The fastest growing GitHub repos this month: 1. affaan-m/everything-claude-code ( 65.1K stars) Skills, memory, security for Claude Code, Codex, Cursor 2. obra/superpowers ( 61.3K stars) Agentic skills framework. Plug-and-play tools for AI agents. 3. 666ghj/MiroFish ( 41.9K stars) Swarm intelligence engine that predicts anything 4. ruvnet/RuView ( 37.1K stars) WiFi signals → real-time human pose detection. No cameras. 5. bytedance/deer-flow ( 32.5K stars) ByteDance's open-source SuperAgent. Researches, codes, creates. 6. koala73/worldmonitor ( 29.1K stars) Real-time global intelligence dashboard 7. shareAI-lab/learn-claude-code ( 24.9K stars) Build a Claude Code clone from scratch. Bash is all you need. 8. shanraisshan/claude-code-best-practice ( 19.9K stars) The best practices repo for building with Claude Code 9. moeru-ai/airi ( 19.0K stars) Self-hosted AI companion with real-time voice chat 10. NousResearch/hermes-agent ( 17.0K stars) The agent that grows with you The theme this month: agent harnesses took over GitHub. Bookmark this. April's list will look completely different.

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

mlx-vlm v0.4.3 is here 🚀 Day-0 support: 🔥 Gemma 4 (vision, audio, MoE) by @GoogleDeepMind 🦅 Falcon-OCR Falcon Perception by @TIIuae 🪨 Granite Vision 4.0 by @IBMResearch New models: 🎯 SAM 3.1 with Object Multiplex by @facebook 🔍 RF-DETR detection & segmentation by @roboflow Infra: ⚡ TurboQuant (KV cache compression) 🖥️ CUDA support for vision models (Sam and RF-DETR) Get started today: > uv pip install -U mlx-vlm Leave us a star ⭐️ github.com/Blaizzy/mlx-vlm

Delhi Air Pollution: Stop blaming farmers and cars! Our Policy Experts have failed miserably on solving our Air Pollution problem. Our solutions are not working because we don't understand the problem well. Chinese cities used to be the most polluting cities in the world a decade ago. Beijing and Shanghai were the most polluting cities in the world. Now most of the major Chinese cities are not even in the top 200 polluting cities in the world. How did they solve it? Our policy is focused only on reducing vehicular emissions, and we have spent lakhs of crores on it without any results? If vehicular emissions were the problem, US would have had the most polluting cities of the world. California state alone consumes nearly same amount of Petroleum as India does. And why is San Francisco or Los Angeles not in the top polluting cities of the world? Watch this video to understand the root cause of Indian pollution. A correct diagnosis is three-fourths the remedy as Gandhi said. It only needs a thousand crore to solve Delhi's pollution problem. Amplify the message if you agree with our diagnosis and remedy.