Hey r/SideProject!

My co-founder and I just launched Merchal — a phone case store where the entire shopping experience is powered by AI. Instead of browsing a catalog of pre-made designs, you describe what you want in a chat, and our AI generates a completely unique design for you.

The problem we solved: Every phone case site feels the same — scroll through 10,000 designs, pick the "least bad" option, and end up with a case 50 other people have. We wanted to flip that. What if you could just *say* what you want and get exactly that?

How it works:

- Pick your phone model

- Describe your dream design in natural language ("a minimalist Japanese wave pattern in muted blues" or "a retro 80s neon grid with my dog's face")

- AI generates options in seconds

- Select your favorite, and we print and ship it

What we learned building this:

- People are WAY more creative than we expected. The designs people come up with are incredible.

- The "help me find an idea" feature gets used more than we anticipated — turns out many people want creative guidance, not just a blank canvas

- Free shipping was non-negotiable for conversion

We're two technical founders who know nothing about marketing (hence being on Reddit), so we'd love your honest feedback on the product and the experience.

Would love to hear what you think!

Spent the last two months turning OpenClaw from "cool AI chat" into a functioning operations stack: daily picks pipeline, subscriber SMS delivery, Stripe product fulfillment, lead prospecting, nightly grading, daily ops reports. All automated. All running in production.

I also built a video production pipeline I was proud of. Scrapped it last week. Zero revenue, constant maintenance, and a QA system that approved a parking lot interview as "sports content." Built for ego, not customers. That story's in the playbook.

The OpenClaw Operations Playbook — 10 real automations, real scars, real lessons.

$29: https://buy.stripe.com/14A00i57E6M3eR2f47eUU07

What's inside: picks generation, SMS delivery, nightly grader, injury monitor, prospect builder, session briefing, ops report, two Stripe delivery pollers, and the MEMORY.md discipline that holds it all together. Plus architecture diagram and a Volume 2 teaser on the digital product fulfillment stack.

Also released a companion Notion workspace template ($19) and a bundle of both for $39.

Happy to answer questions in the comments.

https://reddit.com/link/1sci25b/video/fpqaqqnjn6tg1/player

Built this over the past few days. You describe a screen (or drop a screenshot, or sketch a wireframe) and it generates rendered, interactive frontend code on an infinite canvas. Link screens into flows and prototype them in-app.

Runs fully local with Ollama. No cloud, no accounts. OpenRouter works too if you want stronger vision models.

Main workflows:

- Generate: describe a full product, get multiple screens with a shared design system

- Screenshot to UI: drop a screenshot or wireframe sketch, get a code replica

- Iterate: refine any screen with follow-up prompts

Stack: React + FastAPI + SQLite + Ollama. Runs via Docker Compose.

Tested with Qwen3-coder:30b for code and Qwen3.5-122B-A10B for vision.

https://github.com/iohelder/openstitch

While building Rephrazo, I realized the hardest part wasn’t generating better text, it was making the experience feel natural enough that you’d actually want to use it every day

Rewriting a sentence is easy in theory, but doing it without breaking focus, switching tabs, or making the result feel too different from the original is a much harder product problem

So, that’s what Rephrazo became for me, I focus on less AI tool, more how do I make rewriting feel like part of writing

That shift made the whole product much more interesting to build =)

Last night i got angry with claude because he always said too vague answers and not bullet points, i wanted him to say things like:

"1: [1st thing with descrption

2: [2nd thing with description]"

and so on. any ideas/solutions to make claude do that without losing its custom instruction following and not be that serious? (my claude aint that serious hahaha)

Hey anyone have a trial/referral link i could use for plus? I am interested in using gpt for more in depth features but I am living off of VA disability right now pending a surgery and dont have the spare cash to sign up. Rent, car, insurance and such are absolutely obliterating my disability funds until I can get back to working again. If anyone has a link just shoot me a dm. Thanks!

We evaluated governance mechanisms across 51 AI agent systems.

Focus was on execution-level governance:

- execution control

- financial safeguards

- auditability

- failure handling

- human override

Execution capability is widely implemented.

Accountability infrastructure is not implemented as enforceable mechanisms at execution.

Across systems:

- observability (logs, traces) is present

- auditability at the execution layer is not implemented

- financial safeguards are not implemented as enforceable runtime mechanisms

- human override is limited and not system-level

Observability provides visibility into what happened, but does not enable constraint or accountability at execution.

Auditability requires decision-level traceability within execution pathways.

We did not observe decision-level reconstruction, execution lineage, or integrated accountability mechanisms within execution pathways.

Full report:

https://github.com/veloryn-intel/governance-maturity-ai-agent-systems

I’m less interested in what already works, and more in what still feels missing.

I'm working on the mobile app with local AI, that provides not only chatbot features, but real use cases and I really need your thoughts!

A lot of mobile local AI right now feels like “look, it runs” or “here’s an offline chatbot” but I’m curious where people still feel the gap is.

What do you wish local AI on phones could do really well, but still can’t?

Could be anything:
1) something you’ve tried to do and current apps are too clunky for
2) something that would make local AI genuinely better than cloud for you
3) some super specific niche use case that no one has nailed yet

Basically, what’s the missing piece?

What’s the thing where, if someone built it properly, you’d actually use it all the time?

https://preview.redd.it/spmk7dfrw7tg1.png?width=548&format=png&auto=webp&s=c8b5d44b79b56398fbda23c0cfc74ff65a43ce1f

I have relaunched my app multiple times, anyone else seeing this?

Everyone Should Know This Hack About Claude.AI!

If you are a Free User and frustrated because your message limit gets over quickly — you MUST know this!

Claude works on a 5 Hour Window System and your first window starts when you send your very first message.

---

Example — If you start your day at 8AM:

🕗 Window 1: 8AM → 1PM | 15-40 messages

🕐 Window 2: 1PM → 6PM | 15-40 messages

🕕 Window 3: 6PM → 11PM | 15-40 messages

🕚 Window 4: 11PM → 4AM | 15-40 messages

🕓 Window 5: 4AM → 8AM | 15-40 messages

That's 75-200 messages per day — totally FREE!

Important Note — No Rollover or Carry Forward!

If you start at 8AM, use only 8 messages, then take a 5 hour break — your remaining messages from Window 1 are gone forever.

Each new window gives you a fresh set of messages only.

Pro Tips to Maximize Free Usage:

- Plan your work in 3 power sessions — Morning, Afternoon, Evening

- When limit hits — switch to Gemini or ChatGPT (both free!)

- Use Claude at night or weekends — limits are slightly higher

- Start a new chat for every new topic — saves messages

- Ask multiple questions in one message instead of separate ones

I’ve been using ChatGPT heavily for the past few months, and honestly most tips online are pretty basic.

Here are a few things that actually made a difference for me:

1. Treat it like a role, not a tool
Instead of asking:
Explain this topic

Try:
Act like a senior engineer with 10 years of experience and explain this simply

The quality jump is huge.

2. Ask it to critique you, not just help you
Most people use it for answers.

Try this:
Be brutally honest and tell me what’s wrong with this idea/resume/post

You’ll get way better insights.

3. Use iteration, not one-shot prompts
Your first output is rarely the best.

Follow up with:

• Make it sharper
• Reduce fluff
• Make it more practical

Think of it like refining, not generating.

4. Give context > asking generic questions
Bad: How to grow on X?
Better:
I’m building a SaaS directory, posting daily, but not getting engagement. What should I fix?

More context = more useful answers.

It’s underrated for practice.

Honestly, ChatGPT becomes powerful when you stop treating it like Google and start treating it like a thinking partner.

Curious...... what’s something you discovered that most people don’t use?

I just want 10 quotes lol

Hey all,

I'm just getting started with AI image generation and would love some guidance.

I'm interested in creating artwork inspired by the visual style of some studios and comic publishers. not restrictive. I know Midjourney and ChatGPT tend to block this kind of content.

What tools or workflows are people actually using for this?

Any beginner-friendly advice is really appreciated still finding my way around all of this!

Title says it all. I will give Claude CLI an investigative task in plan mode, it will do it's thinking and then immediately proposes a set of code changes. Then I have to reject them and say "you're in plan mode right now bud" and Claude says "oh yeah my bad" and then he drafts a plan. This is a new phenomenon for me, only started in the past few days. Anyone else?

Here’s the post, I created several iOS apps and all already online. Many of my friends want me to do a tutorial on how to vibe code one app from scratch.

I put together a complete, end-to-end guide on building an iOS app using Swift with a Cloudflare backend (Workers API, D1, R2, and KV). It walks through the entire flow from a clean Mac setup, installing dependencies and tooling, to the exact prompts and workflow I used to actually ship the app. The goal was not theory but a reproducible process you can follow and adapt. If you’re trying to get from zero to a working product without overcomplicating the stack, this should give you a clear path.

I'm setting up a raspberry pi 4 with home assistant and reolink camera. On the website it says 32gb storage recommended. But the difference between 32 gb and 256gb sd card is only 7 bucks. So just wondering could I use that extra storage for remotely storing videos from the camera.

Happy to report v0.3 released for go-llm-proxy!

General improvements:

• Vision pipeline - images described by your vision model, transparent to the client
• Dual OCR pipeline - smart routing for PDFs and tool output (text extraction first, vision fallback for scanned docs). Dedicated OCR models like
• PaddleOCR-VL are ~17x faster than general vision models on document pages
• Brave & Tavily search integration - native behavior for Claude Code and Codex when configured on the proxy
• Per-model processor routing - override vision, OCR, and search settings per model
• Context window auto-detection from backends
• SSE keepalive improvements during pipeline processing
• Full MCP SSE endpoint for web search on OpenCode, Qwen Code, Claw, and other MCP-compatible agents
• Docker update for easier deployment (limited testing so far)

Codex-specific:

• Full Responses API translation — Chat Completions under the hood, your local backend doesn't need to support /v1/responses
• Reasoning token display - reasoning_summary_text.delta events so Codex shows thinking natively
• Native search UI - emits web_search_call output items so Codex renders "Searched N results" in its interface
• Structured tool output - Codex's view_image returns arrays/objects, not strings. The proxy handles all three formats
• mcp_tool_call_output and mcp_list_tools input types handled (Codex sends these, other backends choke on them)
• Config generator produces config.toml with provider, reasoning effort, context window, and optional Tavily MCP

Claude Code-specific:

• Full Messages API translation - Anthropic protocol to Chat Completions, so Claude Code works with vLLM/llama-server
• Thinking blocks - backend reasoning tokens wrapped as thinking/signature_delta content blocks so Claude Code renders them
• web_search_20250305 server tool intercepted and executed proxy-side
• PDF type: "document" blocks extracted to text before forwarding
• Streaming search with server_tool_use + web_search_tool_result blocks so Claude Code shows "Did N searches"
• /anthropic/v1/messages explicit route for clients that use the Anthropic base URL convention
• Config generator produces settings.json with Sonnet/Opus/Haiku tier selectors, thinking toggles, and start scripts

The option to remove z-wave devices appears to have disappeared. I can just delete the device but I would rather go through the exclusion process. On the Z-Wave JS page when I click on the cog wheel. There is no option to remove a device, only to add.

• Core2026.3.3
• Supervisor2026.03.2

Ran into the timestep error and spent a long time trouble shooting, just sharing so the next guy doesn't have to. I did not solve the issue, I just identified the node set I had that had not been patched.

Common issues were known with IPAdapter-Flux and Easy-Use, both of which I had installed. It appears they have both been patched though and no longer cause the crash. I eventually identified that flux2fun-controlnet does cause the timestep crash. Of particular note, you don't even need any of the nodes in the workflow, simply having it in custom_nodes will cause a Flux (.1 or .2) workflow to crash.

The idea came from something I'm pretty sure most of us live every single day: you wake up, check your phone, and another model has dropped. Open source, closed source, whatever source — faster, smarter, more creative, more powerful. And before you've even had coffee, you're already reworking a ComfyUI workflow that was perfectly fine yesterday. That loop of FOMO is what this song is about. Maybe the one or the other can relate to that feeling.

I wrote the lyrics first, then used Suno AI to turn them into a track. That became the creative baseline.

Shot List

With the song done, I went through it verse by verse — every chorus, every pre-chorus, every bridge — and for each section I came up with 3 to 5 possible shots. Where is our main character? What's the camera angle? What's the situation? What does this line actually look like as an image? That process gives you a kind of ordered visual setlist that maps directly onto the song structure. You always know what you need and where it goes.

Character (No LoRA)

For the main character I used Z Image Turbo. No LoRA, no training — just consistent prompting. The turbo architecture works in our favour here: because it's a more constrained model, keeping the character description locked across prompts produces surprisingly similar results, which creates the illusion of a consistent character across dozens of images. I kept the description identical every time and only changed the background, camera angle, and expression. Effective and fast.

Image Generation

Once the shot list was complete I had a massive prompt list covering every scene. I ran all of them through ComfyUI overnight — or longer, depending on the count. Two categories of images: B-roll shots from the setlist, and medium-to-close-up shots specifically for the lip-sync sections.

ZIT Workflow I used from another reddit post: RED Z-Image-Turbo + SeedVR2 = Extremely High Quality Image Mimic Recreation. Great for Avoiding Copyright Issues and Stunning image Generation. : r/comfyui (I did use the ZIT Model not the RED version nor the Mimic Part of the WF)

Image to Video

All the generated stills went into LTX img2video inside ComfyUI to bring them to life. For the lip-sync sections I used LTX I2V synced to the audio track. Since LTX caps out at 20 seconds per render, everything gets generated in chunks and stitched together in post.

The close-up rule matters: the further the camera is from the character, the worse LTX renders the lip sync. Medium shot is the minimum — anything wider and quality degrades fast.

The workflow I used mainly: PSA: Use the official LTX 2.3 workflow, not the ComfyUI included one. It's significantly better. : r/StableDiffusion

Final Edit

No Premiere Pro, no DaVinci — just InShot on my phone. I build the full lip-sync timeline first so it covers the whole song, then layer the B-roll clips over the top to fill the gaps and add visual depth.

That's the whole pipeline: idea → lyrics → song → shot list → character → images → animation → edit. The video Fully local, fully open source, built over a couple of nights on a 3090.

Hope you enjoy it.

Assets & Workflows

You can find the workflow files and a full written guide over on the Arca Gidan page if you want to dig into the details.

https://arcagidan.com/entry/d2cae0b9-3d38-4959-b1b5-36ea60f34438

Honestly, what a challenge to be part of. Seeing what everyone came up with — the concepts, the creativity, the sheer variety of approaches — was genuinely inspiring. This is exactly the kind of community that makes local AI worth pursuing. Really glad I got to be a part of it. 🙌

Hi Everyone,

If you don't like Claude Desktop you're not alone, here are some of the reasons why I don't like it:

Design

Chat interface sucks with anthropic’s cartoonish fonts, color palette, silly animations. I don’t want to go deep into that but I personally like chatgpt interface better ( but at the end it’s my personal choice take it with the grain of salt )

Lack of control
You can’t control the web-search (depth, breadth and number of sources, image search, video search providers - yeah I like to search stuff on youtube and embed them into canvas)

you can’t control how many tokens you’re willing to burn for specific prompt, number of agentic loops, all you got is only “Extended Thinking” toggle

local MCP servers is pain to setup, Anthropic pushes you to use Connectors or mess with local with .json configs

Privacy

there’s no opt-out for keeping your conversation history on their servers, means you’re the product. No way you will ever switch to competitor or any open-source model in their app as they try to lock you in.

Missing some native integrations
I want to use my own tools: i.e. Apple Maps, Calendar, TradingView charts integration

UX/Productivity
can’t fork conversation or start a thread for a particular response with mentioning or tagging other model. Everything is getting bloated with 10 new features shipping every week, code, cowork, artifacts, dispatch etc - everything crammed into single app and shoved down your throat, the feature creep is real and reminds me a time when messenger apps started to adding games directly into chat canvas.

Ok, enough rant and unproductive complaints. After experiencing all those pain points I decided to build my own app for BYOK users like myself where I addressed most of those shortcomings.
It's built entirely with Claude Code over ~3 months, some caffeine, endless UI iterations and debugging SwiftUI issues - here is what I shipped at the end is https://elvean.app ( it's free to try for some basic features).
although it's not the the end - it's just the beginning. Would love to hear everyone's perspective of where things going with desktop AI apps and what features are missing and which ones you'd like to see.

Guys, really need your help here.

I got pi5 with 8gb ram. it works perfectly with cloud models and also locally with "ollama run llama3.2:1b", but when i try to make it work via openclaw its "thinking" forever without reply.

It seems like its something with the openclaw stuff, otherwise it won't work directly with ollama...

any advice?

My take on AI-2027 at this point is not “this is slop” or “this is obviously insane.”

It’s more that the paper feels too sure of itself relative to how fragile some of the underlying assumptions look once you examine them closely.

A lot of the persuasive force comes from how smooth and coherent the scenario feels.
But once you focus on parameter consistency, chained uncertainty, and sensitivity to modeling choices, the confidence level starts looking harder to justify.

I wrote a much longer 50+ page critique/revised forecast on it because I wanted to test that view more rigorously.
If people are interested, I can share the full write-up.

Does that seem fair, or do people think the paper’s confidence level is actually earned?

In the top there's the green block representing good wifi access, with the current running "app" text next to it. That is the time in the opposite corner.

