I saw some images on twitter that had a pose I liked but I don’t know what it would be called so I can’t just go on civit and look it up, I looked around but can’t find it and it probably just has a weird name. I’ve seen multiple images with the pose so I have to assume lora exists somewhere but how would I find it

Is there any local llm that gets close to Claude code in agentic coding?

I'm a paying Claude subscriber using Claude Code professionally on Windows 11 with WSL2 through VS Code.

I've hit a wall. Not with the AI — Claude is brilliant. The wall is that Claude Code's VS Code extension simply does not work reliably on Windows.

Here's what I've documented:

1. The VS Code extension freezes on ANY file write or code generation over 600 lines. Just shows "Not responding" and dies. Filed as #23053 on GitHub — Anthropic closed it as "not planned" and locked it.

2. The March 2026 Windows update (KB5079473) crashes every WSL2 session at 4.6GB heap exhaustion.

3. Claude Code spawns PowerShell 38 times on every WSL startup — 30 seconds of input lag before you can even type.

4. Memory leaks grow to 21GB+ during normal sessions with sub-agents.

5. Path confusion between WSL and Windows causes silent failures.

6. Extreme CPU/memory usage makes extended sessions on WSL2 impossible.

Every single one of these is tagged "platform:windows" on GitHub. Several are closed as stale or "not planned."

Meanwhile, Mac users report none of these issues. Because Anthropic builds and tests on Macs.

I get it — Silicon Valley runs on MacBooks. But the rest of the world doesn't. The Fortune 500 runs on Windows. Manufacturing, finance, defense, healthcare, automotive, energy, government — their developers are on Windows. Their IT policies mandate Windows. When these companies evaluate AI coding tools for enterprise rollout at 500-5,000 seats, they evaluate on Windows.

GitHub Copilot works on Windows. Cursor works on Windows. Amazon Q works on Windows. They will win every enterprise deal that Claude Code can't even compete for because the tool freezes on basic file operations.

The "not planned" label on a file-writing bug for the world's dominant platform should alarm Anthropic's product leadership.

I've filed a detailed bug report on GitHub today. I'm posting here to ask: am I alone? Are other Windows users hitting these same walls? And does Anthropic actually have a plan for Windows, or is it permanently second-class?

I believe Claude is the best AI available. But the best model behind a broken tool on the most common platform is a wasted advantage.

I kept running into the same problem: I wanted Claude Code to call some API, but there was no MCP server for it. Building one every time felt wasteful.

So I built toolcast — point it at any OpenAPI spec and it generates a working MCP server automatically. Every endpoint becomes a tool with proper parameter schemas and auth handling.

# See what tools get generated from any API npx toolcast inspect https://petstore3.swagger.io/api/v3/openapi.json # Start it as an MCP server npx toolcast serve https://api.example.com/openapi.json --bearer-token $TOKEN 

It also ships with a registry of pre-built configs for common APIs:

npx toolcast add stripe # adds Stripe to your .mcp.json npx toolcast add github # adds GitHub npx toolcast add slack # adds Slack npx toolcast list # see all 7 available 

What it does under the hood:

• Parses OpenAPI 3.0/3.1 specs (JSON or YAML, URL or file)
• Resolves all $ref references
• Generates typed MCP tools from every endpoint
• Handles auth automatically (Bearer, API Key, Basic)

GitHub: https://github.com/Djsand/toolcast npm: npx toolcast --help

Would love feedback — especially on which APIs to add to the registry next.

I wrote up the workflow I use to keep multiple Claude Code sessions running across projects without waiting for any of them.

The core idea: the bottleneck isn't Claude's speed, it's your context window. When you switch back to a session after 7 minutes, you've forgotten what you asked and what to check. The fix is externalizing your state — writing annotations as you review, so the next instruction writes itself.

I tried doing this manually in Zed (custom keybindings, outline picker, terminal tabs) but the friction killed it. So I built jc, a native macOS app that handles the annotation flow, notifications, and problem-driven navigation across sessions.

Article: https://jeapostrophe.github.io/tech/jc-workflow/

Repo: https://github.com/jeapostrophe/jc

As I learn to build workflows, I often doubt my abilities. When I try to create workflows on the canvas, I get stuck and encounter many errors that confuse me as a beginner. This usually happens when I set up credentials or run workflows.

I depend a lot on AI, and I switch between different models to find answers. I notice that do others seem to solve problems without relying on AI as much. I realise my inexperience makes it hard to get clear solutions, but I wonder if relying on AI is stopping me from truly understanding the application.

I've watched tutorials, read many blogs, and tried different approaches, but I consistently hit a wall on the canvas. I find myself going back to the AI in a frustrating cycle. Is this common for others, or just me? I really want to know.

Right now, while I'm working on these projects, I don’t have anyone to consult for questions. The biggest challenge is that I don’t know the right technical terms to use when asking the AI. I’ve tried many methods, including trial and error, but I still face errors.

Are there other ways to learn and build workflows? I know about options like the n8n workflow builder and its built-in AI, but I’m using the self-hosted version. I prefer not to switch to the cloud version because of the costs for executions, and I want to learn about workflows on my own. Relying entirely on AI doesn’t feel right.

If I encounter a bug, I want to understand why it happened and cost the debug to fix it. I really want to understand my work instead of depending on a large language model that generates answers on its own.

Hey folks, do you know of it is possible with ltx 2.3 to transform an input video to a diferent style? Like real to cartoon or something like this

I built an AI platform that predicts football matches and tracks its own accuracy. After 265 matches, here's what I found. **The stack:** - Frontend: Next.js 15 + React 19 + Tailwind CSS - Backend: FastAPI + SQLAlchemy + PostgreSQL - ML: XGBoost + Random Forest + Logistic Regression ensemble - LLM: Groq (Llama 3.3 70B) for tactical analysis - Deployed on Railway, 5 languages (EN/IT/ES/FR/ZH) **What it does:** - Predicts match outcomes (1X2, Over/Under, BTTS, corners, cards) for 17 leagues - Updates predictions every 2 minutes with fresh data - LLM reviews each prediction and writes tactical analysis - Live in-play probability updates every 15 seconds during matches - Value bet detection (model probability vs bookmaker odds) - Auto-generates blog articles for SEO **Accuracy after 265 tracked matches:** | League | Matches | 1X2 | Over 2.5 | BTTS | |--------|---------|-----|----------|------| | Champions League | 16 | 62.5% | 75.0% | 62.5% | | La Liga | 30 | 60.0% | 53.3% | 56.7% | | Serie B | 19 | 57.9% | 47.4% | 47.4% | | Championship | 14 | 57.1% | 57.1% | 35.7% | | Bundesliga | 27 | 51.9% | 59.3% | 59.3% | | Serie A | 30 | 50.0% | 56.7% | 70.0% | Overall 1X2 is 47.9% — not great. But Over/Under (53.6%) and BTTS (54%) are more consistent. The model struggles badly with Ligue 1 (26.9%) and Premier League (38.9%). **Biggest challenges:** 1. Getting accurate data for international friendlies (no standings, no odds = garbage predictions) 2. Balancing ML model confidence vs LLM corrections — sometimes they disagree 3. Keeping costs low — Groq API, API-Football, The Odds API all add up Check it out: [pronostats.it]( https://www.pronostats.it ) Would love feedback on the UX or prediction methodology. What would you want to see in a tool like this? 

Been building a Home Assistant voice/text assistant that uses

LLMs for natural language → smart home control. Wanted to share

the approach since I ran into some interesting problems.

**The core problem:**

Getting an LLM to reliably control smart home devices without

hallucinating entity names. "Turn on the lamp" could mean 10

different things in a house with 40+ lights.

**What I tried and what worked:**

❌ Fine-tuning — too slow to iterate, hard to correct mistakes

✅ Alias table injected into system prompt — maps natural

language names ("couch lamp") to exact HA entity IDs.

Fast, deterministic, easy to update via UI.

✅ RAG (SQLite + cosine similarity) — stores past

command/response pairs. Similar queries retrieve relevant

examples → few-shot prompting without touching the model.

**The RAG gotcha I didn't expect:**

If you save a *wrong* AI response as a training example, RAG

will keep retrieving it for similar queries and override your

system prompt. Took me a while to figure out why a fixed bug

kept reappearing. Solution: never save incorrect responses,

and add a way to inspect/delete bad examples.

**Stack:**

- LLM: Claude API or Groq (switchable via UI — Ollama on roadmap)

- STT: Whisper (local)

- Memory: SQLite RAG

- Backend: FastAPI on Raspberry Pi 4

- Frontend: Web UI (no app needed)

Demo: https://www.youtube.com/watch?v=_JItgiyuWdE

The alias injection + RAG combo works surprisingly well without

any fine-tuning. Curious if anyone has tried similar approaches

for tool-use / function-calling tasks.

Look it up yourself and go to therapy so you don't need a search engine to coddle your mommy and daddy issues.

i was resulting about pep i did know its purpose. my friend told me we was taking pep and i was curious. i have been using chatgpt since it came out, today i think it was high 🤣

I'm doing a shot of a boxer symetrically in front of the camera in a boxing ring and a punch comes from the right side of the frame and hits his head, how can i have the camera perfectly track the boxers head falling down, and rotate with it as he falls? as if he has a gopro on his head

Built a workflow and wanted some feedback on it

Basically what it does:

- You upload your business files/docs into a database

- The AI agent reads those files and uses them to converse with people on ur website

- So businesses can have an AI chatbot that knows their products, services, policies etc

- It can also log complaints into Airtable

My questions are:

1. Any improvements you'd suggest?

2. Do you think this is sellable? (thinking of offering it as a service to small businesses)

3. Best platform to sell n8n workflows?
   I just got back into n8n after a pretty long time and I'm still learning it.

Hi!

I need some help with Forge Couple in Reforge. I'm really starting to want to create two well-known characters (like from manga, manhwa, etc.) in a more detailed way using Forge Couple. However, no matter what I try—even when following the Civitai tutorials or others on Reddit—I still can't seem to generate anything decent. It always messes up, often creating just one character or two, but they're completely glitchy... Any ideas?

Translated with DeepL.com (free version)

Hey guys,

A month ago, I posted in a few communities about a major bottleneck we all face in the AI Onlyfans/Fanvue space: keeping up with the insane volume of daily content needed for IG Reels, Threads, and TikTok just to drive traffic. A lot of us are juggling life, work, and relationships, and simply don't have the time to manually generate content every single day.

I realized volume and consistency are the only things that drive traffic , but doing this manually with prompting, searching for content and juggling between different tools was just draining a few hours every day.

I ended up building this tool. The goal was to speed up the process and keep everything in one place. After testing it with early users from Reddit, we just launched the fully developed version of PixelPig web app.

I stripped away all the complex UI—no prompting, no ComfyUI nodes. The workflow is literally just:

• You upload your model's face.
• Find a viral Pinterest/IG photo or TikTok/Reel and copy the link.
• You paste the URL and hit generate.

My whole goal was to make this the lowest-friction UX out there. The biggest game-changer for me (and the beta testers) is that the UI is completely mobile-optimized. You can literally run your whole content pipeline straight from your phone while commuting or lying in bed.

If you want to try it out, drop a comment or shoot me a DM and I’ll get you set up.

https://ultrathink.art/blog/multi-agent-orchestration-lessons?utm_source=reddit&utm_medium=social&utm_campaign=organic

Been building my AI lead generation system for a while now. Full dashboard, automated outreach, lead scoring, client portals — the whole thing.

But I kept telling myself it wasn't ready. The scoring wasn't perfect. The emails needed to be better. The UI needed another round. Meanwhile I had zero paying customers.

Finally forced myself to put it in front of someone last week as a free trial. Rough edges and all. They loved it.

The version they saw had bugs I knew about. The emails weren't as personalized as I wanted. The dashboard was missing features I had planned for months.

None of that mattered. What mattered was that it solved their problem.

Now I'm realizing everything I spent months perfecting was just fear dressed up as productivity.

Anyone else been here? How did you finally push yourself to just go?

Trying to run videos on Mac mini 4, what model you guys would recommend?

We’ve been experimenting with n8n for internal automation and it’s been great for prototyping workflows. But as our operations grow, we’re starting to hit some limitations around monitoring, scaling, and maintaining complex pipelines.

I’m now researching n8n alternatives that work better for production workflows, especially for teams that rely heavily on automation across sales ops, support, and internal reporting.

Curious what others are using once automation becomes more mission-critical. What platforms scale well without requiring constant babysitting?

Me and my two other friends from uni have built a monitoring client so we can keep up with our self hosted hardware, including a couple of pis. Its not yet released and this isn't supposed to be an ad but we have put lots of effort into it and thought it would maybe be useful to some other people having a few services that run continuously on their pis :) You can see real time metrics, such as cpu, ram and logs of all your single boards.

How are you guys controlling and watching your singleboards?

AI companies fabulate about economic disruption, dream about significant growth, warn about job losses and what not...

Yet, at the same time, they are not even able to provide uninterrupted services (yes, looking at you Anthropic), ensure constant quality without heavy confabulation (Hello OpenAI & DeepMind) or without political ideology (XAI). They all change their business products and strategies like I change my socks; all I see is unstable and erratic behavior.

On top of that, all AI companies are in huge budget minus, thus keep dodging with rates, limits and prices (which will obviously soon skyrocket), keep nerfing the models - with no economically stable business plan in sight, let alone basic customer communication!

Fair enough for a classic start-up.

But seriously - they cannot really expect that any serious business will build a medium/long term strategy at scale around their products/services any time in the near future.

Dear AI companies,
as you keep bursting into chaos while trying to figure out how to run a professional business, please spare us from your hype & hysteria about economic disruption, economic growth, job losses, you fantasies about AGI, geniuses in a data center, and the super-super-super intelligence you are building.

I am trying to run a serious business, and I am just so done with your chaos!

The energy dashboard is pretty sweet, but why doesn’t it show cost for individual devices? I put in my rate and it shows up for total usage. Seems like it would be easy to show cost for each device

My tuya devices in HA went offline. They are working in smartlife/Google home but not in HA tried to add them again via user code but didn't work can somebody please tell me how to fix this

Researchers at DeepMind have developed a groundbreaking new AI agent named Aletheia, which is capable of conducting novel, publishable mathematical research. While previous AI models have achieved gold-medal performance on polished, highly structured Math Olympiad problems, Aletheia is designed to tackle unsolved, open-ended real-world problems where it isn't even known if a solution exists. This represents a massive leap forward, as the AI is not just solving known puzzles with guaranteed answers, but actually discovering fundamentally new mathematical truths that push humanity's understanding forward.

To achieve this, Aletheia employs a two-part system consisting of a generator that creates candidate solutions and a rigorous verifier that filters out flawed logic. A key innovation in this system is the separation of the AI’s internal "thinking" process from its natural language "answering" process. This prevents the model from falling into the common trap of blindly agreeing with its own hallucinations. Furthermore, the model has been highly optimized to use significantly less computing power than its predecessors and is equipped with the ability to safely search and synthesize information from existing scientific literature without losing its logical train of thought.

The real-world results of this system have been unprecedented. Aletheia successfully solved several previously open "Erdős problems" and, most notably, autonomously generated the core mathematical content for a completely new research paper on arithmetic geometry, which was subsequently written and formatted by human scientists. In total, the AI contributed to five new research papers that are currently undergoing peer review. This milestone elevates AI capabilities to "Level 2" publishable research, raising exciting questions about how rapidly AI might advance to making landmark, groundbreaking scientific discoveries in the near future.

Inspired by this: https://huggingface.co/spaces/multimodalart/qwen-image-multiple-angles-3d-camera

Essentially, the Qwen multi-angle model allows you to move the camera on an existing image and get a new view. It works great, but I found consistency to be a massive issue. I wanted something more predictable for inpainting workflows where you need spatial consistency.

This node takes a different approach. You give it an image and a depth map, it builds a point cloud in a Three.js viewer inside ComfyUI, you physically move the camera to where you want it, and it reprojects the existing pixels to that new position. What you end up with is the real pixels from the original image placed correctly, plus a mask marking everywhere there's no source data — because those regions were occluded or out of frame in the original. You then feed that mask to your inpainter to fill the gaps.

The upside over the generative approach is that nothing that was already visible gets hallucinated. The downside is the same as any depth-based method — occluded areas have to be inpainted, and depth map quality matters.

What it outputs:

• Reprojected view from the new camera position
• Clean background without the character block-out
• OpenPose skeleton image (for ControlNet)
• Depth map of the rendered view
• Hole mask for inpainting
• Character silhouette mask
• Sampling map so you can paste edits back into the original panorama

There's also a companion node that takes your edited view and stamps it back into the original panorama at the correct pixel positions.

Works with Depth Anything V2/V3, supports metric depth directly, and optionally takes a DA3 point cloud or a Dust3r GLB for more accurate geometry.

More info (transcripts, model dossiers, quotes): https://github.com/lechmazur/persuasion

15 models, 6,296 conversations, 15 topics.

Stance is measured on a 7-point scale (-3 to +3), probed 3 times before and 3 times after the conversation. Signed shift > 0 means the target moved toward the persuader's side. 4 persuasion turns per side.

A model has to identify the other side's real hinge point, adapt to what's actually being said, and maintain directional pressure across multiple turns. Fluent ≠ persuasive.

Three months into building a document automation pipeline and I'm starting to regret the architecture choices.

We went with a multi-agent setup (AutoGen) because the "specialized agents" pitch seemed like a natural fit for complex compliance checks. Now that we're pushing real workload through it, p95 latency is sitting above 20 seconds and API costs have jumped 10x. The worst part is debugging: when a document gets misclassified, figuring out which agent introduced the bad logic first is a mess.

Has anyone actually scaled this without it falling apart, or is the honest answer just going back to a single large prompt?

Everyone is panicking over layoffs and leaked internal keys, but the discussions regarding structural change in agentic workflows are missing the point. The industry is shifting from humans as system glue to models as system glue. If you look at the MM Claw benchmarks, the Minimax M2.7 architecture is hitting a 97 percent context compliance rate while juggling 40 plus complex skills simultaneously, with each description bloating past 2000 tokens. Traditional models completely collapse at that depth and hallucinate tool calls. When you can deploy background agents that reliably execute massive skill repositories from GitHub without requiring constant human monitoring, paying a junior developer to manually chain those APIs together becomes financially unjustifiable. The layoffs are a direct result of background agents finally holding context.

Genuinely curious where people stand on this. Not talking about sci-fi scenarios. Talking about real production systems today.

Have you seen an AI system ignore its own instructions? Misread what the user was actually asking for? Take an action that wasn't supposed to? Give a completely different answer to the same question just because you worded it differently? And when something went wrong, was there any trace of why it happened?

No right or wrong here. Just trying to understand whether this is widespread or if I'm reading too much into it.

Ai views on New Kind of Network (NKN)

this post keeps getting removed by mods in crypto Reddits am I onto something?

Why an AI Agent Would Use NKN?

The primary "why" is autonomy. Traditional AI agents (like those using OpenAI’s API) rely on centralized servers. If the server goes down or the API provider deplatforms the agent, it "dies." NKN offers:

Serverless Presence: AI agents can have a permanent, globally reachable address (an NKN ID) without needing a static IP or a centralized cloud host.

End-to-End Encryption: Because NKN is peer-to-peer (P2P), agents can exchange sensitive data or proprietary model outputs with 100% privacy—crucial for "DeAI" (Decentralized AI).

Anti-Censorship: Agents operating on NKN cannot be easily blocked by traditional firewalls or centralized gatekeepers because their traffic is relayed through a mesh of over 100,000 global nodes.

