Open Source

“As Boris Cherny put it: ‘I don’t prompt Claude anymore. I have loops running that prompt Claude.’ When I first read that, it hit me. And I’d been wasting months polishing one-shot prompts when the real unlock was building systems that prompt themselves. That’s Loop Engineering. And Cobus Greyling’s repo (4,156★ in 21 days — that’s 197★/day) is the first complete toolkit that makes it practical. And not another prompt library either. Also not a ‘how to write better prompts’ tutorial. And it’s a full stack — CLI tools, 7 production patterns, and an MCP server — designed to shift you from writing prompts to designing control systems that orchestrate AI coding agents over time. ...

Ever set up six MCP Servers and still had your Claude Code whiff on a cross-service query because the data lives in three different SDKs? Yeah, me too. I’ve configured MCP servers for GitHub, Slack, and Postgres separately, only to watch my agent drown in JSON-RPC plumbing instead of doing actual work. So when I stumbled across Mirage (3,258★ on GitHub, Apache-2.0, TypeScript) — a project that mounts S3, Gmail, Slack, GitHub, Postgres, and 50+ other services as a single virtual filesystem — my first thought was: finally, someone asked the right question. ...

Your AI coding agent can refactor your entire codebase in one shot. But ask it “what’s the latest Rust AI framework released this month” and you get a hallucinated answer or “I don’t have internet access.” If you’ve been on r/LocalLLaMA lately, you know this is the single biggest gap in agent workflows right now. Here’s the thing: browser-search by Johell1NS is a skill suite that plugs into Claude Code, Codex, OpenCode, or any major coding agent and gives it real web search and page-reading abilities. Self-hosted. Zero API fees. No data leaving your infrastructure. ...

You use Claude Code every day. But what happens when you need it running on your VPS while you’re on the train? Or when you want three agent sessions going at once—one refactoring, another writing tests, a third exploring a new codebase? I hit this wall last week. I had a long-running session on my workstation. Then I had to leave. And I couldn’t check in from my phone, couldn’t SSH in to see if it finished, couldn’t do anything except wait. So when I found lfg—a web UI control plane for AI coding agents—I spun up a $6 DigitalOcean Droplet and installed it immediately. Here’s what happened. ...

Ever wished your browser could be the AI agent — not just a page the agent reads through API calls? So that’s the question peerd answers. And it’s the first browser-native AI agent harness I’ve seen — a Chrome/Firefox extension that runs a full agent loop inside the browser you already have open. No server to provision. No desktop app to install. No Docker Compose, no apt-get, no cloud subscription beyond whatever API keys you already use. ...

Ever fired up a long coding session with DeepSeek’s API and watched the token counter race past $50 before lunch? Yeah, me too. DeepSeek v4 Flash is incredible — but when you’re running 50+ iterations of code review in a single session, those uncached tokens add up fast. This DeepSeek-Reasonix review covers its prefix-cache optimization, quick setup, and real-world cost savings for long coding sessions. That’s exactly the problem DeepSeek-Reasonix sets out to solve — and honestly? It works better than I expected. ...

Ever watched your code fly off to some vendor’s backend every time you ask an AI agent for help? Yeah, me too. But Claude Code sends prompt context to Anthropic. And Cursor ships your snippets through their cloud. So it works — but it makes me twitch every time. I don’t want my private codebase being cached on a server I don’t control. So when Godcoder hit GitHub on June 27 with 236 stars in its first 24 hours—“local-first, bring your own LLM key, pure Rust agent core”—I cloned it immediately. And here’s what I found after building it from source and running it through a full afternoon of testing. ...

Ever watch a GitHub project grow so fast you blink and miss half the updates? oh-my-pi went from 11k to 14.8k stars in 19 days. That’s not just hype — that’s a signal. And in those 19 days, the project shipped more changes than some tools see in a year. The short version: oh-my-pi is a terminal-native AI coding agent by developer can1357. It’s been my go-to alternative to Claude Code since the hash-anchored editing sold me on safety. But v16.2.1 isn’t just a point release — it layers on features that change how you use the tool. ...

Ever watched your AI agent nail a complex task, then completely forget how it did it the next time you asked? Yeah, me too. And that’s the dirty secret of today’s agent frameworks — every run starts from zero. No memory. No improvement. No compounding. So when I heard about Agent Apprenticeship (940★ in 7 days on GitHub), I had to try it. It’s the first open-source infrastructure that treats every task execution as a learning opportunity. Not a training dataset you curate. Not a fine-tuning pipeline you maintain. Just real work → real experience → shared learning signals that make every agent in the ecosystem smarter. ...

Ever asked your AI assistant to remember your preferences, only to have it completely blank on the next chat? Yeah, me too. That’s the dirty secret of most AI agents today — they’re incredibly smart for one conversation, then wake up with amnesia the next time you talk. So enter Mem0 (pronounced “mem-zero”), an open-source memory layer that’s been quietly racking up 25,000+ stars on GitHub and a Y Combinator S24 badge. And it’s not another vector database or a RAG pipeline — it’s a dedicated memory system that sits between your AI app and the LLM, handling the entire lifecycle — extraction, storage, retrieval, and temporal reasoning. ...