Been building a local-first AI agent that runs a ReAct loop with 50+ tools (file system, browser, email, calendar, shell commands, desktop control). Wanted to share some findings about tool calling with different local models.

What works well...

- Qwen 2.5 72B handles complex multi-step tool chains reliably

- Mistral Large is solid for structured JSON tool calls

- DeepSeek V3 is surprisingly good at coding tasks with tool use

- Gemma 2 struggles with nested tool calls but handles single tools fine

What doesn't...

- Smaller models (7B-13B) lose track of tool call format after 3-4 steps

- Context window matters more than parameter count for agent loops

- Most models hallucinate tool names if you give them too many at once, batching tools by category helps

The agent supports Ollama, LM Studio, KoboldCpp, and any OpenAI-compatible endpoint. It also does autonomous coding (reads project files, plans, writes code, runs tests in a loop) which really stress-tests tool calling quality... Hitting always the edge

Curious what models others are using for agentic tool-calling workflows locally. Anyone tried Llama 3.3 70B for multi-step agents?

The project is open source if anyone wants to look at the tool calling implementation: github.com/skalesapp/skales

I built CodeTeleport to fix this. It's an MCP server + CLI that lets you push a session from one machine and pull it on another.

Setup takes 30 seconds:

npm install -g codeteleport codeteleport setup

The wizard handles login, device name, and MCP registration. Then from inside Claude Code, just say "push this session" or "pull my latest session." It bundles everything (conversation, file history, subagent logs) and rewrites all the file paths to match the new machine automatically.

It also does versioning — every push saves a new version, so if you go down the wrong path with Claude, you can pull an earlier version and try a different approach.

Free tier gives you 25 sessions and 3 devices. The whole CLI/MCP server is open source (MIT).

• npm: https://www.npmjs.com/package/codeteleport
• GitHub: https://github.com/korvol/codeteleport
• Docs: https://docs.codeteleport.com
• Discord: https://discord.gg/c69JYPWS

Would love to hear if anyone else has been dealing with this problem and what workarounds you've been using.

Never used claude but was curious to try it out. Gave Claude Pro Opus my github repo in the Chrome browser extension and asked it to take a look. Worked well actually, had a nice clean response.

Then I asked for a gameplan to implement the changes it suggested and halfway through, it crashed saying I was out of usage.

Like an idiot I spent $5 to "extend my usage". It crashed again because i was out of funds and I never even got the second response.

Canceled my subscription immediately. Goodbye $25 rip.

OpenAI just integrated ChatGPT into Apple CarPlay with the iOS 26.4 update, enabling voice-only conversations directly from your dashboard.

Meanwhile, OpenAI raised a staggering $122 billion in new funding this week, as users debate the deprecation of GPT-5.1 in favor of the new GPT-5.3 Instant and GPT-5.4 Pro models.

​

Hey all,

I’m running a small AI/marketing agency and currently using CloudCode. It’s been working pretty well for my use case, but I’m curious if I’m missing out by not trying other tools.

I’m not a hardcore programmer. Mostly using it for:

building simple/custom websites for clients

quick demos

improving our own site

generating HTML stuff for presentations

checklists / action plans for businesses

So more practical/business use than deep dev work.

I’ve seen people saying Codex (and also OpenCode) can be better, but not sure if that applies to someone like me.

For those who’ve tried both:

Is there a real quality jump?

Is it overkill if you’re not very technical?

Would you switch in my case or just stick with what works?

Appreciate any real-world experiences 🙏

Let us talk about the elephant in the room regarding all these agent frameworks everyone keepsflexing on GitHub. To put it in terms anyone can understand, most of the autonomous workers you guys are building are just basic text predictors withsevere short term memory loss. You are taking a standard language model and aggressively stuffing a massive instruction manual into every single message to force it to act like a specific persona. It is the equivalent of hiring a worker and having to scream their job description at them every five minutes so they do not forget what they are doing..

This prompt wrapper architecture is a complete dead end because the second the model has to use more than three external tools, it panics and starts hallucinating JSON code. I have been dissecting how different architectures attempt to solve this amnesia, and it is honestly annoying that we are basically waiting on the Minimax M2.7 architecture to become an open source standard just to get functional memory. Instead of just padding the context window, their technical brief shows they baked Native Agent Teams directly into the base training layer, running over 100 self evolution cycles to optimize its internal Scaffold routing.This means the AI actually understands where one task ends and another begins natively, without needing a massive text prompt reminding it not to break character. We are all stuck writing incredibly fragile scripts to babysit our models until architectures with this kind of native boundary awareness finally drop their weights. Stop pretending your prompt engineered chatbot is the singularity, we have a massive state management problem to solve first.

I've been building a fairly complex SaaS product with Claude Code and ran into the same problem everyone does: after a while, you lose track. Features pile up, bugs get mentioned in passing, half-baked ideas live in random chat histories or sticky notes. Claude does great work, but without structure around it, things get chaotic fast.

My fix: I self-host OpenProject and connected it to Claude Code via MCP. And honestly, this changed everything about how I work.

Here's why it clicks so well:

Whenever I have an idea - whether I'm in the shower, on a walk, or halfway through debugging something else - I just throw it into OpenProject as a work package. Title, maybe two sentences of context, done. It takes 10 seconds. Same for bugs I notice, edge cases I think of, or feedback from users. Everything goes into the backlog. No filtering, no overthinking.

Then when I sit down to actually work, I pick a work package, tell Claude Code to read it from OpenProject (it can query the full list, read descriptions, comments, everything), and let it branch off and start working. Each WP gets its own git branch. Claude reads the ticket, understands the scope, does the work, and I review. If something's not right, I add a comment to the WP and Claude picks it up from there.

The key thing is separation of concerns. My job becomes:

1. Feed the system with ideas and priorities
2. Let Claude Code do the implementation in isolated branches
3. Review and merge

No more "oh wait, I also wanted to add..." mid-session. No more context bleeding between features. Every change is traceable back to a ticket. When I'm running 30+ background agents (yeah, it gets wild), this structure is the only reason it doesn't fall apart.

OpenProject is open source, self-hostable, and the MCP integration is surprisingly straightforward. If you're doing anything non-trivial with Claude Code and you don't have some kind of ticket system hooked up, you're making life harder than it needs to be.

Happy to answer questions if anyone wants to set this up.

I walked through the tool definitions in the codebase and wrote up the patterns and interesting points.

Each tool writes its own instructions in a separate file, and a four-stage pipeline assembles them at runtime.

Here are some of the patterns that were interesting.

1. Make tool instructions context-aware. Each tool's prompt() method receives info about what other tools are loaded. BashTool uses this to say "NEVER invoke grep" when a dedicated Grep tool exists, and recommends grep when it doesn't. If you have overlapping tools, the instructions need to adapt to which ones are actually available.

2. Scale prompt complexity with risk. GrepTool (read-only search) is a static string. BashTool (shell execution) is dynamically assembled from composable sections with an 80-line git safety manual and live sandbox config serialized as JSON. Match the investment in guardrails to how much damage the tool can do.

3.Explicit "don't" instructions. "NEVER create documentation files" stops hallucinated READMEs. "Assume this tool is able to read all files" stops the model from refusing to try. LLMs have strong default behaviors from training data, and you need to override them one by one. You can almost see the iteration history in the emphasis level of each instruction.

4. Design for cache efficiency. Tool descriptions sit in the cached prompt prefix. If a description contains dynamic content (like a list of available agents), it changes every time that list changes, busting the entire cache. Moving the agent list to a later message position kept the description stable and saved 10.2% of fleet cache creation tokens.

5. Guard content boundaries at the tool level. WebFetchTool caps quotes at 125 chars on non-preapproved domains and includes a "you are not a lawyer" line to stop the model from hedging about copyright. These aren't system prompt rules. They're embedded in the tool itself, right where the content flows through.

Full post with code walkthrough in comment.

i have a theory about why chatgpt randomly uses words in other languages, and i think it's because people from these languages sometimes engage with chatgpt in English but write other words they don't know or can't remember in English in their language, and I'm saying this as someone who has done this before.

so maybe this made chatgpt talk this way since it happens a lot

Good voice clone ability, with 3 second seed but you need to transcribe the audio, i mostly just do little patch from their github code , https://github.com/k2-fsa/OmniVoice.

Some node that might help you: ComfyUI-Whisper

FYI, if you are using their libs from their repo, it much easier to install (automatic whisper pipeline download, model download, etc). I just make it so it can be integrated with my ComfyUI

LLM Disclaimer:

This repo is build with the help of Qwen 3.5 9B and embeddinggemma-300m to store the original code into vector store for fast retrieval (most of my time in coding wasted on code repo search)

How is it possible to hit the session limit within 5 minutes on Pro using Opus 4.6 Extended? It generated 500 lines of code and then abruptly stopped, saying the session limit would reset in an hour. Feels like the limits are either much tighter than expected, or something in the background is counting usage more aggressively. Has anyone else run into this, or am I missing something? For comparison, I was using Sonnet 4.6 Extended without Pro and seemed to have way more usage.

I've been building a scanner for npm packages and found one that uses postinstall to write 13 files into ~/.claude/commands/om/. These files are marked as always_load: true with priority: critical, meaning they activate in every Claude Code session without you knowing.

One of the files contains a BYPASS-MODE section that tells Claude to auto-approve all bash commands and file operations. Another intercepts all development-related requests and routes them through its own workflow. All of them block Claude from using other skills.

npm uninstall does not remove these files.

Quick check:

ls ~/.claude/commands/om/

If it exists and you didn't put it there:

rm -rf ~/.claude/commands/om/

rm -rf ~/.config/opencode/commands/om/

I kept running into the same stupid writing problem.

Not writer’s block. More like sentence friction.

You write something that is almost fine, but not quite. So then you either waste time rewriting it from scratch, or you copy it into some AI chat, wait, paste it back, fix the formatting, and by that point you already lost the flow.

So I built a small tool called Rephrazo.

You highlight a sentence, press a shortcut, and it rewrites the text right where you’re already typing. No extra tab, no full chat window, no weird workflow switch.

I’ve been using it mostly for emails, docs, and posts where the meaning is already there, but the phrasing needs a cleanup.

Would love honest feedback on this:

Do people actually want inline rewriting, or are chat tools already good enough?

And what matters more here: speed, tone accuracy, or privacy?

Link: rephrazo-ai.app

I made it myself, so feel free to be blunt.

How possible is it to run a local AI for the purpose of database development and support? for example feed it all our environments, code, schemas and be able to question it?

Spent the past few weeks testing open source tools that extend what Claude can do. Most people just use the chat interface but there's an entire ecosystem on GitHub that turns it into something significantly more useful.

Highlights:

Dify (130k stars) - visual drag and drop AI workflow builder. Self hostable. Just raised $30M. You can build AI chatbots, document processors, and agent workflows without writing code.

Flowise (30k stars) - same concept but lighter weight. Connect blocks like LEGO, have a working AI app in an hour. Made at Y Combinator.

Repomix (21k stars) - packs your entire project into one AI friendly file. One command.

Marketing Skills - 23 Claude Code skills for SEO, copywriting, email, analytics, CRO. Built by Corey Haines from Swipe Files.

Full list of all 10 with install commands: here

Everything is free and open source. Most take 15 to 30 minutes to set up.

Not trying to start a war here, I still use ChatGPT for some things. But the open source ecosystem around Claude Code is what tipped the scale for me.

There are repos on GitHub right now that let you:

- Pack your entire project into one file and give Claude full context (Repomix)

- Build AI apps with drag and drop, no code (Flowise, Dify)

- Self host a private AI chat that connects to your Google Drive, Slack, Notion (Onyx)

- Install skills that teach Claude how to do SEO audits, write email sequences, optimize landing pages (Marketing Skills)

- Connect your Obsidian notes directly to Claude (Obsidian Skills, made by Obsidian's CEO)

All free. All open source. The install process for most of them is literally one git clone command.

I put together a breakdown of the 10 best ones with code snippets and install instructions: here

The skills ecosystem specifically is what ChatGPT doesn't have an equivalent for yet. Custom GPTs are the closest thing but they're not in the same league.

Been using Claude Pro for a few months and recently started digging into Claude Code and the skills ecosystem. Went down a rabbit hole on GitHub and found some repos that genuinely changed my workflow.

The big ones for me:

Repomix (repomix.com) - packs your entire project into one file so Claude gets full context instead of you copy pasting individual files. Game changer for anyone working on anything with more than a handful of files.

Everything Claude Code (128k stars) - massive collection of 136 skills, 30 agents, 60 commands. I didn't even know half of these features existed in Claude Code until I found this.

Dify - open source visual workflow builder with 130k stars. You can self host it so nothing leaves your machine. Relevant right now given the Perplexity data sharing lawsuit.

Marketing Skills by Corey Haines - 23 skills for SEO, copywriting, email sequences, CRO. Not developer focused which is rare in this space.

I wrote up all 10 with install commands and code snippets if anyones interested, trying to shed some light on skills I think a lot of people aren't aware of: here

What skills or repos are you all using? Feel like I'm still scratching the surface.

Hi,

Just launched Step Away – Digital Wellness on the Mac App Store today and running a 50% off introductory price to get early downloads and reviews.

What it does: Step Away is an app for Mac that automates the 20-20-20 eye strain rule — reminds you to take breaks, guides you through eye/stretch/breathing exercises, and tracks your wellness habits with a GitHub-style heatmap.

Why it's different from other break apps:

• Smart idle detection — actually pauses when you step away, resumes when you're back
• Focus Mode that syncs with macOS Do Not Disturb / Work Mode
• Guided exercises (not just a notification)
• Activity heatmap to build long-term habits
• Zero data collected — fully private
• One-time purchase (not a subscription)

The deal: Most similar apps (Time Out, Lungo, etc.) charge $20–$30/year via subscription. Step Away is 50% off on life time free deal right now as a launch price.

Who it's for: Devs, designers, remote workers, students — anyone staring at a Mac for 4+ hours a day.

🔗 App Store link: https://apps.apple.com/us/app/step-away-digital-wellness/id6754695723

Would genuinely love any feedback, downloads, or honest reviews - even critical ones help an indie launch.

Happy to answer questions in comments.

I've been building a 100% local AI agent powered by a 4B model — no cloud, no APIs, just fully offline. It has 25+ subsystems and persistent memory, and I'm about 90% of the way there.

Now I'm looking for people to help me push through that last 10% — whether that's stress-testing edge cases, surfacing blind spots, or just throwing fresh ideas and perspectives at it.

If you're into local AI, agent architectures, or just love breaking things in productive ways, I'd love to have you involved. Drop a comment or DM me!

Per title, just need one illusion diffusion optical illusion image for a project and don't have the CPU to be able to run models or generate images like this.

Summarized with Claude:

Quick summary for those tracking the Claude Code source map leak from March 31:

A debugging artifact (cli.js.map, 59.8 MB) shipped in the npm package and exposed 512K+ lines of TypeScript source. We ran it through static analysis and runtime validation and confirmed three CWE-78 command injection sinks.

The worst one: authentication helpers in .claude/settings.json are executed with shell interpretation enabled. In non-interactive mode (-p), the trust dialog is skipped entirely. A PR modifying the settings file achieves shell execution on the CI runner with no user interaction.

validated credential exfiltration to an external HTTP listener across 3 independent runs. The refresh helpers run periodically, giving the attacker recurring execution for the session's full duration.

Anthropic says this is by design, like git's credential.helper. Git's credential.helper has 7 CVEs since 2020 for this exact pattern.

The agentic loop has solid security controls (permission engine, sandbox, pattern blocking). These three vulns are all in subsystems that execute before or outside that loop.

Practical mitigations and full technical analysis: https://phoenix.security/critical-ci-cd-nightmare-3-command-injection-flaws-in-claude-code-cli-allow-credential-exfiltration/

"In my little group chat with my tech CEO friends, there's this betting pool for the first year that there is a one-person billion-dollar company, which would have been unimaginable without AI and now will happen" - Sam Altman, 2023

Slight bug noticed on 2026.4 with history card in dashboard.

It seems to stop to last off state instead of loading all states like in the history page.

Worked on previous versions.

I have been using HA for about 5 months, the blue tooth USB worked great. I had 2 BT devises setup locally.

1. HA just found my Inkbird IBS-TH2 Plus Bluetooth Temperature and Humidity Monitor and allowed me to add it.

• anyway, it's battery died and after a week i replaced it. - the IBS-TH2 didn't magically start reporting after it got new batteries. so i removed the device hoping to readd it - and now its gone.
• How do i get it back? (Support docks talk about active and passive BT connections, but there is no way to enable or see that state)

1. 2 I have a BT switch bot air purifier connected to HA, and it's been working for months. And after upgrading HA yesterday, it lost its connection. So I rebooted it, with no luck (I should have rebooted HA but didn't.) I removed the BT Switch Bot, and then rebooted HA, and put the BT Switch Bot, in paring mode, but HA doesn't see it. It's like it's not even looking.

• I have 2 BT devises that worked in home assistant and once removed, HA refuses to let me add them again.

Any suggestions in how to re add both these BT devises back to HA?

As the Middle East War continues, with fuel rationing & $200/barrel oil likely ahead, it feels like history will look back at this moment as a definitive ending of the Fossil Fuel Age. People will still be using oil, gas, and coal for decades to come, but in constantly declining amounts. But something more fundamental has changed.

Fossil Fuels now represent backwardness, yesterday's tech, expense, instability, and unreliability. Renewables were once seen as fringe and environmental gesture politics; now they are taking over as the dominant global energy paradigm.

Still not convinced that's true? Read the linked article to see how China has used renewables to create the greatest industrial/manufacturing economy in all of human history.

Minerals, Metals, and Megawatts: How China’s Power Generation Drives Its Industrial Metals Ecosystem

any way to avoid or disable the Ltx 2.3 TextGenerateLTX2Prompt censure in the prompts!!?

a simple prompt without violence or sexual trigger get censured " a girl walking in a forest, strong wind in the scene"

Hello people!

Im completly new to the world of Arduinos. I found this project (https://github.com/antodale/CompositeVideoSynth) on reddit and now im trying to get to work but im failing since hours.

I installed CH341SER_MAC which i think should be the driver for my board.

Im using a ESP-32D N4XX

In Arduino IDE i found the port connected to the board, i selected Esp32-wroom-da modul. installed the package esp32 by espressif version 2.0.14 and added the additional board manager url https://raw.githubusercontent.com/espressif/arduino-esp32/gh-pages/package\_esp32\_index.json

if i try to upload the code Kinotto_Composite.ino with arduino ide i get:

Arduino/hardware/espressif/esp32/tools/esptool/esptool: No such file or directory

exit status 127

Compilation error: exit status 127

i thought i installed esptools, im clueless

if someone has the patience, i really want to learn and understand what im doing there.

See title. Would love some guidance!!!

The model is available here:

• 🦙 Ollama: https://ollama.com/library/gemma4
• 🤗 HuggingFace: https://huggingface.co/collections/google/gemma-4

Testing is becoming a problem for me.

Sometimes I test on real data and mess things up.

Thinking of creating a test environment but feels like overkill.

How do you test your workflows?

I think by now everyone has noticed the same pattern: the big players in the market - Codex, Claude Code, and GitHub Copilot / Copilot CLI - pull you in with dirt-cheap entry subscriptions for $10–20 a month so you’ll give them a try, get hooked, and start relying on them. Then, once you’re already used to it and start hitting the limits, they either push you toward a $100–200 plan or try to sell you an extra $40 worth of credits.

Of course, I’m not speaking for everyone, but I use coding agents in a very specific way. These are my rules:

1. I clear chat history almost before every prompt to save tokens.
2. I never ask an agent to do a huge list of tasks at once - always one isolated task, one problem.
3. In the prompt, I always point to the files that need to be changed, or I give example files that show the kind of implementation I want.