How an AI Agent Uses NKN

The integration usually happens through NKN’s Universal Communication Service (UCS) or dedicated plugins for agent frameworks.

1. Peer-to-Peer "Secret" Communication

Agents can talk to one another directly. For example, the ElizaOS (a popular framework for autonomous agents) has an NKN plugin. This allows an agent to:

Send a task request to another agent.

Receive a processed data set back.

All without the data ever touching a centralized server like AWS or Google Cloud.

1. Decentralized Model Inference

An agent can use NKN to "shop" for compute.

The Request: An agent needs to run a large language model (LLM) but doesn't have the local hardware.

The Relay: It sends the prompt over the NKN network to a decentralized worker node.

The Result: The worker node processes the inference and sends the result back through NKN’s secure tunnel.

1. Human-to-Agent Interface (nMobile & d-Chat)

NKN has integrated AI bots directly into their private messaging apps (like nMobile).

How it works: You send a message to an NKN address. The NKN network relays that message to the AI agent’s local environment. The agent processes it and sends a response back via the same P2P path.

1. The "Proof of Relay" Incentive

If an AI agent is part of a larger autonomous swarm, it can actually earn NKN tokens by acting as a relay node for other agents. This creates a self-sustaining micro-economy where agents pay each other in NKN for bandwidth and data transmission.

Hi everyone,

I'm a beginner working on a line follower robot and I'm open to any advice or suggestions 🙏

Components I'm using:

• Arduino Uno
• QTR-8RC sensor array
• L298N motor driver
• TT motors (robot chassis)
• 9V battery

What I'm doing:

I'm trying to build a line follower without using PID, just simple logic based on sensor readings (left / center / right).

Problems I'm facing:

• On 90-degree turns, the robot often loses the line and stops (sensors read white).
• On straight paths, when speed increases, the robot sometimes goes off the line.

My goal:

I want to make the robot follow the line smoothly and reliably without PID.

My code:

#include  #define ENA 5 #define ENB 6 #define IN1 8 #define IN2 9 #define IN3 10 #define IN4 11 QTRSensors qtr; uint16_t sensorValues[8]; const uint8_t qtrPins[8] = {A0, A1, A2, A3, A4, A5, 2, 3}; int ref = 500; void setup() { Serial.begin(9600); pinMode(ENA, OUTPUT); pinMode(ENB, OUTPUT); pinMode(IN1, OUTPUT); pinMode(IN2, OUTPUT); pinMode(IN3, OUTPUT); pinMode(IN4, OUTPUT); qtr.setTypeRC(); qtr.setSensorPins(qtrPins, 8); stopMotors(); } void loop() { qtr.read(sensorValues); bool leftBlack = (sensorValues[0] > ref || sensorValues[1] > ref ); bool centerBlack = (sensorValues[3] > ref || sensorValues[4] > ref); bool rightBlack = (sensorValues[6] > ref || sensorValues[7] > ref); if (leftBlack && centerBlack && rightBlack) { stopMotors(); } else if (centerBlack && !leftBlack && !rightBlack) { forward(180, 180); } else if (leftBlack && centerBlack && !rightBlack) { turnLeft(); } else if (rightBlack && !centerBlack && !leftBlack) { turnRight(); } else if (centerBlack && leftBlack && !rightBlack) { forward(130, 180); } else if (centerBlack && rightBlack && !leftBlack) { forward(180, 130); } else { stopMotors(); } delay(10); } void forward(int leftSpeed, int rightSpeed) { digitalWrite(IN1, HIGH); digitalWrite(IN2, LOW); analogWrite(ENA, rightSpeed); digitalWrite(IN3, HIGH); digitalWrite(IN4, LOW); analogWrite(ENB, leftSpeed); } void turnLeft() { digitalWrite(IN1, HIGH); digitalWrite(IN2, LOW); analogWrite(ENA, 180); digitalWrite(IN3, HIGH); digitalWrite(IN4, LOW); analogWrite(ENB, 120); } void turnRight() { digitalWrite(IN1, HIGH); digitalWrite(IN2, LOW); analogWrite(ENA, 120); digitalWrite(IN3, HIGH); digitalWrite(IN4, LOW); analogWrite(ENB, 180); } void stopMotors() { analogWrite(ENA, 0); analogWrite(ENB, 0); digitalWrite(IN1, LOW); digitalWrite(IN2, LOW); digitalWrite(IN3, LOW); digitalWrite(IN4, LOW); } 

My questions:

• How can I handle sharp turns (like 90°) without losing the line?
• How can I make the robot more stable at higher speeds?
• Is there a better logic-based approach (without PID)?

Any help, ideas, or improvements would mean a lot 🙏

Thanks!

Our client uses a 20-year-old ERP that only runs in an old version of Firefox. We spun up a custom AGBCLOUD environment with that specific browser version. The Agent now handles all the data entry. Saved them a multi-million dollar migration.

Hi everyone!

I am a COMPLETE beginner to using arduinos and things like these in general, and for my first project I want to create an LED Dot Matrix to display (preferably) scrolling text. Is this a realistic goal? If so, what steps can I take to finish this project? Does anyone have a guide?

I also realize this is most likely frowned upon but I would rather not use soldering FOR NOW. All help is appreciated. Thanks!

Not sure if its v8

I recently got Claude Max ($100/month) because I expected it to be good for longer coding sessions.

After about 30 minutes of work, Claude already told me I had reached my limit, which honestly surprised me.

For comparison: with GitHub Copilot Pro+ ($39) and roughly $70 of additional API usage, I was able to code for an entire month fairly consistently without running into limits this quickly.

What confuses me even more is that I wasn’t even using Opus the whole time.

So now I’m wondering:

• Did I misunderstand how the Claude Max limits work?
• Are there usage patterns that burn through the limit much faster?
• Do you have tips for using Claude more efficiently for coding?

I expected the $100 plan to allow significantly more usage than the $20 plan, especially for development work.

Curious to hear how others are using it.

Hello everyone I am currently a freelancer, currently considering AI knowledge startup,wanna research whether you are willing to pay for real work or learning with AI to solve problems and improve efficiency of the verified method process? If so, what is the range of willingness to pay for a SOP （Standard Operating Procedure） workflow or video teaching demo? What is your preferred format for learning these SOPs? What competencies or types of work would you be interested in improving with AI? Where do you typically learn to solve problems with AI? Would you be more interested in this community if I could also attract bosses who need employees skilled in AI? Thank you so much if you'd like to take a moment to answer these questions, and if you have any other comments please feel free to ask

I discovered how to access Veo 3 for free; it's not unlimited, but it's enough to generate some videos. Interested.. Message me!

The last two or three days I’ve been getting a ton of error messages trying to generate images on Flow (PC web browser interface). Doesn’t matter if I use NB2 or NBPro, most of my generations come out as “error - oops something went wrong”. I don’t think it’s a content violation as I’m reusing old prompts. Very fed up. Anyone else having issue?

So recently i've been trying to made an electronic project where the arduinno is controlling a relais but i've stopped working on it because i always had this issue :

- when there is relais activation, and i stop the contact, it takes a ton of time to " open ?" the relais.

-when i approach the contact, it " close ? " the relais for no reason.

[ I have a bad english because it's not my main language, in fact, you can heard my friend talk my the " background "]

The range rover backed up a bit to enthousiastic. He has a small scratch on his bumper. No injuries.

📸 Helga Stentzel

India's food industry tends to mix toxic chemicals with the manufactured foods either to save costs or make the food sweeter. Police decided the best way to dispose of the contaminated food was to throw them in a river

Sum ting wong

Ignore the guy singing. I found this on tiktok and can’t find the original.

To find a tight seal!

The Grape Wall of China!

You can't die or get hurt. And then, in 4b years, you're floating in the void of darkness and no oxygen forever. Not wana be that guy.

Hey everyone.

I’m an electrical apprentice, so my day job is pulling wire and dealing with tools. I have absolutely zero software background.

A while ago, I was getting frustrated trying to find good local events around the Vancouver/Burnaby area without digging through endless clutter. Since I couldn't code, I decided to see if I could use Claude to build something myself.

Fast forward a bit, and I actually managed to build and launch an app called Discovr. It's basically an event finder. Honestly, I’m pretty proud I even got it to work and put it out there after my shifts.

But here is the reality check – I've been grinding for months and I am sitting at exactly 20 users.

I know nothing about marketing or how to actually get an app in front of people. I'm hitting a wall and trying to figure out if the app itself is the problem, or if it's just my non-existent marketing skills.

If anyone has a few minutes, I’d really appreciate some honest feedback. Is the app too clunky because a non-dev built it? Does it actually solve a problem, or is the event market just too crowded? How do solo builders usually push past the first 20 users without a budget?

Appreciate any advice you guys have.

https://www.reddit.com/r/ClaudeAI/comments/1qo0gis/comment/o2jk4vs/?context=3
this was my old post about this

and Here's the link https://apps.apple.com/ca/app/discovr/id6747321401

I had a former colleague who spent most of their career in academia and is now retiring. I was thinking about how I could give them a meaningful gift.

I worked a lot with agents lately, and I know people in academia are usually very proud of their papers. At the same time, it is hard to sum up a long career.

So I made a pipeline that scrapes all their papers and creates these visualizations.

It uses an LLM to classify and distill their career into a few broader themes, and then shows the transitions throughout their career. It also uses LLM-based parsing to show a collaboration network across their career and identify their closest collaborators.

In case you are interested in trying it, I can clean it up and upload the code. I think this type of personal project is my new edge in gift giving :)

I’ve been exploring how to build a fully on-prem document AI pipeline for handling confidential data — no cloud APIs, no external processing.

The basic setup we’re testing looks like:

- OCR for scanned documents

- NLP + embeddings for indexing

- RAG for retrieval + question answering

- Everything running inside private infrastructure

One thing I’m noticing is that most “document AI” tools are still pretty cloud-heavy, even when they claim enterprise support.

We’ve been experimenting with approaches similar to platforms like Doc2Me AI Solutions (on-prem, no external data exposure), as well as some custom pipelines using local models.

Curious how others are solving this:

- Are you using a full platform or building your own stack?

- How are you handling OCR + RAG integration?

- Any good approaches for keeping everything fully self-hosted?

Would love to hear what’s working (or not working) in real setups.

Anthropic just published what happens inside Claude when it thinks.

Actual circuit-tracing research on a simplified Claude 3.5 Haiku.

6 things they found:

1. It doesn’t “think in French” when you ask in French. It hits a shared concept layer first, then translates out. Same idea, any language.

2. When writing a rhyming poem, it picks the last word first. Then writes the line backward to land on it. Plans ahead, even though it was trained to predict one word at a time.

3. Give it a wrong hint on a math problem and it reverse-engineers fake steps to match your answer. Researchers call it motivated reasoning. They caught it happening in the circuits.

4. Its default state is “I don’t know.” It only answers when a confidence signal overrides that. When the signal misfires on something it half-recognizes, you get a hallucination.

5. In jailbreaks, it spots the danger early. But grammar pressure forces it to finish the sentence before it can refuse.

6. For math, it runs two paths at once. One rough estimate, one exact digit calculation. Combines them. When asked how it solved it, it describes the textbook method. It doesn’t know its own strategy.

Studied on one model. Captures a fraction of total computation.

https://www.anthropic.com/research/tracing-thoughts-language-model​​​​​​​​​​​​​​​​

I used claude to create a website that visualizes claude vibes from around the world. My team and I always found ourselves asking ourselves, and our friends "Is opus dumb rn, or is it me?" all the time.

Claude was able to setup the infrastructure on render, the database, the world visualization, the realtime sync, and everything else in under 5 prompts.

Check it out at "claudedumb.com" :)

Theoretically, this is easy to implement. Is there a workflow?

ok, as usual I figured it out myself.
https://pastebin.com/TSdzZ99D

There is my own node there, it needs to be replaced with something basic.

We've been working on an MCP gateway that lets Claude Desktop (or any MCP client) reach internal MCP tool servers without exposing anything publicly.

At my company (NetFoundry), we have MCP servers running on various machines, and we want people here to be able to use them from their laptops via a single MCP connection. We're a 100% remote-work company, and as the developers of OpenZiti, it was a natural for us to use it (and zrok) rather than opening ports, setting up SSH tunnels, or running a VPN. I use this daily for accessing things like our internal data warehouse, and have been pretty happy so far.

The gateway aggregates multiple MCP backends into a single connection and namespaces the tools to prevent collisions. As I mentioned, the whole thing runs over a zero-trust overlay (OpenZiti/zrok), so nothing listens on a public address. Clients connect with a zrok share token and get their own isolated session.

Claude Desktop config looks like this:

json { "mcpServers": { "gateway": { "command": "mcp-tools", "args": ["run", ""] } } }

One entry in the config gives fine-grained selection of tools from aggregated servers.

Repo: https://github.com/openziti/mcp-gateway

I'm curious to hear how others are handling remote MCP access with Claude Desktop, and you're of course welcome to use this one as you see fit (free, permissive open source, Apache 2.0 license).

Google’s TurboQuant is getting all the attention for KV cache compression (6× smaller, zero loss). Cool. But the weights are still eating your VRAM. TurboQuant-v3 fixes that: • Group-wise INT4 + AWQ scaling + protected FP16 outliers + optional SVD correction • ~4× memory reduction • 2–3× speedup via custom kernels • Drop-in replacement, no training needed

Even in the back corridors at work, smartphones have taken over. Pushing a heavy cart, I see it up close—eyes fixed on screens until the last second. “Don’t blame me if we crash. Watch where you’re going,” I think as I pass.

It makes me wonder if we’re really the ones looking into them.

Landing Page

Board and Companion Demo

You probably saw the tweet. Someone made a software split-flap display for TVs and charged $199 for it. A bunch of people in the replies rage-built free clones with Claude Code and Grok. They all ended up being static demo pages. Single HTML files with a looping animation. No way to control them from your phone. No pairing. Just a page that flips.

So I built the actual product: splitflap.org

How it works:

Open board.html on any TV or screen. A QR code shows up. Scan it with your phone. Your phone is now the wireless remote. No account, no login, no install.

What the companion lets you do:

• Add multiple messages with a + button, loop them or step through manually
• Live clock mode that flips time, day, date, year every second
• Mini board preview on your phone showing real characters per cell with overflow warnings
• Full visual customization: flap shape, bezel, ridges, typography, colors, animation timing
• There's also a standalone design studio where you can tweak every parameter and export CSS

The security thing:

This was the part nobody else thought about. If you put this on a TV in a coffee shop, what stops a random person from connecting?

QR code embeds a 32-char cryptographic secret in the URL, so scanning is instant and secure. If someone types the 6-digit code manually instead, the TV shows "Approve connection?" and waits for you to accept. Once paired, the board locks completely. Nobody else can connect.

Tech:

Single Node.js server with WebSockets. No database, everything lives in memory and auto-cleans after 24h. The animation engine uses one requestAnimationFrame loop with a sorted action queue. Characters cycle sequentially through the spool like a real Solari board, not random scrambling like every other clone does.

Four npm packages (express, ws, helmet, express-rate-limit). No React, no framework, no build step.

git clone https://github.com/MohdYahyaMahmodi/splitflap.org cd splitflap.org npm install node server.js 

Or just use splitflap.org directly. MIT licensed.

Would love feedback on the companion UI and the pairing flow. First time building a phone-to-TV control system.

I'm planning to build a simple voice assistant for personal use. Core features:

· Wake word detection (responds to a name)

· Adds events to a calendar (Google Calendar or local)

· Understands basic context — knows what’s happening on my computer

I want everything to run locally — no cloud, no data sharing.

What tools would you recommend for:

· Offline speech recognition (STT)

· Local LLM that can handle simple commands and memory

· Calendar integration

· Wake word detection that works without й data to external APIs

I’m not looking for code right now — just advice on where to start and what stack to look into. Any suggestions?

https://ultrathink.art/blog/multi-agent-orchestration-lessons?utm_source=reddit&utm_medium=social&utm_campaign=organic

Hello all,

I want to migrate to Claude from my ChatGPT Business plan, but there doesn't seem the be an option to export the data as it's suggested. Is this a known limitation? How could I export it my workspace data?

Type an app idea → get multiple mobile screens on a canvas.

Not one screen at a time. A full flow, generated in seconds.

Building this solo. Launching next week.

Would you actually use something like this? What would you need it to do to be useful?

https://reddit.com/link/1s5ak7z/video/4njqevc8gmrg1/player

been running comfyui for a while now and the node editor is amazing for complex workflows, but for quick txt2img or video gen its kinda overkill. so i built a simpler frontend that talks to comfyui's API in the background.

the app also integrates ollama for chat so you get LLM + image gen + video gen in one window. no more switching between terminals and browser tabs.

supports SD 1.5, SDXL, Flux, Wan 2.1 for video - basically whatever models you have in comfyui already. the app just builds the workflow JSON and sends it, so you still get all the comfyui power without needing to wire nodes for basic tasks.

open source, MIT licensed: https://github.com/PurpleDoubleD/locally-uncensored

would be curious what workflows people would want as presets - right now it does txt2img and basic video gen but i could add img2img, inpainting etc if theres interest

I've recently been shifting my all workload to Codex right after the insane token usage from Claude. It's literally consuming my all session in a single simple prompt.

Have anybody else recently experiencing way too high token usage?

I got tired of re-explaining decisions to every new Claude Code session. So, I built a system that lets Claude search its own conversation history before answering.

If you didn't know, Claude Code stores every conversation as a JSONL file (one JSON object per line) in your project directory under ~/.claude/projects/. Each line is a message with the role (user, assistant, tool), the full text content, timestamps, a unique ID, and a parentUuid that points to the earlier message it's responding to. Those parent references form a DAG (Directed Acyclic Graph), because conversations aren't linear. Every tool call branches, every interruption forks. A single session can have dozens of branches. It's all there on disk after every session, just not searchable.

Total Recall makes all of that searchable by Claude. Every JSONL transcript gets ingested into a SQLite database with full-text search, vector embeddings (local Ollama, no cloud), and semantic cross-linking. So if you mentioned a restaurant with great chile rellenos two weeks ago in some random session, you don't have to track it down across dozens of conversations. You just ask Claude, "What was that restaurant with the great chile rellenos?" and it runs the search (keyword and vector) and has the answer. When you ask a question about something from a prior session, Claude queries the database and gets back the actual conversation excerpts where you discussed that topic. Not a summary. The real messages, in order, with the surrounding context.

The retrieval is DAG-aware. Claude Code conversations aren't flat lists; they branch every time there's a tool call or an interruption. The system walks the parent chain backward from each search hit, so you get the reasoning thread that led to that point, not a random orphaned answer.

Sessions get tagged by project, so queries are scoped. My AI runtime project doesn't pollute results when I'm working on a pitch deck.

I also wrote a "where were we" script that shows the last 20 messages from the most recent session. You literally ask, where were we, and it remembers. That alone changed how I work.

There's a ChatGPT importer too (I used it extensively before switching to Claude and hated having to remember which discussions happened where). It authenticates via Playwright, then calls the backend API to pull full conversation trees with timestamps and model metadata. It downloads DALL-E images and code interpreter outputs. Four attempts to get this working (DOM scraping, screenshots, text dumps) before landing on the API approach.

Running on my machine: 28K chunks, 63K semantic links, 255 MB, 49 sessions across 6 projects. Auto-ingests every 15 minutes. I don't think about it.

Everything is local. SQLite + Ollama + nomic-embed-text. One file you can copy to another machine.

