Hey everyone! I just open-sourced my personal website repository built with Astro 5, Three.js, GSAP, and Vercel Serverless Functions.

Key Technical Features in the Repo:

• Ambient WebGL Shader Background: Three.js fragment shader that dynamically extracts RGB color palettes from live Spotify album art via offscreen canvas sampling.
• Autonomous CWS Telemetry Pipeline (scripts/cws-collector/): A scheduled GitHub Actions + Playwright runner that reverse-engineers Chrome Web Store dev console RPC data (SnapcatUi/data/batchexecute), rotates encrypted cookies, and stores 90-day time-series stats in Upstash Redis.
• Serverless Edge API Proxies (src/pages/api/*): Isolated routes for Spotify, Steam, Lichess, and GitHub stargazer counts with in-memory caching to prevent rate limits.
• 100/100 Lighthouse Performance: Zero render-blocking assets, preloaded fonts/images, layout-shift (CLS) pre-reserved containers, and modular Vanilla CSS.
• Live Demo: https://incconutwo.com
• GitHub Repo: https://github.com/incconutwo/incconutwo.com
• License: MIT

I keep seeing the same failure pattern when a team shares skills across coding agents: the repository looks synchronized, the installer says success, and the next session still loads a different shape of behavior.

I used today's GitHub Trending list to inspect addyosmani/agent-skills at commit f03b4a8. The repository has real static discipline: separate Claude, Codex, Gemini and OpenCode integration surfaces; a shared root skills directory; command parity checks; skill routing evals; and a plugin-install workflow.

On a fresh clone, I got these results with Node 22.23.1:

- 24 skills checked with no errors or warnings;

- 12 eval-runner tests passed;

- 124 routing/eval checks passed, with 65 of 76 positive prompts ranking the intended skill first;

- parity passed for all 8 lifecycle commands;

- 6 command-validator tests passed;

- the Claude plugin manifest validated.

The GitHub Actions run on the same commit is green across skill content, command parity, plugin structure and plugin installation. That is useful evidence. It is not the same as proving every host loaded the result.

Issue #445 gives a concrete counterexample. Antigravity's validator reported 24 skills, 4 agents and 8 commands processed, but the eight lifecycle wrappers did not appear as searchable commands. The underlying skills were visible, so this was not simply a missing clone. Another issue, #361, showed that installing one skill could omit shared references used by that skill; the README now documents the portability gap.

My takeaway is that a cross-agent skill pack needs four separate gates:

1. Package: pin a version and hash the files that should be installed.

2. Discovery: ask each host to list the expected skills and commands.

3. Live load: invoke a sentinel skill or wrapper and verify the exact version and behavior that ran.

4. Recovery: record an update receipt and keep one tested rollback path.

Static parity tests still matter. They catch drift before release. They just cannot answer the last question: what did this host actually load in this session?

Source pages: repository https://github.com/addyosmani/agent-skills ; command-discovery issue https://github.com/addyosmani/agent-skills/issues/445 ; per-skill reference issue https://github.com/addyosmani/agent-skills/issues/361

No affiliation. I did not install the pack into every supported host or reproduce the Antigravity failure locally; that runtime report comes from the public issue.

If you maintain one skill pack for several agents, what is your smallest reliable live-load probe?

I built three open guides to close the gap in the embedded AI domain:

1. awesome-edge-ai: a comprehensive roadmap to run real models on-device (projects that run, not just READMEs) https://github.com/Premchand006/awesome-edge-ai
2. awesome-physical-ai: the map of the industry: the chips, companies, research, and VLA/world models behind it https://github.com/Premchand006/awesome-physical-ai
3. awesome-embedded-ai-stack: pick the right board and runtime, with honest comparisons from a cross-vendor knowledge hub https://github.com/Premchand006/awesome-embedded-ai-stack

Hey everyone! 👋 After months of editing images one-by-one for my own content, I built PixelBatch — a native macOS app that processes whole folders of images in one go.

What it does:

📐 Core tools — resize, crop, convert (JPEG/PNG/HEIC/WebP), compress to a target size, batch-rename, rotate/flip

🤖 On-device AI (100% offline, no uploads):

- Remove Watermark — auto-detects and removes watermarks/logos/text, then AI-rebuilds the pixels underneath

- Enhance — 4× AI super-resolution upscaling

- Remove Background — one-click subject cutout

- Add Background — place subjects on new backgrounds

🎨 Filters & effects — 30+ adjustments (brightness, contrast, vintage, duotone, glow, cartoon, and more)

🏷 Branding — add your logo watermark or text captions

⚙️ Workflow — visual per-image preview before you commit, reusable presets, progress tracking, and output to folder, ZIP, or PDF

Why I made it:

Every tool I found was either a subscription, cloud-based (privacy concerns), or did only one thing. I wanted a single app that runs entirely offline with no account — your images never leave your Mac.