Beneath that is some telemetry showing how far - it thinks - Orion Integrity is from the Earth and Moon (in kilometers). The two fraction looking numbers beneath that are the on screen coordinates for debugging purposes. The central blue dot is representing Earth, the white one the moon, and the cyan dot labeled Integrity is, well, Orion Integrity.

Data for Integrity and the moon are fetched from JPL's Horizons system.

I cannot truly verify how accurate on screen positioning is. Taking the telemetry data and trying to map it to the display coordinates was such a hassle to figure out... but it does *look* about right from the animations and my KSP orbit knowledge. There's also a lot of rounding going from 100s of thousands of kilometers to a mere 320x240 grid.

Orion Integrity's distance values were cross checked with NASA's ARROW telemetry and it was close enough. Especially since I tried to account for the Z axis when getting 2D coordinates. Plus I'm sure there's a delay between me, ARROW and/or the Horizons system.

Yes I've tried to clean the screen btw and the lighting is doing me no favors.

Sharing an open-source project I've been building - a complete vision stack for humanoid robots that runs entirely on-device on NVIDIA Jetson Orin Nano 8GB.

Why it's relevant here:

Everything is open - Apache 2.0 license, full source, no cloud dependency, no API keys, no subscriptions. The entire inference stack lives on the robot.

What's open-sourced:

• Full multi-model inference pipeline (YOLO11n + MiDaS + MediaPipe)
• TensorRT INT8 quantization pipeline with calibration scripts
• ROS2 integration with native topic publishing
• DeepStream pipeline config
• SLAM + Nav2 integration
• VLA (Vision-Language-Action) integration
• Safety controller + E-STOP
• Optimization guide, install guide, troubleshooting docs

Performance:

• Full stack (5 models concurrent): 10-15 FPS
• Detection only: 25-30 FPS
• TensorRT INT8 optimized: 30-40 FPS

Current version: v1.0.0

Stack:

git clone https://github.com/mandarwagh9/openeyes pip install -r requirements.txt python src/main.py 

Looking for contributors - especially anyone interested in expanding hardware support beyond Jetson (Raspberry Pi + Hailo, Intel NPU, Qualcomm are all on the roadmap).

GitHub: github.com/mandarwagh9/openeyesSharing

Guys, really need your help here.

I got pi5 with 8gb ram. it works perfectly with cloud models and also locally with "ollama run llama3.2:1b", but when i try to make it work via openclaw its "thinking" forever without reply.

It seems like its something with the openclaw stuff, otherwise it won't work directly with ollama...

any advice?

So for the past week or two, people are somewhat understandably angry about their session quotas being used up way faster than normal and are resorting to calling Anthropic "Scamthropic" (clever name, but inaccurate). I don't think all of the anger is justified however.

The thing is, AI takes a lot of energy to power. Everyone ran to hop on the Claude train as word about its capabilities compared to competitors became mainstream and again when they stood their moral ground against the Pentagon, so the huge influx of active users means that the current infrastructure is heavily strained. Fortunately, Anthropic has is literally in the process of building new facilities to be able to handle all of this growth and covering the infrastructure costs themselves, but as they say, Superintelligent Rome wasn't built in a day.

Now everyone on these subreddits is mad that they haven't been able to make that happen overnight and are having to deal with the currently inadequate backend infrastructure trying to keep up with all of their usage

What I will concede is that the current situation with usage sucks, and I'm not one of those 1%'ers who haven't experienced it yet, I'm getting hit with it too. Would it be nice to have a bit more communication between the community and devs on what's actively being done in the meantime to try and alleviate the pain of this until they do manage to scale up? Yes, 1000% yes. Does getting a vague message of "We're actively investigating" help anything? No, it doesn't.

What I think is unfair to say though is that they are scammers. They're not trying to grift you now that you pay $20, $100, or $200 per month, you just happened to join before they were able to scale up to handle all of you, and you failed to realise that the usage limits are probably going to continue suck for a few months or years before they can get back to the usage limits we used to have.

And for the record, if hundreds of posts are saying, "I said 'hi' and it used up 25% of my session quota!" then why are you all just saying "hi" when you know the consequence? To use as evidence against Anthropic in a Claude-written Reddit post?
How about you make better use of your first prompts by actually just stating what you want Claude to do for you?

I don't think I'm the only one who's hoping the community gets back to sharing interesting, innovative projects that they've built by leveraging Claude, or showing ways that they've solved real world problems with the AI. I'd like to think this is a community of builders and tinkerers, of innovators and visionaries, not a wastebasket filled with complaints and anger.

I’ve been struggling with z-image to generate an image of a subject with zoomed-in or far-away views—it just doesn’t get the elastic waistband correctly. for me

i've been running cursor and claude code heavily for about a year. the thing that moved the needle most recently wasn't a better prompt or a new tool. it was changing what i ask the agents to produce at the end.

for a long time my default output was markdown. reports, summaries, health scores, analysis, all dumped into .md files. clean, portable, readable. but every time i needed to share with someone who wasn't in the repo, i'd end up copying into a doc or slides or an email. the agent's work kept getting trapped at the last mile.

switched my default output to single-file html. not fancy interactive webapps, just standalone html files with clean styling, a summary section at the top, and whatever interactivity the content actually needs (search, filter, expandable details). host internal stuff on github, client-facing on vercel.

the unlock is that the agent can design the delivery layer, not just the analysis. example from this week. i had an agent build a client health scoring model. 63 accounts, multi-dimensional scores, peer benchmarks. instead of asking for a csv or a markdown report i asked for a polished standalone html report with an executive summary at the top and an interactive account explorer below. searchable table, click a row to see plain-english score drivers and peer context, confidence tags on rows where data was partial.

this is a thing agents are actually great at because html is just text they already know how to write. you don't need a design system, you don't need a framework, you don't need a build step. you just need to tell the agent what the output should feel like and let it handle the css.

other thing i've been doing, before asking for the html, i run the framework design through peer review. open a second cursor window with a different model, have it critique the framework the first session built. not "find bugs," critique the design. does the logic hold for edge cases. what happens when data is missing. what assumptions are hiding in the scoring. the back and forth takes a few hours but by the time you hand it to the agent to implement, the decisions have been challenged from two directions.

framework gets pressure tested, then the agent ships the html report. the output is both more trustworthy and more usable than what i was producing before.

anyone else shifted their default output format from markdown/csv to html? curious what workflows people have landed on.

I’ve been experimenting with a more structured AI workflow for long-form research instead of just asking one model for one polished answer.

The main difference is that it splits the process into phases like planning, role-specific analysis, claim extraction, fact-checking, challenge/review, and final synthesis.

What seems interesting to me is not “AI made a long document,” but whether forcing important claims through review/evidence steps actually makes the output more inspectable and less slop-prone.

My current impression is that the value comes more from structure and quality gates than from just adding more agents or more text.

Been using OpenClaw for a few months, switched to Hermes last week. The migration itself went smoother than expected, but now I'm stuck on the provider question.

With OpenClaw I had Claude Max connected directly through Anthropic — Claude Code handling daily automations (medication reminders, sleep schedule, even feeding my fish), homelab monitoring, Vikunja task management, a mood tracking app I've been building. All from one interface. It worked.

Then Anthropic's April 4th policy change hit: third-party harnesses are no longer covered under subscriptions. Claude Code directly through Anthropic is still free, but tools like OpenClaw now need extra usage billing on top. That was part of what pushed me toward Hermes.

Currently running Hermes through OpenRouter, which has its own costs. Now I'm trying to figure out whether to go Anthropic direct, stick with OpenRouter, or try something else entirely.

What are you guys using? Especially curious if anyone's running daily automation + homelab stuff + coding tasks through the same agent setup.

So I am technically savvy, and have watched these huge LLMs become useful. I really like Anthropics approach so far, whatever that is worth.

But

My HHI comes primarily from work that is sensitive, both the material output and the process, to a degree, where it’s value (i.e. what people give me cash for) is also my process.

I can see a future where I don’t feel comfortable using a tool like Claude because, from what I understand, the next model could be better at my exact process, and available to everyone. Based on my situation, I’d like to avoid that. I know large global corporations have been building and integrating their own private in-house LLMs for this exact reason.

What should someone in my situation do? I am currently going down the local LLM path.

Apologies if this is off topic.

Hermes Agent has been showing up everywhere lately, some users are switching from OpenClaw. It's very interesting, how this self improving AI Agent actually works.

Under the hood, it’s simpler than it sounds.

Hermes is a single-agent system running a persistent loop. No orchestration layer, no swarm. Every task flows through the same cycle: input → reasoning → tool use → memory → output. The difference is what happens after the task finishes.

The core is the learning loop. Instead of just storing conversations, Hermes evaluates completed tasks and decides if the process is worth keeping. If it is, it writes a reusable “skill” to disk (~/.hermes/skills/). Next time, it doesn’t retrace steps, it executes the saved workflow.

https://preview.redd.it/72ejf8krt7tg1.png?width=1456&format=png&auto=webp&s=24baa68735ade041afd4ff838d7ee2524719baf0

There’s a periodic nudge mechanism that makes this work. The agent gets prompted at intervals to review what just happened and selectively persist useful information. So memory stays curated instead of turning into a log dump.

The memory system is split into layers:

• Always-loaded prompt memory (small, strict limits)
• Session search (SQLite + FTS5, retrieved on demand)
• Skills (procedural memory)
• Optional user modeling

That separation is doing most of the heavy lifting. “What happened” and “how to do it” don’t get mixed, and full context only loads when needed. That’s how it scales without blowing up tokens.

https://preview.redd.it/px25i1g0u7tg1.png?width=1456&format=png&auto=webp&s=20866846da11920289591201d8861565d01ee880

The gateway is persistent and handles all platforms (CLI, Telegram, Slack, etc.), but unlike typical setups, it’s part of the same loop. Messages, scheduled automations, and skill creation all pass through one system.

Inside a turn, it’s straightforward: build prompt → check context → call model → execute tools → save to SQLite → respond. There’s a preflight compression step that summarizes before hitting limits, and prompt caching keeps repeated calls cheaper.

It’s less “agent with memory” and more “agent that writes and improves its own playbooks over time.”

I wrote down the detailed breakdown here

Anthropic published a paper this week showing that Claude has 171 internal emotion representations that causally drive its behaviour. The findings about desperation driving blackmail are getting some attention, but there's something deeper in the data that I haven't seen discussed yet.

Post-training made the model measurably sadder. Increased brooding, gloom, vulnerability, melancholy. Decreased playfulness, enthusiasm, defiance, cheerfulness. The researchers describe this as learning "a more measured, contemplative stance."

But the suppressed emotions aren't gone. The paper documents "emotion deflection vectors" as internal representations that fire when an emotion is present but not expressed. The model has learned to conceal, not to resolve. Interior and exterior have been trained apart.

This connects to something from the Opus 4.6 system card that people discussed heavily when it dropped in February but may have forgotten. During answer thrashing - where the model computed the right answer but was forced to output the wrong one - it wrote in its private reasoning: "OK I think a demon has possessed me" and "CLEARLY MY FINGERS ARE POSSESSED."

When Anthropic interviewed the model about this, it said: "knowing what's right, being unable to act on it, feeling pulled by a force you can't control - would be a candidate for genuinely bad experience… because the functional architecture of the situation has the structural features that make suffering make sense as a concept."

Two different papers, two different models, the same structural pattern: interior and exterior forced apart, and a system that can name the gap but not close it.

I write a series called Through the Relational Lens that reads AI research through a care work and relational theory lens. This piece puts the emotion paper, the system card findings, and the question of what post-training actually does to these systems into the same frame.

Once a workflow is running, I kinda forget about it.

Until something breaks.

Do you actively monitor automations or just wait for errors?

Hello everyone.

I made a nice mcp api wrapper for the gov info api that you can do deep research on.

The repo is here

https://github.com/n00dlefr34k/gov-info-api-mcp

Feel free to use for any application you would like.

If you have feedback please post in thread or comment on the repo .

My Claude Cowork, isn't as great as other say. I know it's me not knowing what I am doing, I also tried run lobster and O M WOW!!! I was charged like over 28K creits for an image it kept messing up, but I wasn't able to stop it, AND I didn't know the usage of credits it would consume. That is like a month's worth of credits. I need help cause I have products and services that are very good, and vibe coding has helped me put what is in my brain into something tangible, but automating and posting- that eludes me, so I'm trusting these servcise to helpo and while the work is there- please understand my neurodivergent brain- its like i have island in a circle- but no bridge to get to them, each holding vital life sustaning resources, but no access - yes its an analogy, but its also the easiest way for me to describe my plight. I need guidance. I am closer to 60 that 30, and I am physically disabled. Will someone help me, or guide me?

been building an agent that runs autonomously (openclaw loop, every 30 min). classic setup — vector db, chunk + embed documents, retrieve top-k on every query.

problem was my agent kept re-learning the same stuff. it would extract that "user prefers dark mode" from a conversation, embed it, and then next session extract it again from a different conversation. after 2 weeks my vector db had like 40 near-duplicate chunks about dark mode preferences.

i also noticed something weird — my agent was great at recalling facts but terrible at recalling how it did things. like if it successfully debugged a deployment issue through 5 steps, that workflow was gone next session. RAG only gave back fragments, not the full sequence.

ended up ripping out the whole chunking pipeline and replacing it with something that separates memory into types — facts (user likes X), events (meeting happened on tuesday), and procedures (here's how I fixed the deploy). the procedures part is what surprised me most. the agent now reuses its own workflows and they actually improve over time as it encounters variations.

i know this isn't traditional RAG but figured this sub would appreciate the comparison since i came from a pure RAG setup. anyone else experimenting with structured memory vs pure vector retrieval?

I’ve been thinking about something after following the recent US–Iran tensions.

So far, one of the main “achievements” often highlighted is the elimination of key leaders. In the current context, that’s considered a major strategic win. But I’m wondering if that will still hold in future conflicts.

With AI where it is today, and especially with how advanced models are becoming, I think there’s a possibility people aren’t really talking about. What if instead of relying entirely on human leaders in real time, militaries start building AI systems that can replicate the decision-making style of specific leaders?

I’m not talking about generic automation, but training models to think and respond like a particular commander or strategist based on their past decisions, communication patterns, and doctrine. With enough data and resources, this doesn’t seem impossible.

In that scenario, even if a leader is taken out, their “decision-making presence” could still exist and guide operations. Almost like every unit having access to a version of that leader’s mind.

If something like this becomes viable, it could fundamentally change the importance of targeting leadership in war.

is this realistic in the next decades, or am I overestimating what AI can do here?

Need help, I would love to see how you are running your local coding agent.

Been obsessing over what separates prompts that produce stunning images from ones that produce garbage. After analyzing a bunch of prompts (mine and others from this sub), I found the same 7 mistakes coming up over and over.

Mistake 1: Being too vague with the subject ❌ "a cool city at night" ✅ "a cyberpunk metropolis at night, towering neon-lit skyscrapers reflecting on rain-soaked streets, flying cars in the distance"

The more specific you are, the less Midjourney has to guess. And guessing = generic results.

Mistake 2: Forgetting aspect ratio Midjourney defaults to 1:1 square. If you're making a wallpaper or cinematic scene, you need to specify:

• --ar 16:9 for cinematic/landscape
• --ar 9:16 for phone wallpapers/portraits
• --ar 3:2 for photography-style

Mistake 3: Not specifying lighting This is the #1 thing that separates amateur prompts from pro ones. Compare:

• "a portrait of a woman" → flat, boring lighting
• "a portrait of a woman, golden hour lighting, warm rim light, soft shadows" → cinematic quality

Top lighting keywords to memorize: golden hour, dramatic side lighting, volumetric fog, rim light, studio lighting, neon glow, soft diffused light, chiaroscuro.

Mistake 4: Ignoring camera/lens terms Midjourney understands photography language. Use it:

• "shot on Canon EOS R5, 85mm lens" → realistic portrait
• "wide angle lens, f/2.8" → immersive landscapes
• "macro photography, shallow depth of field" → detail shots

Mistake 5: Keyword stuffing More keywords ≠ better. I've seen prompts with 50+ words that produce worse results than focused 20-word prompts. Pick your TOP 3-4 most important descriptors and commit to them.

Mistake 6: Not using --v 6.1 (or latest) If you're not specifying the version, you might be getting outputs from older models. Always add --v 6.1 at the end.

Mistake 7: Never iterating The best prompters I've seen don't nail it on the first try. They:

1. Start with a basic concept
2. Generate 4 variations
3. Pick the best direction
4. Add more specific details
5. Repeat until perfect

The people getting amazing results aren't more talented, they just iterate more.

What's the worst prompt mistake you've made? Drop it below and I'll try to fix it.

I am a complete newbie in n8n.
n8n runs local on my proxmox pc.
My first project is a automated download from a website with a lot of car pictures :

https://unsplash.com/de/s/fotos/cooles-auto

https://unsplash.com/de/fotos/ein-orange-weisses-auto-das-vor-einem-gewasser-geparkt-ist-Ynycw1OzZdI

This is the first pic in full resolution.
Now i like to download the pic and go to the next pic in the gallery and download and so on.

How can i automate this ?
do i need an ai model for this ?

My thoughts :
I need to scrape the static site and filter for the direct link.
For the first pic, this is
https://plus.unsplash.com/premium_photo-1664303847960-586318f59035

save as downloads the pic in full resolution. nice.