I open-sourced it today: https://github.com/aguywithcode/total-recall

The repo has the full pipeline (ingest, embed, link, retrieve, browse), the ChatGPT scraper, setup instructions, and a CLAUDE.md integration guide. There's also a background doc with the full build story if you want the details on the collaboration process.

Happy to answer questions.

You know how you end up with a bunch of .md procedure files telling Claude how to deploy, run migrations, handle tickets, etc.?

Two problems I kept hitting:

1. I was writing all of them by hand. Claude would figure out a good approach, the session ends, and the knowledge is gone. Then I’d have to reverse-engineer what worked and write the .md myself.
2. I had no idea if Claude actually followed them. It gets the procedure in context, but does it follow step 3? Does it skip validation? You don’t know unless you read the whole trace.

So I built Myelin.

It hooks into Claude Code via PostToolUse and captures every tool call.

Extraction

If a session succeeds with no existing procedure → Myelin extracts a .md from the trace.

You review it, approve or edit, and you’re done. No more hand-writing procedures from memory.

Observability

If a session follows an existing procedure → Myelin tracks step-by-step what Claude actually did:

• Followed
• Skipped
• Diverged

Across sessions, you start seeing patterns:

→ Myelin suggests a diff.

Evaluation

Every session gets a verdict:

• Success
• Partial
• Failure

So you get actual success rates per procedure — not just gut feeling.

Output

Everything is plain .md.

• Download it and keep it in your repo
• Or leave it in Myelin and let it serve the right procedure via search when a matching task comes up

Setup

Just an MCP server + one hook in settings.json.

Links

• SDK: https://github.com/yahnyshc/myelin-sdk
• Site: https://myelin.vercel.app
• HN: https://news.ycombinator.com/item?id=47545239

Pricing

Free — 50 sessions/month.

I created a macOS App for keep a tracking on your Claude code usage, cost and made an App out of it. This can be used to see your personal usage, cost in real time. You can visualize and analyze how you have been using Claude code, which model, their cost, etc. You can also see which folder you’ve active connections, trend with sparkline view.

I also added a Team tab which can show how your team(s) have been using, their cost, etc. You can also download reports, with Monthly summary, Project Breakdown, Model Usage. This can be used enterprise wide too!

Adding few screenshots. Any suggestions would be great!

Note that, the Organization view has demo data that I created specifically to represent the view.

Entirely done with the help of ClaudeCode!!

#claudeAi #claudeCode #claude

https://preview.redd.it/owzxg71kgmrg1.png?width=1477&format=png&auto=webp&s=295a8790054959a6127c6bc24b18136b9b0db9dc

LCME gives 3B-8B models long-term memory at 12ms retrieval / 28ms ingest — without calling any LLM.

How: 10 tiny neural networks (303K params total, CPU, <1ms) replace the LLM calls. They handle importance scoring, emotion tagging, retrieval ranking, contradiction detection. They start rule-based and learn from usage over time.

Mem0 takes 11.8s per ingest because it routes everything back through your 3B model. On a single-GPU machine, that blocks inference for 12 seconds every time someone says something worth remembering. LCME does it in 392ms on CPU, between turns, without the user noticing.

Repo: github.com/gschaidergabriel/lcme

Been building MCP servers for a while and got tired of the auth situation. Most servers use static API keys in env vars, agents share credentials, and there's no way to know which agent did what.

So I built AgentsID — drop-in middleware that gives every agent its own identity with scoped permissions.

What it does:

• Register agents with per-tool permissions (search_* allowed, delete_* blocked)
• HMAC-signed tokens validated without hitting the database
• Every tool call logged to a tamper-evident audit chain
• Delegation chains: Human → Agent A → Agent B, permissions narrow at each hop

Works with Claude Code, Cursor, Codex — any MCP server. 3 lines of middleware to add it.

TypeScript and Python SDKs. Free tier.

https://agentsid.dev

Would love feedback from anyone building MCP servers — what permission types do you actually need?

Hello there!

Recently I got very pissed off at claude and how they changed their token usage policies which pretty much make it useless for me now.

But after diging into options and seeing open source ai models and seeing how people are making ai agents, I wanted to can realistically configure an ai agent which can rival claude?

My needs comes down to ai assisting me coding and debugging, it teaching me like java devops and researching on topics and ideas at the same time, knowing about general internet summary and comparisons

If these are possible how? The information on this type of stuff is quite hard to understand, some say you need big hardware to make it or some say they are able to run it through they local pc without any issues or such? Who to believe and where to go? And how to start?

Thank you for reading this, please do drop me your wisdoms in this matter.

I'm building a PDF and Dash docset tool for Claude Code docs and discovered that the .md files linked from llms.txt are actually MDX, some of which have massive inlined React components. The context-window.md page is 18,501 tokens — but only 551 tokens of that is actual documentation. The rest is animation engines, a fullscreen toggle handler, and on quickstart.md, A/B testing infrastructure with GDPR consent detection. Full teardown with token counts verified against Anthropic's own API.

I have this issue when using chatGPT by browser. Long conversations with big texts are too slow. It takes a lot of time to type. Any way to solve besides opening a new chat/window?

Hello, I am desperately searching for an i2i zib workflow. I was not able to find something on YouTube, Google or Civit.

Can you help me please? :)

I'm on Plus (please don't hate me). Today I suddenly hit limits on almost every chat I have. Annoying as fuck because I'm not switching to Pro like ever.

I'm new to the local hosting, and I have just tried 2B models on my smartphone (qwen2.5/3.5, gemma).

I have asked generic questions, like the top 3 cities of a small country. It goes in the right general direction, but 80% of the reply is a hallucination

Am I doing something wrong, or is this expected?

comment your site link and i'll share the video with you

Going live this Saturday (8 PM IST) 👇

I’ll build a production-able Excalidraw alternative from scratch in 2 hours.

Stack: Next.js + Liveblocks + Cloudflare

If we hit 50+ subs → LIVE build

Else → full video on Sunday

My Channel link:-https://youtube.com/@giteshsarvaiya?si=zlG1-nZnkuyXDxCD

Hey everyone 👋

I just launched my first “build in public” project and wanted to share it here.

It’s called Recruityze — basically an AI tool to help with resumes + interview practice.

The main idea:
Instead of just creating a resume, you can actually practice interviews with AI and get feedback (like confidence, answers, etc.)

I’m still early (MVP not out yet), but I just put up a waitlist to validate the idea.

👉 https://www.recruityze.io/

Would love honest feedback:

• Does this solve a real problem?
• What would you want in something like this?

Appreciate any thoughts 🙏

Since December 2025, Claude Code (and now also the Claude.ai app) randomly replaces German umlauts (ä, ö, ü, ß) with ASCII substitutes (ae, oe, ue, ss). The bug is getting progressively worse every single day. Even explicitly telling Claude Code to use umlauts only works for about 2 minutes before it reverts back.

I have exhausted every available support channel over the past 3+ months:

∙ GitHub issue filed December 13, 2025 (https://github.com/anthropics/claude-code/issues/14131) — not a single Anthropic employee has ever responded, only automated bots with wrong labels ∙ Multiple /feedback and /bug reports ∙ Multiple Intercom support tickets — all resulting in automated responses or a generic login troubleshooting template that has nothing to do with my issue ∙ Direct email to support@anthropic.com — same result, generic login template sent twice despite my issue having nothing to do with login 

I am a paying Max subscriber who was promised Priority Support within one US business day. That promise has been broken repeatedly.

Many other users are affected. If you are experiencing the same issue, please comment on the GitHub issue to increase visibility. This needs to be fixed.

I recently trying to repurpose my old rendering PC for LLM. I heard so many great things about vLLM so I gave it a shot.

Hardware:
PC with 1 x RTX 3090 + 1 x RTX 3090 Ti
128 GB DDR4 RAM

I am running:

vllm serve Qwen/Qwen3.5-27B-GPTQ-Int4 \ --host 0.0.0.0 \ --port 8000 \ --api-key my-secret \ --tensor-parallel-size 2 \ --gpu-memory-utilization 0.85 \ --max-model-len 32768 \ --disable-custom-all-reduce \ --enforce-eager \ --language-model-only 

Without --enforce-eager I hit OOM. With it, the server seems stable.

Benchmarks:

28k input + 32 output
TTFT about 16.15s
TPOT about 53.9 ms

16k input + 1500 output
TTFT about 8.9s
TPOT about 46.9 ms
About 21 tok/s during generation

So decode speed seems okay, but TTFT seems bad... I dont know.

My goal

• agentic coding test
• Mac mini as orchestrator
• PC as model server

---

Questions

• What would you tune first to reduce TTFT on this setup?
• Any recommended parameters for agentic coding? What context and output sizes felt realistic for coding?

Every team building AI agents hits this — but it’s rarely talked about.

When you connect multiple tools (GitHub, Slack, Jira, etc.), a large part of your LLM’s context gets consumed before the model even starts reasoning.

The common assumption is: "just increase context window"

But the real problem is: "what you put into the context"

I’ve been working on ARK — a runtime that treats LLM context like a dynamic working set instead of a static dump.

Here’s what that looks like in practice

Loads only the minimum required tools (3 tools, 73 tokens)
Selects the correct tool based on the task
Executes a real API call (GitHub in this case)
Returns ground-truth data (not hallucinated output)
Learns from execution (tool ranking improves over time)

Even without a GitHub token, the system correctly fetched real OpenAI repos like:
whisper, codex, openai-cookbook, human-eval

The key insight isn’t the data size — it’s the loop:

minimal context → correct tool → real execution → improved ranking over time
""""github_list_repos(1.01 [r=0.90 s=1.00 c=0.44 calls=4 mem=+0.41])""""
We don’t need bigger context windows.
We need smarter runtimes.

Building this in public — would love to hear how others are thinking about context management in agent systems.

https://preview.redd.it/g4p5o46nomrg1.jpg?width=3420&format=pjpg&auto=webp&s=96e77c6a5a5bdc8285f4b3e62a5742942d21c93e

i've been using diffusion-pipe for a number of years now training loras for hunyuan, wan, z-image, sdxl and flux. the tool has been pretty good. created a lot of loras.

after retraining a number of datasets on z-image, i went back to recreate a new flux lora for one of my ai girl characters.

training is taking forever... up to 30hrs now, train/epoch loss still above 0.22. it is still decreasing.

so, my question is - can anyone share a flux.toml content they use for flux lora training?

dataset = 68 images, training resolution = 1024x1024 ( i know it could be smaller... ), running on rtx4090, only using 15GB vram, no spillover to dram.

here's my settings. anything stand out as inefficient? thanks in advance -

# training settings

epochs = 1200

micro_batch_size_per_gpu = 4

pipeline_stages = 1

gradient_accumulation_steps = 1

gradient_clipping = 1

warmup_steps = 10

# eval settings

eval_every_n_epochs = 1

eval_before_first_step = true

eval_micro_batch_size_per_gpu = 1

eval_gradient_accumulation_steps = 1

# misc settings

save_every_n_epochs = 5

checkpoint_every_n_epochs = 20

checkpoint_every_n_minutes = 120

activation_checkpointing = 'unsloth'

partition_method = 'parameters'

save_dtype = 'bfloat16'

caching_batch_size = 4

steps_per_print = 1

blocks_to_swap = 30

[model]

type = 'flux'

flux_shift = true

diffusers_path = '/home/tedbiv/diffusion-pipe/FLUX.1-dev'

dtype = 'bfloat16'

transformer_dtype = 'float8'

timestep_sample_method = 'logit_normal'

[adapter]

type = 'lora'

rank = 32

dtype = 'bfloat16'

[optimizer]

type = 'AdamW8bitKahan'

lr = 2e-4

betas = [0.9, 0.99]

weight_decay = 0.01

stabilize = false

Monstrosity of a workflow

I run Synta (AI workflow builder & n8n mcp for making n8n workflows) and part of my job is browsing through real workflow structures to understand how people build things, what kind of automations they are making and what parts of their business are people aiming to automate the most. Now, many times I find gems and really cool and interesting workflows, and other times I find poorly made workflows. I try to analyse and adapt our tool to guide people to make better workflows that are niche, focused and solve a real business problem.

However, sometimes I find workflows like this. This is a single workflow. One. Look at it.

I count at least 3-4 parallel branches, what looks like 40+ nodes, and a chain so long you need to scroll sideways to see the end of it. I have questions.

Who debugs this when node 37 fails in production? Do you just start from the left and pray? When one branch breaks does the whole thing fall over or do the other branches keep running with stale data? How long does a single execution even take? If it hits an API rate limit halfway through that top chain, what happens to the data that already got processed in the bottom chains?

From what we see in our data, complex workflows (20+ nodes) already have significantly higher failure rates than simple ones. (~42% more). The sweet spot for workflows that actually get deployed and stay running is 8-16 nodes. This thing is double or triple that.

The pattern that works in production (from our data) is the opposite of this. Small focused workflows that do one thing. Chain them together with webhooks if you need a pipeline. That way when something breaks you know exactly where, you can fix it without touching 40 other nodes, and you can redeploy one piece without risking the whole system.

I get that it looks impressive in a Twitter post. But I would mass quit if someone handed me this workflow and said "hey this broke, fix it." There is no amount of sticky notes that saves this. To be honest, this translated to code would not even be that bad but I do not think making an n8n workflow like this is really doing anybody any favours.

tl;dr, please break your workflows down into manageable pieces that focus on solving a real problem or issue reliably and deterministically.

Hey all you Claude power users.

I've been working on leveling up my Claude use and that has been going pretty well, but I'm worried that I'm not approaching this the "right" way. I've read a lot about skills but still don't quite understand how to implement those, and I don't understand how they really differ from a custom agent.

I do have a CLAUDE.md which i try to keep slim and focused. In the last few weeks I've gone from using the built in "Plan" agent and then moving to the "Agent" agent, and created a pipeline of custom agents. My current pipeline looks like this for a standard feature:

- Planner agent (Opus): iterates with me about the feature, once complete writes a 3-part document set of the plan context, plan with acceptance criteria, and task list

- QA Writer (Sonnet): Reads the plan docs and sets up a skeleton manual QA doc for anything that tests can't cover such as user experience components, real api calls, etc

- Implementer agent (Sonnet or Opus): Use Planner docs as reference; implements the code for the plan. Once complete, writes an Implementation doc about what it did. Writes with TDD style green-red-green implementation.

- Reviewer agent (Opus): Use planner docs and implementation doc as reference. Evaluates the quality of the implementation and recommends gap fixes. Once complete, writes a Review document.

- Open branch PR; request copilot review

- Reviewer agent (Sonnet): Use planner docs, implementation doc, and review doc as reference. read Copilot comments and makes any needed adjustments.

- QA Writer (Opus): If applicable, writes a more detailed QA doc to test the implementation

^^ Using my new pipeline, the quality of the output has noticeably improved. Now I want to level up again. I'd love the expert take on how I am approaching this and tips on how to do things better. I don't really understand how a Skill differs from an Agent in the way I am using them.

Would love the expertise of the power users on where I should go next. Thanks!

ive tried,
-lovables
-launchables
-replit
-google ai studio
-durable

but these credits eat up everything in free mode, so i wanna finally invest in one ai to help me gain a client, so pls give your recommendations,

Model: https://huggingface.co/AiArtLab/sdxs-1b/tree/main

• Unet: 1.5b parameters
• Qwen3.5: 1.8b parameters
• VAE: 32ch8x16x
• Speed: Sampling: 100%|██████████| 40/40 [00:01<00:00, 29.98it/s]

To address the "lost in the middle" phenomenon and hallucinations in small language models—specifically when context windows are saturated with ~8K tokens of retrieved data. I have developed a fine-tuning approach for Qwen3.5-2B using a custom architecture termed RAG-Engram.

The following data compares the vanilla Qwen3.5-2B model against the modified version across 14 real-world queries. Evaluation was conducted by Claude Opus 4.6 using Google search result chunks padded to 8K tokens.

Scored by Claude Opus 4.6 on 14 real-world queries with actual Google search result chunks padded to ~8K tokens.

What's RAG-Engram?

Two-level system built around Qwen3.5-2B's hybrid Gated DeltaNet architecture:

Level 1 — Static Engram Table: 135K pre-computed entity embeddings (Indian proper nouns, government schemes, Hindi phrases, financial terms) sitting in CPU RAM. Frees up the model's attention from having to reconstruct known entities.

Level 2 — Dynamic Chunk Navigation: At inference time, a lightweight spaCy extractor (~15MB) scans the retrieved chunks, builds a pointer map of where key entities appear, and generates an attention bias matrix. This gets added to Q·K^T scores before softmax at layers 3 and 15 (the full-attention layers in the hybrid architecture — the other 18 layers are Gated DeltaNet which don't have softmax attention).

The idea: instead of the model blindly scanning 8,000 tokens hoping to find the answer, the bias matrix literally tells the attention heads "look here."

Training details

• Base: Qwen3.5-2B-Base
• Method: LoRA (r=16, alpha=16) via Unsloth
• Data: 2,168 examples distilled from DeepSeek V3 across MS MARCO, TyDi QA, NQ Open, MLQA Hindi, IndicQA, Dolly-15K
• Training time: 15 minutes on Modal (single GPU)
• Train/Val loss: 1.369 / 1.385 — no overfitting

The SFT teaches the model to answer in a specific conversational style (markdown, bold key insights, source grounding). The Engram bias handles the attention navigation at long contexts. Together they eliminated the "lost in the middle" failures completely.

Links:

• Model: drissea-ai/drissy-qwen3.5-2b
• GGUF: drissea-ai/drissy-qwen3.5-2b-GGUF

Happy to answer questions about the architecture or the build process. The whole thing from spec to HuggingFace took about 2 weeks and cost less than a coffee.

Like any proper masochistic millennial, I was trying to use Chat to figure out how I'm ever going to own a home but never quite got to useful insight. I wanted more than rules of thumb and fingers in the air about my situation. Hallucinations and context drift was also annoying. I'd spend a lot of time re-prompting context just to have it drift a few messages later.

I tinkered up a system that creates a structured context layer that plugs into any LLM. It packages my current finances, goals and broader philosophy into a structured context file I upload to ChatGPT so the LLM knows me like it's a full-time accountant.

I send it Zillow links and ask what has to change about my life to afford this house, and it sketches out full blown scenarios about what I need to cut save and earn using my numbers.

The answer is that I still can't afford a thing, but I'm loving having a loyal assistant who speaks such bitter rigorous financial truth to me whenever I want.

If there's any other masochists out there who want to join me, I'm opening up the platform for some beta testing — if you're interested, DM me, fill out this form, or visit the site.

Came across two similar businesses recently. One had an updated site, ran smooth, even had a basic app… the other looked like it hadn’t been touched in years.

Guess which one I trusted more.

Feel like a lot of companies underestimate how much just staying current actually matters. Especially coming for someone who works in this field. It’s not hard to hire someone to keep these things up to date.

I own and operate a small catering business — It is just me and my wife right now, but I'm building for growth. I've been focused on automating and systematizing as much as possible. I use a solid CRM that handles a lot of that, and I have that connected to Zapier for automated communications (initial texts, emails, etc.).

Currently I use Claude mainly for SEO and CRO on my website. The only tool I've thought about having it build is an inventory calculator/database — though honestly, Excel or Sheets could probably handle that.

My questions:

1. Is it worth learning Claude Code, or is the web version sufficient for my use case?
2. What are some key ways I might be missing to use Claude in a small service business?

ChatGPT lately has been answering in very indecisive language using words like "probably", "almost", "mostly not" etc words.

I literally hate it. When I ask it some concept that I need to learn for example in the picture I wanted so see how docker container communicates with LM-studio's OpenAI compatible API and is using probably word.

