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LunarFS
Dropbox for AI agents and devs, forks in milliseconds
Running multiple AI agents on the same repo means cloning it per agent, waiting 30-140s for pnpm install on Mac, and hoping they don't stomp each other. Git's branch-lock means two agents can't share a branch at all. LunarFS fixes this at the filesystem level. Fork a new agent workspace in under 5ms. Files stream in on demand — touch 200 of 50,000, pay for 200. Each agent writes to its own private overlay. Agents push normal Git commits when done. Open-source. Works on Linux, Mac, and Windows.
🌙 Hey Product Hunt — Ethan here, builder of LunarFS!
The origin story is pretty simple: I was watching Theo's video where he ran through a list of things he wished existed but didn't have time to build — and "Dropbox for devs" was one of them. That hit home immediately, because I was hitting the exact same wall.
The problem: I work in parallel across a lot of environments — spinning up new workspaces, switching contexts, running multiple AI coding agents at once. Every time, I'd either wait forever for a full clone/copy, or lose track of files between environments. It was death by a thousand cuts.
So I built LunarFS:
⚡ Fork a full workspace in milliseconds, not seconds — lunar ws fork forked the entire Linux kernel (94k+ files) in 13ms vs. 7.4s for git worktree add, with zero bytes copied
🧬 Content-addressed (BLAKE3) — files are stored once and hydrate lazily on first read, so spinning up a new workspace is basically instant
🔄 Cross-machine sync — push/pull workspaces between machines without a full re-clone
🤖 Built with AI agent workflows in mind — perfect for running multiple coding agents in parallel without juggling clones
Why open source: This solves a problem I know other devs and agent-builders have, not just me. Engine is AGPL-3.0, clients are Apache-2.0 — so you can build on top of it freely. Self-hosting is free forever.
If you'd rather not run your own store, we're also building a hosted version — you can join the waitlist at lunarfs.com 🚀
Huge thanks to Theo for the spark on this one, and to everyone who tested early builds with me 🙏
Would love your feedback, bug reports, and feature requests — and if you've been fighting the same parallel-workspace pain, I'd love to hear how you're solving it today!
PS — if anyone knows Theo, slide him this link lol. Been a fan of his for years, would mean a lot if this ever crossed his desk 😄
About LunarFS on Product Hunt
“Dropbox for AI agents and devs, forks in milliseconds”
LunarFS was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #7 on the daily leaderboard. Running multiple AI agents on the same repo means cloning it per agent, waiting 30-140s for pnpm install on Mac, and hoping they don't stomp each other. Git's branch-lock means two agents can't share a branch at all. LunarFS fixes this at the filesystem level. Fork a new agent workspace in under 5ms. Files stream in on demand — touch 200 of 50,000, pay for 200. Each agent writes to its own private overlay. Agents push normal Git commits when done. Open-source. Works on Linux, Mac, and Windows.
On the analytics side, LunarFS competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how LunarFS performed against the three products that launched closest to it on the same day.
Who hunted LunarFS?
LunarFS was hunted by Ethan Ashi. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of LunarFS including community comment highlights and product details, visit the product overview.
Ethan Ashi