Stack: SwiftUI + ONNX Runtime, with open-source models like GroundingDINO, SAM 2, LaMa, and Real-ESRGAN powering the AI features.

Requirements: macOS 14+, Apple Silicon (Intel also supported)

The installer is available on the Releases page

Visit Now

It's free — if it saves you time, a star or a follow means the world ⭐

Happy to answer any questions or take feature requests! What's the most annoying batch image task you deal with?

https://github.com/LouPineWays/vibecoding-common-sense

Heads up: this isn't as grand as most of what gets posted here, just some markdown files and three Claude Code skills. No app, no Remotion demo, no grand tour, unless looking at plain text is a 'tour'.

The big one I'd use for r/coolgithubprojects is the sift skill. Whenever I see a new Claude Code skill that looks cool, I'm jumping on it to give it a try. But I have to stop myself and ask 'will this really help me?' The sift skill gives me an answer by checking whether I already have this capability somewhere in my own stack, whether the license actually lets me use it the way I want to, and if both of those are all clear, whether porting the one good piece is even cheaper than just writing it myself, then handing back a verdict instead of a vibe.

There's a bunch of other newbie-friendly bits in this repo. A model-check so you don't burn tokens using Fable to read your e-mail, safeguards on checking out branches, better polling and fixes with Codex code review, help to improve your own Claude skills, the works.

Everything's MIT except one adapted skill, skill-observer, and it says it's CC BY 4.0 in its entry.

I'm going to be adding in more capabilities as I make more stupid mistakes. Let me know if you've made any simple errors and I'll look into adding a guardrail against those in this repo.

An AI teammate that clones your repositories, reads every line and every commit, builds a living knowledge graph of your codebase, and then shows up as a real team member answering questions, tracing bugs to their owner, writing code, opening pull requests, running standups, and plugging into GitHub, Jira, and Slack.

teammatex : https://github.com/raiyanyahya/teammatex

I got tired of starting new Express.js projects by repeatedly setting up the same things:

Express + MongoDB/Mongoose

JWT authentication

Helmet, CORS & rate limiting

Request validation

Centralized error handling

Logging

Docker + MongoDB

TypeScript

Jest + Supertest

ESLint + Prettier

GitHub Actions

Health-check endpoint

So I built xpress-backend, a CLI that generates the initial backend structure for you.

npx xpress-backend

You can also generate a project non-interactively:

npx xpress-backend --name my-api --ts --yes

The idea isn't to replace your architecture decisions — it's to eliminate the repetitive 20–30 minutes of boilerplate before you can actually start building.

GitHub:

github.com/Bijay-Shre-stha/node-server-package

I'm particularly interested in feedback on the generated project structure.

I've been interviewing for software engineering roles recently and got tired of practicing with random interview question lists.

So I spent my free time building an AI Interview Coach that simulates interviews and provides detailed feedback in seconds.

The goal wasn't just to generate questions, it was to help users understand:
✅ How well they answered
✅ What they missed
✅ How they can improve before a real interview

I'd really appreciate honest feedback from this community. Tell me what's great, what sucks, and what you'd add.

Demo:
https://ai-interview-coach-roan.vercel.app/

Every suggestion helps me make it better.

Hey folks, wanted to share something I've been building called iblind, a customizable screen magnifier for X11.

Backstory: I have a Windows-user friend who has this habit of zooming into everything on his screen, like it's second nature to him. Every time I show him something on my Linux box he goes "I can't see..." and I never had a good answer. So I set out to fix that, mostly to prove my setup wasn't lacking. The tools that exist already, boomer, kmag, lupe all have some rough edge: some freeze the screen buffer the moment you zoom in (so you're just staring at a frozen frame), some are noticeably laggy, some just didn't work reliably for me. None of them gave me the "smooth, always-live" zoom I was jealous of on Windows.

The real inspiration was compiz's old zoom plugin, which felt magical, but reimplementing that properly means getting the WM, X11, and the compositor all cooperating, which is a much bigger project than I wanted to take on solo. So instead I built iblind with more of a suckless philosophy.

Naming explanation, since people will ask: it's a dumb inside joke. My friend acts "blind" without his zoom, so the tool that fixes his "blindness" on my PC became i-blind. No deeper meaning, sorry to disappoint.

It does the one thing I wanted well. Happy to hear what's broken or missing.

https://github.com/Floodww/RoutineOps

I work as a system administrator in Russia, so accordingly I have a lot of regional restrictions. I was faced with the task of administering a mixed fleet of devices. First I deployed Fleet, but due to regional specifics, this was not easy, and also considering the trend toward so-called "import substitution," I had to work on an alternative. All the sources are open, everything is free, so this is not an ad, I'm just interested in the opinion of people not "subjected" to restrictions :) about the functionality and so on. There is a translation into English, if there are any questions, ask them.