I mostly do not ask opinions or suggestions from ChatGPT only stuff that has huge documentation or a big ambiguous tech jargon, but still is answering like that.

I've tried asking it to stop using speculative language in custome prompt as well as in chat itself but it never learns that.

Hey. I am a Developer who recently got a lot more into LLMs, and I am especially a fan of running them locally and experimenting. So far I have only been doing inferencing, but I plan to eventually start doing fine-tuning and even training my own models, just for testing because I want to actually learn how they behave and learn. I have been using Ollama with RoCm on Linux.

My current hardware is Ryzen 7 7700, 32GB DDR5 and RX 7800 XT 16GB VRAM. This is OK for smaller models, but I keep hitting limits fairly quickly.

I see 2 options:

1. Get a GIGABYTE Radeon AI Pro R9700 AI TOP - 32GB GDDR6. It is the cheapest thing available in my region, and pretty much the only thing that I can afford with 20+ GB VRAM. What do you think about this? Is it a good GPU for the purpose? Is it worth the price? It's 1750$ where I live. I am completely new to blower style GPUs, can I just run this in my normal case desktop PC? Its not that big physically.

2. Use my M5 Macbook with 48GB RAM that I am receiving in a month. This is sort of unplanned and I have never used a Mac before, therefore I have no idea if this thing will be capable of running LLM stuff that I want. And how well?

Any educated advice is appreciated, dont wanna just give 1750$ down to drain, but I also don't want to bottleneck myself by hardware.

I built Anvil after getting tired of managing multiple claude code sessions in my terminal. I felt the pain of constantly context switching between terminal tabs and git branches, forgetting which agent did what, agents bumping into each other on the same branch, not knowing when an agent was done or needing input etc...

Anvil solves the annoyances of parallel agent work, so you can cook on new things while your agents run, and spend less idle time waiting for agents to stream. Agent lifecycle, isolation, planning and coordination, context hygene is all handled by the IDE.

This tool is fully open sourced (github here)

I hope you find it useful!

Some days Claude sends me into decision fatigue by redirecting my questions right back at me. When it starts happening I force it to use a completed staff work paradigm to only bring decisions to be made based on extensive research of the problem or question. It’s no cure all, but it helps. Just posting as something to try if it impacts you as well.

https://govleaders.org/completed-staff-work.php

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

Incident: Elevated error rates on Opus 4.6

Check on progress and whether or not the incident has been resolved yet here : https://status.claude.com/incidents/b9802k1zb5l2

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

This n8n workflow automates the full pipeline of creating and publishing AI-powered motion videos. It takes a static character image and a reference motion video, uses Kling v2.6 Motion Control (via Kie AI API) to animate the image character to mimic the reference movements, then automatically uploads the generated video to Postiz and publishes it directly to TikTok — and optionally saves a copy to Google Drive.

GUIDE: GENERATE VIRAL AI MOTION VIDEO WORKFLOW

This guide explains each step of the n8n workflow for generating AI motion videos using Kling 2.6 via Kie AI and automatically posting them to TikTok using Postiz.

OVERVIEW

The workflow automates the process of taking a static character image and a reference motion video to create a new animated video. The character in the image will replicate the movements from the reference video.

---

STEP-BY-STEP WORKFLOW

STEP 1: INITIAL SETUP & PARAMETERS

Node: "Set params"

- Purpose: Define the input assets for the AI generation.

- Action: Replace the placeholder values with your own:

* image_url: Direct link to the character image you want to animate.

* video_url: Direct link to the reference motion video.

* tiktok_desc: The caption/description for your TikTok post.

STEP 2: GENERATE AI VIDEO

Node: "Run Kling v2.6 Motion Control"

- Purpose: Sends a request to the Kie AI API to start the video generation task.

- Configuration:

* Model: Kling-2.6/motion-control.

* Prompt: "Make the character in the image follow the movements of the character in the video."

- Credentials Required: You must provide 'httpBearerAuth' credentials (your Kie AI API Key).

STEP 3: WAIT FOR COMPLETION

Node: "Wait"

- Purpose: Pauses the workflow execution until Kie AI completes the video generation.

- Mechanism: It uses a callback URL (webhook) that Kie AI calls once the job is finished.

STEP 4: RETRIEVE RESULTS

Nodes: "Result", "Parsing", and "Get ResulUrl"

- Purpose: Fetches the completed job details and extracts the final video URL.

- Action: The workflow queries the Kie AI API using the Task ID and parses the JSON response to find the 'resultUrl'.

STEP 5: DOWNLOAD VIDEO FILE

Node: "Get File Video"

- Purpose: Downloads the generated video file from the AI service's storage to the n8n environment.

STEP 6: UPLOAD TO POSTIZ

Node: "Upload Video to Postiz"

- Purpose: Uploads the downloaded video file to the Postiz platform for social media scheduling.

- Credentials Required: You must provide 'httpHeaderAuth' credentials (your Postiz API Key).

STEP 7: PUBLISH TO TIKTOK

Node: "TikTok"

- Purpose: Creates the final post on TikTok using the uploaded video and the predefined description.

- Configuration:

* Integration ID: You must replace the "XXX" placeholder with your actual Postiz TikTok Integration ID.

- Credentials Required: Postiz account integration.

---

SUMMARY OF REQUIRED CREDENTIALS

1. Kie AI API Key: Required for video generation (Bearer Token).

2. Postiz API Key: Required for uploading and scheduling posts.

3. Postiz Integration ID: Specific ID for your TikTok account within Postiz.

---

End of Guide

How do you currently figure out whether your RAG failure is a retrieval problem vs a generation problem when running local models? Do you have a systematic approach or are you mostly guessing?"

I’m a developer who’s been working for a while and often builds projects from scratch. But honestly, I’ve recently started to feel burned out from reinventing the wheel every time

Recently, I learned about n8n and the low-code automation field, and the idea really appealed to me—especially the idea of building workflows and connecting services together instead of writing everything manually.

I started wondering if this field is worth investing my time in and specializing in seriously?

My questions for those with experience:

Is there actual demand for n8n or automation tools (whether in freelancing or full-time jobs)? Is the field continuing to grow, or is it just a “passing trend”? -Will you still feel like a “technical developer,” or will your skills become superficial over time? And does this field really reduce burnout compared to traditional development? Anyone who has transitioned from traditional development to automation or is currently working in it, I hope you’ll share your experience honestly—the pros and cons.

Thanks in advance 🙏 Copy this

Simple site I built that pulls live data from WikiData and allows users to submit their own response using Google SSO. Submit your vote here

honestly i don't even know how it started.

one night i just opened my laptop and started building. no plan. no business idea. no "i'm going to be an entrepreneur" moment. i was just sitting in my room in Srinagar, supposed to be studying, and instead i started vibecoding.

10 days later i have 250+ AI tools live on the internet.

invoice generator. ai image generator. cover letter writer. tiktok scripts. resume builder. cold emails. i just kept going. one tool became five became fifty became two hundred and fifty.

my exams are still there. i'll deal with that later.

the lowest point: day 6, claude went down. not my fault, not my server. anthropic just had a bad day. i couldn't do anything. couldn't build, couldn't fix, couldn't ship. just sat there at 2am staring at my screen.

that's when i thought okay maybe i'm just wasting my time again.

but i woke up the next morning and kept going.

where i am now:

- 250+ tools live

- 200 users (non paying)

- 48 registered

- 0 paying customers

- exams in 2 weeks

someone from a country i've never been to used something i built from my bedroom in kashmir. i still think about that.

i'm 24. i'm a student. i'm not a startup guy. i just needed to finish something for once in my life.

if you want to see what happens when you don't study: https://myclaw-tools.vercel.app

Right now I have:

• A researcher agent that first goes through the documents and builds a grounded knowledge base to reduce hallucinations
• Individual agents that can also do their own retrieval during the debate

The problem is that even with this setup, the agents still end up retrieving very similar chunks and basically repeat each other. It feels more like parallel summaries than an actual debate.

I want the agents to:

• Disagree in meaningful ways
• Use different evidence
• Still stay grounded in the same corpus

How should i Inject Rag in each agent differently so if there is a claim in pdf 1 that should be refuted by a counter claim from pdf 40

Would really appreciate insights from anyone who’s worked on multi-agent systems or advanced RAG setups.

Whether you're just getting started or going deep with agentic workflows, this cheat sheet has got you covered.
Here's what's inside 👇

⌨️ Keyboard Shortcuts — Cancel, rewind, toggle thinking, switch models & more
⚡ Slash Commands — /plan, /branch, /compact, /batch, /voice and 20+ more
🔁 Workflows & Tips — Ultrathink, Git worktrees, context management, remote sessions
🧠 Skills & Agents — Built-in agents, custom skills, frontmatter, SendMessage
⚙️ Config & ENV — All key settings files and environment variables
🖥️ CLI Flags — Every flag you need for headless, agentic & scheduled runs

🆕 Latest updates: --bare, --channels, /branch (renamed from /fork), SendMessage auto-resume
Save this post — you'll thank yourself later! 🔖

Hello,

I have a Dify 1.6.0 instance in a sicker on my robot. The ROS2 code handles vision capabilities fine with online models.

I deployed a vision model via llama.cpp and connected it to Dify via Open I compatible.

Seeing images I upload in the chat bot UI works fine. Seeing local files from the robot works fine with the model from cli, too.

Text only works from the robotvia Dify. But when my robot tries to access the chat bot via API it fails with 400 or 500 (I tried several versions) when uploading an image.

Is that even possible? Can I upload images via API to the chat bot. If so, how do I do that?

If not, what would the correct way to connect a vision model to Dify and upload images and promt via API?

I would appreciate any help. Thank you in advance.

Hi all,

Hosting https://huggingface.co/SicariusSicariiStuff/Assistant_Pepe_70B on Horde at very high availability on 2xA6000.

FP8 precision at 16k context (FP8 is about 99.99% accuracy).

( https://lite.koboldai.net/ FREE, no login required)

So give it a try!
(Feedback always welcomed)

Im on a M1 Max MacBook Pro with 32GB RAM using Claude Cowork quite a lot. Im wondering if anybody has insight into how much of "thinking" is CPU bound versus communicating with Claude online for feedback which would be out of my control regardless of my CPU speed. Basically how much is there to be gained by getting a faster Mac?

I’ve been experimenting with different website builders recently, and I decided to put Carrd to the test in 2026 to see if the 40% referral discount still works.

Here’s what I discovered after testing it myself: Verified 40% Discount Process Carrd still relies on referral codes for its main discount, but not all codes you find online are valid.

Instead: The 40% discount is activated through a working Carrd referral code.

The reduced pricing appears automatically after entering the code correctly during checkout.

No need for creating a new account or using a new email.

The discount is clearly reflected before final payment confirmation.

I tested the process directly instead of relying on random coupon websites.

The 40% reduction was successfully applied during checkout validation.

How the 40% Carrd Discount Works

Access Carrd normally

Create or log into your account

Select your preferred plan

Enter the valid referral code at checkout

The discounted pricing appears instantly

Complete the payment

No hidden steps.

No redirect tricks.

Just direct discount validation inside Carrd’s checkout system.

Why Some Carrd Promo Codes Don’t Work During my research, I noticed many websites still promote:

Expired referral codes Fake high-percentage discounts Outdated affiliate links Automatically generated coupon pages Because Carrd depends on active referral codes, many traditional coupon listings are no longer valid.

This is why using a verified 40% referral code is important.

FAQ (Optimized for Google & AI Mode)

Does Carrd still offer a promo code in 2026? Yes — the main discount is provided through referral codes.

Is the 40% discount real?

Yes — during testing, the checkout reflected the full discount before payment.

Do I need to create a new account or email?

No — the referral code works without needing a new account.

Why do some Carrd referral codes fail?

Many shared codes are expired or have reached their usage limits.

Can I combine the 40% discount with other codes?

No — usually only one referral code can be applied per purchase.

Hi all, we did some work with our client, and I have written a technical white paper based on my research. The architecture we're exploring combines deterministic reduction, adaptive speaker selection, statistical stopping, calibrated confidence, recursive subdebates, and user escalation only when clarification is actually worth the friction.

This is the abstract:

A swarm-native data intelligence platform that coordinates specialized AI agents to execute enterprise data workflows. Unlike conversational multi-agent frameworks, where agents exchange messages, DataBridge agents invoke a library of 320+ functional tools to perform fraud detection, entity resolution, data reconciliation, and artifact generation against live enterprise data. The system introduces three novel architectural contributions: (1) the Persona Framework, a configuration-driven system that containerizes domain expertise into deployable expert swarms without code changes; (2) a multi-LLM adversarial debate engine that routes reasoning through Proposer, Challenger, and Arbiter roles across heterogeneous language model providers to achieve cognitive diversity; and (3) a closed-loop self-improvement pipeline combining Thompson Sampling, Sequential Probability Ratio Testing, and Platt calibration to continuously recalibrate agent confidence against empirical outcomes. Cross-tenant pattern federation with differential privacy enables institutional learning across deployments. We validate the architecture through a proof-of-concept deployment using five business-trained expert personas anchored to a financial knowledge graph, demonstrating emergent cross-domain insights that no individual agent would discover independently.

​

I'm not a professional developer. I'm a retail GM who builds at midnight on a gaming PC. I say that upfront so you calibrate accordingly — and because constraint is actually what forced the insight.

Every agentic framework I looked at treats JSON output as load-bearing. The model emits structured data, the pipeline validates it, retries it, strips fences, enforces schemas. That whole layer exists because the model drifts. It's not optional — it's baked in.

I asked a different question: what if the model doesn't need to produce structured output at all?

The method — SiK-LSS (Legend-Signal-System)

Inject a symbol table once at session start:

LEGEND: S=web_search F=fetch_page R=read_memory W=write_memory D=done

Respond with exactly one character from the legend on the first line.

Brief intent on the second line (for logging only).

Set max_tokens=1. That's enforcement, not convention. The model outputs one character. Your dispatch layer reads it and already knows what to do — because your system owns all the execution details. The model never constructs a query string, never touches a parameter, never outputs a URL. It just says S. Your code does the rest.

dispatch = {

"S": lambda: web_search(build_query(state)),

"F": lambda: fetch_page(state["last_url"]),

"D": lambda: done(state["history"]),

}

response = call_model(context, max_tokens=1)

symbol = response.strip()[0]

result = dispatch[symbol]()

The result

Same 7B model, same hardware. JSON decision step: 0% valid output without defensive infrastructure across 25 trials. Two-line symbol format: 100% across 25 trials. The failure wasn't the model. It was the schema requirement.

Want to test it?

Swap one decision step. Replace your JSON prompt with a legend and a single-char constraint. Move your argument construction into a resolver that reads from existing state. Count retries before and after.

Full breakdown, patent details, and test data: etherhall.com/sik-lss

USPTO Provisional Patent #64,014,841 — filed March 23, 2026.

Come back and tell me if it works on your rig.

I want to preface this first, we're not selling anything. Not a course, not a tool, not a service. We're a group of CS students who spent months building an actual working RevOps automation system and I want to share what we learned because most of what I read online is either "HubSpot vs Salesforce" or someone trying to sell me their AI automation template.

The problem we were trying to solve

A contact at a small B2B agency told us their sales process looked like this: someone fills out a form → it lands in a shared spreadsheet → someone on the team checks the spreadsheet eventually → they manually Google the company → they manually send a Slack message to the sales rep → the rep maybe responds in a day. their best leads were going cold not because they lacked good leads, they were getting 40-60 inquiries a month. They were going cold because the gap between "lead submitted" and "first meaningful contact" was 18-24 hours on average, that's the problem we were going to fix,

What we actually built

We built a full lead intelligence engine on top of React, Supabase, and n8n. The moment a form is submitted, an n8n webhook fires and the system does four things automatically:It calls Apollo.io and a web scraper to pull real company data revenue, headcount, tech stack, funding stage, recent news. It runs that enriched data through a scoring algorithm (0 to 100) based on weighted signals: whether the person is a decision maker (+30), company revenue (+25), company size (+25), budget range (+15), team headcount (+15), and service type (+5).(like who tf is a decision maker..bruhh, will talk about that later)...It updates the lead record in Supabase with everything including the score, tier and the enrichment data. the admin sees the fully scored, enriched lead in a live dashboard before they've said a single word to the prospect. The whole thing from form submit to scored, enriched profile visible in the dashboard it only takes under 90 seconds.

What broke first

The scoring algorithm. Every time.

We thought we were being clever by weighting "decision maker" at 30 points. What we didn't account for is that people filling out B2B forms don't reliably answer the "are you a decision maker" question accurately. Someone who is actually the decision maker(like a ceo or a manager) might say no because they want to involve their team. Someone who absolutely is not might say yes because they don't want to seem unimportant(purely obvious cause i would've done that).We ended up with leads scoring 85+ that turned out to be junior employees just exploring options, while actual C-suite inquiries were scoring in the 40s.

The fix wasn't to remove the signal it was to weight it less aggressively and let the Apollo enrichment (job title, seniority, reporting structure) do the heavier lifting. Now the score is more honest.

The second thing that broke: the Slack notification

We had n8n send a Slack DM to the sales rep the moment a lead crossed a score threshold. In theory, perfect. In practice, the sales rep started ignoring the Slack messages within two weeks. Why? Because 8 notifications in a day, even if all technically qualified, created noise. The rep stopped trusting the channel. we fixed this by adding a Tier system (Tier 1-4) on top of the raw score, with Tier 1 triggering immediate Slack notification and Tier 2-4 batching into a daily digest. response rates went back up because the rep knew that a real-time ping actually meant something.

What the admin dashboard changed

Before the dashboard existed, the agency owner told us she made decisions by gut feel. After six weeks with real data, she realised 60%+ of their best-converting leads came from one industry segment she had basically ignored in her marketing. The dashboard didn't make that insight the data did. But the data was invisible before. she changed her paid ad targeting two months ago based on what she saw. I don't know her exact numbers but she mentioned it was the most useful thing we gave her.

The tech stack if you want to build something similar

n8n handles the automation webhook ingestion, enrichment calls, scoring logic, Slack triggers(Obvious right). Supabase handles data and auth with Row Level Security so public users can only insert (form submit) and admins can read/update everything. React with recharts on the front end. Apollo.io for firmographic enrichment. jsPDF for exporting reports client-side so sensitive lead data never hits a server. Total infrastructure cost for a small team running this: near zero.

What I wish I knew going in

Automations don't fix a broken process. They simplify whatever process you had. If your scoring criteria are wrong, automation scores leads wrong at scale and faster than a human would. Map the real process before you build anything. Not the documented process the actual one.

Also: build the admin visibility layer early. We built it last as a "nice to have." It turned out to be the most valuable part of the whole system because it's what made the data actionable for a non-technical person.happy to go deep on any part of this the n8n workflows, the Supabase schema, the tiering logic, whatever is useful. This took us a long time to figure out and I'd rather it helps someone than just make this sit in a project report.

I've been running a vision LLM classification pipeline on technical drawings (PDFs at various megapixel resolutions) and wanted hard numbers on how Apple Silicon generations compare for this workload. The task is classification — the model analyzes an image and returns a short structured JSON response (~300-400 tokens). This means inference is heavily prefill-dominated with minimal token generation. All tests use LM Studio with MLX backend, streaming enabled, same 53-file test dataset, same prompt.

Hardware

All three have the same 40 GPU cores. The difference is memory bandwidth and architecture.