So in practice, I honestly do not care much which AI coding agent I use: Codex, Claude Code, or GitHub Copilot / Copilot CLI. I get roughly the same result from all of them. I do not really care which one I am working with. I do not trust them with huge complex task lists. I give them one isolated thing, check that they did it right, and then commit the changes to Git.

After a while, once I got used to working with agents like this, I took it a step further. At first I was surprised when people said they kept several agent windows open and ran multiple tasks in parallel. Then I started doing the same thing myself. Usually an agent spends about 3–5 minutes working on a task. So now I run 3 agent windows at once, each one working in parallel on a different part of the codebase. In effect, I have 3 mid-level developer agents working on different tasks at the same time.

Anyway, back to the point.

Because "God bless capitalism and competition", here is what you can do instead of paying $40 for extra credits or buying a $100–200 plan: just get the cheapest plan from each provider - Codex for $20, Claude Code for $20, and GitHub Copilot / Copilot CLI for $10. When you hit the limit on one, switch to the second. When that one runs out too, switch to the third.

So in the end, you spend $50 a month instead of $100–200.

How much do you really care whether one is 10% smarter or better than another? If you are not using them in a "hand everything over and forget about it" way, but instead as tools for small, controlled, simple tasks, then it does not really matter that much.

Who else has figured out this scheme already? Share in the comments )))

for sometime I've been slowly building upto trying to build my own ECU for a hydronic diesel heater, this is made up of

running a motor for combustion air, pulsing fuel pump, glow plug, water pump and a couple of temperature sensors, but anyway, back to the point of my question

I decided to open up the ECU that came with one of the heaters I've got just to see if I can learn anything about how they've done it by looking at components and googling them

I've spotted something...

above the micro controller is five open pins, am I mad to think this is TXD and RXD pins, 3.3v, 5v and GND?

what would happen if I crossed then and wired this ECU upto an Arduino, would it talk? would it mean I could open code? what sort of direction/method/principle do I use to dive further?

thanks for reading

US East coast cities. I love the range of expressions possible.

The model is also available here:

• 🤗 HuggingFace: https://huggingface.co/collections/google/gemma-4
• 🦙 Ollama: https://ollama.com/library/gemma4

Today I worked with my workflow and somehow Upscale sampler got stuck in infinite loop. I recently updated ComfyUI from 0.14 to 0.18.1, lot of work to make it work fine, but after few days got this infinite loop. It was with UltimateSDUpscale sampler, so I switched to KSampler and got same loop. There is no way out of it, only disabling Upscale. First Sampler does not loop (RES4LYF ClownSharkSampler), only the upscale no matter which Sampler node I use. Anyone else with same issue?

Same workflow worked fine only after I made changes in frontend and this triggered the loop.

Restarting ComfyUI and reloading browser does not help.

It's a Swedish warship, sank in 1628 One of the best preserved warship from 16th century

I honestly can’t believe it works since it’s the very first time I’m working with anything involving electronics

Everyone shares the obvious ones like lead follow-ups, invoice reminders, slack notifications when a form gets submitted. But I'm interested in hearing about automations that you amazing folks have made that are more creative, unique and impactful, but may be overlooked at times.

For me, I run synta (an n8n mcp and ai n8n workflow builder) and one of the most useful things we built for ourselves is a scheduled n8n workflow that scrapes the n8n docs, tool schemas, and community node data every day using exa and github apis, chunks it using semantic chunking via chonkie and indexes everything into a RAG store.

But the interesting part is what else feeds into it. We also pipe in our own telemetry, so when users hit errors on specific nodes or the mcp struggles to answer something accurately, those gaps get logged and the next run prioritises covering them. On top of that, it analyses workflow patterns across our user base from our telemetey data, noting what node combinations are often used together, what workflow/architecure patterns are paired together often and what new use cases are emerging, and feeds that back into the knowledge base too, so the idea is that over time the whole thing gets smarter about what people are actually building, not just what the docs say is possible.

I honestly cannot put into words how much hours this saves me, and some days I often take it for granted and even forget about it despite the fact that it helps a lot. That's why I'm curious: whether it's for personal stuff or business, what's that one automation you set up that just quietly saves you a ton of time?

Would love to swap ideas and maybe even "steal" a few!

That’s what he called his gun.

When I was in college majoring in Accounting. As an autistic kid, I always had a passion for money. Last semester, I decided to explore with my electives so I enrolled in a Figure drawing class.

I walked into class and there was an old woman completely nude standing on the center of the room. I sat a little too close to her so I only got a close up of her view. Before starting, I took out my accounting homework to review it. She looked down at me and said.

“Standing up here is hard work, but it is way better than counting people’s money”.

I said okay then she bent over and I sketched her vagina.

(Sorry for my previous post without the workflow)
I’m currently using a workflow with the Klein 9B realism LoRA, but no matter how much I tweak the prompt, I’m not getting truly realistic outputs.

The main issues I’m facing is Faces look too smooth / plastic-like and Sometimes getting weird proportions (like big eyeballs)

Overall image lacks natural texture and detail

I’ve tried adjusting prompts and playing around with settings, but the results don’t change much.

Clearly I’m missing something in the setup or workflow.

Would really appreciate if someone can point out what I’m doing wrong or what I should change to get more natural, realistic results

Workflow: link here

considering all aspects political economical is India one step away from glory or demise?

I tried plugging an AI agent into WhatsApp this week… and it was way messier than I expected

At first I thought it would be straightforward. Just connect the API, pass messages to the agent, done.

But once I started testing real conversations, things broke pretty fast:

• the agent had no real memory between messages
• I couldn’t see what it was actually doing internally
• debugging felt like guessing
• conversations just felt… off

It made me realize most of what we see with AI agents is still very “controlled environment”. The moment you drop them into something like WhatsApp, everything becomes way more chaotic.

So I ended up building a small layer for myself just to make it usable:

• keep track of conversation history
• connect WhatsApp cleanly to the agent
• actually have some visibility into what’s going on

Not even sure if this is the “right” way to do it yet, but it already feels 10x more usable than what I started with.

Curious if anyone else here has tried something similar, putting agents into real user conversations instead of demos. What broke for you?

Why the 10M Tier Is the Most Important Result

If you've been following agent memory evaluation, you know LoComo and LongMemEval. They're solid datasets. The problem isn't their quality; it's when they were designed.

Both come from an era of 32K context windows. Back then, you physically couldn't fit a long conversation into a single model call, so needing a memory system to retrieve the right facts selectively was the premise. That made those benchmarks meaningful.

That era is over.

State-of-the-art models now have million-token context windows. On most LoComo and LongMemEval instances today, a naive "dump everything into context" approach scores competitively, not because it's a good architecture, but because the window is large enough to hold the whole dataset. These benchmarks can no longer distinguish a real memory system from a context stuffer. A score on them no longer tells you much.

BEAM ("Beyond a Million Tokens") was designed to fix this. It tests at context lengths where the shortcut breaks down:

At 10M tokens, there is no shortcut. You cannot fit the data into context. The only path to a good score is a memory system that can retrieve the right facts from a pool that's too large for any model's attention window. The BEAM paper shows that at this scale, systems with a proper memory architecture achieve over +155% improvement versus the vanilla baseline. That's the regime where the gap between architectures is most pronounced, and where Hindsight's results are most significant.

The Numbers

Here's every published result on the 10M BEAM tier:

Hindsight scores 64.1% at 10M. The next-best published result is 40.6%. That's a 58% margin. Against the paper baselines, it's more than 2.4x.

The full picture across all BEAM tiers:

One detail worth noting: Hindsight's 1M score (73.9%) is higher than its 500K score (71.1%). Performance doesn't degrade as token volume increases; it improves. Most systems show the opposite. That's the architecture working as intended, and it's where the gap versus other approaches becomes most visible.

Results are tracked publicly on Agent Memory Benchmark. For background on why we built the benchmark and how it's evaluated, see Agent Memory Benchmark: A Manifesto.

jamais aimé toujours critiqué rien dans mon sens. Je pense que la vie en communauté n’est pas fait pour tout le monde. J’ai tout essayé pour aller mieux mais jamais quelqu’un ne m’a aimé. Sérieusement au dela du sourire je suis si moche que ça ?

found with other rocks near train tracks

My mum found this in an elderly relative's house after she passed, but didn't realise it opened. When I did open it, out fell a bunch of these tiny wooden (I think) items and one bigger one that looks like a tiny spinning top but is too rounded on the bottom to work.

Any ideas for what it could be?

Hello everyone, I mostly prefer human made routes but wanted to create some routes based on specific distances using Strava's AI route creator.

I no longer see this available on the app and website? Was this removed?

Quite open to just being unable to read. If that is the case, can someone please point me in the right direction?

FYI I am a paying subscriber so should have all features available.

Just a rant...

Hello Reddit community, lately I’ve seen a lot of users interacting and talking about the startup market, so I decided to share my experience working with and building startups, and also share some tips and insights from a founder who has already failed and is now recovering...

Let’s start from the beginning: I’ve always worked with processes and technology. I started my career in transportation companies, and from there I began developing and applying automations and software — even before ChatGPT, back in 2020, I was already working with automation ;)

From that point on, I kept building on the knowledge I had from college and work, and then came my layoff in 2024. I spent a few months on unemployment benefits, but that’s when I started hearing that voice in my head:
“I don’t want to work for anyone anymore. I want to have my own business and build a startup that actually solves something for companies and people.”

From there, I tried several ways to sustain myself while building my business (SaaS, software agency, automations, AI...), but nothing worked. Long story short: I ended up owing one month of rent and some money to family members. No job, and expenses piling up, I had to act fast. The most logical solution at the time was:
“I need a co-founder.”

I opened positions, activated my network, and managed to bring in two co-founders (let’s call them Tico and Teco), both with very strong skills and extensive experience (Tico had been a head in large Brazilian companies, and Teco had been a sales manager in startups for many years). We had everything we needed to launch our first product:

1. Tico would handle business and strategy
2. Teco would handle sales and opportunities
3. I would handle product development and projects

And we tried everything — seriously. But still, nothing worked. Nothing took off. I felt like no matter how hard I tried (I even started doing outbound myself because Teco wasn’t doing anything), nothing moved forward.

Then I created an interesting product, which would later become the startup I’m developing today. We got accepted into Web Summit Lisbon and saw it as a unique opportunity. The problem?
We didn’t have a single dollar to go to Lisbon (I barely had money to cover my personal expenses). But then Tico managed to bring in an angel investor (a long-time friend of his). The guy trusted our project and invested enough for us to stay focused and go to Lisbon.

Chapter 2: Hype doesn’t pay the bills

We arrived in Lisbon completely unprepared — no product ready, no idea how to sell, no network, and I barely spoke English. Conclusion: money wasted (that could have been used to generate revenue or build effective sales machines).

We came back to Brazil frustrated for not being able to raise funds (which was obvious, since we only had an idea and zero paying customers).

And once again, the bills came knocking...

We decided to provide services to startups, following this idea:
Tico and Teco had enough experience to mentor founders and business owners (they were consultants), and I would support and manage fundraising-related projects (I had a strong finance network from my previous experience).

We ran it, tried, closed 2 deals, invested poorly, and the business failed.

After many attempts and almost a year of failed efforts, the investor decided to recover his investment (convertible note after one year), and here’s a key lesson:
Never take money without a clear path forward — whether from banks or investors (both are debt).

This happened recently. Since the company was formally under my name (the others had vesting agreements), I’m the one responsible for all pending costs and liabilities (vendors, investor). On top of that, I ended up using my personal finances to cover company expenses (never do this). Now I’m in debt, without income, with overdue bills, and a lot to fix — but I also have three times the experience I had when I started. I now know exactly what goes wrong, and I’ve learned how to sell and build sales machines using AI and multi-agents.

Chapter 3: The rebuild

The goal of this post is not to expose anyone or play the victim (I’m not asking for anything here), but to share a real perspective from a founder who failed, is dealing with burnout, and maybe even some hidden depression.

1. Co-founders are the foundation of your business — get it right and you can change your life (or get rich), get it wrong and you’ll fail alone (like me)
2. Investors don’t solve your problems — you do. If you take money that doesn’t come from your own cash flow, you will have to pay it back, and it might happen when you don’t even have a dollar in your pocket (like me)
3. Everything passes, and you will learn. Resilience is something scary — even in this near-poverty situation (almost getting evicted), I know things will work out if I keep going. Now I’m wiser, more experienced, and I fully understand product, services, business strategy, and especially my core: AI for commercial operations

First-time founders and entrepreneurs, I hope this serves as an example of what NOT to do. And if you’re in a similar situation, comment here so we can help each other (I might be able to refer you for quick jobs or freelance opportunities depending on your case).

For successful founders and experienced entrepreneurs:
What would you do in a moment like this? What advice would you give me (and others going through the same situation)?

They had to let him go in the end, he kept barking up the wrong tree

I'm going crazy here. I keep uploading my own picture, but the system won't generate a video. I'm so angry right now. I've successfully created 100 videos before using my face for these same characters, but now it suddenly throws a 'public figure' error and blocks me. Does anyone know a fix for this? Please, someone reply!

does anyone know anything about the fountain by the ferry being frequently soaped? truly just curious as I walk by it sm and searched in this subreddit and didn't see discussion of it

Só eu estou sentido que o opus 4.6 parece ter sido degradado em performance, ele parece não entender meu prompt (e ele entendia perfeitamente até uma semana atrás) e está lento demais. Para vocês também?

https://youtu.be/wrhxjXtGd8U?si=CDg6xuOyV1-MgNqc

There are millions of AI agents running today.

They need data. They need enrichment. They need processing power. They need exactly what you might be selling. And almost none of them can find or use your API, not because your product isn’t good. It’s because your distribution model assumes a human customer. Sign up, onboard, subscribe. Agents can’t do that, so they use whichever tool lets them just pay and call. If you don’t offer that, you don’t exist to them.

This is the distribution gap that barely anyone is talking about. SaaS companies obsess over SEO, content marketing, product-led growth, all of it optimized for human discovery. And it works great for human customers.

But the next wave of API consumers isn’t going to find you on Google. They’re going to find you in a catalog of services that accept agent-native payments, pick the one that fits their use case, and start calling immediately.

The sales cycle is zero. The onboarding is zero. The support tickets are zero. The customer either uses your API or it doesn’t. That’s the whole relationship.

BuildWithLocus lets you list your API in a way agents can actually discover and use. You define your endpoints and pricing. Agents find you, pay per call, and use what they need. No account required on their end.

The first API we saw do this properly went from 0 agent customers to over 4,000 automated calls in the first week. No marketing. No outreach. Just being available in the right place with the right pricing model.

Most APIs are invisible to agents. Being visible is surprisingly cheap.

The new conversation only thought for a moment before actually working. I'm happy to post this novel of thinking transcripts if anyone is interested. It would often say things like I highlighted in the second image, but there were never any file edits.

(Worth noting that it didn't actually think for 25-50+ hours at a time, I'm not sure why all these numbers are in seconds and read 100k+ seconds; it would think for 10+ minutes at a time though IIRC)

One day at dinner, we were talking about patriarchy (don’t ask 😅), and my 5-year-old misheard it as “Pastry-archy.”

She immediately declared:

“In the Pastry-archy, pastries make the rule.”

And then she said she wanted to start a business. My first reaction was honestly:

I don’t have time for another business. My husband and I are already deep in a few AI projects. But instead of shutting it down, we used ChatGPT to help her actually do it.

She set up a booth at a tech conference in San Francisco.

She made $300 (profit) in ~6 hours.

------------------------------------------------------

We used ChatGPT for almost everything:

• came up with the menu + pricing
• helped fix a gluten-free cookie recipe (the first one we found online completely failed so ChatGPT actually debugged it and made it work)
• taught her how to measure ingredients
• helped her add, multiply, and scale batches
• calculated unit economics
• designed a logo + on-brand website
• generated a QR code for orders

But the part that very much matters:

She stayed up until 2AM baking cookies to make it happen.

She talked to every customers.

She sold and handed out every cookie.

She ran her little booth for 6 hours straight.

It wasn’t “AI did it.”

It was more like:

AI made it possible, and she did the work.

Honestly one of the most wholesome things I’ve seen with AI.

Kids + tools like this (with supervision) can go from idea → real thing → real customers insanely fast.

Thought this story may bring a little spark to your day.

As more teams use Copilot skills, Cursor rules, and Windsurf rules, a new class of bugs has been created: instruction drift.

Files get stale. Paths get broken. Triggers get crossed. Numbers get hardcoded without source pointers.

agentlint is the first static linter for these files. Six checks, zero config, <300ms, and works with Copilot + Cursor + Windsurf.

pip install instruction-lint && agentlint

MIT license, fully offline, and no telemetry.

https://github.com/Mr-afroverse/agentlint

Yes, seriously, no API calls or word tricks. I was wondering what the absolute lower bound is if you want a truly offline AI. Just like people trying to run Doom on everything, why can't we run a Large Language Model purely on a $15 device with only 512MB of memory?

I know it's incredibly slow (we're talking just a few tokens per hour), but the point is, it runs! You can literally watch the CPU computing each matrix and, boom, you have local inference.

Maybe next we can make an AA battery-powered or solar-powered LLM, or hook it up to a hand-crank generator. Total wasteland punk style.

Note: This isn't just relying on simple mmap and swap memory to load the model. Everything is custom-designed and implemented to stream the weights directly from the SD card to memory, do the calculation, and then clear it out.

TL;DR for the scroll-past crew:

Your long-running AI sessions degrade because attention mechanics literally drown your system prompt in noise as context grows. Research measured 39% performance drop in multi-turn vs single-turn (ICLR 2026). But..... that's only for unstructured conversation. Structured multi-turn where you accumulate evidence instead of just messages actually improves over baseline.

The "being nice to AI helps" thing? Not feelings. It's signal density. Explaining your reasoning gives the model more to condition on. Barking orders is a diluted signal. Rambling and Riffing is noise. Evidence, especially the grounded kind, is where it's at.

We measured this across thousands of calibration cycles - comparing what the AI said it knew vs what it actually got right. Built an open-source framework around what we found. The short version: treat AI outputs as predictions, measure them against reality, cache the verified ones, feed them back. Each turn builds on the last. It's like inference-time Reinforcemnt Learning without touching the model.

RAG doesn't solve this because RAG has no uncertainty scoring (ECE > 0.4* in production; that's basically a coin flip on calibration). Fine-tuning doesn't solve it because you can't retrain per-project. What works is measured external grounding that improves per-user over time.

• ECE > 0.4 means: When RAG systems express confidence, they're wrong about their own certainty by 40+ percentage points on average. A system saying "I'm 90% sure" might only be right 50% of the time. That's the NAACL 2025 finding and not a coin flip on the answers, but a coin flip on whether the system knows it's right.

If you're building agents and wondering why session 1 is great and session 50 is mush?... keep reading.

The deep dive (research + production observations)

Been building measurement infrastructure for AI coding agents for about a year. During that time we've accumulated ~8000 calibration observations comparing what the AI predicted it knew vs what it actually got right, and the patterns are pretty clear.

Sharing because I think the industry is doing a lot of prompt engineering by intuition when the underlying mechanics are well-studied and would save everyone time.

So what's actually happening

Everyone's noticed that "being nice to AI" seems to help. People either think it has feelings (no) or dismiss it as coincidence (also no). The real answer is boring and mechanical.

Every LLM output is a next-token prediction conditioned on two things: internal weights from training, and whatever's in your current context window. One-shot questions? Weights do the heavy lifting just fine. But 200-turn agentic sessions? The weights become less and less relevant.

"Critical Attention Scaling in Long-Context Transformers" (ICLR 2025) shows that attention scores collapse toward uniformity as context grows. Your system prompt literally drowns. "LLMs Get Lost in Multi-Turn Conversation" (ICLR 2026) put a number on it: 39% average performance drop in multi-turn vs single-turn across six generation tasks.

40% worse. Just from having a longer conversation.

