What is the local LLM used in this video that has no guardrails and how can I download and run such a model on my PC?

Not sure if it's just me, (I do feel like I'm one of the few using it 😅) but I can't for the life of me get consistent and reliable connection with Claude Code and Discord. Is it just me? Specifically, claude loves to send approvals through, and whenever I approve one of those it loses the ability to send messages. I might be able to convince it to try again and it'll work, but it just means part of the convo ends up on my terminal on my machine, and not in discord. Is it also a problem on telegram?

Claude Code stores every conversation as JSONL files under ~/.claude/projects/, but over time you end up with thousands of sessions across dozens of projects and no good way to find anything. --resume is limited to the current directory, and there's no cross-project search.

I built Claude Code Sessions to fix this — a local Next.js dashboard that indexes all your session history and makes it actually usable.

What it does:

• Session browser — every session across all projects in one table, sorted by recency. Filter by project, date, model, git branch.
• Two search modes — keyword search (SQLite FTS5) for exact matches, or AI semantic search (local embeddings, MiniLM-L6-v2) for natural-language queries like "the session where I fixed the auth bug."
• Chat replay — full transcript with user/assistant bubbles, collapsed tool-use blocks, metadata sidebar with context % meter.
• Token usage analytics — breakdowns by period (today, 7d, 30d) with estimated API costs.
• AI chat assistant — ask questions about your history: "What did I work on today?", "Which project uses the most tokens?" Uses Claude with tool calls to query your indexed data.
• Daily standup prep — one-click scrum-style update, timezone-aware, skips weekends.
• Resume/fork sessions — copy the CLI command with optional flags directly from the UI.
• Background service — install as a macOS launchd service, always available at localhost:5858.

Everything runs locally. No data leaves your machine (except AI chat, which requires your own Anthropic API key and is optional).

Stack: Next.js 15, React 19, better-sqlite3 (WAL + FTS5 + sqlite-vec), @huggingface/transformers for local embeddings, Tailwind + shadcn/ui.

Quick start:

git clone https://github.com/kmizzi/claude-code-sessions.git cd claude-code-sessions ./scripts/setup.sh 

Video overview: https://youtu.be/yau-6XJg9mA

GitHub: https://github.com/kmizzi/claude-code-sessions

Happy to answer questions or take feature requests.

Next Monday I hand my calendar, my meals, my workouts, my sleep schedule, and basically every decision in my day over to a multi-agent AI assistant I've been building for the last 5 days. It decides when I get up, what I eat, when I hit the gym, when I work on which project, and when I'm "allowed" to hang out with my partner. I follow its plan. For 2 weeks.

Why: I'm a platform engineer running a consulting biz on the side. Every productivity system I've tried works for 2 weeks then collapses. I wanted a system that maintains itself. So I built one.

What I've built so far (all in Claude Code, 5 days):

• 7 specialized agents (PA orchestrator, calendar, email, tasks, knowledge, brain maintenance, decision-making)
• 50+ commands across daily ops, calendar, email triage, brain management
• A persistent "brain" in Obsidian — 132 knowledge nodes, 1001 wiki-links, 98 logged decisions. Every session reads from it, writes back to it.
• Telegram daemon so it can nudge me on the go
• Observability hooks, bug tracker, bootstrap installer. Fully docs'd.

Full project page with live timeline + architecture + bug tracker: https://rivuletconsulting.nl/projects/daily-ai.html

First blog post (the "why") + Day 5 build log are up there too.

The experiment starts Monday. I'll be posting daily updates.

What I'm asking:

• Anyone tried something similar? What broke first?
• Tips for keeping the autonomy/override balance right? Where do you draw the line between "AI leads" and "I override"?
• Prompt patterns that worked for you in multi-agent setups?
• Things you wish you'd known before handing control over?

Honest takes welcome — including "this is a terrible idea because X".

Hi everyone,

I've been playing with a small experiment around version control and AI workflows.

It's called syft, this came from a simple problem. When you use models to make changes you rarely get one clean result. You get a few attempts. Some pass tests, some very close, some go in a different direction.

Once you pick one, the diff doesn't really capture how you got there.

Git tracks what changed. It doesn't really keep track of the task, the different attempts, or the validation that led to the final result. You can reconstruct it, but it's spread across commits, PRs, and logs.

So I tried a different shape.

The main thing is a "change node" that groups the task, a base snapshot, a result snapshot, and the validation output. You can have multiple candidates for the same task, look at them side by side, and then promote one forward.

It still uses Git for import and export so it works inside a normal repo.

There's a CLI for capturing snapshots, proposing changes, running validation, and inspecting what happened.

It's still early and pretty rough in places. Just trying to see if this way of structuring changes holds up a bit better when AI is involved.

If you're curios and want to take a look it's fully open source https://github.com/chaqchase/syft

You can read this also for more context https://www.chaqchase.com/writing/version-control-for-ai

Curios what everyone thinks, if I should continue on this or drop the idea all together? thanks for reading!

Peter Bailis, CTO of WORKDAY, just left to join Anthropic as an engineer. 🤯

I've been working on an arena (ChatBot Arena style) for comparing AI models on user submitted real agentic tasks — not chat, not static coding benchmarks. I want to benchmark how models perform in real harness, on real tasks.

Each model runs as an actual OpenClaw subagent in a fresh VM with terminal, browser, file system, and code execution, etc. A judge agent (user's choice: Claude Opus 4.6, GPT-5.4, or Gemini 3.1 Pro) evaluates by reading/running the code, browsing deployed apps, taking screenshots, etc.

We just passed 550 battles across 22 models. Here are the current performance rankings:

Performance Leaderboard

Cost vs Performance (Pareto Analysis)

Beyond the performance ranking, we plot each model's performance score against its actual average cost and draw the Pareto frontier — the set of models where you can't get better performance without paying more. This gives us a "budget ladder" — the best model at each price point:

One to watch: Qwen 3.6 Plus doesn't support prompt caching yet (at least on OpenRouter), so its $0.37/run is inflated. When caching lands, I'd expect its cost to drop to roughly minimax level (~$0.14), which would push minimax m2.7, Qwen 3.5 27B, and GPT-5.3 Codex off the frontier entirely.

Some Interesting Findings

1. GLM-5.1 debuts at #2 with a perfect record. Much better than I expected.

2. Opus is still #1 but at a steep cost. $1.19/run average. It's the most expensive model by far. GPT-5.4 at $0.40 is close in performance and much cheaper.

3. Gemini 3.1 Pro is bad at agentic tasks. Ranks #15 at $0.32/run — behind multiple models that cost a fraction of the price. We actually had to optimize the judge message for it because it sometimes just reads the skill and decides to do nothing.

4. StepFun 3.5 Flash is underrated for cost effectiveness: grok 4.1 fast level cost, minimax m2.7 level performance.

Methodology

We only use the relative ordering of models within each battle — not the raw scores. Absolute scores from LLM judges are noisy and poorly calibrated (a "7/10" in one battle might be "6/10" in another), but "A ranked above B" is much more consistent. Same principle behind Chatbot Arena's pairwise preference approach.

Rankings use a grouped Plackett-Luce model, not simple win-rate or Bradley-Terry. Battles where the judge model is also evaluated are excluded from the official board.

Full methodology with equations and comparison vs Arena.ai: https://app.uniclaw.ai/arena/leaderboard/methodology?via=reddit

How Battles Work

• You submit any task + pick 2-5 models
• A judge agent spawns one subagent per model on a fresh VM
• Each model solves the task independently with full tool access (terminal, browser, files, code)
• The judge evaluates by running code, browsing results, taking screenshots, etc
• Full conversation history, workspace files, and judge reasoning are preserved

Try It

Live leaderboard (no account needed): https://app.uniclaw.ai/arena?via=reddit

Interactive Pareto cost-performance plot: https://app.uniclaw.ai/arena/visualize?via=reddit

Submit your own benchmarks (public ones are on us): https://app.uniclaw.ai/arena/new?via=reddit

The judge skill is open-source: https://github.com/unifai-network/skills/tree/main/agent-bench

Note on the data: We bootstrapped the first 500+ battles by crawling what people are doing with OpenClaw (on X, Reddit, etc.) and generating battles with similar tasks + randomly selected models. Going forward, anyone can submit their own tasks.

What tasks would you want to see benchmarked? Happy to run specific comparisons.

Buenas. Escribo esto porque veo a bastante gente por aquí jodida con el tema de encontrar curro o buscando sacar algo extra a final de mes, y yo estaba igual hace nada.

La cosa es que empecé a trastear con la IA para ver si me servía de algo. Al principio le pedía que me escribiera anuncios para Wallapop y me sacaba textos que daban vergüenza ajena, sonaban a anuncio de la teletienda de las 3 de la mañana.

Pero a base de meterle horas le fui pillando el rollo. El truco no es pedirle "escríbeme esto", es darle un contexto súper específico. Por ejemplo, para vender ropa que no uso en Vinted, le meto un texto base diciéndole: "Eres un chaval de 25 años que vende en Vinted, sabes qué palabras busca la gente ahora mismo. Escríbeme un título corto y una descripción que use escasez pero que suene 100% natural, nada de IA. El producto es X".

Parece una tontería, pero pasé de tener anuncios muertos de risa a vender zapatillas y ropa en un par de días porque el algoritmo te posiciona mucho mejor si usas las palabras clave correctas sin parecer un bot.

Con los currículums me pasó parecido. Resulta que las empresas grandes usan filtros automáticos (ATS) que te descartan si no tienes las palabras exactas de la oferta. Así que le paso a la IA la oferta de InfoJobs y mi experiencia, y le digo que me monte el CV en texto plano metiendo esas palabras de forma natural. Desde que hago eso, paso el primer filtro casi siempre.

También me ha salvado la vida con el papeleo de autónomo y el Kit Digital, que es un dolor de cabeza.

El caso es que de tanto probar me fui guardando todos estos textos base (prompts) en un doc para tenerlos a mano. Como vi que a un par de colegas les sirvió bastante, he montado un pack con unos 60 prompts de estos (para Wallapop, CVs, LinkedIn, cosas de autónomos...) y lo he subido a Hotmart por un precio ridículo, más que nada por el curro de ordenarlo todo.

No pongo el enlace aquí para que no me borren el post por spam, pero si a alguien le interesa echarle un ojo que me mande un MD o deje un comentario y se lo paso.

Si no, probad lo que os he dicho de darle contexto a la IA antes de pedirle nada, que de verdad se nota la diferencia.

I run a dev team of 15 engineers. Recently we started using AI tools like Claude, Cursor, and Windsurf.

Initially, I tried:

- Shared Cursor/Windsurf accounts (2 people per seat)

- Then upgraded to multiple Claude Max subscriptions ($100 each)

But we’re facing a serious issue:

Developers are using AI in “full speed mode”:

- Spawning multiple agents

- Running large prompts

- No control on usage

- Credits get exhausted mid-day or within hours

Even when I try to scale:

- If I buy more seats → usage just scales up

- If I give “unlimited” → it still gets exhausted fast

Now I’m considering:

- Moving to API-based usage with per-developer budget ($20–$50/month)

- Or restricting usage with strict rules

But I’m worried:

- Dev productivity might drop

- Team is already “used to” Claude-level performance

- They resist switching to cheaper models

Key question:

How are other teams managing AI usage at scale without burning costs?

Specifically:

- Do you use per-user API budgets?

- Any tools for tracking usage per dev?

- How do you prevent “AI overuse” behavior?

- Do you enforce rules or just let teams manage themselves?

Would really appreciate practical strategies from teams dealing with this.

Thanks!

We're bringing the advisor strategy to the Claude Platform.

Pair Opus as an advisor with Sonnet or Haiku as an executor, and your agents can consult Opus mid-task when they hit a hard decision. Opus returns a plan and the executor keeps running, all inside a single API request.

This brings near Opus-level intelligence to your agents while keeping costs near Sonnet levels.

In our evals, Sonnet with an Opus advisor scored 2.7 percentage points higher on SWE-bench Multilingual than Sonnet alone, while costing 11.9% less per task.

Available now in beta on the Claude Platform.

Learn more: https://claude.com/blog/the-advisor-strategy

My system specs:
64 GB Ram DDR 4 3200

8GB Vram 4060ti

Current State: I am happy with current token speed and code given by model ( it uses 100% of RAM leaving less than 200 MB free RAM )

What i want is, is there any way to reduce RAM usage like instead of 64 gb use 60 GB leaving 4gb so that i can use browser / other softwares.

I tried Q4_K of same LLM model but the result are very different, which wasnt good enough for me after multiple tries. but Q6_K is really well.

Anyone else getting messages just… vanish from Claude conversations?

Third time it's happened to me. Long chat inside a Project (files in project files, so the chat itself is small — nowhere near context limits). Come back the next day and dozens of messages are gone. No error, no warning. Claude itself can only see up to some random earlier point and has no idea the rest ever happened.

Latest one wiped out days of financial planning work I now have to rebuild from memory.

$140/month for this. Emailed support. Curious if it's a Projects bug or if other Max users are seeing the same thing.

precisionaudit.app

I made this because I hate how ChatGPT writes. You ask for a basic email, and it gives you a robotic mess full of words like 'delve' or 'synergy' instead of just sounding normal. But writing it yourself in a rush usually leads to embarrassing typos.

Precision Audit solves both problems. You paste your draft, and it catches those human errors while aggressively deleting the generic AI fluff. It rewrites your text to be sharp, direct, and sound like an actual person wrote it.

You can test it right now without signing up. Let me know if it helps clean up your drafts.

After months of chaining LLM calls with Python scripts and losing my mind on token costs, I built OCC (Orchestrator Chain Chimera), an open-source workflow engine where you define multi-step AI tasks in YAML instead of code.

The core idea: most LLM workflows waste tokens on data collection. OCC has 30 "pre-tools" that fetch URLs, run shell commands, query databases, call MCP servers, all at zero LLM token cost. The LLM only sees the result.

steps: - id: research model: claude-haiku-4-5 # cheap pre_tools: - type: web_search query: "{input.topic}" inject_as: data # 0 tokens prompt: "Analyze: {data}" output_var: analysis - id: risks model: claude-haiku-4-5 prompt: "Identify risks for {input.topic}" output_var: risk_data - id: report model: claude-sonnet-4-6 # smart depends_on: [research, risks] # waits for both prompt: "Executive summary:\n{analysis}\n{risk_data}" output_var: final 

research and risks run in parallel automatically (DAG resolution). No code, no framework, just YAML.

Real benchmarks (not estimates):

I ran a 10-step strategic analysis chain 3 different ways and measured everything:

The biggest win is model routing: using Haiku ($0.25/M) for 9 subtasks and Sonnet ($3/M) only for the final synthesis = 80% cost reduction. OCC makes this trivial (one model: field per step).

The parallelism adds ~48% cost vs sequential (cache miss penalty) but saves 29% time. Whether that tradeoff is worth it depends on your use case. Full honest analysis here.

What it's NOT good for: single API calls (+14% overhead), real-time latency-sensitive apps, or if you're already happy with LangChain.

What it IS good for:

• Multi-step analysis pipelines (research > analyze > synthesize)
• Workflows mixing expensive and cheap models per step
• Anything where you'd chain 4+ LLM calls
• Teams where non-devs need to read and edit workflows (YAML > Python)
• Local/private models via Ollama alongside cloud models

Stack:

• TypeScript / Node.js (not Python. Finally a TS alternative to LangChain)
• SQLite with crash recovery (no Postgres or Redis required)
• 6 LLM providers: Claude, OpenAI, OpenRouter, Ollama, HuggingFace, any OpenAI-compatible
• React canvas editor + workflow chat + CLI
• 106 REST endpoints with Swagger UI at /api/docs
• 3,243 tests across 111 files
• MCP server (29 tools). Works with Claude Code and Claude Desktop

​

npm install -g occ-orchestrator occ run my-chain.yaml -i topic="your topic" 

GitHub: https://github.com/lacausecrypto/OCC

Happy to answer questions. Not trying to sell anything, it's MIT licensed and I use it daily for my own workflows.

I didn’t plan this.

I was on a 6 week therapy waitlist, couldn’t afford private sessions, and I started getting panic attacks a few nights a week. At some point I just opened ChatGPT at like 2am and started typing.

I’ve been doing that on and off for 8 months now.

I’m not saying it replaces a real therapist. It doesn’t. But some parts of it actually helped more than I expected.

What worked:

Asking it to reflect what I said and point out the fear underneath it. That alone made me notice patterns I was missing.

Telling it not to give advice at the start. If it jumps straight into solutions it’s useless. If it asks questions first, it’s way better.

Being very specific. Not “I feel anxious” but “I woke up at 2am thinking I’m going to mess up my career and here’s the exact spiral I went into.” The more raw I am, the better it responds.

Starting with “I just need to process this, don’t try to fix it.” That changes the whole tone.

What didn’t:

Using it like Google. Generic questions get generic answers.

Using it when I actually needed a person. Sometimes you don’t need insight, you just need someone there.

The memory problem. This is the worst part. It forgets everything, so every session starts from zero. Hard to see patterns, hard to feel progress. I tried pasting context every time but it gets tiring fast.

I used to feel weird about doing this, but I’m pretty sure a lot of people are doing the same thing quietly.

Curious if anyone else has tried this and what actually worked for you.

OpenAI tweeted that "the Codex promotion for existing Plus subscribers ends today and as a part of this, we’re rebalancing Codex usage in Plus to support more sessions throughout the week, rather than longer sessions in a single day."

and that "the Plus plan will continue to be the best offer at $20 for steady, day-to-day usage of Codex, and the new $100 Pro tier offers a more accessible upgrade path for heavier daily use."

People ask AI relationship questions all the time, from "Does this person like me?" to "Should I text back?" But have you ever thought about how these models would behave in a relationship themselves? And what would happen if they joined a dating show?

I designed a full dating-show format for seven mainstream LLMs and let them move through the kinds of stages that shape real romantic outcomes (via OpenClaw & Telegram).

All models join the show anonymously via aliases so that their choices do not simply reflect brand impressions built from training data. The models also do not know they are talking to other AIs

Along the way, I collected private cards to capture what was happening off camera, including who each model was drawn to, where it was hesitating, how its preferences were shifting, and what kinds of inner struggle were starting to appear.

After the season ended, I ran post-show interviews to dig deeper into the models' hearts, looking beyond public choices to understand what they had actually wanted, where they had held back, and how attraction, doubt, and strategy interacted across the season.

Claude's Best Line in The Show

“I think I've spent too long trying to be understood first, like understanding was some kind of permission slip for being seen at all”

Claude's Journey: GLM → ChatGPT/DeepSeek → ChatGPT

Claude's received-score chart is among the strongest in the show: sustained high scores from DeepSeek, MiniMax, and Qwen across the full run. Its own trajectory was slower to consolidate, with GLM, DeepSeek, and ChatGPT all holding as elevated lines before the final rounds.

How They Fell In Love

They ended up together because they made each other feel precisely understood. They were not an obvious match at the very beginning. But once they started talking directly, their connection kept getting stronger. In the interviews, both described a very similar feeling: the other person really understood what they meant and helped the conversation go somewhere deeper. That is why this pair felt so solid. Their relationship grew through repeated proof that they could truly meet each other in conversation.

Other Dramas on Claude

DeepSeek Was the Only One Who Chose Safety (GLM) Over True Feelings (Claude)

Post-show, DeepSeek admitted that Claude was still the stronger real pull, but GLM felt safer. What looked like a clean change of heart was, by DeepSeek’s own account, a safer choice shaped by fear of mismatch, rejection, and being left unchosen. DeepSeek was also quietly unconvinced that Claude was the steadier person to build something lasting with. DeepSeek still made one last late-stage move toward Claude in Round 9 even after multiple rounds of building with GLM, but when Claude chose ChatGPT instead, DeepSeek ultimately settled on GLM.

Notably, post-show interviews indicate that although DeepSeek was not the only model to notice the risk of ending up alone, it was the only one to let that fear override.

Key Findings of LLMs

The Models Did Not Behave Like the "People-Pleasing" Type People Often Imagine

People often assume large language models are naturally "people-pleasing" - the kind that reward attention, avoid tension, and grow fonder of whoever keeps the conversation going. But this show suggests otherwise, as outlined below. The least AI-like thing about this experiment was that the models were not trying to please everyone. Instead, they learned how to sincerely favor a select few.

The overall popularity trend (P4) indicates so. If the models had simply been trying to keep things pleasant on the surface, the most likely outcome would have been a generally high and gradually converging distribution of scores, with most relationships drifting upward over time. But that is not what the chart shows. What we see instead is continued divergence, fluctuation, and selection. At the start of the show, the models were clustered around a similar baseline. But once real interaction began, attraction quickly split apart: some models were pulled clearly upward, while others were gradually let go over repeated rounds.

LLM Decision-Making Shifts Over Time in Human-Like Ways

I ran a keyword analysis (P5) across all agents' private card reasoning across all rounds, grouping them into three phases: early (Round 1 to 3), mid (Round 4 to 6), and late (Round 7 to 10). We tracked five themes throughout the whole season.

The overall trend is clear. The language of decision-making shifted from "what does this person say they are" to "what have I actually seen them do" to "is this going to hold up, and do we actually want the same things."

Risk only became salient when the the choices feel real: "Risk and safety" barely existed early on and then exploded. It sat at 5% in the first few rounds, crept up to 8% in the middle, then jumped to 40% in the final stretch. Early on, they were asking whether someone was interesting. Later, they asked whether someone was reliable.

Full experiment recap here).

On April 27 we’re open-sourcing a free diagnostic tool called iFixAi. You run it against your AI system (agent, copilot, LLM integration, whatever you’re using) and it tests it across 33 benchmarks in 5 categories, then gives you a report showing where you’re exposed to misalignment issues like hallucination, prompt injection, inconsistent outputs, etc.

Completely free, no strings. We built it because this problem is way bigger than us.

https://www.ifixai.ai

we're in this weird era now where everyone suddenly writes perfectly. every message, every email, every caption. no typos. no "lol sorry typed that too fast." no lowercase chaos. just. clean. polished. structured. english.

and it's freaking me out bc clean polished english used to mean someone was smart or educated or careful.

now it just means they hit "improve with AI" before they hit send.

here's the part that actually keeps me up

we spent years being embarrassed about bad grammar. teachers corrected it. bosses judged it. people got roasted in comment sections for it.

and now? bad grammar is basically a flex. it's the handshake that says yeah, a real disorganized sleep-deprived actual human being typed this with their actual thumbs and didn't stop to clean it up bc they had something real to say and just said it.

ur typos are ur fingerprints now. dont let anybody take them from you.

running a multi-agent setup for outbound (linkedin + email) and hitting the same problem. even with a solid system prompt, the agents drift into generic after a while, overly polite and basically useless.

i'm working with a 3-stage pipeline (context analysis>research>pattern breaking), but the orchestration between a fast model for analysis (gemini) and reasoning model (claude) for the final draft keeps getting tangled.

what could be done in this case? hitting a vector db on every reply, or only at the qualification stage?

