It just an anchor. If it were 50k would you say the same down to 25k? And if so how many more times would it apply?

The interesting thing is that it was manageable solo (in many ways it's _more_ manageable solo+AIs than with coworkers+(their)AIs), and in such a short amount of time.

> It's almost guaranteed with agents you could do the same job with less than half of 100k lines.

That's great, non-test code is only ~47k lines of code.

For a startup with limited funding, building a product is no more a bottleneck. every one doesn't have the same access to funding!

I may have missed it but are those tests written by person or generated? Otherwise how do you know they even test anything (like actually test, not appear to test)

IIUC only 50k LoC are non-test code, which improves the metric. Whether that's enough tests still depends on the code. If most are getters and setters, the coverage might be ok.

It is described in the "Code Contracts" section of the article: "Code contracts specify preconditions, postconditions, and invariants for critical functions. These contracts are converted into runtime asserts during testing but can be disabled in production builds for performance". The .NET framework article that he links to: https://learn.microsoft.com/en-us/dotnet/framework/debug-tra...

The feedback loop is the interesting part, if you use standard software engineering practices (modularise, test/document your interfaces, etc) then I find things like Claude Code do an exceptional job: since they can actually run cargo check/test themselves and can validate the tests too.

A lefthook:

format: glob: ".rs" run: cargo fmt -- --check

lint: glob: "

.rs" run: cargo clippy -- -D warnings

tests: run: cargo test

audit: run: cargo audit

+ hooks that shove the lefthook automatically in the ai's face

---

rustfmt.toml:

edition = "2021" newline_style = "Unix" use_small_heuristics = "Max" max_width = 100

The irony of the machines having no mechanical sympathy is just too good

What kinds of programs are you writing and with what models? I'm curious if the lifetimes your programs require are trickier than most.

> I'm also shifting to an vibe coding workflow, but I have a genuine question: whenever I use AI for Rust, it makes an insane amount of lifetime errors. I have no idea how people are churning out so many lines of code so quickly.

What harness and model you've been using? For the last few months, essentially since I did the whole "One Human + One Agent = One Browser From Scratch" experiment, I've almost exclusively been doing cross-platform native desktop development with Rust, currently with my own homegrown toolkit basically written from scratch, all with LLMs, mostly with codex.

But I can't remember a single time the agent got stuck on lifetime errors, that's probably the least common issue in regards with agents + Rust I come across. Much bigger issue is the ever-expanding design and LLMs being unable to build proper abstractions that are actually used practically and reduces the amount of code instead of just adding to the hairball.

The issue I'm trying to overcome now is that each change takes longer and longer to make, unless you're really hardcore about pulling back the design/architecture when the LLM goes overboard. I've only succeeded in having ~10 minute edits in +100K LOC codebases in two of the projects I've done so far, probably because I spent most of the time actually defining and thinking of the design myself instead of outsourcing it to the LLM. But this is the biggest issue I'm hitting over and over with agents right now.

Yes, but it comes with much better “built-in” guardrails to rein in the autocomplete. Especially if compared to something runtime-surprise-prone-if-lovable like Ruby.