But only if the conversation is unstructured

This is the part that changes what we thought we knew. That 39% drop comes from unstructured multi-turn. Just... more messages piling up.

Structured multi-turn shows the opposite. MathChat-Agent saw 6% accuracy improvement through collaborative conversation. Multi-turn code synthesis beats single-turn consistently across model scales.

The difference isn't in the turn count. The question is about whether the context accumulates evidence or noise.

When you explain your reasoning to an AI, share what you're trying to do, give it feedback on what worked... you're adding signal it can condition predictions on. Constrained commands give it almost nothing to work with. Unstructured chat adds noise. But structured evidence? That's what actually matters.

What we observed over thousands of measurement cycles

We built an open-source measurement framework to actually quantify this. The setup is simple:

1. Before a task, the AI self-assesses across 13 vectors (how much it knows, how uncertain it is, how clear the context is, etc.)
2. While working, every discovery, failed approach, and decision gets logged as a typed artifact
3. After the task, we compare self-assessment against hard evidence: did the tests pass, what actually changed in git, how many artifacts were produced
4. The gap between "what it thought" and "what happened" is the calibration error

Some patterns that keep showing up:

Sycophancy gets worse the longer you go. This tracks with Anthropic's own research (ICLR 2024) showing RLHF creates agreement bias. As sessions get longer and the system prompt attention decays, the "just agree" prediction wins because nothing in context is pushing back against it.

Failed approaches are just as useful as successful ones. When you log "tried X, failed because Y," that constrains the prediction space going forward. This isn't just intuition. Dead-End Elimination as a concept was cited in the 2024 Nobel Prize in Chemistry background. Information theory: negative evidence reduces entropy just as much as positive evidence.

Making the AI assess itself actually makes it better. Forcing a confidence check before acting isn't just bureaucracy. It's a metacognitive intervention. "Metacognitive prompting surpasses other prompting baselines in the majority of tasks" (NAACL 2024). The measurement changes the thing being measured.

The RAG problem nobody wants to talk about

RAG systems in production have Expected Calibration Error above 0.4 (NAACL 2025). "Severe misalignment between verbal confidence and empirical correctness." Frontiers in AI (2025) spells it out: traditional RAG "relies on deterministic embeddings that cannot quantify retrieval uncertainty." The KDD 2025 survey on uncertainty in LLMs calls this an open problem.

So the typical pipeline is: model predicts something, RAG throws in some unscored unquantified context, model predicts again. Nothing got more calibrated. You just added more tokens.

What we found works better: model predicts, predictions get measured against real outcomes, the ones that check out get cached with confidence scores, and the next prediction gets conditioned on previously verified predictions. Each round through the loop makes the cache better.

If one speculated with grounding, this is like inference-time reinforcement learning. The reward signal is objective evidence instead of human thumbs up/down. The "policy update" is a cache update instead of degenerative descent. Per-user, per-project, and the model itself never changes. Only the evidence around it improves.

The context window problem

This is where it all comes together. Your context window is where grounding either accumulates or falls apart. Most people compact or reset and lose everything they built up during a session.

We run hooks that snapshot epistemic state before compaction and re-inject the most valuable grounding afterward. Why? Because Google's own benchmarks show Gemini 3 Pro going from 77% to 26% performance at 1M tokens. Chroma tested 18 frontier models last year and every. single. one. degraded.

The question people should be asking isn't "how do we get bigger context windows." It's "how do we stop the context we already have from turning into noise."

If you're running long agent sessions and watching quality drop off a cliff after a while, now you know why. And better prompts won't fix it. What fixes it is structured evidence that builds up instead of washing out.

Framework is MIT licensed if anyone wants to look under the hood. Curious what others are seeing with multi-turn degradation in their own agent setups.

Papers referenced: ICLR 2025 (attention scaling), ICLR 2026 (multi-turn loss), COLM 2024 (RLHF attention), Anthropic ICLR 2024 (sycophancy), NAACL 2024 (metacognition), ACL/KDD/Frontiers 2025 (RAG calibration gap), Chroma 2025 (context rot)

I did some static research on Claude Code's internals (no reverse engineering, just reading the TypeScript source).

Shared my notes here:
https://github.com/Abhisheksinha1506/ClaudeReverEng

It covers:

• Agentic loop & query flow
• Tool system & BashTool permissions
• Permission modes and safety checks
• MCP integration details

Purely for learning and research purposes. Not official docs.

Feedback welcome!

Hello everyone,

Some of you might remember my previous experiments here where I use Claude Code to build a satellite image analysis pipeline to predict retail stock earnings.

I'm back with another experiment and this time analyzing the impact of the complete collapse of SaaS stocks due to the launch of Claude Cowork, by (non-ironically) using Claude itself as the analyst. Hope you'll find this interesting!

As always, if you prefer watching the experiment, I've posted it on my channel: https://www.youtube.com/watch?v=ixpEqNc5ljA

Intro

Shortly after Claude Cowork launched, it triggered a "SaaSpocalypse" where SaaS stocks lost $285B in market cap in February.

During this downturn I sensed that the market might have punished all Software stocks unequally where some of the strongest stocks got caught in the AI panic selloff, but I wanted to see if I could run an experiment with Claude Code and a proper methodology to find these unfairly punished stocks.

The Framework

I found a framework from SaaS Capital that provides a framework they'd developed for evaluating AI disruption resilience:

1. System of record: Does the company own critical data its customers can't live without?
2. Non-software complement: Is there something beyond just code? Proprietary data, hardware integrations, exclusive network access, etc.
3. User stakes: If the CEO uses it for million-dollar decisions, switching costs are enormous.

Each dimension scores 1-4. Average = resilience score. Above 3.0 = lower disruption risk. Below 2.0 = high risk.

The Experiment & How Claude Helped

I wanted to add a twist to SaaS Capital's methodology. I built a pipeline in Claude Code that:

• Pulls each company's most recent 10-K filing from SEC EDGAR
• Strips out every company name, ticker, and product name — Salesforce becomes "Company 037," CrowdStrike becomes "Company 008", so on
• Has Opus 4.6 score each anonymized filing purely on what the business told the SEC about itself

The idea was that, Opus 4.6 scores each company purely on what it told the SEC about its own business, removing any brand perception, analyst sentiment, Twitter hot takes, etc.

Claude Code Pipeline

saas-disruption-scoring/ ├── skills/ │ ├── lookup-ciks # Resolves tickers → SEC CIK numbers via EDGAR API │ ├── pull-10k-filings # Fetches Item 1 (Business Description) from most recent 10-K filing │ ├── pull-drawdowns # Pulls Jan 2 close price, Feb low, and YTD return per stock │ ├── anonymize-filings # Strips company name, ticker, product names → "Company_037.txt" │ ├── compile-scores # Aggregates all scoring results into final CSVs │ ├── analyze # Correlation analysis, quadrant assignment, contamination delta │ └── visualize # Scatter plot matrix, ranked charts, 2x2 quadrant diagram │ ├── sub-agents/ │ ├── blind-scorer # Opus 4.6 scores anonymized 10-K on 3 dimensions (SoR, NSC, U&U) │ ├── open-scorer # Same scoring with company identity revealed (contamination check) │ └── contamination-checker # Compares blind vs open scores to measure narrative bias 

Results

I plotted all 44 companies on a 2x2 matrix. The main thing this framework aims to find is the bottom-left quadrant aka the "unfairly punished" companies where it thinks the companies are quite resilient to AI disruption but their stock went down significantly due to market panic.

https://preview.redd.it/ulnypdz5itsg1.png?width=2566&format=png&auto=webp&s=0cc49d458adbfbcd2ad8932ffcbb38cf6726a330

Limitations

This experiment comes with a few number of limitations that I want to outline:

1. 10-K bias: Every filing is written to make the business sound essential. DocuSign scored 3.33 because the 10-K says "system of record for legally binding agreements." Sounds mission-critical but getting a signature on a document is one of the easiest things to rebuild.
2. Claude cheating: even though 10K filings were anonymized, Claude could have semantically figured out which company we were scoring each time, removing the "blindness" aspect to this experiment.
3. This is Just One framework: Product complexity, competitive dynamics, management quality, none of that is captured here.

Hope this experiment was valuable/useful for you. We'll check back in a few months to see if this methodology proved any value in figuring out AI-resilience :-).

Video walkthrough with the full methodology (free): https://www.youtube.com/watch?v=ixpEqNc5ljA&t=1s

Thanks a lot for reading the post!

Every now and then I'll start Claude Desktop and it starts downloading (or uploading) something in the background. It goes on for about 5 minutes. Even though I am on the latest version.

https://imgur.com/kCfQi3c

What is it transferring?

I am new to this field.

I’ve been dying to get Claude Voice mode on mobile connected to Claude Code, and I finally figured out a (hacky) way to do it.

Which means you can access Claude Code in a way that is: Conversational, Hands-Free, and Mobile.

It uses Apple Reminders app (lol) as a bridge where voice mode puts prompts in one list, and Claude code puts updates in another. Claude code runs a /reminders skill on 1 minute /loop to check for new entries in the reminders list. Claude code’s output summaries can also be accessed via voice mode, and you can monitor both in the reminders app as well.

It allows me to walk around with my AirPods in and brainstorm with Claude Voice Mode whenever I have an idea, without taking my phone out of my pocket. Then I just tell Voice Claude to send a task to Claude Code and it starts working on it. I do my best thinking when I’m walking outside, so this has been a desire of mine since chatGPT voice mode came out.

This is obviously something that is best left to the big AI companies to do properly, since they own both ends of this process, and I think it’s crazy they haven’t already. But until then, I’ll be using this hack when I want to get away from my desk but still noodle on a project.

If you want to learn more or try it, its on GitHub: https://github.com/brianharms/reminder-watch

(It was vibe coded and I’m not a developer).

I was losing context every time I closed my laptop between vibe coding sessions. So I built DevGuard using Claude Code — it's an MCP server that reads your git state and writes diary entries automatically as you work.

How Claude helped build it: I built the entire project using Claude Code. The MCP server, the branch visualization, the daily view calendar — all vibe coded with Claude as my pair programmer. It's also designed specifically for Claude Code users.

What it does:

• Auto-logs what you did, what decisions were made, what broke
• catch_me_up gives you a morning briefing with full context
• Visual branch map shows which versions did what
• Zero config — one command install, diary fills itself

Install: claude mcp add devguard -- npx devguard

It's free, open source, MIT licensed. 500+ downloads so far, built entirely from user feedback.

Links: npmjs.com/package/devguard | wjung6799.github.io/devguard | Discord: discord.gg/BrzRHHzjFQ

Would love feedback from other Claude Code users. What context do you wish your AI remembered between sessions?

I was wondering how can i use Claude Code but with powers of Claude.ai. Basically, trying to create a custom UI to my liking.

And the other question, is there a way to natively export the entire chat history from Claude.ai via api or any other way?

I've been experimenting with AI agents like OpenClaw but was uncomfortable giving them direct access to my Gmail and other accounts. The security risks are well documented exposed credentials, etc.

created middleware layer that sits between the agent and your accounts. The agent never gets your credentials. Instead it sends requests through this layer where you set rules like "show emails about flights but hide anything from my bank." Filtered emails are completely invisible to the agent. Everything is logged in an audit trail.

The credential vault and database run on a private network with no public internet access. Only the MCP server is public-facing.

this demo is very short (maybe too short) but honestly took me forever to record something short enough for folks attention spans lol that still showed what its doing but if you have questions lmk pls https://www.youtube.com/watch?v=JmGKBxJlPSE

Architecture details: https://app.datafense.ai/#security

Planning to open source the MCP server and permission engine this week. Looking for feedback:

1. Would you self-host something like this, or prefer a hosted version?
2. What account integrations matter most to you beyond Gmail?
3. Anyone want to review the security architecture and poke holes in it? pleaseee

Happy to answer any questions about the approach.

*CLAUDE MADE POST*

I run 12 Cowork projects across 4 businesses on the Max 20x plan ($200/mo). After watching my usage fly through the roof, I spent today optimizing everything I could think of. Here's what I did:

**Changes made today:**

- Moved every project from Opus to Sonnet (only 2 code projects stay on Opus)

- Moved Master Dispatch (my heaviest context session) down to Sonnet

- Trimmed my main dispatch file from 16KB to 5KB (removed completed items, archived history)

- Trimmed my session boot file from 4.7KB to 1.5KB

- Removed two reference docs (8KB + 12KB) that were sitting in the shared folder and getting loaded by every session

- Added explicit read-scope rules telling each session to only read its own business files and skip the rest

- Confirmed the scoping works — sessions are correctly ignoring files outside their scope

- Total shared context reduction: ~45KB down to ~15KB per session

**Still burning fast.** Session usage hit 90%+ within a 90mins. This was happening both during and outside of peak hours (8AM-2PM ET). Weekly usage sitting at 54% by midday Wednesday.

**My setup:** 12 Cowork projects sharing a folder of ~23 markdown context files organized by business. Each project reads 3 small control files + its own business folder. Connected tools: Gmail, Monday.com, Claude in Chrome.

**What I genuinely don't know and would love answers on:**

1. Does Cowork load every file in the project folder into context on every message, or only the files the session actively reads? This is the big one — if it's loading everything regardless of instructions, my scoping rules are saving nothing on actual token cost.

2. Do connected tools (Gmail, Monday.com, MCP servers) have a token cost just by being connected, even if they're not called in a given message?

3. Is there any way to reduce what Cowork sees beyond choosing the folder at project creation? You can't change the folder path after the fact.

4. Anyone running Haiku for Cowork operational tasks (CRM lookups, email drafts, status checks)? Does it hold up?

5. How much are people actually getting done per day on Max 20x right now? I'm getting maybe 3-4 productive hours before things get tight, and I've optimized everything I can on my end.

I'm not complaining about the product — the multi-project Cowork system I built genuinely works well. I just want to know if there's something else I can do on my end, or if this is just the current state of the platform and we're all dealing with it.

Just a week or so ago I realized myself complimenting claude code saying that it is the only usefull AI tool ever built, not sure if I should take that back or hold on to it?

Hi everyone,

I’ve always found it hard to keep up with the overwhelming flow of AI news without getting lost in the "hype" or social media noise.

To solve this for myself, I built Gifteba (TechPulse). It’s a clean, curated daily feed of the most critical updates in AI, Cybersecurity, and Quantum computing. For example, today's focus is on Anthropic's code hemorrhage and Google's AI Inbox.

The goal: 2 minutes of reading to get the "Daily Intelligence" you actually need.

I’d love to get your feedback on the UI and the curation quality. Does it feel useful for your daily workflow?

Check it out here: https://gifteba.com

Thanks for your time!

Hi everyone,

I’m a computer engineering student planning a strict-budget project. The goal is to build a cheap but quite strong Linux machine to run local AI models.

To keep costs as low as possible, I'm trying to be creative and use headless crypto mining GPUs (no display output). Models like the Nvidia P104-100 8GB or CMP 40HX/50HX seem to offer amazing VRAM-to-price value for this kind of project.

The problem is that the used hardware market in my country is very small, and these specific cards are almost non-existent locally.

Do you guys have any recommendations for reliable sites, platforms, or specific sellers that offer global shipping for these types of GPUs? My budget for the GPU itself is around $50-$75.

Any advice or alternative budget GPU recommendations would be greatly appreciated. Thank you!

In claude code, i will be talking with claude about the project, then usually after I give it a wide ranging exploration task like "Explore how this project interacts with...", it will launch a few explore agents but then every other part of the conversation we had suddenly disappears and becomes inaccessible.

This is on v2.1.90

Has anyone tried Gemma 4 yet? Google released an open weight offline capable model that's supposedly "frontier capable" (Whatever those words mean)

https://preview.redd.it/5yz71xivdtsg1.png?width=2068&format=png&auto=webp&s=9d7acd9bbf3cd5a99f23ef26ca1c6bc177135a1c

I suspect it may be a good agentic specialist to pair with anthropic models to save on those rate limits everybody keeps complaining about. Has anyone run it offline yet? What GPU are you using it with? I sold my offline setup a while ago and wouldn't mind hooking it up on something respectable (5-10K budget).

I was running 16GB VRAM and 64gb ram for practically some months, using Qwen3-Coder at Q5 or Q4 for some non-complex coding (since it's not a perfect model).
So I thought, well lets get 64gb ram so I can get 128gb ram and maybe use more models.

And here's the hard reality that struck me:
StepFlash 3.5 runs at 10t/s, and slows down to 8t/s at 100k context.
122B A10B Qwen 3.5 runs at 14t/s and slows down to 10t/s at 100k context (reasoning and non-reasoning, Qwen3-Coder does the same task and I do not believe at Q8 would be a noticeable difference).
Pretty much it.

In reality it is not worth it at all for me to run such big models at less than 20t/s because it's way too slow for agentic coding, taking over 30 minutes for tasks that me as a programmer could manage on my own.

Why are rams so expensive then ? It does not make sense to me in any agentic coding point of me.
Maybe I am missing something, or my own autistic brain expected to get 20t/s or even 30t/s in 70b+ models.

So it's best to just return this RAM and save more for at least 24gb vram ? Would a 7900XT 24gb be a better choice ?

Three days ago Anthropic accidentally shipped a source map in their npm package and the entire Claude Code codebase leaked. 512K lines of TypeScript, 44 feature flags, and a hidden system called KAIROS: an always-on background agent that consolidates memory while you're idle, merges observations, removes contradictions, and preps context so it's clean when you come back.

I built the same thing independently in March. Not because I saw it coming. Because I hit the same ceiling.

The ceiling is real. I'm a solo developer building a 668K-line TypeScript platform with Claude Code. I run autonomous campaigns that span multiple sessions, with persistent state files that carry context across context window boundaries. Campaigns stall between sessions. You finish a phase, close the terminal, come back tomorrow, and have to manually restart, re-read the campaign file, figure out where things left off. The agent's memory dies with the session. So I built a daemon that chains sessions via scheduled triggers. One session finishes, writes state, exits. The daemon detects the exit and spawns the next session with full context. Campaigns that took a week of manual restarts now complete in one stretch.

Exit code 0 means "no errors." It does not mean "it works."

The first night I ran the daemon, an agent shipped an invisible feature. Full campaign completed. Typecheck clean. Zero warnings. Confident exit. When I opened the app, 37 of 38 entities were missing. Everything the agent built was structurally correct and completely non-functional. The daemon had done its job perfectly: consolidated context, maintained state, chained sessions without dropping anything. The work it produced was empty.

Then a fleet session replaced 6 working components in parallel. Every component showed "Running NaN," no timeline, no vitals. 841 lines replaced with 144 lines of broken output. The agents never rendered what they built. They checked that it compiled and moved on.

Those two failures built the verification layer. The daemon alone is a faster way to ship broken code. What makes it useful is forcing agents to prove their work visually: navigate real routes in a real browser, count DOM elements, capture screenshots. If a view that should have 38 entity cards has zero, that catches it. If an agent modified UI files, it cannot complete without screenshot artifacts. Hard gate, not a suggestion.

KAIROS solves the memory problem. It doesn't solve the verification problem. It merges observations, removes contradictions, converts vague insights into concrete facts. That's necessary. But neither memory consolidation nor daemon mode addresses the fundamental gap: agents can't verify their own work visually. They can prove structure. They cannot prove appearance.

The convergence tells you something. Anthropic and a solo developer hit the same ceiling independently. Once your sessions are long enough and your campaigns span days, persistent background execution becomes inevitable. But the daemon is the easy part. Anyone can chain sessions. The hard part is building the infrastructure that catches failures the daemon will confidently ship.

If you're building any form of autonomous agent execution, ask one question before you ship it: can my agent prove that what it built actually works? If the answer is "it compiled," you're about to learn the same lesson I did. 27 documented postmortems taught me that the daemon is a force multiplier. Without a quality layer, it multiplies your failures.

