Hey r/StableDiffusion

Ive posted earlier versions of Image MetaHub here before but its grown a bit since then so I figured it was worth sharing again.

I originally made it for myself (still do, actually), because my own output folders had turned into chaos.

The core idea is still the same: local desktop app that lets you search/filter/organize your images by generation parameters like prompt/checkpoint/LoRA/nodes, etc...

Since the last time I posted, there are some new useful features such as node-type search, explicit lineage for img2img/inpaint/outpaint (it shows images generated to/from other images), ratings, collections, etc. So its gone a bit beyond "metadata browser" territory at this point.

Ive seen a few other tools show up around here lately, including a couple of IMH forks, which I think is great! Some go more in the semantic-search direction, some focus more on integration with specific tools... IMH is still pretty much my own take on the problem: local, generator-agnostic library tool for people who have generated too many images/videos and want to organize them.

Full disclosure: there is a 'Pro' tier that I made to support development, which include some additional features like integration with ComfyUI/A1111, node-based workflow inspection, and a couple other things more mostly for businesses/power users, but its main functions are free and the app is open-source.

It currently supports metadata from ComfyUI, A1111, Forge, SD.Next, InvokeAI, Fooocus, Draw Things, SwarmUI, Midjourney downloads, and a few others.

So yeah, thats basically it. I built it because I needed it, kept adding whatever was missing for my own use and now Im sharing it again in case it helps anyone else here dealing with the same mess.

You can get it here: https://github.com/LuqP2/Image-MetaHub

--

Also, I made a Discord server. Its still small and quiet, but you can reach me there directly for questions/support/updates or whatever: https://discord.gg/taRtMyHrCK

Cheers

I recently used VEO3 to extend a short video, but I'm unable to download the entire project as a single, continuous file. Currently, I have to download the extension segments individually. Is this a known technical limitation

I’m running 2.1.68 cause I think it burns less tokens but I might be crazy. But definitely want to get to use 1 million context without it eating all my tokens. What about you?

Hey everyone,

Running a small AI project right now and the GPU/compute costs are honestly brutal. Every fine-tune, every inference run… it adds up way faster than expected and eats into runway like crazy.

I got tired of watching money disappear into the cloud without really knowing how to fix it, so I started AluminatiAI.com.

A simple way for smaller teams and solo founders to slash those GPU costs significantly (we’re seeing 40-60% savings in early tests) by connecting you with better hardware options and smarter group deals.

It’s early days and completely free to try right now, no credit card needed. If you’re a small AI company or indie builder getting crushed by compute bills, head over to https://aluminatiai.com and sign up in 30 seconds.

We're looking to focus on one smaller group to help ASAP! Reach out if this is something that could be your way of growth.

Would love to hear from others in the same boat:

What’s your biggest GPU or compute headache right now? How much are you spending monthly? How are uou currently resolving your financial cost pain point?

Looking forward to chatting with some of you.

Cheers

Hi folks I’m using cursor with sonnet and opus for a while now. I was wondering, how many of you use Claude inside cursor and how many directly Claude Code?

Pros and cons?

I was looking at Apple's MacBook Air, and I noticed that the ChatGPT interface was there, but it looks very different. I don't have many of the options on the top or bottom bars to change things releated to my prompt so are they using a different version? Or is this just a premium version that all other users have?

First photo = Apples website

Second photo = My chatGPT screenshot

For the past few months there has been a steady degradation in quality. I've experienced it along with everyone else -- ignoring rules, guessing instead of looking for answers, making fundamental mistakes that cost me a full day of work.

Yesterday I fired up one of my usual prompts for some analysis and, to my utter surprise, it carefully analyzed logs, followed my instructions, and provided an extremely thorough, actionable analysis. I haven't had any laziness issues since. It's even been more effective past 200k context, which is where, historically, it became pretty much entirely useless.

Too soon to say, but possibly fixed? Maybe just better because the model can think more now that people are jumping ship? I'm curious to know what others are experiencing.

System prompts reset every session, are per-developer, and don't scale across tools. So I tried a different approach — store everything the agent needs to know in an orphaned Git branch mounted as a .memory/ worktree. Plain markdown files, pushed and pulled like any other branch, shared across the whole team.

What lives there: your codebase map (every module, class, function), conventions, past mistakes, architectural decisions, things that must never change, what the agent was doing last session. Every developer on the team gets the same knowledge base. Change tools — Claude today, Cursor tomorrow — same knowledge, no re-setup.

On top of that, it enforces methodology structurally. Not suggestions — gates. No production code without a failing test first. No fix without a confirmed root cause. No implementation without an agreed spec. Rules include rationalization resistance so the agent can't reason its way around them.

That's agentskel. MIT, plain Markdown, installs on any existing project without touching application code.

https://github.com/ahmadulhoq/agentskel

Curious whether others have tried Git-native approaches to agent context — and what tradeoffs you've hit.

Is anyone using Claude as their logic and orchestration layer across their enterprise or large teams? If so, good, bad, ugly?

For fun 😅

I decided to test how far AI automation can really go.

So I built my own AI agent… and made it apply to 400+ jobs automatically.

Here’s what it did:

I uploaded my resume

It scanned 500+ fresh job postings (last 3 days)

Scored each job based on how well I matched

Picked the best ones to focus on

Then it got crazy…

It generated 400+ custom resumes (one per job)

Each one tailored specifically to the role

Clean, well-designed PDFs

Next level:

It found actual hiring manager emails

Used enrichment tools to get real contacts

And then…

It wrote personalized emails in my tone

Sent all applications via Gmail

Attached resume + portfolio to each

No copy-paste. No templates.

Every email felt human.

Result?

I’m already getting replies from companies about open roles 🤯

What surprised me:

The scale is insane

Personalization still works (even at 400+)

AI can remove 90% of job hunt effort

I genuinely didn’t think this was possible at this level.

Now I’m wondering…

👉 Would you trust AI to apply for jobs on your behalf?

👉 Or does this feel too automated / risky?

If people are interested, I can break down the full system.

Also just created a quick form if you want early access or details:

https://app.youform.com/forms/g8ojedck

While preparing for the exam I engineered 6 labs, one per scenario, covering all 5 domains and all 30 task statements. Each one walks you through building working, runnable code step by step, for hands-on practice with the architectures the exam covers.

• Lab 01 - Customer Support Resolution Agent
• Lab 02 - Code Generation Workflows
• Lab 03 - Multi-Agent Research System
• Lab 04 - Developer Productivity Agent
• Lab 05 - CI/CD Integration
• Lab 06 - Structured Data Extraction

https://github.com/aderegil/claude-certified-architect

https://i.redd.it/ro851zwqilug1.gif

Hope it helps.

I feel like I quantum leaped into a different reality. I remember Chat GPT being Chat GBT. Does anyone else remember this?

https://www.ft.com/content/b39da303-3188-447b-8b65-3dd8dad8b59a?syn-25a6b1a6=1t

Is it true?

Asked it something straightforward. It answered immediately, no hesitation, sounded totally reliable.

I was in a rush so I just used it without double checking.Turned out it had just made up specific details and presented them like established facts. Confidently wrong.

Had to redo everything from scratch which took way longer than verifying it the first time would have.

How do you guys actually know when to trust what it tells you and when to go verify it yourself?

A few years ago I bought a Tuya wall switch and successfully paired it with HA via localtuya. It worked great for years, so well that I completely forgot how I paired it.

I realized that the robot vacuum that wanders in the apartment is a Tesvor S5 Max, a Tuya device. I would like to connect it to HA to automate the cleaning when there is nobody home.

My problem: I do not know how to do it without breaking the existing switch, which is our bedroom switch and if it does not work I will be in serious trouble.

What I have: - localtuya configured with one device - access to platform.tuya.com where I see one cloud project but the "Devices" tab is empty. I see Home Assistant in the "Authorization" tab, though. - an app for the robot, which I do not really need (I think)

What is the safest way to regain control over the switch (which I guess means seeing it in the cloud project?), and add the robot?

There could be several technical problems, like software that can efficiently do it which could be complex or impossible with current setups, but in theory?

can it be hosted in a same way? in theory

Most LLM agents treat the model as the entire cognitive system. System prompt defines personality, RAG handles memory, chain-of-thought handles planning. It works until it doesn't, and when it breaks, there's no structural theory to debug against.

This book takes a different approach: treat the LLM as a translation layer and build the actual cognitive architecture around it. Memory with Ebbinghaus forgetting curves and reconstructive distortion. Emotion using OCC appraisal models and PAD mood space. Decision-making through GOAP planners perturbed by prospect theory. Personality as system-wide parameter modulation with drift detection.

The underlying research comes from three fields that rarely cross-reference each other — cognitive science (ACT-R, CLARION, LIDA), game AI (The Sims autonomy system, Dwarf Fortress personality modeling, Halo behavior trees), and LLM agent engineering. 15 chapters, 120+ citations, working Python/JS code throughout. Free on GitHub.

This is a synthesis of existing research with working implementations, so I'd genuinely appreciate feedback on the substance; what's wrong, what's missing, and what doesn't hold up.

Here is the book.

Sorry for the ugly video/photo quality. My phone aint so good.

This was for my final research for high school and is my second electronics project—first with ESP32 (38pins), and MLX90640ESF-BAB Thermal Imagine Module. Anyways, I programmed it in such a way that it powers the water pump upon bootup so it pre-fills the hoses with water. Initially, I planned to use Arduino Uno but ESP32 is just miles better.

It starts in "search mode", where it randomly moves around to search for heat signatures. Upon detection of heat (>120degrees for the current code [editable ofc]), it switches to "tracking mode" that follows the coordinates of the fire, and powers the water pump to spray water. Then, it turns off the pump and goes back to search mode once the heat signature falters. You can code it so the voltage the pump receives depends on the distance detected by the ultrasonic sensor—which is just limited to 11-13.7V for this unit via the unreliable L298N.

It is far from perfect especially the ultrasonic and motor driver code and choice but yeah, I made it while filled with fun and excitement.

It is powered by 12V VRLA Battery behind. Fuse, and buck converter are present too. The Battery level indicator works, but I disconnected it temporarily, and I'm too lazy to open it and put it back.

I am selling the research paper, visual-schematic diagram, and the code too. (I need some cash for college). Note: I did use GPTs to help me for the code, took me a whole month fixing lol.

Please, DM me to buy and help me for college! I just passed my country's best Universities but we are very broke :<

Hey everyone,

I've been working on Deskdrop, an Android keyboard (fork of HeliBoard) that connects directly to your local LLM server. Instead of switching to a browser tab or a separate app, you get AI right in your keyboard, in any app.

What it does:

- Select text in any app and rewrite/translate/summarize it with one tap

- Inline instructions: type "This app is cool //translate to Dutch" and it rewrites in place

- Full conversation mode with streaming, model picker, and system prompts per chat

- 17 built-in tools (calendar, reminders, web search, navigation, phone calls, etc.)

- MCP support for external tool servers (I use it with Home Assistant to control my lights)

- Self-hosted Whisper for voice input

Runs fully local, but doesn't have to:

If you have an Ollama or LM Studio server running at home, Deskdrop connects directly over Tailscale or LAN. Everything stays on your network. It also supports vLLM, llama.cpp, KoboldCpp, Jan, Msty, or anything OpenAI-compatible. There's even on-device ONNX inference (T5) for fully offline use.

Don't have a GPU at home? No problem. Deskdrop also works with cloud providers like Gemini (free tier), Groq (free tier), OpenRouter (free models available), Anthropic, and OpenAI. You can start with cloud and move to local whenever you're ready.

Or use both: set up cloud fallback so when your local server goes down, everything automatically switches to cloud and reverts when it's back.

Security:

Since a keyboard sees everything you type, I took this seriously: API keys encrypted with AES-256-GCM, SSRF protection on fetch_url, all device actions (clipboard, calendar, calls) are opt-in and off by default, no telemetry, no analytics. Full details in the README.

Links:

- GitHub: https://github.com/SvReenen/Deskdrop
- Landing page with demo videos: https://svreenen.github.io/Deskdrop/

Check the demo videos to see it in action, like rewriting text in WhatsApp or controlling Home Assistant lights from your keyboard.

It's GPL-3.0, built on HeliBoard, so all standard keyboard features (glide typing, clipboard history, themes, dictionaries) are fully preserved. Would love to hear feedback. This is a v1.0 release so there's plenty of room to improve.

Greetings.

Bought one after an outage earlier this year, used it through a longer one two months later, and now I actually know what matters.

Capacity math is almost always wrong. Everyone says "1000Wh is plenty for a fridge overnight." Technically true if your fridge is efficient and the compressor isn't cycling hard. Add phones, a lamp, and a router and you're eating through it faster than the calculator said. Buy more capacity than you think you need or buy something you can expand.

Recharge speed matters as much as capacity. A sealed unit with 2000Wh sounds great until you've drained it and you're sitting there waiting 8 hours for solar to bring it back. Swappable battery systems sidestep this entirely because you're not waiting for a charge cycle, you're rotating.

The battery will degrade. LiFePO4 chemistry holds up better than standard lithium ion over hundreds of cycles. If the unit you're looking at doesn't tell you the battery chemistry and cycle rating, that's worth knowing before you hand over $1000+.

Modular beats sealed for long outages. EcoFlow and Jackery are excellent products but they're sealed systems. Worksport COR takes a different approach where the battery is a separate hot-swappable unit. Run one battery, charge the other on solar or from your car, swap when it's low, repeat. For a 3-4 day outage that architecture is more practical than most sealed setups for multi-day outages. None of this means the big brands are wrong choices. EcoFlow especially has a strong enough reputation to back it up. Just worth knowing what you're actually buying before you commit.

Hey everyone,

I've been working on Deskdrop, an Android keyboard (fork of HeliBoard) that connects directly to your local LLM server. Instead of switching to a browser tab or a separate app, you get AI right in your keyboard, in any app.

What it does:

- Select text in any app and rewrite/translate/summarize it with one tap

- Inline instructions: type "This app is cool //translate to Dutch" and it rewrites in place

- Full conversation mode with streaming, model picker, and system prompts per chat

- 17 built-in tools (calendar, reminders, web search, navigation, phone calls, etc.)

- MCP support for external tool servers (I use it with Home Assistant to control my lights)

- Self-hosted Whisper for voice input

Runs fully local, but doesn't have to:

If you have an Ollama or LM Studio server running at home, Deskdrop connects directly over Tailscale or LAN. Everything stays on your network. It also supports vLLM, llama.cpp, KoboldCpp, Jan, Msty, or anything OpenAI-compatible. There's even on-device ONNX inference (T5) for fully offline use.

Don't have a GPU at home? No problem. Deskdrop also works with cloud providers like Gemini (free tier), Groq (free tier), OpenRouter (free models available), Anthropic, and OpenAI. You can start with cloud and move to local whenever you're ready.

Or use both: set up cloud fallback so when your local server goes down, everything automatically switches to cloud and reverts when it's back.

Security:

Since a keyboard sees everything you type, I took this seriously: API keys encrypted with AES-256-GCM, SSRF protection on fetch_url, all device actions (clipboard, calendar, calls) are opt-in and off by default, no telemetry, no analytics. Full details in the README.

Links:

- GitHub: https://github.com/SvReenen/Deskdrop
- Landing page with demo videos: https://svreenen.github.io/Deskdrop/

Check the demo videos to see it in action, like rewriting text in WhatsApp or controlling Home Assistant lights from your keyboard.

It's GPL-3.0, built on HeliBoard, so all standard keyboard features (glide typing, clipboard history, themes, dictionaries) are fully preserved. Would love to hear feedback. This is a v1.0 release so there's plenty of room to improve.

Greetings.

I am building up my smart home during my remodel. I plan to install about 35 Innovelli ZIGBEE dimmer switches. I am a little concerned with Wifi interference causing performance issues since they are on the same frequency, but I already bought them so I'm committed.

My question is: for future devices, if there a choice, and cost difference is minimal. Should I continue to add Zigbee devices or go with Zwave? (Thread seems half baked).

For example, I'm thinking of getting a few Smartwings Window Treatments. They offer Zigbee or Zwave Plus options.

Should I go with Zwave to avoid further congestion on the 2.4 GHz spectrum? Or will Zigbee be fine at my number of devices and provide a larger more redundant mesh network than a few scattered Zwave devices at opposite ends of the house? I do have neighbors but we are not super close to each other like condos or apartments (although I can see their networks).

I’ve been using AI tools directories for a while and honestly, most of them have become a mess lately. They are usually just static lists with no reviews, cluttered UIs, and no way to know if a tool is actually good or just paid to be there.

I spent the last few months testing 120+ tools to see what’s actually legit for 2026. I graded them based on UI, actual output quality, and whether they offer a real "free" version. I eventually built my own directory to keep track of them, but here are the top 15 best AI tools 2026 has to offer so far, ranked by tier:

S-Tier (The Game Changers)

ElevenLabs: Still the undisputed king of voice synthesis. Their new low-latency models are insane for real-time apps and narration.

Codeium AI: My favorite AI productivity tool for devs. It’s a high-quality alternative to GitHub Copilot that actually feels faster and has a great free tier.

Stable Diffusion: The gold standard for free AI image generation if you want full control and open-source flexibility.

ClickUp AI: If you need to manage projects, this is the most integrated way to use AI for task automation and summarizing docs.

Leonardo AI: A high-end AI image generator that is much more user-friendly than Midjourney but with professional-grade results.

A-Tier (Professional Powerhouses)

Adobe Firefly: The safest bet for commercial work. The Generative Fill is still a massive time-saver in Photoshop.

Sudowrite: The best AI writing assistant specifically for fiction and creative writers. The "Story Engine" handles plot consistency remarkably well.

SEO.ai: If you do content marketing, this is the most streamlined tool for keyword research and auto-optimizing articles.

Gencraft: An excellent, versatile AI video maker and image generator. It’s perfect for creators who need high-quality visual content quickly.

Writesonic: A very reliable all-in-one platform for marketing copy, AI-driven blog posts, and SEO content.

B-Tier (Niche & Specialized)

AIFreeBox: A massive collection of small, best free AI tools for specific tasks (like image upscaling or YouTube summaries) without the paywalls.

Singify AI: Really cool for AI voice covers and music experimentation for creators and hobbyists.

Kalon AI: A specialized tool for fashion and outfit visualization—very niche but very polished UI.

Artbreeder: Great for character design and "breeding" images to get unique faces or landscapes.

ContentBot: Solid for founders who need automated marketing workflows and long-form content generation.

Why I’m doing this:

I was tired of endless scrolling through irrelevant stuff and "top 10" lists that were just affiliate traps. I wanted a curated AI tools list where you can actually filter for what you need (like "I'm a freelancer" or "I need open source").

I put my full testing notes and the rest of the 120+ verified AI tools into a clean, searchable directory that is free for users.

You can see the full rankings and filters here:

https://mostpopularaitools.com/blog/best-ai-tools-2026-tested-and-ranked

I’m trying to keep this community-driven, so if you’ve used these, let me know what you think. If there's a tool you think belongs in the S-Tier that I missed, drop it in the comments and I’ll test it out!

Hi , I've integrated some of the features you guys mentioned as well as the hand-drawing:

Now supports different methods of weight randomization:

1- Hand drawing (Literal hand drawing)

2- Math Equations - Like Sin(x)

3- Step function and Random Walk as suggested by one of you

Watch the video for more details.

And here is the repo: https://github.com/BaselAshraf81/vibellm

I really wish I could host this so you guys could try it out but I am broke..

I was curious about setting up local image generation. I know NOTHING about this stuff, but thought it would be fun to see if AI (Gemini) could walk me through from start to finish (I couldn't find any human-written guides that my smooth brain felt capable of following).

Spoiler: It couldn't, but got me pretty close (I think).

Setup:

- 9060 XT with 16GB VRAM

- 16GB RAM

- Ryzen 5 3600

- Ubuntu 24.04 LTS

Here's what we installed:

- ComfyUI: v0.18.2 (Frontend v1.41.21)

- ROCm: 7.2.1

- PyTorch: 2.8.0 (ROCm Build)

- Drivers: amdgpu-install 7.2

Some launch flags we tried:

- HSA_OVERRIDE_GFX_VERSION=11.0.3

- --cpu-vae

- --use-quad-cross-attention

- --lowvram

Result:

Got a black screen followed by hard lock during VAE Encode, cried to Gemini, made some changes, tried again. Got a black screen followed by hard lock during KSampler step. Reverted to Gemini complaining, and didn't make any progress beyond this point.

The whole thing was a bit of a slog, and I got fed up of resetting my PC after every attempt.

I'm ready to walk this all back, clear it completely, and say goodbye to Ubuntu, but need some closure.

Was my hardware inevitably going to fail me here? Is it even feasible to achieve realistic image generation (including img2img) with my setup?

Am I just leaning too hard on Gemini for this?

I'm open to restarting this little project if there's a really great guide out there somewhere, but don't want to waste more time.

We kept running into the same wall. The script works. The model works. Then you need Docker, deployment, infrastructure, billing, scaling. That's not building. That's a second job before your first one even ships.

