AI Agents

“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. ...

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. ...

You know that feeling when you’re deep in a Slack thread debugging a production issue, and you wish Claude could just jump into the conversation? Yeah, me too. And the usual loop is: copy the context, switch tabs, paste into chat, get an answer, switch back, paste the result. Every time. Claude Tag solved this at Anthropic — but it’s closed source, locked to Anthropic’s infra, and has zero audit trail. And I’ve been waiting for an open version since I first tried it. ...

Ever watched your coding agent burn through half its context window on pnpm test output? Yeah, me too. Claude Code runs a command, gets back 2,000 lines of test output, and suddenly it can’t remember the file structure it built three messages ago. That’s the problem tokenjuice solves. And it does it without touching your API calls or modifying your agent’s behavior — it just sits between your terminal commands and the output stream, compressing what’s unnecessary. So your agent keeps more brain space for the actual work. ...

Ever built a prototype agent that worked beautifully in a notebook, then hit a wall the moment you tried to turn it into something that runs 24/7 without you watching? Yeah, me too. And for months I’ve been cycling through agent frameworks — LangChain for flexibility, CrewAI for quick multi-agent demos — and each one left me wishing for something that could do both: be structured enough for production, but not so rigid that I’d waste days on boilerplate. ...

Ever sent your AI agent on a multi-session mission and come back to find it forgot what it was doing? Yeah, been there. But long-running agent work is great only until your assistant loses the plot somewhere around session three. So you explain the project context again, re-establish the goal, and hope it doesn’t drift off into some unrelated tangent halfway through. That’s exactly the problem COMPASS Skills aims to fix — and honestly, it’s the first tool I’ve seen that treats this as a first-class problem. And 204 stars in 2 days on GitHub says I’m not the only one who felt this pain. ...

Ever asked your AI coding agent to simplify something and watched it add three more dependencies instead? Yeah, me too. That’s exactly why ponytail exists — a YAGNI plugin that puts a “stop and think” ladder inside Claude Code, Codex, Cursor, and practically every other AI coding agent out there. 916 GitHub stars in its first 24 hours. Not bad for a tool whose entire philosophy is “write less code.” ...

You’re building an AI agent and you need it to check Gmail, post to Slack, create GitHub issues, and query Notion. Great. Now wire up OAuth for each one, write retry logic, handle token refresh, parse every API response schema. How’s that afternoon looking? Composio fixes this. It’s an open-source platform packing 1,000+ pre-built agent toolkits — Gmail, Slack, GitHub, Notion, Stripe, Jira, you name it — with managed authentication, context persistence, and a framework-agnostic SDK. 28,720 stars on GitHub, which tells you this isn’t a side project. ...