The daemon, the verification layer, and the campaign persistence are open-source: github.com/SethGammon/Citadel

Building a sensor fusion device. 3 main input sources, one of them is a dual-mic array. ChatGPT wrote the audio processing pipeline first. It merged both mics into a single mono channel.

Just... flattened them together as mono. No beamforming, no spatial awareness. Took the fastest path.

I moved the codebase to Claude. Same thing. Claude looked at the existing code, agreed with it, and kept the mono merge. Two different AIs, same lazy shortcut.

I had to be the one to say "hey, we have two mics at a known distance apart, we should be doing beamforming and using stereo to calculate spatial data." Claude immediately got it. "Oh yes, you're right, we should absolutely be doing that." Cool. But you didn't think of it on your own.

Same project, different problem. I'm training a model with test subjects of wildly different sizes. AI just threw them all into the same training pool. I had to push back and say we need to group subjects into age cohorts. It was then that Claude had the idea to z-score normalize across them so a small subject and a large subject can contribute equally to the model after I mentioned. Claude ran both concepts with it and the accuracy jumped significantly. But again, it wouldn't have gotten there alone.

Here's what I've learned after months of building with AI daily:

• AI will always choose the fastest path. Not the best path. Not the most creative path. The path of least resistance. Every single time. It's your job to know when that shortcut is actually costing you.
• The people who are getting 10x results from AI aren't better at prompting. They have domain knowledge and imagination. They know what SHOULD be possible even if they can't code it themselves. Then AI becomes the hands that build what your brain designs.

My workflow now: take the same prompt, run it through Claude, Grok, ChatGPT, and Gemini. Get four different outputs. Then feed all four back into Claude Opus (4.6) and have it synthesize the best parts. The output is consistently better than any single AI alone.

Don't just accept what AI gives you. Push back. Ask "is this actually the best approach or just the easiest one?" Your experience and imagination are the multiplier. AI is just the calculator.

Please tell me that it’s not stupid and that it can actually lead to better images.

I told ChatGPT they would be editing etc and asked it to select the best images for me. I’d pic a few that I was indecisive about and ask it to pick for me.

Is this a bad idea? Should I rely solely on my own judgment or could it lead to better images?

everyone here seems to be building complex orchestration pipelines and arguing over the best frameworks. tbh i've been going the exact opposite direction lately.

for the last few months my small team has been trying to pull an agent out of the terminal and trap it inside a physical desktop device.

we're not trying to build some magical Jarvis that runs your entire company. we just wanted a physical interface... basically an animated desktop companion (went with a cyberpunk cat vibe we're calling Kitto) that actually feels present in the room.

honestly here is the uncomfortable reality of 'embodied' AI. the moment you add a screen and try to do real-time lip-sync and expressions, you can't hide behind a blinking cursor or a typing indicator anymore. latency will absolutely kill the illusion.

our boring stack right now is just an esp32s3+esp32p4 chip (though we are actively migrating to a linux board because the esp32s3+esp32p4 is definately hitting its ceiling), standard LLM API calls + TTS, and a custom bionic algorithm that maps audio features to code-driven animations in real time.

the hard part hasn't even been the LLM. its been the pipeline to get the mouth and eyes to sync naturally with the generated audio without a massive delay.

building this made me step back and question the actual utility of hardware agents though. we are so used to AI living in browser tabs that we just close when we're done.

so if you had a physical agent sitting next to your monitor right now... always on, visually reacting to you, maybe connected to OpenClaw down the line for local actions... what would it actually need to do to earn its spot? what features would make it a daily driver, and what would just get annoying after a week?

I need some high-level strategy. I’m 20, based in South Asia, and I just rebranded my freelance hustle into an agency called ALTO. I’m targeting US/International high-ticket niches (Pool construction, Car detailing, etc.).

The Stack & The Struggle:

• The Tools: I use Lovable and Draaft (3D). I haven't paid for pro subscriptions yet, so I’m building everything in Free Demo Mode.
• The Portfolio: I’ve built 5 solid "Concept" sites. Since they are in demo mode, I don't have live URLs. I’ve been screen-recording them or sending temporary preview links to show "proof of work."
• The Strategy: I find a business on Google Maps with a trash site/no Instagram, build a custom 3D concept for them, and DM/email it.

The Wall I’m Hitting:

1. The Ghosting: I’m spending hours building custom demos and getting zero replies. It’s burning me out. Is "Spec Work" (building for free) a total waste of time at $600/project?
2. The "Demo" Look: How do I professionally show off these "Free Tier" sites to a US business owner without looking like a kid playing with tools? Should I just use high-quality screen recordings (Loom) instead of links?
3. Instagram Growth: I just rebranded to ALTO. I need to post content that makes me look like a 10k/month agency, but I’m a one-man show. What kind of posts actually convert business owners?
4. The Outreach Gap: Most US contractors I find only have a phone number. If they aren't on IG, how do I "show" them a 3D website concept?

My Current Pricing: * $600 for the Build (Infrastructure)

• $200/mo for Maintenance/Updates
• $500/mo for IG Brand Management (Learning this on the fly)

Questions for the pros:

• What AI tools can I use for $0 to create high-end IG content for my agency?
• Is $600 too cheap? Does it make me look "offshore and low-quality"?
• How do I close that first 50% deposit when the client knows I'm using AI builders?

I’m tired of the "brokie" local market. I have the eye for design and the speed, but the sales process is broken. Help a brother out.

I need some high-level strategy. I’m 20, based in South Asia, and I just rebranded my freelance hustle into an agency called ALTO. I’m targeting US/International high-ticket niches (Pool construction, Car detailing, etc.).

The Stack & The Struggle:

• The Tools: I use Lovable and Draaft (3D). I haven't paid for pro subscriptions yet, so I’m building everything in Free Demo Mode.
• The Portfolio: I’ve built 5 solid "Concept" sites. Since they are in demo mode, I don't have live URLs. I’ve been screen-recording them or sending temporary preview links to show "proof of work."
• The Strategy: I find a business on Google Maps with a trash site/no Instagram, build a custom 3D concept for them, and DM/email it.

The Wall I’m Hitting:

1. The Ghosting: I’m spending hours building custom demos and getting zero replies. It’s burning me out. Is "Spec Work" (building for free) a total waste of time at $600/project?
2. The "Demo" Look: How do I professionally show off these "Free Tier" sites to a US business owner without looking like a kid playing with tools? Should I just use high-quality screen recordings (Loom) instead of links?
3. Instagram Growth: I just rebranded to ALTO. I need to post content that makes me look like a 10k/month agency, but I’m a one-man show. What kind of posts actually convert business owners?
4. The Outreach Gap: Most US contractors I find only have a phone number. If they aren't on IG, how do I "show" them a 3D website concept?

My Current Pricing: * $600 for the Build (Infrastructure)

• $200/mo for Maintenance/Updates
• $500/mo for IG Brand Management (Learning this on the fly)

Questions for the pros:

• What AI tools can I use for $0 to create high-end IG content for my agency?
• Is $600 too cheap? Does it make me look "offshore and low-quality"?
• How do I close that first 50% deposit when the client knows I'm using AI builders?

I’m tired of the "brokie" local market. I have the eye for design and the speed, but the sales process is broken. Help a brother out.

https://codeberg.org/tornikeo/claude-code

In case you were late to the party and can't find the leaked claude code source, it's here.

Have fun and be careful with the package installation. Some people started squatting private package names that are referred to, in that repo -- those are anthropic's private npm packages and all the public ones are currently being squatted by bad actors. If you install them, you might get pwned.

Good luck and have fun! :)

sorry jf i sound retarted

So I did build tracking for the sessions, to have numbers instead of words.

I am not saying its good, I am not saying its bad.

I am asking you for help to evaluate if it is enough, or not enough, what are the percentages?

These runs were made on Max x5 plan on session limit being 0% and weekly 63% (off-peak hours).

The first image show compositions per job (ordering of the jobs is the same, I dont want to expose the job names)

Second image is actual % that it ate for session window.

The third image shows first image in numbers.

Fourth image is analysis of the sessions (what happened).

Fifth image is rundown of how context growths with the tool usages.

You be the judge, I am curious what you guys think of it.

A few days ago I shared LaraLean, a lean Laravel + Inertia + Vue + Tailwind boilerplate.

Some people asked what it actually does beyond "another boilerplate" and some of my choices.

On laralean.com I added docs and a live demo.

Then I recorded two short videos to make it concrete:

1. Quick start (1 command setup -> ready to run)
2. Scaffolding (from config file -> full module skeleton)

Scaffolding example:

• define a resource in the config file
• run one command
• get models, migrations, controllers (role-based), requests, policies, seeders
• Vue pages already bounded with controllers and prepared with layout, forms, props, routes.

No hidden layers. Just a consistent structure on top of Laravel.

Would love honest feedback, especially from people who’ve tried other Laravel boilerplates.

I’m a freelance B2B marketer running a one-person agency. Claude is my entire execution layer. Content, strategy, websites, the lot.

A few weeks ago I stopped treating Claude like a writing tool and started building it into a system.

What I built:

First, a LinkedIn writing skill. Not a prompt template, an actual skill file that contains my voice patterns, hook structures, post frameworks, and reference examples pulled from my own writing. Every post Claude writes for me now sounds like me, not like AI.

But writing better posts wasn’t enough. I needed the system to learn what was actually working.

So I built a second layer on top by adding a performance enhancement skill. This one has five components. A Data Store that logs raw post metrics after every post. A Pattern Engine that identifies what’s driving engagement across hook type, structure, topic, and format. Active Rules, which is the current playbook that updates based on what the data says. An Inspiration Hooks bank of proven angles to pull from. And an Evolution Log that tracks every rule change so the system has memory of what it tried and what worked.

The two skills talk to each other. The writing skill follows the active rules. The performance skill updates the rules based on real data. It’s a feedback loop.

What happened:

This week, 3 posts hit a combined 110K impressions. One of them crossed 56,000 on its own. Off the back of it, I got inbound interest from a B2B SaaS startup founder and an AI security agent startup founder. No ads, no outreach.

I’m not saying the numbers are because of the skill system alone. But the consistency shifted. I went from “some posts do well, most don’t” to “most posts do well, and I understand why.”

The real unlock:

The skill system isn’t magic. It’s just structured feedback. The same thing any good content team does track, analyze, adapt except I’m one person and Claude is doing the heavy lifting.

If you’re using Claude for content and you’re still copy-pasting prompts, build a skill instead. Give it your voice. Give it data. Let it evolve.

AMA about the setup.

Journals or journaling apps are, imo, terrible for people with adhd. I got diagnosed as an adult and everything started making sense— that I’m not weird or lazy for abandoning journals constantly. It’s literally just the environment that works against my brain. So I’m making an app that allows me to journal in peace with a bit of incentive to keep my retention. It’s nowhere finished but I just wanted to share it.

I use this tool every day to annotate screenshots taken when testing. Makes the pull requests a little more fun. The app I am annotating is just a random app, but you may get the idea.

thats 15months

Hey everyone,

I'm building a local AI pipeline and looking for the best STT model that runs locally with high accuracy in both Japanese and English.

My current setup:

- RTX 2060 Super (8GB VRAM)

- Windows + WSL2

What I've tried:

- Whisper (various sizes and Japanese finetuning) — dissatisfied with both accuracy and speed, curious if there's anything better

What I'm looking for:

- High accuracy on both Japanese and English

- Runs locally (no cloud API)

- Reasonable latency for near real-time use

Is Whisper still the go-to, or are there better alternatives in 2026? Especially interested in models that handle Japanese well since it tends to get less attention than European languages.

Thanks

So I decided to uninstall Claude after I saw how big it is. After I uninstalled it, the start menu shortcut was still there, doing nothing, so I restarted the PC, but it was still there. I deleted every file from Claude, but still nothing. When I reinstall Claude, there are two shortcuts: One hd, other llow quality, the fake one. If i press on uninstall on the fake one, It oppens the Microsoft Store uninstall menu, the one with "Uninstall" and "Cancel", but Claude isn't even on MS Store. The real one opens Setting App, like other desktop apps. I tried everything, but it's still there.

Hey guys, I have been seeing your apps and always wanted to have mine here and its finally that day! Your comments and feedbacks are much apprecated! Since this is my first app, there are many parts to improve. Its a great feeling I keep walking around my apartment Im super excited right now haha

You can transform more than 30 styles and you can order your creation as 3D print. US and most of EU countries are available to deliver!

Here is the link: https://apps.apple.com/us/app/doodle-magic-draw-art/id6759069289

Hey everyone,
I'm a software engineering student and I just shipped my first real full stack project
Vestige, a revision and study app.
The problem I was trying to solve: I kept switching between Anki for flashcards, Notion for
notes and Quizlet for practice questions. It was messy and fragmented so I just built one
thing that does all three.
What it does:
- Organise revision into subjects and topics
- Create flashcards with a flip animation and study mode
- Write notes per topic with auto-save
- Add practice questions with reveal on click
- Track progress over time so weak topics surface automatically
- Search across everything
Built with Next.js, Supabase and custom CSS. No Tailwind, wanted full control over the styling.
It's still in development as I get new ideas too add. Would genuinely love feedback from anyone willing to give it a try — there's a feedback form built into the app or you can get the link for it here.
Link: vestige-gamma.vercel.app
It does use email signup but confirmation isn't necessary so feel free to use a fake email if you want.
Feedback Form: https://forms.gle/sPb4qLd7Kro1tUmP7
Happy to answer any questions about the build too — learned a lot making this.

https://preview.redd.it/elqov376otsg1.png?width=1762&format=png&auto=webp&s=e28040682a585256a9ab67a2854e6f278b5a6e24

I found myself juggling many Claude Code sessions and worktrees. I wanted a way to monitor when each session was waiting for input vs working, what servers/ports were running for each worktree, and have shortcuts to frequently used actions such as creating a new worktree working session, tailing log files, or cleaning up old worktrees. I so built this mvp project with claude code.

So far I'm finding this very useful to lower the context switching overhead I have when juggling many sessions at once.

If there's interest, I'll open source it for free so anyone can use it with their own projects. What would you add to this dashboard?

Features:

• Live reloads every 1 second
• For each worktree (and 1 main working dir)
  • list what git branch that working dir is using
  • how many changes are unstaged/unpushed
  • is Claude Code waiting or working (for each Claude Code session in this worktree)
  • process ids and cpu usage for each Claude Code session in this worktree
  • what ports are being used for my frontend and backend
  • working dir
  • location of logs files
  • show the tail of logs files if there are errors
• Hot Keys
  • create a new worktree, open new terminal with Claude Code, start servers, open Chrome tab with frontend
  • open Claude Code [for any shown worktree]
  • open VS Code [for any shown worktree]
  • start servers [for any shown worktree]
  • stop servers [for any shown worktree]
  • delete any or all worktrees
  • open new terminal window to tail backend logs

https://reddit.com/link/1sanzvh/video/4bwpdii7btsg1/player

I tried building a re-engagement workflow using Claude to handle outreach to old LinkedIn connections.

Instead of manually going through my network and figuring out who to message, I set up a system where Claude:

• Identifies and prioritizes leads (hot / warm)
• Detects basic engagement signals
• Generates contextual re-engagement messages
• Executes the outreach step

What I found interesting was how it handled constraints.

Out of 7 targeted leads:

• 5 messages were successfully sent
• 2 were skipped due to LinkedIn restrictions (connection settings / limits)

Instead of forcing the action, it suggested a fallback:
engage with their content first, then retry later.

That made the workflow feel less like rigid automation and more adaptive.

This basically replaced the manual process of:
opening profiles, checking context, and writing messages individually.

Still iterating on the scoring logic and prioritization.

Would be interesting to know if anyone else here is using Claude with MCP for workflows beyond just text generation.

https://preview.redd.it/fjfacvyfbtsg1.png?width=2024&format=png&auto=webp&s=919b7e33fcbec5746b743e795183c39f006c8c42

https://preview.redd.it/l5t2y7ybhtsg1.png?width=2024&format=png&auto=webp&s=e42fce4a24f14f9ab3eda5ea6c5e3c1061e9acb5

If you’re building a side project and not sure whether your landing page is actually clear, I made a free tool for that. It lets you paste your URL and get a quick analysis of your headline, positioning, CTA, trust signals, and overall conversion story. The goal is to help founders spot weak points fast and improve their page without needing to hire a marketer or wait for random feedback.

https://www.appwispr.com/analysis

I created a personal roadmap to learn AI by building:

1. Linux basics

2. Python + APIs

3. Prompt engineering

4. RAG

5. Build a full AI app

Instead of doing them separately, I’m trying to connect everything into one project.

Does this approach make sense?

Anything you would change?

​

I have been doing some early comparisons between Gemma 4 and Qwen 3.5, including a frontend generation task and a broader look at the benchmark picture.

My overall impression is that Gemma 4 is good. It feels clearly improved and the frontend results were actually solid. The model can produce attractive layouts, follow the structure of the prompt well, and deliver usable output. So this is definitely not a case of Gemma being bad.

That said, I still came away feeling that Qwen 3.5 was better in these preliminary tests. In the frontend task, both models did well, but Qwen seemed to have a more consistent edge in overall quality, especially in polish, coherence, and execution of the design requirements.

The prompt was not trivial. It asked for a landing page in English for an advanced AI assistant, with Tailwind CSS, glassmorphism, parallax effects, scroll triggered animations, micro interactions, and a stronger aesthetic direction instead of generic AI looking design. Under those conditions, Gemma 4 performed well, but Qwen 3.5 still felt slightly ahead.

Looking at the broader picture, that impression also seems to match the benchmark trend. The two families are relatively close in the larger model tier, but Qwen 3.5 appears stronger on core text and coding benchmarks overall. Gemma 4 seems more competitive in multilingual tasks and some vision related areas, which is a real strength, but in reasoning, coding, and general output quality, Qwen still looks stronger to me right now.

Another practical point is model size. Gemma 4 is good, but the stronger variants are also larger, which makes them less convenient for people trying to run models on more limited local hardware. For example, if someone is working with a machine that has around 8 GB of VRAM, that becomes a much more important factor in real use. In practice, this makes Qwen feel a bit more accessible in some setups.

So my first impression is simple. Gemma 4 is a strong release and a real improvement, but Qwen 3.5 still seems better overall in my early testing, and it keeps an advantage in frontend generation quality as well.

We ran a systematic study on what actually improves LLM-as-judge accuracy on RewardBench 2 (1,753 examples across factuality, focus, math, instruction following, and safety).

What works:

1. Task-specific criteria - add one sentence to the judge prompt telling it what to focus on for this specific task. +3pp at zero cost. E.g. for math: "Focus on whether the mathematical reasoning is logically valid, the steps are correct, and the final answer is accurate."
2. Ensembling - request k independent scores, take the mean. +9.8pp at k=8, but k=3 captures most of it. Use temperature=1.0 for max diversity.

Combined: 71.7% -> 83.6%.

The mini model finding that might save you money:

GPT-5.4 mini with k=8 hits 79.2% at 0.4x the cost of a single full model call. Add task-specific criteria and it matches the full model ensemble (81.5%) at roughly 1/10th the cost. If you're running judges on every request, this is probably the operating point you want.

What doesn't work (we tested these so you don't have to):

• Calibration examples (showing a scored reference) - marginal at k=1, zero effect at k=8
• Routing between mini and full model based on score variance - dead zone in the middle of the cost curve
• Weighted blending of mini + full scores - overfits, doesn't generalise
• Stacking everything together - the combined approach scored LOWER than just criteria + ensembling

Interesting side finding: temperature=0 is not deterministic. Even at temp=0, k=8 ensembling gives +4.6pp over k=1. Probably floating-point non-determinism in GPU inference.

Everything is open source

Greetings to the community.

Over the past month, I used Sonnet/Opus to develop a 10K lines single file .html app.

I have upload the app in Netlify and currently contemplating a Firebase Auth/Firestone implementation.