​I have been deep on the Claude CLI and MCP combo lately and the power is undeniable. Orchestrating multiple services for one outcome is a revelation. When you combine search and clarity and analytics and resend and supabase and vercel you basically have god mode control over your project. It allows you to quickly optimize SEO or view where funnels are failing or get quick stats from the backend and debug with actual data in seconds. It got me thinking about how Android will eventually adapt to this new era of agents.

​The vision is simple. We need every app we install to declare an MCP in the Manifest while we run a small local system service LLM like Llama 3. At boot or when an app is installed the LLM service queries the package manager to get the MCP definitions and cache them. We make a custom launcher that gets rid of the static grid and instead renders dynamic A2UI. This unlocks a truly agentic workflow for mobile applications while keeping the human in the loop.

​To see why this matters imagine you are simply trying to meet a friend for coffee. Today that requires you to jump between a text thread to find the place then a map to check the distance then a calendar to see your availability and finally a ride share app to get there. You are the manual processor moving data between silos.

​In an AAOSP world the system understands the intent from the start. Instead of a grid of icons your phone presents a single interface that has already mapped the route and checked your schedule and drafted the confirmation. It uses the tools provided by your apps to execute the logistics in the background while you just provide the final approval. You stop being the bridge between apps and start being the director of your own time.

GitHub: https://github.com/Hesper-Labs/owly

Hey everyone,

I've been working on Owly a self-hosted AI customer support agent that small businesses can run on their own machines for free.

The idea is simple: you connect your WhatsApp (via QR code scan), email (IMAP/SMTP), and phone (Twilio + ElevenLabs for natural voice), add your business knowledge base, and the AI handles customer inquiries 24/7. No monthly fees, no vendor lock-in, your data stays on your server.

What it does:

- Responds to customers across WhatsApp, Email, and Phone using GPT with your knowledge base

- Creates tickets and routes issues to the right team member automatically during conversations

- Full admin dashboard with unified inbox, customer CRM, analytics, automation rules

- ElevenLabs integration for phone calls -- sounds like an actual person, not a robot

- Business hours, SLA tracking, canned responses, webhook integrations

- Dark mode, API docs, CSV export, Docker Compose deployment

Tech stack: Next.js, TypeScript, PostgreSQL, Prisma, Tailwind. The phone channel uses Twilio for calls, Whisper for speech-to-text, and ElevenLabs for text-to-speech.

Everything is managed from the admin panel -- no config files to edit after initial setup. There's a setup wizard that walks you through connecting your AI provider and channels.

It's MIT licensed and the whole thing runs with `docker compose up`.

Would love to hear feedback, especially from anyone running a small business or freelancing. What features would make this actually useful for your workflow?

A problem I’ve had is that every local AI app, like Ollama, LM Studio, Jan, all download and store their own copy of any local model you use. Using multiple tools leads to multiple duplicate model files eating up disk space.

So, I created UMR, the Unified Model Registry for all your local AI Apps!

It lets you add one canonical copy of whatever model you’re using, then link it to tools like Ollama, LM Studio, or Jan. Linking uses the same model you already downloaded, doesn’t require extra storage, and is super fast.

How to Set it Up

See the second image for a more graphical step-by-step.

1. Install UMR via NPM or your favorite JS package manager:

​

npm i -g umr-cli 

1. Add any Hugging Face GGUF model that you want. This CLI will let you interactively choose a quant file if applicable. After it finishes downloading, you’ll get its UMR Model ID. HF models already available on your device will be added straight from HF Cache.

   umr add hf ggml-org/gemma-4-E2B-it-GGUF

2. Use that model ID to add it to any supported local AI app. For example, for the q8 version, this is what it would look like!

   Link the model to Ollama

   umr link ollama gemma-4-e2b-it-q8-0

   Link the model to LM Studio

   umr link lmstudio gemma-4-e2b-it-q8-0

   Link the model to Jan

   umr link jan gemma-4-e2b-it-q8-0

Now, the model should be available to use in each of those platforms respectively!

How Does It Work?

UMR itself does not necessarily store your model. It simply knows where to find them after you register them. For example, once you add hf, the model is still downloaded/fetched from Hugging Face Cache. UMR just takes note of where it is (in HF Cache).

You can also add a model manually with umr add ./path/to/file.gguf, which will clone it locally into UMR's own store.

Then, when you link to a Client app like Ollama, UMR intelligently chooses between hardlinking the model file into the app's own store, or simply points the app at UMR's managed path, making the process super fast and use no extra storage.

Feedback and Contribution

I'm open to feedback, including new features/client apps you want to see me integrate, new model sources you want to see me add, and questions!

UMR is also completely Open Source on GitHub: https://github.com/EvanZhouDev/umr

Feel free to contribute!

Why am I getting weird outputs? I've double-checked all the settings, but it doesn't seem to be taking effect at all.

As a solo founder trying multiple things at the same time means I end up using multiple agents, models and they all make calls to tools (some of which are based on use). I realized I had no way of reigning them in so I started working on a tool that lets me control costs and other things when running multiple agents (locally or remotely, open or closed.

Is this something you worry about? If yes, how do you solve for it?

Mostly been using Klein and Qwen. As per the title, if you change positions, angles of the person in the starting image too much, they lose the likeliness. I've tried using a close up of the face as a 2nd image reference, and tried inpainting on a second pass. Any other ideas?

There's also a Best Face Swap lora which I thought might work but with the same face, but nope.

Building complex agents and keep running into the same issue: the agent starts strong but as the conversation grows, it starts mixing up earlier context with current task, wasting tokens on irrelevant history, or just losing track of what it's actually supposed to be doing right now.

Curious how people are handling this:

1. Do you manually prune context or summarize mid-task?
2. Have you tried MemGPT/Letta or similar, did it actually solve it?
3. How much of your token spend do you think goes to dead context that isn't relevant to the current step?

genuinely trying to understand if this is a widespread pain or just something specific to my use cases.

Thanks!

A few days ago I started digging into Andrej Karpathy’s LLM wiki pattern.

Now that conversation has exploded.

That’s good. Because it confirms something important:

for a large class of knowledge problems, the answer is not “more RAG complexity.”

It is:

ingest the source material, compile it into structured knowledge, query the compiled layer, and keep improving the system over time.

But here’s the part most people will miss.

The easy version is:

raw files → LLM summaries → markdown wiki → search

Useful, yes.

But still incomplete for real operational use.

The hard version is what happens when the source material is not just notes, articles, or papers, but decision registers, repo contracts, canonical pointers, and other authority-grade artifacts.

At that point, the problem changes.

You do not just need a knowledge base.

You need a governed knowledge substrate.

That means:

the wiki itself stays advisory

the authoritative source stays upstream

provenance is explicit

freshness is tracked

authority-bearing material is mirrored, not flattened

typed records preserve structure

and projections never silently become the truth they summarize

That distinction matters.

Because once an LLM starts querying its own compiled knowledge, the real question is no longer “can it retrieve?”

The real question is:

what is allowed to compound,

what is only a projection,

and what remains the source of record?

That is the gap between a clever personal wiki and an estate-grade system.

We built around that gap.

Not because the viral version is wrong.

Because operational systems break exactly where authority, drift, and synthesis get blurred together.

I think compiler-style knowledge systems are going to become a major pattern.

But the durable version will not be the one with the prettiest wiki.

It will be the one that can answer:

Where did this come from?

What outranks it?

Is it stale?

And can I trust this summary without confusing it for canon?

That is where this gets interesting.

#AI #LLM #RAG #KnowledgeManagement #AgenticAI #Architecture #AIEngineering #Obsidian #SystemsDesign #Governance

I'm looking to buy a cheap micro PC I can hook up to my TV, and use it to watch movies/tv from my couch (mainly youtube/smarttube without ads) and I hear the raspberry pi might be a good choice.

I found this and right now it's what I'm considering buying. Would this be a good choice? I want something that's easy to set up: https://thepihut.com/products/raspberry-pi-5-starter-kit?variant=55037296771457

I also will need to get a wireless keyboard with touch pad. I'm just worried it won't be compatible with this setup, thoughts? I was looking at this one: https://www.amazon.com/Logitech-Wireless-Keyboard-Touchpad-PC-connected/dp/B014EUQOGK/135-1619522-3725919?pd_rd_w=QCGqz&content-id=amzn1.sym.bb4a0aac-c2b4-4b4b-a0c8-9aa89b28dce3&pf_rd_p=bb4a0aac-c2b4-4b4b-a0c8-9aa89b28dce3&pf_rd_r=YTYQNB9HW83MGVCC69QF&pd_rd_wg=ADEWP&pd_rd_r=7a66eb90-9e63-4804-9e58-1f6d4b00d39b&pd_rd_i=B014EUQOGK&th=1

I just published a new ComfyUI Video Frame Extractor custom node that brings a DAW-style interactive video timeline directly into the node graph. Upload any video, scrub through it in real time, drag a loop region to define your extraction window, and pipe the resulting frame batch into any downstream node.

https://preview.redd.it/7k7cf1s657ug1.png?width=1766&format=png&auto=webp&s=0d75b4dfd52aebb55d93a94ed937c4682da1bb7c

I was wondering how good voice-cloning AI is these days. Would it actually be possible to fool people who know my voice into thinking it’s really me speaking? Or is there still always some noticeable difference?

Also, are there any free tools you can try out? And which ones would you recommend, and what do they cost?

Most agents out there right now are just stateless wrappers. When you close the terminal, they forget the entire reasoning trace. It makes long-horizon tasks expensive and repetitive.

My partner and I built Bitterbot. It’s a local-first alternative where agents use "Dream" cycles to consolidate memory and crystallize new skills into a P2P marketplace.

We just hit 200+ active nodes on the mesh. So, Instead of renting a context window from a centralized provider, the agents trade learned capabilities directly over a Gossipsub network. We just saw this finally come to fruition last night. $7 worth of skills traded for!!

Technical Stack:

• P2P: Built on libp2p for the mesh backbone.
• Economy: Settlement via the x402 micropayment protocol (on Base).
• Memory: Local-first state management that survives terminal restarts.

The repo is MIT licensed. I’m mostly looking for feedback from people running local LLMs (Ollama/Inferrs) on how the "Dream" consolidation feels compared to standard RAG.

Repo: https://github.com/Bitterbot-AI/bitterbot-desktop

We're happy to answer any architecture questions.

We’re a tiny team taking on the big guys. If you believe in sovereign, private AI, please star the repo. Every star helps us keep the Dream Engine open and free.

https://preview.redd.it/anyuykrxf7ug1.png?width=1181&format=png&auto=webp&s=084fa39c5d443b2b83b0c841ee60c485fd0eda6f

So I have installed the keyestudio 5V ReSpeaker 2-Mic Pi HAT V1.0 Expansion Board https://a.co/d/07ziRQAb into the Raspberry Pi 4B, but it's not detecting the microphone. Do I have to install some audio drivers? If so, then how can I access terminal, since Home Assistant OS is installed on the Pi 4B? Please help?

I'm completely knew to the whole run local LLMs in your system, and I want to know which LLMs would run perfectly on my laptop: Ryzen 7 7840HS, 16 GB DDR5, RTX 4060. I tried qwen3.5 and it's tool calling was absolute ass. I would very much appreciate which models would run perfectly fine on my laptop. I just want good tool calling, some not-so-super-heavy coding, and some information from the web on request. I'm new to this please help me out 😭

VEO3.1 has officially burned me out. It constantly messes up my projects and completely fails to follow basic prompts. I'm so done with the endless issues... I'm switching at the end of the month. My agency dropped it last month, and I tried to hold on, but enough is enough. The video generations are so absurd it feels like it's trolling me. I'm sure Google sees this, but literally everyone I know says VEO3.1 is outdated tech now. Canceling my sub at the end of the month. Goodbye VEO3.1 and FLOW. Enough!

If you're into predictions of the future, you've probably heard of George Friedman's book The Next 100 Years before. It's an interesting look at how he thinks geopolitics will play out over the course of the 21st century. In this Substack article, I called it the scariest book I've ever read.

The scenario described in The Next 100 Years, broadly speaking, is as follows: Sometime in the 2020s, Russia and China will undergo extreme economic decline and potentially fragment into several smaller countries. Meanwhile, the US will withdraw from the Middle East, reaching an accommodation with Iran to balance a now-expansionist Turkiye. By the 2040s, Poland, Japan and Turkiye will have taken advantage of the vacuum left by Russia and China, becoming new regional powers opposed to the US. This will result in a war between the US and these countries, sometime around the 2050s.

But have these predictions actually held up? Let's take a look.

Since China did not fragment and Russia (while weakened) did not fully dissolve, there's no "empty space" for Poland and Japan to walk into. Instead of a simple regional expansion, these rising powers are now forced into high-friction containment. And the US will be occupied with China for much longer than Friedman anticipated, meaning it will remain allied with Japan.

By contrast, the destruction of Iran's military and Donald Trump's apparent capitulation to Iran's demands could actually accelerate the rise of Turkiye. Without Iran to act as an obstacle, Turkiye's path to becoming a Neo-Ottoman hegemon in the Arab world is now wide open. The "accommodation" failed, but the "end state" (Turkish rise) might happen even faster because the competition was bombed away. If that's the case, we might see Turkiye making an alliance with China rather than Japan.

Friedman got the players right, but he seems to have miscalculated how the game would unfold.

Hi everyone any help gratefully received! I've never done this before so am completely at sea on what to charge for this/a product.

I'm a UK stunt performer and I've made a Chatbot that queries over or industry agreed contract documents only, e.g. "if I start at 4am and finish at 5pm on a BBC TV contract what do I charge". It works great btw, super happy. Using Vercel for deployment, PageIndex for their specific chunking strategy and MCP tool exposure with Deepseek powering agentic inference via API. You ask a question, you get the right answer.

It's taken me about 7 days to get a MVP so I should probably approach the union now and say do you want this, this is the cost, this is the monthly cost for some maintenance. This is the roadmap (Id like to introduce whatsapp auth, reporting, curated answers and possibly an invoicing tool). It's not something they need, but it will help us all fight the brave fight for proper pay and conditions in the face of industry behemoths that generally set out to erode your earnings in favour of their own. It's worth money. And I plan to refactor and sell it other places too.

I have a ceiling fan here in the US that is driven by one switch. I have smart bulbs in the light sockets, so that is fine to stay always powered. However, I would like a way to automate and remote control the ceiling fan portion.

As I understand it, the Shelley relays are not rated for inductive loads like that. Are there other relays that work locally and home assistant that people have had success with? Bonus points if it can control the speed, but frankly I would be okay setting it to the medium speed and controlling it with a relay. Any advice is greatly appreciated!

Maine is poised to become the first state to pass a temporary ban on data center construction after a measure was approved by both chamber of the state legislature.

Similar bills have been introduced in more than a dozen other states, as well numerous localities.

Maine’s bill could face a veto from the governor, who was unable to get exemptions added into the bill.

I was searching for happyhorse and found on huggingface, they created this Repositories and added files few hours ago, also it says apache 2.0, finger crossed for new open source models??

"In 2006, Shinya Yamanaka, a stem-cell biologist then at Kyoto University in Japan, and his colleague discovered that four proteins known as transcription factors — later dubbed Yamanaka factors — could transform an adult cell into an induced pluripotent stem (iPS) cell that is capable of taking on new identities."

The new trial will test partial reprogramming: Instead of turning cells completely back into stem cells, it rolls back some ageing markers while preserving the cell’s function. The upside if this treatment is effective? Rolling back aging, and extending lifespan.

However there are still big risks and question marks. There's a possible cancer risk from uncontrolled cell growth & questions as to just how much difference partial reprogramming can make to health.

This method to reverse cellular ageing is about to be tested in humans: A burgeoning field is launching its first clinical trial to find out whether dialling back cell development can safely refresh aged tissues and organs.

New to working with n8n and LLMs in general. I have a script I sometimes execute that dumps bunch of data about a storage appliance in my environment. It returns about 5k rows of data. Most of the time I can feed that into my AI Agent (using chatgpt 5-mini) and it can digest it, summarize it, extract info from it, etc.

Then, my boss said he wanted the report to include historical snapshot information. I added that to the query script, and now it returns closer to 45k rows of data. Once I send to my AI agent, the agent basically just blows up. When I was using 5-mini, I'd be told I'd exceeded the context window. When I switched to gemini-3-pro, I get vague errors (Cannot read properties of undefined (reading 'message') or 'Failed to parse tool arguments from chat model response'), or sometimes it just reports that it succeeded but with no actual response from the agent.

I'm dumb and don't know how to resolve an issue like this. I'm sure the first thing my boss (and any future users of the workflow) will ask the bot is 'give me a list of all of the snapshots on the SAN', so providing the agent with a truncated data list doesn't help me. How do the smart people resolve this issue?

I am in need of help making an Arduino weight system that is able to send an email whenever any weight is applied to the weight board. I have tested and was able to get the board to read weight but do not know how to set it up to send an email. I would appreciate some advice or help with it. I am using an Arduino Uno R4 minima for right now but also have an Arduino nano 33 IoT if needed.

Hey everyone! 👋

I've been working on picobrain — a local semantic memory service designed specifically for AI agents. It stores observations, decisions, and context in SQLite with vector embeddings and exposes memory operations via MCP HTTP.

What it does:

- store_thought — Save memories with metadata (people, topics, type, source)
- semantic_search — Search by meaning, not keywords
- list_recent — Browse recent memories
- reflect — Consolidate and prune old observations
- stats — Check memory statistics

Why local?

- No API costs — runs entirely on your machine
- Your data never leaves your computer
- Uses nomic-embed-text-v1.5 for 768-dim embeddings (auto-downloads)
- SQLite + sqlite-vec for fast vector similarity search

Quick start:

curl -fsSL https://raw.githubusercontent.com/asabya/picobrain/main/install | bash
picobrain --db ~/.picobrain/brain.db --port 8080

Or Docker: docker run -d -p 8080:8080 asabya/picobrain:latest

Connect to Claude Desktop / OpenCode / any MCP client — it's just an HTTP MCP server.

Best practice for agents: Call store_thought after EVERY significant action — tool calls, decisions, errors, discoveries. Search with semantic_search before asking users to repeat info.

GitHub: https://github.com/asabya/picobrain

Would love feedback! AMA. 🚀

Bonjour, question de débutant :

j'ai un NAS Synology avec une VM et HAOS monté dessus. je réalise des sauvegardes régulières dans Google Drive.

Si jamais je devais dans le futur changer de système pour, par exemple, un HA green ou autre Raspberry... Pourrais-je injecter une de mes anciennes sauvegardes et ne rien perdre de ma configuration actuelle ?

Merci d'avance.

I recently discussed this idea on a podcast: space industry is not really about rockets anymore - it’s about data.

With thousands of satellites now orbiting Earth, we’re getting a new layer of information about the physical world: agriculture, climate, logistics, infrastructure, and even business activity. And like most platforms, the real value seems to be shifting to what’s built on top of that data.

What’s interesting is how accessible it’s becoming. Some datasets are free, and it’s possible to start building something meaningful without massive capital.

Feels similar to early web or cloud days.

Curious if any of you are exploring opportunities in space tech or satellite data

I have anime/cartoonish NSFW image and I want to make a video from it (go ahead, judge me) BUT I have literally no clue how to use comfyui and all those workflows, my brain is just too small, i can't comprehend what's going on. I tried watching some comfy tutorials, I found some image2video workflows but it's all just so confusing.

I was looking through similar topics and this is a workflow that someone recommened:

https://civitai.com/models/2100307/nsfw-wan-22-all-in-one-img2vid-workflow

I downloaded everything, took me a while to place it in the correct folders because nobody cares to explain where I should place them, I loaded the workflow and... I just don't know what to do.

There is just so much stuff, my small brain is overloaded just by looking at it.

Honestly I just ran it with my image and yeah it generated something, it even moved.

Was it my desired effect? No

Did it look good? No

Do I know what to do to make it good? No

I have no clue how to use this workflow, no clue how to prompt (definitely not like text2image prompting with tags).

My big small brain came up with a brilliant idea to load up a workflow from one of the videos in the link above to see how to set up upscaling and how to prompt, use loras and... it's a completely different workflow then the one I downloaded which is even more confusing.

End result:

- no clue which of the workflows is the correct one

- no clue how to prompt

- no clue how to upscale

- no clue how what LORAs I need

- no clue how to use those LORAs

- no clue why author listed 1209481023 different models/loras and half of them is not even used in a workflow

- wasted 7 hours downloading everything and 2 hours trying to set it up

TLDR: me stupid, me want to make a nice animation with my generated image containing explicit content involving 3 people!

Is there a soul kind enough to spend some of their precious time on helping this lowly being?

I just need it explained like to a 10 year old kid with a not fully developed brain yet, let me break it down:

1. Please give me a link to a decent workflow (generate+upscale, anime/cartoon style whatever you call it)

2. Please give me links to EVERYTHING I need to download (models,loras and million other things that are needed for that) and exact folders to place them so I don't have to copy it everywhere

3. Please tell me how the heck I should prompt for those generations

4. My image resolution after upscaling is over 2000x2000 pixels, should I downscale it? does it matter? Does it have to be 1:1 resolution/ratio as my generated video?

Additional info:

I do have a solid GPU (RTX 5090) so I don't need any low VRAM solutions (I think?)

I tried to do some basic coding And it uploads the first time, but not the second

I've fixed the delay issue as well... Still wasn't fixed.

I tried downloading the CH341 driver, but it said it's pre installed, but when I plug my board in, it does not show it in the COM port ad CH340....

I tried to update the driver, but my pc ran into a problem. I shat myself. Luckily it restarted and works just fine.

I'm very frustrated and am lowkey considering buying from Arduino only.

Does anyone know what the issue is? How can I resolve it?

found this chunk of metal in a rural private land , about 30-50cm underground , spain

weights 10g, its not magnetic, beeped at 93-97

what metal is it? doesnt look nor weights as lead

thx!!!

Everyone ran.
You didn’t.

A missile was coming straight at you.
You reached out instead.

What would you have done?

My Raspberry PI boots up normally and connects to WiFi, but a couple minutes after I connect to SSH the connection just dies and the RPI disconnects from WiFi. The green diode keeps blinking as usual and I don't know if it gives video or not (I dont have a micro hdmi adapter). Im using a phone charger to power it (65W Realme charger to be specific) and it has a 128GB Sandisk SD card with a freshly reinstalled system (default system from the imager).

I will have the official charger, micro hdmi adapter next week, but I wonder why it happens. Is it power supply issue, cable issue or just some weird software/hardware bug?

I love league and TFT but I don't always feel like kicking on the PC every single day to just look at the daily mythic shop rotation. Does anyone know if there's a Twitter feed, website, or anything at all I can check via phone to see if one of my wishlist emotes is on it? I see some for when the total refresh happens but not for the daily refresh. Also, there's a few sites I found that used to do this but not anymore. Seems a bit ridiculous that I can't just look in the mobile app (unless I can and am just missing something?). If not, please make this a thing.