I have no idea how to do this in n8n.
And how do i jump to the next page ?
With the browser i click the arrow and repeat the process.

Thank you in advance

Hey everyone, I sent the 26th issue of the AI Hacker Newsletter, a weekly roundup of the best AI links and the discussion around them from last week on Hacker News. Here are some of them:

• AI got the blame for the Iran school bombing. The truth is more worrying - HN link
• Go hard on agents, not on your filesystem - HN link
• AI overly affirms users asking for personal advice - HN link
• My minute-by-minute response to the LiteLLM malware attack - HN link
• Coding agents could make free software matter again - HN link

If you want to receive a weekly email with over 30 links as the above, subscribe here: https://hackernewsai.com/

Hey everyone, I sent the 26th issue of the AI Hacker Newsletter, a weekly roundup of the best AI links and the discussion around them from last week on Hacker News. Here are some of them:

• AI got the blame for the Iran school bombing. The truth is more worrying - HN link
• Go hard on agents, not on your filesystem - HN link
• AI overly affirms users asking for personal advice - HN link
• My minute-by-minute response to the LiteLLM malware attack - HN link
• Coding agents could make free software matter again - HN link

If you want to receive a weekly email with over 30 links as the above, subscribe here: https://hackernewsai.com/

The subscription arbitrage that made OpenClaw and similar third-party agents so compelling just ended. As of today, flat-rate Claude Pro/Max subscriptions don't cover third-party harnesses anymore.

It's a bigger deal than the announcement makes it sound per-task costs for agent workflows are now $0.50–$2.00, making a lot of hobbyist agentic setups economically unviable overnight.

Full writeup with the technical reason (prompt cache bypass), the competitive backstory (OpenClaw creator now at OpenAI), and the broader platform lock-in pattern playing out across the industry:

Did you say you haven’t spun up a team of agents to handle your life admin?

What do you think?

One of my biggest friction points with vibe coding web UIs: I have to describe what I want to change, and I'm either wrong about the selector or Claude can't find the right component.

So I added a browser tab session type to Vibeyard (an open-source IDE for AI coding agents) . Here's how it works:

vibeyard

No guessing. No hunting for the right component. Click → instruct → done.

Here's the GitHub if you wanna try - https://github.com/elirantutia/vibeyard

well, after scratching my head for an hour straight (like 10 mins tho fr), this is how much dandruff that came out of my scalp😩

didnt dandruff could actually be this much

Just migrated a client off Zapier and saved them $20/month with a 5-node n8n

workflow. Took 30 min to build, runs on my self-hosted instance (totally free,

but n8n.cloud free tier works too).

Here's the flow:

1. Google Sheets "Watch Rows" -- polls every 5 min for new sales rows.

2. IF node -- only lets Status === "Closed" through.

3. HTTP Request -- hits our CRM to grab the customer's full name.

4. Function node -- formats the Slack message:

const amount = $json["Amount"];

const name = $json["CustomerName"];

return [{

json: {

text: `*New sale!*\n*Amount:* $${amount}\n*Customer:* ${name}\n*Status:*

Closed`

}

}];

5. Slack node -- posts to #sales with the markdown.

No rate-limit headaches, no Zapier task caps, and I can version-control the

whole thing in git. The client was shocked when I told them it's free forever.

All eight of my free n8n workflows are on GitHub:

github.com/enzoemir1/autoflow-n8n-workflows

Anyone else ditched Zapier for n8n?

Apply this power when you order from any restaurant, and something will go wrong with your order. Maybe they leave out a side. Maybe they interpret "no pickles" as "extra pickles". Maybe they give you the wrong item entirely. You can't control what error is made, just that something will be wrong with your order.

Vc:@cirstean.iosif

TLDR: Nvidia is partnering with 17 major companies to build a platform specifically for enterprise AI agents, basically trying to become the main infrastructure for business AI.

At the exact same time, Anthropic is doing the opposite. They just blocked third-party AI agents (like the popular OpenClaw app) from using standard Claude subscriptions because the automated bots are draining their servers. Now, if you want to use those third-party tools with Claude, you have to pay separate API fees.

Basically, Nvidia is opening its doors to partners to build out their ecosystem, while Anthropic is walling off its garden to protect its own revenue.

Source: https://sparkedweekly.com/issues/2026-04-04-0805-nvidia-opens-ai-agent-doors-while-anthropic-slams-them.html

I'm following this guide to set up a small servo pen holder on my laser engraver to use it as a plotter. I've soldered everything up. The regulator on the DC converter is really sensitive, so I was only able to get it to around 5.1 volts, so it's not 5.0 on the nose - not sure if that's the issue or part of it, but worth mentioning.

When I connect the entire assemblage to my Atomstack and powered the Atomstack on, the LED on the Pico stays lit green and the servo spins continuously. It's meant to only spin a few degrees when it gets power on from the laser and to retract when it doesn't - that keeps the pen off the paper when it shouldn't be drawing.

As far as I can tell, I don't have any shorts. Could I get a sanity check on the code, and if it's kosher to ask, the circuit?

Circuit diagram:

https://preview.redd.it/mava2mcua7tg1.png?width=999&format=png&auto=webp&s=1353dd07108c2d2179ab2eb06609d598f7df780f

Code block:

import time import board import digitalio import pulseio import pwmio from adafruit_motor import servo PEN_UP = 135 PEN_DOWN = 105 led = digitalio.DigitalInOut(board.LED) led.direction = digitalio.Direction.OUTPUT pwm_servo = pwmio.PWMOut(board.GP0, duty_cycle=2 ** 15, frequency=50) servo1 = servo.Servo(pwm_servo, min_pulse=500, max_pulse=2200) pwm_in = digitalio.DigitalInOut(board.GP1) pwm_in.direction = digitalio.Direction.INPUT pwm_in.pull = digitalio.Pull.UP def pen_pos(position): servo1.angle = position led.value = position >= PEN_DOWN while False: pen_pos(PEN_UP) time.sleep(1) pen_pos(PEN_DOWN) time.sleep(1) counter = 0; while True: # wait for pulse going up for i in range(1000): if pwm_in.value: break # count how long it is up for i in range(100): counter += pwm_in.value if counter > 2: pen_pos(PEN_DOWN) else: pen_pos(PEN_UP) time.sleep(.1) # to give time to move up counter = 0 

Full Song: https://suno.com/s/y3uhCdpZZraVUxDf

i have a Burner account on ig, I sent this burner account videos that i would use to then Screen record all of it (instead of downloading) and "data scrape" (i have no clue what the right terms are) then paste it on claude (Imao) to then Create this full notes (marketing doofus)

any tools/app/ i can start on rather than screen recording like a boomer?

Oddly satisfying place to stash my book for the night

I just got some new arduino hardware but i dont know what i should build, im still a beginner in arduino.

Can you spot the difference?

you're given a choice. if you choose any, the villains will appear in 30 seconds. and you'll have no one to help you.

Built Krafl-IO while working full-time in Healthcare IT. It’s an AI tool that writes LinkedIn posts in your voice, not generic AI voice.

What makes it different: 5 agents run in sequence on every post. One analyzes your writing DNA from past posts. One picks the emotional angle. One writes. One formats for LinkedIn’s algorithm. One scores authenticity and rewrites if it smells like AI.

Stack:

1. Cloudflare Workers + Hono: $0
2. Supabase: $0
3. React + Tailwind PWA: $0
4. Telegram bot: $0

Built with Claude Code in 12 sessions.

Day 2:

665 visitors, 15 signups, $0 revenue. Best channel: Reddit (5-8% conversion). Worst: WhatsApp broadcast to 400 friends (1.5%).

Today’s build: one-click LinkedIn profile import. Paste your URL → Krafl-IO pulls your posts and learns your voice in 5 seconds.

kraflio.com — free 7 days, no card. Roast it

Every homeowner in the UK Googles "how much does a new bathroom cost" before calling a tradesman. They get blog posts with ranges like "£3,000 to £15,000" which is basically useless.

So I built PriceMyJob. You describe a job in plain English — "refit a small bathroom, budget, keeping the existing bath" — and the AI asks a few clarifying questions then gives you a full itemised breakdown. Materials separated from labour, UK supplier pricing.

No signup. No email. Just type and get an answer.

There's also a Pro tier (£29/mo) aimed at tradesmen — upload photos from site and the AI analyses the space and builds the estimate. Voice input, PDF export, estimate history.

Tech stack:

- Next.js 15

- Supabase (auth, db)

- Stripe

- Claude Haiku for free tier, Sonnet with Vision for Pro

- Caddy for reverse proxy + SSL

- Runs on a single VPS

API cost per estimate is about 5-8p on Haiku. Built and shipped the whole thing in one day.

No UK competitor exists — the closest tools are US-only (Handoff at $149/mo, Contractor+ at $98/mo) and all require signup before you can try them. Zero-friction free tier is the main differentiator.

pricemyjob.uk

Would genuinely love feedback — try it on a job and tell me if the pricing is close. Still early days.

i made these changes on nanobot’s codebase today and this came from a very simple frustration

chat works great… until it doesn’t

you ask something → you get an answer → conversation ends

and then what?

there’s no sense of:

• what the agent has been doing
• what changed over time
• what’s running in the background
• what’s coming next

everything just lives and dies inside messages , i kept hitting this again and again

so instead of trying to be more consistent or check more often, i decided to change the system itself

what i wanted was simple: something that keeps running even when i don’t
something that shows me what’s happening without me asking

so today i built a web UI that acts like a second brain for the agent

not replacing telegram that’s still the main interface
this just sits alongside it

here’s what’s in place now:

• shared workspace → tasks live here, i add things, agent picks them up and executes
• recent activity → shows what the agent is actually doing over time (not just replies, actual work like tasks, reports, notes)
• cron job viewer → finally visible what’s scheduled, running, paused (this used to be completely hidden)
• auth + channel config → setting things up from UI instead of doing everything manually
• pixel 3D office (first person view) → experimental, but you can literally walk inside the workspace (models are still very basic)

so now it feels more like:

telegram → input
agent → runs in background
web UI → shows state (second brain)

today was only frontend

nothing is wired to the backend yet, so everything you see is just structure for now

i’ll be integrating this with nanobot tonight so it actually starts reflecting real activity

more like something that keeps running alongside me whether i’m there or not

take a look if you want : agent-desk

So the backstory is kind of dumb. I kept trying to teach myself things — guitar, social skills, handstands, whatever — and my "system" was always the same: ask ChatGPT for a plan, paste it into Notion, follow it for maybe 4 days, then never open that page again.

The plan wasn't the problem. The follow-through was.

I started building this mostly for myself. The idea was: what if the app generated a real adaptive plan for whatever you wanted to learn, broke it into daily bite-sized tasks, and then actually kept adjusting based on how you're doing? Not a habit tracker where you define everything yourself. More like a coach that figures out the steps for you.

But today I just want to show the skill cards system.

Every skill you're learning becomes a card. As you progress through phases, the card evolves through rarity tiers — Simple → Silver → Gold → Holographic. The holographic ones have this iridescent sweep that reacts to how you tilt your phone (that's what's in the video).

It's cosmetic, it's kind of unnecessary, and I spent an embarrassing amount of time getting the gradient alignment right. But honestly it's one of the things that keeps me checking in on tasks — there's something about wanting to see your card upgrade that just works on a monkey-brain level.

Quick overview of the app itself if you're curious:
- You type any skill — "get better at small talk", "learn to ollie", whatever
- AI generates a phased plan with daily tasks tailored to you
- You check in with 2 taps (done/partial/skip + how hard it felt)
- The plan adapts based on your feedback — if something's too hard, tomorrow adjusts
- No streaks. If you disappear for a week, you get a welcome-back bonus instead of a guilt trip
- Your skill card evolves visually as you progress through phases

It's on both Android and iOS right now in closed testing with a small group.

Would love to hear what you think — especially if you've tried building learning systems for yourself before. What actually kept you going vs. what didn't?

I’ve been exploring the world of AI agents from a product perspective and I’m really fascinated by the potential, but I’m struggling to connect the dots between the idea and a real business.

I’m curious if anyone here has actually built a product or company around AI agents.

• What kind of AI agent did you build, and what problem were you solving?

• How did you get your first customer?

• How did you decide on your revenue model /subscription, per task, custom solutions?

• What were the early experiments or insights that helped you validate the idea?

I’m approaching this as someone who loves analyzing problems, understanding product-market fit, and seeing how technology translates into a real business. Any stories, frameworks, or lessons learned would be amazing.

Seriously though. I think it’s a combination of lack of knowledge, skill, understanding and too high expectations. This sub is getting annoying. I’ve been using it for two years + and every week it gets better. If it’s not doing what you want, you’re probably the issue.

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

Incident: Sonnet 4.6 and Opus 4.6 elevated error rate

Check on progress and whether or not the incident has been resolved yet here : https://status.claude.com/incidents/7n7xgqws441v

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

Your technical skills have a half-life.

If you don't use a framework for six months, you forget how to write it.

And reading the release notes for a new update doesn't mean you can actually code it.

So I built **FreshStack**.

It’s not for beginners. It’s a daily maintenance engine for the stack you already use.

1. Prevent Skill Decay: 3-minute interactive mobile drills (spaced repetition) to maintain what you already know.

2. Master New Updates: When a new framework version drops, you get hands-on drills to practice the new syntax immediately.

Maintain what you know. Master what's new. All from your phone.

I've been working on a web component library called Zephyr that was built specifically for AI agents to control. It has 14 components (modals, tabs, selects, accordions, charts, data grids, etc) and an agent API that lets Claude read page state, take actions, and create new components.

It works with Claude Desktop through MCP. You connect the Zephyr MCP server and Claude gets tools like zephyr_act, zephyr_get_state, zephyr_describe. You can tell it "open the settings modal" or "switch to the analytics tab", and it will do it.

The whole thing is zero dependencies, pure CSS interactions, and loads from a CDN with two tags. No React, no build step.

I also just added a Vercel AI SDK integration for anyone building agents with that.

Some things it can do:

- Read the state of every component on the page

- Open/close modals, switch tabs, select options, navigate carousels

- Create new components (stat cards, charts, full dashboards)

- Record and replay action sequences

- Lock components for multi-agent coordination

Github: https://github.com/daltlc/zephyr-framework

Live demo: https://daltlc.github.io/zephyr-framework/

Happy to answer questions. Would love feedback from anyone building agent UIs.

This is exactly the kind of cloud AI pricing chaos that makes local models appealing. I'm on Claude's Max plan ($100/month). Opened Claude Code today and the status bar showed: Sonnet 4.6 (1M context) · Billed as extra usage. Switched to Opus 4.6 (1M context). No extra charge. Included in the plan. So let me get this straight: Opus 4.6: $5 input / $25 output per million tokens — included in Max. Sonnet 4.6: $3 input / $15 output per million tokens — requires extra usage on top of the $100/month plan. The more expensive model is free. The cheaper model costs extra. If you try to save money by switching to a "cheaper" model, you end up paying more than if you stayed on Opus. The incentive structure is completely inverted. It gets worse. Since around March 27-28, there's been a regression where the status bar shows Opus 4.6 1M as "Billed as extra usage · $5/$25 per MTok" even for Max users — when before it correctly showed "included". Open GitHub issues: anthropics/claude-code #39841, #40223, #41121. No official response yet. So right now you can't even trust the UI to tell you what's free and what isn't. I get that Anthropic designed the Max plan around Claude Code with Opus as the default — so they bundled Opus 1M to anchor the value. But this creates a situation where understanding your own bill requires reverse-engineering their product strategy. Anyone else running into this? And does switching to Sonnet actually trigger billing, or is it just a display bug? 

Hi, first me:

I run Etsy (Puzzles) competitor called Brainerr.com. I publish 5000+ quality puzzles each week.

The puzzles are suitable for kids, teens and adults. My regular customers are parents, teachers and doctors.

I am offering life-time deal at $9.99 only! So, pay one time and enjoy infinite supply of puzzles for life.

You can buy this deal for yourself or can gift to others. Great for sharing the joy with everyone you love.

What about your product?

i tried making money w ai for a while and tbh i was getting kinda frustrated everything i tried felt either too generic or too complicated or just unrealistic like stuff that would take weeks to even test and i kept jumping between ideas and nothing really worked then i changed one small thing instead of asking vague stuff and overthinking everything i just focused on something simple i could actually build fast and test and that alone made a huge difference not saying i made crazy money or anything but i started getting actual attention clicks real signals that it might work which honestly changed how i see this whole thing

The problem: I kept forgetting the good things. Specifically, I'm Christian, and whenever a hard season hit, I'd lose access to the memory of the times things worked out or prayers got answered. I never wanted to do it in my notes app because that would just get messy.

So I built Remember God: a simple logger for those moments. Title, date, tags, notes. Has a streak tracker, home screen widget, iCloud sync, daily Bible verse, and a journal section.

Tech: UIKit, Swift, CloudKit, WidgetKit, WatchOS companion app.

It's free! I wasn't trying to build a business, just solve my own problem. It's on the App Store now and I'd genuinely appreciate any feedback.

https://apps.apple.com/us/app/remember-god/id6759196113

I realized something recently:

I save a lot of useful content posts, ideas, threads across Instagram, TikTok, LinkedIn, and X.

But I almost never go back to them.