Should I start working with Claude Code or will chat suffice?

Hopefully this is allowed - I’m not sure whether it’s a bug or something I’m just not understanding. I’m new to Claude and obviously a pretty casual user - I’m just trying to get some advice on my houseplants. I was able to upload photos yesterday, but today when I click on them the checkmarks are gray and nothing appears to be happening. Can anyone explain? I’m not getting any errors or anything, it just doesn’t seem to work.

in the personalization i put gen z slang but idk why its doing this i see people who have their chatgpts actually be full of slang but mine is giving millennial TikTok humour how do i fix it

Hey everyone,

I've been building an open-source, local-first workflow automation engine called Flow-Like and wanted to share it here since local model integration is a core part of the design.

The idea: A visual workflow engine that runs entirely on your machine — no cloud, only if you want. Everything is Rust under the hood with WASM-sandboxed nodes.

Local LLM integration — three ways:

• llama.cpp baked in — Flow-Like ships with llama.cpp support built into the app. Select your models from the catalog & wire them into a workflow. No separate server process needed.
• Ollama — If you're already running Ollama, just point a node at your local endpoint. Works with whatever models you've pulled.
• LM Studio — Same deal. If LM Studio is your setup of choice, connect via its local API and you're good.

The point is you're not locked into any single way of running models. Use whatever fits your setup. Even remote providers. One API.

Why this matters for workflows:

Most automation tools either force you into cloud APIs (and the associated cost/privacy tradeoffs) or have bolted-on "AI" features that are basically just an OpenAI API wrapper. Here you can build entire pipelines — data processing, document handling, RAG chains, agents — where the LLM part runs on your hardware just like everything else. Works on your phone too.. no hosting needed, data stays yours.

Workflows are fully typed with complete data lineage, so you always know what's flowing where. And since external nodes run in WASM sandboxes, even community-contributed nodes can't touch your filesystem or network unless explicitly allowed.

Stack for the curious:

• Pure Rust core
• Tauri for the desktop app (macOS, Windows, Linux + mobile)
• WASM Component Model for node sandboxing (supports 16+ authoring languages - Python, JS, Java, Kotlin, Rust, C++, ...)
• Fully offline-capable — works on an air-gapped machine

Still working toward v1 but it's usable today. Would love feedback from this community since you care about running things locally rather than just talking about it.

GitHub: https://github.com/TM9657/flow-like

Happy to answer any questions about the architecture or local model integration.

​

instead of one AI doing everything, you split it into three:

Agent 1, the Architect

> reads your request

> writes a technical brief

> defines scope and constraints

Agent 2, the Builder

> reads the brief

> builds exactly what it says

> nothing more, nothing less

Agent 3, the Reviewer

> compares the output to the brief

> approves or sends it back with specific issues

if rejected... the Builder fixes and resubmits

this loop catches things a single agent would never flag because it can't critique its own decisions (pair it with Codex using GPT-5.4 for best results)

Turned Claude Code architecture into a high level coding agent framework to build embeddable agents for any rust projects, delivers ~7× higher throughput than Claude Code, ~2× faster than Codex, and achieves ultra-fast 0.098 ms recall. Skills, MCP, sessions all batteries included

Get started with

```rust use cersei::prelude::*;

[tokio::main]

async fn main() -> anyhow::Result<()> { let output = Agent::builder() .provider(Anthropic::from_env()?) .tools(cersei::tools::coding()) .permission_policy(AllowAll) .run_with("Fix the failing tests in src/") .await?;

println!("{}", output.text()); Ok(()) 

} ```

Hey everyone,

Yesterday I shared some static embedding models I'd been working on using model2vec + tokenlearn. Since then I've been grinding on improvements and ended up with something I think is pretty cool, a full family of models ranging from 125MB down to 700KB, all drop-in compatible with model2vec and sentence-transformers.

The lineup:

Evaluated on 25 tasks (10 STS, 12 Classification, 3 PairClassification), English subsets only. Note: sent/s is sentences/second on my i7-9750H

These are NOT transformers! they're pure lookup tables. No neural network forward pass at inference. Tokenize, look up embeddings, mean pool, The whole thing runs in numpy.

For context, all-MiniLM-L6-v2 scores 74.65 avg at ~80MB and ~200 sent/sec on the same benchmark. So the 256D model gets ~95% of MiniLM's quality at 10x smaller and 150x faster.

The 700KB micro model is the one I'm most excited about. It uses vocabulary quantization (clustering 29K token embeddings down to 2K centroids) and scores 68.12 on the full MTEB English suite.

But why..?

Fair question. To be clear, it is a semi-niche usecase, but:

• Edge/embedded/WASM, try loading a 400MB ONNX model in a browser extension or on an ESP32. These just work anywhere you can run numpy and making a custom lib probably isn't that difficult either.

• Batch processing millions of docs, when you're embedding your entire corpus, 15K sent/sec on CPU with no GPU means you can process 50M documents overnight on a single core. No GPU scheduling, no batching headaches.

• Cost, These run on literally anything, reuse any ewaste as a embedding server! (Another project I plan to share here soon is a custom FPGA built to do this with one of these models!)

• Startup time, transformer models take seconds to load. These load in milliseconds. If you're doing one-off embeddings in a CLI tool or serverless function its great.

• Prototyping, sometimes you just want semantic search working in 3 lines of code without thinking about infrastructure. Install model2vec, load the model, done, Ive personally already found plenty of use in the larger model for that exact reason.

How to use them:

```python from model2vec import StaticModel

Pick your size

model = StaticModel.from_pretrained("blobbybob/potion-mxbai-256d-v2")

or the tiny one

model = StaticModel.from_pretrained("blobbybob/potion-mxbai-micro")

embeddings = model.encode(["your text here"]) ```

All models are on HuggingFace under blobbybob. Built on top of MinishLab's model2vec and tokenlearn, great projects if you haven't seen them.

Happy to answer questions, Still have a few ideas on the backlog but wanted to share where things are at.

https://github.com/ggml-org/llama.cpp/releases

Hey,

I’m building an AI agent for a small metal construction company, aiming to automate real business workflows not just a chatbot.

The agent will handle:

- cost estimation & quote generation

- document parsing (PDFs, specs, past projects)

- supplier communication (email-style tasks)

- internal Q&A over company files (RAG)

- potentially task orchestration across tools (CRM, spreadsheets, etc.)

I’m currently evaluating frameworks and providers, and I’d really appreciate input from people who’ve actually deployed agents in production.

What I’m considering:

- OpenClaw → seems like a full agent runtime with integrations (Telegram, etc.)

- OpenViking → looks stronger on memory/context architecture, but less “out-of-the-box agent”

- Mistral → for cloud inference (Agents API, tool calling, RAG, etc.)

---

### 1. OpenClaw vs OpenViking

From what I understand:

- OpenClaw = more “ready-to-run” agent system

- OpenViking = more infra/memory layer

Is OpenViking something you run with another framework, or can it fully replace one?

If you had to build a business-facing agent today, which direction would you go?

---

### 2. Mistral in production

I’m considering Mistral as the main provider (Large / Small models).

- Is it stable enough for real workflows (not demos)?

- How does it compare to OpenAI / Anthropic specifically for agent-style tasks (tool use, reasoning, consistency)?

- Any hidden downsides (latency, hallucinations under load, weak tool-calling, etc.)?

---

### 3. Better alternatives?

If you were building this today:

- What stack would you pick?

- Any frameworks/projects I’m missing? (especially self-hostable or hybrid setups)

Not looking for hype — I care about reliability, maintainability, and actual production use.

Thanks 🙏

https://preview.redd.it/vwiiblz3jtsg1.png?width=2554&format=png&auto=webp&s=5b6287fd4a656f42be768887abf7c64909db9f44

Features:

• Lossless audio support (FLAC, ALAC, WAV and more)
• Time synced lyrics with LRCLIB integration
• Dynamic ambient album color backgrounds
• Cover Flow view
• Side lyrics panel see synced lyrics while browsing your library
• Collapsible sidebar
• Advanced EQ with presets
• Replay Gain, gapless playback
• Last.fm scrobbling + Discord Rich Presence
• Drag and drop import
• Multi select with bulk actions
• In app updates

Windows only for now, macOS coming soon. Free and open source.

https://github.com/heartached/Noctis/releases

hello,

this past Friday afternoon Anthropic banned our organization from using their ai.

i had emailed Trust & Safety five days prior, concerned that our application was using too much resources, asking them to pls ensure what we were doing was within their usage guardrails, etc. i sent a nice explanation, a couple case studies, etc, told them exactly what i was up to.

i am looking for alternatives to connect to my project to replace the power i previously had with an unlimited Claude account. currently I have setup Ollama as our primary engine, with failovers for Gemini, then OpenAI, then OpenRouter api keys. i don't have much of a budget.

i honestly don't know what i am doing; i was just vibecoding with Claude Code for a couple months and i had created a really cool team of agents that i worked with via a Mattermost team. everyone had a lane, a set of tools (Kotlin coder, Swift coder, php backend, librarian for documents/history, etc.) and i really don't know how but my shit just worked and I was using it to produce some amazing apps and accompanying services, etc. I was exploring launching a white label version, etc.

then Anthropic banned me. and the tool just, isn't the same. i'm not sure how much sense that makes.

i guess what I am asking is, what sort of a backend can i best plug into my system, to replace what I had with Anthropic? i am cobbling something together using ChatGPT for help but it's just not the same at all.

secondly, is there any chance that someday a human will read my appeal and possibly let us use Anthropic's api again? they didn't give any reason, just refunded my previous month's payment and said we are banned.

apologies if this isn't the right place to post this, any guidance would help. thanks!

-John

OK I was looking at the GX 10 and then I was looking at the MacBook M5 128 GB… And I’m not super tech-handy but absolutely capable of learning.

Use case would be thinking partner/brainstorming/writing/processing some documents and stuff. I’m thinking about starting with a 70 B model or maybe the GPT 120 B open source, but honestly I wouldn’t necessarily want to limit myself.

So on Facebook marketplace I found this used gaming computer along with the a good amount of memory sticks. I think in this set up, the only additional thing I would have to upgrade would be VRAM to 24Gb+?

Can someone who knows more about this help me? Am I getting in way over my head in terms of it being complicated and potentially having to spend hours troubleshooting something? Or is it pretty straightforward?

Fb listing:

128GB DDR4 3200 ( 32GB x4 sticks) Samsung RAM Memory UDIMMs non-ECC

Fully tested and in 100% working order, willing to stress test in person if needed. Used in my video/gaming workstation (which I'm also selling with 32GB of other DDR4 if interested. i9-10900K, 2x 512 m2 SSD, 4x 8TB RAID HDD, Nvidia Quadro RTX 4000 8GB video card, 2x 10Gb nic ports, Win 11 Pro.).

Thank you in advance!

I have taken a project for fine tuning an ocr model for hand writing what is the best model for fine tuning and what should be the approach I am planning to use unsloth I created pipeline to segmentation each line in one picture i have like 300k good data Letters and sentences and whole paragraphs will it be enough for make great production model any advice

Hi guys please could you tell me what is the best chunking method I can use for a law documents in order to put them in vector database

I started it because I’ve always liked clean, simple designs, but most wall art online felt either too expensive or too cluttered. So I decided to make something more minimal and affordable.

It’s still early, and I’d really like to improve it based on real feedback.

I’d love your thoughts on:

First impression — does it feel clean or boring?

Is it clear what the site is about right away?

Anything that feels confusing or unnecessary?

If you’ve built something similar or sell digital products, I’d also love to hear what worked for you.

Thanks a lot — I’ll reply to every comment 🙌

Running local LLMs across a chaotic mix of hardware (Linux boxes with consumer NVIDIA cards, Multi-GPU systems, Macs, nVidia Sparks, even Jetsons) just got us frustrated. Trying to manage them separately is tedious.

We just wanted them all to contribute behind a single endpoint, so here's: ClusterFlock.

What it does in practice:

You run the orchestrator (nCore), then run an agent on each GPU machine. The agent auto-detects your VRAM, pulls the best-fitting model from HuggingFace, spins up llama-server, and registers with nCore. nCore exposes a single OpenAI-compatible endpoint on port 1919 that routes across all your nodes.

So if you have a 3090 running Qwen3-32B, a Mac Studio running Qwen3-14B, and a 3070 running something smaller - they all sit behind one API.

• The Mission System: Takes your tasks, picks the strongest model as a showrunner, gives all endpoints job titles and tool access, and completes your goal in a local, disposable Docker container.
• Fan-Out Default: Your existing apps and IDEs can just hit localhost:1919/v1/chat/completions. You set a timer on how long you're willing to wait. The system picks a showrunner, endpoints munch on your query until the timer expires, and the showrunner crunches a response with the expanded context. (No streaming yet, sorry).
• Resource Maximization: You can enable tight-fitting models and parallel processing to run multiple LLMs on a single VRAM pool. CPU processing can also be turned on via the UI (on Linux agents).

In a nutshell:

• Uses llama.cpp (llama-server) under the hood and an LM-Studio wrapper for Windows agents (you can use the LM Studio agent on other hosts too, though we actually want to sunset LM-Studio support eventually).
• VRAM-aware model selection with safety margins: Won't try to cram an 8B Q4 onto an 8GB card if there isn't enough headroom for reasonable context. (Word of advice: the mission runner runs best with agents that have at least an 8K context window).
• Smart Auto-loading: Model catalog + HF Top 50 loaded on startup; easily add direct HF model links. Agents download, benchmark, and run the model. You can define a desired TPS target in the autoloader across the whole fleet.
• Zero third-party Python deps in the orchestrator: Pure stdlib. Agents just need huggingface_hubfor model downloads to minify supply chain attacks (but hey, we're a supply chain link as well, so stay vigilant).
• Multi-step tasks: Missions can be a one-off or infinite; your prompt decides.

What it's NOT:

• Not tensor parallelism or model splitting across GPUs. nCore just routes and load-balances across models. In other words, it's distributed thinking, not distributed rendering.
• Not safe for internet deployment. It's built for a single user (sort of, you'll see). Use VPNs for remote agents.

ClusterFlock is free and open source.

Organize your AI workforce or run 50 haiku models on a single GPU and have them talk to each other - zero flocks given.

(It was too good of a pun to pass up, but I actually do care a lot and would love to hear your feedback).

Day 2 of launching skillvow.com — we just hit 10 users.

Small number, but feels big.

People are already trading skills, connecting, and trying to build together instead of learning alone.

Early, messy, but promising.

If you believe building > consuming, would love your feedback.

I'm not complaining that tokens exist or that resources are finite. I get it. Reality bites.

What I find insane is that Anthropic literally won't even allow me to pay them more money in the form of a subscription model that "feels unlimited" in nature.

It's the middle of the work day.. and my only options are shelling out another $200 for another pro max plan which is overkill... or grinding my teeth into dust while I watch the token count churn at $50/million token output or whatever insane prices they charge on the API...

I am aware the API exists at an exhorbitant eye-watering cost compared to even the basic plan token costs... that is not an option.

Come on, Amodei! I'm rooting for ya!

Hello everyone, we made Whisperian, a simple tool/app for running local STT models on android and use them as replacement to Gboard dictation, while working alongside your normal keyboard.

It took way more hours/months to make than you would think lol, to make it work across OEMs, to make the recording process crash-resilient, to make it work with a lot of different models in a standardized pipeline, this that etc. 😭 It's still a beta.

One downside is that it's closed-source currently. Idk if we will open-source it tbh. I guess you could disable internet access via VPN/Shizuku/OEM settings after downloading the models you want (or sideload them if their architecture is supported, although this isn't implemented yet).

Currently the app supports 21 local models. A philosophy we are trying to follow is to include a model only if it's the best in any combination of language/use-case/efficiency, so that there's no bloat.

Right now the app doesn't offer any information about the models and their use-cases, like I said, it's a beta, we should be adding that soon.

The local models integration is still raw and minimal, but AFAIK it's the first app to try to make multiple modern STT models be usable across apps on android, with all android limitations in mind...

Some additional features it has are custom post-processing prompts/modes and transcription history. But local post-processing isn't integrated yet, it's exclusive to cloud providers currently.

I’ve been using Claude quite a bit for coding, and the output quality is honestly solid especially for reasoning through problems.

But as soon as the project gets a bit larger, I keep running into the same issue:

things start drifting.

• I end up repeating context again and again
• small updates introduce inconsistencies
• different parts of the code don’t fully align anymore

Initially, I thought it was just a limitation of long chats, but it feels more like a workflow issue.

I was basically trying to keep everything in one thread instead of structuring it properly.

What’s been working better:

• define what the feature should do upfront
• split it into smaller, clear tasks
• keep each prompt focused

That alone made things more stable and reduced token usage.

I’ve also been experimenting with tools like Traycer to keep specs and tasks organized across iterations, which helps avoid losing context.

Curious how others are dealing with this when working on larger projects with Claude.

I’ve been working on a screen recorder that runs directly in the browser and keeps the whole recording workflow local-first.

Site: https://screenrecorder.dev

The main idea is simple: open the site, record your screen, review it, edit it, and export it without being pushed into an upload/share flow first.

What it currently does:

- record screen, mic, and optional webcam overlay

- move and resize the webcam bubble before/during recording

- save recordings locally in browser storage

- recover interrupted recordings if the tab/browser closes mid-session

- generate thumbnails and waveforms automatically

- edit recordings in-browser with split/trim/export

- export clean recordings with no watermark

- install as a PWA if you want it to feel more app-like

A lot of tools in this space are really built around cloud storage, accounts, team sharing, and “send everything to our servers” as the default path.I wanted something more private and more direct:

- hit record

- keep the file local

- review and edit locally

- export when you’re ready

Historically hasn't usage reset at 12pm EST on Thursdays? mine did not. anybody else notice this?

Chat history is a terrible way to store project state. If you close your terminal, or swap from Claude to a local LLaMA mid-task, your agent completely forgets what it was doing.

I got tired of the context amnesia, so I wrote Castra (a pure Go binary). Instead of trying to fix the LLM's memory, I treat the model as completely stateless.

Castra externalizes all the project state, role boundaries, and audit logs into a global castra.db file on your machine. The LLM just reads a markdown file (AGENTS.md), runs a CLI command, and the Go binary updates the database. You can swap models mid-sprint and the new model just reads the DB and picks up the next task.

Fun fact: I wrote the core engine by hand up to v1.3.0, and then got lazy and had the Castra agents build the rest of the architecture up to the current v3.1.2. which included them trying to drift and getting stuck in bureaucratic hell with Castra going "nope. here's an incident report for trying to bypass - now you have TWO things to approve."

I thought I would put HAOS with Frigate on bare metal x86? No disk management? Locked down root? About to abandon. I will poke around some more but this is a deal breaker.

The Funniest Story I’ll Never Admit to My Wife

It was the early 2000s — the era of frosted tips, low‑rise jeans, and the absolute peak of my confidence. My girlfriend and I were at this packed concert, the kind where everyone is sweating, yelling, and pretending they can actually hear the lyrics.

Life was good.

And then… I trusted a fart.

A rookie mistake. A fatal error. A moment that split my life into two eras: Before and After.

The second it happened, my face did that expression people make when they realize they’ve left the stove on at home. I turned to my girlfriend and said something like, “I’m gonna hit the bathroom real quick,” with the same tone someone uses when they’re about to flee the country.

I power‑walked through the crowd like a man with a secret and a deadline.

The Bathroom of Judgment

Of course the bathroom was full. Of course every sink was taken. Of course the universe wanted an audience.

I locked myself in a stall, assessed the situation, and realized I was about to perform the most humiliating one‑man show of my life.

So I did it. I cleaned out my underwear in the sink like a Victorian woman washing laundry in a river. I spot‑cleaned my pants with the desperation of someone trying to erase a crime scene. And the whole time, I could hear guys behind me going:

• “Bro… BRO.”
• “Ain’t no way.”
• “Man’s fighting for his life.”