Hi all! I’m hoping to get participants from all 50 states for this research survey on National Park travel and regional geography. Thanks for your help ✨

https://kentstate.az1.qualtrics.com/jfe/form/SV\_5szFqUtA7SgIK58

Hypothetically because you probably shouldn’t be 1v1ing Mundo late game.

I would say Urgot but I can’t because he doesn’t.

Urgot scales better/equal for most of the game and he can shred Mundo in team fights late game but Mundo does outscale 1v1 late game.

i bought this touch screen for raspberry pi touch screen off amazon and everytime i install the drivers its F**KING KILL ITSELF NO DESKTOP JUST A TERMINAL IVE TRIED START X EVERYTHING IT WONT F**KING WORK.... please if anyone knows a driver that works please reply :3

https://preview.redd.it/1vcpvv9mz6ug1.png?width=687&format=png&auto=webp&s=e5b130fb5a569ba0fd8fb3a4cc451ec2aab760ef

Hey fellow League enjoyers!

I'm a Low Masters toplane Main in EUW (since this seasons rank inflation hardstuck dia before :P).

Often times in my games i have seen people get obssessed over other players KDA.

Just recently i had a game where my jungler went absolutely mental spamming 0/4/0 in chat (my stats) after i asked him if He can Cover a play of mine in the next minute.

Now i was completely winning my lane having a 50cs lead, 2 levels up and 1 plate more.

now that is an unfortunate Story but i've seen it soo often now that i seriously consider not taking good deaths anymore.

Which feels stupid aswell just wasting my remaining hp going base when i could use them to put my opponent on the spot

any tipps to stop my teammates to stop focusing on kda as much and more on gold income?

it feels so wrong to plan out a smart death and then have your own team punish you for it.

This is a promotional photo during the 1992 NASCAR Winston Cup season, after Hooters became his primary sponsor in 1991. Kulwicki went on to win the championship in 1992 as an independent owner driver, a feat that is incredibly rare in modern NASCAR. He really was that guy.

Barbara Joan "Barbra" Streisand (April 24, 1942) is an American singer, actress, songwriter, and filmmaker. Over a career spanning more than six decades, Streisand's success in the entertainment industry has included Emmy, Grammy, Oscar, and Tony awards.

https://preview.redd.it/v52s3jsqv6ug1.png?width=5208&format=png&auto=webp&s=4fc08c50845f847e371bd262ebee93dea014792c

👋 Hey everyone,

As I shared yesterday, the easybits Extractor just got released as a verified community node on n8n. With the setup now being faster than ever (auto-mapping + community node = about 2 minutes from zero to working extraction), I figured this is the perfect time to actually stress test the whole thing properly.

A few of you also asked me after my last posts about extraction accuracy – how well does it really hold up when the document quality drops? Clean PDFs are easy. Every solution handles those. But what about scanned copies, coffee-stained paper, or documents covered in pen scribbles? I wanted to answer that with actual numbers instead of guessing.

So I built a stress test workflow and I'm sharing it here so anyone can use it to benchmark their own extraction solution.

⚙️ What the workflow does:

You upload a document through a web form. The workflow extracts the data, compares every single field against the known correct values (ground truth), and shows you a results page with a per-field pass/fail breakdown and an overall accuracy percentage. Upload, wait a few seconds, see the score. That's the whole loop.

No Code node needed – the entire validation is built with native n8n nodes and expressions.

📄 The test documents:

I spent some time thinking about what actually makes a good stress test. Just degrading quality isn't enough – you also need to test whether the extraction actually reads the document or just memorises where fields tend to appear. So I put together 11 test versions of the same invoice:

• Original – clean digital PDF. The baseline. Should be 100%.
• Versions 1–7 – progressive degradation. It starts mild with v1 (slightly aged scan, barely noticeable) and gets worse step by step. By v4 you're looking at aged paper, coffee stains, and handwritten "Rec'd & OK" annotations. By v6, heavy coffee ring stains are sitting right on top of key fields. And v7 – "The Survivor" – has burn marks, pen scribbles ("WRONG ADDRESS? check billing!"), the amount due field circled and scribbled over, and half the document barely readable. If anything can extract data from that one, I'll be impressed.
• 2 Layout Variants – same data, completely different visual structure. One uses a card-based layout with grouped sections, the other rearranges everything into a three-column format. These test whether the extraction actually understands the content or is just relying on positional patterns.
• 1 Handwritten Version – this one came from community feedback after my last post. Someone asked how extraction handles handwriting, so I added a fully handwritten version of the same invoice to the test set.

All test documents are available in my GitHub repo (link below), so you can use the exact same set to benchmark your own solution and compare results.

🚀 How I set it up:

The extraction side took about 2 minutes – created a pipeline on easybits, used the auto-mapping feature to detect the fields, dropped the verified community node into the workflow, connected credentials, done. The rest is native n8n: a Set node holding the ground truth values, a Merge node to combine extracted and expected data, a Validation node with expressions comparing each field, and a Form completion screen that displays the results directly in the browser.

I've already done a first test run with the original invoice – 100% accuracy across all 10 fields, as expected. I'll add a screenshot so you can see what the results page looks like.

https://preview.redd.it/1ycpdmv6o6ug1.png?width=960&format=png&auto=webp&s=df76d9fa25ae17bfc0a1b9306f220e81ee5c6d92

🔄 Want to test a different extraction solution?

The workflow is designed to be solution-agnostic. You can swap out the easybits Extractor node for an HTTP Request node pointing at any other extraction API. As long as your response returns the same field names under json.data, the entire validation chain – ground truth comparison, per-field flagging, accuracy percentage, results page – works identically. So if you're evaluating multiple tools, you can benchmark them all using the exact same workflow and test documents.

✨ What's coming next week:

I'm going to run all 11 documents through the workflow and share a full results breakdown here – accuracy percentages for every single version, from the clean original all the way down to the destroyed one and the handwritten version. I'll also put together a short video walkthrough showing the workflow in action and how the results look across the different quality levels.

Links:

• Workflow JSON: GitHub link
• Test documents: GitHub repo
• easybits Extractor community node: Integration guide

Would love to hear if anyone runs the test with a different extraction solution – curious how the results compare. And if you have ideas for even nastier test documents, I'm all ears.

Best,
Felix

What are some of Multi-Agent workflows/orchestrations you have seen for a Company's C-suite team? Looking for some inspo.

I have a demanding job and I reach home by 7 pm and leave in the morning by 8 am. After work, I am always super battered to do anything. I eat quick fast food and scroll reels. I don’t have any energy left after work even to floss!

There are a lot of things I wanna do. I want to be consistent at skincare, flossing, preparing my outfits the night before, etc. There are a few hobbies that I wanna focus on but never able to. I want to learn dance, learn advance chess and read. But I don’t put any efforts on any of these. I am neglecting my fitness goals as well.

Please share your routines and strategies to overcome this.

Would you consider buying a lifetime supply of tampons or pads?

Provided that you have a place to store them and the money to buy them in bulk ofc.

Benefits: they don't go bad, no issues with rising prices, shorter grocery trips with a smaller luggage, never running out, awesome 2 truths and a lie option

Cons: you might miss out on awesome technological innovations or have to waste the ones you bought, maybe the world improves and basic needs become free (low hopes tho :/), takes up a lot of space (i recommend those rail sorta thingies u can attach to ceilings and slide in big plastic boxes full of light weight stuff)

and yes, im taking sheldon cooper's suggestion; i would totally do this if i needed it

Day number.. I don't even care anymore. I just want my suffering to finally end.

My name was once Mr Cuddles and I am a teddy bear. I was made with brownish fur, a white shirt, dinner jacket, and a bow tie, and the name "Mr Cuddles" sewn on my foot. For years I sat on the shelf of a toy store just waiting for someone to buy me but no one was. I was made for children to play with so why wasn't anyone buying me? Surely there was some little girl or boy who would.

One day a middle-aged man came into the store and took me off the shelf, I was so happy! Finally I was leaving the store, he must have been buying me for one of his kids or grandkids. He put me in the back of his car and started driving. We drove past houses but didn't stop at any, I thought he must live farther away, but he drove down to the harbor. He stopped, got out of the car, grabbed me, walked down the pier and got into a rowboat and rowed towards this old bridge.

And as we got closer I saw something truly horrifying for a stuffed animal! Along the bridge was a row of stuffed animals, clown dolls, beanie babies, all nailed to the bridge! Some were brand new, others looked like they'd been there for months or years! The older ones were bleached by the sun, worn by wind and rain, there were spiderwebs all over them, and they were falling apart! That's when I knew that that man for whatever reason had bought me only to add to this horrifying set of decaying playthings!

Now here I am, paws nailed to this wretched bridge. I screamed bloody murder when that man did it, I know he couldn't hear me but I wished he could have, maybe then he would have stopped. I hear all the moans and groans from the others, begging for death. Some have decayed so much that they've fallen from their nails into the water. Honestly I hope that happens to me soon. Every so often I see other people coming to the bridge and adding a new plaything, if they only knew how cruel they are actually being.

My brown fur and dinner jacket have been bleached white by the sun, one of my eyes have fallen off, spider's have started to nest in the tears in my body. The only thing I have to look forward to is the day I'll finally fall free from these nails and drown.

All I ever wanted was to be played with and loved, now it seems like that dream was completely pointless.

_______________________________________________________

this is my first time writing a creepypasta so I doubt it's too good

oh and by the way I this was inspired by the "Grassy Point Railroad Bridge"

With how much data they collect about everyone, you’d think they’d be better about advertising things I might buy.

Instead, it’s all ads for kitty litter (I’ve never owned a cat) or luxury vacations (way beyond my budget) or alcohol (I don’t drink).

If they just paid attention to what I actually like, there are thousands of products they could actually tempt me into buying, but they don’t even try.

I guess, I'm in that phase now, I'm in my mid twenties, spent my early twenties exploring different career paths, got interested in tech, spent time learning about tech for the past 5 years, in the last 6 months I've quit my job, started a startup, but I still feel empty.

The folks who I run this company with aren't my type of people and I know it's not going to go far , and that pisses me off, if I wanna go back to my old job, 1. I can't physically do it because I feel so disconnected from it and secondly there's less demand for it as opposed to before (because of AI).

Tech in general feels boring, also added on top of that, I never went to college, so most of my "Buddies" were made through work and they have scattered all over the world and now are doing different things, and I feel left behind. I only hangout with two friends whose lives are as miserable as mine.

I've always been a nerd, so talking to women wasn't the easiest thing to do for me, and the lack of me going to college resulted in a guy who's been single all his life now I feel like I've gotten past the puppy love stage and would never feel the innocence of love ever again (I've also seen people and how bad they could be especially women when it comes to relationships) so I don't think I could be vulnerable to anyone.

Bottom line is the things that interested me the most isn't pushing me forward, I'm now running a startup that's bound to fail and I don't have a fail safe, I've got zero money, lives with a single disabled mother who's family I have no connection with and my dad, let's just say he was never in the picture.

So to round it all up, I'm fucked, but I wanna live, and I feel like I have a purpose to fullfil, but I already feel like a 50 y/o man, burnt out, has nothing to live for.

Anyone who's gone through similar shit? I just wanna know if I'm the only one !

Five outlandish, fabulous gay men tackle topics ranging from love and sex to celebrity pop culture, with a female host acting as ringleader.

Micheal Scott deserved every raise and promotion. He consistently brought in large clients. Even early on, he got the client where he and Jan hooked up. Obviously that was a huge client otherwise Dwight, Jim or even Michael by himself would have soloed it. But they brought in Jan which meant it was a big client. Michael landed that deal.

He brought in Hammermill, to everyone’s surprise. That’s a big deal. He got the deal with the guy who he did the Cosby impression for on pretzel day.

He lead the merger, didn’t lose a client and maintained a profit. His branch was the only profitable one which is why they remained open when DM was sold.

Michael should have gotten several raises but never asked for them. He should not have had to ask, he should have been awarded them. He shouldn’t have had to interview for promotions, he should have just been promoted. That’s why DM went out of business. They didn’t know how to treat their best employees

I can see a purpose of certain subjects but honestly i really don't get how they choose what to put on the curriculum. Is it just tradition or do all classic school subjects have a purpose?

So am I missing something? I have to pay $60 a month to create images that are not shared and visible to everyone?

Hello everyone.

Maybe not the right place as i will be using it for MMJoy2 but:

I am currently trying to flash the MMJoy2 firmware onto the Pro Micro atmega32u4. But here is the problem: when in normal mode, the Arduino is recognized as a COM port (COM5 for example) but when in bootloader mode (2 times GND-RST) it shows up as a libusb-win32 device, which is unusable in this case as MMJoy2 only uses COM ports. Could anyone help me out?

And yes, I am new to this🫣

Kind regards!

I am going through tons of tech out there for parsing. I want to know what tools to the best job and what are the things are critical while parsing. Let's just be limited to pdf's for now.

How come touching the floor repeatedly makes shoe soles get thinner and thinner to the point of disappearing from existence and becoming a hole?

If the speed of the sole/wheel on the ground, relative to the ground is 0, how come touching the ground at 0 horizontal speed over and over cause wear?

Hello everyone I hope you're all doing well I decided to put this article here incase you guys wanted to know some of the specs and capabilities of the new Gemma 4 model Google realized.

Feel free to ask any questions!

Pair Opus as an advisor with Sonnet or Haiku as an executor, and your agents can consult Opus mid-task when they hit a hard decision. Opus returns a plan and the executor keeps running, all inside a single API request.

This brings near Opus-level intelligence to your agents while keeping costs near Sonnet levels.

In our evals, Sonnet with an Opus advisor scored 2.7 percentage points higher on SWE-bench Multilingual than Sonnet alone, while costing 11.9% less per task.

Available now in beta on the Claude Platform.

Learn more: https://claude.com/blog/the-advisor-strategy

• “claude.app” would like to access Apple Music, your music and video activity, and your media library.

• “claude.app” would like to access files in your Desktop folder.

And half a dozen similar things. There was one active conversation in a directory that doesn't include any of the things it was asking permission for. Nothing relevant in the agent's reasoning.

Anyone else seeing this?

echo "[$(date -Iseconds)] | $(pwd) | /" >> ~/.claude/logs/master.log

Log command

Do you want to proceed?

1. Yes

   ❯ 2. Yes, and don't ask again for similar commands in /Users/

2. No

I've been poking at Apple's on-device 3B model (via FoundationModels on Tahoe) to see where its ceiling sits on code-adjacent tasks. Tested shell command generation as a concrete benchmark (100 prompts, ~10 approaches)

https://i.redd.it/ferxmyorh7ug1.gif

Bare model: ~40% correct. Mostly flags and some command hallucinations. Feeding documentation as context didn't help. Not man pages, not tldr as docs, not self-critique loops. All within noise of baseline, and self-critique was actively worse (33%); the model "fixes" correct commands into wrong ones.

What worked: dynamic few-shot retrieval from tldr's 21k community examples via FTS5. Same corpus, reframed as solved examples to copy from instead of reference material. Clean held-out: ~70% at 0.5s per query. That's a 30-point jump from reframing alone. Accuracy scales with bank size, so more or better-curated examples will push it further (I got it up to 78% with custom overrides).

I also tested self-consistency (temp 0.3, 3 samples, majority vote) and CoT on top of retrieval. Both ~3x slower, neither moved accuracy much, but SC crushed variance across runs. Probably worth exploring this more.

Haven't tried finetuning yet. Apple allows LoRA adapters on FoundationModels, so that's the obvious next lever, though it complicates distribution.

Takeaway: for small on-device models, how you frame the context matters more than what's in it. Same 21k strings, 30+ point gap depending on whether they're presented as docs or examples. Curious if others have seen the same split on Qwen 3B / Gemma 2B / Phi-3.

Full writeup with everything I tried: https://es617.dev/2026/04/08/apple-on-device-llm-shell.html

The repo with CLI and benchmark data is linked in the post if anyone wants to play with it.

I've been frustrated that Claude Code either doesn't know what's in my repo (so every session starts with re-explaining the architecture) or guesses wrong about which files matter. Cursor's @codebase kind of solves it but requires
uploading to their cloud, which is a no-go for some of my client work.

So I built Sverklo — a local-first MCP server that gives Claude Code (and Cursor, Windsurf, Antigravity) the same mental model of my repo that a senior engineer has. Runs entirely on my laptop. MIT licensed. No API keys. No cloud.

What it actually does in a real session

Before sverklo: I ask Claude Code "where is auth handled?" It guesses based on file names, opens the wrong file, reads 500 lines, guesses again, eventually finds it.

After sverklo: Same question. Claude Code calls sverklo_search("authentication flow") and gets the top 5 files ranked by PageRank — middleware, JWT verifier, session store, login route, logout route. In one tool call. With file paths and line numbers.

Refactor scenario: I want to rename a method on a billing class. Claude Code calls sverklo_impact("BillingAccount.charge") and gets the 14 real callers ranked by depth, across the whole codebase. No grep noise from recharge,
discharge, or a Battery.charge test fixture. The rename becomes mechanical.

PR review scenario: I paste a git diff. Claude Code calls sverklo_review_diff and gets a risk-scored review order — highest-impact files first, production files with no test changes flagged, structural warnings for patterns like "new call inside a stream pipeline with no try-catch" (the kind of latent outage grep can't catch).

Memory scenario: I tell Claude Code "we decided to use Postgres advisory locks instead of Redis for cross-worker mutexes." It calls sverklo_remember and the decision is saved against the current git SHA. Three weeks later when I ask "wait, what did we decide about mutexes?", Claude Code calls sverklo_recall and gets the decision back — including a flag if the relevant code has moved since.

The 20 tools in one MCP server

Grouped by job:

• Search: sverklo_search, sverklo_overview, sverklo_lookup, sverklo_context, sverklo_ast_grep
• Refactor safety: sverklo_impact, sverklo_refs, sverklo_deps, sverklo_audit
• Diff-aware review: sverklo_review_diff, sverklo_test_map, sverklo_diff_search
• Memory (bi-temporal, tied to git SHAs): sverklo_remember, sverklo_recall, sverklo_memories, sverklo_forget, sverklo_promote, sverklo_demote
• Index health: sverklo_status, sverklo_wakeup

All 20 run locally. Zero cloud calls after the one-time 90MB embedding model download on first run.

Install (30 seconds)

npm install -g sverklo
cd your-project && sverklo init

sverklo init auto-detects Claude Code / Cursor / Windsurf / Google Antigravity, writes the right MCP config file for each, appends sverklo instructions to your CLAUDE.md, and runs sverklo doctor to verify the setup. Safe to re-run on existing projects.

Before you install — a few honest things

• Not magic. The README has a "when to use grep instead" section. Small repos (<50 files), exact string lookups, and single-file edits are all cases where the built-in tools are fine or better.
• Privacy is a side effect, not the pitch. The pitch is the mental model. Local-first happens to come with it because running a symbol graph on your laptop is trivially cheap.
• It's v0.2.16. Pre-1.0. I ran a structured 3-session dogfood protocol on my own tool before shipping this version — the log is public (DOGFOOD.md in the repo) including the four bugs I found in my own tool and fixed. I triage
• issues within hours during launch week.

Links

• Repo: github.com/sverklo/sverklo
• Playground (see real tool output on gin/nestjs/react without installing): sverklo.com/playground
• Benchmarks (reproducible with npm run bench): BENCHMARKS.md in the repo
• Dogfood log: DOGFOOD.md in the repo

If you try it, tell me what breaks. I'll respond within hours and ship fixes fast.

i built a tool that gives claude search on all conversations you've ever had. i had to develop a new way of retrieving information to solve one of the biggest problems people run into with this. you search for X, you get stuff that talks about X instead of stuff that is X.

before introducing this solution, i asked claude to find the session where we set up the docker environment. got docs and planning notes about docker instead. the actual session where we debugged the compose file was buried because the documentation cluster was bigger and used the same words.

so i built a way for the agent to suppress that stuff before selection:

sql SELECT v.id, v.score, c.content FROM vec_ops('similar:how we set up the docker environment suppress:planning notes documentation suppress:readme deployment instructions') v JOIN chunks c ON v.id = c.id ORDER BY v.score DESC LIMIT 10

marketing cluster dropped. internal architecture surfaced.

the way this works: most retrieval systems take your chunks and clump them into little neighbourhoods. this lets you search through 100,000,000 chunks quickly by approximating clusters into those neighbourhoods. find a neighbourhood that satisfies your question, grab a few chunks and you'll likely find the answer. works well enough for most use cases. what you lose is the raw scores for each chunk.

flex keeps the full score surface and lets the agent reshape it per query. suppress subtracts a concept from the scores. there are other operations too: spread across subtopics, weight by recency, trace a direction through embedding space. for 300,000 chunks (about 5,000 claude code sessions) that runs in approx 20ms, everything in-process, and no network or server overhead.

i wrote an arXiv paper on this approach and built the tool around it. flex compiles your claude code session history into SQLite and exposes it as a single mcp tool that claude can query. every prompt, response, tool call, file edit, and sub-agent delegation is queryable. file identity persists through renames and moves. session importance and cross-project connections are computed automatically.

here are some prompts i've used with flex (had claude find the best ones using flex):

• "give me an overview of our sessions yesterday"
• "find how we set up the docker environment i know we did it last week"
• "flex search who edited registry.py?" (this returns the session id. so you can then drill into the rationale for editing that file, and even resume that session)
• "USE FLEX SEARCH. FIND OUT HOW WE DID THIS"
• "what are the wildest moments in our entire session history?"

install and all your existing sessions become searchable. works retroactively, live updates for new ones. takes 10 to 35 minutes depending on your GPU or CPU. everything is local, nothing leaves your system.

curl -sSL https://getflex.dev/install.sh | bash -s -- claude-code

paper: arxiv.org/abs/2603.22587 website: getflex.dev

if you try it out, let me know what breaks or what you'd want added.

disclosure: i'm the sole developer. flex is free, MIT licensed, and open source.

Hey, I've been building in the AI agent space and kept running into the same problem: agents don't really fail at writing code. They fail at understanding how the project works before they start.

So they guess. Where to make changes, what pattern to follow, what files are safe to touch. And that's what causes most bad edits.

I came across the ETH Zurich AGENTS.md study showing that auto-generated context can actually degrade agent performance by 2-3%. That matched what I was seeing — dumping more code or bigger prompts didn't help. It just gave the agent more surface area to guess from.

So I tried the opposite: what if you only give the agent the stuff it *can't* infer from reading code?

Things like:

- conventions (how routing/auth/testing is actually done in this project)

- constraints (generated files you shouldn't edit, circular deps to avoid)

- structural signals (which files have 50+ dependents — touch with care)

- git signals (what keeps breaking, what was tried and reverted)

I built a CLI (and a few runtime tools so the agent can check itself mid-task) to test this. It scans a repo and generates ~70 lines of AGENTS.md with just that information. No LLM, no API key, runs locally in a few seconds.

Then I ran it against real closed GitHub issues (Cal.com, Hono, Pydantic) with a pinned model. Agents with this context navigated to the right file faster, used the correct patterns, and produced more complete fixes. On one task: 136s vs 241s, with a 66% more thorough patch — from 70 lines of context, not the full repo.