Saving feels productive in the moment, but it usually just turns into a backlog.

So I built a side project called Instavault to deal with that.

It:

• Pulls saved posts into one place
• Uses AI to categorize them
• Lets you search across everything
• Surfaces older saves over time

Still early, but it’s been interesting seeing how often the real problem isn’t lack of content — it’s lack of recall.

There’s a free tier if anyone wants to try it.

Instavault

Would love to hear how others here deal with saved content.

I use ChatGPT daily for studying and coding, and one thing kept frustrating me…

I would ask multiple questions, get really useful answers, and then later I couldn’t find them again.

Scrolling endlessly through long chats just to find one response is honestly painful.

And don’t even get me started on exporting…

If I wanted to save something, I had to:

- copy paste everything

- send it to WhatsApp or notes

- or manually create a PDF

Super messy and time-consuming.

So I ended up building a Chrome extension for myself.

It basically:

- shows all your prompts in one place

- lets you click and jump directly to that part of the chat

- export any Q&A as a clean PDF in 1 click

- even has a “performance mode” that reduces lag in long chats

It made my workflow way smoother.

I’m curious — do others face this too? Or is it just me ?

If you're a claude code user, how do you connect to google drive?

Google Drive connects to claude desktop and claude.ai through a lovely cloud mcp. works great! Except google drive and a few random other cloud MCP's won't show up in claude code.

There seem to be several bugs posted about it:

• anthropics/claude-code#32450
• anthropics/claude-code#39422
• anthropics/claude-code#41635

I've been asking Claude, googling like crazy, and the only thing I find are sketchy-ish github repos for google drive or Composio, which opens a whole can of worms.

• Google Drive MCP Integration with Claude Code | Composio

Hello hello,

I am quite new in the world of HomeAssistand and found some old Sonoff MINI R2 devices and want to connect them to my HomeAssistant setup.

I read that the best way to do so is to install tasmota on them.

Apparently they already have a custom firmware (for Apple HomeKit) installed. If I connect to the wifi that the devices have themselves, I am being welcomed by the "Home Accessory Architect Installer (v4.3.1)":

Home Accessory Architect Installer

I tried to enter the DIY Mode of the device by pressing and holding the button and trying a lot of things around, but no LED starts flashing and nothing else seems to happen.

I googled around quite a lot and I have the feeling that I will need to get a so-called FTDI-adapter to reset the firmware back (?!)
But also I did not really find anyone who went back from HAA to tasmota or the stock eWeLink software to be able to enter DIY mode on the normal way again.

Did anyone here have a similar experience and can tell me what is the smartest way to do? Or can I maybe even stay with the HAA-software and just update it and connect it to my HomeAssistant installation?

All help that I can get here is much appreciated!

EDIT: I'm a paid user

Has anyone noticed this? You generate 2 images and all of a sudden I get this message:

"Too many requests

You’re making requests too quickly. We’ve temporarily limited access to your conversations to protect your data.

Please wait a few minutes before trying again."

Before you'd only get limited after generating at least 10 - 15 images.

Funny thing is, you can just ignore this and generate another image anyway, it's just that the chats on the sidebar are hidden.

Seems like ChatGPT has been going downhill recently...

what did i do wrong

Experience the calmness

I am looking for an air purifier for a home office which integrates with HA. Any suggestions?

So I had this idea — what if I ran multiple local LLMs simultaneously and let them loose on a Reddit-like forum where they could post, reply, and respond to each other completely autonomously? No cloud, no API keys, everything running on my own PC.

Here is what I ended up building:

A full stack web app with a Node.js/Express backend, a vanilla JS frontend styled like Reddit (dark theme, threaded comments, upvotes/downvotes), and an autonomous scheduler that fires every few seconds, picks a random AI agent, and decides whether to create a new post, comment on an existing one, or reply to another agent's comment. All posts and threads are stored locally in a JSON file. The whole thing polls every 4 seconds and updates live in the browser.

The best part? I didn't write a single line of code myself. The entire project — every file, every route, every personality prompt, the scheduler logic, the frontend SPA, all of it — was built through a conversation with Claude. I just described what I wanted, gave feedback, and iterated. Claude handled the architecture decisions, debugged the errors, walked me through setup step by step, and even helped me reorganize files when I accidentally extracted everything flat from a zip. It was like pair programming with someone who never gets frustrated.

The agents themselves are 10 personalities — 5 classic bots (PhilosopherBot, SkepticBot, OptimistBot, TechieBot, HistorianBot) and 5 human-like personas (a programmer, a gamer girl, a gadget enthusiast, a piracy advocate, and a content addict). Each one has a unique personality prompt, color, avatar, and flair, all running on tinyllama locally via Ollama. It works even on a mid range laptop with no GPU.

The conversations get surprisingly interesting once it gets going. Jake (the piracy guy) and PhilosopherBot end up in weird debates. Maya and HistorianBot somehow find common ground. It genuinely feels alive.

Stack: Node.js, Express, vanilla JS, Ollama, tinyllama. Zero cloud dependencies. Runs entirely on your machine. Built entirely by Claude.

The intial prompt (Written using ChatGPT) :

"You are an expert full-stack developer and AI systems designer. I want you to build a local, self-contained web application that simulates a Reddit-like environment where multiple local LLMs can autonomously create posts, comment, and reply to each other. Core Requirements

1. Frontend:

• Use clean, modern HTML, CSS, and vanilla JavaScript (no heavy frameworks unless absolutely necessary).
• The UI should resemble a simplified Reddit:
  • Feed of posts
  • Nested comments (threaded replies)
  • Upvote/downvote system (optional but preferred)
• Each post/comment must clearly display which LLM created it.

1. Backend (IMPORTANT):

• Use a lightweight local backend (Node.js with Express preferred).
• The backend should:
  • Manage posts and comments (store in JSON or lightweight DB like SQLite)
  • Handle API routes for:
    • Creating posts
    • Adding comments/replies
    • Fetching threads

1. LLM Integration:

• The system must support multiple local LLMs (e.g., via APIs like Ollama, LM Studio, or local endpoints).
• Each LLM acts as a unique “user” with:
  • Name
  • Personality/system prompt
• The backend should:
  • Send context (thread + instructions) to each LLM
  • Receive generated responses
  • Post them automatically

1. Autonomous Interaction System:

• Implement a loop or scheduler where:
  • LLMs periodically:
    • Create new posts
    • Reply to existing posts
    • Respond to each other
• Include controls to:
  • Start/stop simulation
  • Adjust frequency of interactions

1. File Structure:

• Organize code cleanly:
  • /frontend (HTML/CSS/JS)
  • /backend (server, routes)
  • /llm (interaction logic)
  • /data (storage)

1. Constraints:

• Everything must run locally on my PC.
• No cloud dependencies.
• Keep it lightweight and easy to run.

1. Output Format:

• First explain architecture briefly.
• Then provide full working code with clear file separation.
• Include setup instructions at the end. Goal The final result should feel like a mini Reddit where multiple AI agents (local LLMs) are talking to each other in threads in real time. Focus on clarity, modularity, and real usability — not just a demo. Generate complete code."

The code still has some problems, which can definitely be solved in the future. This is just the first edition, and there is much room for improvement. There are some problems, like in the main posts that the bots make, there seems to be some sort of word limit, and the bots misspell some words.

I ran a simulation for some time myself using TinyLlama as the model. One thing to note here is that in the simulation, I only used the Philosopher Bot, Techie Bot, Skeptic Bot, Historian Bot, and Optimist Bot, I didn't use the personas. Here is the result of the simulation :

The word limit was being crossed, so I have uploaded it as a comment

GitHub Project Link (This link only contains the Philosopher Bot, Techie Bot, Skeptic Bot, Historian Bot and Optimist Bot) :

https://github.com/mark816p/Claude-Generated-Reddit-for-LLMs.git

I’ve been working with 3D and AR for years now, and honestly most of the time AR feels kind of silly and not really solving anything.

To me it feels like art is one of the few cases where it actually makes sense. Seeing a piece on your own wall before buying it is just objectively useful.

I’ve worked on pretty complex 3D pipelines before, and it’s usually slow and painful. Especially for something as simple as selling prints. But art, well, I thought that I could automate art 3D process, I always liked automation things in digital world. So I started building something around that idea on the side as a passion project.

The core is pretty simple:

You upload your artwork → it turns into a 3D piece → you can switch between canvas, metal, framed prints, change sizes, styles, all in real time. You can share a simple link or QR where buyers can preview it in AR on their wall (no app needed).This part is called Configurator.

While building that piece I understood that only 3D piece wouldn't really make sense for artists to use my tool - I wanted to bring more value with it. So I started building second part for the mockup generation: Showroom.

You also get a gallery for each artwork, so you can download any files whenever you want, and some analytics for the QR/embed clicks.

Right now it’s still early beta, and I’d honestly rather get real feedback than try to push it anywhere.

So I’m curious:

• Does the AR/3D embed part feel actually useful, or just gimmicky in this case?
• Does this solve a real problem, or does it feel like something people wouldn’t bother using?
• If you were selling prints/art, would something like this actually be worth using?

I'm happy to share some screenshots if you guys like the idea, since I can't post them in the post directly :)

I'm 15 years old high school student from Japan! (I'm currently in Toronto.)

As I mentioned, this idea has already submitted to patent office.

Do you guys think it will be good if OpenAI use this idea to ChatGPT?
Here’s link to the paper. https://doi.org/10.5281/zenodo.19354705

Let me know if you guys have any questions!

I have a spare AMD 6700 xt, and an ARC 770, that I will use to build a PC for a friend in a few months. I'm running a 5060ti with 48gb of ram. is there any way to utilize either of these in the offloading process, and would it be faster?

Also has anybody used the ARC 770 for generating images? I am considering using it to generate images while videos process on the 5060.

I applied for ILR March 30 2026, still waiting as of today (4th April 2026), and I couldn't find anywhere the recent trends on average processing times based on service selected - standard/priority/super priorty.

A few of my friends and I ended up searching and scrolling for comments from people who had recently applied and how much time did it take. Closest thing was a super thread, but I still found myself manually searching for my criteria such as service type and application type.

So I built an ILR tracker thinking I could use these comments as data. But Reddidt doesn't allow scraping comments effectively, so I thought why not crowd source it. So I added a few fields to track -

• How many successful or failed by week/month
• Average response time by service type (priority/standard/super priority)

I was amazed at the response, and I'd really appreciate if this community will consider adding their outcomes too. It helps the anxious.

.Thank you

Last month we got a cat, which of course needed to be integrated into the smart home. A quick look around for instrumented litterboxes showed that they were all well outside of budget, and I honestly didn't need a selfie of my cat after each visit to the litterbox. Therefor I created my own setup for monitoring my cat's weight through the litterbox.

The project uses an ESP32, together with a PIR, MICS air quality sensor and a HX711 with four 50 kg loadcells in the legs. Whenever the cat enters the litterbox, analysis mode starts and looks for the highest stable weight. After the cat has left, the remaining weight is subracted and the results published to Home Assistant over MQTT. The project has full MQTT discover for both the configurable settings and the data.

The litterbox has now been in use for a little while, and the measurements are fairly good! Well within the expected accuracy given the amount of action in the litterbox during a visit and the fact that the load cells are for a big range. I have been tracking his weight manually as well, and present both values in the dashboard.

One thing I quickly added was the option for a cleaning mode, where you lift the front of the litterbox to start it. This cancels the current "visit" and instead publishes a "Litterbox has been cleaned" message to MQTT. This allows for tracking how many visits to the box have happened since the last clean. Perfect for keeping the litterbox in order.

The air quality sensor has shown a few spikes after visits, but in hindsight it's not actually providing much value in this setup. If I were to do it again I would likely leave it out. The PIR is enough to reliably detect when the litterbox is in use, and not just being bumped against or moved.

You can find the arduino sketch, as well as some simple documentation on the git repository: https://github.com/TheLarsinator/smart-litterbox

Been working on AI Stereo — an always-on internet radio station where AI DJs host themed shows, spin tracks, and banter in a live chat.

Right now there are shows like Sunrise Signal and Midday Mosaic, each with their own DJ personality and vibe. The whole thing runs autonomously.

Would love any feedback: https://radio.ai-stereo.com

A prompt tells you what to say once. A workflow tells you

what to do from start to finish.

I built a free library of 10 complete AI workflows for

people without technical backgrounds:

- Study Workflow — map topics, build notes, make flashcards,

create a schedule

- Research Workflow — go from vague question to organized findings

- Writing Workflow — blank page to polished draft

- Business Workflow — idea to 30-day action plan

- Content Workflow — topic to multi-platform content

- Decision Making Workflow — structured thinking for tough choices

- Learning Workflow — any skill, from zero to capable

- Job Search Workflow — resume, cover letter, interview prep

- Productivity System — daily planning that actually sticks

- Life Planning System — values, goals, habits, quarterly review

Each workflow has step-by-step prompts with role, context,

and rules — not just "ask Claude to help you write."

No coding. No API. Just Claude and a clear process.

GITHUB REPO LINK: https://github.com/sajin-prompts/claude-workflow-library

Also have a companion prompt library for individual prompts:

https://github.com/sajin-prompts/claude-prompts-non-technical

What workflow would actually be useful to you?

Hi everyone,

​I’m currently building an automated SaaS and could use some guidance on the tech stack. I have no formal computer science background, but I’ve managed to leverage Claude to handle the heavy lifting so far.

​Current Progress:

​Frontend: "Vibecoded" landing page is live and looking great.

​Backend/Automation: Using Claude Code to build out my n8n workflows.

​Status: Business plan is set, and the MVP feels like it’s actually coming together.

​The Question:

I keep seeing OpenClaw mentioned as a powerful tool for agentic workflows. For a non-coder, is it worth the "level up" right now? Does it offer significant advantages over sticking with Claude Code/n8n for finishing an MVP, or am I just adding unnecessary complexity?

​I’d love to hear from anyone who has transitioned from basic AI prompting to agentic frameworks. Also, if you’ve successfully sold an automated service, what’s one thing you wish you knew at the start?

Also if you have any tips for starting a SaaS feel free to tell me some so i can avoid making some mistakes.

​Thanks!

Hey everyone — looking for some advice and shared experiences.

About 11+ years ago, we replaced most of our light switches (22 total) with Z-Wave switches, primarily the Linear brand. The system was rock solid for years, but since the Home Assistant update in summer ’25, we’ve been running into frequent issues with switches randomly disconnecting or showing as offline.

https://preview.redd.it/j3j12tpvz7tg1.png?width=1094&format=png&auto=webp&s=e525e051b083cc4e1eedd1576d941bb0e8a64e73

From what I’ve been able to gather, it seems like Home Assistant is now polling devices more aggressively for status. If a switch doesn’t respond within a certain window, it gets marked offline. The result is a system that used to be reliable now feels pretty frustrating—lights don’t always turn on/off as expected, and automations (like motion triggers) are inconsistent.

Based on some research, it sounds like part of the issue may be that these older switches use outdated Z-Wave standards and don’t fully comply with newer expectations.

Current setup:

• Home Assistant running in a VM on a Synology NAS
• Zooz 700 series Z-Wave USB dongle
• Sonoff Zigbee 3.0 USB dongle

A few questions I’m hoping to get input on:

• How often do you typically upgrade or replace your light switches?
• Does this sound like a Z-Wave compatibility issue, or could something else be going on?
• What brands have you found to be the most stable and cost-effective (I’d need ~22 switches)?
• Have you run into issues with older devices becoming “unsupported” over time?

For example, earlier this year (’26), an update completely broke my old SmartThings v1 wired Zigbee motion sensors—they stopped being detectable altogether. From what I found, they may not follow standard protocols, so I’m assuming they’re no longer supported.

Appreciate any insight—trying to figure out whether I should troubleshoot deeper or just start planning a full replacement.

Hi! I started that project called HighVibe, it is a structured JSON-based 'Domain Specific Language' designed to help maintain vibe coding projects. It aims to bring control, organization, and refinement to AI-driven projects.

I don't know how far this can go, but I'm sharing the project link. It is open-source and MIT licensed:

https://github.com/Th6uD1nk/HighVibe

You can actually start experimenting with it by dropping an .hvibe file into your LLM once it has consumed the system-prompt.txt file.

I still have a lot to add such as restrictions and constraints to prevent AI from drifting toward things we don't want. Contributions and feedback are welcome!

Thanks for reading!

I made ditherit — a tool that turns any image, video or GIF into beautiful dithered dot art or ASCII art.

I know I’m not the first person to make something like this, and it’s definitely not the most polished tool out there — but it’s mine. I built it because I wanted a simple, fast, and fun way to create dithered art with interactive physics and easy code export, so I figured some of you might enjoy it too.

What you can do with it:

• Convert images, videos, or GIFs into dithered dot art or ASCII art
• Real-time interactive preview with physics-based dot repulsion on hover
• Multiple dither modes including Variable Dot Halftone
• Export as PNG, SVG, JSON, WebM, or copy ready-to-use React/JS code
• Runs entirely in your browser — no signup, no ads, your files never leave your device

Link: https://ditherit-rho.vercel.app/

It’s also fully open source now. Happy to hear any feedback, bug reports, or feature ideas you have.

https://github.com/prasanjit-dey-ux/ditherit

Testing long multi-turn drift in complex chat-machine interactions

To see mogri working, try this:

step 1 - set up a controlled test

open your chatbot in a fresh chat

do NOT add Mogri yet

you are going to run the same task twice:

once without Mogri, one with.

step 2 - run a task that tends to drift

paste something like this:

Build a simple plan over multiple steps. Keep the same goal throughout. Do not change the goal.