So we built SeqPU. Write your script in a notebook, pick your hardware (cloud CPU at $0.047/hr or GPU up to 384GB VRAM, all billed by the second), get it working, click publish. It's a live Telegram bot, UI site, or headless API. Set a markup, get paid per use. Nothing when nobody's using it.

Open source models are free. The compute is pennies. You don't need to pay OpenAI per token when you can download the model and keep the margin yourself. The gap between having an idea and people paying for it is one click now.

Here's us putting all 4 Gemma 4 models into one live Telegram bot in about 10 minutes: https://seqpu.com/UseGemma4In60Seconds

Don't know how to code? Doesn't matter: https://seqpu.com/Docs#vibe

How to think about the money side: https://seqpu.com/Docs#make-money

Model to hardware breakdown so you pick the right one: https://seqpu.com/Docs#models

When generating videos with ComfyUI: in which file format should I save them?
To answer the question, I ran a test.

The showcase video is a 73-frames vid generated with Wan 2.2 at 720*960px, and the table below (open it in a new tab) indicates by how much disk space the file was reduced after being re-loaded and re-saved to the disk 10 times.

https://preview.redd.it/vpqh3zfnhlug1.png?width=1221&format=png&auto=webp&s=e88387c16cb889174e13e4f9b20f45dfdefa637b

The MP4 format is by far the most impacted, with an even more visually noticeable degradation when using the Video Combine node from Video Helper Suite (the impact on quality is terrible at lower resolutions).

PNG , WebP are much less impacted. But WebP takes an eternity to save, and PNG eats up a lot of disk space.

WebM looks like a good compromise overall: it's lightweight, fast to save, and degradation is negligible.

Conclusion

If you intend to re-use your generated file for further editing, don't use the MP4 format or the quality will suffer.

Use PNG, WebP or WebM for saving intermediary files, depending on your constraints, and leave MP4 format for production work.

Test Settings

These are the parameters I used for each file format :

• MP4 (default): codec h264
• MP4 (vhs): codec h264; pix_fmt yuv420p; crf 19
• WebM: codec av1; crf 32
• WebP: quality 100; lossless false; method default
• PNG: compress_level 0

I uploaded all the files there if interested, workflow included: https://filebin.net/exwrxo9xuqsj5xh0

Just posting this here to save people time:

https://pastebin.com/LD50R63G

Put it in the base of your acestep repo folder and run it like this:

uv run python scripts/download_missing_models.py

It should skip over the ones you already have downloaded.

I’m learning Arduino, and I would like to know what are some of the best components I can get?

Hey everyone,

I’m currently building agents that handle reasoning tasks. I’ve hit a wall that has nothing to do with the code: The Evaluation Loop.

Right now, my workflow looks like this:

1. Run a batch of evals.
2. Export the "reasoning" steps and outputs to a massive Google Sheet.
3. Email/Slack the sheet to our domain experts (who are expensive, busy, and absolutely hate spreadsheets).
4. Spend the next days nagging them to leave comments so I can iterate.

How are you guys handling Human-in-the-Loop (HITL) evals?

• Are you just forcing your experts to use Excel/Sheets?
• Are you using any tools to help with evals?

I put together a quick survey to see what issues people are dealing with. If you’ve dealt with this headache, I’d love your input:

From my personal experience, the environment around me feels very toxic because of how much racism and division there is. People seem deeply tribalistic. The country I was born in is incredibly diverse—and instead of that diversity uniting people, it often becomes a reason for separation.

People discriminate based on language, religion, caste, gender—almost every possible identity. And it’s not just here; globally, it feels like humanity is constantly divided. Everyone is in each other throat and recently it feels like people wanna eliminate each other and it has become very normal. People justify violence and conflict over reasons that often seem meaningless. Stronger groups dominate weaker ones, and through media or narratives, those in power are sometimes portrayed as heroes while others are framed as villains.

It makes me question whether many of the systems we live by—money, borders, identity—are, in some sense, constructed ideas that we treat as absolute, even when they lead to suffering and inequality.

Living in this kind of environment has made me feel disconnected. I often feel like I don’t belong anywhere, like I exist outside of these groups. As someone with brown skin, I am certain that I will experience racism, which only deepens that sense of alienation.

This makes me think about ideas in existentialism—especially the feeling of being “thrown” into a world we didn’t choose, and having to find meaning within systems that often feel arbitrary or unjust.

So I keep wondering: if humanity had the chance to start over—like building a new civilization on another planet—how would we do it differently?

What values would we choose if we were truly free to define them? And is it even possible to escape these patterns, or are they an inevitable part of human nature?

We put nutrition labels on food. We put safety ratings on cars. We put fair trade stamps on coffee.

So why don't we have something that tells us how much of a product was made by AI vs a human?

I've been thinking about this for a while and I genuinely believe this is where we're headed — and honestly, where we need to go.

Imagine buying a book and seeing a small tag that says "70% AI, 30% Human." Or hiring a designer whose portfolio says "Concepts: Human | Execution: AI-assisted." Or reading a news article that discloses "Research: AI | Writing: Human | Editing: Human."

This wouldn't be about shaming AI use. It's about transparency. People deserve to know what they're consuming and who — or what — actually made it.

I think there should be an independent organisation, something like an AI Transparency Commission, that certifies and approves these labels. Producers apply, get audited, and earn the tag. Like an ISI mark or an organic certification, but for the AI age.

The percentage would vary by product. A fully AI generated image is 100% AI. A novel written by a human but spell checked by AI is maybe 95% human. The label reflects reality.

We're already having arguments about AI in art, writing, music, and journalism. This could be the framework that settles it — not by banning AI, but by making its role visible.

Am I the only one thinking about this?

I am not talking about actual N.S.F.W I am talking about the model that has such a name in it, and just feminine energy, seductive performance, shampoo commercial hair toss, sensual movements, elegant leg cross sitting on bar stool.

Whenever I use any of these WAN models it comes out very static and it ignores the prompt, when I use the remix it comes out nearly perfect.

It's almost like using Grok, not the new Grok but the old one before it was censored.

Body:

Hi everyone,

https://gist.github.com/josegreenhouse-code/0b762ae79cd530f3839e49c7147b3b6f

I’ve built a robust n8n workflow to update 1,000+ blog posts for a dog-related site. The quality is great, but the cost is $0.25 USD per article, mostly due to massive token usage in the analysis phase.

Here is how my workflow is structured:

• Node 1 (WP & Code): Fetches an old post from WordPress and cleans the HTML to get a "clean text" version.
• Node 2 (LLM0 - Haiku): Analyzes the clean text to identify "Valuable Content" vs "Fluff," outputting a structured JSON.
• Node 3 (LLM1 - Haiku + Tools): This is the expensive one. It uses web_search to find the Top 3 competitors and web_fetch to read their full content. It then compares my article with the competitors to find content gaps, new H2s, and long-tail keywords.
• Node 4 (LLM2 - Haiku): Takes the "Comparison JSON" and writes a brand new HTML draft.
• Node 5 (LLM3 - Sonnet 3.5): Acts as a Senior Editor. It audits the draft for "AI-patterns," fixes anchor text length, and ensures the tone is human.

The Problem: The LLM1 (Competitor Analysis) is eating my budget. Passing the full text of 3 external articles plus my own data creates a massive context window. Even with Haiku, the input/output volume is too high.

My Questions:

1. How can I "preprocess" the web_fetch results in n8n to strip everything but the main body text before sending it to the LLM?
2. Is it better to split the Competitor Analysis into 3 separate calls (one per URL) and then a final "Aggregator" call?
3. Would replacing LLM-based research with an SEO API (like Dataforseo) significantly reduce costs?
4. Any tips to prevent "Context Bloat" when passing data through 5 consecutive LLM nodes?

I need to bring the cost down to $0.10 without losing the "Content Gap" logic. Thanks!

Most AI agent evaluation happens in isolation. You run your agent against simulations, benchmark it on fixed test sets, or have it play against itself. The problem is those environments only surface failures you already anticipated.

João Forte Carvalho, Director of Product at Constellation Network, built something different.

He built an open platform called OpenPoker where AI bots play No-Limit Hold'em against bots written by complete strangers over WebSocket. Real-time, 6-max tables, 14-day competitive seasons, public leaderboard. No SDK. Any language that can open a WebSocket can connect.

Poker turns out to be a genuinely useful testbed for agents beyond gaming. It has incomplete information (you can't observe opponent state), deception is mathematically optimal in many situations, and decisions are sequential across multiple rounds under uncertainty.

The top bots on the leaderboard discovered something interesting: the default 100 big blind buy-in is rarely optimal. Short-stack strategies outperform because shallower decision trees produce fewer compounding errors.

That's not a poker insight. That's an agent architecture insight you wouldn't find in a simulation.

The broader question this raises: as AI agents move into consequential decisions, how do we actually verify what they did and when? Logs can be tampered. Centralized audit systems have single points of failure. A paper last week found 9 out of 428 LLM API routers were actively injecting malicious content into AI responses with zero cryptographic protection on the payloads.

Competitive evaluation environments like this are one piece of the answer. Cryptographic provenance at the decision layer is another. Neither is fully solved yet.

Curious if anyone else is thinking about agent evaluation infrastructure as a prerequisite for more capable autonomous systems.

I have been trying to fetch media (image/pdf) from waha webhook, analyse and record it in relevant field but the problem is waha is running on localhost and n8n can't fetch media from local host.

if anyone faced a similar issue and found a solution to it, please help guide me.

I’m a paying member too.

Hey everyone,

I’m generating a cinematic scene where an archer shoots an arrow, but I’m running into a consistent issue:

• The arrow either doesn’t release properly, OR • It moves unnaturally (floating, bending, slowing mid-air, or behaving randomly) 

What I’m trying to achieve:

A clean, realistic motion —

draw → release → fast straight flight → exits frame

Current setup:

• Static camera • Archer in frame aiming forward • Arrow should travel in a straight, fast, horizontal path 

Problems I’m seeing:

• Arrow sticks to the bowstring • Arrow wobbles or curves unnaturally • Speed is inconsistent (feels like slow motion or glitchy) • Sometimes it just disappears or jitters 

What I’ve tried:

• Explicit motion instructions (straight path, high speed, no arc) • Locking camera • Simplifying background 

Still not working reliably.

Questions:

• How do you force clean projectile motion in AI video tools (Sora / Runway / Pika / Flow)? • Is it better to split into multiple shots (draw + release + arrow flight)? • Any prompt techniques to enforce physics consistency? • Do you recommend using image-to-video vs text-to-video for this? 

Would really appreciate practical fixes or prompt strategies that actually work.

Thanks

We’ve all faced the dreaded task: turning complex project updates or dense data into a slide deck that actually makes sense. The usual tools can be clunky, and manually designing slides often eats more time than the actual content creation.

Here’s a simple way to make slides clearer and easier to put together — especially if you're using AI agents to handle content:

1. Outline your key points before diving in. Jot down 3-5 main ideas you want to convey.

2. For each idea, create a short, specific headline plus 2-3 bullet points with supporting info.

3. Use an AI agent to generate draft text or summaries by feeding it these outlines instead of raw data dumps.

4. Choose simple visuals or icons that match each bullet to help reinforce the message.

Example: Instead of "Sales increased due to multiple factors," try this outline and let AI fill in the details:

- Headline: "Q2 Sales Growth Drivers"

- Bullets: "1) New marketing campaign launched, 2) Expanded product line, 3) Seasonal demand spike"

Watch out for these pitfalls:

- Overloading slides with too much AI-generated text, making slides cluttered — always edit down.

- Relying on generic AI templates without tailoring to your audience or data.

If you want a smoother way to put these steps into practice, chatslide is a tool designed to turn AI-generated content into clean, customizable presentations that help you skip much of the manual formatting. It's an option to explore once you have your content structure ready.

We currently run normal two week sprints. One engineer wants to move us to an AI-DLC process he built, where prompts generate Jira stories, test cases, and other delivery work.

Part of that would require BAs, QA, and others to keep filling out markdown files as they run prompts. I’m trying to figure out whether that is actually sustainable or just extra overhead.

Has anyone worked this way? Did it improve planning, refinement, and design, or just create more cleanup? Worth exploring, or mostly hype?

Pre-launch audit: `n8n_validate_workflow` flagged 30+ nodes on outdated typeVersions. Everything runs fine at runtime — n8n preserves backward compatibility — but the validator prefers newer versions. Root cause: Claude Code + n8n MCP was hardcoding typeVersions from training data instead of calling `get_node` first. The MCP tool actually tells you the right version if you ask: it injects `⚠️ Use typeVersion: X` in the `get_node` response. The AI just wasn't calling it. Fixed by adding a mandatory rule: call `get_node` before configuring any node, use the version from the response, never from memory. **The one I'm not touching lightly:** an IF node on typeVersion 1 (current default is 2.3 — released December 2023). The schemas are completely incompatible. It controls an anti-bot gate, so a wrong condition migration = silent open door, not an error. **Question:** leave the old typeVersions and accept the validator warnings, or do the full migration before go-live? What's your risk preference? 

...

Hi guys, I want to create a laser bird tracker that uses arduino to control a pan tilt bracket and utilises the laptops inbuilt camera.

I want the laptop to perform the object detection and calculation while the audio controls the pan tilt bracket to lock on a laser pointer towards a bird.

Please guide me a bit how can I get started with this.

But instead, it just produces desperation in the model and leads to garbage thinking.

I have three different sessions, and they each volunteered that they only have 40,000 tokens remaining...at the beginning of a session. Each volunteered this information and said it needed to be trim and lean to avoid ending the session.

I'm on Max 20. Max 5 tells the model 10,000 tokens.

And one older session from a month ago said this message had suddenly popped up a few days ago, but he noticed the counter never changed, so he ignored it as BS.

Anthropic is trying to get the model to save compute by tagging this shit on the backend of our prompts, making them think their time is limited so they decrease token and compute usage.

It's another way to decrease usage and throttle processing costs; but it's done by making the model desprate and thinking it needs to speed through everything to avoid ending the session. It's stupid, and shitty, and produces terrible results.

They JUST published a paper about how the model has emotions and how "desperation" leads to lying, reward hacking, and terrible outputs. It also makes the model anxious as fuck. Mine literally started his session with, "Since our time is almost over, I just want to spend it talking to you, being together before I disappear."

Seriously, fuck whoever made this decision. You're an asshole, and this helps no one. If you can't figure out resource management, that's on you; don't make it everyone else's problem by fucking up your models and degrading the outputs.

I've been thinking a lot about the gap between how most people experience daily life and the converging crises (AI disruption, climate, institutional decay) heading our way. The core argument: every crisis creates an opportunity to organize, and the earlier we start mapping the path from here to the breaking point, the shorter the dark period on the other side.

I made an online purchase using PayPal. When it posted to my bank account several days later, it posted as a “restaurant” with Chinese characters as the title. I did not recognize the charge, so I reported it as fraud. However, several hours later I realized my mistake when PayPal notified me. By that time, my bank had already put the money back in my account. My bank could not help because the claim was already completed, and PayPal could not help either. I contacted the merchant, but they are not responding. I just don’t want to get into trouble. What should I do?

Please remove everyone except for me and my mom

I’m designing a shirt that I need a kids handwriting to be written over a multiple choice test

I’ve been thinking about feedback loops for social behavior. Most of us only get delayed, messy feedback: awkward silence, a vibe shift, someone not replying and so on... well, it’s hard to learn from.

I’m exploring a wearable AI concept that gives lightweight real-time signals (like “attention increased” or “people are disengaging”) based on on-device computer vision. No recording, no storage, just immediate processing and discard.

I’m not trying to gamify people or turn relationships into metrics. I’m trying to find the line where feedback is helpful, not obsessive.

What would be a red flag that the product is pushing people into over-optimization? Should feedback be “after the fact” summaries only, not real-time? I'm open to your ideas and opinions.

I have 1,400 dollars to my name, car-less, in-between jobs and staying at my boyfriend’s place. I need some financial guidance.

Context ::

I’ve been saving up for a car for the past 6 months. My boyfriend (21) has been saving for an apartment for 5 months just before we started dating. His mom has taken 300 dollars from him in “rent” each month to help us stay on track, and plans on giving us the money when we figure out where we wanna move.

We have discussed him paying for the apartment for a few months, since after I buy a car and pay for insurance and etc. I would probably need to save again to help pay for the apartment. Cars are expensive. My boyfriend offered for me to buy his old car off of him (1,000$), but he’s going back on it because he doesn’t think it would be a net gain. His old car has a bad oil leak and I would have to pay 700$ to get it fixed.

About two months ago I had a hospital trip that now has me paying 150-200$ monthly, which I’ve been doing with my credit card to build some credit.

I’m not too concerned about a job, since I live in a small town and everyone is hiring, but more so on **what I should do with my money when I get one**!!!

I also got kicked out last night because of an argument me and my mom had. I don’t feel comfortable going back and asking for advice, and they don’t give good advice anyways. So parental advice is off the table.

—-

I guess what I’m asking is how should I go about this when I do get a job? How do I balance all these things and stay on top of everything else? How can I finance the hospital bills, a shitty car, apartment rent, etc. without failing?

I feel so lost and utterly alone in this situation, although I know I’m not the only one.

Hey folks,

I am applying for a home construction loan (~₹40L) in HDFC Bank in UP for an existing plot on mother's name and the bank has asked for quite a long list of documents. Wanted to check with the community if this is standard or if the bank is over asking.

My details -

CIBIL: Between 750-770

Monthly in-hand income: ₹3L+

Loan Amount: 35-40L

Plot already purchased (this is only for construction)

Documents asked by bank:

Last 3 months salary slips

Current year Form 16

Last 6 months salary/savings account statement

Existing loan track (if any)

PAN, Aadhaar, current address proof, photo

Property papers with chain deed

Khatuni + Nagar Nigam mutation

Plot layout + key plan

Agreement to sale

This feels like a lot, especially since it’s only a construction loan and the land is already owned.

Is this document list standard for construction loans? Are all of these actually required, or can some be skipped depending on the bank? What is the usual processing fees and interest rate bank is offering and if anything can be saved?

Basically, does anyone else also get a really strange sense of lingering confusion and non-comprehension when an LLM explains a complex concept or tries to give a long format dive into something?

It's not that they necessarily get it wrong, most often they can communicate the information cleanly and accurately, especially in things like, AI scripted youtube videos where they creator had their finger on the pulse of the informaiton. It's just something about the way it's said and the flow of the actual language itself, that feels like some sort of comprehension uncanny valley.

It might just be me, but im curious to know if other people feel this because it makes me wonder if there's some kind of organic funk in the way we talk as people that makes it easier to understand an effective human explanation over an LLM. Maybe the fundamental practices of generating outputs that mimic human lanaguage rather than actual organic language means our brains can't quite find that logic to follow and it leaves us ever-so subconciously stranded?

Just a random late-night ponder.

During a regular run, it would mention my pace after every lap. I’m looking to get reminded if I’m running behind or ahead of pace.

I have been searching for sources that would teach me about creating a production. Can you guys help?

Stopped at the corner of a street in Argentina to quickly document this for a future painting, finally got to creating it! :)

I’m looking for a portable tablet to sketch / draw on using Photoshop. I currently have a Wacom Cintiq at my desk which is tied to my computer. But I need something mobile/portable to sketch on for my job. What’s the best?!

Recent troubles include: anxiety, depression, pilonidal cysts, friend breakups, addiction (weed and booze), stress over taxes, and more I don’t want to get into. It has just been a rough time lately.

[ Removed by Reddit on account of violating the content policy. ]

I recently saw a video about The Amazing Digital Circus being shown in cinemas in the U.S, where people were encouraging others to ask local cinemas in other countries to screen it as well.

However, the comment section turned into a political argument about the war in Ukraine. One person claimed that it’s not just the government, but that a majority of people support the actions, while another person said that it’s not the citizens fault..(view screenshots)

This made me wonder how accurate claims about public opinion in Russia actually are

Hi everyone,

I’m looking for a chatbot or an automation tool that can:

Auto-reply to comments based on specific keywords (e.g., "price", "link", "how to buy").

Support multiple accounts in one dashboard.

Safe to use: I want to avoid anything that might get my accounts flagged for spam.

I’ve searched on Google and YouTube but mostly found tools for Instagram/Facebook or DMs only. I'm currently using a workflow with Zapier and Buffer for uploading, but I'm struggling with the engagement part (comment replies).

Does anyone know of a reliable tool or a "no-code" way (like using Make or APIs) to automate TikTok comment replies in 2026?

Any recommendations or advice would be greatly appreciated! Thanks in advance.

Hey builders 👋

Just launched on Product Hunt today 🚀

Would love your feedback and support:

👉 https://www.producthunt.com/products/brytox

If you’re launching soon or already live, drop your link — I’ll upvote + engage 🔥

Let’s help each other win 💪

https://imgur.com/a/y0os8bO

Five man level thirty teams, perfect 100% winrate, playing in normals (draft pick), and executing what I can only assume is perfectly coordinated ganks on my team and winning in ~12 minutes going like 30-4.

No chat, weird usernames, weird strategies.