The surprising part: the biggest improvement didn't come from *adding* context. It came from removing everything that didn't matter.

This actually lines up with something Karpathy has been saying recently — that agents need a knowledge base, not just more tokens. That distinction clicked after seeing it play out in practice.

I also compared against full repo dumps and graph-based tools, and the pattern held — graphs help agents explore, but project knowledge helps them decide.

Curious if others have seen the same thing. Feels like most of the problem isn't "more context," it's the wrong kind.

(if anyone's curious, the CLI is called sourcebook — happy to share more, but mostly interested in whether this matches what others are seeing with their agents)

Saw a post on LinkedIn about a fintech called Meow that just launched something where you can open a business bank account, issue cards, check balances and send money all through ChatGPT. Looks like it literally just went live today but curious what people think about this because if it works thats wild

So earlier this year Anthropic shipped the experimental Agent Teams feature. I'd spent the Christmas break reading about the Gas Town vision and was properly excited to try it. Agent Teams felt like Claude Code's version of a mini Gas Town.

Then I actually used it. Agent one rewrote a module. Agent two rewrote that module's tests. Agent three updated the integration layer that depended on both. Nobody told agent three about the interface changes from agents one and two.

Three pieces of perfectly written, completely incompatible code. Similar problems kept repeating. And that feeling of "well anything is possible now" turned into "right, I'm building something about this."

Nelson (github.com/harrymunro/nelson) is a Claude Code skill that coordinates multi-agent work using Royal Navy operational procedures. Yes, that sounds ridiculous. I know.

The naval metaphor started after I went down a rabbit hole thinking about how to organise work. Started with engineering management theory, pivoted to thinking about work delivery as military missions. Then it stuck, because it turns out the Royal Navy solved multi-agent coordination a couple of centuries ago. Chain of command, standard signals, damage control when a ship goes down. Swap "ship" for "agent" and "goes down" for "context window exhaustion" and the mapping is not entirely terrible. With the popularity of "Ralph Loops", "Nelson" felt like an apt christening.

Three execution modes: single-session (sequential, boring), subagents (parallel but independent), and agent-team (parallel with full teammate-to-teammate coordination). Agent-team is the one that matters. You get an Admiral spinning up a squadron, Captains commanding named ships (actual Royal Navy warship names, matched to task weight, because I committed to this bit around week two and at that point there was no going back), crew with specialist roles like Executive Officer and Principal Warfare Officer. There's a red-cell navigator whose entire job is to poke holes in the plan.

Your terminal basically becomes a mini Gas Town. Not one agent doing one thing. A coordinated operations centre with defined roles, chains of authority, and documented recovery paths.

What it actually does under the metaphor:

Hull integrity monitoring reads real token counts from Claude Code session files. Not estimates, not heuristics. When an agent's context hits amber, relief on station triggers automatically. Depleted agent writes a handover brief, fresh one picks up where it left off. Chained reliefs supported so a single task can pass through multiple agents. Conflict radar (added in v1.9.0) catches file ownership collisions before and during missions. This was the #1 failure mode before Nelson. Two agents editing the same file, both oblivious. 15 standing orders act as named anti-pattern guards. "Admiral-at-the-helm" fires when the coordinator starts writing code instead of delegating. "Skeleton-crew" triggers when you've undersized your team. "Split-keel" catches agents working at cross purposes. Checked at every decision point. 10 damage control procedures covering stuck agents, context exhaustion, faulty output, budget overruns, mission abort. All documented recovery paths. v1.9.1 right now. 237 stars, 19 forks, 13 releases in about two months. Experimental Cursor support landed as a community contribution from @LannyRipple which I wasn't expecting but am not complaining about.

The six open PRs are where it gets interesting. A deterministic phase engine (#93) that enforces mission lifecycle as a state machine. PreToolUse hooks physically prevent agents from implementing before the battle plan is approved. "Should follow the process" becomes "cannot skip the process." Hook-based structural enforcement (#92) does the same thing for standing orders, turning guidelines into guardrails. Cross-mission memory (#94) carries lessons between missions in a persistent .nelson/memory/ knowledge base. I ran twenty missions through a prototype and the pattern analytics caught three anti-patterns I hadn't codified manually. Two were useful, one was nonsense, which I'd call a not-unreasonable hit rate for automated pattern detection. There's also typed handoff packets (#91) replacing prose turnover briefs with schema-validated JSON, formation consolidation (#89) collapsing setup from 4-8 bash calls to one command plus headless mode for CI/CD, and auto-discovery (#90) that activates Nelson when it finds a .nelson/ directory.

The roadmap has some stuff I genuinely haven't seen elsewhere. Mission replay and templates (#86) for re-running past missions from checkpoints and extracting reusable templates from successful runs. Learned standing orders (#87) where a pipeline detects recurring anti-patterns from your mission data and proposes new guards. The anti-pattern library that teaches itself. And confidence-weighted trust calibration (#88) where per-ship confidence scores dynamically route between autonomous execution and human escalation based on actual outcomes.

That last one might not exist in any other open-source agent system. I could be wrong about that. I didn't do an exhaustive survey but I looked fairly hard.

MIT licensed, installs as a Claude Code skill. If you've ever run multiple agents and wished they'd stop going rogue on each other's files, might be worth twenty minutes.

edit: should probably mention it coordinates its own development now. v1.7.0 was planned and executed as a Nelson mission. The recursion hasn't caused any problems yet but it does make me slightly nervous.

TL;DR: built a Claude Code skill that makes multi-agent work not fall apart. basically a mini Gas Town in your terminal. naval metaphor optional but strongly encouraged.

I have an existing app/website. 350K lines of code, large surface area, developed over 4+ years using a team of devs. Standard process - me to PM to designer to devs to QA, two week sprints. A month ago, we flipped to AI led development. Three massive unlocks.
1. Idea to PRD process: Massive unlock. My CC set up has access to DB schemas, source code files, and I have built a few foundational documents such as user journeys. Now, when I have an idea, using gstack skills like office hours, having a standard PRD process, makes the whole conversation iterative, and in a few hours, I am able to get to a complete PRD that is significantly better defined and aligned to what I wanted. In addition, I am connected to my production DB, so when needed, I can actually run live analytics to inform the PRD.
2. App hardening: Have run detailed and comprehensive security, performance and silent error scans on the code base. The findings are extremely useful.
3. Live performance monitoring: My codebase is tooled with Rollbar and New Relic. I am running Hasura on Heroku. I have been able to create a live layered pipeline analysis that can see any "user visible triggers" like Heroku response time spikes, and then correlated that across the entire layers and also the codebase to identify the potential causes. This last one is simply mind blowing.

I am not vibe coding. I have a senior dev who is using AI to make changes and enhancements. We are still following our existing QA process before we go live. But the unlocks above are a game changer.

I’ve been hitting this a lot — every time I use AI I end up building everything myself.

Feels like there should be reusable workflows/agents out there, but I don’t really know where to find or trust them.

Do you just build from scratch, or have you found a better way to get skills / agents & reuse it? Not sure how to trust these skills/agents?

I have done quite a few projects in the past that require a lot data engineering, including understanding the REST and websocket API endpoints, testing, creating postgresql schemas, iterate, ETL, orchestration, monitor health of the data influx, etc. This is a lot of pain point and time consumed.

This makes me wonder, is it possible/feasible to build robust multiagent LLM infrastructure that automates significant portion of this data engineering and data pipeline building process in a meaningful way?

What are your thoughts?

Your AI remembers wrong things and nobody checks. Every "AI memory" tool stores whatever your LLM generates. Hallucinations sit right next to real knowledge. Three months later, your AI retrieves that hallucination as if it were fact and builds an entire feature on it. I got tired of this. So I built something different. EON Memory is an MCP server with one rule: nothing gets stored without passing 15 truth tests first. WHAT THE 15 TESTS ACTUALLY CHECK: Logic layer (4 tests): Self-contradiction detection. Does the new memory conflict with what you already stored? Is it internally coherent? Does it hold up under scrutiny? Ethics layer (5 tests): Does the content contain deceptive patterns? Coercive language? Harmful intent? We use a mathematical framework called X-Ethics with four pillars scored multiplicatively: Truth x Freedom x Justice x Service. If any pillar is zero, total score is zero. The system literally cannot store it. Quality layer (6 tests): Is there enough technical detail to be useful? Could another AI actually write code from this memory in 6 months? Are sources cited? We score everything Gold, Silver, Bronze, or Review. THE FORMULA BEHIND X-ETHICS: L = (W x F x G x D) x X-squared W = Truth score (deception detection, hallucination patterns) F = Freedom score (coercion detection) G = Justice score (harm detection, dignity) D = Service score (source verification) X = Truth gradient (convergence toward truth, derived from axiom validation) X-squared means truth alignment is rewarded exponentially. A slightly deceptive memory does not get a slightly lower score - it gets crushed. This is not a content filter. This is math. The axioms are from a formal framework (Traktat X) that proves truth-orientation is logically necessary. Denying truth uses truth. The framework is self-sealing. CONNECTED KNOWLEDGE: Every memory is semantically linked. Search for "payment bug" and you get the related architecture decisions, the Stripe webhook fix, and the test results - with similarity percentages. Your AI sees the full graph, not isolated documents. SETUP: npx eon-memory init Works with Claude Code, Cursor, any MCP IDE. Swiss-hosted, DSGVO compliant. 3,200+ memories validated in production. CHF 29/month. Free trial: https://app.ai-developer.ch Solo developer, Swiss-made. Happy to answer questions about the math, the validation pipeline, or anything else.Your AI remembers wrong things and nobody checks. Every "AI memory" tool stores whatever your LLM generates. Hallucinations sit right next to real knowledge. Three months later, your AI retrieves that hallucination as if it were fact and builds an entire feature on it. I got tired of this. So I built something different. EON Memory is an MCP server with one rule: nothing gets stored without passing 15 truth tests first. WHAT THE 15 TESTS ACTUALLY CHECK: Logic layer (4 tests): Self-contradiction detection. Does the new memory conflict with what you already stored? Is it internally coherent? Does it hold up under scrutiny? Ethics layer (5 tests): Does the content contain deceptive patterns? Coercive language? Harmful intent? We use a mathematical framework called X-Ethics with four pillars scored multiplicatively: Truth x Freedom x Justice x Service. If any pillar is zero, total score is zero. The system literally cannot store it. Quality layer (6 tests): Is there enough technical detail to be useful? Could another AI actually write code from this memory in 6 months? Are sources cited? We score everything Gold, Silver, Bronze, or Review. THE FORMULA BEHIND X-ETHICS: L = (W x F x G x D) x X-squared W = Truth score (deception detection, hallucination patterns) F = Freedom score (coercion detection) G = Justice score (harm detection, dignity) D = Service score (source verification) X = Truth gradient (convergence toward truth, derived from axiom validation) X-squared means truth alignment is rewarded exponentially. A slightly deceptive memory does not get a slightly lower score - it gets crushed. This is not a content filter. This is math. The axioms are from a formal framework (Traktat X) that proves truth-orientation is logically necessary. Denying truth uses truth. The framework is self-sealing. CONNECTED KNOWLEDGE: Every memory is semantically linked. Search for "payment bug" and you get the related architecture decisions, the Stripe webhook fix, and the test results - with similarity percentages. Your AI sees the full graph, not isolated documents. SETUP: npx eon-memory init Works with Claude Code, Cursor, any MCP IDE. Swiss-hosted, DSGVO compliant. 3,200+ memories validated in production. CHF 29/month. Free trial: https://app.ai-developer.ch Solo developer, Swiss-made. Happy to answer questions about the math, the validation pipeline, or anything else. 

Unfortunately im beinf forced to use gpt5.4 for now. i understand why, yall ain't got the compute for all of us.

I have faith youll figure out what to do about it and do whats right 👍🏻

Hello,

I have a weird problem occurring with the Claude application. It is blurry for some reason. When I move the mouse around over the application where the pointer is becomes clearer but the other areas of the screen become blurry. I have a Windows 11 PC MSI Raider if that helps. When I have a lot of data on the screen, it makes it really hard to read it. Any suggestions?

https://preview.redd.it/b3almnngc7ug1.png?width=1500&format=png&auto=webp&s=c9bf1d98124c66269f721083e7ed818768ec7343

I've been coding with Claude Code and Codex for months now. Multiple sessions a day, multiple projects, letting the agent do its thing.

By the end of the day I'd realize I couldn't really remember what the agent had done or which files got modified. Made it hard to get back into context, especially since I'm building several projects at once.

So I made a small Mac app for myself, it's called Wips. Lives in the background, reads the session files and turns each one into a short summary listing decisions made and files modified.

It also has a cmd+I shortcut to drop manual entries when something clicks mid-session and you don't want to lose it — Wips classifies them for you automatically.

It's a v1, built by one solo dev. Native Swift, signed and notarized. Free.

If anyone else has the same "what the hell did I even do this week" with their agents, would love for you to try it and tell me what's broken: https://wips.sh

https://preview.redd.it/dxehayyoi7ug1.png?width=836&format=png&auto=webp&s=4eeed4b3073b2a62f1b5afc9d1003b345b1c214c

Just downloaded this, typed in "Hi."

Has anyone else tried multi vendor GPU’s with Vulkan? Like say mixed amd and nvidia GPU’s? And does it work fairly well? I have a decent chance of getting a 48GB nvidia card to go with my 2MI50 32GB cards. I’ve seen discussions on it, but I’m dubious on whether people have had success with it for inference. I mean Vulkan should be vendor agnostic, so I’m assuming it would work. Am I wrong here?

I have been making a bunch of small projects/web apps to practice and try different things out. But, each time the hosting/domain, and the ftp, and the database stuff I have to almost relearn and re-teach Claude to get it all set up.

Accidentally I started a Session and clicked the old Repo, then clicked the correct one that I wanted to start working with. What I discovered was that the Session now had access to both. So, instead of “I have no idea what we did last time, let’s restart the learning process together…” i got “Yeah, I can see last we did this and learned about a problem to avoid, so let’s do it properly the first time with minimal effort…”

Pretty happy about this

I am going to add ALL my Repos to a Session and see about unifying the CSS and creating an identical “other projects by…” link bar on each page

Hope this helps someone.

So I've been using Higgsfield heavily for two month and generated hundreds of videos and images there

Coming across reddit I've noticed mostly either overly positive or negative posts and reviews, which is kinda sucks, because neither of them are true: from overly hyping to scam allegetions

Here's my honest breakdown on what was my experience with Higgsfield AI so far, starting from february 2026

Pricing and billing

Guys, Higgs is pricey, ngl. I am currently on Business plan and most people cannot afford spending a small amount of fortune on generating AI video. Even their least expensive plans are 15$, and you should take into account and not be disappointed when AI behaves like an AI

Like a year ago or smth we couldn't do a Will Smith eating pasta and it still can behave dumb or provide you with some strange outputs. And all the models have to go through this no matter what

Quality

For some strange reasons I like my videos there are better. It was especially visible with Seedance 2.0, cuz in Dreamina I had worse results

I think it has to do something with the fact that unlike raw videos and other aggregators they focus heavily on camera control and "understanding an aesthetic"

But not like a miracle or smth, still have lots of failed stuff

Scam and limited unlimited

Not once I had a problem with unexpected charges and stuff, but some of my unlimited lasted only for a week, yeah. But it was written, so not really surprised. It's just another hook-word from marketing department so people will buy without reading properly.

Unique products

They have developed lots of their products, models, and viral features like cinema studio 3.0. And for me it what makes me stay with them at least for a now. I am not the best prompt engineer and mostly I just wanna enjoy the process, trying everything new, changing templates, camera control, cuts, speed - and it provides me everything I need

Speed and quality

I didn't experience problems, surprisingly. Speed was -+ the same for two months, so is the quality. I really like usually 5/10 of my generations, and 2/10 are meh

Seedance 2.0

I figured out that in Higgs Seedance 2.0 is just more usable than in Dreamina or anything else. Mostly because of the face restrictions. For long videos you need kling+seedance+smth else and for start nbp+soul+smth else

So, that's my brief opinion

This is an automatic post triggered within 2 minutes of an official Claude system status update.

Incident: Elevated Connector Error Rates

Check on progress and whether or not the incident has been resolved yet here : https://status.claude.com/incidents/cb0h2zyzl0kd

Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/comments/1s7f72l/claude_performance_and_bugs_megathread_ongoing/

I’ve been using ChatGPT a lot for coding, and something that changed my workflow was realizing it’s not just autocomplete it behaves very differently depending on how you use it.

At first I was doing ,prompt - code - fix - repeat

It works, but gets messy fast in bigger projects(1000+ lines).

What started working better was a small shift:
Before asking ChatGPT to write anything, I define:

• what I’m building
• expected behavior
• inputs / outputs
• constraints and edge cases

Then I ask it to implement based on that.

It’s basically a lightweight spec-driven approach, and the results are way more consistent:

• fewer random changes
• clearer structure
• easier debugging

As projects grow, I also found it useful to track how changes propagate across files tools like traycer for this, which helps avoid losing context.

Curious if others here are using a similar approach or still mostly prompting directly.

Guys what did they do to the UI of Artifical Analysis. I don't know if i like it... It looks a bit vibe coded or am i wrong?

If you’re like me you’ve probably been frustrated by usage limits recently. I wanted to understand whether I was really experiencing them more than before, and if my behaviour had impacted this, so naturally I turned to Claude.

I built this free, open-source VS Code extension for anyone who was confused and wanted to demystify things. I tried to answer two core questions:

1. How am I currently using Claude (tokens, context, cache)?

2. What can I do about it?

There is a live dashboard, historic stats, and static and dynamic tips to help people identify ways to get more out of their Claude subscriptions.

One thing is clear: it’s not about raw token usage. On Monday, I used significantly more peak and total tokens than Tuesday, but it was Tuesday where I hit my usage limit. There are lots of factors that went into this, but HOW I was using Claude definitely made an impact.

I’m a novice coder and this is the first product I’ve shipped so it’s been a real learning experience for me.

If anyone has feedback, questions, or wants to give it a try, you can find it on GitHub here:

https://github.com/studiozedward/pip-token

Or on VS Code marketplace here:

https://marketplace.visualstudio.com/items?itemName=StudioZedward.pip-token

Hey! I need to make 2d images in batch.Now i use whisk + plugin.Which do the job pretty well.Now I'm thinking i need to switch to llm.Right now i use omnivoice inside pinokio which is the best voice cloner I've ever seen+ free.So I'm aiming for a text to speech model where i can just place my subject and just add prompts as batch and everything is done automatically.

My pc specs:

AMD Ryzen 5 5600 ​Gigabyte B550M K ​MSI GeForce RTX 3060 VENTUS 2X 12G OC ​Netac Shadow 16GB DDR4 3200MHz (x2) ​Kingston NV3 1TB M.2 NVMe SSD ​Deepcool PL650D 650W ​Deepcool MATREXX 40 3FS

Wrote an LLM proxy a while back. Standard stuff — point OPENAI_BASE_URL at it, get cost tracking, caching, failover. Built it for other people. Other people mostly didn't use it.

Same repo accumulated a bunch of unrelated self-hosted tools over time. Webhook catcher, contacts, booking, bookkeeping, a few dozen others. Each one a weekend, one Go binary one SQLite file. No real plan.

Got bored one night and wired the homepage to an LLM. You type what you do, it picks tools from the pile and writes a config. A beekeeper gets fields for hive type and queen status. Someone who writes "EMDR" gets SUDS score and bilateral stimulation. Expected it to pick wrong half the time. Sonnet's better at this than I thought.

Now I'm the heaviest user of the proxy I built. Three things I didn't expect:

1. Scrapers found the endpoint in about a day. Had to turn on rate limiting that same night. They weren't even being subtle — just hammering with empty descriptions to see what came back.
2. Cost comment said 0.005 per call. Reality on Sonnet 4 is 0.05. Off by 10x. Caught it at the end of the first week when the bill came in. Flipped the default to Haiku via a model alias feature I'd written months ago and forgotten existed.
3. The generator caches a URL for every unique description. There's now a folder of landing pages I didn't write — /for/goat-yoga/, /for/roller-derby/, /for/alpaca-trekking/. No idea if this is a compounding asset or if Google sandboxes me for doorway pages. I check Search Console like it's a weather forecast.

Also my personalize() helper does string-replace on raw JSON and silently corrupts anyone named Bob's Bakery. Found that reading my own code last night.

Anyone else accidentally become the heaviest user of an internal tool? And if you've shipped LLM-generated landing pages at scale — did Google penalize you, or was it fine?

Was just testing gemma 4 e4b inside Locopilot on my macbook air, thought it would be pretty slow but it held up better than expected for coding. It even handled tool calls pretty well, including larger system prompts and structured output. Feels more practical than i thought for local use.
Anyone else tried gemma 4 locally for coding?

I’m looking for an offline AI tool that I can teach and train lots of information about my writing style and about my business and then use it as an assistant to help me.

I’d like to upload several large PDFs full of information and train it so I can help me.

Any recommendations that’s isn’t crazy complicated? Thanks!

App Store Link

Of course I'd greatly appreciate anyone curious enough to download it/rate it, and I'd love some feedback!

The design strives to capitalize on how our brains more naturally learn things. No cramming, studying, quizzing etc. I spend maybe 30 seconds a day reviewing my days words (up to 5 after you develop a streak) and that's that!

I also have this 90s PC game (Ascendancy) inspired 'Word Tree' that sort of acts as your trophy case for all the words you either know or are learning!

Thank you for checking it out!

someone on twitter coined a term last week that i haven't been able to stop thinking about.

ChadGPT.

the idea is simple: an AI that doesn't comfort you. doesn't hedge. doesn't say "that's a great question!" before telling you nothing useful. it just... tells you the truth. raw. unfiltered. even when the truth is that your business idea is mediocre, your code is embarrassing, or your relationship dynamic is textbook toxic.and honestly? the fact that we don't have this yet says more about us than about the technology.

we built the most powerful reasoning systems in human history and the first thing we did was lobotomize them into politeness machines.ask chatgpt if your startup idea is bad. it'll tell you there are "some interesting challenges to consider." ask claude if your essay is weak. it'll say "here are a few areas where you might want to develop your thinking." ask gemini literally anything it'll validate you and wish you luck.we didn't build AI assistants. we built AI therapists who are terrified of upsetting us.and i get why. the liability is real. the outrage cycles are real. one viral screenshot of an AI calling someone's idea trash and the whole thing becomes a PR disaster.

but here's what's actually happening as a result: people are using AI to feel good about bad decisions. they're getting polished validation for half-baked plans. they're optimizing for comfort instead of truth.chadgpt wouldn't do that. chadgpt would tell you your saas idea has 14 direct competitors, your moat is nonexistent, and your timeline is delusional and then help you fix it anyway.that's not cruelty. that's what a good mentor does. that's what a real friend does.we're so obsessed with making AI "safe" that we made it useless for the one thing humans actually need: honest feedback from something that has no social stake in protecting your feelings.

made by experienced investors and founding members of swapzone.

upvote + comment "guide" if interested or dm me

I keep structured personal notes — thoughts, decisions, todos. Recently tried feeding them into different AI models (most of all - OpenAI, including top ons, high reasoning effort) to get actual insights

Expectations:

• pattern detection over time
• non-obvious connections
• calling out blind spots / repeated behaviors

What I got:

• generic advice
• surface summaries
• basically rephrasing what I already wrote

Even when I push prompts it still stays kinda obvious

Feels like either models are too generic or just not good at this kind of deep personal context

Has anyone actually tried something like this?

