GitHub Discoveries: n8n-io, ollama, affaan-m, anomalyco, langchain-ai

🌟 New GitHub Discoveries — 10 fresh repos 1. n8n-io/n8n ⭐ 184,407 stars Fair-code workflow automation platform with native AI capabilities. Combine visu https://lnkd.in/dgNJVu3Q 2. ollama/ollama ⭐ 169,229 stars Get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemm https://lnkd.in/dKPdRGVt 3. affaan-m/everything-claude-code ⭐ 159,188 stars The agent harness performance optimization system. Skills, instincts, memory, se https://lnkd.in/dXqu9W34 4. anomalyco/opencode ⭐ 144,734 stars The open source coding agent. https://lnkd.in/d5AQXYHy 5. langchain-ai/langchain ⭐ 133,851 stars The agent engineering platform https://lnkd.in/dYV8M_hK #GitHub #OpenSource #Developer #Tech

🌟 New GitHub Discoveries — 10 fresh repos 1. n8n-io/n8n ⭐ 184,407 stars Fair-code workflow automation platform with native AI capabilities. Combine visu https://lnkd.in/dgNJVu3Q 2. ollama/ollama ⭐ 169,229 stars Get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemm https://lnkd.in/dKPdRGVt 3. affaan-m/everything-claude-code ⭐ 159,193 stars The agent harness performance optimization system. Skills, instincts, memory, se https://lnkd.in/dXqu9W34 4. anomalyco/opencode ⭐ 144,737 stars The open source coding agent. https://lnkd.in/d5AQXYHy 5. langchain-ai/langchain ⭐ 133,851 stars The agent engineering platform https://lnkd.in/dYV8M_hK #GitHub #OpenSource #Developer #Tech

🔥 I built an AI Agent that reads, understands, and debugs entire GitHub repositories. Not just file-by-file. The whole codebase , at once. After days of deep building, I'm launching GitHub Smart Agent, a production-ready AI tool that turns any GitHub repo into an interactive, intelligent assistant. What it actually does: 🔍 Ingests any GitHub repository in seconds 💬 Chats with your entire codebase like a senior dev 🧠 Detects bugs using both static analysis + AI reasoning ⚡ Suggests optimizations with context-aware explanations 📊 Generates structured analysis reports — ready to share Under the hood: → LangChain + FAISS Vector Store for RAG pipeline → OpenRouter (Gemini Flash / DeepSeek) as the reasoning engine → Custom embedding pipeline for code-aware retrieval → Streamlit with a fully custom premium UI Why I built this: Every developer has opened a legacy codebase and thought "where do I even start?" This tool answers that question in seconds. 🎬 Full demo video is live — watch it below. 🔗 GitHub repo dropping in the next day or two — I'll post when it's up. Drop a follow so you don't miss it! If you're working in AI engineering, LangChain, LLM tooling, or developer tools , I'd love to connect and exchange ideas. What's the biggest pain point you face when navigating an unfamiliar codebase? 👇 #AI #LangChain #RAG #LLM #DeveloperTools #Python #Streamlit #CodeAnalysis #OpenRouter #FAISS

🤖 The #1 trending repo on GitHub today is CrewAI — and honestly, it deserves it. If you haven't heard of it yet, here's why developers are buzzing: CrewAI is a lean, lightning-fast Python framework for building multi-agent AI systems – built completely from scratch and independent of LangChain or any other agent framework. What makes it stand out: 🚀 Two powerful primitives: → CrewAI Crews — autonomous, collaborative AI agents that work together → CrewAI Flows — enterprise-grade, event-driven orchestration with granular LLM control 🏢 Built for production — not just prototyping. It's the architecture teams are using to deploy real AI automation at scale. 📊 The numbers speak for themselves: → 49k GitHub stars → 6.7k forks → 6 million downloads/month → 100,000+ certified developers through learn.crewai.com And now there's the CrewAI AMP Suite — a full bundle for organisations that need secure, scalable, agent-driven automation, with the Crew Control Plane offering real-time tracing, observability, and unified monitoring. We're moving fast from "AI demos" to "AI infrastructure" – and CrewAI is one of the clearest signals of that shift. Have you tried CrewAI yet? Would love to hear what you're building 👇 #AI #MultiAgent #CrewAI #OpenSource #LLM #AIAgents #Python #BuildInPublic #MachineLearning

158K stars in 2 days? I had to look twice. So I dug in. Here's what actually happened 👇 On March 31 at 4 AM, Anthropic accidentally leaked Claude Code's entire source code. One missing file. 512,000 lines of proprietary AI — exposed. That same morning, a developer named Sigrid Jin stayed up and rebuilt the whole thing from scratch. No stolen code. Just architecture, memory, and one very long night. He called it Claw Code. And the dev world lost it. Why it blew up: → Developers had wanted a free alternative to Claude Code for a long time → This wasn't a hobby project — it was production-ready from day one → Works with any AI provider, not just Anthropic → Runs fully local, no subscription, no cloud lock-in → Open source means you own it completely What it actually does: You connect it to your AI provider of choice, give it an instruction, and it gets to work — writing code, editing files, running tasks — all from your own machine. How to set it up (5 minutes, no tech skills needed): 1. Visit https://lnkd.in/dvsPwZsp and download the ZIP 2. Unzip it and open the folder 3. Open the README — it walks you through everything in plain English 4. Paste in your API key when asked 5. Launch it and type your first instruction That's genuinely it. One person. One night. Something 100,000+ engineers were waiting for. Have you tried Claw Code? #AI #OpenSource #GitHub #DeveloperTools #AIAgents #ArtificialIntelligence #MachineLearning #SoftwareEngineering #Coding #Tech #Innovation #OpenSourceAI #AITools #Developers #FutureOfWork