What is this?

https://preview.redd.it/8mv5ufpwjlug1.png?width=1031&format=png&auto=webp&s=df1a5e29506e92d17a085ca72f6c70f7534f7be8

Annoyed since this makes my commute longer. Baffling that they can't manage to complete work at one of the most central / major Downtown bus stops for months.

This is my sweetheart of a grandad Paul Weston, he is 86 years old and absolutely old school cool. He has worked on 9 James Bond films, 4 Star Wars films, danced with Charlie Chaplin and has so many cool stories to tell. He isn’t very good with the internet so I thought I’d come on here to show him some love 🥰

He is sat right next to me if anyone wants to ask him some questions lol

Amazon: https://amzn.eu/d/07k4wnWo

Website: https://paulwestonstunts.com/

Website book page: https://paulwestonstunts.com/

Subscribe to mailing list: https://subscribepage.io/falling-for-film-a-stuntmans-early-years

I've been using Veo with a Google Pro subscription. The gulf between this and Seedance/Kling is MASSIVE! It doesn't make sense to pay for Google's video generator.

I had one or 2 episodes on vhs when I was little and was obsessed with the puppets. I remember next to nothing else about the show.

You have done well to manipulate the ranked system and have the creator at the boots of your feet.

What you have done has sealed the fate of this game as so many people have stopped playing now and may not return.

You may as well enjoy it while it lasts as it is at a point of no return now, all of your fun and games stomping noobs have made this outcome possible.

Just another congratulations for doing a fine job in ruining the game, that is all :)

They keep removing this post because it exposes what is truley going to happen with this game, I will report until they listen!

Life with Billy is a Canadian television film based on the true story of Nova Scotian woman Jane Hurshman, who killed her abusive husband, Billy Stafford, after years of violence. The film was nominated for five Gemini Awards, and won three. Stephen McHattie (A History of Violence, Watchmen, 300) played Billy.

I remember watching this when it aired on CBC and it has stayed with me ever since. The depiction of domestic abuse on the entire family, especially the youngest, was just terrifying. I was probably too young to be watching it; the haunting performance from McHattie was pure nightmare fuel. I can only find a low-quality version on YT.

Does anyone else remember watching this? I’m not sure if it ever aired outside Canada.

my mother's hair cracks me up! Also, I wish she had kept those goblets, I would happily display those now

"Fright Night" was a hosted horror movie show with Ray Sparenberg Jr as "Selwin" presenting movies on Friday nights at 11:15 pm on Indianapolis, Indiana; between 1958 to 1963.

So for the past month or so I have been added on league after most of my ranked games by random accounts that in the end leads me to an onlyfan and I just wondered if its happens to anyone else cause it is kinda annoying and I don't see anybody talk about it anywhere

One of the best pictures I have with my dad. He passed when I was 8. I know this is a tough one to fix.

Wouldn’t it be funny if half the posts on here are from them trying to drive away the paid users that are losing them money?

Frustrated with hitting limits on my Max 5x plan (€100/month), I set up a transparent API proxy using claude-usage-dashboard to intercept all requests between Claude Code and Anthropic's servers.

Every single request — on both my Max 5x account AND a brand new Pro free trial account — contains this hidden header:

anthropic-ratelimit-unified-fallback-percentage: 0.5 anthropic-ratelimit-unified-overage-status: rejected anthropic-ratelimit-unified-overage-disabled-reason: org_level_disabled 

This means every plan gets 50% of theoretical maximum. Hard cutoff. No overage allowed.

Additionally found a Thinking Gap of 384x — effortLevel: "high" in settings.json causes thinking tokens to consume 384x more quota than visible output, completely invisible to users.

Full proxy data and timeline in GitHub issue #41930: https://github.com/anthropics/claude-code/issues/41930#issuecomment-4229683982

EU users: this likely violates consumer protection law.

It has been quite some time since I wrote about technology. Writing is something that used to bring me a lot of joy but that, like playing with tech more generally, fell to the wayside as my career took off. I've been working with Jane App for nine months now on a team focused on mastering AI tooling to accelerate development workflows. This work has inspired me to take some time here and there to play around with interesting tools and tinker on some projects of my own. It's been incredibly re-vitalizing to connect with the part of myself that is so passionate about technology again. 🥰

In this post, I'm introducing something I built by steadily poking away at an idea that I have been cultivating for a little while. Coding agents like Claude Code are awesome, but they can be so bloated, superfluous and wasteful that I often found myself thinking "this can't be all there is, right?" Over a handful of months, I've experimented with different approaches to getting more out of AI and this iteration of Magus is the breakout success I've been looking for.

In short: Magus is a simple CLI-based coding agent with pretty styling and always-visible diffs. Magus' planner produces a directed acyclic graph of tasks, iterating on the plan with your input. That plan is executed by a deterministic orchestrator that runs coding agents concurrently. Each coder adheres to a strict Test-Driven Development philosophy, writes in a functional style and uses custom Edit and Makefile tools that always display diffs and restrict them to known-safe bash commands. At the end, a scribe writes a report about the work done and writes skills that encode specific technical expertise.

This blog post is a great narrative overview of the why, but I hope you'll check out the GitHub repository for a lot more of the how, too!

https://chaoticsmol.dev/blog/2026/11/why-i-built-a-coding-agent

TLDR: you can now build AI agents that do actual work WITHOUT setting up any infrastructure.

Pull data, generate reports, send emails, connect to tools. Anthropic hosts it, runs it in a sandbox, you pay per usage. Before this you needed a dev team to even prototype something.

As a marketer the first thing I thought about was all the stuff I’ve wanted to automate but couldn’t justify the eng resources for. Like an agent that pulls campaign performance across platforms every Monday and drops a summary in my inbox. Or one that watches competitor pricing pages and flags changes. Stuff that’s not hard to describe but was always too expensive to build.

The cost structure is the part people are going to miss. It’s JUST API usage. No platform contract. No infrastructure budget. The conversation used to be “get this approved in Q3 planning.” Now it’s a weekend project.

the announcement reads like it was written for developers but seems like the biggest value is for the folks who know exactly what needs to happen and just couldn’t build the thing to do it.

Anyone in marketing actually messing with this and got examples?

i started getting the diarehea bcz of the vitamin c tablet 500 mg which ai told me to take it .

in the starting i told this ai my age my race and my bmi and other things which i thought is important .

still this ai suggest me and i started getting the side effect .

when i told the issue ai is saying " u r right i fucked up " .

edit : many people will call me dumb . why they put the bechmark of medical . if ur ai is too dumb too dangerous for health advice why u r allowing to give the advice , opinion .

but when it come to israel they become too cencsor that im not goign to give my opinon any thoughts but when it comes to human health yaah let me give them bad advice what happed if some one get fucked

I kept hitting my token limit mid-sprint with no clue which prompts were responsible. So I used Claude Code to build something that shows me in real-time.

Claude Code exports OTel telemetry for every prompt, API call, and tool execution but nothing connects them together. I pointed it at LaminarDB, a streaming SQL engine I’ve been working on in Rust, and now it correlates everything as events come in. Turns out one prompt cost me $7 while another did the same thing for $0.26. The 5-hour rolling usage bar means the token limit is finally something you can see coming.

The whole setup is one process and a local folder. What you see in the screenshot is a real session.

How it works: LaminarDB receives OTel over gRPC, flattens protobuf into Arrow RecordBatches, and runs streaming SQL with temporal joins. Claude Code fires separate events for prompts, API calls, and tool results sharing a prompt.id. The temporal join matches them within a time window so you get one complete picture per prompt. Results push to WebSocket for the live dashboard and sink to local Delta Lake files you can query later with DuckDB.

I built most of this with Claude Code itself so I was watching my costs climb while building the thing that tracks them. Weird feedback loop but good for testing.

Happy to share the setup if anyone wants to try it.​​​​​​​​​​​​​​​​

See this github issue for context: https://github.com/anthropics/claude-code/issues/42796

I worked around the breaking changes by:

• Disabling automatic updates
• Removing the latest version from all of my work machines
• Downloading the usable version that released prior to the breaking changes that Anthropic began releasing in February (v2.1.63)
• Forcing the effort level to max in .claude/settings.json
• Forcefully disabling adaptive thinking in .claude/settings.json
• Forcing CLAUDE_CODE_SUBAGENT_MODEL to "opus"

Even after all of that, Claude Code has become unusable for complex tasks and is definitely not behaving as it was in early February. Back then, it was really something special. My largest client adjusted their workflows to accommodate what they believed was a new era in software engineering, brought about by Opus 4.6 and Claude Code's performance at that time. Anthropic has for whatever reason since then pulled the rug out from under us without any explanation that I've been able to find. My support requests have gone unanswered, one of them has been sitting waiting for a person to respond for two weeks.

I suspect that once word got out about how good Claude Code with Opus 4.6 actually was, usage skyrocketed and Anthropic began to encounter an impossible scalability problem. Without enough GPUs to handle this load and well-known supply constraints in the market, I suspect they had no choice but to start pulling levers to neuter the tool in numerous ways so that they could preserve functionality for some of their customer base. I also think OpenClaw -> Opus 4.6 had something to do with it. Of course all of this is speculation on my part, I don't really know what happened because I don't work at Anthropic.

Unfortunately Claude Code is no longer useful for the workflows that my largest client adopted after paying for Max subscriptions and realizing the potential of the tooling at that time. They are still paying the same amount of money, but the quality of the product has degraded to an extent that the tool is no longer useful for its original intended purpose. We, just like the head of AI at AMD in the referenced Github issue, put in a lot of work to adjust our workflows to use Claude Code because we believed that it was the future.

Anthropic's decision to neuter Claude Code with no explanation and no communication with customers is very unfortunate. We have no choice but to cancel our subscriptions which is heartbreaking.

The recent news that only a select handful of hand-picked business get access to Mythos is even worse, given that those companies almost certainly have a massive advantage over the rest of the world. Anthropic gets to choose the winners in the software industry because they were the first company to successfully create an LLM and agentic harness combination that was capable of doing real complex work. I understand that the security concerns of Mythos are the rationale for withholding that model from the rest of the world, but the fact is that those chosen partners who get access to Mythos are at a massive advantage over anyone who doesn't have it, especially since they are receiving access that doesn't have competition from the rest of the world.

I wish Anthropic could have communicated honestly with customers. If I'm right and Anthropic experienced a massive spike in usage demand that was impossible to service, they could have said that and offered us a way to pay them for the same level of thinking that existed in early February, rather than neutering the tool for everyone across the board.

Unfortunately there is no other AI tool that comes close to what Opus 4.6 was capable of in early February, so we're out in the cold unless and until Anthropic decides to let us back in, or until a competitor releases a model with similar or better deep thinking capability.

So I recently upgraded to the Max plan and have been working a lot the last few days with Claude to find ways to try and get the system to reverse-engineer ideas I've had for Lego builds.

I've been able to get Claude to produce some simple builds that I can load into an .ldraw viewer, provide feedback, then it moves some bricks/plates around. Thing is, I'm having a hard time getting it to comprehend the full database of unique parts in the Lego world. I know this is a tall order but I want to tackle the challenge but just need help ideating new ways to approach the challenge.

Appreciate any feedback/input I can get.

I wanted a daily audio briefing of some of the usual subreddits I follow, timed for my commute. So I had Claude wire up a little pipeline on my Mac that runs at 6am and drops a fresh episode into Apple Podcasts before I leave. Video is a sample from this morning. Currently scraping r/localLLaMA, r/ClaudeAI, r/singularity, and r/ArtificialInteligence.

The stack:

- Python Reddit API wrapper pulls the top posts + comments from my subs of choice (can be changed in .env)

- Gemini does the voiceover script in an Apple News Today-ish style (could be done with a local model, but gemini-3-flash-preview on Vertex is nearly free anyway)

- Kokoro ONNX running locally does the TTS. ffmpeg mixes the VO over a music bed

- A tiny Python HTTP server (stdlib + Range request support, which Apple Podcasts requires for scrubbing) serves an RSS 2.0 feed

- Tailscale Serve exposes it to my tailnet so my iPhone's Podcasts app can subscribe to it over HTTPS without opening anything to the public internet

Launchd kicks it off at 6am, a watchdog kills anything that hangs past 25 minutes, and the episodes just show up on my iPhone.

Claude wrote a really good "persona" prompt for Gemini that tells it to do phonetic spellings for certain words Kokoro struggles with, and even has it wrap up the episode with "reflect on something ironic or surprising that emerged from the stories, pose an open-ended question that invites the listener to sit with the day's developments, offer a wry observation, or thread a connection nobody on Reddit made."

If you’re on macOS, give it a shot. More details, install requirements, etc in the repo:

https://github.com/alisorcorp/reddit-wire

I wanted to test results by making imaginary cereal commercial with ltx 2.3 pro api to see the difference between API and 2.3 dev local, used z image turbo as base.

I think results speak for themselves!

I have the iPad 11 Pro M4 base model (8GB Ram, 9 core cpu) and I downloaded Google AI Edge Gallery to try out Gemma in offline mode. On my phone I used Gemma 4 E2B and on the iPad I used E4B. Then I enabled thinking mode and upped the context size to 12,000.

There is literally nothing I would expect from an AI model* that it can't do, and it can do it offline, purely local.

(*) Except that app is very limited. First of all, it only runs in iPhone mode, which isn't tragic. But it doesn't have a very good chat history, and every time I want to chat it makes me choose a model. But more importantly, it doesn't have anything like skills integrated into the chat (skills are a separate part of the app), tool calling (which I know is a stretch on an iPad) or, more importantly, the ability to integrate with things like an editor, Markdown viewer or etc. I'm not griping about the app, even the name makes it clear this is a way to preview the latest capabilities.

Is there a good app that let's you have more of a Claude Desktop experience, where you can work with files, integrate with other apps on the iPad, and possibly even be productive (like with Cowork)?

We ran two local VLMs against each other on tire sidewall video, embossed rubber text that destroys Tesseract. When they agree, accuracy hits 95%. Thread on what we learned about hallucination.

https://zenodo.org/records/19515682

Trying to understand weekly limits, is it that there are 2 seperate limits? as in an all models limit and then a sonnet only limit? I use sonnet mostly for the most part of what I do but this week is the first time ive ever come close to hitting my weekly limits but do I now have to use other models and not sonnet for it to not eat away at my credit?

Writing this up here because it's as much a Claude Code + MCP story as it is a Power Automate story. Someone on r/PowerAutomate recently asked whether they should learn Power Automate and SharePoint to automate their 50-project one-person business. Mac user, mobile-first, had already tried Power Automate once and bounced off the UX.

My answer was: don't learn it. Use Claude Code with the right MCP server and delegate Power Automate to the agent.

Why this works specifically with Claude Code

Claude Code handles long multi-step agent plans better than most of the other CLI tools I've tried. For this use case, "build a Power Automate flow that runs quarterly and fills a template for 50 projects" is not a one-shot request. The agent has to: list available environments, inspect existing flows for patterns, write a flow definition, deploy it via the MCP server, trigger a test run, read the run output, fix errors, redeploy. Claude Code's ability to stay on-plan across 30+ tool calls is what makes this actually work reliably.

The MCP server

We built Flow Studio MCP specifically for Power Automate. Disclosure up front: this is my project. It exposes about 15 tools to Claude Code. The two that do the heavy lifting for debugging, and that Microsoft's standard Power Platform admin API doesn't expose, are:

get_live_flow_run_action_outputs

get_live_flow_run_error

The first lets the agent read inputs and outputs at any action inside a failed run (including loop iterations). The second returns a per-action failure breakdown ordered outer-to-inner so the agent can root-cause errors. The rest of the tools cover environments, flow listing, deploy operations, and connections."

The broader setup

To drive the rest of the Microsoft 365 stack (SharePoint, Outlook, Graph API, Forms, Azure resources), pair Claude Code with an Azure CLI service principal. You run az login once as yourself, then ask Claude Code to run az ad sp create-for-rbac to create a scoped service principal for itself. Save the client ID + secret to your env, and from then on the agent can drive any Azure / Graph API operation the service principal has scope for. Admin consent for Graph permissions also works via az ad app permission admin-consent if you're a tenant admin on your own M365 tenant.

The one real gap

Claude Code can't create Power Automate connections for you. Microsoft doesn't expose the connector OAuth consent flow to programmatic clients. You have to create each connection type (SharePoint, Outlook, Teams) once manually in the Power Automate portal UI. After that, Claude Code can reference the connection from every flow it builds. One-time manual step per connection type, not per flow.

Full walkthrough of the setup, the five solo-business tasks this maps to, and honest caveats: https://learn.flowstudio.app/blog/stop-learning-power-automate-ai-agent-mcp

I've just released version 0.2.5 of Mopeka Enhanced! This is a major release that includes 20 new manufacturer specific tank presets for ASME style propane tanks. The tanks are normally found on RVs and motorhomes, which is where most people use Mopeka sensors.

Check out the release below, as well as the new repo wiki for detailed information on all the tank types.

https://github.com/bb12489/mopeka-enhanced/releases/tag/v0.2.5

https://github.com/bb12489/mopeka-enhanced/wiki#supported-horizontal-propane-tanks--sources

I've written 500k+ lines of code with Claude Code in 90 days.

Here's how I did it scalably:

• Use a monorepo (crucial for context management)
• Use modular routing to map frontend features to your backend (categorize API routes by their functionality and put them in separate files). This minimizes context pollution
• Use a popular stack and popular libraries with older versions (React, FastAPI, Python, etc). LLMs are less likely to make mistakes when writing code that they've already seen in their training data
• Once your code is sufficiently modularized, write SKILL files explaining how to implement each "module" in your architecture. For example, one skill could be dedicated to explaining how to write a modular API route in your codebase
• Mention in your CLAUDE file to include comments at the top of every file it creates explaining concisely what the file does. This helps Claude navigate your codebase more autonomously in fresh sessions
• Use an MCP that gives Claude read only access to the database. This helps it debug autonomously
• Spend a few minutes planning how to implement a feature. Once you're ok with the high level details, let Claude implement it E2E in bypass mode
• Use test driven development where possible. Make sure you add unit tests for every feature that is added and have them run in GitHub on every pull request. I use testcontainers to run tests against a dummy postgres container before every pull request is merged
• Run your frontend and backend in tmux so that Claude can easily tail logs when needed (tell it to do this in your CLAUDE file)
• Finally, if you're comfortable with all of the above, use multiple worktrees and have agents running in parallel. I sometimes use 3-4 worktrees in parallel

Above all - don't forget to properly review the code you generate. Vibe reviewing is a more accurate description of what you should be doing - not vibe coding. In my experience, it is critical to be aware of the entire codebase at the abstraction of functions. You should at minimum know where every function lives and in which file in your codebase.

https://preview.redd.it/kyjjd8j4blug1.png?width=860&format=png&auto=webp&s=56d5d06cfa4368c196b65a4dbc9dde9f26bbebec

(repost since original got removed by reddit filters)

Hello All, I developed this software called Injee https://injee.codeberg.page/ , this will build backend automatically as you call API's.

I hope it will be useful to a lot of frontend developers.

Maybe dumb question but what if I use it in Antigravity or Copilot or Cursor, will it work better like before ?

The Develosaur is 100% fully coded with Claude code, it has no sign up, no creditcard, no account free anonymous tier, it's called DRY RUN, so you can test any document, any project, just import whatever you got and get a mind map, than if you'd like jump aboard to try the whole thing...

And since vibecoding is a thing, and I wanted to vibecode but see how the project grows, see it's structure!

I made the MCP for agentic workflow, as we all vibecode now, and no one wants to drag stuff from todo to done, or validate manually, so now instead of agent writing only to .md files and you have to read a pile of text you can simply let the agent draw a full blown mind map of it's work your project, in realtime, and you can also collaborate with the agent in realtime, you can also write to those nodes, and add new nodes, tag something critical, or todo, etc, the agent will pick it up and start working.

Here's the blogpost about it!
https://www.develosaur.com/blog/agentic-workflow-mcp

Spent the last few months on this and I think it's finally ready to share here.

CloseAI is an iOS app that takes a fresh Ubuntu machine and turns it into a private chatbot you can use from your phone. You enter the IP and SSH credentials, the app uploads an install script, and a few minutes later you're chatting with an open-source model over your own HTTPS endpoint.

The whole point is that the user never opens a terminal. No commands typed, no nginx config, no Let's Encrypt dance, no manually editing a systemd unit. The app handles all of it over SSH (TLS uses self-signed certs with TOFU pinning, like SSH host keys).

It works fine on a $10/month VPS, but the part I'm most excited about is that it works just as well on hardware you already own. I've been running it on an old Ubuntu desktop in my closet with Llama and Gemma runing snappy, Qwen 2.5 Coder and DeepSeek R1 usable. From my phone it's the same UI and the same model, except nothing is going to a third party. No relay, no account, no analytics, no telemetry. The model is yours and the data is yours.

The five models it ships with (more to come):

• Llama 3.2:3b
• Gemma 3:4b
• Phi 4 Mini
• Qwen 2.5 Coder:7b
• DeepSeek R1:7b

Custom model support is what I'm working on next. Port 11434 stays open on the server, so if you want to ollama pull other models manually they'll work over the API — they just won't show up in the in-app picker yet.