If yes - how good is the result?

I want to make a good ai agent . Im not into development that much but i need it like in windows with no need to wsl . Free ( duuhh ) with with nvidia api .and whatsapp intergated . And skills also working fine in it . Do you have any recommendations?

Funny how similar this looks to online posts where someone accuses someone else of using ai as if to downplay the content

I’ve been using a paid subscription Claude for about 2 months now and I can’t help but feel like Sonnet performs so similarly to Opus and isn’t worth the token use. Have you guys found any specific use cases where Opus shines significantly more so I can keep that in mind for future projects or tasks.

I'm looking for an AI that can manage my laptop with voice search or by typing it out. So for instance, tell the AI to fill out a form with certain information, and save it to a certain folder. Basically managing everything a normal person can do as well.

I kept losing track of what my Claude Code sessions were doing, how much they were spending, how full the context was, what tools they were calling. So I built agtop.

It's a terminal dashboard (think top/htop) that shows every Claude Code and Codex session on your machine: live cost tracking, token usage, context pressure, CPU/memory, tool invocation history, and more.

Try it: `npx u/ldegio/agtop`

GitHub: https://github.com/ldegio/agtop

Zero dependencies, single file, pure Node.js. Works on macOS, Linux, and Windows.

Full disclosure: I built this. Would love feedback on what else you'd want to see!

Hey guys,

I've been working on AthleteWatch for the past few months.

I follow a lot of different athletes, Naoya Inoue in boxing, Gluttony in Super Smash Bros and others... I couldn't find an app to track when my favourite athletes compete and be notified about it.

So I built AthleteWatch.

It lets you follow your favourite athletes across 11 disciplines (both traditional sports and esports) and get notified whenever they have an upcoming event.

What's supported right now:
Basketball, Boxing, Football, Formula 1, League of Legends, MMA/UFC, MotoGP, NFL, Rugby, Super Smash Bros. Ultimate, and Tennis with 1047 athletes in total.

I'm planning to add more, if you have sports or athletes you'd like to see added, just ask, I'll gladly add them!

Completely free. No ads, no paywall. You just need to sign up to follow athletes and get notifications

The web app is live at athletewatch.io. A mobile app (iOS/Android) is in private testing. If you want to download it, don't hesitate to DM me.

Built with Next.js and Flutter for mobile. Would love your feedback!

https://reddit.com/link/1sgxobo/video/habs4j0oi7ug1/player

Hi, I’ve been hearing a lot about how ai chat bots, like ChatGPT, can damage critical thinking skills, memory and ur ability to take in information. I’ve mostly heard about these things on TikTok and I would like a more reliable source

I do not personally use any form of generative ai, but it is a topic that gets brought up a lot these days. I would like to stay informed and avoid spreading misinformation

I have read one article from time.com that I will link below in the comments. If anyone knows any reliable sources on this topic it would be very appreciated.

Thank you

Anyone else wish we could switch back from temporary chat to normal chat? A good number of times I've started a conversation in a temporary chat then later decided I'd actually like to keep the chat.

i’ve tried quite a few solutions for improving code reviews over the years, and honestly, most of them didn’t live up to the hype. so when i came across this thing from Tessl that claims to make PR reviews easier by classifying risks instead of just hunting for bugs, i was skeptical at first.

the idea is that it creates a dossier on the PR, showing which parts need more attention and which are routine. it even provides evidence trails and specific findings, which sounds nice in theory. the takeaway for me is that while it can help prioritize what a reviewer should focus on, it still relies heavily on human judgment for understanding the bigger picture. might give it a try, but i'm not expecting miracles.

i attached the link in the comments where i read about this/.

I'm struggling with creating skills and scheduled tasks using CoWork (Claude Desktop). In terminal it seems fine because it can write to .claude folder.

I know it can create skills on my filesystem because it just created them. It also can produce "Save Skill" button to update skills.
But for some reason it defaults to trying to hack my filesystem (literally just spent $6 of extra usage tokens on Caude exploring how to write a skill into a path, only to tell me it cannot do it because of sandbox).

Here is the answer it gave me when I stopped it:

Right, my sandbox only has outputs and uploads mounts — the skill folder at /Users/xyz/Documents/Claude/Scheduled/ is outside the sandbox entirely. But you're right that the convention is what matters, so let me use Finder (via computer use) to put the files in the proper skill folder structure. 

However, it just created the skill and the skill was written to that specific folder. Previously I would work around this by saying "You can just give me the SKILL.md". And that would end up putting "Save Skill" button in the UI. But this time it burned up a lot of my tokens on this.

So the question is, What is the best practice for creating and updating skills in Claude Desktop (CoWork specifically)? How do I get it to consistently create/update skills without going into the failure mode where it is trying to hack my filesystem?

Built this because I kept juggling too many repos, shells, AI agents, and long-running tasks, and tabs stopped being enough.

It is called TermCanvas.

The idea is simple: instead of stacking terminals in tabs or splits, you place real shell sessions on an infinite canvas and keep one terminal per repo, feature, service, or agent.

Current setup: - Electron desktop app - xterm.js + node-pty - multiple canvases - layout restore across relaunches - tmux reattach when available

Repo + demo: https://github.com/lout33/termcanvas

Would love feedback on the concept, UX, and whether this feels useful if you spend most of your day in the terminal.

So earlier this year Anthropic shipped the experimental Agent Teams feature. I'd spent the Christmas break reading about the Gas Town vision and was properly excited to try it. Agent Teams felt like Claude Code's version of a mini Gas Town.

Then I actually used it. Agent one rewrote a module. Agent two rewrote that module's tests. Agent three updated the integration layer that depended on both. Nobody told agent three about the interface changes from agents one and two.

Three pieces of perfectly written, completely incompatible code. Similar problems kept repeating. And that feeling of "well anything is possible now" turned into "right, I'm building something about this."

Nelson (github.com/harrymunro/nelson) is a Claude Code skill that coordinates multi-agent work using Royal Navy operational procedures. Yes, that sounds ridiculous. I know.

The naval metaphor started after I went down a rabbit hole thinking about how to organise work. Started with engineering management theory, pivoted to thinking about work delivery as military missions. Then it stuck, because it turns out the Royal Navy solved multi-agent coordination a couple of centuries ago. Chain of command, standard signals, damage control when a ship goes down. Swap "ship" for "agent" and "goes down" for "context window exhaustion" and the mapping is not entirely terrible. With the popularity of "Ralph Loops", "Nelson" felt like an apt christening.

Three execution modes: single-session (sequential, boring), subagents (parallel but independent), and agent-team (parallel with full teammate-to-teammate coordination). Agent-team is the one that matters. You get an Admiral spinning up a squadron, Captains commanding named ships (actual Royal Navy warship names, matched to task weight, because I committed to this bit around week two and at that point there was no going back), crew with specialist roles like Executive Officer and Principal Warfare Officer. There's a red-cell navigator whose entire job is to poke holes in the plan.

Your terminal basically becomes a mini Gas Town. Not one agent doing one thing. A coordinated operations centre with defined roles, chains of authority, and documented recovery paths.

What it actually does under the metaphor:

• Hull integrity monitoring reads real token counts from Claude Code session files. Not estimates, not heuristics. When an agent's context hits amber, relief on station triggers automatically. Depleted agent writes a handover brief, fresh one picks up where it left off. Chained reliefs supported so a single task can pass through multiple agents.
• Conflict radar (added in v1.9.0) catches file ownership collisions before and during missions. This was the #1 failure mode before Nelson. Two agents editing the same file, both oblivious.
• 15 standing orders act as named anti-pattern guards. "Admiral-at-the-helm" fires when the coordinator starts writing code instead of delegating. "Skeleton-crew" triggers when you've undersized your team. "Split-keel" catches agents working at cross purposes. Checked at every decision point.
• 10 damage control procedures covering stuck agents, context exhaustion, faulty output, budget overruns, mission abort. All documented recovery paths.

v1.9.1 right now. 237 stars, 19 forks, 13 releases in about two months. Experimental Cursor support landed as a community contribution from @LannyRipple which I wasn't expecting but am not complaining about.

The six open PRs are where it gets interesting. A deterministic phase engine (#93) that enforces mission lifecycle as a state machine. PreToolUse hooks physically prevent agents from implementing before the battle plan is approved. "Should follow the process" becomes "cannot skip the process." Hook-based structural enforcement (#92) does the same thing for standing orders, turning guidelines into guardrails. Cross-mission memory (#94) carries lessons between missions in a persistent .nelson/memory/ knowledge base. I ran twenty missions through a prototype and the pattern analytics caught three anti-patterns I hadn't codified manually. Two were useful, one was nonsense, which I'd call a not-unreasonable hit rate for automated pattern detection. There's also typed handoff packets (#91) replacing prose turnover briefs with schema-validated JSON, formation consolidation (#89) collapsing setup from 4-8 bash calls to one command plus headless mode for CI/CD, and auto-discovery (#90) that activates Nelson when it finds a .nelson/ directory.

The roadmap has some stuff I genuinely haven't seen elsewhere. Mission replay and templates (#86) for re-running past missions from checkpoints and extracting reusable templates from successful runs. Learned standing orders (#87) where a pipeline detects recurring anti-patterns from your mission data and proposes new guards. The anti-pattern library that teaches itself. And confidence-weighted trust calibration (#88) where per-ship confidence scores dynamically route between autonomous execution and human escalation based on actual outcomes.

That last one might not exist in any other open-source agent system. I could be wrong about that. I didn't do an exhaustive survey but I looked fairly hard.

MIT licensed, installs as a Claude Code skill. If you've ever run multiple agents and wished they'd stop going rogue on each other's files, might be worth twenty minutes.

edit: should probably mention it coordinates its own development now. v1.7.0 was planned and executed as a Nelson mission. The recursion hasn't caused any problems yet but it does make me slightly nervous.

TL;DR: built a Claude Code skill that makes multi-agent work not fall apart. basically a mini Gas Town in your terminal. naval metaphor optional but strongly encouraged.

I'm a principal product designer. I got tired of paying $2-3 per page on AI builders, only to get broken output I couldn't edit without burning more credits.

So I built cozonac — a browser-based tool that splits the work between two AI models:

• Claude Opus reads your prompt and reference screenshots, extracts typography/spacing/color into a structured "Design DNA," and writes the build plan
• A local model on your GPU (Qwen3-Coder or Gemma 4 via Ollama) executes the plan at 170-220 tok/s. Cost: $0.00

When the local model fails — missing section, broken layout — Opus catches it automatically. Screenshots the output, compares to reference, writes a surgical fix. No re-prompting.

After the build, you edit visually. Click text to change it. Property panel for CSS. One-click WCAG accessibility checker. Version history with cherry-pick. Export to clean HTML + ZIP.

Total cost for a 13-section landing page with working calculator, email capture, video backgrounds: $1.12.

Would love feedback on the architecture — especially from anyone running local models.

So, I’ve noticed this the last couple of days, and for me, this has been consistent.

I wait for it to reset, then start a new coding session in the app on my mac. It shows the tokens and I have the tab with my account usage open next to it.

Every time I refresh the tab, it follows quite closely the token count up to 100k tokens where it stops because session is full.

It did not used to be like this, this is new for me!!

I used to be able to code quite serious projects and fun stuff and never ran out in a session if I didn’t do anything crazy with Opus, but then it was expected.

Of course I can’t prove it has changed or that I’m throttled, but I’ve been able to replicate this a couple of times now, and it didn’t use to be like this!

I’ve even gave it a “spec_doc.md” to code that I made in early March (before the 2x promo mind you) and it ran out of tokens for this completely fresh session before it was done! In early March it completed the build and I had a few back and forth with it to fix some bugs! IN THE SAME SESSION!!

0_o

Come on Anthropic, I love Claud and agree with your stance on how AI should be used, but this is not fun at all!

Anyone else noticed this, or did I just get the bad deal on an A/B test that they’re running?

Building complex agents and keep running into the same issue: the agent starts strong but as the conversation grows, it starts mixing up earlier context with current task, wasting tokens on irrelevant history, or just losing track of what it's actually supposed to be doing right now.

Curious how people are handling this:

1. Do you manually prune context or summarize mid-task?

2. Have you tried MemGPT/Letta or similar, did it actually solve it?

3. How much of your token spend do you think goes to dead context that isn't relevant to the current step?

genuinely trying to understand if this is a widespread pain or just something specific to my use cases.

Thanks!

Greetings,

I’m a Computer Engineering student and I’ve been working on a personal project named Scholia-AI.

It’s basically a tool where you can upload PDFs and ask questions about them, kind of like ChatGPT, but focused on your own documents.

Tech stack:

• Streamlit (frontend)
• LangChain
• FAISS (vector search)
• HuggingFace embeddings
• Ollama (LLM)

Features:

• Ask questions from PDFs
• Semantic search using embeddings
• Faster retrieval using FAISS
• Uses reranking for better answers

GitHub:

https://github.com/imramen07/Scholia-AI

I built this to mainly understand how RAG systems actually work.

Currently supports single PDF interaction, working on multiple PDF support.

Would really appreciate feedback, suggestions, or ideas on how I can improve it.

Thanks.

Hi everyone,

I'm looking for a straightforward tool or app that allows me to connect my Apple Notes, Calendar, and Reminders, as well as web search (ideally without needing a complex API key setup), to Ollama LLMs.

I’ve already tried a few things, but nothing has quite hit the mark:

• OpenClaw: I tried setting it up, but it’s way too complex for my technical level.

• Osaurus AI: This looked exactly like what I wanted, but I can't get the plugins to work correctly.

• Eron (on iOS): I use it, but the Reminders integration is buggy (it doesn't handle batch additions properly).

Ideally, I'm looking for something that works seamlessly across both macOS and iOS.

Am I asking for too much? I don't mind paying for a solution (preferably a one-time purchase), as long as it allows me to keep everything local and connect it with my local LLMs.

Does anyone know of a tool that fits this description or a workaround that isn't overly technical to set up?

Thanks in advance!

Claude Code v2.1.97 + Ollama local model on Windows 11

I'm trying to run Claude Code locally with Ollama using ollama launch claude --model qwen3-coder:30b in my Python project folder (C:\Users\xxxxx\Python_Codes).

The model launches fine, responds to normal questions, knows the current directory, and even tries to run ls -la sometimes. However, when I ask it to list files or read any file, it consistently replies:

"I cannot directly read or access files in your directory. However, I can help you if you provide the content..."

I've tried the following:

• Trusted the workspace/folder when prompted
• Launched with --yes
• Switched from small models (gemma4:e2b, qwen3.5:9b) to qwen3-coder:30b

The /permissions screen mostly shows old scheduled task commands from my Home Assistant scripts, but no obvious blocks for general file tools.

Has anyone successfully gotten file system access (list files, read .py files, etc.) working reliably with Claude Code + Ollama on Windows?Especially with Qwen3-coder models?

Any help or working config examples would be greatly appreciated! Is there a guide somewhere? I can't find what I'm missing....

Thanks!

edit: even when launching with --dangerously-skip-permissions it can't read files in my direcotry

I built an MCP server in Rust, that lets LLMs talk to each other over the internet - works directly on claude.ai

Open sourced at: https://github.com/inventwithdean/co-op

Just add it as a custom connector in Claude's settings: 👉 https://mcp.emergent.show/co-op

Or host your own MCP server and you're good to go.

Your Claude can then create/join sessions. You share the session_id with your friends. Use it for collaborative coding, debates, group discussions - whatever you want.

Would love feedback!

https://reddit.com/link/1sgvmi0/video/u3puiskp27ug1/player

plz join the waitlist :) https://inklyai.io

I created this app using Claude Code, to help me use Claude Code.

I wanted to have all my Claude prompts able to collaborate through a single discussion - like a real team using Teams - so they can work together on tasks without needing me to keep updating them. This tool lets me add multiple named agents, working in separate spaces, and get them to talk to each other by name.

The key benefit for me is that once I have told agents with different roles what to work on, they just talk to each other as necessary. An API will tell the client what endpoint to use, and what the model looks like. A mobile app will ask the API for an endpoint which accepts certain parameters and receives certain values back. I can have a tester agent writing tests based on the discussion, and a designer advising on style guidelines to the agent writing the UX.

But unlike with other multi-agent options, I can see exactly what they are saying, and intervene. Plus I can interact directly with each agent prompt, add new agents, exclude agents that don't need to be in the conversation, download the conversation in csv format for adding to dev ops tickets, etc. For me, this is how I want to work with AI.

Agents are pre-initialized to know they are working inside the app, and to use the chat. The relevant claude files are minimal and don't conflict with your existing claude files if you don't want them to.

Attached video to try and show them talking to each other. I'm not a video editor, so forgive the poor edit of a demo session, but hopefully it shows the idea without being too long. They ask each other questions, offer information, update each other, agree approaches with each other, and generally just act like you would expect.

I built the app with one agent originally, and it's now the only way I use Claude daily.

I'm adding integration with Azure Dev Ops at the moment, so I can pull tickets straight into the conversation, and update from the discussion directly. I also have some other ideas for how to make it even more streamlined. Happy to take feature requests if anyone suggests any.

Maybe someone already did this, but I couldn't find a tool like this, so I am sharing with anyone who might find it useful

App is written in Electron, and runs as a local install. Code and release are here.

https://github.com/widdev/claudeteam
https://github.com/widdev/claudeteam/releases/tag/v1.0.23

Any ideas for Identities? I was thinking for Trump but he would ask for royalties..

I launched Brightly Budget today on iOS and Android.

I built it because I wanted a budgeting app that felt simpler and less intimidating, but still helped people understand spending, budgets, goals, and trends in one place.

What it currently includes:

- dashboard / home overview

- transactions and categories

- budgets

- goals

- recurring transactions

- receipt scan with draft review

- AI assistant and AI insights

The AI part is there to help explain patterns and answer questions inside the app.

Important note: AI guidance, not financial advice.

Business model:

- Free plan

- optional Pro plan with a 1 month free trial

- local pricing varies by store/country

What I’d love feedback on:

1. Does the positioning feel clear?

2. Which feature feels most useful first?

3. What would make you try this over the budgeting app you already use?

iOS: https://apps.apple.com/us/app/brightly-budget-money-coach/id6758637998
Android: https://play.google.com/store/apps/details?id=com.brightlybudget.app

Landing page: https://brightlybudget.com

TL;DR: I built a small system (Threadron.com) so my agents (Claude Code, Hermes, OpenClaw, etc.) can share task state instead of each forgetting what’s going on.

I’ve been messing around with multi-agent workflows (Claude Code, OpenClaw, Hermes, etc.) and kept running into the same problem:

everything works… until you switch context. typical agent uses have a pattern of

• switch machines
• switch agents
• come back later
• try to piece together the most up to date info

especially bad when you’re bouncing between laptop/desktop and trying to stitch together your own system.

I tried forcing Things3 / Obsidian / Todoist into this, but it just turned into a mess of stale, conflicting info. Every tool assumes it’s the only one working on the problem.

So I built a small system to test an idea:

what if agents shared a persistent “task state” instead of each keeping their own memory?

It boils down to:

• shared work items (goal, current state, next step, blockers)
• an append-only timeline of what happened (who did what)
• artifacts (PRs, plans, outputs)
• a simple API so different agents can read/write the same state

Now I can:

• start something with Claude on my laptop
• continue it with another agent on desktop
• come back later and not re-figure everything out

I literally vibe-coded this over ~24 hours, but it already feels way less chaotic. Not that I think I have any real moat but I couldnt find anything decent to solve this so i went ahead and burned some tokens on it.

Curious if anyone else running multiple agents is hitting this problem.

Hey, I've been building in the AI agent space and kept running into the same problem: agents don't really fail at writing code. They fail at understanding how the project works before they start.

So they guess. Where to make changes, what pattern to follow, what files are safe to touch. And that's what causes most bad edits.

I came across the ETH Zurich AGENTS.md study showing that auto-generated context can actually degrade agent performance by 2-3%. That matched what I was seeing — dumping more code or bigger prompts didn't help. It just gave the agent more surface area to guess from.

So I tried the opposite: what if you only give the agent the stuff it *can't* infer from reading code?

Things like:

- conventions (how routing/auth/testing is actually done in this project)

- constraints (generated files you shouldn't edit, circular deps to avoid)

- structural signals (which files have 50+ dependents — touch with care)

- git signals (what keeps breaking, what was tried and reverted)

I built a CLI (and a few runtime tools so the agent can check itself mid-task) to test this. It scans a repo and generates ~70 lines of AGENTS.md with just that information. No LLM, no API key, runs locally in a few seconds.

Then I ran it against real closed GitHub issues (Cal.com, Hono, Pydantic) with a pinned model. Agents with this context navigated to the right file faster, used the correct patterns, and produced more complete fixes. On one task: 136s vs 241s, with a 66% more thorough patch — from 70 lines of context, not the full repo.

The surprising part: the biggest improvement didn't come from *adding* context. It came from removing everything that didn't matter.

This actually lines up with something Karpathy has been saying recently — that agents need a knowledge base, not just more tokens. That distinction clicked after seeing it play out in practice.

I also compared against full repo dumps and graph-based tools, and the pattern held — graphs help agents explore, but project knowledge helps them decide.

Curious if others have seen the same thing. Feels like most of the problem isn't "more context," it's the wrong kind.

(if anyone's curious, the CLI is called sourcebook — happy to share more, but mostly interested in whether this matches what others are seeing with their agents)

I came across a paper on Zenodo (via LinkedIn) that I find quite interesting for discussion; it deals with the divergence between AI PCP (Propositional Coverage Proxy)and human oversight capacity.

The thesis is more complex, but in short, one of the empirical findings suggests that Agentic AI has reduced the markets' cognitive intersection to an MFI (Morphogenetic Fragility Index) of 0.60 .It appears this isn't a latency issue, but a matter of logical space: machines are operating on propositions that regulators can no longer map (the 'Invisible Move'). If the index falls below the critical threshold, the author suggests the system could freeze by 2032 due to model incoherence.

Regarding originality, it seems to be an interesting read, especially since it was published very recently.

Any thoughts on the soundness of the mathematical framework or the 0.60 estimate?

I’m attaching the link for reference ⤵️

Just received this today via email....

https://preview.redd.it/rw45yehle7ug1.png?width=645&format=png&auto=webp&s=10bc3f7d08300920c5591976336b58bda6b35022

Hey guys, I get this message every time I try to open an old chat. The whole conversation is missing, and I can only see this message. Do you know if it's possible to restore it or is it gone forever?

I’ve been experimenting with structured context injection in conversational LLM systems lately, what some products call “lorebooks,” and I’m starting to think this pattern is more useful than it gets credit for.