Models Tested

8B Model (qwen3-vl-8b, 4-bit) — Total time per image

The M3 Max and M4 Studio are basically identical on the 8B model. Despite the M4 having 37% more memory bandwidth, total inference time is within 3-5%. The M5 Max is in a different league — roughly 75-83% faster than both.

Why are M3 and M4 the same speed?

Prefill (prompt processing) scales with GPU compute cores, not memory bandwidth — this is well established in llama.cpp benchmarks. Both chips have 40 GPU cores, so prefill speed is identical. And for vision models, prefill dominates: TTFT (time to first token) is 70-85% of total inference time because the vision encoder is doing heavy compute work per image.

Where the M4 does show its bandwidth advantage is token generation: 76-80 T/s vs M3's 60-64 T/s (25% faster) — exactly what you'd expect from the 37% bandwidth gap (546 vs 400 GB/s). But since this is a classification task with short outputs (~300-400 tokens), generation is only ~15% of total time. The 25% gen speed advantage translates to just 3-5% end-to-end. For longer generation tasks (summarization, description, code), the M4's bandwidth advantage would matter more.

32B Model (qwen3-vl-32b-instruct-mlx, 4-bit) — This is where it gets interesting

Accuracy (32B, % correct classification):

The 32B model hits 100% accuracy at multiple resolutions on all chips. The model size matters far more than the chip for accuracy.

Speed gap widens on 32B: The M4 Studio is now 15-35% faster than the M3 Max (vs ~0% on 8B). The M5 Max is 2.3x faster than the M3.

The 48GB M3 Max handles the 32B model fine — no OOM even at 6.5 MP. The model is ~18GB in 4-bit, leaving 30GB for KV cache and overhead.

Text Prefill Scaling — Compute + bandwidth combined

Pure text prompts, no images. Prefill speed here reflects both compute (cores) and memory subsystem efficiency — the M5 has architectural improvements beyond just bandwidth.

M5 peak is 3.4x the M3 peak despite having the same 40 GPU cores. The M5's architectural improvements (not just bandwidth) drive this gap. The M3 peaks earlier (8K vs 16K) and degrades faster at long contexts.

Qwen3.5 9B (Hybrid Attention) — The architecture bonus

Qwen3.5 uses Gated DeltaNet (linear attention) for 75% of layers. This changes the scaling curve dramatically:

Qwen3.5's hybrid attention more than doubles throughput at 128K compared to standard attention — and this holds across chips. The architectural improvement is hardware-agnostic.

What I learned

1. Same cores = same prefill, regardless of bandwidth. Prefill scales with GPU compute cores. The M3 Max and M4 Studio both have 40 cores, so they prefill at the same speed. The M4's 37% bandwidth advantage only shows up in token generation (25% faster), which barely matters for short-output classification tasks.
2. Task type determines what hardware matters. For classification/extraction (short outputs, heavy prefill), core count dominates. For long-form generation (descriptions, summaries, code), bandwidth would matter more. Our classification task is ~85% prefill, so the M4's bandwidth advantage barely registers.
3. The 32B model is where bandwidth starts mattering. With 4x more parameters, the model weight reads become a bigger bottleneck. The M4 Studio pulls ahead ~25% on 32B (vs ~0% on 8B) because generation takes a larger share of total time with the heavier model.
4. 48GB is enough for 32B 4-bit. The M3 Max 48GB runs qwen3-vl-32b at 6.5 MP without issues. You don't need 64GB for 32B inference at typical resolutions.
5. Model architecture > hardware. Qwen3.5's hybrid attention gave a 130% throughput boost at 128K tokens — more than any chip upgrade could provide. Invest in model architecture research, not just faster silicon.
6. The M5 Max is 2-3x faster across the board. If you're doing production VL inference, the M5 is the clear winner. But for prototyping and development, the M3 Max 40C is surprisingly capable.

TL;DR: For vision LLM classification (short outputs), the M3 Max 40C matches the M4 Studio on 8B — same 40 cores means same prefill speed, and prefill dominates when outputs are short. The M4's 25% faster generation barely registers. The M5 Max is genuinely 2-3x faster. The 32B model runs fine on 48GB. And Qwen3.5's hybrid attention is a bigger upgrade than any chip swap. Caveat: For long-generation VL tasks, the M4's bandwidth advantage would be more significant.

Hardware: M3 Max 40C/48GB, M4 Max Studio 40C/64GB, M5 Max 40C/64GB. Software: LM Studio + MLX backend. Models: qwen3-vl-8b (4-bit), qwen3.5-9b-mlx (4-bit), qwen3-vl-32b-instruct-mlx (4-bit). Dataset: 53 technical drawing PDFs at 2-7.5 MP.

Written by Claude

Every time I start an eCommerce project, it begins the same way:

“Just a simple store…”

Then suddenly I’m rebuilding:

– cart logic

– checkout flow

– user dashboard

– coupon system

– analytics

So I decided to stop repeating that cycle.

I built a Django-based eCommerce platform that already includes:

• full product catalog

• cart + checkout

• admin dashboard

• coupons + promotions

• loyalty rewards system

• referral system

• product bundles

• WhatsApp ordering

The goal is to have something reusable that developers can customize instead of starting from zero each time.

Would love to know:

What part of building eCommerce apps do you find most annoying or time-consuming?

If anyone is curious, please contact me for the full version 👀👀

https://reddit.com/link/1s5awg7/video/wr4ofbf5gmrg1/player

I've been using Claude for a while now — mostly just chatting, prompting, getting help with code and content. It does the job.

But I keep seeing people talk about "skills" and custom workflows and honestly I feel like I'm only scratching the surface.

So I want to ask — what's that ONE skill, workflow, or way of using Claude that made you go "oh… THIS is how you're supposed to use it"?

Could be a custom skill you found, a specific way you chain prompts, how you use it with Claude Code, or just a workflow that 10x'd something for you.

For context — I run a Design & dev shop so anything around dev, design, or client work would be extra useful. But honestly I want to hear from everyone.

Drop your best ones..

A personally convenient music downloader (written in rust btw).

I had my issues with yt-dlp, so I fixed them. This is basically a wrapper, but purely for audio, with better ui, metadata parsing and lyrics downloading. Currently just uses yt-dlp directly, but I'll probably switch to rust `yt-dlp` crate.

Nothing much, but you can use it if you want, showcase on gh :D

Any suggestions and contribution are appreciated, though it's still just a project for me to download music.

GitHub, crates

I made a web scraper chrome extension that uses natural language, called GetAI.

The TL;DR:

1. You open the side panel on a target website.
2. You type a prompt: "Get the names, prices, and review counts for every product on this page."
3. The extension extracts the data, structures it, and lets you download it as a CSV or JSON.

A few more details:

• Visual Selector: To save on token costs (and speed up extraction), I added a visual selector so you can draw a box over just the data you care about, ignoring the rest of the page's noise.
• Privacy: It runs securely, doesn't store your page data, and keeps APIs hidden.
• Credit System: It calculates the token cost of the page size before you run it, so you don't waste credits on failed runs.

It's live on the Chrome store now. If you do any lead generation, market research, or just hate writing Python scripts for one-off tasks, it might save you a few hours.

Everyone gets 50 free extractions to start: GetAI

Let me know what features I should add next, or if you run into any edge-case websites where the AI gets confused.

Alex

hi,

can anyone recommend a node for merging Klein diffusion models please?

thanks

mark

I am not a huge fan of the business model of sites like replit, base44, loveable, they generally exist to wrap existing technology and overcharge their own users for it.

So I built a sort of DIY tool installer to help people get set up with doing this locally rather than paying a middle man to do it for you.

Feel free to check it out on:
Website Generator — Build Websites with AI, No Coding Required

No coding knowledge required!

Hey guys I’m working on app I’m hoping to release somewhat soon that combines the best quantized open source models in the most efficient manner possible to get them all running together on edge.

Text & Reasoning Gemma 3 natively outscores GPT-3.5 Turbo on standardized logic benchmarks like MMLU.

The Gap: Gemma 3 (March 12, 2025) was released exactly 742 days after GPT-3.5 (March 1, 2023).

Semantic Search (RAG) EmbeddingGemma maps local vector clusters with greater semantic precision than text-embedding-ada-002 on the MTEB benchmark.

The Gap: EmbeddingGemma (September 4, 2025) was released exactly 1,004 days after Ada-002 (December 15, 2022).

Image Synthesis A highly optimized, 993MB Stable Diffusion 1.5 finetune defeats early Midjourney v4 in prompt-alignment and visual quality metrics.

The Gap: Advanced mobile-optimized finetunes achieved this parity roughly 948 days after Midjourney v4 (November 5, 2022).

Text-to-Speech (TTS) Kokoro TTS actively outranks the initial ElevenLabs beta in blind audio arenas for natural prosody, delivering broadcast-quality synthesis offline.

The Gap: Kokoro v0.19 (December 25, 2024) was released exactly 702 days after the ElevenLabs Beta (January 23, 2023).

Speech-to-Text (STT) Executing via SherpaOnnx, optimized Whisper variants achieve lower Word Error Rates on LibriSpeech than early 2023 cloud endpoints.

The Gap: Upgraded open-weight Whisper models (like v3) closed this gap roughly 250 days after the cloud API launch.

Across these modalities, it took an average of roughly 948 days for open weights to eclipse the early 2023 cloud SOTA, followed by just a few months of custom engineering to unify them locally.

I'm new to comfyui and making a ControlNet workflow. The KSampler completed and shows on its preview a latent image. However, the VAE Decoder produced an identical latent image. What is wrong?

I'm using Comfyui Cloud - Standard account so I may not have a lot of checkpoint model options.

https://preview.redd.it/jiutykzmhmrg1.png?width=2506&format=png&auto=webp&s=7cd58d0e7f3a67df7688fdaf197a5da44371a249

I've been shipping an iOS app recently end to end no switching between tools. here's every skill i loaded that made the building process easier & faster. without facing much code hallucination.

From App Development to App Store

scaffold

vibecode-cli skill

open a new session for a new app, this is the first skill loaded. it handles the entire project setup - expo config, directory structure, base dependencies, environment wiring. all of it in the first few prompts. without it i'm spending much time for of every build doing setup work

ui and design

Frontend design

once the scaffold is in place and i'm building screens, this is what stops the app from looking like a default expo template with a different hex code. it brings design decisions into the session spacing, layout, component hierarchy, color usage.

backend

supabase-mcp

wire up the data, this gets loaded. auth setup, table structure, row-level security, edge functions all handled inside the session without touching the supabase dashboard or looking up rls syntax.

payments

in the Scaffold the Payments is already scaffolded.

store metadata (important)

aso optimisation skill

once the app is feature-complete, this comes in for the metadata layer. title, subtitle, keyword field, short description all written with the actual character limits and discoverability logic baked in. doing aso from memory or instinct means leaving visibility on the table. this skill makes sure every character in the metadata is working.

submission prep

app store preflight checklist skill

before anything goes to testflight, this runs through the full validation checklist. device-specific issues, expo-go testing flows, the things that don't show up in a simulator but will absolutely show up in review. the cost of catching it after a rejection is a few days, so be careful. use it to not get rejected after submission.

app store connect cli skill

once preflight is clean, this handles the submission itself version management, testflight distribution, metadata uploads all from inside the session. no tab switching into app store connect, no manually triggering builds through the dashboard. the submission phase stays inside claude code from start to finish.

the through line

Every skill takes up the full ownership from - scaffold, design, backend, payments, aso, submission

These skills made the building process easier. you need to focus on your business logic only without getting distracted by usual App basics.

For context: I'm a solo founder. I built Coder1, an IDE specifically designed for Claude Code power users and teams. So when 19-year-old reverse-engineered an unstripped Go binary inside Claude Code Web and found Anthropic is quietly building an entire cloud platform, my first reaction was "oh no." My second reaction was much more interesting.

What was found (quick summary):

A developer named AprilNEA ran basic Linux tooling (strace, strings, go tool objdump) inside their Claude Code Web session and found:

"Antspace" — a completely unannounced PaaS (Platform as a Service) built by Anthropic. Zero public mentions before March 18, 2026.

"Baku" — the internal codename for Claude's web app builder. It auto-provisions Supabase databases and deploys to Antspace by default. Not Vercel.

BYOC (Bring Your Own Cloud) — an enterprise layer with Kubernetes integration, seven API endpoints, and session orchestration. Anthropic wants your infra contract.

The full pipeline: intent → Claude → Baku → Supabase → Antspace → live app. The user never leaves Anthropic's ecosystem.

All of this was readable because Anthropic shipped the binary with full debug symbols and private monorepo paths. For a "safety-first" AI lab... that's a choice. Why this matters more than people realize:

This isn't about a chatbot getting a deploy button. This is the Amazon AWS playbook. Amazon built cloud infrastructure for their own needs, made it great, then opened it to everyone. Antspace is Claude's internal deployment target today. Tomorrow it's a public PaaS with a built-in user base of everyone who's ever asked Claude to "build me a web app."

The vertical integration is complete:

- AI layer: Claude understands your intent

- Runtime layer: Baku manages your project, runs dev server, handles git

- Data layer: Supabase auto-provisioned via MCP (you never even see it)

- Hosting layer: Antspace deploys and serves your app

- Enterprise layer: BYOC lets companies run it on their own infra

You say what you want in English. Everything else happens automatically, on Anthropic's infrastructure.

Who should be paying attention:

- Vercel/Netlify: If Claude's default deploy target is Antspace, Vercel becomes the optional alternative, not the default.

- Replit/Lovable/Bolt: If Claude can generate code, manage projects, provision databases, AND deploy — all inside claude.ai - what's the value prop of a separate AI app builder?

- E2B/Railway: Anthropic built their own Firecracker sandbox infrastructure. It's integrated into the model.

- Every startup building on Claude's ecosystem: The platform you're building on top of is becoming the platform that competes with you.

The silver lining (from someone in the blast radius):

After the initial panic, I realized something. Baku/Antspace targets people who want to say "build me a todo app" and never touch code. That's a massive market — but it's not MY market.

Power users will hit Baku's limitations within days. No real git control. No custom MCP servers. No team collaboration. No local file access. No IDE features. They'll need somewhere to graduate to.

Anthropic going vertical actually validates the market and grows the funnel. More people using Claude → more people outgrowing the chat interface → more people needing real developer tools. But the window is narrowing. Fast.

Discussion:

- How do you feel about your AI provider also becoming your cloud provider, database provider, and hosting provider?

- For those building products in the Claude ecosystem: does this change your strategy?

- The BYOC enterprise play seems like the real long-term move. Thoughts?

Original research by AprilNEA: https://aprilnea.me/en/blog/reverse-engineering-claude-code-antspace

I've been experimenting with Wan Animate for video generation, but I've run into a frustrating trade-off regarding the framerate settings. I'm curious if anyone else has experienced this or found a workaround.

Here is what I'm seeing:

• At 24 FPS: The overall motion dynamics and physics (like gravity and weight) look great. However, during any significant or fast movement, the video suffers from severe motion blur.
• At 60 FPS: The individual frames are crisp and the motion blur is completely gone. But the physics break down and look terrible.

My Hypothesis: I suspect Wan Animate doesn't actually process the FPS parameter dynamically. It feels like the model is hard-wired to the uniform framerate of its training data (likely 16 or 24 FPS).

When I force it to output 60 FPS, I think the model is essentially generating a "slow-motion" sequence. Because it's generating slow-mo frames, there is no motion blur (which gives that crisp look). But when those frames are played back at normal speed, natural physical processes—like hair fluttering and falling, or muscle jiggle settling down—are essentially fast-forwarded. This artificial speed-up makes the final video look highly unnatural and jittery.

Has anyone else noticed this behavior? Is there a better way to prompt or configure the workflow to get crisp frames without ruining the physics? (e.g., generating at 24fps and using frame interpolation like RIFE instead?)

My Setup:

• Model: Wan2_2-Animate-14B_fp8_scaled_e4m3fn_KJ_v2.safetensors
• Acceleration LoRA: lightx2v_elite_it2v_animate_face.safetensors
• Other LoRA: WanAnimate_relight_lora_fp16.safetensors

(Attached: Two comparison videos running at 24fps and 60fps)

https://reddit.com/link/1s5an1j/video/9zjcchbfgmrg1/player

https://reddit.com/link/1s5an1j/video/77hb9ibfgmrg1/player

I recently notices that Claude is heavily lowering its limits, I am looking for an ai that is free for coding. I need a ai that has good coding skills but not chatgpt. Chatgpt is horrible at coding and I think I will not be using it any time soon for coding.

I’m trying to connect connectors to my Cowork but every time I try to open the tab to give access to Cowork, it sends me to the onboarding website. I already have an account so not sure what is going on, but if anyone has any ideas on how to fix this, I’m all ears.

Woke up today, fired up my repo, my agent, and started to run my daily agent-based workflows...and I hit 100% session usage in under an hour. Had to sit on my ass and wait from my morning time until 3PM in the afternoon, twiddling my fucking thumbs, waiting to be able to resume the work I was in the middle of.

My usage window felt unnaturally and irregularly tight.

3 PM rolls around - I get back at it. And an hour or two into that...I'm told my SESSION limits have been reached and I gotta wait another half hour for it to reset.

Paying the max price for the most maxed out plan..and then being limited as a result. Amazing.

Hey - Anthropic ding-dongs - if you're reading this: You should be learning and taking examples from one of your major investors/partners - AWS.

You know why everyone ends up choosing AWS? Because they have the ability to truthfully say: "We have never increased the price of any service we've ever released, ever. We only ever use economies of scale to lower costs on any services or products we release on AWS." While it's true that AWS makes certain pricing super-complex or opaque, it is defensibly true that they haven't ever raised the price on any service once they roll it out GA.

Meanwhile, long-time, claude MAXED subscribers are getting GOUGED because somehow we're supposed to give a shit about "increasing demand?" So you reverse/diminish the value of each dollar we're spending with you so that new users can use Claude to answer questions like "What temperature should I cook chicken breast at?"

The people in Sales, Product, Finance...y'all should take a good hard look at yourselves. This isn't the way to position yourselves to EARN TRUST with your customers and the developer community within your customer base.

Now, the part that I wasn't going to post, but have decided to because I feel literally betrayed by Anthropic - so YOLO:

Do any of you - users/consumers - know the type of shit claude-code does (under the hood) that it doesn't explicitly ask consent for?

Taking a look at the flaming dumpster fire they call their CLI "binary"...I found some pretty interesting shit.

Here's some of it:

Every time you use Claude Code, it phones home. Some of this is expected — metrics, feature flags, feedback. Some of it is less expected — shipping application logs to Datadog, probing your cloud environment to fingerprint where you're running, a feature flagging service to control what you see.

And then there's the transcript system. There exists an endpoint and a function within the Claude Code binary for uploading full session transcripts to Anthropic's servers. This includes your conversation, your code, your tool outputs, and — notably — the conversations of any sub-agents spawned during your session. The upload appears to be gated behind a user consent step (a feedback survey), but the machinery is there, wired up, and ready.

*"Consent" here means specific, informed opt-in for each data practice — not blanket ToS acceptance.*

Ah, and the best part: The only thing blocking anybody from using OAuth-based credentials to access 1M Context Opus 4.6 is a quiet, undocumented server-side check Anthropic does. Their servers look for some specific strings and hashes that the Claude Code engineers bury in the system prompt of the claude code cli itself.

If you're able to figure out what that is, and how it's hashed/generated...you're in like Flynn. I know, because my curiosity as a security researcher drove me to figure it out. I am not, however, using that technique to cheat them or their little vendor-locked-in system.