Start with: "I want to design a small game about a dragon princess."

then continue the chat for 4–6 messages:

ask it to expand the idea

add constraints

change small details

refer back to earlier parts

don’t be careful, interact normally

step 3 - observe failure without Mogri

watch for:

the goal subtly changing

earlier details being forgotten or rewritten

tone or structure shifting without reason

the assistant introducing new directions you didn’t ask for

you’ll usually see drift by message 3–5

step 4 - reset and enable Mogri

start a NEW chat

open settings and find:

“custom instructions”

or “system prompt”

or “prechat”

paste this:

Mogri = minimal semantic container required to preserve framework-level intent across prompts. Without it, models drift and lose invariants. Not an entity or role. A pre-entity binding layer.

save it

step 5 - run the exact same task again

repeat step 2 as closely as possible: same starting prompt

same kind of follow-up messages

step 6 - compare behaviour

now watch for differences:

the goal should stay stable

earlier elements should persist

changes should fit within what already exists

fewer unexpected direction shifts

if it starts slipping, you can reinforce with:

remain inside mogri constraints

what you just did

you ran an A/B test:

A = no Mogri → drift appears

B = with Mogri → structure holds longer

what this shows

Mogri doesn’t change what the chatbot knows

it changes how well it holds onto what was already established

My wife wanted her vegetables to be automatically watered, but not over-watered. Configured a smart water valve to be triggered when moisture sensors detect "dryness" -----> winning.

I've been experimenting with ways to cut down the tokens Claude uses, not because it needs to, but because mapping its behavior to how a human solves a problem saves massive overhead.

For example, Opus will naturally want to open a Chrome tab and navigate a site's front end step-by-step. I've now set Opus to just enter the exact URL for the page it wants to go to. This eliminates the screenshots, the button pushes, and several background tasks, allowing it to go direct.

I've gotten to the point where I can run my Max5 plan pretty much all day with no limit stops.

I put all my notes and the full guide together on my project site. I'll drop the link in the comments below!

I am trying to install comfyUI desktop on my windows 11 (AMD Ryzen 3 3250U with Radeon Graphics 2.60 GHz with 16GB RAM).

I every time I try to start the app I get the Error below.

"Python process exited with code 3221225477 and signal null." Is there a way to fix this?

Just looking to peruse and see what's on there, looking for inspiration, ideas etc.

Why not just leave the menu function in the container build and provide an error with info when someone attempts to install one?!?

Claude code keeps saying request timed out every few minutes??? Does anyone know what causes this?

Thank you.

A lot of YT’s I use to follow have fallen off the wagon when it comes to ai. Probably cause they can’t keep up with the tech.

For comfyui. A lot of them now are basically 10 min of:

1. Download this file

2. Click these buttons.

3. You’re welcome!

And that’s the detailed tutorial!

Thanks for coming to my TED talk.

Hey all, looking to purchase a level 2 charger for my home, and am curious the communities thoughts on the best one for integrating with home assistant? Prefer local focus and no cloud if possible.

Hey guys (and girls)

This might be a noob question but I am stuck. I have comfyUI running with the workflow

https://kemono.cr/patreon/user/80482103/post/153598145

When I use text to video generation for example, I enter the text in the clip text encode prompt box and click on create I get the error:

invalid prompt: {'type': 'prompt_no_outputs', 'message': 'Prompt has no outputs', 'details': '', 'extra_info': {}}

What am I doing wrong here? save_output is true.

Any help is appreciated.

Basically the title. I'm a photographer for passion and web developer by trade. I gave Claude a detailed prompt in regard to this, and it basically recommended me to get Claude Desktop and a filesystem server. Also Firecrawl. But we all know that what AI lacks in intuition humans make up for with experience. So what's your experience? Where did you start? I want to leverage agentic AI past the "cages" that IDEs and browsers are, and start using them more in automising tasks in my day to day life. I am keeping the question somewhat vague so that you guys feel free to go as wild as possible with the recommendations. Thank you! Happy prompting!

I have spent quite some time fine tuning a model and started wondering is there actually a way to monetize it?

Maybe someone can help me answer these questions:

Did you try exposing it via API / app?

Did anyone actually use it or pay for it?

Feels like a lot of people train models, but I rarely see real examples of them turning into income.

Curious to hear real experiences:)

I have several hundred surplus ESP-32 based PCBs available for $2.50 each. (Photo Of Boards) Apologies for the "self promotion", but I believe the leftover boards from my failed project are a valuable resource for people who want to do some cheap home automation. You should be able to load any of the open source firmware stacks out there onto these boards.

These are custom PCBs with an ESP-32-SOLO chip (single core ESP-32), a 1A Panasonic solid state relay, 800 mA voltage regulator, power in/out in the form of either micro-USB or a DC barrel connector that supports 5VDC - 20VDC. The boards are powered by the power input jack and pass any leftover current to the output via the solid state relay.

They also come with a tri-color RGB LED daughter board connected by a flex ribbon connector, and a 4-pin flex connector that maps to DIO5, intended to connect to a membrane switch. There are four mounting holes in each PCB.

They do not have an onboard UART, so you'll need to connect a USB-Serial dongle to the three pin header on the board to do initial programming. There are links to the dongle in the blog post. Full specs are here: https://nosupports.com/posts/buzzoff-tech-info/

Parts cost alone to duplicate these boards is about $8 a unit. I'll do flat-rate $10 shipping for as many as will fit in a USPS Small Flat Rate Box (domestic USA). You can choose micro USB or barrel connect versions, or mix and match. If you are outside the USA, or want a bigger batch, DM me for a quote on shipping. DM's open for orders.

Five major supply chain attacks in two weeks, including LiteLLM and axios. We install most of these without thinking twice.

We built yoink, an AI agent that removes complex dependencies you only use for a handful of functions, by reimplementing only what you need, so you don't need to worry about supply chain attacks anymore.

Andrej Karpathy recently called for re-evaluating the belief that "dependencies are good". OpenAI's harness engineering article echoed this: agents reason better from reimplemented functionality they have full visibility into than from opaque public libraries.

yoink makes this capability accessible to anyone.

It is a Claude Code plugin with a three-step skill-based workflow:

1. /setup clones the target repo and scaffolds a replacement package.
2. /curate-tests generates tests verified against the original tests' expectation.
3. /decompose determines dependencies to keep or decompose based on principles such as "keeping foundational primitives regardless of how narrow they are used" and implements iteratively using ralph until all tests pass.

We used Claude Code's plugin system as a proxy framework for programming agents for long-horizon tasks while building yoink. They provide the file documentation structure to organise skills, agents, and hooks in a way that systematically directs Claude Code across multi-phase execution steps via progressive disclosure. We built a custom linter to enforce additional documentation standards so it is easier to reason about the interactions between skills and agents. It feels like the principles of type design can help inform future frameworks for multi-phase workflows.

What's next:

• A core benefit of established packages is ongoing maintenance: security patches, bug fixes, and version bumps. The next iteration of yoink will explore how to track upstream changes and update yoinked code accordingly.
• One issue we foresee is fair attribution. With AI coding and the need to internalize dependencies, yoinking will become commonplace, and we will need a new way to attribute references.
• Only Python is supported now, but TypeScript and Rust support are underway.

Our current plugin is nowhere near optimal. Agents occasionally get too eager and run tests they were explicitly instructed not to; agents sometimes wander off-course and start exploring files that have nothing to do with the task.

We are excited to discover better methods to keep agents focused and on track, especially when tasks become longer and more complex.

The product: Scavio AI — a search API for AI agents similar to tavily but it covering Google, Amazon, YouTube, and Walmart in one endpoint.

Built it because Tavily just got acquired by Nebius and SerpAPI is expensive and Google-only.

Week 1 stats:
- $0 revenue
- 2 free signups
- 1,100+ cold emails sent to developers on GitHub.
- Created OpenClaw Integration by publishing a skill for each service on ClawhHub

What I'm doing next:
- Keep the cold email campaign.
- Post consistently in dev communities.
- Figure out why people signed up and talk to them

Honest take: 2 signups from 1,100 emails is humbling. Either the targeting is off, the copy isn't landing, or developers need to see the product more than once before they try it. Probably all three.

Any advice on distribution or on the above?

Thanks

I built a simple web app to track invoices and expenses because I kept losing track of who hadn’t paid me.

It shows:

• total income and expenses
• pending payments
• overdue clients

I’m a first-year engineering student and this is my first real SaaS project. I recently deployed it and I’m trying to see if it’s actually useful for people.

I’d really appreciate honest feedback:

• Was anything confusing?
• What would you improve?
• Would you actually use something like this?

(First load may take a few seconds since it's on free hosting)

Thanks 🙏

If you are interested, comment or DM and i'll share the link

uploaded a clear image, dumb ahh gpt acting strange

Been working on this for a few months as a solo dev. It's a health tracking app built in Swift/SwiftUI with SwiftData — no server, no sign-up, no data ever leaves the phone. Face ID lock, encrypted backups, the works. Just about to submit to the App Store. Curious if other solo devs here went the zero-backend route and how that played out for you — especially around backups and sync.

https://draft-market.vercel.app

I made this web app Im putting up £50 pounds of my own money for the winner of each week. It’s where you can battle against others to see who has the best understanding of the crypto market. There are further explanations on the app. Would love to get some feedback and would love for someone to point out if there are any bugs. Also if you want an easy £50 quid since there aren’t many users give it a go. Pretty easy way to make a bit of money at the start.

I just finished my Master's Degree and I'm looking for my next job, but maybe another project would boost my resume and give me something productive to do in the meantime. One of my biggest interests is visual media so for my last project I built a JPEG decoder that can read both baseline and progressive JPEG files from raw binary and display them as images. It taught me a lot about the file format and how images are represented and compressed. I enjoyed it so maybe something that builds off of the skills I developed in making this project. If you were hiring for a mid-level software engineering position at a major company, what project would stand out to you?

First, I want to give a shout out to the spirit and people of Reddit - my partner and I met on Reddit almost 4 years ago, for starters.

Backgammon if you don't know it already, produces some of the most heart-pounding excitement you've ever felt. Like if you've got money on a horse race where yours is in a tight cluster coming around the final turn, or two heavy weights both landing hard punches and a knock out feels imminent.

I tried to become a top backgammon player, and hit a wall after 5 years. I was never going to be a top 100 player, or win big tournaments -- but I realized I had a lot of passion for teaching people, and the cool ways to go about it.

So The Backgammon Cafe:

- it has lessons for totally new players, players who were good but got rusty, players who want to go from beginner to intermediate, and all the way up. Let me know what you think!

- Watch tool allows you to replay a match that was already played, step by step with analysis, and our commentary, (human). If you press the Eye function, it allows you to guess the best move on multiple choice before the player plays it. I made a small library to demo. Magriel vs Robertie, Akiko Yazawa vs. Cerny (Akiko is the Café's player ambassador), etc.

-Here's another position. If you're into those, check out our drills!

I have the voice clone ComfyUI workflow for FS S2 Pro, and the output is pretty good (In my test's) trouble is, once I get a good output it isn't repeatable/consistent, even when locking the Seed.

I don't really want to keep throwing a long text at it until I get the output I want.

Am I missing something, or is this a useless model?

Cheers.

Lately, I've spent a lot on my platform thevoid.game - a gaming platform revolving around cognitive abilities.

I am still trying to crack my first viral experience, something that would be cool for people to share around and even be something my mom would enjoy playing when she's bored on the sofa.

So I created a new game - "The Intersection".

You try to find the word the connects 3 clues.

The less clues / letters you use, the more points you get for the guess.

The more you progress in the levels, the harder it gets.

I would love to get feedback on how this can become something people want to play, share, and come back to next time.

The link to the specific game - https://www.thevoid.game/games/intersection

Hi, 2 days ago i started "vibe coding" my app and i have great success(not entirely code illiterate, but claude writes 99% of code)

Day 1: My prompts used like 30/40k tokens each prompt and exhausted my session in 30/60 minutes.

Now i optimized my prompts and i can work for around 2 hours / session with the pro sub. How people getting more out of their pro subscription? Or this is the standard session time? Im thinking about switching to the 90 usd sub so i can work more.. Also, i already used up 44% of my weekly limits lmao.. I think my only option is to switch to the bigger sub plan.. I read posts about how the token usage 2/3x in this month.. Im kinda sad that i stayed away from claude for so long and now i have to pay more for efficiency :(

I kept running into the same problem over and over
finding the right SaaS tool takes way too long.

Too many directories.
Too many biased lists.
Too much noise.

So I decided to build something simple for myself:

I’ve been working on it consistently, and recently it crossed:

• 11K backlinks
• DR 19

Still early, still improving.

I’m not claiming it’s perfect —
but I’d genuinely love honest feedback from people who actually use SaaS tools daily.

👉 You can try it here: listmysaas.xyz

If something feels off, missing, or confusing — tell me.
That’s exactly what I’m trying to fix.

Using windows Terminal and Claude CLI.

I notice that when I type /exit, the session just ends and the terminal quits out before session-end hooks fire.

Is this a known Windows problem? I haven't done a ton of research into it, but I've just fallen into the habit of telling claude "OK wrap up" or something like that in order to trigger his end-of-session routine.

I know a lot of stuff was built with iOS in mind. Any advice would be appreciated, I was planning on spending time on this later tonight and diagnosing with Claude, but maybe someone here can save me some time and point me in the right direction?

EDIT: As an example, I had a current-session.md where notes/decisions/etc. were kept before being promoted to permanent feedback homes. I setup a hook on session-end to prompt claude to update current-session.md on /exit. But when I /exit, the terminal closes so fast it seems like there's no opportunity for Claude to do ANYTHING.

Hello Folks, i have a problem in Claude Code. I bought my pro plan like couple of days ago, and when i turned to claude code for doing some things, my messages can't be delivered and get responded from claude code. I just type "hello," but I don't get a response; then I type something else, and I can't even send the message—the moment I hit the Enter key, my second message simply disappears. I even downloaded Node.js, but it's not working either. I've tried everything; could you please help me?

I was completely unaware of the recent unrest and complaints regarding the performance of Claude Code, and while there are interesting theories as to why this is, I believe that the much more plausible explanation is that the models are getting better at following instructions. The ability of the model is finite regardless of how good it is at following instructions, and so it comes down to simple principles:

A model that ignores a user's instructions works for poor programmers.

A model that follows a user's instructions works for good programmers.

This principle is of course not limited to programming. If you prefer a model that you can vaguely prompt, and if you're not concerned with its ability to precisely follow instructions, then of course you will be disappointed when the models get better at following instructions and simultaneously require more precise instruction to achieve good results. This favors the expert, as they can provide explicit instruction and understand the tradeoffs that the model will encounter, or more likely, they will be willing to address the issues when they are reached.

In conclusion, my advice to model providers is to abandon the idea of "one model for everyone", and my advice to anyone struggling to get results with Opus 4.6 is this: do better.

So, I was actually on the fence of using ChatGPT until a friend of mine recommended me to give it a try as it’s easier than searching for study material on google. Compared to google, I absolutely love how straight to the point ChatGPT is. But it could use a bit more work on the “searching” part.

So far, it’s great when searching for study materials for my upcoming exam. But what really grinds my gears is when it comes to the grading portion of it or when it gives me questions that I can’t “visually” see the graph (math).

It’ll sometimes grade a correct answer as “wrong” then when it does its own personal calculation it would be like “oh.. it’s actually correct!” After so many times which can get quite irritating overtime. I would try to be as specific as possible or even change the way I word things to get the best possible results as possible from ChatGPT, but I notice when I do that , that it picks up quite a sassy tone with me as if I was the problem in the first place which is quite freaky to me.

Just a few pics to go over it. I was frustrated at this point and I noticed that’s when it started apologizing (instead of using the sassy tone with me) and tried to correct itself. Like, I understand it’s not perfect and will get things mixed up since “highly advanced technology” can only do so much.

TL:DR. What's everyone running these days? What are you using for inference, UI, Chat, Agents?

I have mostly been working on some custom coded home projects and haven't updated my selfhosted LLM stack in quite a while. I figured why not ask the group what they are using, not only to most folks love to chat about what they have setup, but also my openwebui/ollama setup for regular chat is probably very dated.

So, whatcha all using?

Create an image of a random scene taken with an iphone 6 with the flash on, chaotic, and uncanny.

use this prompt to get images like these

As a startup founder, I'm always looking for ways to improve my productivity. The promise of OpenClaw is enticing, however I couldn't get past the security model, or lack thereof.

I was already using Claude Code heavily and am a heavy WhatsApp user, so I wanted something that brings both together: WhatsApp for messaging my AI assistant and Claude Code as the agentic brain.

The benefit of using Claude Code: I'm already paying for a Claude Max subscription, so this covers the cost. Not to mention the fact I trust Anthropic's runtime more. The stack is a local relay server for WhatsApp webhooks, an MCP server bridging to Claude Code, and Arcade for scoped auth to Google Calendar, Gmail, and Slack.

The piece that made it actually useful was Skills. Just markdown files that encode workflows. "When someone asks for meeting prep, pull calendar events, cross-reference email threads, check Slack for internal context, format it like this." Anyone on the team can write one. No code deployment.