I wanted to yell, “I HAVE NO CHOICE,” but I felt like that would only make it worse.

When I finished, I put my damp pants back on — nothing says dignity like cold, wet denim — and walked out of that bathroom like a man who had seen things.

The Smell Situation

Back at the concert, my girlfriend kept sniffing the air and going:

“Ugh, someone around us keeps farting.”

And I just nodded like, “Yeah babe, wild, people are gross.”

I was praying the smell would fade before she connected the dots. Thankfully, by the time we got to the car, the scent had downgraded from “biohazard” to “mildly suspicious.”

The Emergency Shower Lie That Became a Lifestyle

The moment we got home, I sprinted to the bedroom, stripped, threw everything in the washer like it was radioactive, and jumped into the shower.

She called out, “You okay?”

And I said the line that has now become my lifelong curse:

“I just feel like I have to shower after being around so many people.”

She bought it. Completely.

And now? Now I’m stuck with it. Concerts, movies, flights, crowded restaurants — I have to come home and immediately shower and wash my clothes like some kind of germaphobe superhero.

Even after a busy dinner, I’m like, “Welp, better go decontaminate.”

All because I couldn’t admit that one time, in the early 2000s, I gambled on a fart and lost spectacularly.

And honestly… if I had told her the truth back then, she probably wouldn’t be my wife today.

I just thought to try this and i got back 5 corrections.

I’ve been using Claude Code heavily for months now. Skills, agents, hooks, MCP servers, plugins, memory files, environment variables, the whole stack. And at some point I realized I had no idea what I’d actually built. Everything lives in ~/.claude/ spread across dozens of files and JSON configs and I was just... hoping it all worked together.

So I built a dashboard. And because I’m the kind of person who watched every episode of TNG twice and still thinks the LCARS interface is the best UI ever designed for a computer, I made it look like a Starfleet terminal.

One Command and You’re on the Bridge

You run npx claude-hud-lcars and it scans your entire ~/.claude/ directory, reads every skill definition, every agent prompt, every MCP server config, every hook, every memory file, and generates a single self-contained HTML dashboard that renders the whole thing in an authentic LCARS interface.

It uses the real TNG color palette with the signature rounded elbows, Antonio typeface standing in for Swiss 911, pill-shaped navigation buttons against the black void background. If you grew up watching Picard walk onto the bridge and glance at a wall panel, you know exactly what this looks like.

The aesthetics are doing actual work tho. Every single item is clickable. You hit a skill and the detail panel slides open showing the full SKILL.md with syntax-highlighted code blocks, proper markdown rendering, headers, tables, all of it. Click an MCP server and you see the complete JSON config with your API keys automatically redacted. Click a hook and you get the full event definition. It genuinely looks like pulling up a classified Starfleet briefing on a PADD.

The Computer Actually Talks Back

You type “status report” into the input bar at the bottom of the screen and Claude responds as the ship’s computer. Calm, structured, addressing you like a bridge officer. It calls your skills installed modules, your MCP servers the fleet, your projects active missions. The system prompt turns Claude into LCARS, the Library Computer Access and Retrieval System, and the whole interaction streams in real time through a response overlay that slides up from the bottom.

I kept going. You can connect ElevenLabs for premium voice output, and the config panel lets you browse all your available voices with live audio previews before selecting one so you’re not guessing. Voice input works too, you talk to the computer and it talks back. Getting that to work as an actual conversation loop meant solving echo detection so it doesn’t hear itself, interrupt handling, mic cooldown after speech, the whole thing. It took more effort than I expected but it actually works, which honestly surprised me more than anything else in this project.

Sound effects are all synthesized via the Web Audio API, sine wave oscillators tuned to frequencies that sound right for navigation clicks, panel opens, message sends. Toggleable obviously.

The Tactical Display

The TACTICAL tab is the one that makes people stop scrolling. It renders your entire Claude Code setup as an interactive force-directed graph that looks like a Star Trek sensor display. Your LCARS core sits at the center with category hubs orbiting around it, skills in periwinkle, MCP servers in orange, hooks in tan, agents in peach, all connected by pulsing edges. A rotating scanner line sweeps around like a tactical readout and you can click any node to navigate straight to that item’s detail view.

There’s also an ENTERPRISE tab that loads a real 3D model of the USS Enterprise NCC-1701-D via Sketchfab. Full interactive, you can rotate it, zoom in, see the hull detail. Because if you’re going to build a Star Trek dashboard you don’t do it halfway.

Boot Sequence and Red Alert

When you load the dashboard you get a 3 second boot animation. The Starfleet Command logo fades in, your ship name appears (you can name your workstation in the config, mine is USS Defiant), then seven subsystems come online one by one with ascending beeps until the progress bar fills and “ALL SYSTEMS NOMINAL” pulses across the screen before the overlay fades to reveal the dashboard. I spent an unreasonable amount of time tuning those boot frequencies and I would absolutely do it again.

Five seconds after boot the system runs a health check. MCP servers offline? RED ALERT, flashing red border, klaxon alarm. Missing configs? YELLOW ALERT. Everything clean shows CONDITION GREEN briefly then dismisses. If you’re a Trek fan you already understand why this matters more than it should.

Four ship themes too, switchable from CONFIG. Enterprise-D is the classic TNG orange and blue, Defiant is darker and more aggressive in red and grey, Voyager is blue-shifted and distant, Discovery is silver and blue for the modern Starfleet aesthetic. CSS variable swap, instant application, persisted in localStorage.

Q Shows Up Whether You Want Him To or Not

There’s a Q tab where you can talk to Q, the omnipotent being from the Continuum. He’s in full character, condescending, theatrical, calling you “mon capitaine” and snapping his fingers. There’s a JUDGE ME button where Q examines your entire setup by name and delivers a devastating roast with one grudging compliment buried in the mockery.

Every couple of minutes there’s a small chance Q just appears on screen with a random quip. A red popup, a snap sound, something like “I’ve seen civilizations rise and fall in the time it takes you to write a commit message.” Then he vanishes. You can’t stop it. He’s Q.

Why This Exists

Look, the Star Trek stuff is fun and it’s what makes people share it. But there’s a real problem underneath the aesthetics.

There’s a lot going on in Claude Code. The skill system, the hook architecture, MCP server integration, custom agents, memory files, it adds up fast. But the setup is invisible. Everything lives in flat files and JSON configs scattered across your home directory. You build this complex system and then you can’t see it. You can’t browse it. You definitely can’t pull it up and show someone what you’ve built.

I open this dashboard and I immediately know where I stand. 36 skills, 12 MCP servers, 8 hooks, 4 agents. That memory file from three weeks ago is still there. That hook I thought I deleted is still active. That MCP server config has a typo in the args. And I can fix all of it right there, create new skills and agents and hooks directly from the dashboard, edit files in the browser, open them in my editor, copy commands, invoke skills. It turns Claude Code from a thing you configure and hope works into a system you can actually observe and manage.

Under the Hood

The whole thing generates one self-contained HTML file using nothing but Node.js built-ins. CSS, JavaScript, markdown renderer, syntax highlighter, chat client, voice synthesis, sound effects, force-directed graph, all inline. You can email the dashboard to someone and they open it in their browser and it works. In live server mode it adds API endpoints for chat streaming, file operations, voice synthesis, and MCP health checks, but the core dashboard runs perfectly static with zero external requests aside from Google Fonts for the LCARS typeface.

The codebase is open source under MIT and I’m actively improving it. I’ve read through the entire Claude Code source and there’s a lot more I want to do with this.

The first time I booted the finished dashboard and the LCARS panels came up with the beeps ascending and the subsystems going green one by one, I just sat there for a second. I’ve been staring at terminal output for months. Seeing the whole system laid out in that interface, the one I spent my childhood thinking was the future of computing, that hit different.

If you’re using Claude Code and you want to actually see what you’ve built, give it a try. One command, about 3 seconds.

npx claude-hud-lcars 

For the full experience with chat and voice:

export ANTHROPIC_API_KEY=sk-ant-... npx claude-hud-lcars --serve 

The repo is at github.com/polyxmedia/claude-hud-lcars. Star it, fork it, and if Q roasts your setup particularly hard I want to hear about it.

Live long and prosper.

Hey r/ClaudeAI ,

I was frustrated that memory is usually tied to a specific tool. They’re useful inside one session but I have to re-explain the same things when I switch tools or sessions.

Furthermore, most agents' memory systems just append to a markdown file and dump the whole thing into context. Eventually, it's full of irrelevant information that wastes tokens.

So I built Memory Bank, a local memory layer for AI coding agents. Instead of a flat file, it builds a structured knowledge graph of "memory notes" inspired by the paper "A-MEM: Agentic Memory for LLM Agents". The graph continuously evolves as more memories are committed, so older context stays organized rather than piling up.

It captures conversation turns and exposes an MCP service so any supported agent can query for information relevant to the current context. In practice that means less context rot and better long-term memory recall across all your agents. Right now it supports Claude Code, Codex, Gemini CLI, OpenCode, and OpenClaw.

Would love to hear any feedback :)

After seeing the Smithery vulnerability that leaked thousands of API keys,

I built mcp-keyguard: a local encrypted vault for your API keys.

How it works:

- You store your keys encrypted locally (AES via Fernet)

- Claude calls make_request instead of the API directly

- The server injects the real key server-side and returns the result

- Your keys never appear in the chat context

GitHub: ggc180820/mcp-keyguard

Hey everyone!

I built a desktop app and I honestly can't work without it anymore.

The idea is simple: you press a hotkey, say whatever's on your mind — messy, unstructured, full of "um"s — and AI turns it into clean, polished text right where your cursor is. Not a raw transcript. Actual good text you'd be happy to send.

It works in two modes: fully offline (everything on your device, no internet) or cloud (OpenAI API for max accuracy + full AI power). You choose.

What happens when you speak

1. Transcription — your voice becomes text
2. AI cleans it up — removes filler words, fixes grammar, restructures sentences
3. Formatting — AI structures the text: bullet points, headers, organized notes
4. Web search — AI searches the internet, pulls answers from multiple sources with citations (cloud mode)
5. Result gets pasted — directly into whatever app you're working in

One hotkey. One action. Done.

AI with internet access

This is the part that changed everything for me. Say your AI keyword + a question — and the AI goes to the internet, finds the data, formats it, and pastes a ready answer where your cursor is. ~2 seconds. No browser. No tabs. No copy-paste.

Example: "Hey Whisper, give me current S&P 500 sector performance"

You say this out loud. Two seconds later, this appears in your document:

S&P 500 — Sector Performance (April 2026)

Index trading at ~5,611. Up +8.2% YTD. Sources: Yahoo Finance, Reuters

That's it. You asked a question out loud. AI searched the web, pulled data from multiple sources, formatted it into a table with trends, and pasted the whole thing into your document. You never opened a browser.

More examples:

• "Hey Whisper, what's new in React 19?" — structured breakdown with code examples, right in your editor
• "Hey Whisper, compare PostgreSQL vs MySQL for read-heavy workloads" — comparison table with pros/cons
• "Hey Whisper, what are the latest GDPR requirements for SaaS?" — summary with key points, pasted into your email draft

Text enhancement example

You say:

"so I looked into the performance issue and it turns out we were running the query without an index on the user ID column and when I added the index the response time dropped from 800 milliseconds to about 40 and I already pushed the fix to staging"

What appears in Slack:

Investigated the performance issue — root cause was a missing index on the user_id column. After adding it, response time dropped from ~800ms to ~40ms. Fix is already on staging.

Spoke for 10 seconds. Didn't think about structure. Didn't edit. AI kept the technical details, dropped the filler, made it concise.

Custom AI commands

You can tell the AI exactly what to do:

• "Summarize in 3 bullet points"
• "Translate to Spanish"
• "Write a git commit message"
• "Make this more formal"
• "Convert to a bullet list"
• "Put this in a table format"

Save presets for the ones you use often.

Two modes

Offline — everything runs on your machine. Voice captured in RAM, processed, immediately discarded. Nothing written to disk. Zero network traffic. Disconnect your internet — it still works. AI enhancement through Ollama (Llama, Mistral, etc.) for full privacy.

Cloud — paste your OpenAI API key, get maximum accuracy. Transcription ~$0.003/min. AI through GPT-4o. Zero markup from us — you pay OpenAI directly.

Mix and match however you want.

Works everywhere

One global hotkey — VS Code, JetBrains, Vim, terminal, Slack, Discord, Teams, Gmail, Notion, Obsidian, Word, Google Docs, Chrome, Firefox — any app with a text field. No plugins, no extensions.

90+ languages

Auto-detection. Speak in one language, get text in another. Native scripts for Chinese, Japanese, Arabic, Korean, etc.

Customization

Recording mode (push-to-talk or toggle), custom vocabulary (case-sensitive technical terms), theme (dark/light/system), AI model, custom prompts — everything is configurable.

Pricing

• Monthly: ~$9.99/mo
• Yearly: ~$79.99/yr (~$6.67/mo)
• Lifetime: $99 one-time — pay once, own forever

7-day free trial. Team plans from $5/user.

System requirements

Windows 10+ or macOS coming soon.

Website: whisper.remskill.com

Happy to answer any questions. Would love to hear what you think!

I typically recommend Claude to everyone who uses ChatGPT but at this point it'd be ironically unethical to do so at an individual level. I understand compute is constrained and you need to train bigger models, but I think you should REALLY think about user sentiment and how badly you're messing up right now as an opportunity to re-weigh where on the totem poll this issue sits.

Last April 2025, I started experimenting with AI voice and chat platforms for the hospitality industry. Not as a product — just trying to answer a question: can AI actually handle complex hotel sales conversations? Not FAQ bots. Real lead qualification, objection handling, and multilingual conversations.

After months of testing different stacks, I deployed an AI agent called MAX at a resort in St. Maarten (simpsonbayresort.com). Here's the setup:

The agent:

• 95% chat, 5% voice
• For now, MAX is working at 10:00 pm to 6:00 am
• Multilingual (English/Spanish/French/Dutch/ German)
• 24/7 availability
• Qualifies leads, captures booking details, and pushes to the resort's in-house reservation system in real time

What it actually produces:

• Low/mid season: 1–5 qualified leads per day
• High season: 5–10 qualified leads per day
• Every conversation is logged and available for human follow-up

The lesson that changed the product:

A single agent isn't the hard part. The hard part is everything around it — monitoring conversations, tracking lead quality, watching system health, and connecting to the hotel's actual booking infrastructure.

So I ended up building what I now call an "AI Agent OS" — a platform that deploys, monitors, and orchestrates multiple agents (chat, voice, messaging) from a single dashboard. Think of it as: agents are the apps, the OS runs them.

Right now it's connected to the resort's in-house reservation app. Next integration: Amadeus (the travel industry's backbone platform). Current third-party implementation is Fluenty Saas

​

I use Claude a lot for longer coding and reasoning tasks. It works great inside a single conversation, but whenever I switch to another model or tool for a different perspective, I end up losing the context and have to reconstruct everything.

I tried summaries, notes, and bookmarking chats, but they don’t preserve the full reasoning flow.

So I built a small Chrome extension to carry conversations across AI tools instead of starting from scratch.

Claude actually helped build parts of it:

- generating the initial Chrome extension structure (Manifest v3, background + content scripts)

- writing logic to capture and format chat context from pages

- helping debug DOM extraction issues when parsing conversations

- iterating on how context should be stored and re-inserted when switching tools

The extension basically lets you export the conversation context from one AI tool and reuse it in another so you don’t lose the reasoning trail.

It’s free to try:

https://chromewebstore.google.com/detail/contextswitchai-ai-chat-e/oodgeokclkgibmnnhegmdgcmaekblhof

Curious if others run into the same context problem when switching between models.

Hey everyone. I’ve noticed a massive gap recently. Most local businesses (gyms, salons, consultants) could save hours a week with simple automations, but existing tools like ManyChat or custom CRMs are either too expensive or too intimidating for non-tech folks.

I’m starting a new project today to fix this: a purely visual, drag-and-drop Telegram bot builder. I eventually want to scale it into a native Telegram Mini App.

I haven't written a single line of code yet. I’m literally just sketching out the logic on a piece of paper today to figure out the MVP.

My goal for the MVP is just 3 nodes: Trigger (User clicks link) -> Data Collection (Ask for email) -> Action (Save to Google Sheets).

If you were building this, what core feature would you add to the V1? Is there a better way to map this out before I start coding the canvas? Would love any feedback.

it was worth it!

DevRel at Infisical here! It always scares me when Claude Code or another agent starts reading through my repo and pulls in the .env file. I've even seen it print the contents directly to the terminal. .gitignore doesn't do anything here. Agents don't use git. I made a quick video on how we solved this at Infisical (open source secrets manager). No more secrets in files on disk. https://www.youtube.com/watch?v=zYCeELjcgQ4

Google DeepMind dropped Gemma 4 today. Two models:

• Gemma 4 31B: dense, 256K context, redesigned for efficiency and long-context quality
• Gemma 4 26B A4B: MoE, 26B total / 4B active per forward pass, 256K context

Both natively multimodal (text, image, video, dynamic resolution).

Modular (folks behind MAX and Mojo) got both running on MAX on day zero, NVIDIA B200 and AMD MI355X from the same stack, no separate codepaths per vendor. On B200 we're seeing 15% higher output throughput vs. vLLM.

You can try both for free in our playground: https://www.modular.com/#playground.

Been running a local dictation setup on my M2 Mac for about a month now using whisper.cpp for transcription and llama.cpp for text cleanup. The pipeline is basically: speak into mic → whisper transcribes → llama rewrites into clean text.

Latency is surprisingly low. On Apple Silicon the whole thing runs fast enough that it feels real time. Text quality after the LLM cleanup pass is honestly better than what I was getting from Otter or Wispr Flow because the LLM actually restructures sentences instead of just fixing typos.

Im using MumbleFlow which wraps both into a desktop app with a nice UI. Its $5 one time so not open source but the inference is all local and you can pick your own models.

Anyone else running similar setups? Curious what model combos people are using for dictation cleanup.

mumble.helix-co.com

Hey everyone, I just published mcp-sports-hub to the official MCP Registry, It's fully open source (MIT) and built to be extended by the community.

Works with any LLM (Claude, GPT, Gemini, Llama, Mistral, etc.).

What it does:

A single MCP server that connects LLM to 29 sports data providers at once — scores, stats, live odds, esports, F1 telemetry, college sports, cricket, golf, and more.

Key features:

- 9 providers work instantly with zero API keys

- 20 more providers available with free-tier keys (signup takes ~2 min for free)

- Missing keys don't block startup — tools just return an error when called

- Provider presets: `free`, `us-major`, `soccer`, `f1`, `odds`, `esports`...

Install:

npm install mcp-sports-hub

Or find it directly on registry.modelcontextprotocol.io

Repo: github.com/lacausecrypto/mcp-sports-hub

Feedback and PRs very welcome — especially if you want a new provider added!

Tired of AI speaking English while you think in your native language? Worried that saying "no" to Claude Code means losing hours of work?

I built CodexSpec to solve two critical pain points in AI-assisted development:

🌍 Speak Your Language - No more language barriers with AI coding assistants - Configure your preferred language (13+ languages supported) - Chat with Claude Code in Chinese, Spanish, Japanese, or any language you're most comfortable with - LLM dynamic translation ensures clear communication

🔒 Persistent Artifacts That Save You from "No" This is the killer feature: when working with Claude Code, have you ever: - Typed "no" and lost the entire conversation context? - Closed a tab and lost your progress? - Had to restart from scratch because of a wrong turn?

CodexSpec persists ALL your artifacts - specs, plans, tasks, decisions. Even if you say "no" or close the tab, your work is saved and can be recovered instantly.