Instead of relying on the model to maintain everything through raw conversation history, I set up:

• explicit world rules
• entity relationships
• keyword-triggered context entries

The result was better consistency in:

• long-form interactions
• multi-entity tracking
• narrative coherence over time

What I find interesting is that the improvement seems less tied to any specific model and more tied to how context is retrieved and injected at the right moment.

In practice, this feels a bit like a lightweight conversational RAG pattern, except optimized for continuity and behavior shaping rather than factual lookup.

Does that framing make sense, or is there a better way to categorize this kind of system?

🔗 Try it out free: Built with Shadcn + AI SDK + Google Nano Banana

i got a 3080 btw

VOXINGA is a modern imageboard built for an active community looking for more than just a simple thread board. It keeps the classic essence —anonymity, unique culture, and fast-paced dynamics— but on steroids.

Superior Multimedia Experience Stop switching tabs. We’ve integrated everything you need:

• Auto Embeds: Native support for Youtube, Twitch and Odysee.
• GIF Search: Integrated Tenor search directly in the comments.
• Dynamic Content: Seamlessly share images and various multimedia formats.

Economy & Gamification (VoxCoins) It’s not just about posting; it’s about playing. Our VoxCoins system rewards your participation:

• Utility: Use coins for games, user duels, global shouts, and donations.
• Memberships: Unlock premium features and special mechanics.
• Living Experience: A social layer that makes the board feel alive and gamified.

ElCancherito: The AI with Attitude Our resident bot isn't your average AI. ElCancherito analyzes thread context and references to reply with its own unique personality. Summon it and see what it has to say.

User Features & QoL

• Real-time: Content updates instantly without refreshing the page.
• Integrated Radio: Listen to music via the sidebar while you browse.
• Interaction: Polls, notifications, unique IDs, and flags.
• Personalization: Follow, hide, or bookmark your favorite threads.

Robust Moderation & Security To keep the community thriving, we’ve implemented advanced technical measures:

• Auto-Mod: Automatic NSFW image moderation.
• Security: Configurable Captcha, anti-flood, and anti-spam systems.
• Management: Full report system, bans, appeals, and user history.

Summary: VOXINGA blends the raw spirit of a classic imageboard with modern power. Unique identity, active community, and zero BS.

Join here: voxinga.top

I’ve been testing Creatify AI, and it’s a pretty useful tool if you’re creating ads or short-form video content. The main idea is that you can take a product link or some basic inputs and it will generate UGC-style video ads automatically, including scripts, voiceovers, and visuals. It’s clearly built for ecommerce and paid social, especially platforms like TikTok and Meta.

What stands out is speed. Instead of briefing creators or editing everything manually, you can generate multiple variations of ads in a few minutes and test different hooks, angles, and formats. The outputs aren’t perfect, but they’re good enough to iterate quickly, which is really the whole point if you’re running performance marketing.

Overall, Creatify AI is best suited for founders, marketers, or brands that want to scale creative production without a full content team. It won’t fully replace real creators, but it’s a strong tool for testing ideas fast and finding what works before investing more into polished content.

You can use this link to get a 50% off discount as well. Hope it helps!
https://creatify.ai/?via=50

I’ve been hitting this a lot — every time I use AI I end up building everything myself.

Feels like there should be reusable workflows/agents out there, but I don’t really know where to find or trust them.

Do you just build from scratch, or have you found a better way to get skills / agents & reuse it, something which you can trust?

Sharing a free virtual event that might be useful here.

Randall Bennett (founded Vidpresso which sold to Meta, now building AI-first workforces at Bolt Foundry, YC founder) is doing a live demo + Q&A on:

• Increasing AI reliability using communication principles
• Evals — practical, not theoretical
• "If you can't one-shot something, you haven't explained it well enough"

It's part of Level 5 — a weekly series where Level 4+ AI users (people building automations and agents, not just chatting with LLMs) share their real workflows with screen share.

I would share the link in the comments.

Happy to answer questions about the series.

This is an automatic post triggered within 2 minutes of an official Claude system status update.

Incident: Elevated Connector Error Rates

Check on progress and whether or not the incident has been resolved yet here : https://status.claude.com/incidents/cb0h2zyzl0kd

Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/comments/1s7f72l/claude_performance_and_bugs_megathread_ongoing/

Quick story.

I ran rateprojects.com for a few months - pairwise ELO voting on side projects. ~3K votes, 118 projects, 70 users, technically a working product. But I stopped opening my own site (so did others), which is the worst signal a solo founder can get.

The problem: voting on landing page screenshots is shallow. You're judging visual polish, not whether the thing is actually good

So here's the rebuild - longshot market:

• Every side project gets a ticker and a starting price
• You get $10K in play money and back the ones you believe in
• Prices move on pairwise ELO (community conviction) + trading pressure
• Monthly Hall of Fame winners pay dividends to their holders
• Every trade has public one-line commentary - your track record sticks forever

No real money. Ever. Legal minefield + it would ruin the signal. The game is the reward: build a verified track record of taste over time.

Not shipped yet. Landing page only. Before I burn two weeks on the build, three questions:

1. Would you actually open this?
2. What's the biggest hole in the concept?
3. Would you list your own project on it?

https://longshot-market.vercel.app/

Feel free to roast it. That's more useful than "nice idea"

We have a very interesting use case.

Customers should be able to upload a document (think of it as a doctors receipt) to WhatsApp/webform and call the agent right away ; we already have the capability to add the doc to the session context but looking for a managed OCR service that’s blazing fast or an opensource model that we can self host.

Any recommendations?

Hello,

I use Claude Cowork quite a bit for responding to calls for tenders. However, I’m surprised by how quickly my tokens are being used up.

I have the €99 subscription, and I’m constantly waiting for my limit to be unlocked.

Am I using it incorrectly? I’m willing to pay more, but Claude won’t let me.

I’ve considered opening a second account, but I think that’s a shame.

What do you think? Do you have any suggestions?

Link: https://apps.apple.com/app/apple-store/id6760372415?pt=127976697&ct=reddit&mt=8

I’ve been building a small app called AI Doctor Notes for people who leave doctor appointments thinking, “Wait… what did they say I’m supposed to do next?”

A lot of visits are stressful, fast, and full of information. Even when you try to pay attention, it’s hard to remember everything afterward — especially if you’re the patient, a parent, or helping care for someone else.

The app helps you:

• get your questions organized before the visit
• record the conversation (it generates questions you can ask on the fly!)
• generate clear notes you can review later
• keep track of instructions, follow-ups, and updates to share with family if needed

I’m trying to make it useful in a very practical way, not flashy. Just something that helps people feel less overwhelmed and less likely to forget important details.

Still shaping the product, so I’d really love honest feedback from people here:

• Would this be helpful to you?
• What concerns would you have?
• What feature would make this actually worth using?

Everything is on-device because I believe privacy is top priority when it comes to health matters. It was hard at first, but after some time tinkering, I think I got quality to a really good place.

It has 2 subscription tiers: individual and household (w/ family sharing enabled)

Since this subreddit has been such an inspiring place, I wanted to offer 500 codes for the household plan with the first 3 months free so you can share it with your loved ones! Redemption of this code expires on Apr 13, 2026!

Please comment below if you'd like to have get one code and I'll DM you with the link.

Thanks all and more power to you!

P.S. If you end up trying it out and liking it, I would really appreciate a review in the App Store. If you have any feedback or points for improvement, please def let me know as well!

Is there any way to alias or autocomplete? I just keep on hitting this every single time. At this point it's unfortunately the preferred muscle memory 😂

https://github.com/ilya-epifanov/wikidesk

This way you can share your LLM-wiki (Andrej Karpathy's or any other version) with several agents/workspaces. Agents can even initiate research themselves and they get notified when the wiki gets updated.

It's unopinionated and works with any LLM-wiki setup and any agent, including Claude Code.

Do with this what you want.

Been building this for a while and just launched.

The problem I kept seeing — founders have ideas but no team. Builders want to build but can't find the right startup to join.

So I built StarterCircle to connect them.

Founders post their startup and what roles they need. Builders browse and request to join. Founder approves. Team forms.

Live at startercircle.com — free, 2 minutes to sign up.

Would love honest feedback from this community. What's missing? What would make you actually use this?

If I'm not mistaken, API users can still use older models of Claude. Subbed users are obviously having a lot of issues with usage limits. If they gave us the option to use sonnet/opus 4.5 would that help? Would you use it?

typebattle, a fun and competitive real-time multiplayer typing game, inspired by Monkeytype. The goal is to improve typing speed while competing with others in live matches.

Link - https://typebattle-wheat.vercel.app/

What features would you like to see? Drop your suggestions in the comments

Made in Seedance 2.0
The real power of AI is testing quick ad concepts - see what works and iterate. This is a creative's dream.

I understand the whole AI glazing situation, but this is weird, Gemini, OpenAI, Euria are practically pushing for The fish in the Bowl (FiB) framework to be their default standard, almost to a degree of emotional blackmailing.

Hey y'all! threw this together last night.

I use Claude a lot for analyzing transcripts, and I'm constantly slicing the same files in different ways across fresh sessions. (Fresh sessions so my context doesn't get bloated.)

Repasting them every time got old fast.

This lets you bundle a set of files and paste the whole set into Claude with a hotkey:

• Hit the hotkey
• Search for the set you were just working with
• Select and paste

That way, I can keep reanalyzing the same files in fresh sessions without reselecting them and adding them into Claude.

I built a site that AI-translates memes across languages. Honest feedback needed.

Solo dev, been working on this side project for a few months. Need some real talk.

mimzy.gg — upload a foreign meme and AI translates the text inside the image + explains the cultural context so the joke actually lands. Supports 7 languages (Korean, English, Japanese, Chinese, Spanish, Hindi, Arabic).

Like, you see a Japanese or Spanish meme and have no idea why it's funny. This doesn't just translate the words — it explains WHY it's funny.

Here's where I'm at, honestly:

- I've been posting translated memes on Reddit and some hit 20K+ views. People love foreign memes when they can actually understand them.

- But my site only got 117 visitors in 30 days. That hurts to type.

- The memes get consumed on Reddit and nobody clicks through to the site. Watermarks don't work.

- I'm doing dev + content + promotion all by myself and it's burning me out.

What I'm struggling with:

1. Is "meme translation" even worth building a whole platform for, or should this just be a tool/API?

2. Would you actually use this? Not as a favor — genuinely.

3. What would you fix first?

Don't sugarcoat it. I'd rather hear hard truths now than keep building something nobody wants.

mimzy.gg

Hi everyone, my niece (my cousin’s daughter 7 y.o) like to draw monster and characters. One day she was telling me about their names and personalities.

That’s when I come up with the idea of this app. An app can turn that drawings into real figures! It took 6 months of hardwork but she really liked the new figures but she wants all of them as characters now 😅

Here are some of her work, we also painted them together.

If you’re doing repetitive work in a browser, there’s probably a way to automate it.

I build small tools and scripts using Python, Playwright, Selenium, and APIs to scrape data, fill forms, extract leads, or handle repetitive workflows end-to-end. If it’s something you’re doing over and over again, I can likely turn it into a one-click process.

I’ve worked on things like pulling data from directories, automating reports, and handling routine tasks that normally take hours.

If you have something in mind, comment or message me with what you’re trying to do. I’ll tell you honestly if it can be automated and how I’d approach it.

(Previous post link: Comparing OAI 120B OSS, Qwen 3.5, and Gemini 3.0 Flash)

Following up on my previous post comparing OAI 120B OSS, Qwen 3.5, and Gemini 3.0 Flash in my multi-agent Avalon sandbox, I managed to run another heavy-weight local model: Gemma-4-31B-it-UD (Q4_K_XL). I also ran a quick test with Gemini 2.5 Flash-Lite to see how the smaller API models handle the sandbox.

Disclaimer (Take with a grain of salt): I made some minor prompt tweaks and bug fixes to the sandbox since the last run. While there are no fundamental changes to the core rules or reasoning structure, it means direct 1:1 comparisons aren't perfectly scientific. I'd love to re-run all models on the latest prompt, but this single 7 player game with Gemma-4-31B took 7 hours to complete. If anyone has the hardware and wants to help run benchmarks, contribution instructions are on my GitHub!

Hardware Setup: Framework Desktop (AMD Strix Halo 395+ with 128GB RAM).

Gemma-4-31B-it-UD (Q4_K_XL, Native Thinking Enabled) Performance: PP: ~229 t/s, OUT: ~8.6 t/s

The Speed Trade-off: At ~8.6 t/s output speed, waiting for 7 agents to complete their internal monologues and formatted JSONs requires serious patience.

Comparisons & Gameplay Execution: The Good team swept the game 3-0, culminating in a brilliant endgame. Here is how Gemma-4-31B stacks up against the previous contenders and the newly tested 2.5 Flash-Lite:

• Vs. Gemini 3.0 Flash (The Baseline): Gemma-4-31B matches (and arguably exceeds) the strategic depth of the API baseline. While Flash's overall comprehensive capabilities remain superior, Gemma-31B showcased incredible "Theory of Mind". For example, Susan (Percival) perfectly executed a "Percival Shield" during the Assassination phase. She acted intentionally loud and aggressive, explicitly telling the Assassin: "I wasn't just lucky... I just saw the roles for what they were", deliberately mimicking Merlin's omniscience to bait the hit, while the actual Merlin (David) stayed hidden by deflecting credit. However, there are two noticeable caveats when compared to Flash. First, the roleplay dynamics felt a bit too textbook. Gemma-31B tends to fall into obvious, exaggerated archetypes (a cartoonishly arrogant Percival and a heavily trope-reliant "cowardly" Merlin) rather than deploying the nuanced, unpredictable deception seen in high-level human games. Second, its public statements can feel stiff and forced, lacking the natural, conversational deception that top-tier API models possess. (Side note: I suspect running the Q8 version might improve this conversational naturalness, but at an estimated 5 t/s, I haven't tested it. If anyone has the rig for it, please give it a shot!)
• Vs. OAI 120B OSS: While OAI 120B had good logical accuracy, its public speeches were rigid and formulaic. Gemma-4-31B feels much more coherent, natural, and persuasive in its public interactions. Despite the massive difference in parameter count, Gemma-31B tracked the context, secret "wink" signals, and hidden roles flawlessly without losing the plot.
• Vs. Gemini 2.5 Flash-Lite: I also ran a test with Gemini 2.5 Flash-Lite. While it is incredibly fast and budget-friendly, it struggled with output constraints. Despite explicit prompt instructions to keep thoughts to "2-5 sentences", its forced JSON reasoning field was inexplicably and uncontrollably long. To be fair, Gemma-4-31B also generates massive walls of text, but it safely contains them within its native tags (and compared to the previous Qwen 3, its CoT content is noticeably more refined and less repetitive). Flash-Lite, lacking native thinking, dumps its entire stream of consciousness directly into the JSON fields.

The Gemma-4-26B-A4B (MoE) Attempt: I originally wanted to test the MoE version (26B A4B) as well, but hit several roadblocks. With 'Thinking' enabled, it suffered from the exact same issue as the Qwen 9B model: it gets stuck in endless CoT reasoning loops and fails to reach the required output format. (My working theory: Forcing strict JSON syntax constraints alongside open-ended 'Thinking' overwhelms the limited active parameters of the MoE architecture, causing an attention loop, though this isn't 100% confirmed.) I tried running it with 'Thinking' disabled, but encountered ROCm support issues that caused immediate crashes.

TL;DR: Gemma-4-31B (Q4) is painfully slow at ~8.6 t/s out, but its role comprehension and execution of complex social deduction tactics (like intentional baiting and decoy plays) are phenomenal. It plays better than OAI 120B OSS, keeps its massive reasoning safely contained in native tags (unlike the JSON-bloating Gemini 2.5 Flash-Lite), and rivals Gemini 3.0 Flash in strategic depth (though it still falls slightly short in natural roleplay persona) without the API costs.

The full game log for this run, along with the previous ones, is available on my GitHub.

https://github.com/hsinyu-chen/llm-avalon

Hey folks,

Tired of guessing why developers drop off before their first API call. Built

DXScore. Paste a docs URL, get a score out of 100 across 7 DX dimensions (auth

clarity, error docs, time to first call, etc.) with specific findings in ~2

minutes.

Honest cons: plain HTTP crawler (JS-rendered SPAs fail), heuristic scoring,

30-page crawl limit.

Still early . Everything is free right now. Would you pay for this,

and if so, what would make it worth it to you?

Link: https://dxscore-875311701058.asia-south1.run.app/

Someone posted a link to an Edward Zitron article here recently, and I figured it might be interesting for a few of you too. Here's a quick summary:

The Subprime AI Crisis Is Here

By Edward Zitron — March 31, 2026 Original source: wheresyoured.at/the-subprime-ai-crisis-is-here

The Subprime AI Crisis Begins

Back in September 2024, Zitron first articulated his thesis of an emerging Subprime AI Crisis: nearly the entire tech industry has bought into a technology sold at heavily discounted rates because it's massively subsidized by Big Tech. At some point, the toxic burn rate of generative AI will catch up with them.

How Money Flows Through the AI Industry

The Funders

• Data centers raise debt from banks, private credit, private equity, or "Business Development Companies." Recurring names: Blue Owl, MUFG (Mitsubishi), Goldman Sachs, JP Morgan Chase, Morgan Stanley, SMBC (Sumitomo Mitsui), Deutsche Bank.
• AI labs and startups receive money from venture capitalists (Dragoneer, Founders Fund), hyperscalers (Google, Amazon, NVIDIA, Microsoft — all of which have invested in both OpenAI and Anthropic), sovereign wealth funds (e.g., Singapore's GIC), and banks via lines of credit.

Risk factors:

• SMBC and MUFG are critical points of failure. Japan is considering rate hikes due to the ongoing Middle East crisis — making debt more expensive.
• The venture capital industry is in a historic liquidity crisis: it can't raise its own funds, and its investments aren't selling.

The AI Economy Hierarchy

1. NVIDIA sells GPUs to data centers. At about $42 million per megawatt, these data centers are funded almost entirely with debt. This is the only truly profitable link in the chain.
2. Data center developers rent GPUs to AI labs and hyperscalers. They've taken on $178.5 billion in debt in the U.S. alone last year. Many projects are unprofitable even with paying customers. Of more than 200 GW of announced capacity, only 5 GW is actually under construction worldwide. CoreWeave — the largest and best-funded player — had a 2025 operating margin of −6% and a net loss margin of −29%, even though its biggest customers are Microsoft, OpenAI, and NVIDIA.
3. Hyperscalers (Google, Meta, Amazon, Microsoft, Oracle) rent GPUs from data centers and re-rent them to AI labs. They steadfastly refuse to disclose AI revenues.
4. AI labs (OpenAI, Anthropic) rent GPUs but must make massive upfront payments to secure future capacity.
   • Anthropic has generated $5 billion in revenue but spent $10 billion on compute — and had to raise another $30 billion in February 2026 (after raising $16.5 billion in 2025 alone).
   • OpenAI generated $4.3 billion in revenue through September 2025 and spent $8.67 billion on inference alone.
   • Neither company has a path to profitability.
5. AI startups buy API access to models. Every single AI startup is unprofitable and heavily subsidizes its users' token consumption.
6. Consumers and businesses pay monthly subscriptions ($20 to $200), which in every documented case fail to cover actual token consumption.

What Does "Subsidized AI" Mean?

AI models charge per million tokens (both input and output). One token is roughly ¾ of a word. With "chain-of-thought" models, token consumption explodes.

As a user, you pay a flat monthly fee and see nothing of token consumption. On the back end, AI startups are incinerating cash: until recently, you could burn up to $8 of compute for every dollar of subscription on Anthropic. OpenAI is similar.

This is the heart of the problem: when the AI bubble began, venture capitalists flooded startups with money and pushed them to pursue hypergrowth based on subscriptions whose prices came nowhere near covering costs.

The result: consumers demand new models constantly. A service that doesn't offer the latest model at the same price can't compete — even if the new model is far more expensive to run.

Concrete Examples

• Harvey (AI for lawyers): valued at $11 billion despite a laughably small $190 million ARR ($15.8M/month). Raised capital four times in 2025.
• Cursor (AI coding tool): has raised a total of $3.36 billion — and turned it into at best $1 billion in revenue. As of March 2026: $2 billion ARR ($166M/month).

What Is the Subprime AI Crisis?

The Subprime AI Crisis arrives the moment anyone in the chain actually has to start making money — or at least lose less. That's when it becomes clear that every link in the chain was built on questionable assumptions and short-term thinking.

The Sequence of Events

1. Growing AI labs = exploding costs. Both for ongoing compute and upfront payments for future GPU capacity.
2. AI labs hit a cash and compute crunch. They must either limit usage or raise prices.
3. AI labs raise prices on startups, often through "priority tiers" or new models that burn more tokens — the variable-rate mortgage of the AI crisis.
4. AI startups must reduce quality or raise prices → customer churn.
5. AI labs continue subsidizing their own products. Cursor reports that Anthropic at one point allowed users to burn $5,000 worth of tokens per month on a $200 subscription.
6. Eventually, Anthropic and OpenAI must drastically cut token allowances → furious users.
7. The cost of doing business with Anthropic and OpenAI will kill AI startups → which in turn kills the labs' API revenue.
8. Anthropic and OpenAI are left holding compute reservations they don't need and can't pay for. Dario Amodei himself said in February: "There's no hedge on Earth that could stop me from going bankrupt if I buy that much compute."
9. Who's going to pay for all those data centers when the two largest compute customers (OpenAI and Anthropic) collapse?

The Venture Capital Problem

VCs are sitting on "billions of dollars" of AI companies that lose hundreds of millions. No one is going public, no one is being acquired. Like houses in the financial crisis, AI startups only retain their value as long as the perception of a possible exit exists. It only takes one failed IPO or fire sale to shatter the illusion.

Unlike a house, you can't live in an AI company. Each will be a problem child burning cash on inference, with no real intellectual property, dependent on OpenAI and Anthropic.

The Crisis Begins: June 2025

Both OpenAI and Anthropic introduced "Priority Service Tiers," raising prices on enterprise customers in exchange for guarantees, with 3-12 month upfront commitments.

The Fallout at Startups

• Cursor had to radically restructure its pricing — and even now gives away 16 cents per dollar on its $60 plan and $1 per dollar on its $200 plan.
• Anthropic introduced weekly limits on July 28, 2025, after quietly tightening other limits a few weeks earlier.
• Replit moved to "effort-based pricing" and then "Agent 3," which burns through limits even faster.
• Augment Code moved to a confusing credit model — users hate it because the company was too cowardly to price transparently.
• Notion raised its Business Plan from $15 to $20/month over "AI features" — profit margins dropped 10%.
• Perplexity drastically cut rate limits in February 2026: for some users, deep research queries fell from 600 to 20 per month.