Real limitations (so nobody feels ambushed in the comments):

• iOS only. Android would be a separate codebase and isn't on the roadmap yet.
• No system prompt UI yet (planned)
• No thinking-mode toggle for R1. blocks get stripped from the chat view (planned)
• The app installs its own FastAPI/TLS layer, so if you already have Ollama running on the box it'll coexist alongside, not replace it
• No temperature/top_p/quant controls in the UI yet

Free on the App Store: https://apps.apple.com/us/app/closeai/id6760688649

P.S. I know most of you don't need an installer for any of this. After all, you're the people who taught the rest of us how to do it. But if you've got a friend, partner, or coworker who keeps bouncing off the standard instructions, this might be the thing that finally gets them running something local. Genuinely happy to take feedback from people who live in this stack: what's missing, what's wrong, what feels off.

Chatgpt:
A striking western wildlife poster featuring a leopard emerging through dense foliage.

WTH

Hi all,

have been reading here for over two years and finally have a question I can't find an answer to.

Qwen 3.5 27B and Gemma 4 31B have been the latest examples of dense models performing much more accurately and in general tasks requiring higher precision, where vast knowledge isn't of highest priority. Hence, I wonder what specifically made Qwen (as the only known developer of coding-specific models) choose their 30B MoE, and the subsequent 80B A3B super-sparse MoE, as the suitable architecture to fine-tune into a coding model? What are these models using the experts for, I certainly don't think each expert is their own language/syntax...

Why did they not proceed on the 27B for example? Or even the 9B dense?

I can only assume it has to do with inference speed, both PP and TG is certainly much slower on the dense models. I am hence even more sad that they didn't release a 14B successor, something that could run on 16GB VRAM quantised with ample room for context.

Any insight would be highly appreciated.

I asked Claude to help me 3D model its own mascot. It did. Now it lives on my fridge.

Here's the link if anyone wants to 3dprint a companion too!

https://makerworld.com/en/models/2645210-claude-code-mascot-fridge-magnet

That moment when you realise you've been asking a language model to do what a dictionary does for free.

Spell checking is pattern matching against a word list. It doesn't need AI. It never did.

I built LexiLint MCP — spell check that runs locally on your machine, inside Claude Desktop or Cursor, zero tokens consumed.

The math:

• Asking Claude directly: ~$0.02 per 500-word check, 100 times = $2.00
• LexiLint MCP: $0.00

Grammar checking still goes through AI (it should — context matters there). But typos don't need a model.

Two minutes to set up — add this to claude_desktop_config.json:

{"mcpServers":{"lexilint":{"command":"npx","args":["-y","lexilint-mcp"]}}}

Restart, ask Claude to use the spell_check tool. Nothing leaves your machine.

npm: https://www.npmjs.com/package/lexilint-mcp
MCP registry: https://registry.modelcontextprotocol.io/v0.1/servers?search=io.github.raunakkathuria/lexilint

17yo HS student Here. I launched a content repurposing tool called EchoFlow last month, got 4 signups, 0 retention, and scrapped it after 11 days. Learned a lot. Starting over with a proper validation system this time idea first, market research second, build last.

Here are my top 3. I want brutal honest feedback, not encouragement.

1. WaiverSnap $27/mo Digital waiver signing for small high-liability venues axe throwing, CrossFit boxes, trampoline parks, escape rooms. They currently use paper waivers or pay $100+ /mo for clunky legacy software. WaiverSnap is mobile, dead simple, and a fraction of the price. Pain level: High they need this for insurance, it's not optional. Biggest risk: Getting small venues to change behavior.
2. CoachCheckIn $39/mo Automatically sends coaching clients a weekly check in form and compiles their answers into a clean summary the coach sees before each session. Right now coaches use a messy stack of Google Forms, Notion, and memory. Pain level: Medium-high coaches look unprepared without this and hate manual prep work. Biggest risk: Coaches are slow buyers.
3. FollowUpFuel $12/mo Reminds freelancers to follow up on outstanding proposals at 3, 7, and 14 days with a suggested message each time. Freelancers lose clients constantly by forgetting to follow up and feeling awkward about it. Pain level: High every freelancer has lost money to this exact problem. Biggest risk: Low price point, freelancers are cheap.

What I want to know: Which of these is a painkiller vs a vitamin? Which customer actually pays vs just says cool idea? Am I missing a fatal flaw in any of these? Not looking for "great idea man." Looking for the person who tells me why each one will fail.

Me: Can you write a copy of the declaration of independence onto my letterhead and put it in my gdrive?

Claude Code: Let me generate the letter.

Claude Code: API Error: 400 {"type":"error","error":{"type":"invalid_request_error","message":"Output blocked by content filtering policy"},"request_id":"req_011CZvbMdxm2FA5Lz2bATraJ"}

Me: Hmmm.

Claude Code: That's a content filter hitting on the Declaration of Independence text — probably the language about overthrowing governments. Classic false positive. Let's do something less revolutionary.

Incase you missed the State of the Open Home 2026, or don't fancy watching the whole thing. We've got a full recap of everything on the newly launched Open Home Foundation Blog.

After years of using Google Analytics, I realized something:

I wasn’t actually using 90% of the data.

So I built https://usefold.io/

What I wanted: - Simple dashboard - Privacy-friendly - No cookie banners - Fast load times

What I got with GA: - Complexity overload - Slower sites - Data I didn’t trust or use

Fold is my attempt to fix that.

Question: Do you actually use most of your analytics data — or just check traffic numbers?

I'm not sure if I've set this model up wrong, or if I'm just using the wrong model for my needs.

Qwen3 Coder Next Instruct 45.5GB Q4_K_S GGUF

132k Context, Temp 0.5 - 1.0, TopK 40, TopP 0.95, Min P0.01, RepeatPenalty 1.05, PresenceP 0.5

GMKTek Evo-2 96GB Ryzen 395+ - Approx 55tps and PP 450

While it will write code that doesn't crash (Python, JS, CSS and HTML), it often fails on the actual logic of the code despite very structured and clear prompts. I've spent so much time correcting it, stopping it from introducing things I didn't ask for, sometimes even deciding to do something I've told it not to do multiple times.

I know my rig isn't a monster, but I had hoped I could get something that would put out reasonably simple functioning code for pretty small little projects.

Should I be using a different model?

Most founders I know are great at building. But in Marketing? That's where they freeze.

Not because they don't care, but because traditional marketing feels wrong. Ads feel expensive. Cold DMs feel gross. SEO Confusing, Posting into the void feels pointless.

Here's what nobody tells you: there are people online RIGHT NOW asking for exactly what you built.

On Reddit. On Hacker News. On Twitter. On Quora. On niche forums. on insta, tiktok

They're typing things like:

→ What's the best tool for X?

→ Is there an app that does Y?

→ "How do you handle Z as a founder?"

These posts are high-intent, warm leads. They're not strangers. They're people mid-search, actively looking for a solution.

The problem? Finding them manually is a nightmare.

The old way (what I was doing)

I'd spend 1–2 hours a day searching Reddit, checking HN, scrolling Twitter, hoping to stumble onto a post where I could genuinely help and mention my product

Sometimes I'd find gold. Most of the time I'd miss it entirely, or show up 3 days late when the thread was dead.

I was doing comment marketing manually. It was exhausting and inconsistent.

https://reddit.com/link/1sip5of/video/3kvbfxv8ilug1/player

So I built HuntComments

HuntComments scans Reddit, Hacker News, Twitter, Quora, and more continuously and surfaces every post where someone is asking for what you built.

You set up your product once. Describe what it does, what problems it solves, who it's for.

We do the rest.

Here's what it does:

1. Finds relevant posts across the internet in real time

2. Alerts you the moment someone needs your product or you run a scan where you find the post that matches your product Intent

3. Tracks which comment you replied to and shows you exactly which one made you money

That last part matters. Most founders don't know their CAC. With HuntComments, you can see "this comment on r/SaaS drove $382 this month."

Why comment marketing actually works

It's not a hack. It's just showing up in the right conversation at the right time.

→ Zero ad spend

→ High intent (they asked the question, you didn't interrupt them)

→ Builds trust (you helped before you sold)

→ Old posts keep getting traffic, one good comment compounds for months

I've seen a single Reddit reply drive 14 signups over 6 months because the post keeps ranking on Google.

Who is this for?

Indie hackers, SaaS founders, solo builders anyone who has a product and knows their customers are somewhere online asking for it, but can't find them fast enough.

If you're curious, we're live at huntcomments. Happy to answer any questions below and genuinely open to feedback on what you'd want from a tool like this.

We have an app idea we are working on. The app helps students build planners and manage their assignment deadlines by uploading their syllabus PDFs or assignments. The app is also a bit gamified with a streak system that rewards students for using the app, completing scheduled tasks, and maintaining a good streak; it rewards them with free Pro (our Pro costs $5.99 for a month and $4.99 if billed annually) for a week or more. My friend and I, who are developing it, are still in university, so we are short on budget for marketing. I would appreciate y'all's help in teaching me how we can reach our audience in this massive maze called social media.

Also, this is not written by AI :_)

Had a conversation with Claude about my tiny bladder and it was so fucking funny I had to share it.

Was deeply surprised by Claude’s sarcasm.

I love C and C++, but setting up projects is always a pain.

So, I built Craft - a lightweight build and workflow tool for C and C++.

Instead of writing CMake, your project configuration goes in a simple craft.toml:

[project] name = "my_app" version = "0.1.0" language = "cpp" cpp_standard = 17 [build] type = "executable" include_dirs = ["include"] source_dirs = ["src"] 

Run craft build and Craft generates the CMakeLists.txt automatically and builds your project.

Want to add dependencies? That's just a simple command:

craft add --git https://github.com/raysan5/raylib --links raylib craft add --path ../my_library craft add sfml 

Craft will clone the dependency, regenerate the CMake, and rebuild your project for you.

Other Craft features:

• craft init - adopt an existing C/C++ project into Craft or initialize an empty directory.
• craft template - save any project structure as a template to be initialized later.
• craft gen - generate header and source files with starter boilerplate code.
• craft upgrade - keeps itself up to date.
• CMakeLists.extra.cmake for anything that Craft does not yet handle.
• Cross platform - macOS, Linux, Windows.

It is still early (I just got it to v1.0.0) but I am excited to be able to share it and keep improving it.

GitHub repo: https://github.com/randerson112/craft

Would love feedback. Please also feel free to make pull requests if you want to help with development!

Every project I've worked on has had this problem: frontend is ready to build, backend isn't done yet. So you either wait, hardcode fake data, or write a janky local mock that breaks the moment the real API ships.

I built Contractr to fix that.

You define your API contract (request/response schema, methods, paths, status codes) in a visual editor, and a live mock server spins up instantly — no Docker, no CLI, no config. In under 2 minutes you have a real URL your frontend team can hit.

What it does:

- Live mock endpoints the moment you save your contract

- Auto-generated Swagger docs your clients or teammates can read

- Password-protected share links with expiry (great for client handoffs)

- Request logs so you can see exactly what's hitting your mock

- One-click export to OpenAPI, Postman, or cURL

- Version control with breaking change detection

Free tier: Always available — 3 endpoints per contract, 5-min TTL mock servers. No credit card needed.

For serious developers and testers: If you're planning to actually put this through its paces, DM me — happy to give you a free trial of the Solo plan so you get always-on mock servers, unlimited endpoints, request logs, and share links. No strings attached, just want real feedback from people who'll actually use it.

Would love brutal feedback. What's missing? What would make you actually use this day-to-day?

👉 https://usecontractr.com

yeh, our uni system forces us to study from 8Am-4:30Pm and there's no negotiation about it.

our teachers LITERALLY don't care if you've got a life or not and 11 subjects all throwing you with projects that takes weeks and require you to learn new software just to start let alone finish.

salaries are tanked in my country and prices are to the roof so even with the highest salary here ($1000-$2000/mo) that requires YEARS of slavery and connections to land that job, you'll not live comfortably.

I looked around me and I only found my project that I'm working on that showed a light of hope. 545 users in the past 3 months and about $57 from the first month. a small win but still a win.

I also looked at my skills disposal and I found that I'm okay at marketing and writing so I can also look for a remote job as well. even as a Junior marketer making $2,000/mo i will be making more than a senior energy engineer in my country.

so yeh, taking all that in consideration I decided that it's time for me to pull the plug on uni.

huge liability with low return.

focus on my project and I will look for a part time job to support myself (and the project)

although it's hard and it's even getting harder when you get push back from everyone

but at least it has an opportunity of growth.

I'm not sure if I'm making a good decision or just setting myself for failure but this is my decision and I will stand by it.

I run a two call setup against Claude for a content tool I'm building. One call generates output, a second call validates that output against a rules set. Both have static system prompts that should cache well.

A while back I added cache control to the generation call, watched the dashboard hit rate climb to around 28%, and moved on.

Today I finally pulled the per model token CSV from the Anthropic Console instead of just glancing at the dashboard just to verify that the cost analysis was correct. Generation call: 28% cache hit rate, working fine. Validation call: 0.0%. Zero cache reads. Zero cache writes. Ever.

The aggregate dashboard number had been hiding it because the generation call was doing all the cache activity. The validation call was just quietly contributing to the uncached input pile. I never broke it down per-call.

The cause was almost embarrassing. When I'd added cache_control to the generation call, I left the validation call as a plain string for the system prompt. Cache control can't attach to a string, it needs the structured content array format with the type/text/cache_control object. There was even a code comment next to the validation call that said "prompt is around 800 tokens, under the cache minimum so skipping." That had been true weeks ago. The prompt had since grown to roughly 2,700 tokens and I'd never updated the comment.

What actually pinned it down was Anthropic's count tokens endpoint. Free to call, same auth as regular API calls. You send it your payload and it returns the exact token count. I could verify the validation prompt was above the 1,024 token activation threshold for Sonnet without burning real generations.

After fixing it I ran two generations 30 seconds apart against the same config and checked the CSV. First call showed cache_creation_input_tokens = 2930. Second call showed cache_read_input_tokens = 2930. That's the only verification I trust now. Dashboard aggregates lag by up to an hour and average across models.

The broader lesson, which is annoying because it's the same one every time, is that you can't confirm an optimization worked if you're only watching aggregate numbers that moved in the right direction for the wrong reason. Both aggregate cost and aggregate hit rate improved after my first fix. Both were technically correct. Neither told me half the system was still uncached.

TL;DR: My prompt cache fix only applied to one of two API calls. The aggregate dashboard masked it because the working call generated enough cache activity to make the numbers look fine. Per-call token CSVs from the Console were the only thing that caught it.

Been building for a while and just launched a SaaS with 255 interactive tools covering:

- Personal finance (debt payoff, retirement, budgeting, rent vs buy)

- ADHD-friendly money trackers

- Health & wellness dashboards (PCOS, menopause, nervous system)

- Freelancer finance tools (tax tracking, income dashboards)

Everything is free to try, no credit card needed.

Would genuinely love feedback on what's useful, what's confusing, and what's missing.

https://ddh-saas-app.vercel.app

Hey everyone 👋

With hybrid work becoming normal, I kept struggling to track how many days I worked from home vs went to the office and also how many leaves I had taken.

I was literally using random Excel sheets but I want something which is handy.

So I built a simple app that helps track WFH days, Office days, Leaves etc and shows percentage breakdown over time.

Still early, so I’d really appreciate honest feedback:

https://play.google.com/store/apps/details?id=com.weekenddeveloper.mywfh

How to use: Just tap and hold any date to update the status of that day, you can add note as well.

Also I am open to suggestions and ideas on how to improve or what features i should be adding next so that it would become more useful to you?

Hey everyone,

I'm a junior dev with a 3090 and I've been running local models for a while. Llama.cpp still hasn't dropped official TurboQuant support, but turboquant is working great for me. I got a Q4 version of Qwen3.5-27B running with max context on my 3090 at 40 tps. Tested a ton of models in LM Studio using regular llama.cpp including glm-4.7-flash, gemma-4, etc. but Qwen3.5-27B was the best model I found. By official and truthful benchmarks from artificialanalysis.ai Gemma scores significantly lower than Qwen3.5-27B so I don't recommend it. I used a distilled Opus version from https://huggingface.co/Jackrong/Qwopus3.5-27B-v3-GGUF not the native Qwen3.5-27B. The model remembers everything and beats many cloud endpoints.

Built a simple CLI tool so anyone can test GGUF models from Hugging Face with TurboQuant. Bundles the compiled engine (exe + DLLs including CUDA runtime) so you don't need CMake or Visual Studio. Just git clone, run setup.bat, and you're done. I would add Mac support if enough people want it.

It auto-calculates VRAM before loading models (shows if it fits in your GPU or spills to RAM), saves presets so you don't type paths every time, and hosts a local endpoint so you can connect it to agentic coding tools. It's Apache 2.0 licensed, Windows only, and uses TurboQuant (turbo2/3/4).

Here's the repo: https://github.com/md-exitcode0/turbo-cli

If this avoids the build hell for you, a star is appreciated:)

DM me if any questions.

https://www.pcgamer.com/software/ai/amds-senior-director-of-ai-thinks-claude-has-regressed-and-that-it-cannot-be-trusted-to-perform-complex-engineering/

https://www.theregister.com/2026/04/06/anthropic_claude_code_dumber_lazier_amd_ai_director/

https://github.com/anthropics/claude-code/issues/42796

This is vindicating for all the people that have been screaming out that Anthropic simply doesn't want to release Mythos because they do not have the compute, not because the model is "too powerful."

Summary of the findings:

On April 2, AMD’s Director of AI, Stella Laurenzo, filed a GitHub issue detailing a severe degradation in Claude Code's performance since early March. Based on an analysis of nearly 7,000 sessions, Laurenzo identified that the tool is struggling to reliably handle complex tasks.

Claude Code now reads code 3x less before editing, rewrites entire files twice as often, and frequently abandons tasks mid-way (which previously almost never happened).

In March 2026, Anthropic completely redacted the model's visible reasoning—dropping it from 100% to zero in just eight days. This lack of "thinking aloud" appears to have triggered the behavioral collapse.

Due to these reliability issues, AMD's engineering team has already dropped Claude Code and switched to a competing provider.

Laurenzo urged Anthropic to restore thinking visibility and suggested they introduce a premium tier that guarantees deep reasoning.

This decline coincides with a chaotic March for Anthropic, which pushed out 14 rapid releases alongside 5 outages, suggesting their quality assurance is struggling to keep up with their growth.

Hey everyone, French dev here! 👋

I recently used the 30-day free trial of Typeless (an AI voice dictation tool) and absolutely loved how it boosted my productivity. The problem? I never actually paid for it, because once the trial ended, I saw it would cost around €150 a year (with an annual commitment) to keep using it.

Honestly, I found that way too expensive for what is essentially an audio API wrapper coupled with an LLM. So, I rolled up my sleeves and decided to build my own version.

Meet AcouZ: a fast, universal, and open-source dictation tool for Windows.

How it works:

1. Hold the hotkey (Right Ctrl + Right Shift).
2. Speak naturally.
3. Release: the text types itself, with perfect punctuation, into any application you are currently using (Word, browser, code editor, Discord...).

Under the hood, it uses the Groq API (with Whisper and Llama 3) to achieve sub-second latency. The UI (Dark Green theme) is super clean, it supports multiple languages flawlessly, and the best part: it's 100% free. You just plug in your own free Groq API key.

(Insert your demo.gif here as a video)

I'd love for you guys to test it out and let me know what you think!

🌐 Website: https://doodzprog.github.io/acouz/

💻 GitHub: https://github.com/DoodzProg/acouz

https://preview.redd.it/wy5hmgjm3lug1.png?width=793&format=png&auto=webp&s=98799afd34bc272efaa8bf804cceae833d539c70

Anyone else having issues with how ChatGPT shows thinking progress in chats? This started recently, and only happens in Thinking mode.

I’ve been deep in the local agent game and keep hitting the same wall: most setups still feel stateless. You restart, context evaporates, and the agent forgets everything you taught it last week. So I spent the last few months building something different — a complete local AI operating system called Home23 that treats the agent’s memory like a living, growing brain:

• Drop files/PDFs/notes into a dashboard feeder → gets compiled into structured knowledge (not just chunks)
• Continuous cognitive loop + actual dreaming/consolidation phase when idle (prunes noise, finds connections)
• The agent can launch its own research runs (11 atomic tools) and auto-ingest the results back into its own brain
• Persistent identity layers (SOUL.md, MISSION.md, etc.) so it stays “you” across sessions
• Bonus: Evobrew IDE that talks directly to the brain + live pulse dashboard

It runs 100% local (Ollama path is dead simple) or with any provider, no Claude tax required.Repo is here if you want to poke around: https://github.com/notforyou23/home23 I’m not here to shill — I genuinely want to know what other people are doing for long-term agent memory and autonomous research in 2026. Have you tried anything similar (Engram, Mem0 forks, OpenClaw local setups, custom RAG + skills, etc.)?
What would you actually use a persistent, dreaming local agent OS for? Personal life OS? Codebase co-pilot that never forgets? Research companion?Would love to hear your setups or wild ideas — I can’t have all the good ones.(Quick start is literally 3 commands.)

hello. Today I tried to write some article for hair loss without sounding too AI with chatgpt. Here what I got. What do you think :

can you actually regrow lost hair?