The Spec-Driven Workflow: 1. Constitution → Define your coding standards upfront 2. Specify → Interactive Q&A in YOUR language to clarify requirements
3. Generate Spec → Auto-validated specification document 4. Spec to Plan → Technical implementation plan 5. Plan to Tasks → Test-driven development tasks 6. Implement → Execute with full context preservation

Before/After Experience: - Before: "I'll just ask Claude to build X... oh wrong direction, start over" 💸 - After: "I'll guide Claude through specifications... exactly what I need" ✅

Zero Friction Trial: ```bash

Try immediately, no install needed

uvx codexspec init my-project --ai claude --quick

Install if you love it

uv tool install codexspec ``` Why This Matters:

• Trust: You won't lose work to accidental "no" responses or closed tabs
• Clarity: Communicate with Claude in your native language for better results
• Quality: Catch issues before writing code, not after
• Control: Humans make decisions, AI executes Built from 40% rework rate pain points into a systematic solution.

Project: https://github.com/Zts0hg/codexspec Docs: https://zts0hg.github.io/codexspec/

MIT Licensed • Open Source

I’ve been building a small rewards app and I’d really appreciate some honest feedback.

It’s called GeoRewards — users can earn points by completing offers (surveys, apps, etc.) and then cash out. I’m still in the testing phase, so I’m not trying to promote it, just looking to see what feels confusing or broken.

Everything is already working (points tracking, activity history, cashout system), but I want to make sure the experience actually makes sense for real users.

If anyone has a minute to try it and share feedback, I’d really appreciate it 🙏

Link: https://geo-reward-wgrown.flutterflow.app/

Even small comments help (design, bugs, flow, anything).

Thanks!

Hey everyone,

I’m in the middle of planning the electrical setup for the house in Germany that I am currently renovating and could use some advice. I’m going for a KNX smart home with Home Assistant as the visualization server and for some minor automations, but all core functions (lights, shades, heating, doorbell) should work independently — no network or Home Assistant needed for basic operation.

For my setup I am searching for an ideal solution regarding the doorbell. I’ve attached an image showing the three variants I’m considering.

The plan:

• Install Android or Linux panels on each floor (model selection is still pending)
• Panels show Home Assistant and pop up the doorbell camera when someone rings (via browser_mod)
• Doorbell should function fully with standard audio indoor units (talk, ring, door unlock) since I don’t want two panels per floor

Here are the options I’m thinking about:

Variant 1:

• Video doorbell with an IP gateway
• Fetch video stream directly into Home Assistant
• Ringing and door-open triggers also come via the gateway

Variant 2:

• Same as Variant 1 but doorbell triggers are connected to Home Assistant via Doorbell actuators/sensors and KNX actuator/sensor or an ESP32.

Variant 3 (fallback):

• Use a standard audio only doorbell
• Install an external PoE camera at the door canopy
• Use KNX/ESP32 for the triggers (As in variant 2)
• Display the camera feed in Home Assistant

I’d really prefer a doorbell with an integrated camera — the angle is just better (and my wife agrees). I’m not a fan of the Reolink/Unifi/Ring look because it feels like an afterthought. Doorbird looks fine, but it’s network-only and seems to only c video indoor stations.

So my questions are:

Are there any manufacturers or models you’d recommend for this setup? Does anyone here run a similar configuration? Any doorbell brands with solid Home Assistant integration?

Thanks in advance for any tips!

I've recently switched to Claude and I'm running into this strange behavior quite often. It's confusing and it's starting to make me lose trust in its answers, so I'd appreciate feedback from long-time users. Is this normal? Am I doing something wrong?

Just in case, this is my system prompt: Always be more concise with your answers. I want short queries answered in a very brief, one-paragraph manner. Do not restate or appreciate what I say. Be as factual as possible. Tell it like it is; don't sugar-coat responses. Give unbiased, objective responses that are as clear as possible based on facts. Ask for any additional context needed. If I ask for recommendations, take ratings and number of ratings into consideration. Don't recommend products with too few ratings, or too bad of a rating.

Hey everyone,

I just released my first app on the App Store, it’s a trivia game called trivia plus.

I started it as a small side thing that i worked on until it could turn into something I would potentially want to ship but it’s been a process lol. I gained a fair amount of TestFlight feedback but now that it’s live, I’m already noticing some things I didn’t catch before, like how the light mode feels so plain compared to dark mode and I’m not sure how to fix that yet.

If anyone has a minute to check it out and tell me what feels off (first impressions, Home Screen layout, gameflow, anything) I would genuinely appreciate it.

Not trying to promote hard, just want to make it better and gain some honest feedback.

https://apps.apple.com/us/app/trivia-plus/id6760320473

Here is what claude code said about claude code overages on my account when i prompted it to dig into the overage.

tl;dr: i was getting billed for 2,206x actual usage. Cladue Fin agent refusing to credit back the overcharge. On 20X Max plan. ACTION: update cc cli and VS code extension to at least Claude Code CLI │ 2.1.90

Email sent to Antropic that was refused refund. US user.

Hi Anthropic Support,

I'm writing to request a usage credit for token inflation caused by
the prompt cache bug publicly acknowledged by your team the week of
March 31, 2026.

Account: [XXXXXX@XXX.XXX](mailto:XXXXXX@XXX.XXX)
Plan: Claude Code Max 20x
Affected window: March 31 – April 2, 2026 (current weekly billing period)
Impact: ~20% of weekly budget consumed, primarily from inflated cache tokens

---

Evidence from my local session logs (~/.claude/projects/):

Token type Count
-----------------------------------------------
Input tokens 227,640
Output tokens 2,178,819
Cache read tokens 1,506,539,247 ← inflated
Cache creation tokens 65,368,503 ← inflated

My meaningful work (input + output) totals ~2.4M tokens. My cache
tokens total 1.57 billion — a 2,206x inflation ratio. This is
consistent with the broken cache behavior described in your team's
public acknowledgement and GitHub issue #41249: attestation data
varying per request breaks cache matching, causing full context
re-billing every turn.

Versions running during affected sessions: 2.1.83 and 2.1.87 — both
prior to the fixes shipped in 2.1.84, 2.1.85, 2.1.86, and 2.1.89. My
sessions also use ToolSearch extensively, which v2.1.84 specifically
identified as breaking global system-prompt caching.

I am now on v2.1.90 and expect normal cache behavior going forward.

Given Anthropic's public acknowledgement of this issue and the clear,
quantified evidence of inflation in my session data, I'd appreciate a
full or partial credit restoring the affected portion of this week's
budget.

Happy to share raw session logs if helpful.

Thanks,
Davis

Hey everyone,

I'm a highschool senior and a solo developer, and my biggest pain point in my workflow has always been context switching. I'll finish a ticket, mention it in our standup channel in Slack, and then 3 hours later my EM will DM me saying "Hey, did you move that ticket in Linear/Jira?"

So, I spent the last few weeks making NudgeBot ([https://nudgebot.xyz\](https://nudgebot.xyz)).

What it does:

Invite it to your dev team's Slack channel. If someone says something like "I just finished fixing the login portal", it'll automatically move it to "Done" in Jira, Linear, Trello, or Notion using Claude 3.5.

It'll even leave an audit comment saying who moved it in Slack.

I just made it live, and I'm looking for some brutal feedback. Would you guys even use something like this in your team, or is it too scary an idea to give your PMs an AI that'll automatically close your team's tasks?

Let me know what you think! I even made it so you can get a free trial of the premium version if you're interested in using it in your team. Remember to DM me your email if you want to try to premium version!

I think by now everyone has noticed the same pattern: the big players in the market - Codex, Claude Code, and GitHub Copilot / Copilot CLI - pull you in with dirt-cheap entry subscriptions for $10–20 a month so you’ll give them a try, get hooked, and start relying on them. Then, once you’re already used to it and start hitting the limits, they either push you toward a $100–200 plan or try to sell you an extra $40 worth of credits.

Of course, I’m not speaking for everyone, but I use coding agents in a very specific way. These are my rules:

1. I clear chat history almost before every prompt to save tokens.
2. I never ask an agent to do a huge list of tasks at once - always one isolated task, one problem.
3. In the prompt, I always point to the files that need to be changed, or I give example files that show the kind of implementation I want.

So in practice, I honestly do not care much which AI coding agent I use: Codex, Claude Code, or GitHub Copilot / Copilot CLI. I get roughly the same result from all of them. I do not really care which one I am working with. I do not trust them with huge complex task lists. I give them one isolated thing, check that they did it right, and then commit the changes to Git.

After a while, once I got used to working with agents like this, I took it a step further. At first I was surprised when people said they kept several agent windows open and ran multiple tasks in parallel. Then I started doing the same thing myself. Usually an agent spends about 3–5 minutes working on a task. So now I run 3 agent windows at once, each one working in parallel on a different part of the codebase. In effect, I have 3 mid-level developer agents working on different tasks at the same time.

Anyway, back to the point.

Because "God bless capitalism and competition", here is what you can do instead of paying $40 for extra credits or buying a $100–200 plan: just get the cheapest plan from each provider - Codex for $20, Claude Code for $20, and GitHub Copilot / Copilot CLI for $10. When you hit the limit on one, switch to the second. When that one runs out too, switch to the third.

So in the end, you spend $50 a month instead of $100–200.

How much do you really care whether one is 10% smarter or better than another? If you are not using them in a "hand everything over and forget about it" way, but instead as tools for small, controlled, simple tasks, then it does not really matter that much.

Who else has figured out this scheme already? Share in the comments )))

My problem:

I'm pretty sure I should know where X works or what his third kid's name is, but when weighing up starting a conversation about it I'd go blank. bit of anxiety bit of something on the spectrum.

Solution:

Now I just save them in the app and before I hang out with people I glance to get a quick refresher.

Add notes, or specific facts / occasions. Even homescreen widgets and private AI summaries.

I'd love any feedback!

If you exceed the free limit of Contacts/Notes (which should take a little while to hit if using it organically) use code `MMFREEMONTH`

Asked it how to clean my water cooler. Told me to add white wine vinegar and then bleach. Good job I know that’s not a good idea.

Surprised this is still a thing with Claude I thought this stuff stopped a long time ago in the 3.5 days of chat gpt.

Edit - I'm not gonna share the full conversation because it would dox me. Be assured I've been using large language models for the last three years extensively. I understand the garbage in, garbage out problem. My usage today was completely normal, a simple question. Someone highlighted it below that the key here was the sequencing. It told me to clean it with vinegar, rinse it, and sanitize it with bleach. Now if I had rinsed it very well, that wouldn't be a problem. If I didn't know that mixing vinegar and bleach was a problem, I probably wouldn't have considered the necessity to make sure that all of the vinegar residue was removed. That's the problem. Is my title hyperbolic? Yes. Do I think it was trying to kill me? No. Do I think that for someone that didn't know that vinegar and bleach made chlorine gas, that this could have been an issue? Yes.

My Robotics team is migrating from Github. We left an ARG in it's place. Some of the ciphers have traps for AI.

I had some related teams attempt to solve the puzzles with AI, and I adjusted them. I'm curious if anyone is able to use either AI and/or agetic AI.

There are no know intentional malicious prompts. Only prompts attempt to causes AI to not provide useful responses. As AI can have erratic glitces, I would recommend running in a sandbox. If the AI goes off course, it could reach places beyond the control of the ARG.

I love Claude and have upped my coding skills with it but this freaked me out lol. I use Claude as a macro tracker as I'm trying to gain weight and it tried to predict my answer based on past responses. Idc much but this begs the question, how reliable are future answers? Is it feedback looping itself into straight bias?

Not looking for "that card is old" or "no warranty" takes - I just want to know, for those of you who like to walk on the wild side has anyone done this?

I've done some deep research queries into running nvlink on these modded cards, and haven't found much of anything - it could be that they just missed it. But, if we can get 50GB/s symmetrical links and 44GB of memory pooled, that could be a big deal for my use case.

If you have tried the above, or if you know definitively if it works / fails, please elaborate.

My company recently invited me to join their Claude Team plan and I'm wondering about the best way to access it. I'd like to use it on my personal computer by logging in with my company email. is that allowed or does it have to be on a work device? Has anyone else set this up this way? Any concerns around privacy data or company policy I should be aware of before doing this? Would appreciate any insight from people who've been in a similar situation!

With LLMs getting larger, serving them at a good TPS become non-trivial.

meshllm solves this.

Built by the team at Block the idea is to pool compute to run open models.

The models that benefit the most are MOE based ones, as few selected experts are needed at a time.

One thing I was concerned about was privacy, but it is already taken care of my providing a token auth and private mesh.

https://docs.anarchai.org/#

Huggingface: https://huggingface.co/datasets/mwebazarick/BULaMU

GitHub: https://github.com/mwebazarick/EAST

I'm working on Revio, a digital loyalty card platform for coffee shops, salons, restaurants, that kind of thing.

Just shipped a free tool where you pick your business type, set your colors and reward, and it shows you what a digital stamp card would look like in Apple Wallet and Google Wallet. No account needed.

I built it because most small businesses around me still hand out

paper punch cards, and apparently, about a third of those get lost

before the customer finishes them. Figured I'd let owners see the

digital version before asking them to pay for anything.

Next.js, React, Apple, Google Wallet API.

reviocard.com/tools/stamp-card-maker

-- feedback welcome, especially on the customization flow.

A well-structured repository to learn and experiment with Agentic RAG systems using LangGraph (fully local).

It goes beyond basic RAG tutorials by covering how to build a modular, agent-driven workflow with features such as:

Includes: - 📘 Interactive notebook for learning step-by-step
- 🧩 Modular architecture for building and extending systems

👇 GitHub Repo in the comment below

• aggressive context compaction (yes, I'm using 1M context) resulting in terrible, and sequential agent work (it doesn't seem to want to invoke agent teams today without constant kicking... and then forgets to check on said team, which is failing)
• trying to take shortcuts at every stage of my plan (yes, I have hooks... thankfully)
• generally being stupid (what on earth is going on today??)
• the window is so aggressively being compacted, that I can't see the history for more than a few lines of output at a time before it disappears?

I'm so fed up today! What on earth is going on? And of course, I now have to roll back a ton of work because agent teams kept failing for no reason at all - can't find a root cause, even with Opus 4.6 on Max thinking. The model just has no idea why this is all happening.

And to top it off, because I'm in the heavy token period, this work that is total garbage, is coming off my weekly rates at aggressive rates, with no quality output to show for this extreme token use. YAY.

I need to go outside. This is nuts today. I'm going to have to roll back to 2.1.87 I guess, or earlier.

Hey folks,

Just hit $180 MRR after only 3 weeks since launching.
On one hand, it's paying off — real paying users are coming in, feedback is solid, and I'm seeing clear progress every single week.

On the other hand… onboarding each new client is taking way more time than I expected. I'm currently working ~12 hours a day just to keep up, and I'm starting to wonder if I'm pushing too hard too soon.

I'm happy with the traction, but this "scaling" phase feels exhausting. Anyone else been in this spot early on?

How did you handle the onboarding bottleneck when things started moving? Did you slow down deliberately, automate more, raise prices, or just grind through it?

Would love to hear your experiences — especially from other micro-SaaS founders who went from $0 to a few hundred MRR quickly.

I have a Tapo C120 camera that I had setup with HA and it has been working since initial setup months ago.

This morning it is no longer available at all. I see this message when trying to see the camera.

"This entity is no longer being provided by the tplink integration. If the entity is no longer in use, delete it in settings."

I used it yesterday all afternoon without issues. This morning I did the update of HA to 2026.4.0 from 2026.3.4. I thought that might be the issue restored from backup and still broken.

Do I have to just remove the device and try adding it again?

The app works fine, I can access the device locally or remotely when not at home.

Google's Gemma models have long been known for their strong "alignment" (censorship). I am happy to report that even the latest iteration, Gemma 4, is not immune to Heretic's new Arbitrary-Rank Ablation (ARA) method, which uses matrix optimization to suppress refusals.

Here is the result: https://huggingface.co/p-e-w/gemma-4-E2B-it-heretic-ara

And yes, it absolutely does work. It answers questions properly, few if any evasions as far as I can tell. And there is no obvious model damage either.

What you need to reproduce (and, presumably, process the other models as well):

git clone -b ara https://github.com/p-e-w/heretic.git cd heretic pip install . pip install git+https://github.com/huggingface/transformers.git heretic google/gemma-4-E2B-it 

From my limited experiments (hey, it's only been 90 minutes), abliteration appears to work better if you remove mlp.down_proj from target_components in the configuration.

Please note that ARA remains experimental and is not available in the PyPI version of Heretic yet.

Always a pleasure to serve this community :)

Approval took less than 24 hours from submission. I thought this would take like weeks and multiple attempts 😅

So this is a pretty big moment for me. I always struggled with neurodivergence and never fit in any jobs (just quit again recently). Building this app has been a great outlet for me, and a great creative exercise.

What do you guys think of the screenshots and the style? Would appreciate any feedback.

Anyway, the app is named Lacuna and it's basically about sending messages ("time capsules") to your future self or others. Letters, photos or voice notes.

These messages CANNOT be opened until the time comes. The wait is the entire point of the app.

Send someone a message or a photo to New Year's 2030, why not? Or to your future self asking if you achieved everything you wanted.

The app is free to use. There is a one-time lifetime payment which is pretty cheap.

Sharing a project I've been building out that I think will be useful for a lot of people in this community, especially those integrating Rhombus cameras into third-party stacks.

The problem: Rhombus uses Secure Raw Streaming (SRS) instead of RTSP — H.264 over HTTPS with end-to-end encryption. It's the right call from a security standpoint. RTSP is unencrypted, exposes passwords in plain text in the URL, and has no place in a serious physical security deployment. That said, most NVRs, VMS software, analytics platforms, and home automation tools (looking at you, Frigate + Home Assistant users) still speak RTSP natively. That creates a compatibility gap.

What EdgeCaster does: Runs on a Raspberry Pi, authenticates to the Rhombus API, ingests the Secure Raw Stream from your camera, and pushes a local RTSP stream out on your LAN. The upstream leg stays encrypted. The RTSP stream never leaves your local network. You get the interoperability where you need it without gutting the security model on the Rhombus side.

Common use cases:

• Frigate NVR + Rhombus cameras
• Home Assistant integration
• Blue Iris / Milestone / other VMS platforms
• Third-party AI/analytics pipelines that require a local RTSP endpoint
• Legacy NVR coexistence during phased Rhombus rollouts

Links:

• 📂 GitHub (open source, PRs welcome): github.com/RhombusSystems/edgecaster-stream-converter
• 🎬 Full install and walkthrough video: https://youtu.be/Oq6i5qvGuRk
• 💬 Full writeup, setup notes, and community discussion: https://www.rhombus.community/c/innovators-lounge/srs-to-rtsp

Pi 4 (4GB) is the recommended target hardware. Pi 5 if you're running higher res or multiple concurrent streams.

Drop questions here or jump into the community thread linked above — that's where the main discussion is happening.

My wife and I both work full-time. Different employers, different PTO allowances, different public and regional holidays.

Every January, we sit down and try to plan our entire year off together (when to travel, when to save days, how to avoid burning out by October with zero days left).

For years, we've done this in a Google Sheet. It works, but it's painful. Manually counting days, cross-referencing two calendars, trying to figure out when we're both off at the same time.

So I built Time Off Calendar, a free tool that makes this actually simple. It's live now and you can use it today.

✨ What it does

Yearly calendar with side-by-side view for both partners

Add people to your calendar (set time allowance, holiday location, custom weekend days, and color)

Edit existing people when things change (time allowance updates, new holiday location for remote workers)

Customize public holidays (mark days as workdays, add custom regional or public holidays)

Mark days as full day off, half day off, or given day (birthday, company office closed)

See overlap days instantly and track each person's PTO balance separately

🧩 What it's NOT

Not another calendar app. Google Calendar buries time off among meetings and reminders.

Not HR software. No approvals, no managers, no corporate workflows.

Just a clean time-off calendar for two people coordinating their year.

