Ask HN: Do you have any evidence that agentic coding works?

Yes. Over the last month, I've made heavy use of agentic coding (a bit of Junie and Amp, but mostly Antigravity) to ship https://www.ratatui-ruby.dev from scratch. Not just the website... the entire thing.

The main library (rubygem) has 3,662 code lines and 9,199 comment lines of production Ruby and 4,933 code lines and 710 comment lines of Rust. There are a further 6,986 code lines and 2,304 comment lines of example applications code using the library as documentation, and 4,031 lines of markdown documentation. Plus, 11,902 code lines and 2,164 comment lines of automated tests. Oh, and 4,250 lines in bin/ and tasks/ but those are lower-quality "internal" automation scripts and apps.

The library is good enough that Sidekiq is using it to build their TUI. https://github.com/sidekiq/sidekiq/issues/6898

But that's not all I've built over this timeframe. I'm also a significant chunk of the way through an MVU framework, https://rooibos.run, built on top of it. That codebase is 1,163 code lines and 1,420 comment lines of production Ruby, 4,749 code lines and 521 comment lines of automated tests. I need to add to the 821 code lines 221 comment lines of example application code using the framework as documentation, and to the 2,326 lines of markdown documentation.

It's been going so well that the plan is to build out an ecosystem: the core library, an OOP and an FP library, and a set of UI widgets. There are 6,192 lines of markdown in the Wik about it: mailing list archives, AI chat archives, current design & architecture, etc.

For context, I am a long-time hobbyist Rubyist but I cannot write Rust. I have very little idea of the quality of the Rust code beyond what static analyzers and my test suite can tell me.

It's all been done very much in public. You can see every commit going back to December 22 in the git repos linked from the "Sources" tab here: https://sr.ht/~kerrick/ratatui_ruby/ If you look at the timestamps you'll even notice the wild difference between my Christmas vacation days, and when I went back to work and progress slowed. You can also see when I slowed down to work on distractions like https://git.sr.ht/~kerrick/ramforge/tree and https://git.sr.ht/~kerrick/semantic_syntax/tree.

If it keeps going as well as it has, I may be able to rival Charm's BubbleTea and Bubbles by summertime. I'm doing this to give Rubyists the opportunity to participate in the TUI renaissance... but my ultimate goal is to give folks who want to make a TUI a reason to learn Ruby instead of Go or Rust.

You are welcome to use whatever definition of "small/medium/large" you like. Like you, 1-2 weeks is also far from the largest project I've worked on. I don't think that's particularly relevant to the point of my post.

The point that I'm trying to emphasize is that I've had success with it on projects of some scale, where you are implementing (e.g.) multiple related PRs in different services. I'm not just using it on very tightly scoped tasks like "implement this function".

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I've found it useful for getting features started and fixing bugs, but it depends on the feature. I use Claude Sonnet 4.5 and it usually does a pretty good job on well-known problems like setting up web sockets and drag and drop UIs which would take me much longer to do by hand. It also seems to follow examples well of existing patterns in my codebase like router/service/repository implementations. I've struggled to get it to work well for messy complicated problems like parsing text into structured objects that have thousands of edge cases and in which the complexity gets out of hand very quickly if not careful. In these cases I write almost all the code by hand. I also use it for writing ad-hoc scripts I need to run once and are not safety critical, in which case I use it's code as-is after a cursory review that it is correct. Sometimes I build features I would otherwise be too intimidated to try if doing by hand. I also use it to write tests, but I usually don't like it's style and tend to simplify them a lot. I'm sure my usage will change over time as I refine what works and what doesn't for me.

> would call out the AI hype bubble

Which is what it is by describing it as a tool needing thousands of dollars and years of time in learning fees while being described as "replaces devs" in an instant. It is a tool and when used sparingly by well trained people, works. To the extend that any large statistical text predictor would.