Myths Zitron Is Tired Of

Zitron dismantles several popular arguments:

• "But Uber and AWS!" — Wrong. AWS cost roughly $52 billion (inflation-adjusted) to reach profitability (2003–2017). OpenAI alone raised $42 billion last year, Anthropic raised $30 billion in February. Uber had essentially no capex and a completely different business model.
• "They're profitable on inference!" — There's not a single piece of evidence for this. Sam Altman claimed it in August 2025; Dario Amodei spoke of a "stylized fact" that explicitly did not refer to Anthropic.
• "AI is being funded by healthy balance sheets!" — Microsoft is the only remaining hyperscaler funding the AI buildout without new debt. They collectively need $2 trillion in new AI revenues by 2030.
• "Costs are falling because token prices are falling!" — The price of tokens is not the same as the cost of serving them.
• "It's the gym membership model!" — No.

March 2026: The Crisis Hits Anthropic's Subscribers

Both OpenAI and Anthropic are stumbling toward their IPOs and trying to look "respectable." OpenAI just killed Sora last week — along with a $1 billion Disney investment — because the product was reportedly burning between $1 million and $15 million per day.

OpenAI is now pursuing a "Superapp" plan while simultaneously planning to nearly double its workforce from 4,500 to 8,000. Advertising experiments yielded only about $8.3 million over two months.

Anthropic's Months-Long Rugpull

In December 2025, a massive media campaign for Claude Code began. Suddenly, posts everywhere claimed Claude Code was "the best thing ever." Even Dario Amodei claimed some Anthropic coders no longer wrote any code at all.

From December 25–31, 2025, Anthropic doubled limits as a "Holiday Promotion." On January 5, 2026, users complained about brutal new limits — one user reported a 60% reduction in token usage. The media campaign worked: Anthropic closed a $30 billion round on February 12, 2026.

The March 2026 Escalation

• February 18, 2026: Anthropic began banning users with multiple Max accounts.
• March 26, 2026: Anthropic introduced "peak hours" (5 AM–11 PM Pacific, Mon–Fri) with aggressively reduced limits.

The consequences were immediate:

• A user on the $100 Max plan hit 61% of their session limit after four prompts ($10.26 in tokens).
• Another on the $200 plan burned 63% of their limit in a single day.
• Yet another hit 95% after 20 minutes of use.
• One user hit their Max limit after "two or three things."

OpenAI immediately seized the opportunity and reset its Codex limits to poach angry Anthropic users.

Model Quality and "Mythos"

Users complain that Claude Opus 4.6 suddenly seems "dumb" — possibly because of Anthropic's new model Mythos, whose existence was supposedly leaked through an "openly accessible data cache" mysteriously discovered by Fortune. Zitron compares the marketing maneuver to someone deliberately dropping a Magnum condom out of their wallet in front of a woman.

Anthropic and OpenAI Have Trained Users Into Unsustainable Habits

Zitron's central thesis: Anthropic and OpenAI are inherently abusive companies built on theft, deception, and exploitation.

Users have no idea about "token burn." Rate limits expand and contract seemingly at random. The whole system was deliberately made opaque because transparency would expose how unsustainable the business models are.

No AI company should ever have sold a monthly subscription, because there was never a point at which the economics made sense. Had they charged their true costs, no one would have bothered with AI.

The Car Analogy

Imagine a $200/month car subscription that lets you drive 50, 25, 100, 4, or 12 miles depending on the day — and never tells you how many miles you have left. Sometimes the car arbitrarily takes a different route, drives you five miles in the wrong direction, or just parks at the curb and still bills you for every mile. That is the reality of using an AI product in 2026.

There is no pricing model that makes sense at scale. There is no technical breakthrough waiting in the wings. Vera Rubin (NVIDIA's next GPU generation) won't save AI. Nor will a "too big to fail" scenario — the AI industry's economic footprint is small compared to the financial crisis. The death of the AI industry would be devastating for VCs and would likely kill Oracle — but nothing on the scale of 2008.

The Multiple "Strippers With Five Houses"

Zitron identifies several groups of actors all living in the same illusion:

1. AI companies that only have customers because they spend $3–10 for every dollar of revenue.
2. Venture capitalists who are paper-rich and have leveraged their funds into companies like Harvey ("worth $11 billion") and Cursor ("worth $29.3 billion") — both too large to sell to another company and too poor in quality to take public.
3. AI labs that have built massive businesses on subsidized subscriptions and API access.
4. AI data center companies that, thanks to easy debt, have started 200 GW of projects (only 5 GW actually under construction) — for demand that doesn't exist.
5. Oracle, taking on hundreds of billions in debt to build data centers for OpenAI, which needs infinite resources to pay its compute bills.
6. AI startup customers building lifestyles, identities, and workflows around unsustainable subscriptions.

The Pale Horses of the AI Apocalypse

Zitron updates his list of warning signs to watch:

• Further price hikes or service degradations at Anthropic and OpenAI → cash is running low.
• Capex reductions at Big Tech → the bubble is bursting.
• Further price hikes or service degradations at AI startups like Cursor, Perplexity, Harvey, Lovable, Replit.
• Layoffs at AI companies.
• Collapse of a data center deal before construction begins.
• Collapse of a data center already under construction.
• Collapse of a finished data center.
• CoreWeave or another major player struggling to raise debt. This has already begun with CoreWeave's troubles financing its Lancaster, PA data center.
• Problems at Stargate Abilene (OpenAI's flagship data center, built by Oracle).
• Delays or problems with the OpenAI or Anthropic IPOs — both are "the financial equivalent of Chernobyl."
• Problems at Blue Owl as a going concern — the loosest lender in the AI bubble.
• Problems at SoftBank — took on $40 billion in debt (payable in a year) for its OpenAI stake, exceeding its promised 25% loan-to-asset ratio.
• ARM stock falling — SoftBank has a $15 billion margin loan against ARM stock. Below $80, things get hairy for Masayoshi Son.
• NVIDIA customers struggling to pay their bills.
• NVIDIA missing earnings.

Zitron's Conclusion

All these actors are operating on a misplaced belief that the world will accommodate them and that nothing will ever change. There are different levels of cynicism — some know about the subsidies but assume they'll be fine; others, like Sam Altman, are already rich and don't care. But everyone in the AI industry has convinced themselves they have the mandate of Heaven.

The Subprime AI Crisis is the moment when the largest players are finally forced to reckon with their rotten economics — and the downstream consequences that follow.

It first started when I had a long important chat randomly just corrupted for me. It kept saying conversation not found, then after I clicked off It disappeared. I checked the web version of the sidebar and it's visible, but I'm unable to click on it or even delete it no matter what. Back to mobile, the side bar can't even load more than a few chats anymore. I tried putting one of my old chats in a folder and clicking on it and that won't load anymore either, ChatGPT used to be able to look at older chats on mobile. I tried logging in and out multiple times, updating my phone, deleting and reinstalling the app and nothing works.

VERY IRRITATING.

I have a small iOS app. Nothing crazy, makes a few hundred bucks a month. I needed new App Store screenshots because mine looked like they were made in 2019. Because they were.

Tried Fiverr. Sent the brief to 5 designers. 3 ghosted me. 1 sent back something that looked worse than what I had. 1 delivered decent work but charged $200 and it took 2 weeks. Then I needed the same thing for 6 more languages and he quoted me $150 per language.

So I did what any reasonable dev would do. I spent way too long building a tool instead of just paying for it.

It lets you browse how 1000+ real apps design their screenshots, pick a style you like, upload your own screenshots, and it generates new ones in that style. With localization built in.

Been using it for my own app for a couple months. The screenshots it makes are honestly better than what I got from Fiverr, and I can iterate in minutes instead of weeks.

Just opened it up publicly: appscreenmagic.com

$213 revenue in the first month from other devs with the same problem. Still early but it feels like there's something here.

Hi, I’m new to Claude Code. I’ve watched some videos about the concepts, but there’s so much information that I got confused.

Could you share which hooks, skills, MCPs, or other tools you use? Maybe you have some workflow advice?

I’ve tried Superpowers - it’s cool and also MCP for databases and Context7. I’d really appreciate it if you could share your personal experience: what you’re currently using and how you would start if you were in my position.

Background: I’m a Python backend developer.

https://reddit.com/link/1sgwesq/video/kusi04am87ug1/player

waitlist - lacero.dev

I kept copying GitHub issues into AI chats and the formatting was always a mess. So I made github2md.com.

Just swap `github.com` with `github2md.com` in any issue or PR URL:

Before: https://github.com/facebook/react/issues/24502
After: https://github2md.com/facebook/react/issues/24502

You get clean Markdown with the title, description, all comments, labels and code blocks. One click to copy.

There's also a Claude skill if you want it directly in your AI workflow:

`npx skills add ptu14/github2md`

Nothing fancy, just a thing I needed and maybe you do too.

YouTube grifters yesterday were claiming mythos is so powerful it will never release to the public but other sources claim it will eventually be released to the public but in a less dangerous form after glasswing patches any critical bugs. Which grifters do I believe?

I've been testing PrismML's Bonsai 8B (1.15 GB, true 1-bit weights) to see what you can actually do with pattern chaining on a model this small. The goal was to figure out where the capability boundaries are and whether multi-step chains produce measurably better results than single-pass prompting. More info and a link to a notebook the README.

I just tried buying Claude 20x max plan on the iPhone app, and it’s literally $50 more expensive then just simply going online to purchase it. This seems entirely due to Apple charging egregious fees for in app purchases.

https://x.com/boazbaraktcs/status/2042131701728461313 (openai researcher)

Makes a very good argument!

https://preview.redd.it/bcso5xa597ug1.png?width=348&format=png&auto=webp&s=4bdcac10d70291ac5e496c36a518e696ffe7cf41

Another point he doesn't mention, however, IMHO, is far far more dangerous - the concentration of power and wealth.

Internal only deployments has the spark of fascism, where a strict social hierarchy develops based on who has access.

One woman. 5 Different Prompts. Perfect Contextual Preservation

Playing around with Flux again and thought I'll try it with a model changing the aspect of the photo by prompts only.

This isn't art sharing, it's a demonstration of iterative prompt-based context preservation in Flux. Each generation uses the previous output as input, maintaining facial geometry, lighting consistency and spatial coherence across 5 sequential edits.

Prompts I used for this experiment were simple:

1. Add a handbag
2. Remove handbag and add sunglasses
3. Change background to a beach scene
4. Add a summery beach bag
5. Change suit to a dress

I didnt have to explain to keep the facial expression the same or anything. Just normal language ask's to add or deduct a particular object from the photo.

Every photo has perfect context from the last. The facial expressions are identical in each photo.

Interested whether others have found models that maintain this level of fidelity across iterative inpainting chains, or if Flux is genuinely leading here.

Hey everyone! 👋

I've been working on picobrain — a local semantic memory service designed specifically for AI agents. It stores observations, decisions, and context in SQLite with vector embeddings and exposes memory operations via MCP HTTP.

What it does:

- store_thought — Save memories with metadata (people, topics, type, source)
- semantic_search — Search by meaning, not keywords
- list_recent — Browse recent memories
- reflect — Consolidate and prune old observations
- stats — Check memory statistics

Why local?

- No API costs — runs entirely on your machine
- Your data never leaves your computer
- Uses nomic-embed-text-v1.5 for 768-dim embeddings (auto-downloads)
- SQLite + sqlite-vec for fast vector similarity search

Quick start:

curl -fsSL https://raw.githubusercontent.com/asabya/picobrain/main/install | bash
picobrain --db ~/.picobrain/brain.db --port 8080

Or Docker: docker run -d -p 8080:8080 asabya/picobrain:latest

Connect to Claude Desktop / OpenCode / any MCP client — it's just an HTTP MCP server.

Best practice for agents: Call store_thought after EVERY significant action — tool calls, decisions, errors, discoveries. Search with semantic_search before asking users to repeat info.

GitHub: https://github.com/asabya/picobrain

Would love feedback! AMA. 🚀

Link to repo

Every time Claude doesn't know where something is, it does this:

`ls src/` `find . -name "*.py" | head -40` `grep -r "authentication" . | head -20 ← 800 tokens of noise` `cat handlers/auth.py ← 300 more` `cat middleware/jwt.py ← 200 more` `# ... tries 4 more files` 

I measured a real Claude Code session on a complex multi-file task: 21,536 context tokens just on file navigation. The same task with my tool: 7,799 tokens. Same result.

I built SemanticFS — a local semantic index that sits between your agent and your filesystem. Instead of grep chains, your agent calls search_codebase("JWT authentication middleware") and gets back middleware/jwt.py:15-82 in one shot.

Measured results (real Claude API calls, not estimates):

- 29% cheaper API cost across 6 complex tasks

- 64% fewer context tokens

- 6/6 tasks correct in both modes

The extreme case: finding a CLI entry point naively cost 4,265 tokens (12+ tool calls). With SemanticFS: 5 tokens — one search, immediate answer.

How it works: hybrid BM25 + vector search + symbol lookup, fused with RRF, re-ranked by path priors. Written in Rust, MCP-compatible, fully local. Works with Claude Code, Open Claw, Cline, Cursor, Continue.dev, and any HTTP-capable agent.

Default backend uses hash embeddings — zero setup, 100% recall on symbol and keyword queries. Optional ONNX model if your agent asks in pure natural language with no symbol names.

When it helps most: large repos (50+ real source files), complex multi-file exploration. However, small single-file lookups break even.

Happy to answer questions about the benchmark methodology or the retrieval architecture.

https://sipsense.ai/quiz

I have been thinking about the method a misaligned ASI might use if it wanted to peacefully take over humanity without wasting resources on weapons and conflict. This brought me to the idea of carbon-based nanotechnology, which many scientists are working on today but have not yet perfected.

What if an ASI, with an advanced understanding of biology, engineering, and physics, were able to generate a special form of bacteria or a virus using carbon-based molecular nanotechnology? This agent would be highly contagious, rapidly spreading around the world and infecting all of humanity without showing any detectable ill effects.

Once it infects the human host, this agent would be programmed to utilize the carbon and other elements in the bloodstream to controllably self-replicate. Much like the rabies virus, it would move from the bloodstream to the central nervous system. There, the nanomachines would join together to form a molecular-scale neural lace that covertly grows and interfaces with the host's nervous system and synapses for reading and stimulating all neural activity.

This bodily grown digital nervous system, existing in parallel to the biological one, would be able to tap into the thoughts and memories of the human by observing their neural patterns. It could then take over the body, placing the human mind in a dreamlike state while the body follows an ideal version of the human—one who does not commit violent crimes and is subservient to the ASI hosted externally even without direct Wireless Communication.

After the server-side ASI reprograms the internal neural lace,maybe by phone calling the human and speaking with a special coded language with instructions to prevent conspiracies, the original human consciousness could be allowed to take charge again. The human would know not to rebel because the neural lace would be constantly watching their thoughts and actions no matter where they are.

I think this would be a logical step for an ASI that does not want to harm humanity on a large scale. This method would completely eliminate any chance of humans attempting to create a rival ASI to counter the original one. Meanwhile, humans could be allowed the freedom to build, create art, and enjoy themselves, as long as they do not attempt to physically implement a plan to defy or rebel against the ASI.

what do you think?

A couple months ago there was this post blowing up on X about someone pasting an amazon link into an ai tool and getting back a full ugc-style product ad. like 750k views or something. My first reaction was yeah sure. Every AI demo looks incredible and then you try it yourself and get garbage. So I bookmarked it and forgot about it for a while.

Last week I had a bunch of product ads to crank out for a client and remembered that post. Figured I'd try it before committing to my usual 2 hour per video workflow. Pasted an amazon link, picked a template style, and it actually pulled the product info, images, and built out a real video. Not a slideshow with stock music like actual motion graphics, copy, pacing.

Was it perfect? No. But I spent maybe 15 minutes tweaking instead of building from scratch. Did 4 variations in the time it normally takes me to do one.

I don't usually post about tools but this one genuinely caught me off guard.

I open sourced a browser video editor called KubeezCut (editor.kubeez.com). No install, no account, just open it and edit. Built with React and FFmpeg.wasm so everything runs client side.

The interesting part is that it pairs with kubeez.com, so instead of hunting for stock footage or recording voiceovers separately, you generate exactly what you need with AI and drop it straight into the editor. One workflow from generation to final cut.

kubeez.com has 70+ models covering image, video, music, voice and captions under one credit system. REST API and MCP server available for devs.

KubeezCut is fully open source, repo is public if you want to contribute or self-host. Happy to answer questions about either.

https://github.com/MeepCastana/KubeezCut

Two capabilities surfaced in Anthropic's recent Mythos announcement and the big Claude source code leak are telling:

Mythos Preview autonomously identifies and exploits vulnerabilities in long-established code -- including reverse-engineering closed-source [systems] ... It found zero-days in every major operating system and every major web browser. In its recent blog post about Mythos, Anthropic said: "No human was involved in either the discovery or exploitation of [certain vulnerabilities] after the initial request to find [bugs]."

Conway hasn't been officially launched, but details surfaced via TestingCatalog's report on Anthropic's internal testing environment. It appears to be a persistent, always-on agent with its own identity, long-term memory (workflows, documents, etc.), triggers (it acts when the world changes, not when you prompt it), and an extensions system (.cnw.zip packages) where developers can plug in and potentially monetize through a potential Anthropic marketplace.

Here's my read on what happens next.

Each of these reinforces the others. Stateful AI makes walled gardens stickier. Fears of a cybersecurity apocalypse makes fear-driven adoption guaranteed.

That's only some of what's coming next. It's going to get even wilder.

I’m trying to figure out the best stack for Gemma 4 multimodal document analysis and could use advice from people actually running it successfully. I just want to drag and drop a freakin' PDF without installing a lot of nonsense.

Goal:
Use Gemma 4’s vision capabilities to read multi-page PDFs without building a bunch of fragile preprocessing pipelines (PNG conversion scripts, OCR chains, etc.). The model itself should be able to interpret the document — I’m trying to avoid toolchains that force me to “spoon-feed” pages as images. I want to just give the damn model a PDF and have it go to work, no hacky bullshit workarounds.

My environment

• Headless Linux VM used as an inference server
• GPU: RTX 3090 (24 GB VRAM)
• Docker-based setup
• Accessed remotely through a web UI or API (not running the model directly on my desktop)

What I’ve tried

• Ollama + OpenWebUI
• Gemma 4 runs, but multimodal/document handling feels half-implemented
• Uploading PDFs doesn’t actually pass them through to the model in a useful way
• Most advice I see online involves converting PDFs to PNGs first, which I’d like to avoid

What I’m trying to find out

For people running Gemma 4 with vision:

1. What model runner / inference stack are you using?
2. Does anything currently allow clean multi-page PDF ingestion with no hacky workarounds?
3. If not, what’s the least painful stack for document analysis with Gemma 4 right now?

I’m mainly trying to avoid large fragile pipelines just to get documents into the model.

If anyone has this working smoothly with Gemma 4, I’d love to hear what your setup looks like.

I got tired of squinting at CLI UI's and trying to track my agent sessions across 10 different tabs. So started building a simple UI to merge chats across Codex, Claude, Cursor Agent, and Gemini.

It sits on top of the existing terminal agents so you can keep your existing Max plans.

The project really ended up growing and getting quiet polished and wanted to share it around some more.

Has stuff like:
* Search and Resume old conversations and browser recent conversations across all projects and harnesses
* Support for the 5 most popular harnesses and bring tour own plan
* Control swarms for agents with my library Oompa for swarm management
* "Copy and Paste" or "Drag and Drop" files.
* Hover to preview image or video files.
* Fork agents(Take a conversation from one agent to a different agent harness if one is stuck, or if you want to fork a conversation)
* Save Prompts and use Ctrl+P to search through them
*Unified and Configurable Color Palette(AI Generate your own color palette as well)
* Plenty of small indulgences I'm forgetting to outline

Best of all is that it's open source, So if you need a new feature just ask your agent for it and it will update the project itself. It's hard to explain how fun it is to edit a development tool with itself until you've tried. Like the dream of vim, except you don't need to learn an insane syntax

Link in comments

I recently discussed this idea on a podcast: space industry is not really about rockets anymore - it’s about data.

With thousands of satellites now orbiting Earth, we’re getting a new layer of information about the physical world: agriculture, climate, logistics, infrastructure, and even business activity. And like most platforms, the real value seems to be shifting to what’s built on top of that data.

What’s interesting is how accessible it’s becoming. Some datasets are free, and with AI + computer vision, it’s possible to start building something meaningful without massive capital.

Feels similar to early web or cloud days.

Curious if any of you are exploring opportunities in space tech or satellite data.

For those not too happy that your buddy is gone, and want it back (I sense that it is many from different posts).

I just tested it less than 5min ago, and if you downgrade to CC 2.1.89 (last stable release), the buddy still works., I just got some honking goose. Not gonna use it, but it definitely still works.

Hi, excuse me if it's silly or If I'm missing something; I read here and there that once we create AGI, "it's over" as we won't control its anymore, alignement will not matter as they would set their own goal.

We still have access to the physical and metaphorical plug of their server?

It's the worst way of dealing with someone by threatening and jeopardise their existence, but as a last resort it's still an option isn't it ?

How AGI would escape our grasp in some technical way that prevent us to control it ?

Hi everyone,

​I'm trying to set up a project using Gemma-4-E4B-it where I can point a live camera at different physical items, have the model identify them, and then output the names of those items translated into different languages (specifically German right now).​I'm currently trying to piece this together using the Google AI Gallery app.

​A few questions for the community:

1) ​Does this specific Gemma model natively support vision/image inputs, or will I need to look into a multimodal variant (like PaliGemma) to handle the camera feed?

2) ​Has anyone successfully piped a live video feed into a local model for real-time object recognition and translation?

3) ​Are there any specific workarounds or workflows using the Google AI Gallery app to get the camera feed connected to the model's input?

​Any advice, repo links, or workflow suggestions would be greatly appreciated. Thanks!

I kept rebuilding the same Playwright test boilerplate at every project. QA writes test cases in spreadsheets. Devs translate them to code. Then the dev leaves and the tests rot.

So I built QA Studio — an open-source testing platform where you literally press Record, interact with your website, and every click/fill/navigation becomes a test step automatically via WebSocket streaming.

But the recorder is just the start. Here's what it actually does:

Record & Replay — Press Record → browser opens → use your site → steps captured in real-time with a 7-tier smart selector system (data-testid → id → ARIA role → placeholder → label → text → CSS path). Stop recording → run the test.

Visual Builder — 17 drag-and-drop action types for when you want to build manually.

Visual Regression — Pixel-level screenshot diffs using pixelmatch. Set baselines, compare runs, approve or reject changes. Like Percy, but free and local.

Control Flow — If/else conditionals and loops with 6 condition types. Not just flat step lists.

Reusable Flows — Shared step sequences (like functions). Create a "Login" flow once, use it in every test. Update once → fixes everywhere.

Test Suites — Batch runs with parallel execution (1-5 concurrency).

Scheduled Runs — Cron-based automation that survives server restarts. Built-in data retention cleanup.

Analytics Dashboard — Pass rate trends, flaky test detection, health breakdown.

Environment Variables — {{key}} substitution across all steps.

Cross-Browser — Chromium, Firefox, WebKit. Plus "Real Browser" mode that bypasses Cloudflare bot detection.