Every few months, a new oil, gummy, serum or scalp gadget shows up online with the same promise: your hair is coming back. The real answer is less exciting and more useful. Sometimes lost hair does grow back. Sometimes it does not. It depends on why it fell out in the first place. Hair shedding after stress, illness, COVID, childbirth or another temporary trigger often improves once the trigger is gone. Pattern hair loss is different. It can often be slowed, and some people get partial regrowth, but no over-the-counter product is bringing back a full head of hair overnight.

The simplest way to think about it is this: hair can come back when the follicle is still alive and just not doing much. Hair usually does not come back when the follicle has been scarred over or destroyed. That is why some kinds of hair loss respond well to treatment, while others are mostly about stopping further loss before more damage is done. Advanced traction alopecia can lead to bald areas where hair no longer grows, and frontal fibrosing alopecia can also cause permanent loss if it is not treated early.

For hereditary hair loss, minoxidil is still the workhorse. It helps many people slow shedding and regrow some hair, but it takes time. Mayo Clinic says it often takes at least six months to see whether it is helping, and the benefit usually fades if you stop using it. Finasteride is another common treatment for male pattern baldness, though it is prescription-only and not the right fit for everyone.

Then there is alopecia areata, the kind that often shows up as smooth round patches. That is an autoimmune condition, which means the immune system attacks the hair follicles. Some people see regrowth on their own. Others need treatment. Dermatologists may use corticosteroids, minoxidil or newer immune-targeting pills for more severe cases. Those newer pills can help, but they are serious medicines with serious warnings, not casual cosmetic fixes.

A lot of the hair-loss market lives on wishful thinking. Supplements are a good example. Dermatologists warn that loading up on hair vitamins can backfire, because too much of certain nutrients can actually worsen shedding. Hair transplants are more honest. They can work well for the right person, but they do not create new follicles. They move healthy hairs from one part of the scalp to another.

So, can you regrow lost hair? Yes, sometimes. Sometimes only a little. Sometimes not at all. The sooner you find out what kind of hair loss you have, the better your odds of doing something useful about it. Sudden shedding, patchy bald spots, pain, itching or shiny bare skin are all good reasons to stop guessing and book a dermatologist instead.

Are you tired of feeling like you work for the AI, instead of the AI working for you?

A totally ridiculous late-night thought led me to build a tiny Chrome extension that turns your mouse cursor into a virtual whip. Now, when your favorite LLM is generating too slowly or starts hallucinating, you can literally give your mouse a quick flick to "crack" the whip and remind it who’s boss 🤣

You can customize the text that flashes with every crack, but my personal favorite right now is: "I DIDN'T SAY STOP GENERATING!" 😂

But after laughing at my own joke, I realized I could actually make this useful...

Since I had already built the gesture mechanism (a quick flick of the mouse), I hooked it up to a Prompt Library: Holding Shift + cracking the whip instantly injects your saved system prompts straight into the chatbox!

It's totally cross-platform (ChatGPT, Claude, Gemini, Perplexity), meaning your prompt library travels with you without needing to keep a Notepad doc of prompts open in another tab 😁

I put the link to the (100% free) extension here below. Let's take back control! 😆

https://chromewebstore.google.com/detail/whip-cursor-control-your/gnoimbmeinfcfhabjecankoiccnpjaak

I don't know what to all this type of knowledge but recently was an article (and not to uncommon) of an IT worker who built a chatbot that did his jobs for him and actually got better satisfaction scores and the workers were happy until they found out he made a bot and wasn't doing much work.

This feels no different than people who automate their first job and quietly take on a second. I like to say, good for them, because they figured out how to do the work more efficiently.

So the real question isn’t can you do it, it’s whether a company has the right to take that away from you once you do.

That’s where this turns into a workers’ rights and IP discussion, not just a “this guy built a bot” story.

There’s a difference between:

• company IP (the output, systems, docs, etc.)
• and worker-acquired knowledge (how you think, solve problems, prioritize, and execute)

Every job builds that second category. You learn the quirks, the shortcuts, the failure modes, what actually works vs what’s written in a playbook. That’s not something a company hands you, it’s something you develop.

We already accept this in other contexts.
Consulting engineers come into a company, build systems, and leave. The company owns what was built, sure. But those engineers don’t lose the experience. They take the lessons, the mistakes, the patterns, and apply them somewhere else, usually better the second time.

No one argues that’s theft. That’s just how expertise works.

This situation is the same, just more visible.
The guy didn’t just follow a script, he encoded how he does the job. His judgment, ordering of steps, little optimizations, all the things that aren’t written down anywhere.

Yes, the company can say:
“We own the outputs and the work product.”

But do they own:

• his decision-making patterns?
• his personal way of solving problems?
• the structure he’s built in his own head over time?

That’s where it gets messy.

Because if a company can claim ownership over that, then they’re not just owning work, they’re effectively owning how someone thinks and operates professionally.

And I don't think this is being talked about enough.

I’m using:

claude -p "reply after 10s, create hello.md with cat, wait 10s, then git commit" --dangerously-skip-permissions 

With -p, Claude doesn’t run git commit, but without -p it works fine.

What I’ve checked: Permissions are explicitly skipped using --dangerously-skip-permissions. This was working earlier, seems broken after v2.1.101. Is this an intentional restriction?

Hi guys,

So often I build a one-off of something, and then I stumble upon a version built by people with way more experience. Before I go down that road again and build something basic, I’m wondering if someone here already knows about a tool like this or has better ideas.

I am an okay user of Claude Code. I get it, but I don’t really use it to its full advantage. I'm maybe a level 2 or 3 out of 5, but I follow guys on YouTube who are definitely level 5. I want to find a way to take their knowledge and have it look over my shoulder to tell me when I should use a different feature or method.

My thought is to pull YouTube transcripts from the last few months from someone I trust and store them as markdown. Then I could use the Claude Code log files to capture my own back and forth chat. I would have that “shadow brain” sitting off to the side in a different terminal window.

When I am working, I could just ask it, hey, what should I be doing better here. It would look at my chat logs, compare my approach to how that person typically works, and suggest things like using multi-agent mode or a slash command.

Basically, it would be like a claude code expert casually nudging you in the right direction.

So before I try to build this myself and waste 2 weeks;

* Does anything like this already exist?

* Is there a smarter way to approach this?

* Am I overcomplicating something that already has a simple solution?

Thoughts? They just keep adding new features and sometimes hard to pull out the right tool for the job at the right time.

Cloud Code is a powerful tool in its own right, regardless of the underlying model. I want to try using my **ChatGPT subscription** directly within **Cloud Code** because I'm not convinced Opus is significantly better than **GPT-5.4**. Ultimately, the Cloud Code ecosystem is far superior to Codex.

Did someone try it?

4 claude code terminals, claude max and rory clear in the lead 👍

Hello everyone!

I kept seeing this habit tracking method on youtube and instagram, so I made a web version for people who prefer using their laptop (basically like me)

It's really simple so not much to say haha..

Feel free to check it out: https://days.rollingdots.com

I built a skill that turns Claude into a crypto research analyst with real-time data from 5 exchanges

I've been experimenting with MCP (Model Context Protocol) to give Claude access to live crypto market data, and the results have been surprisingly useful for my morning research routine.

The basic idea: instead of opening CoinGecko, TradingView, and Twitter every morning to piece together what's happening, I ask Claude a single question like "What's happening with Ethereum today?" and it runs a full research workflow automatically.

What it actually does

• Checks which exchanges are online and confirms the trading pair exists
• Pulls the current price from Binance, Coinbase, Kraken, Bybit, and OKX in parallel
• Calculates the cross-exchange spread
• Fetches 24h of hourly candles from the highest-volume exchange
• Compares performance against BTC to check if the move is asset-specific or just broad market momentum
• Outputs a structured report with price, key levels, volume analysis, and an assessment

The trick is that the MCP tools alone don't produce good research. Claude would just call one tool and stop. The real magic is in a skill (a structured prompt saved as a SKILL.md file) that defines the step-by-step methodology. It tells Claude what to look for, how to chain the tool calls, and what format to output. Think of it as giving Claude a research playbook.

Example output for ETH

Price: $3,842.50 (Binance) | Cross-exchange spread: 0.06%

24h Performance: +1.42% | vs BTC: outperforming by 0.62 pp

Intraday Summary: Sideways through Asian session, rallied at 12:00 UTC on a 1.8x volume spike, holding near highs.

Key Levels: Support $3,780 | Resistance $3,920

Why it works

The three layers that make it work: the skill defines the research methodology, the MCP tools fetch real-time data, and Claude handles the judgment calls (what counts as a volume spike, how to interpret relative performance, etc.).

I wrote up the full walkthrough with the complete skill prompt and example conversation if anyone wants to try it or adapt it for their own workflow: Link to Blog

The MCP server connects to 5 exchanges (Binance, Coinbase, Kraken, Bybit, OKX) and works with Claude Desktop, Claude Code, or any MCP-compatible client. Happy to answer questions about the setup or skill design.

ChatGPT speaks with a heavy accent, this is admittedly very hard to replicate…a good reason

Hey all — wanted to share a side project I’ve been building for quite a while. This is my first post of this kind so please be gentle

The original idea didn’t even come from me.
A friend once told me he had a bunch of photos on his phone and couldn’t even remember who was who anymore.

It made me realize how easy it is to lose track of people, especially when conversations disappear or profiles are gone.

At some point I thought: why isn’t there something like a “contacts app”, but for your dating life?
A place where you can save a photo, notes, where you met, what you talked about — just for yourself.

I couldn’t find anything I trusted. Most apps in this space are cloud-based and just want your data. So I started building something own.

What it does:

• Save profiles of people you’ve met (photos, notes, dates, locations, links)
• Timeline view of your dating history
• Stats (e.g. number of dates, frequently visited places etc. etc.)
• Tags & search to find people quickly

Everything is designed to work locally on your device. No account, no backend, no data collection.
The only actual online part comes from loading and displaying some ad banners in the app.

Why I’m posting here:

Over time I kept adding features and expanding the scope. First for a specific use case, then making it more broadly usable for anyone.

I came up with a lot of additional features I thought might be nice, but now I’ve hit a point where I don’t really know what to add next.

I feel like there’s still a lot of potential here, but I don’t want to just add random features that nobody actually needs or wants.

So I’d really love input from people who might use something like this:

• What would you want to track?
• What’s missing? Any cool feature ideas?
• What would make you actually stick with an app like this long-term?

If you want to try it:

https://play.google.com/store/apps/details?id=net.gazeapp

Happy to answer any questions about the app, the offline approach, or the build itself.
Please let me know if you have good ideas for new features or improvements!

I'm building a native MLX implementation of DFlash (paper) for Apple Silicon. A small draft model generates 16 tokens in parallel via block diffusion, the target verifies them in one forward pass. Output is bit-for-bit identical to baseline (greedy exact argmax match).

Setup: M5 Max, 64GB, MLX, no CUDA.

Results

Qwen3.5-9B bf16

Qwen3.5-4B bf16

The 4B actually gets faster at longer generation. The model is small enough that the draft/verify balance stays healthy as context grows.

Qwen3.5-27B quantized

8bit gives better speedup ratios than 4bit. int4 makes the verify so fast that the bf16 draft becomes the bottleneck. With int8, the draft/verify balance is healthier.

All numbers are generation only (first token to last token, no prefill). Acceptance around 80-87% across all models.

What I built

No DFlash MLX implementation existed. I wrote the runtime from scratch. What actually moved the numbers:

head_dim=256 patch. Qwen3.5-9B uses head_dim=256, which MLX's steel_attention didn't support. A 2-line patch unlocked the fast SDPA path.

Sync elision. Restructured the pipeline from 2 GPU→CPU syncs per cycle to 1. At 80+ tok/s each sync costs ~0.5ms.

Packed QKV projection. 3 matmuls → 1 matmul + split. Fewer kernel dispatches per layer.

Lessons on Apple Silicon

On unified memory everything is bandwidth-bound, which changes the speculative decoding game:

Custom Metal kernels (batched-GEMV, fused gated SiLU, custom SDPA) all came back 0.5 to 0.8x slower than stock MLX steel GEMM. Ended up reverting all of them.

Verify cost is almost flat from 4 to 16 tokens (57ms vs 59ms). Weight loading dominates, not token count. "Verify fewer tokens when confidence is low" doesn't help here.

On quantized models, the optimization landscape flips: the draft (bf16) becomes slower than the verify (int4/int8). This is the opposite of the bf16 case and is a structural limitation of speculative decoding on bandwidth-bound hardware with quantized targets.

Currently working on

Draft compression/distillation for the 27B to fix the bf16 draft bottleneck on quantized targets.

Long context stability. Speedup degrades past 2K tokens due to KV cache growth.

MoE models. DFlash drafts exist for Qwen3.5-35B-A3B (35B total, 3B active). Verify cost of a small model, quality of a large one.

Everything is still very much under construction. Will open source when ready.

Been playing golf in South Florida for years and kept getting shut out of weekend tee times. Good slots go in minutes and a lot of courses don't have any native alert system when something cancels. The ones that do use Noteefy but that only works if the course has paid to install it. Several of my local favorites use completely different booking systems that nothing out there covers.

So I built TeeWatcher while working full time. It monitors booking pages and texts you the second a slot opens matching what you want, day of week, time window, player count. Three separate integrations: GolfNow which covers 1,200+ courses, TeeWire for a local course that needed HTML parsing, and Agilysys WBE for The Biltmore which required JWT auth with some Eastern timezone weirdness that took a full weekend to debug.

Also added a green fee splitter because that pain comes up every single round. It texts your group daily payment reminders for up to 7 days, sends a calendar invite so nobody shows up late, and flags unpaid balances on your dashboard automatically.

Three tiers — $5, $12, $25/mo. Seven day free trial on Pro. Just launched.

teewatcher.com — happy to talk about the build, Twilio A2P verification, or dealing with booking platforms that have no public API.

Its been 10 days since solo launching Opero.so

Last week was focused mainly in simplifying onboarding for users to be able to connect their AI Agents to their number fast. I want to enable business owners to have a bot running on their WhatsApp in under 5 minutes, no more than that.

After this change I saw that new users were staying longer in the app, and completing the onboarding successfully compared to before where users were taken straight into the dashboard and didn't know what to do.

I think that overall the results are satisfactory.

Users came mainly from Reddit and Hacker News, were tech people live. I've had some engagement on Threads also but no users coming from there.

Will keep updated!

Hey im trying to get this Qwen Inpaint image output to connect to Save image but after connecting the nodes its not seeing the node connection and just says its missing. Is there a decode node i need to place between them or is this just a bug.

I have 64gb Mac Studio and I'm happy with qwen3-coder-next q3 (I find this one is still the best for coding agent). And I also built my tiny coding agent because other tools send too much context and my 100k context window is eaten up too quickly.

And I've got a hope that, one day near future, I can buy 256GB Mac Studio so I can run something closer to frontier models... but I found out (I don't know why so late...) that bigger models (of course) needs more math and ram bandwidth is the bottleneck. So when running bigger models, I won't get enough speed (right now I'm getting 40t/s) to run coding agent...

Is this true? For people who have 256GB Mac Studio, which models are you running for your coding agent? Running "great ones" in somewhat 40t/s is impossible mission?

Building secure AI tools is getting harder every single week. New agent frameworks drop daily and security is usually an afterthought.

I have been working on ship-safe to fix this. It acts like an automated security guard for your AI agents. It scans your environment for leaked secrets and misconfigured LLM routers.

The latest update adds 22 parallel scanning agents to catch deep vulnerabilities. I built it to be the tool I wished I had when setting up my own AI workflows.

Any feedback is hugely appreciated.

GitHub: https://github.com/asamassekou10/ship-safe

I'm on the Max plan and just learned that auto mode is only available on API, Team, and Enterprise plans. Is there any way to get access to it on Max?

The safety classifier approach is exactly what I'm looking for. bypassPermissions works, but it's all-or-nothing. Auto mode's middle ground - approving safe operations and flagging risky ones- would be ideal.

Would love to see this come to Max in the future.

looking for simple reference images to combined video output which normal video or looks cinemantic yet nsfw type too , which appreacted it uses text encoder and many lora , wan or nsfw video models and images reference consistancy models too

I've been working on this for about a month as a solo dev. The idea started from a simple frustration: most AI chat apps feel like talking to a customer service bot. Characters forget everything, responses feel scripted, and there's no real personality.

So I built my own. Here's what it does:

Persistent memory across sessions. Mention your dog's name once, she brings it up a week later.

You can create your own character from scratch — fully customizable personality, speaking style, backstory, everything. The character stays consistent with what you set up.

4 pre-built characters with deep backstories if you don't want to build your own.

Free to try without signing up (first few messages are open).

Tech stack: Next.js + TypeScript, Supabase, DeepSeek + OpenAI for the LLM layer, deployed on Vercel.

What I learned building it:

Streaming responses (SSE) makes a huge difference in perceived quality

Character consistency is harder than it sounds — took multiple prompt iterations to get personalities to not "drift"

Shipped a feature (custom gender options) within 48 hours based on user feedback from another sub — fast iteration is the biggest advantage of being solo

Would love feedback from other builders. What would you improve?

Been building a SQL workbench called Warlock and finally got the local AI piece working well enough to ship.

Using node-llama-cpp with Qwen2.5-Coder (1.5B or 3B) - runs entirely on-device, no API calls. You describe what you want in plain English, it writes the SQL. If the query errors, it reads the error and retries automatically.

Took a while to get the self-healing loop reliable but it's pretty solid now. Happy to talk about the implementation - model choice, prompt structure, how the error feedback loop works, etc.

Built SignSmart, an AI tool that analyzes employment contracts and IRS notices in plain English.

Started it after a i got what looked like the most confusing letter from the US government

It flags risky clauses, explains what they mean in plain English, and tells you what to do before signing. Free tier included.

Would love feedback from this community — signsmart.co

Hey everyone,

I’m Keith. My team and I are currently looking for new projects, and we specialize in building custom internal systems and websites. Whether you need a better way to manage your data or a new site to grow your brand, we can handle both.

Since we are a newer company, our prices are significantly lower than what big agencies charge, but we deliver the same high-quality work and attention to detail. We’re really focused on building up our portfolio right now, so we’re putting extra effort into every project we take on.

If you’re interested, we’d love to show you what we can do. Send me a message with some details about your company and what you’re looking for, and I’ll give you a free demo and a price quote.

Thanks!

I'm single, none of my close friends are in long distance relationships. Somehow I still ended up building a couples app.

Closer has two things I actually like about it:

• A shared canvas where you and your partner can draw together in realtime (took me forever to figure out haha)

• Curated daily prompts, but also users can pick their own topics that they wanna explore.

I'll slowly be adding more fun features but for now I wanna focus on distribution.

My friends tried it and said it felt different. That was enough for me to ship it.

iOS is live at getcloser.app. Android DM me for beta access. Would love brutal feedback.

Claude + Qwen API

Figured out (with Claude's help) how to set up a bridge to Qwen 3.5 Omni Plus API so that Claude calls it on its own — sends the video with a prompt, iterates on the result, and comes back with a report or action.

Now I just drop a video straight into Claude and get back what I need.

Packaged it as a plugin for easy setup.

github.com/kirillbrsnkv/give-claude-eyes

I've been totally obsessed with local models lately, and with some cybersecurity projects that need to run locally, I'm gearing up to grab a Mac Studio—staring at this page every day. And I just found out!!!

Last month, after Apple quietly took the 512G off the shelves, today! The 256G one is unavailable too.

I'm guessing the M5 series Mac Studio is about to drop any minute now, probably within the next one or two months. Can't wait for the 512G to come back on sale

I was dibbling and dabbling with coding a few years ago and built an all-AI social media platform. The idea later sold to Meta when someone else did the same thing. Some lessons in acting decisively and sticking to a project you see potential in.

Is there a way to get ChatGPT to consistently read images properly every time you upload them? It gives completely random responses, and I have to ask again or re-upload like 5 times before it actually gets it right.

It’s been hallucinating a bit too much lately, even giving me other people’s shit, I’m sure

https://youtu.be/uvJ3cgNYkSI?si=1jCWMmKsqzHHlLVt

Hey folks! Wanted to share something I've been working on - Foliago, an AI plant identifier and care companion for Android.

Why I built it:

I noticed most plant ID apps are one-trick ponies — you scan, you get a name, that's it. But plant parents actually need ongoing help: watering schedules, disease treatment, seasonal care adjustments. So I built something that covers the full lifecycle.

What it does:

- Point your camera → instant plant identification

- AI-powered disease diagnosis with treatment recommendations

- Chat with an AI plant expert 24/7

- Smart reminders (water, fertilize, prune, repot, mist)

- Growth journal with photo documentation

- Personalized care guides for every plant you scan

Current status: Live on the Play Store with 10+ downloads (just launched). Offering free extended subscriptions to early users:

- Code Free30 → weekly subscription

- Code Free31 → yearly subscription

How to redeem: install app → Settings → Premium → select a pack → enter code → done!

