AgentFork
Agent fork, everything runs. Instant dev env for any repo
What it does
Every new contributor hits the same wall — Docker, env vars, database setup. Most give up before writing code. AgentFork fixes this. Connect your repo, our AI detects the full stack, and every fork gets a cloud environment with databases and a live preview URL. Zero config — the AI figures it out. No Docker, no terminal, no local setup. Even someone who's never touched a database can fork your project and start contributing in seconds. Your repo, instantly runnable and contributable by ANYONE.
Does a similar job
all alternatives →
Supafork – Share and Fork Sessions Across Harnesses5d ago · supafork.com · ▲15Supafork is the AI Agent Native Version Control system, Git built for autonomous agents. Super-fast forks, instant branches, S3-backed agent filesystems, and full env control. Faster than Mesa, more agent-native than Archil. Track every change your agents make. Now the source of truth for your agent
- RARun AI Agents on your cloud infrastructure2025 · okteto.com · ▲12
Hi, founder of Okteto here! We’ve been experimenting with AI agents in our workflows at Okteto. Running them locally worked at first, but quickly became painful. git worktrees, multiple terminals, and messy context switches slowed us down. So we built Agent Fleets: ephemeral, fully managed environments for AI agents, built on top of Okteto’s development platform. Each agent runs in its own containerized environment on your infrastructure, with the services, tools, and policies it needs. You can spin up agents with a single click or API call. No local setup. No git worktrees. The beta…
- KBKeystone – building self-configuring agentsMar 2026 · imbue.com · ▲5
When cloning a repo, we kept hitting the same wall: no Dockerfile, no dev container, no clear path to running it. Asking an agent to fix it directly on your system is risky, as it can clear Docker config, change kernel settings, downgrade packages without warning. Keystone runs the agent inside a Modal sandbox with its own Docker daemon. It iterates until tests pass, then hands you a .devcontainer/ you can just check in. Now your repo knows how to run itself!
- CACoding Agents swarming your codebaseSep 2025 · infrastructureas.ai · ▲9
I built this because I was tired of creating pull requests in 20 repositories just to change a single line of workflow job version. With Infra as AI, just mention the change. Agents work on all repos in parallel, read the docs, make a bunch of PRs and fill in the description. You can see the demo of the actual dashboard in the landing. Let me know your thoughts :) It means a lot to me!
- RLRun LLMs in Docker for any language without prebuilding containersJan 2026 · github.com · ▲26
I've been looking for a way to run LLMs safely without needing to approve every command. There are plenty of projects out there that run the agent in docker, but they don't always contain the dependencies that I need. Then it struck me. I already define project dependencies with mise. What if we could build a container on the fly for any project by reading the mise config? I've been using agent-en-place for a couple of weeks now, and it's working great! I'd love to hear what y'all think
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 18d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 19d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, March 2026
the whole month →
Switch from ChatGPT to Claude with import memory feature
AI · Mar 2026 · claude.com
