I have a materials engineering degree but knew halfway through that I liked computers more. Software ever since, mostly self-taught — bhatti was partly a project to teach myself low-level Linux properly.
I started by trying every snapshottable-sandbox product on the market for running my own coding agents. sprites.dev came closest but was unstable enough that I'd wake up to broken sessions. Halfway into building bhatti, I realised the market is ten companies racing to be the managed-Firecracker layer, and a Hetzner box is $100/mo — if you're willing to run the orchestrator yourself, you don't need any of them. I now run bhatti for my own agents (some need a browser, some need a full desktop, most just need plain Linux) and the rest of my engineering workflow on top.
If you try it and something breaks, please open an issue. The early adopters who did that have shaped bhatti more than any single design call I made.
On the AI question: a lot of bhatti's code came out of an LLM. The discipline I held onto was the boring kind — long planning docs before each chunk, integration tests against a real Pi cluster at home (no mocks for the VM path), atomic ship/test cycles. Every plan and every experiment is in docs/archive on the repo, both as a credibility receipt and so anyone who wants to fork can see how it was actually planned.
A bit more context on why I built this:
I have a materials engineering degree but knew halfway through that I liked computers more. Software ever since, mostly self-taught — bhatti was partly a project to teach myself low-level Linux properly.
I started by trying every snapshottable-sandbox product on the market for running my own coding agents. sprites.dev came closest but was unstable enough that I'd wake up to broken sessions. Halfway into building bhatti, I realised the market is ten companies racing to be the managed-Firecracker layer, and a Hetzner box is $100/mo — if you're willing to run the orchestrator yourself, you don't need any of them. I now run bhatti for my own agents (some need a browser, some need a full desktop, most just need plain Linux) and the rest of my engineering workflow on top.
If you try it and something breaks, please open an issue. The early adopters who did that have shaped bhatti more than any single design call I made.
On the AI question: a lot of bhatti's code came out of an LLM. The discipline I held onto was the boring kind — long planning docs before each chunk, integration tests against a real Pi cluster at home (no mocks for the VM path), atomic ship/test cycles. Every plan and every experiment is in docs/archive on the repo, both as a credibility receipt and so anyone who wants to fork can see how it was actually planned.
Excited to see this launch, have been using bhatti for some time now!