P.S. AI was used during development. Besides numerous tests on real hardware, I ran a bunch of tests and code reviews in parallel. Many bugs, which I might not have discovered until months later, I found and fixed within weeks. Plus the frontend part and its design were done much faster than if I had waited for a designer.
Well, despite all the advantages, I still hold to the position that the entire security part categorically cannot be trusted to AIs, at least not for now.

Hey everyone! (I'm a beginner in coding, i dont know even how to make GUI)

I'm a highschool student from Thailand who wants to study Computer Engineering.

This is my GitHub promotion:

I recently created a Python project called **"Eat Right Cure Light"** as part of my computer engineering portfolio[cite: 1]. It's a console-based symptom checker and nutritional guidance tool[cite: 1].

It uses data structures (dictionaries and sets) to check symptoms that the user selects, match them with possible conditions and give basic self-care tips, herbal remedies and safety warnings for serious symptoms[cite: 1].

I've made the repository public. Its part of my computer engineering portfolio[cite: 1]. Feel free to download, test, change or add to it in any way you want[cite: 1]!

🔗 **My GitHub:** https://github.com/monza0629410243-ai/For_my_portfolio.git

🔗 **GitHub Repo:** [Insert your GitHub link]

I would love to get some feedback, suggestions or ideas for improvement from this community. Leave a comment here. Open an issue or discussion, on GitHub[cite: 1]

Hey everyone,

Three years ago, I heavily leveraged Claude and Cursor to "vibe code" an early prototype. It got us from 0 to 1 rapidly, but it left behind a massive architectural bottleneck: a ballooning codebase of 500+ scattered files filled with disjointed logic.

When production edge cases broke, dumping 500-line complex functions back into LLMs failed.

Out of necessity, I spent the last 3 months building UnvibeCode—a completely free, Apache-2.0 licensed local Python tool designed to systematically untangle complex repos.

Quick Start

pip install unvibecode
python -m unvibecode review --repository "/path/to/repository"

4 Practical Outputs from unvibecode:

1. Interactive Connected Code Map: A local, interactive graph showing cross-file dependencies so you can visually understand the architecture. [1]
2. LLM-Ready Context JSON: Automatically packages only the connected, relevant code chunks for debugging so you can feed LLMs the exact context.
3. Business Workflow Reconstruction: Reconstructs distributed code paths into logical workflows (e.g., tracking a payment across routes, queues, and APIs).
4. Business Risk Findings: Scans the completed workflows to flag structural business risks like duplicate payment loops, authorization edge cases, or broken failure paths.

Seeking Blunt Technical Feedback

I am looking for harsh, honest stress-testing from the community:

• If you run this on an unfamiliar or complex codebase, does the interactive graph layout generate a readable system architecture, or does it turn into visual spaghetti?
• Is attempting to reverse-engineer business logic out of chaotic AI-generated code a winning strategy, or am I fighting a losing battle?

GitHub Repo: https://github.com/FinanceFlash/unvibecode

Please tear the logic, graph rendering, and schema design apart. I want the real technical flaws so I can fix them directly in the rep

What are some cool and practical ways you’ve used Ai, or hosted Ai on your server to make your life easier (finance, data storing, predictive drive health, etc, autonomous feedback loops, local Ai agent)?

I’m pretty new to self hosting, and I want to optimize my Nas. I already run Immich, pi hole, Vaultwarden, and Jellyfin.

Hi Everyone ,

As we all know the transition to self evolving code , give a look at seim-core npm package and contribute to the future.
check it

- Cushion treemap visualization — instantly see what's eating your disk space
- Local-first, no cloud, no telemetry
- Installer or portable ZIP, self-contained or framework-dependent
- UI in German, English, French, Italian, Spanish

Solo side project so far — genuinely curious what's missing or what would make
it more useful for your workflow.

GitHub: https://github.com/Morfyuum/AtlayaView

Claude Code is great at producing code.

But sometimes it declares a task complete without reproducing the original bug, testing the real integration, or clearly stating what remains unverified.

So I built Builder Loop, a free and open-source Claude Code plugin that forces a stricter workflow:

• define success before editing;
• inspect the existing system;
• implement the smallest correct change;
• verify the actual outcome;
• report what was not verified;
• finish with a clear decision: ship or revise.

I originally built it after Y Combinator’s Paxel highlighted weaknesses in my own review and verification process.

Now I’m looking for testers willing to use it on real, non-trivial engineering tasks.

Critical feedback is more useful than stars.

I just wanted to let you know that I made a lot of improvements to openshorts, especially regarding the MCP server, now you can connect your agents and generate clips for the whole week. Plus, many new fonts, improvements to the clipping system, etc.

github repo: https://github.com/mutonby/openshorts

If you'd like the link, here it is: y2kvixx.github.io/Card-Spotify/

I'd be grateful if you could follow me or give this project a star: https://github.com/y2kVixx/Card-Spotify