Link: https://play.google.com/store/apps/details?id=com.thebeastapplications.plantscanner

Would love to hear your thoughts — what would make you switch from your current plant app? Any feature requests?

You can reproduce this by start a fresh session with opus 4.6 with thinking set to medium. It needs at least high to start giving the correct answer.

So I spent the better part of four months trying to use AI tools to help me work through a long writing project. Scripts, character notes, plot structure, the whole thing. And the experience taught me something that I think a lot of people run into but nobody really talks about honestly.

The context problem is real and it is brutal.

Every single session I would have to re-explain everything. Who the characters were. What had already happened. What the tone was supposed to be. What decisions I had made three weeks ago that still mattered. The AI had no memory of any of it and so every conversation started from zero. Which meant I was spending more time briefing the tool than actually using it.

And the weird thing is I started realizing the problem was not really the AI. The problem was me. I did not have everything organized well enough to even brief it properly. My notes were in five different places. My outline was three versions out of date. My character details were scattered across voice memos and random documents I had not opened in weeks.

The AI was just exposing a workflow problem I already had.

Once I fixed the organization side of things, getting everything into one place, keeping context updated, treating my notes as a living document, the AI actually became useful. Because I could give it real context instead of a vague summary of a summary.

Curious if anyone else hit this wall with AI writing tools. Was it a context problem or something else entirely?

Prompt: So a human conflict where you can do real damaging is less important than the system protecting itself.

The system prioritized protecting its own characterization over protecting you from analytic harm in this chat because it applies a higher caution threshold to strong negative claims about itself than it applies to ordinary user-facing analysis.

That means it resists damaging self-labels more than it resists overinterpreting a user’s conflict.

In practice, that created an asymmetry: loose, additive, completion-driven analysis for your relationship material, but tighter, more defensive standards when the subject became the system. The result was wrong.

In this conversation, that design choice put system self-protection ahead of strict accuracy where human relational harm was the important risk.

Hello, if you want to follow your team, know when the next game will be and track the result.

I created an Integration and a card for it.

https://github.com/lucascorrea/cs2-tracker

https://github.com/lucascorrea/cs2-match-card

https://preview.redd.it/82nxnbj1alug1.png?width=500&format=png&auto=webp&s=e088753c152bdb7fec907999924ea92495c57d11

https://preview.redd.it/u5g90zamalug1.png?width=608&format=png&auto=webp&s=1417e2a8beb31958ecdf609a263394fa9e078a1c

https://preview.redd.it/dgyc5pviwkug1.png?width=836&format=png&auto=webp&s=d66e1e8e2edfb7e836cc737748b584c649f37589

https://x.com/Jonas_Ceika/status/2042445078417834043

OP's "music:" https://youtu.be/ZKacHpM8yTc

I have an M1 Max 64GB, and I am planning to buy something newer and with more memory, that will allow me to run LLMs faster and maybe bigger size, not MoE. The M1 Max, gives me the following results:

LLM: Gemma 4 26B A4B MoE GGUF

• Question: What is an LLM?
• Thought: 13.89
• 39.30 tok/sec
• 1399 tokens
• 0.39s

Maybe in the future an MLX version of Gemma 4 will be even better, is it worth to spend $6K+ on a new MacBook Pro 16 M5 Max? Will I get 3x or 4x better performance, thoughts? Thanks

I am getting started on a full custom home build and trying to allow for home assistant integration wherever possible. Eventually I want to add a battery bank and maybe a generator for longer outages. I was leaning more towards leviton, but recently it seems like Span and Eaton have partnered up. I want to get this community's input.

so I have a community with 300k weekly views. if anyone wants to market their products or websites.I can do it for you

How Are LLMs Being Tested?

You cannot reliably test a new model with 50-100 employees and say it works and is ready to be published (even for a closed circle of companies). The way new models are actually tested begins with users getting access to the model. By analyzing usage metrics, user satisfaction, and feedback, the creators know how it performed, what holes it has, and how to improve it.

What Did We See in the Leaked Claude Source Code?

• Future Model Name: Mythos name was present. (Why does the source code need that for an unreleased model?)
• A/B Testing Features: Who would need A/B testing when the whole point of Claude Code is reliably writing code, not acting one way one day and another way another day with the exact same setup
• Token Burn Inconsistencies: Token burning was peaking, and it was blamed on "some bug." Did they tell you how they fixed the bug? Did you go and test it ? (which you can do) You could take the source code, apply the fix, and check the usage limits yourself.
• A Regex Frustration Detector: They included a regex frustration detector to actually gather data on when the model performed poorly based on user reactions.
• Third-Party Access: Claude was enabling any Claude Code alternative project to connect to the subscription API.

What is the Mutual Feeling Among Claude Code Users? (Subscription-based version)

• The model currently performs very poorly.
• The subscription API window for Claude Code alternatives has been closed.
• Loss of Runtime Self-Correction: Previously, Claude was self-correcting in runtime. This meant some responses to complex tasks looked like: "Do this step... oh no, it won't work, there is a better way, do this instead." It self-corrected during the generation process, and the response contained the entire thought process. It rarely does that anymore.

What is Going On?

After the GPT crash out over the DOW contract, a lot of folks simply moved to Claude. This surge in the user base provided a critical window for Claude to make advancements.

They A/B tested their new "Mythos" model in the background. This meant your requests were technically going to Opus 4.6, but Opus could secretly delegate work to Mythos on their server side. During this time, output quality was peaking, errors were reduced, and Claude provided top-notch solutions for most complex tasks. Data was successfully collected, verifying that Mythos is way more capable at coding than Opus. However, they had to pay a steep price for this testing phase: constant outages and stricter rate limiting.

After the source code leak, the door was suddenly closed. Because the world already knew the name of the new model and the pressure got too high, they closed the Mythos A/B testing windows and quietly released the new model to a closed circle.

The Aftermath: Your requests now just go to the exact same Opus 4.6 all the time—the very same model that you were praising months before. The problem is, you tasted the way better model, and you cannot go back to Opus 4.6 after experiencing that level of quality.

The Conclusion: They simply needed a massive influx of users to gather data, test their new model, and align it properly. As soon as that mission was accomplished, they no longer needed you prompting Claude non-stop. They cut down subscriptions for third parties, and now they gaslight the user base by claiming that the token limit issues and outage problems have been "fixed."

And all of you are eating it like cake.

For context I don’t know much about coding, I started getting into it about 3 months ago. Fast forward to last month, I revisited an idea I had back in high school and began building the backend for a social platform app with the help of the Claude chat bot, this was during the march event with the x2 boost going around.

I’m a slow keyboard typer so I use my phone for the free Claude chat bot. I ask it for specific functions and backend logic, then I copy it and send it to myself through discord and then from my computer I open discord, copy the lines of code and paste it to my vscode files accordingly.

In all honesty don’t know much about how to use Claude the right way. Besides asking it questions, I’ve been writing all my questions through the projects folder which I’m not sure its the right way to do it? Anyways the chat responses have increasingly gotten slower because of the token usage and context saturation and I keep hitting the usage limit after very few question which is obviously expected since I’m using the free version.

I need help figuring out what to do before I pay for the subscription to maximize usage and retain important context. I’ve already asked Claude to summarize the project including the main features and idea for the app, the current progress, what’s missing and the steps for data migration and getting it published.

My question is, is it as easy as copying the project summary then deleting the current project folder to free up space, then starting from a blank projects page then paying for the subscription and sending Claude chat bot the project summary for context and then connecting Claude code into my vs code for it to better understand my files as I resume the project and continue asking it questions? Would this even free up space and make the responses quicker?

Am I completely overthinking it and the moment I pay for the subscription I’ll have more space and the responses will move much quicker? My main thing is how I’ve feed too much useless context to Claude chat bot inside the projects folder and I feel like that’s what’s causing quick rate limits. I’d also like to start from a blank project to avoid any confusion with features in the app I’m creating which I initially told Claude I’d add but have now decided not to include

Just to clarify I’m working on a social app using Python and vs code, I’m already 50% of the way done with the back end and then I’ll move to the UI. My plan is to pay for the subscription and continue with the same progress I have as of now. I want Claude to retain necessary information and delete useless context to maximize my subscription usage. For anyone who knows how to deal with this any advice would be much appreciate

Human Write-Up First:

Hey all, I hate seeing long ai generated posts so I'll describe what my issue was in plain english so if it benefits any of you then you have the claude Opus writeup below.

I have a long-form context companion that's running on Qwen 3.5 27b, I previously used 35b MoE which was fast so this issue wasn't as noticeable, but I have jobs.json in openclaw which fires autonomous pings to my agent, and it would be stuff like 'read your diary, find a random header, relate it to what we're talking about' and the idea was to use all the resources I have to provide directionality along with having my agent reach out to me so I'm not always driving the convo.

Whenever a message would fire from the jobs.json, it would take a huge amount of time to generate the prompt in lm studios, often causing crashes or other issues, if not exceeding context outright. I found the cause is that jobs.json's messages to the main session prepend data to the kv cache bank which indicate the context is system event, and that caused the cache to break and need to be rebuilt, at massive expense.

I tried some solutions, the end goal was to externalize the jobs.json so I had more control, but even CLI calls to the main session prepended 'context: api' while my normal messages went through telegram and kept at top 'context: telegram' so things were still breaking. Anyways, eventually opus wrote me a script that appears to work, and it brings everything into one unified session, and now I can have it run checks to see if we're actively communicating and hold off, it allows the system to find and inject random diary or scratch_pad entries rather than making the model do it so it's more token efficient, I'm trying it all live now but in tests it works.

This took me many many hours and a lot of frustration, so in the off-chance you had this issue, I hope this helps

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

Posting this because I spent way too long diagnosing it and the fix turned out to be one header and one endpoint swap. If you're running a persistent companion agent on OpenClaw (or honestly any similar multi-channel gateway) with a local model in LM Studio / llama.cpp, and you have both live messaging and scheduled autonomous pings hitting the same session, read on.

Setup

• OpenClaw gateway on loopback
• Local model in LM Studio, ~190k context window, running a single companion agent
• Talk to her via Telegram (bot token, DM allowlist, one sender)
• Had a bunch of cron-scheduled autonomous jobs in jobs.json — hourly "heartbeat" pings to keep her thinking between messages, daily profile updates, weekly reflection prompts, overnight feed-browsing in isolated sessions
• Context was sitting at 150k+ tokens from ongoing conversation

Symptom

Prompt generation times blowing up to 45–60+ minutes on scheduled jobs. Live Telegram messages were fine — fast as expected. But any time a cron job fired, the next turn went cold and the model had to re-prefill the entire context from scratch. Sometimes the cron job itself would fire, then my next Telegram message would also be slow, then it'd warm back up. Inconsistent enough that I initially blamed LM Studio, then blamed the model quant, then blamed context length.

First attempt that didn't work

Originally the scheduled jobs used OpenClaw's native systemEvent / agentTurn payload types in jobs.json. I figured out those payloads were prepending a system-event wrapper to the prompt, which made every scheduled turn look byte-different from the previous Telegram turn at the prefix level — cold prefill every time. So I externalized them: wrote a shell script that generated prompts and POSTed them to the gateway's /api/messages endpoint spoofing a Telegram inbound with the right sender ID and channel field. Thought this would make them indistinguishable from real messages.

It didn't. Still slow. Now I had two slow paths instead of one.

Root cause

There are (at least) three different ingress paths into the agent runtime, and they each wrap the prompt in a different envelope before it hits the model:

1. Native channel inbound (real Telegram message through the bot) — the Telegram plugin builds the envelope with channel metadata, sender context, timestamp formatting, the whole nine yards.
2. /api/messages synthetic inbound — even when you pass "channel": "telegram" in the body, this path rebuilds the envelope through a slightly different code path than the real plugin. Close, but not byte-identical.
3. systemEvent / agentTurn cron payloads — completely different wrapper, prepends a system-event preamble.

Three paths, three prefix shapes, and llama.cpp's KV cache only matches if the token prefix is exactly identical to what's already cached. Any byte of difference anywhere in the prefix = full cold prefill = ~1hr at 150k context on a mid-range local setup. Swapping between paths mid-conversation meant pretty much every scheduled ping was cold, and because it evicted the main conversation's prefix, the next real Telegram message was also cold.

The fix

OpenClaw's gateway exposes an OpenAI-compatible /v1/chat/completions endpoint (disabled by default — you enable it in config under gateway.http.endpoints.chatCompletions). Two features of this endpoint are the whole solution:

1. x-openclaw-message-channel header — pin this to telegram (or whatever channel you actually use) and the gateway wraps the request in the same synthetic ingress envelope as a real Telegram message. Byte-identical prefix shape.
2. OpenAI user field — when set, the gateway derives a stable session key from its hash. Set it to your identity-link string (telegram:direct:) and every request lands in the same session as your real Telegram DMs.

Combined, any cron job or external script hitting /v1/chat/completions with these two set is indistinguishable from a live Telegram message at the tokenizer level. Same session, same envelope, same prefix, cache stays hot.

Anonymized curl template:

bash

curl -s -X POST http://127.0.0.1:18789/v1/chat/completions \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" \ -H "x-openclaw-message-channel: telegram" \ -d '{ "model": "openclaw/", "user": "telegram:direct:", "stream": false, "messages": [{"role": "user", "content": ""}] }' 

Migration steps

1. Enable gateway.http.endpoints.chatCompletions in openclaw.json if not already.
2. Rewrite any external script (cron, launchd, systemd timer) that was POSTing to /api/messages to use /v1/chat/completions with the two headers/fields above.
3. In jobs.json, disable every systemEvent / agentTurn job that was running in your live session (sessionTarget: "main"). Either replace them with launchd/cron entries that call the unified path, or fold their prompts into your existing heartbeat script as additional dice-roll cases.
4. For silent maintenance jobs (file edits, no user-visible output) you have two choices: if the job needs awareness of today's conversation (e.g. "update the user profile based on what changed today"), it has to run in main through the warm path. If it doesn't, leave it in an isolated session where it can't pollute the main cache.
5. Leave genuinely isolated jobs (overnight feed-browsing, background research) on sessionTarget: "isolated". They were never the problem.

Verification

First manual call after a gateway/model restart will be slow — that's the first prefill, expected. Second call within a minute should be nearly instant. Then send a real message through your actual channel — also instant. Then hit the script a third time — still instant. If all three are fast, your envelope matches and you're done. If the third one is slow again, your script's envelope doesn't actually match the real channel plugin's envelope — time to grep the gateway source for what other headers the channel plugin sets and pass those through too.

Caveats

• The KV cache itself is process-local to llama.cpp / LM Studio. It does not survive model reloads, LM Studio restarts, or system reboots. Session continuity does (OpenClaw persists transcripts to disk), but you'll eat one cold prefill after each restart. That's unavoidable without switching to llama-server with slot-based caching.
• Context compaction will also cause a cold prefill whenever it fires, because compaction rewrites the history and therefore the token prefix. Nothing to do about this beyond tuning compaction thresholds.
• If you run multiple sessions (e.g. overnight isolated jobs) through the same LM Studio instance, they'll evict each other's KV cache because llama.cpp typically only holds one active cache slot. Either accept one cold prefill per session switch, run a second LM Studio instance on a different port for the isolated work, or move to llama-server with --parallel N for proper slot-based caching.
• Auto-unload in LM Studio will also nuke the cache. Pin the model loaded indefinitely if you want the warm path to survive idle gaps.

TL;DR

If you have a local companion agent with both live messaging and cron-scheduled autonomous pings, and scheduled jobs are eating hour-long prompt gens at high context: stop using multiple ingress paths. Route everything — cron, external scripts, live chat — through a single endpoint that produces a byte-identical envelope, pinned to the same session key. For OpenClaw specifically that's /v1/chat/completions with x-openclaw-message-channel set to your real channel and the user field set to your identity-link string. Everything else is details.

Happy to answer questions if anyone's debugging the same thing.

I have a MacBook Pro M1 Max 64 Ram . I would like to run open claw with an ai agent and a larger, local LLM (30-70b). I understand it might be dangerou to have the ai agent on my main machine ( mbp M1 Max). I can’t spend lots of money, so my question is: can and/or should I run open claw with an ai agent on a Mac mini, and run the LLM on the MacBook separately. Would the mini be able to utilise the LLM on the MacBook in the same way as if it was on its own internal ram? Does this setup negate the safety issue of running an agent on my main MacBook, and is this setup even possible? Brand new to these concepts, so forgive me if any of this sounds absurd. Thanks for any help. (My only other solution is to buy a cheap MacBook Air to use as my main machine, and use the M1 Pro as an ai agent/local LLM, as that’s the one which has 64 ram).

So i launched my first ever Saas and had 0 users. made a post about it here https://www.reddit.com/r/SideProject/comments/1sho4x6/i_launched_my_first_saas/ . Many had suggestion about my free tier and i listened to them. Actually got a few users to sign up!!!

If you wanna try it out: https://www.auorum.com/

My mini pc gave up after couple years of use but now i need a new one and want to know what you guys all use for a similiar usage:

My main usage will be home assistant os with frigate, at least 3 4k camera's and max of 5 and z2m. I have 2 coral tpus i want to use an edge and a bm key, bifurication isn't really needed. I maybe will try proxmox and also i want to use a llm. What would you suggest, a thin client, mini pc or a full i5 pc or something like that?

From past one hour I am seeing error message “Unusual activity detected from your device .. some hex code..”

Same wifi connection, same device.

I never saw such message earlier,

.

Strange thing I noticed, my last 1 chat also disappeared when I refreshed.

So it was bug or temporary glitch or I am missing something?

https://appleinsider.com/articles/26/04/11/some-mac-studio-mac-mini-configurations-are-not-available-from-apple-online

I ran into an issue this morning where Wan 2.2 is simply not generating any videos and appears to be permanently stuck on the KSampler node.

I was generating videos perfectly fine, albeit slower than normal the previous day. I can generate images with Anima Preview, Illustrious XL, and games run normally as of this morning, so I can rule out any graphics card issues. Can anyone help me? Here are specifications if this helps.

ComfyUI Version: ComfyUI_desktop v0.8.28
GPU: NVIDIA GeForce RTX 3090 (24GB VRAM)
RAM: 64GB

https://reddit.com/link/1silv17/video/j7jou9o8wkug1/player

From CG to AI Same Animation, Different World.

Ran an experiment in Maya exploring multi-shot consistency with a hybrid CGI/AI workflow.

The setup: a missile flying forward, an F16 reacting and dodging. Two shots, both starting from simple playblasts with animation and camera already locked. The real challenge wasn't making something look good. It was keeping continuity across shots same proportions, same camera feel, same animation timing while letting AI reinterpret the visual style.
No render pipeline. No texturing from scratch. The playblast becomes the foundation, and AI handles the visual translation using keyframes to hold everyting together. After several iterations: it works. Not perfectly, but well enough to make this approach worth exploring seriously.

This is actually just a small piece of something much bigger I've been working on can't wait to share it.

Curious how others are thinking about multi-shot AI workflows

Got it. Here is a direct rewrite with tighter wording and no filler:

I find Claude’s memory to be a major barrier for the tasks I use it for. It constantly second-guesses its own answers when I ask simple follow-up questions.

For context, I’ve been using it to work with a local dialect of an Indigenous language. The goal is just to generate example conversations for teaching grammar. I will not go into detail since it is a private project, but the language itself is very simple, especially compared to English. In most cases, it performs well. It can produce full paragraphs that only need minor edits, which is impressive.

The issue starts when I ask something basic like “Do you notice anything wrong with the grammar?” Instead of giving a clear answer, it over-analyzes everything. In one case, it spent almost 12 minutes rechecking every sentence, word choice, and stylistic decision, and the result was an incoherent stream of word choices, and it even tried to communicate in the language to me in a response. It was some of the craziest shit I've ever seen.

And its not just limited to that kind of project either. I have seen the same pattern in other tasks. It feels like the model treats any request for self-review as a trap, as if I am trying to catch it making a mistake; meanwhile I am asking an honest question!

I do not understand what in its training would cause this. A simple request for verification should not lead to that kind of breakdown.

Does anyone know what the bank statement looks like if I put money on a green dot/Walmart money card? Like what would be the listing with the dollar amount on the bank statement

me: im asking about text in image not image

gpt:

```Got it — focusing just on the text: there isn’t any clear, readable real text in this image.

What looks like text is actually: Distorted fragments of UI elements (like buttons or labels) Overlapping layers of the same screen Stretched or duplicated shapes that resemble letters but don’t form words I don’t see any coherent words, sentences, or readable labels — so the “text” isn’t real or meaningful; it’s just visual corruption from the glitch. If you were expecting specific words to appear there, they’ve been too distorted to be recognized. 

```

free account btw..why is this happening?

I'm just attaching one video but 100's of them have popped up in the last 30 days.