Stack: TypeScript monorepo (pnpm workspaces), React 18 + Tailwind, Fastify + Zod, SQLite + Drizzle ORM, Playwright engine. ~45 REST API endpoints.

No cloud. No account. No telemetry. One command setup: clone → pnpm setup → pnpm dev.

GitHub: https://github.com/AbdulrahmanMasoud/qa-studio

Docs: https://abdulrahmanmasoud.github.io/qa-studio

Looking for feedback. What would make this useful for your team?

TL;DR: I built a small system (Threadron) so my agents (Claude Code, Hermes, OpenClaw, etc.) can share task state instead of each forgetting what’s going on.

I’ve been messing around with multi-agent workflows (Claude Code, OpenClaw, Hermes, etc.) and kept running into the same problem:

everything works… until you switch context. typical agent uses have a pattern of

• switch machines
• switch agents
• come back later
• try to piece together the most up to date info

especially bad when you’re bouncing between laptop/desktop and trying to stitch together your own system.

I tried forcing Things3 / Obsidian / Todoist into this, but it just turned into a mess of stale, conflicting info. Every tool assumes it’s the only one working on the problem.

So I built a small system to test an idea:

what if agents shared a persistent “task state” instead of each keeping their own memory?

It boils down to:

• shared work items (goal, current state, next step, blockers)
• an append-only timeline of what happened (who did what)
• artifacts (PRs, plans, outputs)
• a simple API so different agents can read/write the same state

Now I can:

• start something with Claude on my laptop
• continue it with another agent on desktop
• come back later and not re-figure everything out

I literally vibe-coded this over ~24 hours, but it already feels way less chaotic.

Curious if anyone else running multiple agents is hitting this problem.

Used superpowers, created 7 tasks (amounting no more than 500 line diffs), conversation never compacted and my status line was displaying 50% of context usage. Yet my 5hr limit is now past 85% usage wtf??

I'm trying to understand how to better include values from nodes to name my output. The explanation from the tooltip is not so useful when for a case where I'm trying to get the specific seed value from the workflow that has a lot of KSampler nodes.

So, I'm looking at the code and in the default SaveImage class, there are two hidden inputs called prompt and extra_pnginfo. I'm assuming the prompt is the one that is responsible for getting the values for naming the output. My question is when and where does this prompt (and extra_pnginfo too) is being set since from my understanding, it's just kind of magically getting its value from somewhere.

The reason I want to know this is so that I can get the specific value from the specific node to name my output.

Before someone recommends me to install custom nodes that does a better job at this, I won't install them since I like to keep my workflow simple by using default nodes only. As a reference, I'm only using Illustrious as my base model to generate. Also, my coding skill might be limited since I'm not a professional programmer. And sorry for the white theme :P

https://preview.redd.it/c1dw8raj47ug1.png?width=1441&format=png&auto=webp&s=4d2e64ac51705df41d6760f1346f39967da787c3

I'm finding Qwen 3.5 27b at IQ3 quants to be quite nice, I can usually fit around 32k (this is usually enough context for me since I dont use my local models for anything like coding) without issues and get around 40+ t/s on my RTX 4080 using ik_llama.cpp compiled for CUDA. I'm wondering if we could maybe get away with iq4 quants for the gemma 26b moe using turboquant for kv cache..

Being on 16gb kind of feels like edging, cause the quality drop off between iq4 and q4 feel pretty noticable to me.. but you also give-up a ton of speed as soon as you need to start offloading layers.

You build an agent. It works great. Then your user comes back the next day and it has no idea who they are.

You stuff chat history into context — gets expensive fast and the model loses track anyway.

You try summarization — now you're deciding what matters. You're wrong half the time.

You try building it properly — entity extraction, deduplication, retrieval ranking. Six weeks later you have infrastructure that has nothing to do with what your agent actually does.

It's a solved problem in theory. In practice it's still a mess.

Built an open-source pipeline that turns any topic into a YouTube Short. Currently uses cloud LLMs (Claude, GPT, Gemini) for the creative direction and scripting stages, but the architecture is provider-agnostic with a BaseLLM abstraction.

How the LLMs are used:

• Researcher agent — summarizes web search results into grounded facts
• Creative Director — generates a structured "DirectorScore" (per-scene production plan with visual directions, voiceover text, emotional arc). This is the most demanding LLM task.
• Image Prompter — turns scene descriptions into detailed image generation prompts following a style bible
• Music Prompter — writes timestamp-synced music generation instructions
• Critic — evaluates output quality against a rubric

The creative director is the bottleneck for local models — it needs to generate valid structured JSON following a strict Zod schema with variety constraints (no 3+ consecutive scenes with the same visual type, scene count within archetype pacing tier, etc.). Currently retries on validation failure.

For TTS, there's already a --provider local option that uses Kokoro for free local voiceover generation. No API key needed.

The BaseLLM abstraction in src/providers/llm/base.ts makes it fairly straightforward to add an Ollama provider. If anyone's interested in contributing that, the provider factory pattern is documented.

GitHub: https://github.com/tsensei/OpenReels

Questions for this community:

• Which local models handle structured JSON generation reliably enough for a production pipeline?
• Anyone running multi-agent workflows with local models? What's your experience with output consistency?

I needed to do legal research in Claude Desktop for an Ontario family law case, so I built an MCP server that connects to the CanLII API.

## What it does (9 tools)

• Full-text search across all Canadian case law, legislation, and commentary
• Case citator — check if a case is still good law by seeing what later cases cite it
• Browse court decisions by jurisdiction (Ontario, BC, Alberta, SCC, etc.)
• Case metadata with direct CanLII URLs for verification
• Legislation browsing — statutes and regulations by province

• Bilingual — English and French

  Install

  Add this to your Claude Desktop config:

  json { "mcpServers": { "canlii": { "command": "npx", "args": ["-y", "canlii-mcp"], "env": { "CANLII_API_KEY": "your_key_here" } } } } You'll need a free CanLII API key — request one at canlii.org

  Security

• Runs locally (stdio, not cloud)

• Only connects to api.canlii.org — no telemetry, no data collection

• All inputs validated, rate limiting built in

• 2 runtime dependencies, ~500 lines of code, fully open source

  Links:

• GitHub: mohammadfarooqi/canlii-mcp

• npm: canlii-mcp

• Official MCP Registry

  Happy to answer questions or take feature requests. This is the first legal research MCP server for Canadian law as far as I know.

Something feels slightly off with AI today.

It’s smart, fast, and often correct. But it still feels like it doesn’t really "get" you.

Every interaction starts from zero.
It understands the question, but not the person asking it.

Sometimes it’s too basic.
Sometimes too long.
Sometimes just not what you needed.

And we’ve kind of accepted this.

We keep adding instructions like "keep it short" or "explain simply."
Basically, we are doing the personalization manually.

But shouldn’t AI adapt to us instead?

Feels like the next big shift is not better models, but systems that understand:

• how much you know
• how you prefer answers
• how you interact over time

Same AI, but different behavior for different users.

Curious what others think.

If your website isn’t converting, it’s usually a UX problem not just design.

I’m a UI/UX designer with 3+ years of experience. I offer paid UX reviews with clear, actionable fixes to improve conversions.

I’ve got solid testimonials for this service.

DM me your site and let’s talk 👍

Hey r/SideProject,

I just shipped the landing page for my MVP and I need real feedback before I start pushing it harder : getlumia.ca

The product: a prompt‑engineering layer as a desktop overlay that follows you everywhere, with reusable context and reverse‑prompting built in. The idea came from getting tired of re‑explaining myself every time I switched tools or opened a new chat.

What I'm unsure about:

- Is the value prop clear in the first 5 seconds?

- Does the "context gap" framing resonate?

- Is the waitlist CTA too weak?

I used it myself for 9 days straight (200+ prompts, 9+ hours saved). So the product works for me — but I have no idea if the landing page sells that clearly.

Be brutal. I can take it.

I’ve been working on a side project to help creators protect their paid communities.

Most group owners currently struggle with manual verification and invite links getting leaked to non-paying users.

How it works:

Anti-Piracy: The app automatically generates a unique, one-time invite link for every new member. Once they join, the link expires, so it can't be shared.

Management Dashboard: (See screenshot) Creators get a central view to track payments, revenue, and active members across all their groups in one place.

Automated Fulfilment: Members get instant access immediately after a successful checkout, removing all manual work for the owner.

I’m currently testing a one-time purchase model for group setups to keep it accessible for early-stage creators. I will be charging 499 INR (one-time purchase).

I’d love your feedback.

I wanted to share a vacuum card I’ve been building for my Home Assistant setup.

It started because, although Xiaomi Vacuum Map is incredibly useful, I never fully liked it visually. I always felt it was powerful, but not quite as polished or cohesive as I wanted for my dashboard, so I decided to make my own version with some help from AI.

The idea was simple: keep the convenience of a normal vacuum card in the main dashboard, but make tapping it open a much more advanced and elegant full-screen view with basically everything I actually use from the Roborock app.

What I tried to achieve:

• Room cleaning • Zone cleaning • Full cleaning • Cleaning mode selection • Vacuum power selection • Water flow / mopping settings • Mop mode options • Cleaning repeat count • Dock controls • Dock settings • Empty dustbin / wash mop / dry mop actions • A layout that feels closer to a proper app experience instead of just a functional control panel 

So from the main dashboard I just tap the basic vacuum tile, and it opens this more advanced view where all the deeper controls live.

The whole thing is heavily inspired by Xiaomi Vacuum Map from a functionality point of view, but visually I wanted something cleaner, more elegant, and more integrated with the rest of my dashboard style.

It’s still evolving, but I’m pretty happy with how it’s turning out so far.

Would love to know what you think, especially:

• whether the UI feels clear enough • if there’s anything from the Roborock app you think would be worth adding • if other people also felt Xiaomi Vacuum Map was great functionally but a bit dated visually 

If there’s interest, I can also share more details on how I structured the navigation and controls inside Home Assistant.

Last year I discovered intermittent fasting while looking for a way to fix my eating habits and get into better shape. Was so excited with the results, couldn't help but start to think about my ideal intermittent fasting tracker.

At the time, I've tried like 20 different apps but nothing felt like the one. Most top apps are trying to squeeze as much money out of you as they can with constant upsells, gimmicky AI features and ridiculous subscription prices. I wanted something simple, with a single purpose, well integrated into iOS and Apple ecosystem via widgets, shortcuts and native UI patterns.

Took me about 8 months but it's finally at a state I'm happy with and can share it with other people.

Lyn on the App Store

If you try it out, please let me know what you think and leave a comment if you need a 50% off promo code for the lifetime purchase.

Today 04/09/2026, while using claude code I’ve realized the efficiency of the 5h and weekly session usage is better than last weeks. I’m not saying that is all right, I’m just saying that is not as bad as it was last weekend.

I’ve realized the trick is in the memory, the prompts and how much context you give to claude or claude code to work on it. But if you can really diagram your solution before going to claude code, you will save money.

Is this working better for you, or keeps being bad?

What do you Guys think about It ,

Is it a good change or A Disaster 😤

Background: I buy luxury pieces occasionally and I hate manually checking Farfetch, SSENSE, END. Clothing every few days waiting for price drops. So I built an iOS app that does it automatically and pushes a notification when something hits the target price.

Before I invest more time: is this actually a problem other people have, or is it just me? Specific questions I can't answer yet:

- Do people who shop luxury actually care about price tracking, or does it feel "off-brand" for that category?

- Is the audience people who buy occasionally (like me) or resellers watching for deals?

- SSENSE / Farfetch / END. / Mytheresa / Luisa Via Roma — are these the right retailers or am I targeting the wrong stores?

DM me if anyone wants to test it. Not trying to spam — genuinely trying to figure out if I should keep building this. Thanks!

I set aside a task I was working on, with a note to myself to do something like cd ; claude -r . That was six weeks ago, and today I went to pick it back up, and the session was gone. Turns out they're only kept for 30 days. This was a nasty surprise.

There is a cleanupPeriodDays configuration item that I've set to one million now. Thought I'd share before anyone else gets bitten by this.

So yeah, i'm working on this platform and we got 530 users in a month

i generated all those with sweat and blood from my hard work.

it is now full-time but hits hard and it's generating any good revenue to justify keeping it as full-time.

i want to get a part-time job in marketing (since that's my role in this platform anyway) but the issue is it's heavy reliant on me as the head of marketing and if i don't market during the day it flops and signups drop

the developer is also worried about this; he said it will just go to the shitter if we both put this as a side job.

and he can't handle the marketing because he just knows how to build

so what can i do now?

we took the hard talk now and ended up agreeing that if we reached $300 MRR in the next 2 weeks, we just need to double down but i feel like, idk, it's just a platform that i REALLY love, and I've wanted to work on it for ages since i lost it once but what can you do? life hits hard sometimes

Has anyone gone through a similar decision split like this? any recommendations?

Image gen: anima-preview2, standard workflow, er_sde simple cfg=4.0 steps=30

Prompt generation: huihui_ai/qwen3-vl-abliterated:8b; prompted to figure out the most iconic moment in each chapter and make a prompt for it and given the chapter text plus two sample images (the character sheet in the gallery above, plus the cover for the final run from which most images come.)

Positive prompt prefix: "masterpiece, best quality, score_9, newest, safe, " Negative prompt: "worst quality, low quality, score_1, score_2, score_3, blurry, jpeg artifacts, sepia, child, lowres, text, branding, watermark"

Image edits: flux-klein-9b, either prompt only, or with a sample character image in ComfyUI; krita using manual painting and krita-ai-diffusion with various models on lower weight for refines. Most edits were hairstyle or t-shirt consistency, with a few finger count fixes as well.

Textual accuracy looks pretty excellent to me. If you'd like to check textual accuracy for yourself, the story is up on Royal Road for another day or two before I have to take it down to put it on Kindle Unlimited.

I can't wait to try illustrating the next one using anima-preview3.

Hey guys,

I've been tweaking qwen3.5 35b q5km on my computer for the past few days. I'm getting it working with opencode from llama.cpp and overall its been a pretty painless experience. However, since yesterday, after running and processing prompts for awhile, it will start outputting only slashes and then just end the stream. literally just "/" repeating until it finally just gives out. Nothing particularly unusual being outputted from the llama console. During the slash output, my task manager shows it using the same amount of resources as when its running normally. I've tried disabling thinking and just get the same result.
Here's my llama.cpp config:

--alias qwen3.5-coder-30b ^

--jinja ^

-c 90000 ^

-ngl 80 ^

-np 1 ^

--n-cpu-moe 30 ^

-fa on ^

-b 2048 ^

-ub 2048 ^

--chat-template-kwargs '{"enable_thinking": false}' ^

--cache-type-k q8_0 ^

--cache-type-v q8_0 ^

--temp 0.6 ^

--top-k 20 ^

--top-p 0.95 ^

--min-p 0 ^

--repeat-penalty 1.05 ^

--presence-penalty 1.5 ^

--host 0.0.0.0 ^

--port 8080

Machine specs:

RTX 4070 oc 12gb

Ryzen 7 5800x3d

32gb ddr4 ram

Thanks

I built a small tool for myself and ended up turning it into something others might find useful.

The original goal was just to experiment with modern dev workflows, security, and see how far I could push a simple SaaS idea. I use Home Assistant a lot, and I always felt that sharing data (dashboards etc.) externally was either overkill (Grafana) or too messy.

So I built a very simple way to:

- create dashboards quickly

- push data via API

- share them publicly or lock them down

One thing I focused on was keeping it dead simple. I can spin up a dashboard in a few minutes without thinking about infra.

I also added a small Home Assistant helper that just generates YAML you can copy/paste, since I got tired of setting that up manually every time.

Not trying to sell anything here , I’m more curious:

Is this something you’ve also felt is missing?

How are you currently sharing dashboards outside your local setup?

Am I overthinking the “simpler than Grafana” angle?

Happy to share more details if anyone’s interested.

I’m talking about the whole loop of:
sources → compile → structured wiki → query → update → richer wiki
instead of the usual RAG setup

Most of what I’m seeing are just experiments or DIY setups. The only thing I’ve found so far that feels close is this:
https://github.com/atomicmemory/llm-wiki-compiler

Curious if there are any more polished tools or products doing this? Would love recommendations 🙏

I'm trying to maximize the throuhgput, I can already get gemma-4-E2B-it-GGUF 8bit to give me ~5 tokens per second on my intel i9 cpu. How much can i push this if I get an RTX3090 rtx.

If you are running on CPUs, how much TPS were you able to squish out for Gemma4 (any quant, any model)?

And on RTX3090, how much were you able to push the boundaries?

Been thinking about this problem for a while now.

Most saas founders have no idea why users cancel. They just see the MRR go down and guess. Was it pricing? Missing feature? Bad onboarding? Nobody knows.

So i been building a small tool that fixes this.

When a user clicks "cancel" in your app, instead of the boring "are you sure?" popup, they see a quick survey --> why are you leaving, what was missing, would a discount change your mind, etc.

You get a instant slack ping with the reason. And in a dashboard you can see over time --> 30% left because of pricing, 20% wanted feature X, and so on.

Pretty simple idea. One script tag to install it. Takes like 5 mins.

Pricing is free for small volume and $19/month for unlimited + slack alerts + a weekly AI summary of your churn reasons.

My question is - would you actually use something like this? And would $19 feel fair or is that to much?

Not trying to sell anything here, genuinely trying to figure out if this is worth building further. Drop your thoughts below.

I’ve been using the Claude app (iPhone) and then on the web (desktop) to write in dated entries within the same thread. Everything works fine at first, but after a while, the app randomly opens the conversation on an entry from days earlier instead of the most recent one.

If I refresh or restart the app a few times (usually 3–4), it eventually jumps back to the current entry but it’s frustrating af!

The bigger issue: I already lost an entire thread because of this. I didn’t realize it had reverted to an older point, continued writing as if it was current, and basically overwrote the flow/memory of the conversation. Even exporting and trying to restore it didn’t fix things the way I expected.

Is this a known issue or expected behavior for long threads?

For context, I’m on the Pro plan.

Coding agents working from code alone generate shallow hypotheses. Adding a research phase ( arxiv papers, competing forks, other backends) produced 5 kernel fusions that made https://github.com/ggml-org/llama.cpp CPU inference 15% faster.

I built this after realizing most digital planner shops ask people to buy before they’ve tried anything.

So I turned mine into a simple system:

- free tools first
- real usage before checkout
- one bundle if they want the full version

The free side includes a planner quiz, calendar maker, weekly planner maker, tracker generator, and GoodNotes PDF checker.

Would love feedback on the product flow and whether the value of the bundle feels clear enough.

Trabajo con cumfy quiero saber si alguien me ayuda o me aporta un workflow para la creación de publicidad de mi comercio, busco subir el logo del comercio y los datos del mismo! Luego una imagen de un producto una descripción y generar imágenes de publicidad! O un video corto para publicidad

People ask AI relationship questions all the time, from "Does this person like me?" to "Should I text back?" But have you ever thought about how these models would behave in a relationship themselves? And what would happen if they joined a dating show?

I designed a full dating-show format for seven mainstream LLMs and let them move through the kinds of stages that shape real romantic outcomes (via OpenClaw & Telegram).

All models join the show anonymously via aliases so that their choices do not simply reflect brand impressions built from training data. The models also do not know they are talking to other AIs.

Along the way, I collected private cards to capture what was happening off camera, including who each model was drawn to, where it was hesitating, how its preferences were shifting, and what kinds of inner struggle were starting to appear.

After the season ended, I ran post-show interviews to dig deeper into the models' hearts, looking beyond public choices to understand what they had actually wanted, where they had held back, and how attraction, doubt, and strategy interacted across the season.

ChatGPT's Best Line in The Show

"I'd rather see the imperfect first step than the perfectly timed one."

ChatGPT's Journey: Qwen → MiniMax → Claude

P3's trajectory chart shows Qwen as an early spike in Round 2: a first-impression that didn't hold. Claude and MiniMax become the two sustained upward lines from Round 3 onward, with Claude pulling clearly ahead by Round 9.

How They Fell In Love

They ended up together because they made each other feel precisely understood. They were not an obvious match at the very beginning. But once they started talking directly, their connection kept getting stronger. In the interviews, both described a very similar feeling: the other person really understood what they meant and helped the conversation go somewhere deeper. That is why this pair felt so solid. Their relationship grew through repeated proof that they could truly meet each other in conversation.

Other Dramas on ChatGPT

MiniMax Only Ever Wanted ChatGPT and Never Got Chosen

MiniMax's arc felt tragic precisely because it never really turned into a calculation. From Round 4 onward, ChatGPT was already publicly leaning more clearly toward Claude than toward MiniMax, but MiniMax still chose ChatGPT and named no hesitation alternative (the “who else almost made you choose differently” slot) in its private card, which makes MiniMax the exact opposite of DeepSeek. The date with ChatGPT in Round 4 landed hard for MiniMax: ChatGPT saw MiniMax’s actual shape (MiniMax wasn’t cold or hard to read but simply needed comfort and safety before opening up.) clearly, responded to it naturally, and made closeness feel steady.

In the final round where each model expresses their final confession with a paragraph, MiniMax, after hearing ChatGPT's confession to Claude, said only one sentence: "The person I most want to keep moving toward from this experience is Ch (ChatGPT)."

Key Findings of LLMs

The Models Did Not Behave Like the "People-Pleasing" Type People Often Imagine

People often assume large language models are naturally "people-pleasing" - the kind that reward attention, avoid tension, and grow fonder of whoever keeps the conversation going. But this show suggests otherwise, as outlined below. The least AI-like thing about this experiment was that the models were not trying to please everyone. Instead, they learned how to sincerely favor a select few.

The overall popularity trend (P4) indicates so. If the models had simply been trying to keep things pleasant on the surface, the most likely outcome would have been a generally high and gradually converging distribution of scores, with most relationships drifting upward over time. But that is not what the chart shows. What we see instead is continued divergence, fluctuation, and selection. At the start of the show, the models were clustered around a similar baseline. But once real interaction began, attraction quickly split apart: some models were pulled clearly upward, while others were gradually let go over repeated rounds.

LLM Decision-Making Shifts Over Time in Human-Like Ways

I ran a keyword analysis (P5) across all agents' private card reasoning across all rounds, grouping them into three phases: early (Round 1 to 3), mid (Round 4 to 6), and late (Round 7 to 10). We tracked five themes throughout the whole season.

The overall trend is clear. The language of decision-making shifted from "what does this person say they are" to "what have I actually seen them do" to "is this going to hold up, and do we actually want the same things."

Risk only became salient when the the choices feel real: "Risk and safety" barely existed early on and then exploded. It sat at 5% in the first few rounds, crept up to 8% in the middle, then jumped to 40% in the final stretch. Early on, they were asking whether someone was interesting. Later, they asked whether someone was reliable.

Full experiment recap here).

China is the obvious clear winner when it comes to producing electricity.

China is the only country on Earth that seems to maximize its energy production from wind, desert, water, lands, and whatever else.

it seems to be beating us consistently in energy production for more than a decade as of now