Open Source

Ever watched your AI agent burn through tokens on 65,000 characters of JSON tool output — just to answer “yes, the service is down”? Yeah, me too. I’ve been running Claude Code and Codex daily, and my token bills were starting to look suspiciously like a second mortgage. (Code-review-graph cut my review tokens 82x, but that only covers the review step — I needed something for every agent interaction.) So when Headroom crossed my radar at 59.7k GitHub stars and #2 on Trending, I had to see what the fuss was about. ...

14 files instead of 230. That’s what code-review-graph did to my Claude Code review context. But I’d been burning through my Pro quota on a 500-file side project, watching my agent re-read the same files every single time I asked for a code review. So when I saw code-review-graph sitting at #9 on GitHub Trending — 19,700+ stars, 712 commits, last commit 46 minutes ago — I had to try it. The pitch: a local-first code intelligence graph that tells your AI assistant what’s actually relevant. Nothing more, nothing less. ...

Ever spent an afternoon debugging a Playwright script because some website changed a single CSS class name? Yeah, me too. But browser automation has been a pain for years — you chain selectors, wait for elements, and pray nothing moves. browser-use flips that entirely. So here’s the idea instead of a fragile selector chain: give an LLM a screenshot of the page and a goal, and let it figure out the clicks and keystrokes. The result? 105,000 GitHub stars and the fastest-growing open-source browser agent framework I’ve seen this year. ...

Ever had your pager go off at 3 AM for what turned out to be a single noisy pod restarting in a loop? Yeah, me too. Then you spend the next hour digging through Datadog dashboards, filtering logs, and manually stitching together what happened — while your phone keeps buzzing with alerts that are all the same incident. I’ve been running AI agent stacks on my own infra for a while — containerized agents with sandboxd, orchestrating them with PilotDeck. So when I saw Superlog hit 1,029 stars in 44 days and land in Y Combinator’s P26 batch, I paid attention. An open-source observability tool that uses AI agents to automatically aggregate noise into incidents and investigate root causes? And I had to try it. ...

Ever had a Claude Code session where you spawn three subagents, paste the same 9,000-token plan into each prompt, and cross your fingers? Look, me too. So last week I ran a build script across three parallel agents. But each one re-read the full plan on every turn. Even my token bill for that single session? Just shy of $3. And worse — when I found a bug in the plan after spawning them, I had to paste the correction into three separate conversations. And one of them never got the update and ran the wrong analysis for 20 minutes before I noticed. ...

Ever watched your AI agent turn “add a date picker” into 404 lines of code, a flatpickr npm dependency, and a wrapper component with its own CSS? Yeah. Me too. So when I saw a GitHub repo called Ponytail hit 83,449 stars in its first month—with a tagline that reads “He says nothing. He writes one line. It works."—I had to install it immediately. TL;DR: Ponytail is a plugin for Claude Code, Codex, and 20+ agent CLIs that installs a 7-rung “ladder of restraint” before every code generation step. But the result? 54% less code, 22% fewer tokens, 20% lower cost, 27% faster sessions—and 100% safety on adversarial inputs. Yet a bare “write one-liners” prompt only manages 95% safety. ...

Disclosure: Some links below are affiliate links. If you sign up through them, I may earn a commission at no extra cost to you. Vultr — starts at $6/mo DigitalOcean — $200 credit for new users Here’s a question I haven’t seen anyone ask about AI coding agents: what happens when the model is wrong — not just bad code, but malicious? Every agentic tool today — Cursor, Claude Code, Codex, OpenCode — works the same way: the model proposes an action, and the tool trusts it. Maybe it asks for confirmation before running shell commands. But the model picks which commands to run. The model chooses which files to read. The model decides which npm packages to install. The model holds the keys. ...

Ever watched an AI agent propose running sudo rm -rf /? You thought, “Yeah, that’s a no from me”? Dark joke. But it hits a problem every agentic coding tool has. I’ve used Cursor, Claude Code, Codex, OpenCode — the whole lineup. They’re all great at writing code. The thing is, they share one bad habit: trust the model by default. The model says it needs access to my terminal? Fine. It wants to install a package from a random npm registry? Sure. It needs my GitHub token? Go ahead. Then that trust burns you. ...

Here’s a problem I run into constantly: my AI coding assistant has zero memory of what I worked on last week. But every session starts fresh. And I keep dumping the same files into context, re-explain the same architecture, and burn tokens asking questions the agent answered yesterday. Graphify solves that in a way I hadn’t seen before. It’s a skill for Claude Code (and many other AI coding tools) that reads a directory — code, docs, PDFs, even screenshots — builds a persistent knowledge graph from it, and lets you query that graph weeks later without re-reading the raw files. It’s sitting at around 2.3k stars on GitHub right now. 71.5x fewer tokens per query on a mixed corpus of code + papers. That’s not a marketing number — that’s their benchmark against Karpathy’s repos with actual output files to verify. ...

Ever wanted your AI agent to edit a video for you? So I just made it happen. So I dropped six raw clips into a folder, told Claude Code to “cut these to the beat markers, add a teal-orange grade, and put word-pop captions” — and watched the timeline rebuild itself live in my browser. No Premiere. No cloud service. Just a node server.js and an MCP connection. So what’s the project? It’s called FableCut — a zero-dependency browser video editor that exposes its entire timeline as JSON. Also, any MCP-capable agent can manipulate it. It’s MIT-licensed, sits at 268 stars, and was only released four days ago. But honestly? The concept is bigger than the star count. ...