TL;DR: Overplane is an AI build system that pairs popular AI coding agents with containers, spec driven development, and automatic lightweight formal verification for a safer, replayable AI codegen workflow.
Like many others, I’ve watched AI code pile up at work, and get increasingly lighter reviews due to its size and maddening uniformity. On my side projects at night, I see the full speed and fragility of vibe coding. Neither practice seems sustainable, so I’ve been playing around for the last few months looking for a balanced, middle ground.
The key idea here is similar to chip design: if AI written software functionally does what you intended it to (by virtue of increasingly sophisticated testing against a codification of your intent), then it’s perhaps less important to manually review every line of generated code.
Overplane is a labor of love that brings that idea to life as an open-source experiment, combining some old personal loves: containerization, content addressed build systems, and formal verification.
The example I’d start with is rustdis, a partial, wire-compatible Redis clone in Rust with an empty [dependencies] section. Created with seven short specs and about $45 of Claude Opus, in about three hours: https://www.overplane.dev/examples
What sold me on the approach is that the one IR generated by Overplane from the specs paid for itself downstream: Z3 checks at build time, 48 generated proptest properties, and Kani proofs on the parser and arithmetic core.
It also holds up against redis-benchmark better than I expected, within about 90% of real Redis on my box unpipelined and a bit ahead on some pipelined workloads, which I mostly attribute to rustdis doing less than Redis does.
It’s v0.0.8 and rough. If you’ve tried spec-first workflows or lightweight formal methods in anger, I’d love to hear where they broke down for you.
TL;DR: Overplane is an AI build system that pairs popular AI coding agents with containers, spec driven development, and automatic lightweight formal verification for a safer, replayable AI codegen workflow.
Like many others, I’ve watched AI code pile up at work, and get increasingly lighter reviews due to its size and maddening uniformity. On my side projects at night, I see the full speed and fragility of vibe coding. Neither practice seems sustainable, so I’ve been playing around for the last few months looking for a balanced, middle ground.
The key idea here is similar to chip design: if AI written software functionally does what you intended it to (by virtue of increasingly sophisticated testing against a codification of your intent), then it’s perhaps less important to manually review every line of generated code.
Overplane is a labor of love that brings that idea to life as an open-source experiment, combining some old personal loves: containerization, content addressed build systems, and formal verification.
The example I’d start with is rustdis, a partial, wire-compatible Redis clone in Rust with an empty [dependencies] section. Created with seven short specs and about $45 of Claude Opus, in about three hours: https://www.overplane.dev/examples
What sold me on the approach is that the one IR generated by Overplane from the specs paid for itself downstream: Z3 checks at build time, 48 generated proptest properties, and Kani proofs on the parser and arithmetic core.
It also holds up against redis-benchmark better than I expected, within about 90% of real Redis on my box unpipelined and a bit ahead on some pipelined workloads, which I mostly attribute to rustdis doing less than Redis does.
It’s v0.0.8 and rough. If you’ve tried spec-first workflows or lightweight formal methods in anger, I’d love to hear where they broke down for you.