On March 31, 2026, one missing line in a .npmignore file changed the AI industry's open-source conversation forever. Anthropic accidentally shipped 512,000 lines of Claude Code's full TypeScript source — 1,906 files — inside a routine npm update. Within 2 hours, the repo had 50,000 GitHub stars. Within a day, 84,000 stars and 82,000 forks. A Python clean-room rewrite appeared. Decentralized mirrors went live with the message: "Will never be taken down." Then came 8,000+ DMCA takedowns — many hitting legitimate public forks that contained zero leaked code. I want to be clear: this was genuinely a human error, not malice, and I have deep respect for what the Anthropic team has built. But the reaction to the leak is the real story. In under 48 hours, developers worldwide were: → Reverse-engineering the agent orchestration → Rewriting pieces in cleaner architectures → Identifying security gaps before bad actors could exploit them That's not piracy. That's the open-source immune system working. Here's my honest question for the AI industry: If the community's first instinct — when handed your source code — is to make it better, safer, and more transparent... what does that tell you about the value of keeping it closed? I've been shipping open components since CodeProject days in the early 2000s — custom Win32 controls, media stacks, crypto tools, and now AI apps. The best code I ever wrote got better because strangers on the internet told me what I got wrong. My next project is building the world's first open-source AI stack for pet health monitoring — models, hardware designs, APIs, all of it in public. No accidental open-source moments. No DMCA panics. Just deliberate transparency from day one. Because trust in AI doesn't come from secrecy. It comes from showing your work. What do you think — should foundational AI coding tools be open-source by default? #OpenSource #AI #ArtificialIntelligence #AITransparency #ClaudeCode #Anthropic #OpenSourceAI #AIRobotics #BuildInPublic #PetTech #AIEthics #SoftwareEngineering #TechLeadership

Claude Code just got the one thing it was missing: memory Every AI coding session has the same tax. You explain the codebase again. You re tell the decisions. You paste the same context until the window breaks. Claude Mem flips that. It runs as a Claude Code plugin that quietly records what happened in your session, summarizes it, and brings back only the relevant pieces next time. The repo says it captures tool use and injects context across sessions, with a layered search flow designed to cut token waste. The result is what people wanted all along: you can stop resetting the assistant every morning. The speed of adoption is the signal. It surged past 46k GitHub stars in about 48 hours and is now around 49.5k stars on GitHub. One command install. Local storage. Pick up where you left off. If you use Claude Code daily, this is worth testing just to feel what continuity does to your workflow. 👉 https://lnkd.in/g76xUFqD #ClaudeCode #AIcoding #DeveloperTools #OpenSource #Productivity #LLM #AgenticAI

So this weekend I tried recreating CodeRabbit from scratch. I just wanted to understand how it works under the hood. But then one thing led to another and now I have CodeFox , a fully working AI code review platform. You connect your GitHub repo, whenever you open a pull request, a webhook fires. Code Fox grabs the diff, pulls relevant context from the vector DB (pinecode), sends everything to llm, and posts a detailed review as a PR comment. Same thing for issues. it reads the issue, understands what part of the codebase it relates to, and gives you a proper analysis. The wildest part? It can read an issue and auto-generate a full pull request to fix it. All of this running on Next.js, Inngest, Pinecone, and Gemini. If there's a tool you use every day and you're curious how it works just build it. You'll surprise yourself. #BuildInPublic #AI #CodeReview #SoftwareEngineering #NextJS #GitHub

Excited to share that my first meaningful open-source pull request has been merged 🎉 I contributed to waifu-code, an open-source CLI project that routes Claude Code requests through alternative AI providers. My contribution: -> Added support for OpenRouter, Groq, and Ollama -> Fixed tool-call formatting issues where local models returned raw JSON instead of the structured format Claude Code expects -> Documented real-world testing to identify which models actually work reliably in practice Some interesting challenges I ran into while testing: • Groq’s free tier token limit (~6k) is far below Claude Code’s large system prompt size (~50k) • Smaller local models often hallucinate invalid tool names • Larger hosted models (like 120B via OpenRouter) handled workflows much more reliably This was a great learning experience in debugging real-world LLM integration issues beyond just API wiring. Huge thanks to the maintainers for building the project and reviewing contributions. https://lnkd.in/dnsgJsP3 Project repo: https://lnkd.in/dvciVt44 First solid contribution down - more to come 🚀 #opensource #github #ai #buildinpublic

We did the math: Our coding agents were working maybe 15 minutes an hour. The other 45 minutes? Waiting on us. That’s when it clicked: the real bottleneck wasn’t the agents. It was the human coordination layer around them. So we built WaveCode. ▸ Claude Code, Codex, and Aider running in parallel ▸ One chat interface across all agents ▸ Agents review each other before code reaches you ▸ Self-hosted, open source, Apache 2.0 The result: 5 agents, 23 minutes, one full feature shipped — backend, API, tests, docs, and a bug caught before the human saw it. The video below shows it running live. GitHub: github.com/dbenic/wavecode More: wavenetic.com/wavecode #OpenSource #AI #DevTools #CodingAgents