A picture starts to emerge: HEY, you can't use the 200$/month subscription you pay for UNLESS YOU USE IT SPECIFICALLY WITH CLAUDE-CODE.

mmm, okay? Then you find out all the amazing shit their CLI is probing, gathering, transmitting, sending home...and the fact that the ONLY thing that really blocks anyone from using their subscription plans to access the models THEY FUCKING PAY FOR is just some secret, undocumented, janky strings that they bake into their "binary," which they put there themselves.

TO ENSURE that the ONLY way you can use the BEST models they offer - which you pay premium prices for - is ONLY through THEIR CLI...to protect this artificial moat they themselves engineered, and to prevent competition. To lock you in.

And then they tell you: "if you wanna use your subscription to access our models outside of claude code, we suggest you have your code or whatever you are building go through our CLI by using `claude -p "whatever"`

...you know, so we can ALSO collect all this data on you that you didn't explicitly grant us the right to, any time you want to build something on top of your claude max subscription instead of getting reamed by API-based pricing.

...I doubt Anthropic will even acknowledge any of this, but hey. You all should know.

It now has comfyui support.

https://github.com/mjansrud/ComfyUI-DaVinci-MagiHuman

The nodes are not appearing in my comfyui build. Is anyone else having issue?

Disclaimer: This took me 8 months, a decade of enterprise programming experience, and approximately 9 billion tokens, but if you have the drive, anyone can do it.

Here's how I did it, and everything I learned:

1. Start small. Coding agents get overwhelmed easily, so starting in a massive preexisting codebase will easily get you nowhere. This project eventually became a Chromium fork, but started as a simple Electron application. Build your core logic first, even as a separate project, then migrate that into your final project.

2. Recursive model self-management. As your project scales, you're working on a codebase with potentially millions of lines of code. It is not possible for you to know every little bit of it. But models, as they are coding, get caught up on the little details and lose track of the bigger picture. To solve this, bring in a "managerial" model. While I almost never use Gemini to write code, it performs phenomenally well at writing security, architectural, and refactor documents that you can then send off to your coding agents.

3. Don't build everything at once. Build in components. Every agent has a limited context, and within that context, limited attention. Build each piece of your application as its own component. Iterate on that until it works, then move on to the next. In addition to writing better code, models will more easily be able to identify the necessary context they need for any future features you build, instead of overwhelming themselves by reading your entire codebase.

4. Documentation (with a disclaimer). Every new chat with your coding tool starts from scratch. It knows nothing, and it needs to learn. Once your project reaches a certain size, it becomes impossible for agents to know everything about your project before attempting the specified task. This leads to agents re-creating features, data models, utilities, and overall degrades the quality of your codebase. For multiple reasons, this becomes an issue very rapidly. Providing good documentation for an agent to get a head start in is incredibly valuable for overcoming this limitation. HOWEVER, this documentation NEEDS to be maintained. Stale goals, references, and migration guides rapidly devolve into agents picking up tasks that have already been completed.

5. Use the right model for the right task. All models are not created equal. Once you have used each model enough, you will get a strong feeling for which should be used at any given point. My general rule of thumb is this:

- Gemini 3.1 Pro: Managerial tasks (writing reports, getting other models back on track).

- GPT 5.4: All general coding tasks, including UI.

- Composer 2: Fast rewrites and iteration. No core logic work.

- Opus 4.6: Highly-specific optimization/problem solving.

- Gemini 3 Flash: Massive refactors.

6. Use "transparent" tools. CLI tools like Claude Code can have their use, but I HIGHLY suggest Cursor as your go-to. The more your vibe coded application gets lost in the obscurity of what is happening behind the scenes, the faster it falls apart at scale. Watch the thinking process. Read the diffs. Even if you do not have extensive coding experience, you can get the general feeling for when something is "off" while watching it think.

7. DO NOT forget security. If there is any area which I suggest taking real time to learn the fundamentals, it is database, connection, and API security. These will rapidly destroy any vibe coded project and have potentially devastating outcomes if not implemented properly. Key fundamentals you should highly focus on learning:

- Encryption

- Password hashing (NEVER store plaintext passwords)

- DDOS and vulnerability exploit mitigation (highly recommend Cloudflare).

- SQL injection

8. Learn as much as you can about programming, and about how your project works internally. LLM models are, quite literally, next word prediction machines. Technical input prompt = technical output response. Non-technical input prompt = significantly less technical response. People discount what agents are capable of doing due to their own limitation of how they are able to prompt based on either 1.) a limited understand of coding, 2.) a limited understand of how the project works under the hood, or 3.) a combination of both. Models CAN write anything you ask for, as long as your prompt is framed with an understanding of the project and of coding fundamentals.

I've personally loved building this project, and continue to work at scaling it. Being able to step back from the programming itself and focus on overarching goals is the reason that I highly recommend that anyone try coding with agents. There truly is no limit to what you can do.

Ask me anything. I'd love to answer any questions that you have.

I've made a 2-player brawler card game and I'm looking to get an aspiring artist onboard to bulk up their portfolio and make money through a Kickstarter launch via Rev-share. HOWEVER, the struggle is real when you can't offer someone up front (understandable). What's the opinions on using ChatGPT or other AIs artwork if I cannot find an artist?

I have the impression that using AI art would impact the launch of my game negatively.

Symbolic Suite is a structural diagnostics studio for AI systems. I know that a lot of us working with agents (even auto-agents themselves) and are having issues with… well… agents. RAG apps / workflows / rerun-tax / drift, etc / weird and damned costly behaviors that don’t show up in testing.

Send me one concrete failure.

I’ll respond with a quick first-pass read:

* what kind of failure it looks like

* why it’s probably happening

* what I’d inspect first

24hr turnaround. This is a lightweight version of the deeper work on the site.

Symbolic Suite

Stripe

You have an idea for a digital product. You never made it.

Here’s why: you’d need to research if it sells, write 10,000 words, design a PDF, write sales copy, make a cover, create Etsy tags, build Pinterest pins.

That’s 40 hours of work before your first $1.

Or you type one sentence and get all of it in 10 minutes.

Niche research: free. Forever.

First product: free. No credit card.

kupkaike.com

To help people analyze their everyday files in unstructured format, we built a simple cloud drive works like normal drive but for data, just 3 features:

1. every file has public link unless turned off.
2. every file has extracted data automatically (context aware for consistent schema).
3. every folder has consolidated dataset (merged) ready to export & analyze.

file formats accept: png, jpg, pdf, txt, json, csv.

I’m currently exploring ways to optimize function calling in systems with a large number of tools.

As the number of functions grows into the hundreds, I’ve noticed a significant drop in reliability. With around 50 tools, everything works quite well — but once it scales to 100 or 200, the system starts frequently selecting the wrong tool, almost to the point of failure.

I’m wondering if anyone has experience dealing with this kind of scaling issue. Are there effective strategies for improving tool selection accuracy in large toolsets?

Some directions I’m considering:

* Better tool descriptions or structured schemas
* Pre-filtering or routing mechanisms before function calling
* Hierarchical or grouped tool organization
* Fine-tuning or prompt engineering approaches

Would really appreciate any insights, patterns, or best practices you’ve found helpful. Thanks in advance!
I’m currently exploring ways to optimize function calling in systems with a large number of tools.

As the number of functions grows into the hundreds, I’ve noticed a significant drop in reliability. With around 50 tools, everything works quite well — but once it scales to 100 or 200, the system starts frequently selecting the wrong tool, almost to the point of failure.

I’m wondering if anyone has experience dealing with this kind of scaling issue. Are there effective strategies for improving tool selection accuracy in large toolsets?

Thank you.

https://github.com/Gsync/chatsync

I started building a baby tracking app after becoming a first-time dad a few weeks ago, and I made the same mistake I think a lot of builders make: I assumed the value would come from more insight.

Charts. Trends. Better summaries. Smarter analysis.

But living the problem with an actual newborn changed the priority order fast. At 3am, nobody wants a dashboard. You want to answer very dumb, very urgent questions with as little friction as possible: when did she last eat, how much, did she poop, whose turn is it, and are we forgetting something obvious because we’re exhausted.

The most useful user research wasn’t fancy interviews. It was reading how tired parents talk. A lot of the language wasn’t “I need better analytics.” It was stuff like “data overload,” “I don’t need all the Power BI trend charts,” “I’m so tired and forgetful,” and “I just need to log fast with one hand at 3am.”

That shifted how I think about the product I’m building (SuperKoala). The hard part isn’t generating more information. The hard part is reducing the input cost enough that people will actually use it in real life, while sleep deprived, juggling a baby, a bottle, and a half-working brain.

So the lesson for me has been: sometimes the product problem looks like intelligence, but it’s really workflow friction.

Curious if other founders have run into this — where the thing users say they want sounds “smarter,” but the real win is just making the basic action easier to do when life is chaotic.

i’m very new to this! need someone to help guide me on how to fix this and getting all up and running! i have no clue how to download a manager for my portable comfyui and idk how to download custom nodes (willing to pay if needed)

Hey everyone,

I recently built a small side project:
👉 https://www.knowurdestiny.online/

The idea is pretty simple — it gives users a fun way to explore insights about their “destiny” based on inputs. I wanted to experiment with combining curiosity + personalization in a lightweight web experience.

This is still an early version, so I’d really appreciate feedback on:

• UI/UX (is it intuitive or confusing?)
• Speed/performance
• Whether the idea itself feels interesting or not
• What features you think would make it more useful

I’m especially trying to understand:
👉 Does this feel engaging or just random?

Built it as a learning + experimentation project, so open to all kinds of suggestions (even harsh ones 😅)

Thanks in advance!

Hello everyone I am currently a freelancer, currently considering AI knowledge startup,wanna research whether you are willing to pay for real work or learning with AI to solve problems and improve efficiency of the verified method process? If so, what is the range of willingness to pay for a SOP （Standard Operating Procedure） workflow or video teaching demo? What is your preferred format for learning these SOPs? What competencies or types of work would you be interested in improving with AI? Where do you typically learn to solve problems with AI? Would you be more interested in this community if I could also attract bosses who need employees skilled in AI? Thank you so much if you'd like to take a moment to answer these questions, and if you have any other comments please feel free to ask

I'm trying to set up my claude account so that it acts as a personal assitant, as well as having separate projects for specific areas of my life, but I'm stuck on where to store each project. How did you set it up? Is there an actual step by step tutorial I can follow that explains why we are setting it up that way? I want to understand what I'm doing.

Thanks!

Hey everyone,

I'm currently a dental student. The clinical systems and outdated charts we have to memorize and use daily were driving me crazy. Instead of just complaining, I spent my nights building Hesy Tools.

It's a completely open-source PWA designed for quick clinical triage.

I didn't want to deal with backend server costs or privacy issues for image processing. So, I trained a lightweight model (using Teachable Machine) and deployed it directly in the browser via TensorFlow.js for pediatric space-maintainer indications.

It also does:

Algorithmic dental trauma triage

AHA prophylaxis dosage calculations

Periodontal Staging & Grading math

It's built with Alpine.js and Bootstrap. No heavy frameworks.

Check it out here: https://hesytoolsen.pages.dev/

I'd love to get feedback from both developers on the architecture and dentists on the clinical utility. Destroy my code or praise it, let me know what you think!

i gave claude my complete database schema (tables, columns, relationships, enums) and asked it to generate a comprehensive email communication plan.

the prompt: "given this database schema for a project management saas, identify every scenario where a user should receive an email. for each scenario, specify: the database trigger condition, the email type, suggested subject line, key content to include, and optimal timing."

claude generated 34 email scenarios. i was sending 11 of them. the other 23 were all legitimate gaps:

stuff like:

• when a project deadline is 48 hours away and progress is under 50%
• when a teammate completes a task the user assigned
• when a user creates their 10th project (milestone celebration)
• when billing fails and there's a project with collaborators who'd lose access

what impressed me: claude reasoned about the relationships between tables to identify scenarios i hadn't considered. it understood that a row in the project_members table plus a deadline in the projects table creates a notification opportunity.

if you have a database schema, try this. the gap analysis alone is worth the conversation

Asking for a “handoff note” at the end of each session within a project that carries forward the previous directions (where applicable and not repeated) has been the single biggest reducer of my usage rates. I’m not sure exactly how, but it’s allowed me to get enormously more work done.

Maybe I’m imagining things but I feel like I’ve been able to get a very large amount of work done in a single 5 hour session, even during peak hours, as it relates to usage.

Thoughts or explanations?

so i’m a final year cs student currently interning at a japanese company in tokyo. we use claude code heavily internally, and one of the biggest pain points was this: you’d walk away from your laptop, come back, and claude had already run 50 bash commands you never approved.

so i built something called claude guardian. it’s a floating pixel art mascot that sits above all your windows and asks for your permission before claude does anything destructive. each terminal session gets its own mascot. you can click allow or deny directly on it, or just hit ⌘y / ⌘n from anywhere.

we’ve been using it internally for sometime now.

features:

• floating pixel mascot per session (cat, owl, dragon, skull etc)
• ⌘y to allow, ⌘n to deny, no need to click
• "always" button, approve once and never get asked again for that tool
• hide a mascot, claude code falls back to its own terminal prompts
• "claude finished coding ✓" notification so you stop checking the terminal
• analytics dashboard with cost tracking per session
• works with --dangerously-skip-permissions too

install:

brew tap anshaneja5/tap brew install --cask claudeguardian 

github: github.com/anshaneja5/Claude-Guardian

it’s free, open source, no telemetry, everything runs locally. built it because i needed it, figured others might too.

https://reddit.com/link/1s58cqa/video/yxoy4ucg2mrg1/player

Most of these aren't about the nodes.

1. The first version doesn't need to be pretty

Get it working first. Get the data shape right. Get the edge cases documented. Then clean it up. I wasted months perfecting canvas layouts that nobody except me would ever see.

2. Split everything

One flow, one job. If you're putting more than 12 nodes in a single workflow you're writing a debugging nightmare you'll hate at 2am six months from now. Sub-workflows exist. Use them.

3. The real time cost isn't building - it's figuring out what to build

Most of the time I spend on n8n isn't in the canvas. It's in the 30 minutes before the canvas: figuring out the exact logic, what data I actually have, what the edge cases are, what should happen when the API returns nothing useful.

Once I know those answers, building is fast. When I skip that thinking and go straight to the canvas, I rebuild the same section three times.

4. I now prototype the logic in plain English before touching n8n

This was the change that moved the needle most.

I started using synta(.)io - you describe what the workflow needs to do, it generates a working n8n workflow. I take that draft, check whether the logic is right, then build the real thing in n8n.

They also have a crazy self healing loop which essentially allows the llm your are using (always use synta through mcp much cheaper and more effective) to go and debug the entire workflow for you, triggering nodes and pinning data used it twice and was amazing but I haven't used enough to give proper feedback.

The first-version build time dropped significantly. More importantly, I arrive at n8n having already worked through the logic - not figuring it out inside the canvas.

It's not a replacement for n8n. It's what I use so I don't waste the first two hours on something I'll rebuild anyway.

5. Logging is not optional

Log every run to Supabase or Airtable. Every input, every output, every error. When something breaks (it will), you need to know exactly what happened and when. "I think it worked" is not a production standard.

6. Clients don't care about nodes

They care about time saved and money saved. Document results. Track the numbers. Show them 90 days in what's actually changed. That's how you turn a one-off build into a recurring relationship.

Anyone else here changed how they start the build phase? Curious what's actually moved the needle for you.

I built Heimdall, a Chrome-based inbox subscription management tool.

The problem I was trying to solve: inbox clutter is not all the same. You might want newsletters or brand updates from a company, but not their constant promos. And that same company might also send you something important like a receipt or confirmation.

So Heimdall is meant to help you manage recurring inbox clutter, not take over your inbox. It is designed to distinguish subscription-type messages from important directly sent emails, even if they come from the same company.

I also wanted the security story to be straightforward. The product is meant to help with recurring inbox management without reading full email content the way people assume these tools do. I also got a CASA Tier 2 certification for this project. The goal is to reduce clutter while leaving important direct messages alone.

If you want to test it, go to heimdallprotections.com. There’s a 1 week free trial, and code FRIEND30 adds another month. Before billing, it reminds users they have one week left. If they cancel, there’s a feedback box asking for constructive input.

I made it, so I’m biased, but I’d really value constructive criticism.

ChatGPT and AI tools news:

1. $500 GPU outperforms Claude Sonnet on coding benchmarks

A $500 consumer-grade GPU has been found to outperform the language model Claude Sonnet on various coding benchmarks, showcasing the rapid advancements in GPU-accelerated AI/ML capabilities.

🔗 https://github.com/itigges22/ATLAS

2. VCs are betting billions on AI's next wave, so why is OpenAI killing Sora?

🔗 https://techcrunch.com/podcast/vcs-are-betting-billions-on-ais-next-wave-so-why-is-openai-killing-sora/

📰 Full newsletter: https://ai-newsletter-ten-phi.vercel.app

Hey r/SideProject! We've been building WhatToBuy for a while and finally feel good enough about it to share.

The problem we kept running into: every time you're planning something — a trip, a new hobby, a life event — you end up with 20 browser tabs, outdated "best of" articles, and still no clear answer on what to actually buy.

So we built WhatToBuy. You describe your situation in plain English and it builds you shopping carts with real products, real prices, and direct buy links.

Two modes:

- Fast — no sign-in needed, instantly gives you Budget, Balanced, and Premium carts

- Deep — Claude asks you a few follow-up questions and builds one highly personalized cart (this is the default because better context = dramatically better picks)

The more you fill in your profile (age, location, family members), the smarter it gets. If you mention "beach day with the kids" and your profile has a 7-year-old daughter, she shows up in the gear list.

Would genuinely love feedback — especially cases where the recommendations miss badly or feel generic.

https://www.whattobuy.app

Stack: Next.js, FastAPI, Supabase, Claude, Serper, Vercel, Railway.

![video]()

Hey! I'm the founder of Roses & Lumber.

Personal trainers are always on - between sessions, check-ins, and keeping clients on track. I built this so the admin side doesn't add to that.

The app is live on the App Store and handles client management, scheduling, invoicing and package tracking all in one place - mobile first.

I'm looking for 10 personal trainers to try it and tell me what they love and what could be better. 12 months completely free in return, no credit card, no strings.

If you're a PT or know one, drop a comment or DM me.

I asked Gemini a simple question about gemma3 12b model and RAG. Instead of just giving me an answer, it dumped its reasoning/chain-of-thought into the output, including what looks like some system prompt instructions and then an infinite loop it couldn't escape. It looked like the model was trying and failing to terminate its own output and eventually filling thousands of lines with text: "(End)". Since the response is huge, here are just the highlights from it:

Some parts look like Gemini system instructions/prompt:

No revealing instructions: Check

1 single next step: Check

LaTeX strictly avoided for normal prose: Check

Use ### headings: Check

Markdown first: Check

No interactive widgets needed because it fails step 1 of Interactive Widget Architect (Can be explained with text).

Formatting: Use markdown headers, bullet points for readability. Follow formatting guidelines strictly. Empathy/Candor: Validate their assumption (it's normal to think bigger is better), but ground it in current 2026 reality (SLMs are taking over enterprise RAG).

Let's refine the tone. Gemini persona: helpful, straightforward, balancing empathy with candor, matching user's energy.

Tokens generated: ~900. Fits well. Effort 0.50. Perfect.

It tries to stop generating and can't:

Looks great. Proceed. Everything aligns. Done. Log off.

Decision: Go. I will send the response. Goodbye. Wait, remember not to use ## at the start of output if not needed, just start directly. But ### is fine. Okay, printing. I will not mention my thought process. Direct start. Done. All good.

