It does, but I have no mental model of what would be required to efficiently coordinate a bunch of independently operating agents, so it's hard to make a judgement.

Also about half of it seems to be tests. It even has performance benchmarks, which are always an distant afterthought for anything other than infrastructure code in the hottest of loops! https://github.com/steveyegge/beads/blob/main/BENCHMARKS.md

This is one of the defining characteristics of vibe-coded projects: Extensive tests. That's what keeps the LLMs honest.

I had commented previously (https://news.ycombinator.com/item?id=45729826) that the logical conclusion of AI coding will look very weird to us and I guess this is one glimpse of it.

>Meta measured a 6-12% uplift in productivity from adopting agentic coding. Thats paltry.

That feels like the right ballpark. I would have estimated 10-20%. But I'd say that's not paltry at all. If it's a 10% boost, it's worth paying for. Not transformative, but worthwhile.

I compare it to moving from a single monitor to a multi-monitor setup, or getting a dev their preferred IDE.

I mention a few here: https://news.ycombinator.com/item?id=45379452

> ... just looking at LOC or PRs, which of course is nonsense.

That's basically a variation of "How can they prove anything when we don't even know how to measure developer productivity?" ;-)

And the answer is the same: robust statistical methods! For instance, amongst other things they compare the same developers over time doing regular day-job tasks with the same quality control processes (review etc.) in place, before and after being allowed to use AI. It's like an A/B test. Spreading across a large N and time duration accounts for a lot of the day-to-day variation.

Note that they do not claim to measure individual or team productivity, but they do find a large, statistically significant difference in the data. Worth reading the methodologies to assuage any doubts.

> A Stanford case study found that after accounting for buggy code that needed to be re-worked there may be no productivity uplift.

I'm not sure if we're talking about the same Stanford study, the one in the link above (100K engineers across 600+ companies) does account for "code churn" (ostensibly fixing AI bugs) and still find an overall productivity boost in the 5 - 30% range. This depends a LOT on the use-case (e.g. complex tasks on legacy COBOL codebases actually see negative impact.)

In any case, most of these studies seem to agree on a 15 - 30% boost.

Note these are mostly from the ~2024 timeframe using the models from then without today's agentic coding harness. I would bet the number is much higher these days. More recent reports from sources like DX find upto a 60% increase in throughput, though I haven't looked closely at this and have some doubts.

> Meta measured a 6-12% uplift in productivity from adopting agentic coding. Thats paltry.

Even assuming a lower-end of 6% lift, at Meta SWE salaries that is a LOT of savings.

However, I haven't come across anything from Meta yet, could you link a source?

If the software has tens of thousands of users without expecting to get any at all, does the code even matter?