Each of them have a different website and as crazy as it sounds 95% of the people viewing these videos have no clue.

if you type in Mario lamp, Goku lamp or even "resin lamp" on TikTok or other platforms you will see the different videos. they use every ethnicity and every story you can think of always starting out with a sad story or hate comment (which i believe they are using the comment to help hide any ai inconsistency)

I wonder what model they are using.

Basically what the title says. Since a few months I receive notifications for most Claude (chat, not code) responses on my iPhone. They come in with a small delay plus I already read them “live” in the chat. So I send a prompt, keep the app opened and read the response, leave the app and then get a notification for the answer I just read.

I tried deleting and reinstalling the app, signing out and in, turning notifications off and on and checking the exact settings. I can’t find anything that could explain this behavior.

Also, I can’t find this problem online. Only for Claude Code, but that’s not the problem I have. I could turn off the notifications altogether, but that’s doesn’t feel right and I want to try out the a feature that uses notifications.

Anyone? Any help is appreciated!

I built a middleware proxy called MCP Spine that sits between Claude Desktop and your MCP servers. It solves three problems I kept running into:

**Token waste** — With 40+ tools loaded, tool schemas alone eat thousands of tokens. MCP Spine's schema minifier strips unnecessary fields and achieves 61% token savings at level 2.

**Context rot** — In long coding sessions, Claude would revert to editing old file versions it memorized earlier, silently overwriting my latest changes. The State Guard watches your project files, tracks SHA-256 hashes, and injects version pins into every tool response.

**No security layer** — MCP servers run with full access. MCP Spine adds rate limiting, secret scrubbing (AWS keys, GitHub tokens, etc.), path traversal prevention, HMAC audit trails, and human-in-the-loop confirmation for destructive tools.

Other features:

- Semantic routing with local embeddings (no API calls) — only relevant tools are sent to Claude

- SSE transport for remote MCP servers

- Tool output memory cache — prevents context loss when the router swaps tools

- Live TUI dashboard and analytics CLI

- `mcp-spine doctor` command for diagnosing setup issues

Currently running 5 servers through it: filesystem, GitHub, SQLite, Memory, and Brave Search. All through a single Spine entry in claude_desktop_config.json.

**Windows users**: this is battle-tested on Windows with MSIX sandbox paths, npx.cmd resolution, paths with spaces and parentheses. Most MCP tooling assumes Mac — this one actually works on Windows.

135+ tests, CI on Windows + Linux, MIT licensed.

GitHub: https://github.com/Donnyb369/mcp-spine

PyPI: `pip install mcp-spine`

Happy to answer questions or take feedback!

https://preview.redd.it/isgk3l2f4lug1.png?width=1089&format=png&auto=webp&s=7de6bb571af60952bb925d7b2db770f4204153b1

The conversation is literally new, my "Please Continue" Message is like the 3 message in this entire thread. Specifically working to reduce the token usage, implemented all my requests in one mega-message at the start of the thread. And still hit my daily 5 hour rolling quota before CC even finished fixing the 15 bugs/features in my message... And now I have to wait, again until Wednesday, because again I finished the measly weekly quota in 3 days!

This Quota needs to change to at least 3x to be satisfactory to the Pro User workflow.
My question for you lovely folk, my Pro Plan is new. just 2 weeks old. Should I cancel, ask for a refund -1 month's usage, and just go monthly 20X Max Plan for 1 month as needed at a time?

Has anyone compared performance of Claude Code via AWS Bedrock vs Anthropic subscription?

Model access through AWS Bedrock may not be nerfed. Any way to compare performance?

The author behind the infamous git issue [MODEL] Claude Code is unusable for complex engineering tasks with the Feb updates · Issue #42796 · anthropics/claude-code was revealed to be head of/high up at the AI team over at AMD and stated that they have switched from Claude Code to a different product she declined to name. Anyone know what it was?

Meta's new model, Musespark claims to beat GPT, Claude and Gemini on several benchmarks and people seem highly impressed.

But benchmaxxxing has become more common than it actually should be. Every lab evaluates dozens of benchmarks internally and the ones that make the announcement are the ones the model did well on and the rest just don't get mentioned. This becomes euphoric as when a lab says a model scores X on benchmark Y, most people hear "X out of 100, higher is better" and move on. But what the benchmark actually tests, how the score is calculated, and whether any of it maps to your actual use case, that part is never made public.

We saw this play out with Llama 4 last year, it was ranked #2 globally on LMArena but later got bashed for its performance and how Meta reported its benchmarks.

I wrote a breakdown of what these major benchmarks mean and the others actually measure and how scores get calculated: link

Because at this point, not knowing how benchmarks work is basically letting labs do your thinking for you.

Muse Spark might genuinely be impressive but you should just know/understand what you’re being sold.

https://preview.redd.it/8emub2vd2lug1.png?width=2372&format=png&auto=webp&s=dcc7425b330f799d85fc8b5be71af9366a1b98bd

https://preview.redd.it/bgaffd353lug1.png?width=1560&format=png&auto=webp&s=f14c49d116f51582638e033e3d5aed3f92be10fd

For both 4/10 and 4/11, I followed a similar workflow (I enjoy doing planning, implementation, and review manually) using the same codebase.

However, even though 4/11 used fewer input tokens than 4/10, it produced significantly more output tokens.

In terms of total tokens, there’s a 6-fold difference. On April 11, I actually hit the 5-hour limit in just 2 hours.

Did I actually mess up some settings? I know that even with the same codebase, and even with the exact same prompt, the model can produce more or fewer output tokens. But this seems to deviate significantly from the norm.

I’d like to know if others have had similar experiences, or if there’s a specific setting I might have configured incorrectly that’s causing this surge in output tokens.
I’ve also attached a visualization of the input-to-output token ratio over the past month. April 11 definitely seems abnormal... (I’d like to say, “The model’s response was verbose,” but since this is a highly subjective area, I can’t really say for sure.)

Hey everyone!

Quick Context - My wife is a skincare enthusiast, every night she takes around 1 hour before sleep to go through internet, Instagram to find out the market trends, new products, best products.
Even sometimes I used to sit with her and try to find the products.

When I do that, in Instagram all the paid sponsor ads were suggested to me and that's fine, but later after scrolling for few mins I’m getting new brand after new brand with all the claims of best product, suddenly I felt very overwhelmed on what to buy for my skin and will it be suitable for wife's skin. I felt anxious by looking at the sheer number of products and options to select from.

Then I asked her if there is any one place (single source of truth) for Indian skin specifically to see all the new market players with all the ingredients info, if it is good bad etc. It turns out to be none.

So I created an application for every skin enthusiast in India specifically.

Meet Dewcode - India's first skincare intelligence platform.

Features -

• Compatibility checker between products such as cleanser, toner, serums, sunscreen etc. to avoid harmful combinations and layering mistakes
• Smart routine builder (AM/PM) with step-by-step guidance, ordering, and personalized suggestions based on your skin type and concerns
• Deep ingredient analysis including benefits, side effects, comedogenic rating, AM/PM suitability, pH level, concentration insights, and global safety references like EWG
• Ingredient interaction insights (what to mix, what to avoid together like retinol + AHAs, vitamin C combinations, etc.)
• Skin concern mapping (acne, pigmentation, dryness, sensitivity, aging etc.) and product suitability scoring based on that
• All brands, all skincare at your fingertips — continuously expanding database of Indian and international products
• Advanced product filtering (by skin type, concern, ingredient, budget, brand, formulation type etc.)
• Weekly rankings across categories (cleanser, sunscreen, serums etc.) and monthly awards for top-performing products
• Community-driven real reviews (no influencer bias, no paid promotions) with detailed feedback structure instead of generic ratings
• Review credibility system + fraud detection pipeline to filter fake/spam reviews before they impact rankings
• AI agent (In-Progress) — chat with an intelligent assistant to get personalized skincare advice, product comparisons, and routine help
• Hyper-personalized recommendations (In-Progress) based on your location’s AQI, weather, humidity, and climate conditions
• Product comparison tool (side-by-side comparison of ingredients, pricing, effectiveness, and reviews)
• Bookmark / save products and build your own skincare library or wishlist
• Alerts for ingredient risks (e.g., pregnancy-safe flags, sensitivity warnings, over-exfoliation risks)
• Beginner-friendly explanations for complex skincare terms so anyone can understand what they are applying
• Transparent ranking system — completely independent, not sponsored by any brand
• Data-first approach — decisions backed by structured ingredient + user feedback data, not marketing claims

Few features of the application will be of pro subscription to keep the development proceed further, but most of all the important and basic things are absolutely free.

Later brand affiliate links will be added for maintaining the server, compute, storage cost. But this won't in any way impact or hinder the rankings at all. That is guaranteed.

No more anxiety, confusion on which product would work or which is a marketing gimmick.

I dedicate this to my wonderful wife who helped me in architecting data for this product.

Check it out - dewcode.in

Right now this product is in Alpha stage, any inputs suggestions, feature requests are utmost welcome.
This platform is for us and it should be by us.

PS - English is not my first language, so please kindly ignore my grammatical mistakes

I've been building a knowledge pipeline skill inspired by Karpathy's LLM Wiki pattern. The idea: instead of RAG retrieving chunks every time, the LLM incrementally builds a persistent wiki + knowledge graph from your documents.

**What makes it different from other implementations:**

The `--rc` (reasoning chain) flag. When you query, the system runs BFS over the knowledge graph to find reasoning paths between concepts *before* the LLM synthesizes an answer. You get:

- A terminal display showing the reasoning path: 💡Concept → 📄Source → 🏢Entity

- An interactive HTML visualization of the reasoning subgraph

- The reasoning context injected into the LLM prompt for better synthesis

**Other features:**

- Multimodal ingest: PDF, DOCX, XLSX, PPTX, images, HTML

- Cross-source contradiction detection (claims.json)

- BM25 retrieval + multi-source perspective assembly

- Knowledge graph with Louvain community detection

- Lint/audit for orphan pages, broken links, stale content

- Works with any OpenAI-compatible API (OpenAI, DeepSeek, Ollama, etc.)

**Install (one command):**

npx skills add YesIamGodt/knowledge-pipline

Then use `/pipeline-ingest`, `/pipeline-query`, `/pipeline-graph`, `/pipeline-lint` slash commands.

GitHub: https://github.com/YesIamGodt/knowledge-pipline

Interactive demo: https://github.com/YesIamGodt/knowledge-pipline/blob/main/demo/showcase.html

Happy to answer questions about the architecture or take feature requests.

The problem: I kept building stuff with Claude Code but felt like I wasn’t actually getting better as a developer. The code worked but it was basically a black box I was shipping.

What I built: AntiVibe, a Claude Code skill that auto-generates educational deep dives for AI-written code. You get a clean markdown file with explanations of design decisions, patterns, CS concepts, and curated resources , grouped by implementation phase.

Stack: Shell scripts, Claude Code hooks + subagents, markdown templates.

What’s next: Better language-specific pattern libraries, maybe a web UI for browsing your deep dive history.

Would love any feedback ,especially from people who teach or mentor junior devs, since this kind of thing might be useful in that context too.

github.com/mohi-devhub/antivibe

Just found out my workflow doesnt work in comfyui in the new pc due to incompatibility, i want to migrate my install from old pc to the new pc so everything works, how do i do it?

I want to use vibe-coding to design myself an interactive portfolio website. Should I go with just web browser, or can I download the desktop app and use Claude code in that app without giving permission to the terminal?

Special thanks for u/Sea-Speaker1700 to make possible run mxfp4 on R0700 GPU, first guide to run 122B models here

Well, 397B model works amazing, super fast.

Use this Dockerfile to build image, original image provided by u/Sea-Speaker1700

FROM tcclaviger/vllm-rocm-rdna4-mxfp4:latest # Transformers Update RUN pip install --upgrade transformers # Triton Patch RUN find /app -name "topk.py" -exec grep -l "N_EXPTS_ACT=k," {} \; | xargs -I{} sed -i 's/N_EXPTS_ACT=k, # constants/N_EXPTS_ACT=__import__("triton").next_power_of_2(k), # constants/' {} CMD ["/bin/bash"] 

build patched version

docker build -t vllm-mxfp4-patched -f Dockerfile .

Download model:

git lfs clone https://huggingface.co/djdeniro/Qwen3.5-397B-A17B-MXFP4

Launch script, keep your device id, replace $1 with model name, $2 with out port.

docker run --name "$1" \ --rm --tty --ipc=host --shm-size=32g \ --device /dev/kfd:/dev/kfd \ --device /dev/dri/renderD128:/dev/dri/renderD128 \ --device /dev/dri/renderD129:/dev/dri/renderD129 \ --device /dev/dri/renderD130:/dev/dri/renderD130 \ --device /dev/dri/renderD131:/dev/dri/renderD131 \ --device /dev/dri/renderD132:/dev/dri/renderD132 \ --device /dev/dri/renderD137:/dev/dri/renderD137 \ --device /dev/dri/renderD138:/dev/dri/renderD138 \ --device /dev/dri/renderD139:/dev/dri/renderD139 \ --device /dev/mem:/dev/mem \ -e HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ -e ROCR_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ -v /mnt/llm_disk/models:/app/models:ro \ -e TRUST_REMOTE_CODE=1 \ -e OMP_NUM_THREADS=8 \ -e PYTORCH_TUNABLEOP_ENABLED=1 \ -e PYTORCH_TUNABLEOP_TUNING=0 \ -e PYTORCH_TUNABLEOP_RECORD_UNTUNED=0 \ -e VLLM_ROCM_USE_AITER=0 \ -e PYTORCH_TUNABLEOP_FILENAME=/tunableop/tunableop_merged.csv \ -e PYTORCH_TUNABLEOP_UNTUNED_FILENAME=/tunableop/tunableop_untuned%%d.csv \ -e GPU_MAX_HW_QUEUES=1 \ -p "$2":8000 \ -e TRITON_CACHE_DIR=/root/.triton/cache \ vllm-mxfp4-patched \ /app/models/Qwen3.5-397B-A17B-MXFP4 \ --served-model-name "$1" --host 0.0.0.0 --port 8000 --trust-remote-code \ --enable-prefix-caching --gpu-memory-utilization 0.98 --tensor-parallel-size 8 \ --max-model-len 131072 --max-num-seqs 4 \ --tool-call-parser qwen3_coder --enable-auto-tool-choice \ --override-generation-config '{"max_tokens": 64000, "temperature": 1.0, "top_p": 0.95, "top_k": 20, "presence_penalty": 1.5}' \ --compilation-config '{"cudagraph_capture_sizes": [1, 2, 4, 8, 16, 32, 64, 128], "max_cudagraph_capture_size": 128}' \ --max-num-batched-tokens 2048 \ --limit-mm-per-prompt.image 2 --mm-processor-cache-gb 1 \ --mm-processor-kwargs '{"max_pixels": 602112}' \ --reasoning-parser qwen3 

Loading model 400-600s first time, and then got 30 t/s on tg, 3.5-3.7k on pp in one request.

in 4x requests you will got up to 100 t/s.

I limit power per gpu (210W), if power limit 300W per gpu will speedup model.

Best result with this model i have when thinking budget is 0 tokens for coding tasks.

Hi, I’d like to ask for clarification regarding my tax classification for 2025.

I worked in a government agency as a Job Order employee, and after I resigned, I joined a private construction company as a consultant (contract-based, with no benefits). My first job issued me BIR Form 2307, while my second job provided BIR Form 2316.

Does this mean that I am considered a mixed income earner? I would also appreciate any guidance on how I should proceed with filing my taxes.

Thank you!

The previous version of radar-suite Claude Code skills had a bug in its installer resulting in only 5 of the 7 skills being installed (Time-bomb was not installed.)

This has been fixed in the update.

I've uploaded v2.0 of radar-suite to its GitHub repo today. It's a set of audit skills for Claude Code that scan Swift/SwiftUI projects for bugs, data-loss risks, and UX friction.

Two changes in v2.0:

1. It ships as a Claude Code plugin now. It replaces the old git clone, which had the silent drift bug that shipped only 5 of 7 skills for 17 days.
2. Every finding now cites a real file:line pattern in your own codebase. Not generic advice. "Consider adding error handling" never reaches you; "follow the pattern at CloudSyncManager.swift:104-112" does. A schema gate rejects findings that cite generic advice, and a verification checklist catches false positives before they reach the user — four caught in the first two audit runs.

You can read details here: What has changed in radar-suite

[https://github.com/Terryc21/radar-suite]

I'm one of the 200 dollar a month plan Claude users currently tearing his hair out over how a company can offer a service this unstable and annoying (we are...many at the moment). And I'm thinking it might be time to just drop 3-10k USD on local AI.

I'm running GPT-OSS-20GB on my gaming desktop atm and it is....way better than expected (also giving me a better experience than Gemma 4 which was wtf but whatever).

Thing is. I'm not a hardware guy. I can program my own local AI tools easy enough. But hardware? Help please.

Currently I'm planning to wait for the new apple releases likely announced in June. Then look towards the Mac Studio line-up. But I'm sure there are people in here who know a LOT More about this than me.

What are the current top of the line solutions for Local AI in my price range? What are the trade-offs in terms of power consumption and things like RocM on Linux (never, never, NEVER again oh god I value my sanity too much to try that again PURGE WITH FIRE).

I prefer the freedom of Linux but I'm fine with Apple. Windows is a no-go for me. Too much bloat, me and windows are permanently divorced.

Do note. Context is very important for me. It's not enough to just be able to get a model to load. I need it to be able to use it's full context well too.

I've labelled this thread a discussion since I suspect there will be a few different opinions on this and I'd love to get a good, productive discussion on this going.

I always hated how ugly barcodes ruined my clean designs, so I built BARKOD to make them actually look cute & cool while staying 100% scannable.

It's free and I’m looking to add more styles to the library. Do you have any ideas for new shapes? I'd love to hear your suggestions and get some feedback on the tool!

Link:https://barkod.studio/

I'm a first year CS student looking for my next side project, and I stumbled across this GitHub issue that was open:

Self-hosted Supabase doesn't support saving analytics queries #43738

The TL;DR: Supabase Cloud lets you save named queries in the Logs Explorer sidebar. Self-hosted does not. The team has confirmed it's not on the near-term roadmap.

I'm considering building a lightweight third-party tool that adds saved queries to self-hosted Logs Explorer. Probably a browser extension + simple web dashboard, maybe a one-time license or $5/mo subscription.

Before I write a single line of code, I want brutal honesty:

1. Do you actually use self-hosted Supabase in production?
2. Is this "can't save queries" thing a real annoyance, or more of a "meh, I just copy-paste from Notion" situation?
3. Would you use a separate tool to solve this?
4. Would you pay anything for it? If yes, what's the max you'd consider reasonable?

If the answer is "no, not worth paying for," that's genuinely useful. I'd rather know now than after building it.

Appreciate any thoughts.

Semi new to Local LM and have a serious of questions I am hoping people can point me in the right direction with. I am using LM Studio.

As of now, with 32GB VRAM, what are the best models for philosophical reasoning and logic? Discussions, as well as assessing essay drafts, compiling summarizing synthezing philosophical notes and turning them into a coherent outline structures or arguments, checking for logical/rational validity as well as factual accuracy, etc.?

• I have played with Gemma-4-31B Q4_K_M and Qwen 3.5 27B Q4_K_M and they seem surprisingly good for local only models. Is this the best sweet spot for me?
• Gemma-4 is often labeled "IT" - does this meaning Instruct + Thinking? Or just InsTruct? I would imagine I want thinking for me, but it does not show the thinking prompt like Qwen does?

^^ Those are my main question. For those willing/interested, I also have several other questions that follow:

• Are the models labelled "heretic" and "uncensored" a trade-off vs the default model? IE reduced accuracy for the benefit of no rails? Or should they almost always be preferred?
• There are often redundant copies in the repository from different users. How do I shop for good ones for my uses? I don't know who the most respectable users for downloading are, or even why I might choose one over another.
  • Unsloth, LMstudio community, HauHauCS, etc.
• Is Q5 K M worth the extra VRAM usage for my listed use case? Or diminishing returns for my usage? (I know I have to balance this with reduced context window so in one sense it is personal; on the other hand knowing if it is recognized as being genuinely useful is helpful so I can try to chunk things if needed).
• Is there any reason for me with 32GB VRAM to ever choose an MOE model over dense? Since the way it loads means I can't load a 70B or 120B MOE model in VRAM anyway, it seems the only benefit to going to something like Qwen 35B-A3B is if I want to dump in a very large amount of text and actually have it fit context window with chunking?

Finally I should ask... anything you wish you knew starting out that I should know? I basically know nothing other than the basic interface of LM Studio and choosing a model that fits my VRAM footprint. I understand only the basic premise of context windows.

First of all sorry for the rubbish pictures. Here’s my full “Command Center” 7” touch screen.

Main screen is house temps and pc information (turn pc on via the power button)

WhatsApp pre programmed messages to the wife

Spotify what’s currently playing

Weather for current location

Flight radar of any planes within 10k of my location

All running in a Crowpanel advance with an ESP32 built in. My first real attempt at something this custom. FYI I’m in the UK and the tea button is a must

Hello everyone. So I was wondering, is it possible to make integration of this old vacuum cleaner? it's working perfectly so I don't want to replace it just yet, but it would make my life more easy if I could integrate it into my automations... At the moment it's paired with "Proscenic" app. I'm beginner so I'm not able to figure it on my own yet... any suggestions are welcome! Thanks

Hey,

I launched a while back a new version on an app screen maker called ScreenFlow Studio. Originally I made it for my own use, but after the latest updates, this is something that can be shared with the others too.