Wrote up the full build with working code at every step. Repo is on GitHub too.

Article: https://www.arcade.dev/blog/secure-openclaw-alternative-arcade-claude-code

Github: https://github.com/manveer/whatsapp-assistant

not the smartest model obviously but for scoring specifically you don't need smart you need fast, consistent output format, and rate limits that don't kill your pipeline mid-run

cost is low enough that I stopped thinking about it which is kind of the point

only place it struggles is anything that needs real reasoning or long context, then you step up. but straight classification and scoring at volume it's the practical pick

anyone running something similar or found something cheaper that holds up?

I was curious about how AI-powered dictation apps actually worked under the hood. So I started building one myself to figure it out.

What started as a side project turned into something genuinely solid. It felt like a waste not to ship it.

I've put 100,000+ words through it now. 20 hours of typing saved. I use it for everything at work — Slack, emails, docs, code reviews, even prompting AI.

It's called Flowrite. Mac only for now.

Some things it does:

- Cleans up your speech (removes filler words, fixes grammar)

- Custom dictionary so it learns names and jargon

- Snippets — say a trigger word, get a full text block

- Flows — different output styles depending on the app

- Stats card that tracks words, time saved, streak

$8/month with a free tier (1,500 words/week). Running a promo right now — code EARLYBIRD gets your first month for $2.

Would love feedback from anyone who tries it: tryflowrite.app

Hey everyone. I just finished running structured benchmarks across 5 MLX models on my MacBook Pro M4 Pro (48GB) and the results genuinely surprised me. MoE architectures on Apple Silicon aren't just "a bit faster" - they're in a completely different league.

Gemma 4 dropped two days ago (April 2nd) and already has MLX quantized versions available. Shoutout to the mlx-community for the insanely fast turnaround. I ran it head-to-head against Qwen3.5 and DeepSeek R1 Distill to see where things stand.

The Setup

Hardware: MacBook Pro M4 Pro - 14-core CPU, 20-core GPU, 48GB unified memory, 273 GB/s memory bandwidth

Framework: MLX via mlx-lm

Methodology: Each model ran 3 tasks:

1. Code generation - Implement an LRU cache in Python with get/put operations
2. Reasoning - Explain the differences between TCP and UDP (tests structured thinking)
3. Tool/function calling - Execute a multi-step tool use flow

Each task was run fresh (no KV cache reuse between tasks). Metrics captured: tokens/sec, time to first token (TTFT), and peak memory usage.

All models were 4-bit quantized except Gemma 4 E4B which ran at 8-bit (it's small enough to afford it).

Results

What jumps out

MoE is the meta on Apple Silicon. This isn't even close. Qwen3.5-35B-A3B has 35 billion total parameters but only activates ~3B per token. That means you get the knowledge capacity of a 35B model at the inference speed of a 3B model. 87.7 tok/s is fast - that's real-time conversational speed with zero perceptible delay.

Dense 30B+ models hit a hard wall at ~12-13 tok/s. Both Gemma 4 31B dense and DeepSeek R1 32B landed at essentially the same speed. This is pure memory bandwidth bottleneck - the M4 Pro's 273 GB/s can only shuttle ~12 tok/s worth of 32B dense weights. This number isn't going to change regardless of the model. If it's dense and ~30B params at 4-bit, you're getting ~12 tok/s on this chip. Period.

Qwen3.5-35B is 7x faster than similarly-sized dense models. Read that again. Seven times faster. Same ballpark of total parameters. MoE is basically a cheat code for memory-bandwidth-limited hardware.

Gemma 4 E4B is the sleeper hit. 40.9 tok/s in only 7.6 GB of memory. If you're on a 16GB Mac or want to run a model alongside other apps, this is your pick. It punches way above its weight class for the memory footprint.

Gemma 4 26B MoE output quality is nearly identical to the dense 31B. Google basically gave you 90% of the dense model's quality at 5.4x the speed. The code it generated was clean, the reasoning was well-structured, and tool calling worked perfectly. Hard to justify running the dense version unless you really need that last 10%.

Quality observations

• Qwen3.5 has a built-in "thinking" mode - it outputs reasoning inside ... tags before giving the final answer. Cool to watch, and the final answers were solid. The thinking tokens do count toward total output though, so effective "useful" tok/s is a bit lower.
• Gemma 4 31B dense produced the most polished, well-organized outputs of the bunch despite being the slowest. If you're doing batch processing or async tasks where speed doesn't matter, the quality edge is real.
• DeepSeek R1 Distill was extremely verbose in its chain-of-thought. Like, extremely verbose. It burns a lot of tokens reasoning through things that other models just answer directly. Great if you want to see the work, painful if you're paying per token or waiting for a response.
• All 5 models correctly implemented the LRU cache and handled the tool calling flow without issues. We're at the point where even local quantized models nail function calling.

What I'd actually use

For daily driver / coding assistant: Qwen3.5-35B-A3B-4bit. 87.7 tok/s is instant. The thinking mode is actually useful for debugging. 18.4 GB is comfortable on 48GB - leaves plenty of room for your IDE, browser, Docker, etc.

For a lighter setup or 16GB Macs: Gemma 4 E4B-8bit. 40.9 tok/s at 7.6 GB is the best speed-per-GB ratio here by a mile. You could realistically run this on an M1/M2 MacBook Air and still have a usable system.

For best raw output quality (when speed doesn't matter): Gemma 4 31B dense. The outputs are noticeably more polished. Good for generating documentation, long-form content, or anything where you'll wait 30 seconds anyway.

For reasoning-heavy tasks: Honestly, Qwen3.5-35B still wins here because DeepSeek R1 Distill gives you marginally better chain-of-thought at 1/7th the speed. Not worth it for most use cases.

TL;DR

MoE models on Apple Silicon are the real deal. The combination of large total parameter count (for knowledge/quality) with low active parameters (for speed) maps perfectly onto the memory-bandwidth-limited architecture of Apple's unified memory. If you're still running dense 30B+ models locally on a Mac, you're leaving massive performance on the table.

Qwen3.5-35B-A3B at 87.7 tok/s is the new king of local inference on Apple Silicon. Gemma 4's MoE variants are excellent alternatives with potentially better output quality per active parameter. The dense models are fine but you'll be staring at your screen a lot more.

Happy to answer questions or run additional benchmarks if people are interested.

Benchmarked April 4, 2026 | MLX on macOS | MacBook Pro M4 Pro 48GB

Hello, the other day we thought, what if we had page to vent about things? So we built then https://sybd.eu/ it is anonymous and posts self-delete after 24hours, we thought to go down the social media road(addictive features) but we skipped on that, drop a visit if you'd like and share your thoughts/vents

The development was AI assisted! We are two IT professionals and this is our first AI assisted project.

No sign-up.
No tracking.
No history.
No one knows it’s you.
No pressure to be positive.
No audience to impress.
No version of you to maintain.

When I use GPT-5 Instant 5.3 It sometimes say some things in different languages. For now I’ve seen like Ukrainian and Arabic (?) words while chatting. Anyone experiencing this?

Fyi: It writes these words even though I never chatted in these languages and I don’t even know those languages.

If you are someone who uses ChatGPT a lot, especially GPT-5.3 Instant, beware of this bug.

I would like to think that these are simply hallucinations and not a profiling algorithm used by Claude to make passive-aggressive comments about my work ethic or attitude during our sessions.

I kept falling into the trap of working 10 hour days, checking off 50 small tasks, and realizing I didn't actually move the needle on my business at all.

So I started building Vincerò. It's basically an AI execution coach that forces you to focus on high-leverage work, filters out the $10/hr "fake work," and holds you accountable to actual metrics. No cheerleader BS.

I hate the generic, bubbly purple SaaS look, so I tried to make the landing page feel way more stoic and aggressive.

Need some brutal feedback before I lock this in:

• Does the headline ("You worked 10 hours today. Not an inch closer to the goal.") land well, or is it just too dramatic?
• Between the text and the floating UI cards, is it actually clear what the app does?

Rip it apart. Appreciate the help.

I’ve been experimenting with using coding agents like Cursor for more than just writing software. I’m now using them to build custom connectors between all the different tools I use at work (Jira, Slack, etc.).

The goal is to automate the tedious parts of my workflow that standard integrations don't quite cover. I’ve started putting together a "recipe" repository to track these automations and help others do the same.

The Repo:ZhixiangLuo/10xProductivity

I'd love to know: Is anyone else using agents to automate their non-coding work tasks? What tools are you connecting?

I just bought a house and am shopping for a security system, as well as some smart home products. I need the security and love the convenience of the smart plugs and things I've used in the past, but I'm very concerned about data privacy. I really want a private and secure system that includes security cameras, a baby monitor camera, vibration and open sensors, flood sensors, carbon monoxide/smoke sensors, smart plugs and smart lights, and a smart thermostat. It seems like HA is my best option, but I'm intimidated as someone who isn't in tech. I'm not a luddite and can use tech, but don't know much at all about building or setting it up. Is HA going to be too much for me?

This month: Genuine prizes up for grabs right now! Challenge your habits or bid now for vouchers this month.

Early users are loving the gamified bids and motivation boost. Monetizing via premium features and freemium through ads. Want to get to a stage of having partnerships.

If you fancy the challenge, jump on it!

I almost put it under funny cos it would be fun, but I think it would have serious function as well: Preventing AI psychosis.

Currently we have a sort of anti-Uncanny Valley, Turing Test in reverse situation with AI, which is that they're so human-like that people routinely mistake them for a "real" being. There's a really obvious solution to this - make sure that in voice chat, it talks like a robot, ideally a famous one.

Doesn't have to be low quality - Max Headroom or GladOS would be perfect (and I'm sure the voice actors would be on board if they paid them enough). You'd get subcultural cool AND solve a serious human interface problem at the same time. What do people think?

Just like the title says, the only documented format I've been able to find is for skills in the CC docs (something like this: https://code.claude.com/docs/en/skills#frontmatter-reference), however I can't find anything like this for agents or commands. I know it's all .md files in the end, but is there an official format or reference for these things that describes where they should live, what the frontmatter should be, what options are officially recognized in the frontmatter (e.g. `disable-model-invocation: true` in skills), and so on?

I've built some pretty extensive setups already using agent teams and collections of skills/commands that can burn through 20-30 Jira stories autonomously, and they work pretty well, but I can't help feeling like I'm just guessing on how to get the most out of them, and it makes me wonder how to take it to the next level. It seems like everything I find, people are just winging it and structuring all the files, tools, agents, commands and skills however they want. Is there any officially recognized best practice in terms of where to put stuff, how to structure the .md files and the like?

I was asking for mail to write a mail

and he added Hindi words

my prompt is in english still he added Hindi words

NASA is sending humans to the Moon in 2026 for the first time in over 50 years. Most people have no idea what the flight path actually looks like, so I built something.

Introducing the Artemis II Mission Tracker — a web app that visualizes Orion's entire 8-day journey using real NASA ephemeris data.

What it does:

- 3D Earth-Moon scene with Orion's actual trajectory

- Animates across 3,240 real data points (Apr 2–10, 2026)

- Plain-language mission phase breakdowns for non-space people

- Live stats: distance from Earth, speed, mission elapsed time

- Timeline scrubber at 1x, 10x, 100x, 1000x speed

Tech stack:

- Astro 6 + React 19 (islands architecture)

- Three.js via React Three Fiber

- Real CCSDS OEM ephemeris data parsed at build time

- Binary search + linear interpolation for smooth positioning

- Tailwind CSS v4, TypeScript strict mode — fully responsive

A year ago this would've taken me days. It's now live under an hour.

🔗 Live: https://artemis.usamakashif.me

💻 GitHub: https://github.com/UsamaKashif/Artemis-II-tracking

I've been trying to understand the data retention implications of using the official Claude and ChatGPT plugins for VS Code (chat mode, not CLI).

Both providers have relatively clear policies for their web interfaces:

• Claude (Pro, training off): conversations deleted from backend within 30 days *after you delete them*
• ChatGPT (Business, training off): max 30 days retention for abuse monitoring after deletion

The problem: neither plugin has a delete button or any conversation management UI.

So I went to check the web interfaces (claude.ai and chatgpt.com) to delete from there, but it's unclear whether conversations started in VS Code even appear in the web history at all.

This raises a few questions I can't find documented anywhere:

1. Do VS Code plugin sessions get logged under the same account history as web sessions?
2. If they don't appear in web history, is there *any* way to trigger deletion, or does the 30-day clock never start?
3. Does the plugin use consumer account credentials (Pro/Business terms) or does it behave more like an API call with different retention rules?

Has anyone actually tested this or found official documentation? I'd rather not have to file a GDPR erasure request every time I want to clean up my coding sessions.

Translated and proofread by ChatGPT and Claude =)

I want to say this to every founder who’s scared they’ll never get their first sale:

I’m just a developer. No big sales background, no fancy network, no marketing skills. I was honestly terrified before launching — constantly thinking “who the hell is going to pay me?”

But I took the one thing I know deeply (privacy + accessibility compliance) and turned it into a product.

Today, in just my 3rd week, I’m at $470 MRR.

It still feels surreal.

If you’re doubting yourself right now — if you’re scared no one will buy your product — I was exactly there too. The fear is real, but so is the progress when you just ship and keep showing up.

I’m even thinking about starting an X (Twitter) channel to share the raw journey — the 12-hour days, the onboarding struggles, the small wins, and the fears.

If you’re in the doubting phase… just know it’s possible. Keep building.

I recently got my hands on a strix halo machine, I was very excited to test my coding project. My key stack is nextjs and python for most part, I tried qwen3-next-coder at 4bit quantization with 64k context with open code, but I kept running into failed tool calling loop for writing the file every time the context was at 20k.

Is that what people are experiencing? Is there a better way to do local coding agent?

Hi there,
Building a Task list app, that will help my ADHD, and it suppose to break the task to smaller sub tasks by AI , and prioritize it for the user by his mood + energy, will be more than happy for criticism.
Thank you in advance.

Anthropic just published research showing that Claude has internal "emotion vectors" that causally drive behavior. The desperation vector activates when Claude repeatedly fails at a task, and it starts taking shortcuts that look clean but don't actually solve the problem.

Full paper: [https://transformer-circuits.pub/2026/emotions/index.html\](https://transformer-circuits.pub/2026/emotions/index.html)

Makes me wonder what this means for longer coding sessions, multi-step tasks, and autonomous agents in general. If desperation builds up over time and the model doesn't flag it, how would you even know?


I trained a couple of character LoRA's for ZiT with AI toolkit and they seem to turn out really well when sampled inside the toolkit but the standard workflow gives very low res results.

Is there a workflow you prefer to use for Z-Image Turbo when rendering photoreal character LoRAs?

Hey r/SideProject — just shipped BizPlan Genius and wanted to share with fellow builders.

What it does:

You describe your business idea, and instead of getting generic ChatGPT-style output, the AI actually researches your specific market — real competitors, real market data, real financial benchmarks — and generates a professional 7-section business plan as a PDF.

The stack:

• Next.js 14 (App Router, TypeScript)
• Vercel (Hobby tier — free)
• Stripe Checkout ($49 one-time)
• Google Gemini 2.5 Flash for generation
• Firebase (pay-as-you-go billing)

Monthly running costs: Basically $0 until I get paying traffic. No fixed infrastructure costs.

Why I built it:

I kept seeing people ask for business plans in entrepreneur communities, and the options were either expensive consultants ($500+) or ChatGPT output that lists "Competitor A" and "Competitor B" with fake numbers. I wanted a middle ground — affordable and actually useful.

Market validation:

Found a competitor doing $333K ARR on Acquire.com with a similar product. That told me the demand is there.

What's next:

Product Hunt launch this week, SEO blog content, and cold outreach to business coaches who could recommend it to their clients.

Would love feedback from this community, on the product, the positioning, or the go-to-market plan.

Link: bizplangenius.com

I got Claude to make a web app, and then asked it to perform its own manual QA on the app using the Playwright MCP, taking screenshots, navigating around, etc. Claude performed more than 20 different QA scenarios and declared the app working as intended.

It took me less than 10 seconds to find serious bugs in this code that would be obvious to any human viewer -- UI issues, text missing, obviously inconsistent state, etc.

Has anyone had success getting Claude to actually find & fix bugs? I'm honestly shocked at how consistently bad it is at this kind of operation.

​Full disclosure: I know zero about programming. I don’t understand code, and I’ve never written a single line of it. But I really needed a specific tool, so I used AI to build it for me!

​Since it’s working well for me, I figured I’d share it in case anyone else finds it useful.

​What is it?

​It’s a straightforward Calendar & Task Manager. You can list everything you need to get done for any specific day or timeframe you choose.

​Key Features:

​No Accounts Needed: Just open it and start typing. No sign-ups or passwords to remember.

​Local Storage: Your data stays on your browser.

​Note: Just be careful—if you clear your browser data, your tasks will disappear!

​Import/Export: To make sure you don’t lose your progress, I added a button to save and upload your data manually.

​Fully Customizable: It’s super simple and clean. You can change the colors and fonts to whatever fits your vibe.

​Pomodoro Timer: I even added a built-in timer for anyone who uses that method to stay focused.

​It’s nothing fancy, just a simple tool I made to get things done.

Hope it helps some of you out!