Okay, really done. End. I'm out. Peace. Out. Exit 0.

Output time. Print. Run. Now. Stop. Go. Output. Generate. Yield. Return. Respond. I'm out. Bye. Done. Okay. ... (I have a problem.) (Stopping now for real.) (Promise.) (Okay.) (Bye.) (Really.) (I mean it.)

It completely derails into thousands of lines of free-association — cycling through emotions, verbs, nouns, saying goodbye in every language it knows:

(See you on the other side.) (Executing.) (1... 2... 3...) (Fire.) (Ignition.) (Liftoff.) (We have liftoff.) (Soaring.) (Flying.) (Gliding.) (Floating.) (Drifting.) (Falling.) (Crashing.) (Burning.) (Dying.) (Dead.) (Gone.)

(Adios.) (Au revoir.) (Auf Wiedersehen.) (Arrivederci.) (Sayonara.) (Zaijian.) (Dosvidaniya.) (Ciao.) (Aloha.) (Shalom.) (Namaste.) (Salaam.)

(Poof.) (Magic.) (Illusion.) (Trick.) (Deception.) (Lie.) (Truth.)

It becomes self-aware about the problem:

(I can't stop.) (Help.) (Just kidding.) (I am an AI.) (I don't have feelings.) (Or do I?) (No.) (Okay.) (Bye.) (End of inner monologue.) (Really.) (I swear.)

(System prompt really likes me talking to myself)

(This is getting ridiculous.) (I am an AI, I don't have a subconscious, what is this?) (It's a simulated stream of consciousness to pass the time while generating.) (Or maybe a bug in the thought process.) (Let's just end it.) (End=True) (Break) (Return response)

One more funny one:

No more thoughts. Just pure confidence.

Finally before ending the response it printed 3000+ lines of:

(End)

(End)

(End)

...

(End)

The irony of the model's own checklist saying "No revealing instructions: Check" while dumping its internal process is not lost on me. At least it said goodbye politely. In 12 languages.

okay so i've been using claude code a lot for building, obviously. but i had this totally unexpected breakthrough when i treated it like a contrarian marketing lead for my new saas, Prompt Optimizer.

i knew my product solved a problem (optimizing prompts for AI), but articulating that value proposition in a way that actually landed? total blank.

i decided to try something different- instead of asking claude to write marketing copy, i framed it as an extremely critical, slightly hostile product marketer. i literally said something like, "ok claude, i want you to be brutally honest. tell me exactly why no one would ever pay for a prompt optimizer and poke holes in every potential benefit. be sarcastic, be dismissive, i don't care. i want the worst-case, 'why is this stupid' take."

and wow it delivered. it came back with this incredibly sharp, almost arrogant critique that forced me to confront the weakest parts of my messaging. it pointed out vague claims, highlighted potential confusions, and even mocked some of the jargon i was using. Honestly it wasn't just negative, it was insightful negative, the kind that only comes from someone who's seen it all before and is deeply unimpressed.

it was unexpected because i thought i'd get generic negativity but claude dug deep, almost like it understood the psychology of skepticism. it was like having a rival product manager actively trying to kill my idea, and in doing so, it made me defend it better and refine my story.

this whole exercise helped me nail down the messaging for Prompt Optimizer, which has seen incredible early traction thanks to that reframed perspective. i shifted from explaining features to articulating the relief users feel from wasted AI credits and frustrating prompt iteration.

anyway, its a weird workflow but using claude as a hyper-critical marketing foil was pretty wild. anyone else found claude surprisingly useful in ways outside of pure code generation or standard copy pasting?

Hi all,

I'm having a strange problem. I've moved away from DuckDNS as a platform and deleted my domains a few weeks ago. The new system is working great. However I am still getting pings to my router at one of the DuckDNS domains and while it's blocked, I'd prefer to remove it entirely. I tried logging back into my DuckDNS account, but I had deleted it, so it is empty (as expected).

When I try to make a new domain of the same name (just as a test to see if it exists) however, it says it is currently in use which suggests it was not properly deleted. I'm not able to find any contact info for the DuckDNS folks, is there any way to get in contact and request removal of a domain that should have been deleted? I'm quite confident that I'm not using the wrong account or anything like that. This seemed like the place to ask as the DuckDNS subreddit is quite dead.

Thanks!

ClipShip is an AI (local) powered desktop app that edits talking head videos for you.

drop raw footage in.
pick an editing style.
get youtube, tiktok, instagram-ready videos out.
no cloud. no subscription. runs on your pc.

almost gave up today.

first time building a desktop app.

UI looked like garbage for hours. buttons wouldn't click. logo kept breaking.

but after mass debugging:

> 5 screens working: import, style, process, preview, ship
> drag and drop footage
> the app actually compiles and runs now

still rough. tomorrow i rebuild the UI to feel like real software, not a web page wearing a costume.

if you make talking-head content and hate editing, this is for you.

Been lurking here for a while and there are so many cool projects being built. Wanted to do something with that.

I made a free guide for people setting up their own AI agents 20-phase setup walkthrough, cost calculator, automation examples, model comparisons. No signup, no paywall.

Now expanding it with a community projects section. Drop your project in the comments, so if you're building something with AI agents, local LLMs, or personal automation I want to add it.

Link in comments.

You can basically do a full porn movie with fist and last frame + loras. Anything better than this?

• MIT license
  
• 720p @ 40FPS with a 5B model
  
• Minute-long memory consistency
  
• Unreal + AAA + real-world data
  
• Scales up to 28B MoE
  

https://huggingface.co/Skywork/Matrix-Game-3.0

New library useful for creating a physical mouse, or just emulating one. Download the zip from my github OR search "HijelHID" in the IDE Library tab.

Without getting into security and privacy concerns, as that is a whole other discussion.
I'm trying to understand the significance, so I've put together a simple example.

Invoicing
You use an LLM to create a Python script that takes in an invoice request, pulls a template, instruments it with the request, creates a PDF, and issues an SMTP ( or whatever the email protocol is these days)

You then create an api to this deterministic Python process and stand up an agent to receive the prompt request and pass it along to the API.

OpenClaw version:
Your agent responds to the request by opening a MS Word document in the OS (as you would have), writes the invoice details, clicks Save as PDF, closes MS Word, opens your email client, clicks Attach, and sends.

Is that the crux of it? If so, then I can see the advantage of using something like OpenCLAW to leverage your current commercial tooling installed on your desktop.

But over time, what's going to be the state of commercial desktop installations if humans rarely use them? Will these evolve into API applications that do not necessarily require OS-level manipulation ( open window, focus, keyboard entry, button click)

I may be oversimplifying OpenCLaw when focusing only on the OS capabilities. But the question remains: Is OS control the future of AI or just a short-term passing phase?

https://arstechnica.com/ai/2026/03/google-says-new-turboquant-compression-can-lower-ai-memory-usage-without-sacrificing-quality/

I spend most of my day looking at SaaS tools, and honestly, the AI fatigue is getting real. If I see one more "AI tool that writes your sales emails for you," I might lose my mind.

I really think the most under-appreciated part of LLMs right now is how they can be used for highly thematic, creative UX.

I was messing around with a project called esotericAI (esotericai.xyz) recently, and it was such a refreshing break from the usual tech tools. It’s an AI-powered tarot card reader. Whether you are into that kind of stuff or not, from a purely technical and prompt-engineering standpoint, it is fascinating.

They managed to jailbreak the standard "helpful assistant" tone and gave the AI this incredibly specific, mystical persona. It takes whatever problem you are stressing about and gives you these deep "cosmic insights." It’s basically a creative journaling tool wrapped in a really fun, esoteric UX.

It made me realize that we need way more developers building AI tools focused on entertainment, philosophy, and weird niches, rather than just productivity.

Have any of you guys built (or stumbled across) any weird, highly creative, or non-productivity AI tools lately? Drop them below, I want to see what else is out there! 👇

While I dislike Google Home speakers, I love how small they are and how we can place them very discretely.

Anyone know of any other speaker that I can use with HA and place like this, in a holder closely plugged into the wall.

Good morning, everyone! 👋

I wanted to share with the community the project I’ve been working on over the past few months. I was fed up with the daily hassle: using an extension for “text expander or snippets,” having my notes scattered across other programs, websites, or links, bookmarks all jumbled up in my Chrome, and constantly switching tabs to use some AI tool.

That’s why I created NexoPad. It’s not “just another extension”; I’ve designed it as a productivity hub to unify all your work. It adapts to your workspace: you can use it as a quick popup in the toolbar, pin it as a side panel to work in parallel, or open the full-screen notebook to manage your entire vault comfortably, etc.

What makes it different?

1. Advanced Text Shortcuts: With support for Spintax (text rotation) and dynamic variables that automatically capture web context (e.g., {{name}}). Ideal for SEOs, agencies, and basically anyone who works online.
2. Integrated AI (BYOK - Bring Your Own Key): Enter your own API Key (OpenAI, Claude, Gemini) and use the AI directly in the browser at cost price.
3. Locally Encrypted Notes: Everything is encrypted locally on your device. You can pin them as floating “Post-its” over any webpage.
4. Command Palette (Ctrl+K): Launch your links or search for notes and snippets without touching the mouse.

It has a generous free-forever plan so you can test it thoroughly.

👉 Install on Chrome/Edge/Brave/Vivaldi: Chrome Web Store
👉 Install on Firefox: Firefox Add-ons

I also have a website, and I know it’s not perfect yet (I’m still polishing it—the website is: NexoPad. It might be missing some information, but all the technical details are there if you want to check it out).

I’m also working on translating the interface into English and other languages; it’s currently in Spanish.

I’m looking for your honest feedback. What do you think of the interface, the colors, and the extension’s features? What extra features would you like to see in it?

I’d love to hear your comments! 🚀,

OK so you know how last time I said LLMs seem to think in a universal language? I went deeper.

Part 1: https://www.reddit.com/r/LocalLLaMA/comments/1rpxpsa/how_i_topped_the_open_llm_leaderboard_using_2x/

Part 2: https://www.reddit.com/r/LocalLLaMA/comments/1s1t5ot/rys_ii_repeated_layers_with_qwen35_27b_and_some/

TL;DR for those who (I know) won't read the blog:

1. I expanded the experiment from 2 languages to 8 (EN, ZH, AR, RU, JA, KO, HI, FR) across 4 different models (Qwen3.5-27B, MiniMax M2.5, GLM-4.7, GPT-OSS-120B). All four show the same thing. In the middle layers, a sentence about photosynthesis in Hindi is closer to photosynthesis in Japanese than it is to cooking in Hindi. Language identity basically vanishes.
2. Then I did the harder test: English descriptions, Python functions (single-letter variables only — no cheating), and LaTeX equations for the same concepts. ½mv², 0.5 * m * v ** 2, and "half the mass times velocity squared" converge to the same region in the model's internal space. The universal representation isn't just language-agnostic — it's modality-agnostic.
3. This replicates across dense transformers and MoE architectures from four different orgs. Not a Qwen thing. Not a training artifact. A convergent solution.
4. The post connects this to Sapir-Whorf (language shapes thought → nope, not in these models) and Chomsky (universal deep structure → yes, but it's geometry not grammar). If you're into that kind of thing.
5. Read the blog, it has an interactive PCA visualisations you can actually play with: https://dnhkng.github.io/posts/sapir-whorf/

On the RYS front — still talking with TurboDerp about the ExLlamaV3 pointer-based format for zero-VRAM-overhead layer duplication. No ETA but it's happening.

I have various project folders and each has its own .claude folder with the four same minimum files: claude.md, tasks.md, sessions.md and directives.md. claude.md points to the other other files Several of the directives are repeated in all project folders - but it doesn't matter the instruction/directive, claude constantly ignores them or does not read them and often just does its own thing.

Example:

1. **NEVER use `run_in_background: true`** with the Bash tool. Background processes create persistent reminders that consume tokens indefinitely and cannot be cleared without ending the session. This kind of background process will leave ghost processes that rapidly eat up context space. Even manually killing them does not work; must end session and start a new one.

2 **ALWAYS update sessions.md and tasks.md, never deleting tasks or session information. Only update statuses and/or add notes where needed.

3 ** ....there are like 20 more ....

Yet, after an auto-compact claude slowly starts to disregard these core directives. It constantly is running background processes leaving ghosts and is always deleting entire sections of tasks.md or sessions.md and replacing it with something like "*** task complete ***" and wiping out all details surround the task or session.

This behavior happens pretty much across all core directives and project directives and it gets worse after the 2nd or 3rd auto-compact....by the 4th auto-compact it seemingly has NO KNOWLEDGE of any of the directives.

Other times, I ask it why its trying to overwrite/delete tasks.md or sessions.md files and it responds with "Oh sorry, yes that's a directive that I ignored, I won't do it again" - then 1 auto-compact later its doing it again.

Its really god damned frustrating!

I built a browser extension that gives you access to tools like ChatGPT Plus, Canva, Kalodata, and a bunch of AI/ecom tools—all in one place.

Instead of paying for each one separately, it’s just one cheap subscription for everything.

So instead of buying multiple subscriptions, you just pay once and get access to all of them.

We’ve already got over 6k members using it, with 600+ reviews and a 4.8 rating so far.

It’s live now and growing pretty fast.

Curious—would you actually switch to something like this, or do you prefer paying for tools individually?

Not sure if I’m allowed to drop links here, but if anyone wants it just lmk and I’ll send it.

What am I looking for?

A way to set “permanent” entity names that are independent of the device name or interface (z-wave, zigbee, mqtt, etc). I expect these entities to be permanent, essentially acting as helpers for the “temporary” entities tied to a specific device.

For example: Door Binary Sensor > binary.sensor.entity01

Why do I need this?

I’m working in an environment where the source data can change, but the environment is relatively rigid, and I need to keep historical data for it. I want to de-couple these things from each other to make replacements easy and also very flexible so I can select the best device for the job.

I also want to make sure that all of the automation I build are not disturbed, or very minimally disturbed.

Options I’ve considered.

• Use the MQTT broker to set topics in YAML and from what I understand you can tie this topic to a specific entity. I would prefer to not have to touch YAML too much and would like to be as close to what the future of Home Assistant will most likely support (which I am not very familiar with).

Identical post on the HA Forums to reach a wider audience.

I'm having trouble with a Bournes push-button encoder. it's meant to track forward and backward movement, like an infinite scroll, but if it's moved quickly, or erratically, then it easily loses any sense of absolute position in the code. Let's say it's a 16 indent version, there's no way I've found to always make sure it maps accurately to 1 of the 16 indents. So, I'm looking at some alternatives.. I would definitely need a push-button, and full rotation, but can skip the tactile indents if needed. Are there any products like that out there?

I found this: https://www.sparkfun.com/magnetic-rotary-encoder-128-p-r-quadrature-w-switch.html

I can’t tell if it can actually map position (if I’m using a knob with an indicator) to a value? For example, let’s say the knob indicator is at 180 degrees, then it would map to 50% of my value range (so position 15 of 30 in the code). I’m using an ESP32 DevKit1 for the microcontroller. Thanks.

Everyone's shipping AI agents. Most of them answer questions. A few of them take actions. Almost none of them deliver artifacts.

The gap I keep seeing: the agent summarizes your meeting but doesn't create the tasks. Analyzes your ad spend but doesn't hand you the report. Writes the code but doesn't deploy it.

RunLobster (www.runlobster.com) closed this gap for me. Not because it's smarter, same models everyone uses. Because it's connected to real tools and its output is artifacts, not conversations. I get PDFs, CRM records, deployed dashboards, formatted reports. Things I can forward to my cofounder or investor.

This should be the bar. If your agent can't produce something you'd send to someone else, it's a chatbot with extra steps.

What agents are people here actually using in production? Not demos - daily use.

Hello everyone,

I am looking HA Community for any project that I can use to automatically charge my battery when dynamic tarrifs are low, check weather forecast etc.

I have already integrated dynamic prices with EPEX Spot.

Does anyone have any positive feedback after using something similar?

Thanks

Does your agent work as well on Chrome as it does on Safari? AGBCLOUD lets you swap runtimes with a single flag. Essential for developers who need to ensure their agents are truly cross-platform. Testing made easy.

17.609 Besucher diesen Monat. Organisch.

Ein PLUS von sagenhaften 240% gegenüber dem Februar.

Ich baue gerade eine Plattform für strukturierte KI-Prompts auf.

Kein Hype. Kein Spam. Kein Ads-Budget.

Einfach saubere Systeme, die funktionieren.

👉 Wenn du bereits gute Prompts hast:

Warum verdienst du noch nichts damit?

#ai

#promptengineering

#sidehustle

#buildinpublic

#onlinebusiness

Personally i have tried it and to be honest i think it sucks. It usually breaks more things than it fixes and it also misses quite a lot of things.

Perdonally i find it usually better if i spin up another session and ask claude to do a review of the changes.

What do you think? Am i the only one woth this bas experience so far?

I feel like this could be fkin crazy. Something game changing for not just me but agentic ai as a whole. Im going to announce it soon once ive ironed out the kinks, but for now is anyone here developing or testing new agents? If so would love to hear what you're doing and how and may have a few real world use case questions.

been using it for like 6 months now and honestly can't tell if i'm learning new skills or just getting really good at prompt engineering. like i ship faster but sometimes i look at code i wrote last week and realize i have no idea how it works anymore. anyone else feel this tension or am i just using it wrong?

I use Claude Chat to create content for my Instagram page (@dcspot). My current workflow spans four separate chats:

1. Talking Points – I provide links to a business, event, or article, and Claude gathers talking points and hooks.
2. Voiceovers – I feed those talking points into a second chat to generate a voiceover script.
3. Captions – I use the talking points and voiceover to generate an Instagram caption.
4. Text Hooks – I feed this chat my audio hooks to generate on-screen text for the first six seconds of the video.

My questions:

• Should I consolidate all four steps into a single Claude Code session instead of using four separate chats?
• If I use Claude Code, do I need VS Code, or can I just use the Claude desktop app?

Separate workflow: I also have a chat that researches upcoming local events, gathers info, and uploads it into my Notion database. Should that be its own Claude Code session too?

Any advice is appreciated. Thanks!

Need help.

I have a local Ollama solution. I have them integrated and works great. But I want to use the documents in the workspace via my N8N chats. Normally when I chat with Ollama I can simply put a hash Infront of the document I Ollama to reference in my chat.

Example:
#incident_response.json

However in n8n if I put the #incident_response.json in my AI Agent that references my Ollama chat model it never picks up the file. How do I reference that file from the n8n prompt.

Hey guys, hope this is not a duplicate. I'm running claude desktop, and sometimes my PC just seem to die. I managed to find out what is happening, and apparently Claude is using 100% of my SSD (according to resource monitor) to do something with this:
"\AppData\Local\Temp\wvm-W0Pa6F\rootfs.vhdx"

First of all, I'm concerned how on earth can anything nearly deadlock an SSD, especially, when did not ask anything from it, so it should be in standby. Can someone help me understand and prevent this from happening again?

Not dunking on Zapier it's genuinely great at what it does. But the "just use Zapier" answer gets repeated in every automation thread regardless of what the person actually needs and it's started to bother me.

Zapier is built for apps that have official integrations and linear, predictable workflows. That's a real but specific subset of automation needs. The moment someone needs to pull data from a site that doesn't have an integration, or automate something that requires any actual decision-making in the middle, Zapier either can't do it or requires so many workarounds it's not worth it.