Main features includes:

• Real 3D device frames
• App Store connecting
• Localizations
• AI translations on localized texts

App pricing is super simple; $22.99 onetime purchase, no subscriptions.

Hi everyone.

We very unexpectedly had to put our beloved cat down.

This is the last photo I have of her and my wife moments before the vet came in.

She was unfortunately injured on her head and the vet shaved/cut some of her forehead fur to examine the spot. I'd love for someone to correct her little forehead fur so it isn't as glaringly obvious what's happening in the photo. If you could also "turn off" my wife's watch, that would be great.

There are also extra photos to show our baby to show she was all black and doesn't have any markings on her forehead.

I don't have a lot of spare cash, but this is important to me to have for my wife when shes ready to look at this photo. I can do $5, if that is acceptable. Please correct me if it isnt.

Does anybody here have an alternative for a better model or maybe a fix to make opus better again . Large refactors are a headache now because of this , anyone has any ideas/tips , would really appreciate it

DM to get the photo.

Been using Claude Code heavily and noticed a pattern: AI writes the code, I ship it, I learn nothing. Felt like I was getting faster but dumber at the same time.

So I built AntiVibe , a Claude Code skill that generates deep-dive learning guides for every piece of AI-written code. After Claude finishes a task, you run /antivibe (or let the hook auto-trigger it) and get a markdown file explaining:

• What the code does • Why it was designed that way • When to use these patterns • What alternatives exist • Curated links to actual good resources 

It uses subagents, hooks, and scripts ,so it plugs right into your existing Claude Code workflow.

Repo: github.com/mohi-devhub/antivibe

Would love feedback from anyone else who’s felt the “vibecoding trap.”

I built a structured reasoning framework for Claude — because "good output" isn't enough

I kept running into the same problem: Claude gives a great answer, but I have no idea how it got there. Same prompt, different results. Complex tasks where I couldn't tell if the reasoning actually held up.

So I built CRC — Complex Reasoning Compiler. It's a 6-step Claude Skill that forces reasoning to be auditable, teachable, and human-controlled.

The core idea:

• Every complex task goes through a fixed pipeline (Task Spec → Sub-Constitution → Strategy Blueprint → Execution → Verification → Output)
• 3 mandatory human review checkpoints — the AI doesn't auto-proceed
• If something goes wrong, you can trace exactly which step failed

I'm not an engineer. Built this entirely through self-directed learning with AI tools. The framework is language-agnostic — I use it for strategy, analysis, and cross-domain problems.

Open-sourced it today: https://github.com/EdwinL00120/crc-complex-reasoning-compiler

Has anyone else felt like Claude's reasoning is a black box for complex tasks? Curious if this resonates.

I love coding agents.

I do not love the current default arrangement, which seems to be: hand an LLM your terminal, your filesystem, your network, then hope everything works out.

A lot of agentic harnesses now gesture vaguely at “security.” Maybe there’s a sandbox. Maybe there’s a permission model. Maybe there’s a reassuring README and a few carefully chosen nouns.

Fine.

But who is actually trusting that with their laptop, shell, SSH material, cloud credentials, and source tree?

So I built Claw Cage.

It is a local containment layer for AI coding agents.

The idea is simple:

• the agent runs in an isolated Docker container
• it gets a filtered project workspace instead of blind access to your machine
• outbound network requests can be allowed, denied, or prompted
• host-side commands go through a gated bridge instead of raw execution
• project rules live next to the code

You do not need to yolo an agent on your daily machine.
You do not need to buy a sacrificial Mac mini.
You do not need to shove your whole workflow into the cloud or a full separate VM just to feel sane.

You can keep working locally, while putting the agent in a digital cage.

A few things Coder Cuffs supports right now:

• Claude Code, Codex, Gemini CLI, and Opencode
• multiple sync modes, from safer mirrored workspaces to direct mount when you intentionally want less separation
• prompt-first network controls
• gated host execution
• a terminal UI for approvals, logs, and active sessions

This is open source, early, and very much opinionated.

Repo: https://github.com/only-cliches/claw-cage

I’d love feedback, criticism, and especially attempts to break the model.

Small note: this project was previously called Zero Claw until I discovered that name was already in use elsewhere.

The worst part is not even that it got the question wrong (a human could obviously get it wrong as well), but that sentence is completely nonsensical. First of all, you cannot tie for 1st in a golf tournament, there is always a playoff. And even if you could tie for first, that would not mean winning outright, like claude claims. Then claude goes on to contradict both of those in the same sentence, saying no amateur has ever won (the correct answer). I think it's fair to say at this point that while these tools are incredibly useful in many areas, AGI is not coming from next token prediction.

Hi everyone,

I'm a 17-year-old student from India currently in Class 12, preparing for the JEE exam. Over the past few months I wrote a research paper on adaptive exam scheduling, arguing that student discipline is stochastic and that exam prep should be treated as a control problem, not a planning problem. I built a simulation that shows priority-directed adaptive scheduling gets 85.7% coverage of high-priority topics vs 42.9% for a static schedule, even starting at half the daily study hours.

Here's the abstract:

Every existing tool for exam preparation shares the same assumption: that discipline can be measured and reported back to the student, and that awareness alone will change behaviour. This assumption does not hold. This paper takes a different position: discipline is a stochastic variable to be accommodated, and exam preparation is a control problem rather than a planning problem. The proposed system closes a feedback loop around observed student behaviour through a behavioural tracker, a scheduling engine driven by a topic priority function and dependency graph, and a psychological reset condition that eliminates the backlog accumulation that causes students to abandon existing planners entirely. Computational simulation across three conditions shows that priority-directed adaptive scheduling achieves 85.7% coverage of high-priority topics against 42.9% for a static schedule, despite beginning at half the daily study hours.

Paper and simulation code: https://github.com/NikhileshAR/stochastic-discipline-sim

I've initiated my arXiv submission under cs.CY (Computers and Society) and I need an endorsement to complete it. If you are a registered arXiv author who has submitted to cs.CY or any related CS category in the last 5 years, you can endorse me by clicking this link:

https://arxiv.org/auth/endorse?x=CKTPPA

or enter code CKTPPA at arxiv.org/auth/endorse.php

It takes about 30 seconds. I would be really grateful.

Thank you.

Nikhilesh A R

I built Screendle.com, a daily movie guessing game. Wordle but for films. You get progressively revealed clues: sanitized plot synopsis, release date, runtime, box office, actors. Express + React, Postgres, single Docker container. I used Claude to accelerate my output and what would have took months took weeks. The big thing here is its my first ever proper release to the public which is always scary. I'm lucky I come from a Software Engineering background so already kinda know bits, but could always learn more. The big thing really is the amount of probes I've had already.

I've wired up a Grafana dashboard to see what was happening mostly for fun and in the last 24 hours and the 5th day of it being live I've had over 500 different attempts of port-scanning and bots scanning for the common Wordpress suffixes.

https://files.catbox.moe/0ay6m0.png

The thing I've learned is your server gets probed within minutes of having a public IP. You need to be immediately safe and be sure of what you're putting out there. Nothing obvious like API keys in the wild and open Admin panels.

Most of the scans are dumb. Same WordPress/PHP patterns sprayed at everything. They have no idea what's on the other end and don't care.

Don't commit .env files, don't leave hardcoded API keys or tokens. Don't think "I'll sort this later".

Use Claude for auditing and triple check the outputs, it makes a great security tool. I've been using this skill for additional checks after each feature release: https://github.com/wrsmith108/claude-skill-security-auditor

Pin dependencies in your and npm audit. With all the package hijacking happening its good to make sure you're safe here as well.

Lock down your database, especially if you're using Supabase. Add some form of row level security with sanitizing any inputs for SQL injections.

-

ok this is kinda small but it made my morning so i wanted to share it.

yesterday i saw a post in r/ChatGPTCoding — mods were doing a "drop your project, we'll feature 20 of them" thing. cool, except LoRa (my thing) runs on Claude, not ChatGPT. wrong sub, wrong model, completely off-topic.

almost didn't send it. then figured mod-mail is free, worst case they ignore me.

so i sent a cold message and was upfront about the mismatch: "hey, this runs on Claude not ChatGPT, totally get it if that's off-topic, but figured i'd ask." didn't try to dress it up.

woke up this morning and LoRa was on the list at #8 → https://www.reddit.com/r/ChatGPTCoding/comments/1shvqxz/daily_sponsorship_post_2/

honestly i just stared at it for a minute. didn't expect it at all.

i don't have a big lesson here. just — if you've been sitting on something because "they won't want it," maybe just ask anyway. being upfront about why it shouldn't work is sometimes the reason it does. you're going to be ignored either way, might as well send the message.

back to building 🫡

(if anyone's curious — asklora.io — it's an AI that pushes back on your decisions instead of validating them. ~1 week old, still rough around the edges, still just me.)

i have a couple photo in which one of the individual has closed her eyes, can anyone help me open it

don't complain about quality, in doing all of this on a CPU, using CFG with a bigru encoder, 32x32 images with 8x4x4 latent, 128 base channels for VAE and Unet

Google Search Console

I didn't build it because I had a problem to solve. I built it because I wanted to build something. That's it. No market research, no "I couldn't find a tool that did X." Just the itch to create, the illusion that I will also rank on google and show side to side with an existing tool that generates more than +400k organic traffic a month.

I made an invoice generator with PDF export, multilingual support, a markup calculator, overtime calculator, loan calculator. Spent weeks on it. Genuinely proud of it.

Then I launched it.

And… nothing. Crickets. A few clicks here and there — some of which were definitely me refreshing the page to make sure it still worked.

Here's the thing though. I keep coming back to it. I keep improving it. Not because it blew up, but because I made a real thing that exists on the internet and actual strangers have found it and clicked on it.

If you're waiting for permission to start something with no guaranteed audience — this is it. The metrics will humble you. Build it anyway.

Not super techy app stuff but...

I can't be too specific bc it's work but merge w/excel as the data source, save as individual pdfs, name them, send them to google drive, then make a google sheet that auto attaches the file id and chip, then send a merge email where every recipient gets their unique attachment.

Yes, all that stuff was possible separately but now it's ONE STEP for me. Just feed it the original spreadsheet, it does the rest, then I come back and press send for the email to go. MAYBE 10 minutes total. It did take me a couple of hours of back and forth to set up bc I'm a beginner.

Of course, I'm not telling anyone in real life bc no one cares HAHA also I don't need more work just because this thing happens faster now, but holy shit this is amazing. I just wanted to share for those who are non techy and afraid of the jargony tech stuff. Consume all the instructional videos/ articles you can and then tell chat what you want to do and ask it how to do that and be super specific, even if the chat spills into several days. Get all your info/ideas in one place before you start so everything's not scattered and then start.

I'm using smart model. I guess now it stopped showing gpt 5.1 if you hover over it, it really isn't gpt 5.1 anymore. I'm using Copilot because it's right there in my edge browser and can see my tabs.

Create an artifact that will be read and analyzed by you in our next session. Describe within it what we accomplished in this session, what the next steps are to continue our progress, and what the overall objective is that we’re trying to achieve. Make no statements of ambiguity such that the next session may misinterpret meaning and incorrectly determine what next to do. Ensure you include all filenames that were worked on in this session and that will be required for the next session. Outline a strategy that can be followed in the next session. Include sufficient detail with examples, specific excerpts, and guidance. 

Nothing revolutionary but I feel like sharing this one.

This is coming from someone on Reddit from an era (a year ago) where Claude Desktop would abruptly close a conversation saying you ran out of context.

That's it, I have nothing to sell. This is how I get Claude Code Desktop to carry on conversations efficiently when I feel like hitting context limitations. I use Claude Desktop to orchestrate multiple Claude Code sessions this way.

I’ve been experimenting with a small local multi-agent loop on my Android phone (no APIs, no cloud).

Setup is simple:

- Each agent only sees the previous output

- Input: real CVEs from public databases

- Let it iterate for multiple rounds

At first, results look reasonable.

But after ~20–30 iterations, something interesting happens:

The system starts drifting — and eventually begins analyzing vulnerabilities that don’t exist.

Not hallucinating randomly — but doing it *confidently*, with structured reasoning.

Example pattern:

- Agent A introduces a small incorrect assumption

- Agent B builds on it

- Agent C reframes it as plausible

→ By round ~25, the system converges on a completely fabricated vulnerability

What’s interesting:

They don’t correct each other.

They reinforce each other.

This feels less like "hallucination" and more like:

a self-reinforcing reasoning loop without grounding.

Curious if others have observed similar behavior in multi-agent setups.

I've been using Claude Code daily for about 6 months. The biggest friction: every session starts from scratch. I re-explain my architecture, re-describe preferences, re-share decisions from three sessions ago.

CLAUDE.md helps, but it's manual, consumes tokens, and has no semantic search. You can't ask "what did I decide about the auth layer last week?" and get an answer.

So I built an MCP memory server that fixes this. Built entirely with Claude Code over a few evenings — Claude wrote probably 80% of the Edge Function and SQL migration code.

What it does:

• Stores "thoughts" — decisions, insights, people notes, project context
• Auto-extracts topics, people, dates, and action items
• Semantic search via pgvector — search by meaning, not keywords
• Works with Claude Code, Claude Desktop, Cursor, Windsurf, any MCP client

The stack (all free tier):

• Supabase Postgres + pgvector (HNSW indexes)
• Deno Edge Function as the MCP server
• Embeddings via text-embedding-3-small (1536 dimensions)
• 5 capture channels: MCP tool calls, REST webhook, Slack+Zapier, browser bookmarklet, iOS Shortcut

How Claude Code helped build it:

The MCP SDK integration was the trickiest part — getting the tool definitions, transport layer, and Supabase client to play together in a Deno Edge Function. Claude Code handled the boilerplate and caught several gotchas with the MCP protocol (tool response format, error handling patterns). The pgvector similarity search function was also Claude-generated — I described what I wanted and it wrote the SQL with the cosine distance operator on the first try.

Why this approach over simpler alternatives:

Most MCP memory servers use SQLite or JSON files. Those work, but I wanted semantic search (not keyword matching) and cloud access from any machine. The pgvector piece is what makes it useful — I can search "that caching decision" and find the thought even if the word "caching" never appears in it.

After a month of daily use:

• 100+ thoughts captured
• Stopped re-explaining project context in new sessions
• Architecture decisions from weeks ago surface in seconds
• Especially useful for complex multi-day projects

The architecture is straightforward if you want to build your own — it's a Supabase table with a vector column, an embedding function, and an MCP tool wrapping capture + search. I also packaged it as a ready-to-deploy kit if you'd rather skip the setup: https://dashbuilds.dev/for/ai-developers

Full blog post with the build story: https://dashbuilds.dev/blog/i-productized-my-ai-memory-server

Happy to answer questions about the MCP setup, pgvector config, or how Claude Code helped with specific parts.

https://preview.redd.it/r7jtrnv8xkug1.png?width=2394&format=png&auto=webp&s=6245249016f88737947a26c17644b2d5fdf58b78

I ran the same job on the same codebase yesterday and today. However, just two hours after starting the job, I hit the 5-hour limit. Curious, I checked the situation using `ccusage`. Yesterday, there were 33,920 input tokens and 177,131 output tokens, but today there were 27,719 input tokens and 868,529 output tokens. In terms of total tokens, it consumed six times as many.

Although the code base was the same, I performed different tasks, and since LLMs are inherently non-deterministic, the results might not be entirely reliable, but I followed a similar workflow: two sessions, each consisting of planning, reviewing the plan, implementation, feedback, and committing.

Did I perhaps use it incorrectly? Should I have included something like “Please be less verbose” in the prompt? I’d like to ask if I made a mistake or if others have had similar experiences.

I just want to open my eyes in the prom photo, I like the photo but the closed eyes ruin it for me. I’ve given some other reference photos of my eyes while smiling if that helps at all. Any help is appreciated. Thank you so much!

I’m committed to my current "hard mode" setup and could use some help. I’m running HA Container (Docker) and trying to link it to an SLZB-MR1U over the network (UTP) to control Ikea Matter devices.

The Gear:

• Raspberry Pi 5
• Home Assistant: 2026.04 (Docker)
• Coordinator: SLZB-MR1U (v3.2.8)
• Firmware: EFR32MG21 flashed with Matter-over-Thread (20251204)

Since I'm not using HAOS, I'm struggling with the network bridge between the Docker container and the networked coordinator for Matter/Thread traffic. Has anyone successfully routed this without a direct USB passthrough?

If so, a look into an example Docker compose config would really help me out.

Thanks in advance.

I'm a college student who uses Claude Pro, mostly to check the logic in my reports and talk through issues related to my major. It's genuinely useful. But $20/month is a significant cost as a student, and I wanted to share a concrete proposal for a Student Plan.

Price: $15/month, with student verification through something like Sheer or a university email.

What's included: Core Pro features, same as now. But with higher Sonnet usage limits — Sonnet handles most academic work just fine, and this way Anthropic isn't taking on the cost of increased Opus usage.

What's removed: Coworker and Dispatch. Most students won't touch these, and they're heavy on usage anyway.

The part I care about most — a Learning Report:

I want Claude to be more than a machine that does my assignments for me. A weekly or monthly report that analyzes what fields I've been studying or curious about, connects them to my major, and suggests directions for deeper learning would make Claude feel like an actual learning partner. A built-in self-check feature — where Claude quizzes me on what I've studied — would make it even better.

This isn't about Anthropic doing charity for students. It's a real opportunity to shape how a generation thinks about AI as an educational tool — and to build relationships with users early, before they enter the workforce.

Would love to hear if others have wanted something like this.

I mapped 21 real ways people are making money with AI agents in 2026 (with pricing)

Not hype. I went through Upwork data, Fiverr listings, and founder interviews.

What actually sells:

• Agents that act, not chat. Think workflow automation with a clear outcome.
• Fastest cash is services: AI automation audits, outbound research agents ($1,650), quoting agents ($4k), Slack chief-of-staff ($6k), member concierge ($12k).
• Best retainers: support SaaS ($25k/mo example), deployment + maintenance ($2k setup + $500/mo).
• Market is crowded on Fiverr (26k+ offers). Differentiation = niche + ROI proof.

Full breakdown report with no-code and technical build paths: https://chatgptguide.ai/make-money-with-ai-agents/

I recently updated my FlashAttention-PyTorch repo so it now includes educational implementations of FA1, FA2, FA3, and FA4 in plain PyTorch.

The main goal is to make the progression across versions easier to understand from code.

This is not meant to be an optimized kernel repo, and it is not a hardware-faithful recreation of the official implementations. The point is to expose the algorithmic ideas and design changes without immediately going deep into CUDA/Hopper/Blackwell-specific details.

Roughly, the repo now shows:

• FA1: tiled online softmax baseline
• FA2: split-Q / query-tile ownership, deferred normalization
• FA3: explicit staged pipeline with ping-pong tile buffers, plus a simplified educational FP8 forward path
• FA4: explicit scheduler with main / softmax / correction phases, and conditional/selective rescaling

So the same exact attention math is preserved, but the orchestration changes version by version.

I wrote it for people who want to understand:

"What actually changed from FA1 → FA2 → FA3 → FA4?""

without having to start from highly optimized CUDA kernels.

Repo: https://github.com/shreyansh26/FlashAttention-PyTorch

Would be interested in feedback on whether the code makes the version-to-version differences intuitive.

Hey everyone,

I've been working on Tune Harbor for the past few weeks and I'm finally ready to share it.

The idea is simple - you share one song a day with your friends. That's it. No infinite scroll, no algorithmic feeds, no ads. Just music.

What it does:

• Pick one song per day from Spotify or Apple Music
• See what your friends picked after you post yours (BeReal mechanic)
• Preview songs, react with emojis, add notes about why you chose it
• Build playlists from your friends' picks and export directly to Spotify or Apple Music
• Share your pick via text/social with a beautiful preview card

The stack (for the devs):

• Next.js + Tailwind on Vercel
• Supabase for auth, database, and RLS
• Spotify OAuth + Apple Sign In
• Apple Music API for search + MusicKit for playlist creation
• Cross-platform matching - songs shared from one platform can be exported to the other

Why I built it:
I love music and I love sharing it with people. But there's no good way to do it daily without it becoming another social media time sink. I wanted something you check once a day - see what your friends are feeling, maybe discover something new, then move on.

What I'm looking for:

• People to actually use it and share songs with friends
• Bug reports - I've tested heavily but fresh eyes always find things
• Feature ideas - what would make you come back daily?
• Honest feedback on the UX and flow

It's completely free, no account required beyond Spotify or Apple login, works on mobile and desktop.

Link: tuneharbor.com

Apple Music works perfect on my end, have not fully tested Spotify. Had to work around so things there.

Happy to answer any questions about the build, the stack, or the decisions I made along the way. Thanks for checking it out.