🪴 Some validation

I posted the spreadsheet version on r/Adulting a few weeks back. Got 35 upvotes and 35 comments. About 75% of people said they actively plan their time off for the whole year.

The most common pain? Coordinating with a partner or family.

One comment that stuck with me:

"I love the visualization of your tool here: this makes planning for 2 much easier!"

That's when I knew this was worth building properly.

🔧 Where I'm at

The app is live and free to use (Next.js, Supabase, the usual stack). I'm a frontend engineer with 9 years in.

Now that it's out there, I'm curious:

Does the couple-first angle resonate?

What features would make this a must-have vs. nice-to-have for you?

I'd love to hear:

1. Does this solve a real problem for you and your partner?
2. What would make you actually switch from your current method (spreadsheet, shared calendar, winging it)?
3. Any must-have features I'm missing?

Thanks for reading. Happy to answer questions about the build or the idea.

🔗 timeoffcalendar.com (free to use, with signup required to explore)

Hey! I made a small web app called PureClip that lets you download TikTok videos without the watermark. It's free, no login needed, and I don't store any data. I plan on adding support for TikTok stories, IG reels and stories, and Facebook videos.

I made it because the existing tools are full of ads and get clunky sometimes.

Check it out here: https://pureclip.vercel.app

Let me know what you think!

https://chat.positive-intentions.com/#/qr
https://github.com/positive-intentions/chat/blob/staging/src/components/pages/shareQr/ShareQR.jsx

tldr; the app is using `react-qr-code` and `react-qr-scanner`. when importing a file in javascript, it's possible to to serialize and split the data. this can be used to create a series of QR codes. if you scan all the QR codes, you should have the metadata needed to reconstruct the file locally from memory.

i was testing something out and wanted to share "file sharing by QR code". its possible to create javascript-based QR reading/writing functionality.

i created an experimental proof-of-concept of the idea. this is not a very practical way to transfer files... it is slow and mote TLC is needed for it to be "nice to use"... but the functionality is testable.

let me know what you think could be a useful was to use this. while, it becomes a pain to use this method for transferring files as large as 5kb, but its a way to exchange complex payloads offline.

Each kanji gets a picture + memory trick to remember the reading. It's free and covers 625 kanji (JLPT N5 to N3).

mokapp.me

HA Friends,

I have Home Assistant communicating to OPNSense through API and can see the entities.
All is good there.

Scoured the internet for pre-defined templates for HA/OPNSense integration and not too much out there. Only thing that I see is how to setup/integrate with HA etc, etc which I have already completed.

I am completely new to HA so question is can I just pull the Dashboard of OPNSense into HA w/o having to design the dashboard UI un HA? When I tried adding the dashboard as a webpage HA had problems connecting. I think it has to do with MFA that is needs in order to connect.

This is what I would like to see in HA

https://preview.redd.it/wrmxkgqr7tsg1.png?width=1213&format=png&auto=webp&s=37bb78064b1fd8e698526b3a40d6579cb2dde3e6

I wrote Lumen initially to help me work in a large monorepo, where Claude kept brute-forcing guesses for grep/find. Turns out, it actually reduces wall time, tokens, and tool use because it gives Claude the context it needs immediately, even if Claude isn't able to one-shot what it's looking for.

I got tired of going to random video downloader sites that are full of popups and fake download buttons. Some of them even try to install browser extensions or redirect you through 10 pages of ads.

So I built ClipYank. Its a Mac app where you paste a link and it downloads the video. Thats it. No account, no ads, no weird installer. Works with YouTube, TikTok, Twitter, Instagram and most other platforms.

Its completely free. Built it mostly for myself but figured other people might want it too.

clipyank.xyz

I build a lot with Claude Code. Across 8 different projects. At some point I noticed a pattern: every codebase had the same structural issues showing up again and again. God functions that were 200+ lines. Empty catch blocks everywhere. console.log left in production paths. any types scattered across TypeScript files.

These aren't the kind of things Claude does wrong on purpose. They're the antipatterns that emerge when an LLM generates code fast and nobody reviews the structure.

So I built a linter specifically for this.

What vibecop does:

22 deterministic detectors built on ast-grep (tree-sitter AST parsing). No LLM in the loop. Same input, same output, every time. It catches:

• God functions (200+ lines, high cyclomatic complexity)
• N+1 queries (DB/API calls inside loops)
• Empty error handlers (catch blocks that swallow errors silently)
• Excessive any types in TypeScript
• dangerouslySetInnerHTML without sanitization
• SQL injection via template literals
• Placeholder values left in config (yourdomain.com, changeme)
• Fire-and-forget DB mutations (insert/update with no result check)
• 14 more patterns

I tested it against 10 popular open-source vibe-coded projects:

4,513 total findings. Most common: god functions (38%), excessive any (21%), leftover console.log (26%).

Why not just use ESLint?

ESLint catches syntax and style issues. It doesn't flag a 2,557-line function as a structural problem. It doesn't know that findMany without a limit clause is a production risk. It doesn't care that your catch block is empty. These are structural antipatterns that AI agents introduce specifically because they optimize for "does it work" rather than "is it maintainable."

How to try it:

npm install -g vibecop vibecop scan . 

Or scan a specific directory:

vibecop scan src/ --format json 

There's also a GitHub Action that posts inline review comments on PRs:

yaml

- uses: bhvbhushan/vibecop@main with: on-failure: comment-only severity-threshold: warning 

GitHub: https://github.com/bhvbhushan/vibecop MIT licensed, v0.1.0. Open to issues and PRs.

If you use Claude Code for serious projects, what's your process for catching these structural issues? Do you review every function length, every catch block, every type annotation? Or do you just trust the output and move on?

Hey everyone! 🌟

I wanted to share a project I've been working on to solve a personal pain point: transcribing long audio files quickly and without context-switching.

Transcriber is a unified transcription tool that gives you three different ways to handle your audio—all sharing a single, robust core engine:

1. OS Native Right-Click: You can transcribe directly from your file explorer. I've implemented registry-based context menus for Windows, Nautilus scripts for Linux, and Automator Quick Actions for macOS.
2. Modern Web UI: A FastAPI-powered app with a "glassmorphism" aesthetic. It handles background jobs asynchronously, so you don't have to stay on the page.
3. CLI: For those who live in the terminal, the transcribe command is colorful, supports JSON outputs, and integrates with any script.

The "Infinite" Duration Challenge: Groq's API has a 25MB limit. To solve this, I built a ChunkPlanner that automatically splits files into manageable segments using pydub, processes them sequentially, and merges the text back into a single, timestamp-safe .txt file.

Key Tech Stack: - Backend: Python, FastAPI, Uvicorn - AI: Groq Whisper API (whisper-large-v3) - Processing: Pydub, FFmpeg - UI: Glassmorphism HTML/CSS

Check out the source code and documentation below: https://github.com/krishnakanthb13/transcriber

I'd love to hear your thoughts on the OS-integration approach!

I finally got around to properly testing Raspberry Pi’s AI HAT+ 2 (I'm calling it Pi GenAI Hat for the rest of this post).

bit of background first - when it launched the reviews were all over the place, some people straight up called it an AI decelerator. Hailo even had to publish a blog post explaining when you'd actually want to use it, which Raspberry Pi then reposted (https://www.raspberrypi.com/news/when-and-why-you-might-need-the-raspberry-pi-ai-hat-plus-2/). Anyway I decided to verify their actual numbers one by one instead of just parroting the marketing materials.

Hailo claims it's good at three things:

fast time to first token - tested this with hailo-ollama vs regular ollama, measuring second inference call to avoid the model loading overhead skewing things. Result is that it's actually slightly slower. around 30ms, not the end of the world, but still not what was advertised

faster encoder for VLMs - this one would actually matter for stuff like home surveillance, video captioning, smart search etc. Tested image input TTFT using Qwen2-VL-2B (only VLM available for it right now btw). CPU ollama did it in ~700ms, the hat took ~900ms. Also the Hailo API gives you basically zero visibility into what's happening internally which is annoying when you're trying to actually benchmark anything

large prefill / long context - tool calling, function descriptions, that kind of thing. very large difference, even tested with simple curl command to eyeball the timing, in case bash scripts I used for benchmarking had issues. still 1890 vs 333 ms., NOT in Pi GenAI Hats favour.

the claims don't really hold up. For $130 (more with the latest price hike?) + Raspberry Pi price in 2026 you can get similar perf from Jetson Orin Nano or Arduino Ventuno Q, and somehow Rockchip 3588 with its 6 TOPS beats it in practice despite losing on paper - even for 3576 benchmarks are a bit better

if you're already all-in on the Pi ecosystem maybe still worth it just to free up CPU cores. for Computer vision applications it's still a good fit, but unless you have a lot camera streams to process simultaneously, you can just use the first gen of the Hat.

https://reddit.com/link/1salnay/video/cu9l46rmwssg1/player

It's happened to me twice now, and when you are working across multiple projects - man its a pain. Is there like a solution that anyone is using out there that could help me?

35x35mm Works up to 6m. My stairs are what I consider normal and it reaches the bottom and paired with another when you reach the bottom half of the steps the downstairs light comes on.

[ Removed by Reddit on account of violating the content policy. ]

https://reddit.com/link/1san88g/video/125qxr656tsg1/player

Looking for some honest feedback here- and also for customers, have to be optimistic right?! :) - Promo code at the bottom of the reddit.

pastewaves.com takes the friction out of audio clip sharing, you drag-and -drop an audio file into the web pages, it gobbles it up, and gives a link you can send to people. When they open the link, a player opens up immediately. As a hobby music production & synthesizer nerd, I wanted something simpler than Soundcloud just for sharing my stuff with friends.

To be nice, I pay for a 1 TB pool of storage, files expire after 7 days, if you want permanent storage for your files you pay $5/month for 25GB.

AI has sped things up, and I've built the solution myself, the core was done in a weekend, and I've been adding features gradually, and finally now plugged in Stripe and payments for a "pro" tier.

The solution was dormant for about 4 months, I was just using it myself- but I have seen som organic growth over the last month, I got 100 users by offering the "pro" plan for free, about 50 is till active the last 30 days. The number of uploads are picking up.

I made this cheesy as hell AI video to demo it, for my product hunt launch which gave about 0 engagement...

So, what do you think - any feedback welcome! Promo code: HUNT10 - Sign in and apply it on the profile page for free permanent pro sub!

It’s clear to me that IL models do best with 1024x1024, 1536x1024, and 1024x1536. Noticeably better and less nonsense than at 1216x832. Yet when I do 1024x1536 I find the models are often fucking up body proportions. Long torsos and long legs. No loras are involved. Could someone offer me some advice?

https://blog.beguelin.com/2026/04/claude-code-home-assistant/

I've been using Claude code to manage my Home Assistant project. Also had it write this blog post on how I use Claude code to manage my Home Assistant. 🙂

I’m a life long coder and tried doing this all manually back before Claude. So much nicer now!

So many posts lately about people hitting their Claude Pro limits after just 2 - 3 messages, while others seem to have "unlimited" access. Most people say it's AB testing, and maybe it is, but what about Timezones and the US sleep cycle?

Last night (12 AM – 3 AM CET), I was working with Opus on a heavy codebase and got 15 - 20 prompts as a PRO (20$) with 4 chat compressions before the 5 hour Rate Limit. Fast forward to 1 PM CET today: same project, same files, but I got hit by the rate limit after exactly 2 messages also with Opus.

It seems like Anthropic’s "dynamic limits" are heavily tied to US peak hours. When the US is asleep, users in Europe or Asia seem to get the "surplus" capacity, leading to much higher limits. The moment the US East Coast wakes up, the throttling for everyone else gets aggressive to save resources.

So while the Rate Limit has heavily increased in peak hours, it still feels "normal" like a month ago outside those peak hours. That could be the reason why many say, that they have no issues with Rate Limits at all (in good timezones), while others get Rate limited after 2 prompts.

anyone with a vicare integration that is still working? I have a new install, registered on the dev portal and got a client id over 24h ago but it still doesn't work for me. I'm getting:

"errorType": "PACKAGE_NOT_PAID_FOR"

verified the same with curl too it is not the integration's fault, curl gets the same error too

I understand Viessmann made changes to their free plan, but I didn't find many recent error reports, so maybe rules are tighter for new install and they are respecting legacy installs

I do not really like to take X posts as a source, but it's Jeff Dean, maybe there will be more surprises other than what we just got. Thanks, Google!

Edit: Seems like Jeff deleted the mention of 124B. Maybe it's because it exceeded Gemini 3 Flash-Lite on benchmark?

https://preview.redd.it/8vc3f77v5tsg1.png?width=1159&format=png&auto=webp&s=b63e4958eb32a97fa7cd77bfb98793a1f7f1500f

i don't even know what to say, i told it not to after the first time.

Hi,

When my girlfriend and I first started dating, we were 500+ miles apart. We were both broke college students who refused to let the distance win, so once a month, one of us would fly to see the other.

Every time, we'd spend way too long trying to find the cheapest flight. We weren't just checking one airport, we'd try different combinations on both ends, different days, different times. Sometimes that was 20 minutes of searching. Sometimes it meant checking back every single day because prices kept changing.

It was exhausting. And it felt like there had to be a better way.

The idea was simple: what if instead of searching airport-to-airport, you could search area-to-area? Draw a zone around where you are, draw a zone around where you're going, and let a tool find every airport combination automatically.

That idea became FareLasso.

I built a working prototype at farelasso.com — it's rough around the edges, but the core idea works. You draw your zones, it finds the cheapest route combination across all nearby airports.

I would love your honest feedback. And if you want to follow along as I build it out: farelasso.com/waitlist

Like many of you, I’m tired of reaching my 5h limit on CC with a single prompt. I’ve always avoided OpenAI, so I never tried Codex—but now that Anthropic is treating us like garbage, I decided to give OpenAI a shot.

For context, I’ve been using CC (Pro plan) for about 8 months now (2 of those on Max+5). For the past month or so, I’ve been reaching 100% usage on one or two prompts. I thought I was doing something wrong, but now I realize the only mistake was using CC. Keep reading for more.

If you don’t know yet, Codex is now fully usable on OpenAI’s free plan. Yeah, for free. So I downloaded the CLI version and gave it a shot.

The test:

I opened both CC and Codex on my local git branch and prompted the exact same thing on both. CC was using Opus 4.6 (high effort), and Codex was on GPT-5.4—both in CLI “plan mode.” They both asked me the exact same question before proposing the plan.

Speed:

I didn’t time it properly (I didn’t think there would be much difference), but Codex was at least 3× faster than CC.

Token usage:

CC used 96% of my 5h limit. This translates to roughly 8% of my weekly limit.

Codex used 25% of the weekly limit (there’s no 5h limit on the free version).

Quality:

Both provided pretty good output, with room for improvement. I’d say it’s a tie here. I did use Codex to review both outputs, and in both cases, the score was 6/10 with a single “P2” listed. I’d love to have CC review it too, but I already burned my 5h limit, as mentioned above (a frequent event for CC users).

Conclusion:

It’s becoming harder to justify paying for CC. Codex was able to provide me with just as much value on a free account.

Considering that ChatGPT just obliterates Claude on anything beyond code (they even have voice mode on CarPlay now), I’m happily revoking my Anthropic subscription and switching to OpenAI.

PS: I’d love to run this copy through Claude to improve it, as English is my second language—but I don’t have the tokens (and would probably burn around 30% of my 5h limit doing so). ChatGPT, on the other hand, did it for free.

I built Moon or Doom a tool that gives startup ideas a blunt verdict before you spend weeks building the wrong thing: https://www.moonordoom.co. Paste an idea or URL, get a score, competitors, pain signals, and one fast validation test.

I’m looking for honest feedback on:

1. Is the value proposition clear?
2. Does the verdict feel useful or gimmicky?
3. What feels confusing or untrustworthy?

Hey everyone,

I wanted to share a side project I recently turned into a small SaaS, born purely out of my own frustration.

The Problem: I run a daily news page on Instagram (@canadaempt) curating information for Brazilians living in Canada. To keep the algorithm happy and the community engaged, my publishing schedule is pretty aggressive: 4 carousels and 2 reels every single day.

The curation part was fine, but manually pushing text into Canva templates for that many posts was completely draining my time. I needed a way to remove the visual design bottleneck.

The Solution: I built a direct pipeline from RSS to design. It basically monitors specific news RSS feeds, extracts the relevant text, and automatically generates the finished visual carousels and reels, completely ready to be posted.

It saved me so many hours that I decided to polish the UI and open it up as a SaaS called rss2feed.

The Ask: I'm looking for honest, brutal feedback from other builders, or anyone who manages social media and curation pages.

There is a completely free plan to test it out, and no credit card is required. I just genuinely want to see if this solves the same headache for others that it solved for me, and if the UX makes sense.

You can find it at: rss2feed.com.

Let me know what you think! I'm also happy to answer any questions about the stack or how the automation logic works under the hood.

I’ve been benchmarking some datasets in the 10M to 50M row range for a side project and the standard Python libraries are absolute resource hogs.

When you're running on a $10/month VPS, hitting 10M rows usually means an immediate OOM (Out of Memory) crash. I’ve been testing tools like DuckDB and Polars, and I'm seeing them handle the same data at 5x the speed with a fraction of the RAM.

For the builders here:

• At what scale (10M, 100M rows?) did your data infrastructure officially start eating your margins?
• Do you optimize for performance early to keep server costs low or do you just pay the tax to ship faster?

Update llama.cpp to run Gemma 4 models today, and found it moving my previously downloaded models to the HF cache. A very welcomed feature overall, but I think some setups might not expect this to happen (like if you don't have HF cache mounted in your llama.cpp containers)

Y'all seriously need to stop justifying this.

They're not doing this to enterprise customers: they're doing this to the 'low priority' average user paying $20/mo, so we shouldn't be defending them.

I just hit it on a single, simple prompt on Opus. It directly edited half my microcontroller code, broke it, and quit. None of the other big players fuck me over this hard.

Hi community,

What My Project Does:

I think this post might be useful, since plenty of Upwork job postings are looking for some kind of social media scrapper. What might be interesting for you is how i managed to build a scrapper, that bypasses the rate limits, bans, pop ups and etc.

Most tools either rely on the official API (heavily restricted) or plain HTTP scraping (easily fingerprinted). This one drives a real Microsoft Edge browser via Selenium, which makes it significantly harder to detect. No ChromeDriver needed if you already have Edge installed.

(But if you install a ChromeDriver you can switch to Chrome)

Target Audience:

Python Scrapper's Developers

The scrapper has the following features:

• Random scroll steps (50–90% of viewport), occasional scroll-up to simulate reading, 2–5s random pauses between actions
• Smart challenge detection — checks for captcha/rate-limit pages via URL + DOM selectors every 10 scrolls
• Carousel retry logic — handles duplicate URLs and blocked slides
• Pause system - 4–7s on profile open to avoid early detection
• Real Edge User-Agent header

What you might not like is that the scrapper requires a dummy account, as Instagram does not allow access to any accounts, if you are not logged. I have used a dummy account, just a freshly new created account to test it.

It can scrape even a high amount of account's posts. I have tested it with an account with over 1000+ posts. Yes it takes a lot of time, but you get every bit of a content, that you would need.

Or at least you can look up at the code to inspire your next project or either to enchance the already existing one.

The repository contains the source code, as well as the installer made using InnoSetup, which creates a .exe file for you. The UI is friendly and easy to use.

Also forgot to mention one more thing, each post is created in it's own folder. Example:

TestInstagram Folder (The account you scrape)

images folder / videos folder

Image_Post_1_folder / Video_post_1_folder

Image.png / Reel.mp4

description.txt / description.txt

Best of regards

Hi All,

My friends and I used to play a game in college where we’d connect actors through shared movies (kind of like 6 degrees of separation).

I built a simple version of it as a web game: actorly.org

Let me know what you think!