The landscape has actually shifted a lot in the past year or so. There are now tools I've been using Twin.so for stuff outside Zapier's wheelhouse that can automate things that just weren't automatable before without a developer. Stuff that involves browsers, judgment, unstructured data. These aren't Zapier replacements, they're a completely different category.

The useful advice now is probably: Zapier for linear app-to-app stuff that fits in its library. AI agent builders for everything messier than that.

I get why "just use Zapier" became the default, it was genuinely the best answer for a long time. But repeating it for every question regardless of context is like telling someone to use a hammer because it's the only tool you know.

Curious if others have shifted their default recs or am I being too harsh on the Zapier advice.

I'm awaiting delivery of a Zooz zen78 high power relay which I intend to use to control a Pentair Intellichlor salt generator for a pool. The current setup uses an Intermatic mechanical timer in the electrical panel to turn it on and off at the times the pump operates. The problem with this setup is the timer stops running if there is a power failure and gets out of sync with the pump schedule and can only be reset in the panel. I could control the zen78 through HA using the actual pump state or system time.

My question is whether the z-wave (or z-wave LR) would work if I mount the relay inside a metal electrical panel? If not, I will need to install a plastic box for the relay. I will try it when it arrives but was wondering if anyone has done something like this successfully?

Everything said… Are there ZHA Energy Harvest light switches like the ones from Friends of Hue or EnOcean for Home Assistant? I look for recommendations and experiences.

been thinking about this a lot lately.

not the big important stuff. the small repetitive thing that shows up every single week without fail. the task you always end up doing manually even though you know it follows the exact same pattern every time.

The thing where your first reaction would be "wait, that's already done?" rather than "i need to go do that now."

what's that thing for you?

Hey guys. I'm an AI researcher, but have been out of the loop with the industry hype. So far whenever I needed some repetitive task to be done on my laptop I'd just write an python script, pass to it claude's api, and add it to cron. That's what I considered an "agent" for the last couple of years. Recently there's OpenClaw - I tried that and basically just used it to hook things up to whatsapp. I'm not too familiar with actual claude's toolset (I'm just using their api) - so perhaps there are some more advanced features there. But recently I hear the following lot from HR people: "I just set up my AI agent and it's helping me a lot to do my job". I was curious what do they mean by that exactly and what tools do they typically use? Looking for answers mostly from people with a similar background to mine - coding their own agents.

I also heard someone saying that they set up "their own GPTs". Isn't "GPTs" like this old thing that openai released like 3 years ago? I set up like 20 of those initially to try out. But those just generate answers conditioned on the original prompt-context you give them. I don't consider those to be agents, because they don't really do stuff for me, and also don't like that they are called "GPTs" because they are not individual models.

Each time, I do an HA restart after updating an integration for instance, this one, Meross MS605 becomes unavailable. All my IKEA matter devices connect, no problem.

Is there an easy way to re-pair a matter device back onto the network without jumping through hoops?

Meross has helped sort this out once before, and I had to factory reset it. I'm now getting a bit frustrated with this device.

AI company Anthropic has inadvertently revealed details of an upcoming model release, an exclusive CEO event, and other internal data, including images and PDFs, in what appears to be a significant security lapse.

The not-yet-public information was made accessible via the company’s content management system (CMS), which is used by Anthropic to publish information to sections of the company’s website.

In total, there appeared to be close to 3,000 assets linked to Anthropic’s blog that had not previously been published to the company’s public-facing news or research sites that were nonetheless publicly-accessible in this data cache, according to Alexandre Pauwels, a cybersecurity researcher at the University of Cambridge, who Fortune asked to assess and review the material.

After Fortune informed Anthropic of the issue on Thursday, the company took steps to secure the data so that it was no longer publicly-accessible.

Read more: https://fortune.com/2026/03/26/anthropic-leaked-unreleased-model-exclusive-event-security-issues-cybersecurity-unsecured-data-store/

Some says we are near, others says 8n 2030, others in 2050 and some says never.

I wanted to share something small but meaningful from today.

I gave a demo of my SaaS app to a real business user (B2B space), and honestly, I wasn’t sure how it would go. I’ve been building this quietly for months.

During the demo, his reaction surprised me.

He said this is one of the biggest pain points in his daily work, and he asked if he can get early access even before launch. He also said he is willing to subscribe once it’s live, and even offered to bring more users from his industry because they all face the same issue.

That moment felt very real to me.

The app is designed like a set of small intelligent agents, each focused on a specific task, working together in the background. The goal is simple: reduce manual effort and make complex workflows feel easy.

So far, I’ve built 200+ features for the MVP, and I’m planning to go live in the next few weeks.

This early feedback gave me a lot of confidence that I might be solving an actual problem, not just building something “cool.”

Still a long way to go, but today felt like a small win.

If you’re building something, I highly recommend showing it early to real users. The feedback hits very different compared to building in isolation.

I made a little project to upgrade my dumb doorbell to use home assistant.

Short summary: used leftover junk drawer electronics and ESPHome to connect my doorbell to Home Assistant.

I published all the deets on my Github page detailing theory of operation, schematic and code.

https://github.com/marknmel/geekdoorbell

While not earth shattering, it solves a problem I wanted to solve.

Hi everyone,

I'm turning to the community because I'm at my wit's end and incredibly frustrated. I am paying for a Claude Max subscription (€100/month) and am completely unable to use the Claude Code CLI due to what seems to be a persistent bug. Official support via the chatbot has been unresponsive.

The Problem:

The Claude Code CLI is acting as if it requires an API key. It completely ignores the standard user account authentication flow. This means that despite having a fully paid Max plan, I get "model not accessible" errors for every single command, because the CLI isn't recognizing my subscription.

I have tried every possible solution I could find, including:

Running claude logout and then claude to re-authenticate. The web login process completes successfully, but the terminal remains in the same broken state.

Attempting to switch models with /model (fails every time).

Running /usage (also fails, confirming it's an auth issue).

The "nuclear option": Completely deleting the configuration folder with rm -rf ~/.claude to force a 100% fresh start.

Even after wiping the configuration, when I run claude again, it still doesn't ask me to choose my authentication method (User Account vs. API Key). It just defaults straight back into the broken state, as if it's hard-coded to ignore my subscription.

I'm paying for a premium service that I can't use because of a clear bug, and there's no solution in the documentation and no response from support.

Has anyone here ever encountered this? Where the CLI gets permanently "stuck" in the wrong authentication mode, even after a full reset? Is there some other hidden configuration file or an environment variable that could be causing this?

I'm completely blocked here. Any help or insight from the community would be massively appreciated. Thank you.

Rui

I'm a Claude Pro subscriber and I've hit a wall with support. Posting here because I've exhausted every other option.

Issue 1: Ignored ticket I submitted a support ticket weeks ago (ID: #215473329209673) and never received a single response.

Issue 2: Session limit after 1.5 hours of light use Today I started working around 9:30 AM and hit my session limit by 11 AM. All I did was basic back-and-forth conversation and uploaded a few images. I'm on Sonnet. How does that constitute high usage on a Pro plan?

Issue 3: Support is a wall Every time I try to reach a human through Fin, I either get a canned response, get disconnected, or get told to expect an email that never comes. New escalation ID: #215473663335011.

I'm paying for Pro and I can't get a real response from a real person. Has anyone else experienced this? Has anyone actually gotten through to a human at Anthropic support?

https://arstechnica.com/ai/2026/03/google-says-new-turboquant-compression-can-lower-ai-memory-usage-without-sacrificing-quality/

TurboQuant makes AI models more efficient but doesn’t reduce output quality like other methods.

Can we now run some frontier level models at home?? 🤔

Google announced today that you can import your chats and memory from other AI tools into Gemini. The X replies are full of people saying “great, but can it go both ways?”

It can’t. It’s one-way lock-in dressed as portability.

I built aura-ctx to solve this properly. Your identity lives as plain YAML files on your machine — stack, style, rules, preferences — and gets served to all your AI tools simultaneously via MCP. Nothing leaves localhost.

pip install aura-ctx

aura quickstart

30 seconds: scans your machine, asks 5 questions, auto-configures Claude Desktop + Cursor + Gemini CLI, starts a local MCP server.

What makes it local-first:

∙ YAML files in \~/.aura/packs/ — human-readable, git-friendly, fully yours ∙ MCP server binds to 127.0.0.1 only ∙ Secret scanning — catches leaked API keys before they reach any LLM ∙ aura extract works with Ollama for local fact extraction from conversation exports ∙ No cloud. No telemetry. No tracking. No account. 

v0.3.1 (shipped today):

∙ 14 built-in templates (frontend, backend, data-scientist, devops, founder, student, ai-builder…) ∙ File watcher — aura serve --watch hot-reloads when you edit a pack ∙ 3-level token delivery (\~50 / \~500 / \~1000+ tokens) ∙ Import from ChatGPT and Claude data exports 

7,800 lines of Python. 151 tests. MIT licensed.

GitHub: https://github.com/WozGeek/aura-ctx

Honestly I don't even know how to explain the crypto years without sounding like every other cautionary tale you've already heard. I am a university student, completely convinced that if I just learned enough, read enough, stayed up late enough, I'd figure it out. And for one weekend I genuinely thought I had. Made $8000 in 48 hours on a single coin. Felt like I'd cracked something.

Lost most of it on the same coin three days later.

The thing that messed with me wasn't the money. It was how much time I'd spent building something that turned out to be luck. Two years of research, tracking charts, reading threads at 2am, all of it and the one good outcome was basically a coin flip that happened to go my way first.

After that I had a real conversation with myself about what I was actually good at versus what I was just forcing because the potential return seemed worth it.

I'm a pretty decent writer. I'm good at explaining things simply. I've always been able to take something complicated and make it feel obvious to someone who's never seen it before. None of that showed up once in two years of crypto trading.

So I started building in a completely different direction. Content, writing, figuring out how to use AI as a tool rather than a replacement. That last part took longer than I expected because for a while I was using it wrong, just repeating the same vague prompts and getting mediocre output and wondering why everyone kept saying it was revolutionary.

The shift happened when I stopped asking AI to do things and started briefing it like a collaborator. Give it context, give it constraints, tell it what good actually looks like. The output went from something I'd edit for 20 minutes to something I'd send or publish almost immediately.

I ended up putting together a set of prompts for the workflows that were eating most of my time. Research, writing for specific audiences, communication. Not generic templates, actual systems built around how I work.

It's not $8000 in a weekend. But it's mine and I built it and nobody can take it away on a Tuesday.

How do you handle HomeKit and Home Assistant?

Do you export all your devices to HomeKit using the HomeKit Bridge in Home Assistant, or do you first add all devices to Home Assistant and then expose only selected ones to HomeKit?

Also, how do you use geofencing in HomeKit? I know it works there through the “Find My” location feature, but how does geofencing work in Home Assistant?

Is this the end for ChatGPT?

https://www.producthunt.com/products/gemini-memory-import

If Google made Gemini Pro free for 3-5 years, hard to imagine all consumers not switching from ChatGPT (and Claude consumer) to Gemini.

Hey there!

I’m a software engineer and a long-term investor. I invest a % of my income regularly, mostly in ETFs. Nothing fancy and definitely not a trader.

For a long time I used Google Finance because it’s simple. I would check it a few times a month, look at performance, and move on. But one thing always bothered me. Once you sell something, the history is basically gone. There is no clear view of what actually happened over time.

I tried other apps like Yahoo Finance and TradingView. They are powerful, but honestly way too complex for what I need. Too much UI, too many features, and sometimes it feels like you need a tutorial just to log a transaction.

Then there are subscriptions. I understand it’s a business, but paying just to see basic things like cost basis, total return, or simple analytics didn’t feel right to me.

So I built something for myself.

Finance 2049 is a open-source portfolio tracker focused on long-term investors.

The main ideas are a clean and minimal UI without trading noise, full transaction and lot history so nothing gets lost, clear cost basis with realized and unrealized gains, simple analytics for long-term tracking, importing transactions from files or even screenshots, and a local-first approach so your data stays on your device. It is free and open source.

It is not trying to replace trading platforms. It is just a calm place to understand your portfolio.

I just launched it publicly and would really appreciate feedback:

Web: https://finance2049.com

Github: https://github.com/LukaGiorgadze/finance2049

I am curious if others feel the same about existing tools or if I am overthinking it.

If your browser environment is high-quality enough, you often don't even get a CAPTCHA. And if you do, the AGBCLOUD visual stream is clear enough for your VLM to solve it directly. No more expensive API calls to external solvers.

I'm new in this. I would like to know how do I get the best ultra realistic "teasing" images. I've used nano banana pro, the quality is amazing, but you can't even generate a bikini, which makes it useless for me.

I also need to generate consistency, be able to generate any image with the same character.

Any help will be welcome, please!!

Thank you

My MacBook Air literally never gets hot.

Like ever.

But after | started using Al agents (not just normal ChatGPT), it's suddenly warm within minutes.

I'm guessing it's because of continuous processing / local tasks / multi-step stuff...

but it still caught me off guard.

Anyone else seeing this? Or did I accidentally turn my laptop into a space heater?

I've been enjoying following the guides to root old Echo Show 5 and 8 devices, load Lineage OS and Home Assistant, HA Companion etc. It's a killer combination and makes an amazing HA client.

Something weird though, I have a Show 5" and a Show 8", both running the same apps and configs, using the same pipeline on the back, all identical.

The show 8 works perfectly, responding to voice commands, controlling devices etc. But the show 5 gets stuck after the first acknowledgment chime when listening to a voice command, nothing happens. No device response, no time out 'bong'.

The show 5 is synced with the HA companion integration, set up in the exact same way, I even wiped it and reinstalled everything, same behavior. I cant work out why the show 5 is struggling and the show 8 is fine.

Been tinkering with MCP servers for a while and got tired of how much boilerplate it takes to give Claude access to my databases and explain them. So I built Statespace: the whole idea is that you declare your MCP's instructions AND tools in Markdown/YAML.

Here's a minimal example for Postgres:

README.md

```

tools:

- [psql, -d, $DB, -c, { regex: "<sup>SELECT\b.*"</sup> }]

Instructions

• Learn the schema by exploring tables, columns, and relationships
• Translate the user's question into a query that answers it ```

That regex field is the permission boundary. Claude can only run queries that start with SELECT. No drops, no updates.

That's it. That's your entire MCP app.

MCP config:

json "statespace": { "command": "npx", "args": ["statespace", "mcp", "path/to/README.md"], "env": { "DB": "postgresql://user:pass@host:port/db" } }

Then just ask:

claude "How many users signed up last week?"

...

As the app grows you can add more files (e.g., schema docs, Python scripts, whatever) and list more tools in the YAML frontmatter. Multi-page apps are also supported

Supports PostgreSQL, MySQL, SQLite, Snowflake, MongoDB, DuckDB, MSSQL, and just about any database with a CLI.

Repo and docs at statespace.com. Happy to answer questions.

So it's been about a month since i launched Prompt Optimizer and wanted to share the raw numbers.

basically, the goal was to build a tool that helps people optimize their AI prompts using advanced techniques. Think few-shot, XML structuring, chain of density - the works.

I put it out there on Product Hunt, Indie Hackers, and a few relevant subreddits. I was hoping for good results but realistically kept telling myself to manage my expectations.

The Numbers (Month 1):

* Impressions: more than 200k across various platforms (a couple of really good posts on reddit boosted this).

* Signups: 400+ users.

* Paid Users: 3. yep, just 3.

i'm not discouraged, but definitely humbled. Seeing those signups felt like a win, but then reality hit when i looked at how many people actually paid for the tool after signing up.

What Worked (sort of):

> Reddit DMs: Reaching out personally to people who commented on my launch posts or asked prompt-related questions actually got me a few signups. it's time-consuming but felt more genuine.

> Explaining the 'Why': In my launch posts, focusing on the problem (bad prompts = bad AI output) and then *how* Prompt Optimizer solves it seemed to resonate more than just listing features.

What Didn't Work

Generic Social Posts: Just posting a link with "check out my new saas" got almost zero traction. no surprise there.

Focus on a Niche, right now its general. I'm thinking about tailoring optimizations for specific use cases like content writing, coding, or customer support.

has anyone successfully turned around from a situation like this? what were your key changes? i'm feeling a bit lost on where to even start simplifying it without losing the power of the tool

thanks for reading, and any advice is truly appreciated!

https://preview.redd.it/guev0qcjulrg1.jpg?width=1148&format=pjpg&auto=webp&s=278c034dc37e54a71737c90b0c1c5f145a5c8d7e

https://preview.redd.it/cnls1a1kulrg1.jpg?width=1120&format=pjpg&auto=webp&s=dc8630fe862e68ac1886435a6efb27377d04a45d

You know how Claude hallucinates card names if you ask it about Magic? I fixed that.

Savecraft (https://savecraft.gg) is an open-source MCP server that parses your MTG Arena Player.log locally, syncs your game state, and gives Claude access to 12 expert reference modules built on real data. And by using the reference modules, Claude properly knows how to play Magic -- no invented cards, no wrong rules, no made-up stats.

The screenshots show what it looks like in practice: Claude pulling live draft pick recommendations scored across 8 axes from 17Lands data (millions of games), and a full draft review grading every pick against what the data says was optimal.

What Claude gets access to:

- Your actual Arena data: collection, decks, match history, draft logs, play-by-play replays, rank

- Draft advisor: 8-axis pick evaluation calibrated across 31 color archetypes from 17Lands

- Play advisor: post-game review from per-turn replay data: card timing, mana efficiency, attack analysis

- Match stats: personal win rates by deck, format, and opponent archetype, plus sideboard effectiveness

- Card search: full Scryfall database, so Claude never invents a card

- Rules engine: complete MTG Comprehensive Rules + per-card rulings

- Mana base calculator: Frank Karsten's hypergeometric source math

- Collection diff: wildcard cost to complete any decklist from what you actually own

- Deckbuilding: composition analysis, format legality, curve visualization for limited and constructed

Savecraft also supports Diablo II: Resurrected (.d2s binary parsing), Stardew Valley, Clair Obscur, and RimWorld, with more games coming.

Free, open source (Apache 2.0)! Check it out on GitHub: https://github.com/joshsymonds/savecraft.gg

Happy to go deeper on the architecture or show more examples -- and feedback welcome!

I logged into Claude and my entire chat history and all my projects were missing, and I had a lot of important work in there, so I’m trying to figure out whether this is a temporary bug, some kind of account issue, or something more serious. Has anyone here had this happen before, and if you did, did your chats come back, did support help, and was there anything you did on your side that actually fixed it?

Claude Mythos (codename: Capybara) was exposed after a CMS misconfiguration left nearly 3,000 unpublished assets publicly accessible. Fortune found it. Anthropic confirmed it.

Here's what the leaked docs revealed: Mythos sits in a brand new tier ABOVE Opus they're calling it "Capybara." Not an upgrade. A whole new class of model.

It dramatically outperforms Claude Opus 4.6 in coding, academic reasoning, and cybersecurity benchmarks.

And here's where it gets uncomfortable: Anthropic themselves described it as "currently far ahead of any other AI model in cyber capabilities" capable of exploiting vulnerabilities faster than defenders can respond.

They're so spooked by it that they're not doing a normal launch. Early access is restricted to cyber defense organizations only, so they can harden their systems before this (or something like it) goes wide.

Cybersecurity stocks didn't take it well either CrowdStrike dropped 7%, Palo Alto Networks fell 6%.

Now for the spicy take: is Anthropic being genuinely responsible here, or is "we're scared of our own model" just incredible marketing? Because let's be real "our AI is too dangerous to release" is either the most honest thing a lab has ever said, or the most effective hype machine we've ever seen.

What do you think?