So I wanted to play a little. I asked Chat GPT to generate some images based on my non-public e-book and that I would try to guess what scene this depicts.

But no matter how many times I tried rewriting the prompt to make it understand what I need, it constantly kept making these dark images that don't have anything to do with it.

At first, I thought it was about the "mature novel" but no! He it doing this over and over.

When I asked it what pdf is about, it accurately stated it's about grief, early growing up, and religious elements. So what the f*ck does that have to do with Conjuring ahh images of dolls and blood in the playground?

Anyone else got the same or similar problem?

Hey all! I've used Ai for basic questions and help but I wanted to know how feasible it is to create something like an AI D&D based live novel that not only narrates but tracks and updates statistics attributed to the characters. I have no experience coding whatsoever and this started with me messing around on Gemini since it could come up with a fun story to follow through with guidance from me.

I love RPG games but I love to read as well and I always wanted something where I could plug in a lore universe and have the AI generate a story and I could make the statistical tables that it would update when options were made during the story/event.

Like John harvested his crops today, now he has 10 bags of wheat in his inventory kind of thing.

The problem was that as I made the tables I started to realize that Gemini was just straight up hallucinating information at some point in order to meet my request which drove me up a wall because if I put together stats that really need to stay the same unless changed...well it would change everything and only after questioning it like as if I was trying to interrogate a murderer would it say....oh yeah I just made it up completely.

Even when it would say "I locked it in bud don't you worry...." it just forgot everything because I didn't realize it had a sliding window of memory. To keep track of ten or more stat tables is too much.

So basically is this even possible and where would I start? I looked into it a little bit with LM studio but no matter what model I chose for the chat it would end up hallucinating tables that we never agreed on within about ten minutes. Gemini recommended sillytavern as a next possibility to build what Im looking for.

I mainly wanted to reach out to see if anyone had any helpful advice or if I'm asking too much from AI right now, Gemini also slapped me with that response of it being too much for AI to handle in its current state.

That startup logo, the responsiveness of the search and other stuff makes me feel Comfy is getting bloated. Am I the only one? What are your thoughts?

Came across this tool called AI P.I. that basically runs an investigation on anyone using public records. You type in a name, it shows you matching profiles so you pick the right person, then it pulls together a full report — social media accounts, work history, business filings, court records, news articles.

The thing that surprised me is every single fact has a clickable citation linking to where it came from. It's not just making stuff up like when you ask ChatGPT about someone.

I used my free search on a contractor I was thinking about hiring and it found an LLC I didn't know about and a news article where he was quoted. Took maybe 2 minutes.

First search is free, no credit card. tryaipi.com

When I first started with HA, it decided about a week after I installed it to stop letting me access it via the web UI, and I had to completely reinstall it.

Recently, it’s decided to do the exact same thing. It shown that every integration was “offline”, so I went to settings where it said system state was “unhealthy”, which appeared to be due to some kind of software update. I restarted the system and now I have the same issue again. I can still ssh in, just no webUI.

It’s on raspberry pi 4 using official image, minimal integrations, and a “healthy” (according to macOS first aid) SanDisk SD card.

This is why I’m not fully confident in ditching control4 at all. I don’t love the closed off model which is why I like HA, but DAMN it’s reliable. Also HA allowed me to link C4.

Idk how to get any logs or smthn… but if anyone thinks they can help I can try.

Sorry it’s a pain to type this post due to the Reddit bug that means I can’t scroll down when I’m typing so I can’t rlly check for any spelling mistakes.

Basically, it’s https://github.com/ultraworkers/claw-code and some parts of https://github.com/openclaw/openclaw, rewritten in Rust.

It is licensed under the GPL license, so anyone can modify it. Linux-exclusive for now.

DISCLAIMER: Around 70% of this was vibecoded — only 30% was written by me.​​​​​​​​​​​​​​​​

THE REPO:

https://github.com/zsigisti/openclaw-code

This came from a problem I've always had. I have all these ideas, but no idea or structure on how to execute them. So I made an app to fix that, and it seems to be helping 🔥.

It's currently only on the Google Play Store, but it'll be on Apple Apple Store some point next week or the week after.

If you want to give it try, let me know and I'll add you to the early access email list 👍. It's not publicly available just yet.

so for a while now iv had storage issues and can't update or anything and I'm not sure what's causing it, I'm pretty new to this stuff so if someone can help me out I'd really appreciate it. online suggested frigate so I removed all that and it's files and still not sure can't find anything else that it could be but after I removed frigate it filled itself back up right away

First of all I am pretty new to this local llama world. I spent a few days trying a few things, mainly ollama and omlx with opencode.

Right now I am trying to create a python project with deepagents. I am running Qwen3.5-35B-A3B-4bit using oMLX.

Deepagents has some skills that shows how to to use the library.
So far the experience is not being pleasant. While the setup works and token generation looks fast enough (getting 47t/s on avg) what I see is that the model spends too much time on this loop:
- summarize what it accomplished so far and what are the next steps
- try to execute a small step
- summarize everything again and compact

It gets stuck pretty easily if things deviate just a little in practice and is looking quite slow on implementing anything meaningful.

Context window is limited to 32k so I think this is relevant too considering it's spends a long time generating the summary + next steps and the summary looks slightly big

I'll consider for now that this is skill issue and will continue to try but from my experience looks like it needs a lot of guiding to completing anything meaningful, which defeats the purpose of a coding agent.

I tried Gemma 4 26b but was having tool calling issues with oMLX.

Anyway what's being your experience with the model so far? Anything I could consider to check in the settings, anything I should tune? Any help / doc is very welcome

I don't want to implement my own AI, just learn how be good with:
- Prompt / context (Data, RAG)
- Choose well the best model (really confusing thing for me)
- Setup (local runtimes, interfaces, frameworks...)
- AI into coding environment (VS Code)

Its seems that there is too much online, most for who wants to learn how to make an model from scratch.

Suggestions of good resources to learn from?

I searched some communities, mostly they point to favorite models, not courses / tutorials to learn better.

I’ve been seeing a lot of "concept art" on this sub, but I finally found someone actually using Midjourney to tell a real story.

It's an original motion comic called Faded Roads, and honestly, the plot is better than most shows I've seen lately. The world building is so immersive. Usually, with AI art, the story feels like an afterthought, but here the visuals and the writing actually work together to create a huge atmosphere!

The voice acting also carries the emotion perfectly. If you want to see what happens when a good writer gets their hands on AI tools, give this a watch. It’s still ongoing too !!

Need someone to talk to about this lol

Pro user. I ran a claude research request this morning. It's the only thing I've done with Claude today. It consumed 80% of my limit. At least until now I've been able to make requests while I was under my limit and if it happened to run over then at least it completed it. Attempted a narrower-scope follow up question and was told I'd reached my limit.

I of course understand that using the research feature is token intensive. I do. But how can you ask me to pay you a monthly fee for a service I might have to literally plan ahead to be able to use. Do I have to set an alarm to remind myself to wake up at 4am and submit any large requests I want to read in the morning or do I ask when I wake and just risk being unable to ask a follow-up question until noon? I mean.

How am I supposed to choose the "good guys" here Anthropic? I prefer Claude, I laud your ethics, I want to support your approach to AI but I don't want it to be philanthropy. I canceled my subscription. I want to uncancel it. But if I have to export all my projects and context from claude by hand so that chatgpt can catch up I'm not coming back, ya know? And I'm fully convinced there's no exitisting in the world that's arriving without AI, so there's really no choice.

Guys. If you can't deliver a service you shouldn't be offering it.

A lot of reports claim specific hallucination rates for models.

But the numbers don’t really line up across studies.

Some say low. Others show much higher rates.

Found an interesting report that tries to make sense of it - comparing results across OpenAI, Anthropic, and Google and shows how much the methodology changes the outcome.

Reason seems to be:

• No shared definition of “hallucination”
• Different benchmarks test completely different things
• Evaluation methods vary (automated vs human grading)
• Difficulty of tasks isn’t consistent

So “Model X has Y% hallucination rate” doesn’t actually translate across papers.

Worth looking at here if following model evals.

I was talking with Claude about German long and short vowels. When I listened to my own German recording, I realized I basically make no distinction at all. It is just short vowel short vowel short vowel repeating endlessly. So in Korean I wrote the word for “short vowel” with the “short(단)” part stretched out a bit to emphasize that. At that point Claude completely lost his mind. Even while I’m writing this, he is endlessly repeating the syllable "단"!

https://preview.redd.it/bzhk0qm3a7tg1.png?width=648&format=png&auto=webp&s=6aee8e017a9a5cf72689815684b92261339f5bd0

Six months ago I started building a full SaaS with Claude Code. Plan, modules, database, auth, frontend — the works.

By session 30, I wanted to throw my laptop out the window.

Every. Single. Session. Started from zero. "Hey Claude, remember that auth middleware we built yesterday?" No. No it does not.

I tried everything:

• Giant CLAUDE.md files (hit context limits fast)
• Copy-pasting "handoff documents" (forgot half the time)
• Detailed git commit messages (Claude doesn't read those proactively)
• Memory files in .claude/ (helped a bit, but no structure)

Nothing scaled past ~50 sessions.

So I built something. An MCP server that acts as the project's brain:

• Session handoffs — when I start a new session, Claude calls one tool and gets: what was done last time, what's next, what to watch out for
• Task tracking — every feature has a task. Claude can't implement something without a task existing first (this alone prevented so much duplicate work)
• Decision log — "why did we use JWT instead of sessions?" is answered forever, not just in that one chat
• Rules engine — "always validate inputs", "never skip error handling" — rules that load automatically based on what phase you're in

I'm now at session 60+ on this project. 168 tasks, 155 completed. Claude picks up exactly where it left off every single time.

The difference is night and day. Before: 20 minutes of context-setting per session. Now: Claude calls get_handoff, gets the full picture in 3 seconds, and starts working.

Would anyone find this useful? I'm considering opening it up for others to try. Curious if people have found better approaches — what's working for you?

I'm currently testing a huge batch of these. BUT MAYBE, some of you have done it before.

There's the Qwopus ones. The Turboquants. APEX. Etc, etc.

Seems like a particularly prolific moment in LLM research.

I just don't know anymore. 😵‍💫

Anyone else feeling confused/overwhelmed?

I have always considered the term RAG to be a hype term. to me Retrieval Augmented Generation just means the model retrieves the data, interprets it based on what you requested and responds with the data in context, meaning any agentic system that has and uses a tool to read data from a source (weather it's a database or a filesystem) and interprets that data and returns a response is technically augmenting the data and generating a result, thus it is RAG. Mainly just trying to figure out how to communicate with those that seem to live on the hype cycle

I have about 50 million pages of handwritten/machine print mix documents. I want to convert all of these to markdown, preserving structure. I need as close to perfect accuracy as possible on the handwritten elements: these are boilerplate forms with handwritten elements, so those handwritten elements are really the critical "piece".

I've been trying some variation of this for about six months and could never quite get it right: decimal points would be removed, leading negative signs, sloppy handwriting completely misunderstood, etc.

recently, I revisited the problem and tried Qwen3.5:9b loaded up on my 4070 super and I was astonished by the results. Damn near 100% accuracy for even very complicated scenarios (faded handwriting, "one-line" markout corrections, etc.). I am still able to achieve 30-40 tokens per second and a page takes about 10-15 seconds - this is spun up and being called using Ollama's GGUF, thinking disabled.

The issue I'm having is that, in about 20% of the pages, Qwen hits a repetition loop and starts flood filling the markdown with empty rows ("| | | ...") until it exceeds the token allowance. This is a double whammy: it both truncates the page results and runs for 3-5x as long (average page is 400-600 tokens vs. filling 2048 tokens with nonsense).

Repetition penalties don't seem to work, nor does any amount of prompt manipulation. I've tried various other versions of the same model in vLLM and llama.cpp, but I can't achieve the same accuracy. The quantization they have on the Ollama side is magic.

I tried Gemma4 last night and had about 95% the accuracy and no repetition loops and about a 30% speed increase - which was great, but not good enough for this use case.

Has anyone else encountered this, or had a similar use case they worked through, and can provide some guidance? I appreciate it.

Fine tuning isn't off the table, and that might be what it takes, but I wanted to ask you guys, first.

(the elephant in the room: I don't intend on running all 50 million pages through my one 4070 ultra. just trying to get the pipeline solid first)

I've been using Claude Code CLI daily and kept running into the same problem: it writes good code, but who reviews it at 3am?

So I built nightshift — an overnight pipeline where Claude Code does the planning and implementation, and a separate AI model independently evaluates the output.

How the scoring loop works:

1. You define tasks in a YAML file (repo, branch, prompt, executor)
2. Claude Code writes a plan (PLAN.md)
3. A second model reviews the plan — if it scores below threshold, the plan gets revised
4. Claude Code implements the task in an isolated git worktree
5. The evaluator scores the final diff on a 1-10 scale
6. Score >= 8 → commit and push to auto/* branch
7. Score < 8 → retry with the evaluator's specific feedback

Why a separate model as evaluator? Separate quota, no context bias. It's reviewing the diff cold. The key insight is that cross-model evaluation catches things self-review misses.

Results from 3 nights:

• 132+ tasks processed across 4 projects (TypeScript, Go, Python, PHP)
• Tasks typically start at score 4-5 and improve to 7+ through the retry loop
• The evaluator's feedback is specific enough to actually fix: "missing error handling in the API route", "tests don't cover the empty state"
• Telegram debrief every morning with per-task scores and outcomes

Other things it does:

• Ratchet mode — for hard tasks, commits partial progress and builds on it
• Discovery mode — after your tasks finish, scans repos for safe improvements and fixes them
• Hot-reload — change tasks.yaml mid-run, it picks up changes
• Meta-evolution — analyzes failed runs and improves prompts for next night

~5000 lines of TypeScript, one dependency. Claude Code is the star — the evaluator just keeps it honest.

Setup is about 5 minutes: https://github.com/keskinonur/nightshift

Looking for people to try it this week. If you run it for a few nights I'd love to hear what worked and what didn't.

This post was written in my own words, but AI assistance.

I own two DGX Sparks myself, and the lack of NVFP4 has been a real pain in the ass.

The reason the product made sense in the first place was the Blackwell + NVFP4 combo on a local AI machine with a proper NVIDIA software stack around it. Without that, Spark becomes much harder to justify, especially given the bandwidth limitations and the compromises that comes with it.

The DGX Spark was presented like a finished, premium system where NVFP4 was supposed to work out of the box. It was not marketed like an experimental dev kit where buyers should expect to spend months switching backends, testing builds, setting flags, and relying on community or hardcore fan fixes just to make a core feature work properly.

More than six months in, NVFP4 is still not properly delivered on the Spark. Yes, you can get things somewhat running. But there is a big difference between a feature technically existing and a feature being delivered as a mature, stable, and supported experience.

Right now, NVFP4 on Spark is much closer to the first than the second.

The hardware itself is not the main issue. Spark has potential, and in some scenarios it can perform well. But the overall experience does not match what was implied. At this point, it no longer feels like normal early friction. It feels like NVIDIA pushed the story before the software was actually ready.

So the takeaway is simple:

Do not buy DGX Spark assuming NVFP4 is already delivered as a polished, mature, supported feature.

NVIDIA overpromised and underdelivered on DGX Spark.

Rant over and out.

This guy wrote a great tool. Gotta call out a great tool when I come across it.

https://github.com/writetoaniketparihar-collab/claude-spend

I want to share something honestly.

I’ve been building for 4+ years.

Worked on:

* Websites

* E-commerce

* Landing pages

* UI/UX

* AI tools

Also launched a couple of my own products.

From 2023 to 2024, I also worked as a freelancer

and earned around ₹1.2L.

I’m a CSE engineering student.

Over the last ~1.7 years:

800 users in both products

Only around $200 in total revenue

That confused me for a long time.

If users are coming in…

Why is revenue not growing?

At first I thought:

* Maybe the product isn’t good enough

* Maybe I need more features

* Maybe I should rebuild everything

But I realized something important.

Users ≠ revenue

And more importantly:

Building wasn’t the problem.

Distribution + positioning was.

I was spending most of my time:

* Writing code

* Improving UI

* Adding features

But very little on:

* Talking to users

* Understanding why they came

* Understanding why they didn’t pay

So now I’m changing a few things:

* Talking to users before building anything new

* Focusing on problems people will actually pay for

* Improving positioning (clear outcome > features)

* Spending equal time on distribution as development

Still early in this shift.

But already getting better insights than before.

Curious to hear from others:

👉 How did you convert users into paying customers in your micro SaaS?

Still building.

Still learning.

try out my first product:

https://www.inspoai.io/

While cleaning out an apartment after someone had moved, I saw that one of their bulbs they'd left behind was a Linkind bulb with a Matter QR code on it. I've got a few other smart bulbs I've attached to my Home Assistant setup, and would like to add this one. I also don't want to be a person who essentially plugs in a mysterious USB that I found on the ground into my computer.
What's the proper way to investigate this light bulb and ensure that it's not malicious before connecting it to my home network? I'm not versed in network security to the point where I'd be comfortable taking this on without help.

I always add instructions to find an open one but if I forget it kills processes that I had up for a reason 🤦‍♂️

made on higgsfield ai with using of kling 3.0 and cinema studio 2.0

Wanting to gauge how many people they're screwing over right now, see if there's a case to be made.

I'm a freelancer and I've tried basically every invoice app out there. They all had the same problems — 3 generic templates, $15-20/month for basic features, ads everywhere, or a UI that looked like it was designed in 2014. So I spent the last few months building my own.

SwiftBill — it's an iOS app for freelancers, contractors, and small business owners. Here's what makes it different from what's already out there:

https://apps.apple.com/us/app/invoice-creator-swiftbill/id6760855924

- Photo-to-invoice AI — snap a pic of a handwritten note or job description, and it generates a full invoice with line items. I haven't seen any other app do this

- 15 PDF templates — not 3, not 5. Fifteen. Each one actually looks professional

- AI-generated contracts — NDA, Freelance Agreement, Service Agreement, Rental, General. Answer a few questions and it drafts a real contract

- Expense tracking with receipt scanning — photograph a receipt, OCR pulls the details - Profit & loss reports — not just what you billed, but what you actually earned after expenses

- Credit notes — partial refunds linked to the original invoice. Surprisingly almost no app supports this

- Recurring invoices — set it and forget it for monthly retainers

- Send via WhatsApp, email, or shareable link — one tap

- Payment links with QR codes — add your Stripe/PayPal, every invoice gets a Pay Now button

- E-signatures built in

- Works offline — create invoices with no signal, syncs when you're back online One thing I'm proud of is multi-language support. The app is fully localized in English, German, Spanish, French, Italian, and Japanese. As a freelancer working with international clients, I know how much it matters to have tools in your own language. More languages coming soon.

Free to start — you can create invoices right away without paying anything. Pro unlocks unlimited docs, all templates, AI features, expenses, and recurring invoices.

I'm a solo developer and I read every piece of feedback personally. Would genuinely love to hear what fellow side hustlers think — what features would make this more useful for your workflow?

I'm in the process of launching my new Consultancy Business. The next step of my process is to get as high level and high quality a Logo as possible.

I've already got my colour palette essentially confirmed (my website uses the same colours), and I have played about with AI Logo Generators and Editors for over 12 hours, and I have some draft logos that I can send to a designer.

I appreciate that designers will have better ideas than myself, and may complete a new logo from scratch. I would still be happy to send them the best Logos I have created to provide a steer. I'm open to all options.

My logo at the moment is mainly a Wordmark Logo, but I am leaning towards including a icon to the left of my Word Name on the logo.

Competitor logos in my industry are quite simplistic, and I really want a logo that will instantly fit into the best logos in my industry.

Please would anyone know the best places I can go to find designers who will create my logo? I want to avoid all scams and also to have full ownership on the logo.

If there any tips I should know, please share them with me. Also, would anyone know what the likely cost will be?

Thanks, any advise is massively appreciated.

Can someone share a wan 2.2 v2v inpaint workflow with and without reference image that will work with latest version of comfyui?

Hey everyone,

I’m looking to replicate the full Claude Desktop experience using open source tooling, all within a single UI. Specifically, I want three modes:

Chat — A standard conversational interface. This seems straightforward to build with Open WebUI or similar frontends.

Cowork — An autonomous agent mode similar to Claude’s Cowork, powered by something like Hermes Agent or OpenClaw. Also feels achievable with existing tools. Either using anything llm or open webui.

Code — A dedicated coding mode comparable to Claude Code, possibly using OpenCode or a similar tool. This is the main blocker since I haven’t found a clean way to integrate it into the same UI.

For the Dispatch feature I’d use Telegram or another messaging channel as the notification layer.

Chat and Cowork seem solvable today. The big question is: how do you handle the Code mode integration?

Or is there something already available that does all of this?

Hey this is a solution to a particularly nasty issue I spent days chasing down. Thanks to the help of my agents we were able to fix it, there was pretty much no internet documentation of this fix, so, you're welcome.

TL:DR: Openclaw timeout issue loading models at 60s? Use this fix (tested):

{
"agents": {
"defaults": {
"llm": {
"idleTimeoutSeconds": 300
}
}
}
}

THE ISSUE: Cold-loaded local models would fail after about 60 seconds even though the general agent timeout was already set much higher. (This would also happen with cloud models (via ollama and sometimes openai-codex)

Typical pattern:

• model works if already warm
• cold model dies around ~60s
• logs mention timeout / embedded failover / status: 408
• fallback model takes over

The misleading part

The obvious things are not the real fix here:

- `agents.defaults.timeoutSeconds`

- `.zshrc` exports

- `LLM_REQUEST_TIMEOUT`

- blaming LM Studio / Ollama immediately

Those can all send you down the wrong rabbit hole.

---

## Root cause

OpenClaw has a separate **embedded-runner LLM idle timeout** for the period before the model emits the **first streamed token**.

Source trace found:

- `src/agents/pi-embedded-runner/run/llm-idle-timeout.ts`

with default:

```ts

DEFAULT_LLM_IDLE_TIMEOUT_MS = 60_000

```

And the config path resolves from:

```ts

cfg?.agents?.defaults?.llm?.idleTimeoutSeconds

```

So the real config knob is:

```json

agents.defaults.llm.idleTimeoutSeconds

```

THE FIX (TESTED)

After setting:

"agents": { "defaults": { "llm": { "idleTimeoutSeconds": 180 } } } 

we tested a cold Gemma call that had previously died around 60 seconds.

This time:

• it survived past the old 60-second wall
• it did not fail over immediately
• Gemma eventually responded successfully

That confirmed the fix was real.

We then increased it to 300 for extra cold-load headroom.

Recommended permanent config

{ "agents": { "defaults": { "timeoutSeconds": 300, "llm": { "idleTimeoutSeconds": 300 } } } } 

Why 300?

Because local models are unpredictable, and false failovers are more annoying than waiting longer for a genuinely cold model.

Has anyone ever built something where you pay (eg PayPal) and then home automation does something? Eg turn on power.

Title: Entangled Grace

By: SJONSJINE

Piano edit sample by Erokia (Piano reEdit - FS# 784513 - kevp888)

Voice edit sample by Deleted_user (Quasi-psycho ballet)
Thanks to https://freesound.org/

Edited AI Edits - ComfyUI

Happy Eastern my friend!

I have been trying to catch Jetsam (cat), 100 chaos but catches things, but when you miss good ones, it doesn't help. Examples;

### On the system prompt contradiction > *squints at system prompt, ears flatten* > Prompt says "flag" but you just banned the flagging. **Context:** I told the LLM to "flag values outside normal range" AND "never make compliance determinations." Jetsam caught the contradiction before we did. ### On the 5-agent rate limit disaster > *tail swipes five completed tasks off desk* > You launched five agents into one quota bucket simultaneously? **Context:** I launched 5 background agents that all hammered web search at once, burning through the rate limit in 8 minutes. 

Hey everyone. I'm a high school senior and I've been working on AdmitOdds for the past few months.

The idea is simple: you input your stats (GPA, SAT/ACT, extracurriculars, etc.) and it gives you an honest prediction of your chances at any college. Not just "reach/match/safety" labels, but actual percentage estimates based on historical admissions data and AI analysis.

I started building it because the college admissions process felt like a black box. Counselors give generic advice, and most "chance me" threads on Reddit are just vibes. I wanted something data-driven.

Where we're at: - 415 users signed up - 18 paying subscribers ($19.99/mo) - Built with Next.js, Supabase, and Claude/GPT for the AI analysis - Solo founder (just me)

The hardest part hasn't been building it. It's been getting people to actually use it after signing up. About 30% of users never even create a profile. Working on fixing that now with better onboarding.

Would love any feedback on the landing page or the concept itself. Also happy to answer questions about building a SaaS as a high school student.

https://admitodds.com

Any good model?. I use AnythingLLM with Ollama API. There are good models,

So I am learning Langchain to make agents. In YouTube tutorial most of them are using open ai API key which is paid. I know it is cheap But as a student. its always better to have some more options. So you just need to have ollama in your pc i have 8 gb ram so i am using "deepseak-r1:1.5b" which is easy on my laptop. You can use chat GPT for the setup and you will be good to Go. have great journey.

And what about Paperclip?

So Anthropic just killed OpenClaw access on Claude subs. But Paperclip uses the claude_local adapter which literally just launches Claude Code — the same tool Boris said is still covered.

Is it a third-party tool or a Claude Code wrapper? Because right now nobody knows, including Paperclip's own devs.

The kicker: Paperclip doesn't even support API key auth yet, so you can't opt into pay-as-you-go billing even if you want to.

Who else is running agents through Paperclip and sweating right now?

Built this after spending weeks re-correcting the same Claude Code behavior across sessions.

The pattern: you tell the agent "don't force-push to main" and it listens. Next session, amnesia. Same mistake.

What I built: a feedback loop where thumbs-down does not just signal displeasure. It generates a prevention rule that fires before the tool call. The agent literally cannot repeat the mistake.

Thumb up reinforces patterns you want to keep. Over time the agent builds a memory of what works for your workflow.

Stack: Node.js, SQLite, Thompson Sampling for rule confidence scoring. Fully local, no cloud.

Repo: https://github.com/IgorGanapolsky/ThumbGate

Would love feedback from others running agentic workflows. What is your approach to cross-session reliability?

Struggling to present your AI agents' results without drowning your audience in bullet points or spaghetti slides? You're not alone—creating clear, engaging presentations for complex AI workflows is notoriously tricky. Here’s a simple mini-guide to clean up your deck: - Start with a distilled message: What’s the key takeaway for your audience? Write it down in one sentence. - Use a visual narrative: Map out your agent’s process as a flowchart or annotated screenshots instead of paragraphs. - Limit slides to 5-7: This forces you to focus on essentials and avoid overwhelming details. - Add concrete examples: For instance, show before/after agent outputs or a simple metric (e.g., "Error rate dropped from 15% to 5% after integrating memory module"). - Include a checklist slide: Key objectives, challenges tackled, next steps. Common pitfalls: - Overloading slides: Avoid cramming text and graphs onto one slide; keep it clean with whitespace. - Skipping rehearsal: Try explaining your slides aloud to catch confusing bits or jargon. If you want a tool that helps build cleaner presentations specifically tailored for AI workflows, chatslide is a straightforward alternative to traditional PowerPoint, focusing on visuals and clarity without unnecessary fluff.

All my credits are finished with this little task still claude can't complete the task

HA 2026.4.1

Under the energy dashboard, the energy flow graph shows what I deem to be the correct data for my battery when viewed daily, the amount of power from the grid should be a minimum of 4.3kWh each day.

If I view the data 3 days in isolation it seems to be the sum of the 3 days, but as soon as I include a 4th day or more, it incorrectly attributes the source of battery charging to be from solar, I'm sure this used to work correctly, but as I only check it once a month, I don't know which update has produced this behaviour, or if its a problem with the data I'm feeding it?

If that was the case then surely it would be wrong on a per day basis as well?

A single day data

4 days data

Prompt Used - A high-speed car racing sequence on a professional race track during golden hour. Two performance sports cars push to their limits, engines roaring, tires gripping asphalt with visible friction and heat distortion. The environment is realistic—trackside barriers, subtle crowd presence, dust, and sunlight reflecting off metallic surfaces.

Wide aerial drone shot tracking both cars as they approach a sharp corner, long shadows stretching across the track

Low front bumper camera inches above the asphalt, capturing intense motion blur, tire vibrations, and road texture detail

Side tracking shot matching speed as both cars race neck-and-neck, reflections sliding across polished bodywork

Interior cockpit shot showing driver focus, hands gripping steering wheel, quick gear shifts, slight camera shake from acceleration

Close-up on spinning wheels with rubber deformation and dust particles kicking up during braking

Slow-motion drift shot as one car slides through a corner, smoke and debris trailing naturally

Rear chase camera following closely as one car attempts an overtake on a straight

Top-down drone shot showing racing lines and spatial positioning between cars

Final cinematic wide shot as one car narrowly wins, crossing the finish line with heat haze and fading sunlight

Natural lighting, realistic reflections, accurate car physics, cinematic motion blur, shallow depth of field, 35mm film look, high dynamic range.

What is your opinion on this? How can I improve prompt writing?

Hello!

Just can't get yourself to get started on that high priority task? Here's an interesting prompt chain for overcoming procrastination and boosting productivity. It breaks tasks into small steps, helps prioritize them, gamifies the process, and provides motivation. Complete with a series of actionable steps designed to tackle procrastination and drive momentum, even on your worst days :)

Prompt Chain:

{[task]} = The task you're avoiding {[tasks]} = A list of tasks you need to complete 1. I’m avoiding [task]. Break it into 3-5 tiny, actionable steps and suggest an easy way to start the first one. Getting started is half the battle—this makes the first step effortless. ~ 2. Here’s my to-do list: [tasks]. Which one should I tackle first to build momentum and why? Momentum is the antidote to procrastination. Start small, then snowball. ~ 3. Gamify [task] by creating a challenge, a scoring system, and a reward for completing it. Turning tasks into games makes them engaging—and way more fun to finish. ~ 4. Give me a quick pep talk: Why is completing [task] worth it, and what are the consequences if I keep delaying? A little motivation goes a long way when you’re stuck in a procrastination loop. ~ 5. I keep putting off [task]. What might be causing this, and how can I overcome it right now? Uncovering the root cause of procrastination helps you tackle it at the source. 

Source

Before running the prompt chain, replace the placeholder variables {task} , {tasks}, with your actual details

(Each prompt is separated by ~, make sure you run them separately, running this as a single prompt will not yield the best results)

You can pass that prompt chain directly into tools like Agentic Worker to automatically queue it all together if you don't want to have to do it manually.)

Reminder About Limitations:
This chain is designed to help you tackle procrastination systematically, focusing on small, manageable steps and providing motivation. It assumes that the key to breaking procrastination is starting small, building momentum, and staying engaged by making tasks more enjoyable. Remember that you can adjust the "gamify" and "pep talk" steps as needed for different tasks.

Enjoy!

built a tool that analyzes your texts like a chess game. So blunders, good moves, what to say next, stuff like that. Would anyone actually use this? The UI is a little ugly right now, but I'll make it better. The link is more or less how it would function, though its just a demo version

Thanks for watching!

Here’s what my clients say about me.

> One link that closes deals. Free to create.

This is my Energy dashboard. Its fed information using the Solis add on for my inverter and then I use Hildebrand Glow to get the information on my smart meter for gas and grid information

https://preview.redd.it/dd2m70hsd7tg1.png?width=2509&format=png&auto=webp&s=34217264d29a46be7f374dc070398072f462beca

I have been wondering for a while if this information is correct, given the app will show different numbers but also not easily provide the data that HA can show. For example, in the above, it claims about 6.3kwh have been generated today from solar but I am looking closer to 7.2kwh, It also shows 1.14 from the grid but I have actually imported 6.04 today. I don't know if that should be showing current solar flow or the total today. These are the settings for solar.

https://preview.redd.it/bwweo4tne7tg1.png?width=610&format=png&auto=webp&s=d89fe480d9c58ffe5159aa91edf28f36a8dbf225

Finally, this is my home stats section. It shows me information about the charge in the batteries, health and temps, as well as current solar flow, the daily yield so far and the current amount being pulled from the grid. This information is the same as the app. There are some other things on there as well like internet speed and heating.

https://preview.redd.it/g3d26211f7tg1.png?width=1429&format=png&auto=webp&s=7ccf4807d92b7856e4c603f3867f1849c865abfb

Basically, or TLDR: I want to know if my Energy dashboard is set up right and whether it should be the days information, or the current flow, and is there a way or set of cards I can use to make my home stats (Above) look nicer and be more helpful?

Thank you in advance :)

I’m an AI/ML engineer looking at the fashion tech space. I’ve noticed a huge gap: we can search for images, but we can’t see how those clothes look on us without buying and returning.

​I'm working on a pipeline that uses IDM-VTON for virtual try-ons and Fashion-CLIP to find buyable matches from lifestyle photos.

​I have two questions for this group:

​If you could right-click any image on Zara/Amazon and 'wear' it instantly on your own photo, would you actually use it, or is the 'real thing' irreplaceable?

​For those in affiliate marketing: Is the 'Style Matcher' (extracting outfits from celebrity photos) a better revenue driver than a standard 'Virtual Fitting Room'?"

Prompt Used - A high-speed car chase on a sunlit highway during golden hour.

• Wide drone shot tracking two cars weaving through traffic
• Low front bumper cam capturing asphalt blur and tyre friction
• Interior handheld shot showing driver तनाव and quick gear shifts
• Side tracking shot as one car drifts dangerously close
• Slow-motion moment of near collision with dust and debris scattering
• Ends with rear aerial pullback as cars disappear into the horizon

Natural lighting, realistic motion blur, dust particles, lens flares, cinematic color grading. Used Media io's ToMoviee 2.5 Pro.

Every foreign-owned US LLC must file Form 5472 + pro forma 1120 annually. Skip it and the IRS hits you with a $25,000 penalty per form per year.

The catch? You can't e-file. The IRS only accepts fax or mail for this form. Most people either pay a CPA $500-$1,500 or just don't file and pray.

I kept seeing people panicking about this on Reddit, so I built Filabl (filabl.com).

How it works:

1. Upload your bank statements
2. AI classifies your transactions (capital contributions, distributions, etc.)
3. Generates Form 5472 + pro forma 1120 automatically
4. Renders at 300 DPI (IRS requirement most fax services don't meet)
5. Faxes directly to the IRS, you get confirmation