LLMs can't justify their answers–this CLI forces them to
wheat.grainulation.comhow is everyone reviewing ai-generated tools/ai-generated websites like these? I cannot keep up with all of these
I just ignore it all, its just a bunch of useless cruft that nobody needs that people are throwing out there to seem AI-relevant.
If you need to know "Should we migrate to GraphQL?" (the example on the site) and your brain is already AI-mushed to the point where you can't deduce this yourself, just ask the model directly, it doesn't need 9 layers of slop-built bullshit stacked on top to answer this question.
I broadly agree with you but if you use claude code you should give this a try, the website doesn't really do it justice but wheat really solves for a lot of pain points when using claude for longer sessions
Another one on the front page today: https://news.ycombinator.com/item?id=47660262
You don't have to review them just ask your llm to make the same thing if you really want it;)
I literally just tell claude, here is the deekwiki please use this tool for the next task lol
I only stay on this particular variant of one of the 5 site designs every LLM spits out for long enough to check that - yes - it has the annoying thing where content fades in as you scroll, before closing the tab.
They got that from humans who made websites.
Ergo, the existence of the effect is not an effective filter for determining use of an LLM in the creation of the website.
if you like wheat, check out farmer & orchard (both are really cool)
Evals or GTFO
5x productivity boost in merged PRs (lots of open PR & merge rate goes down, but net positive)
Starting to build custom tooling around new "friction" points in dev cycle
(eng IC perspective)
Evals let us agree on the baseline, measurement, etc, and compare if simple things others do perform just as well. For same reason, instead of 'works on my box' and 'my coding style', use one of the many community evals vs making up your own benchmark.
That helps head off much of many of the unfalsifiable discussions & claims happening and moves everyone forward.
a rust version of that compiler (that the project runs on) ran at 480k claims/sec and it was able to deterministically resolve 83% of conflicts across 1 million concurrent agents (also 393,275x compression reduction @ 1m agents on input vs output, but different topics can make the compression vary)
natively claude (and other LLM) will resolve conflicting claims at about 51% rate (based on internal research)
the built in byzantine fault tolerance (again, in the compiler) is also pretty remarkable, it can correctly find the right answer even if 93% of the agents/data are malicious (with only 7% of agents/data telling us the correct information)
basically the idea here is if you want to build autonomous at scale, you need to be able to resolve disagreement at scale and this project does a pretty nice job at doing that
My question was on claims like "5x productivity boost in merged PRs (lots of open PR & merge rate goes down, but net positive)", eg, does this change anything on swe-bench or any other standard coding eval?
The ecosystem is 8 tools plus a claude code plugin, the unlock was composing those tools (I don't regularly use all 9). The 5x claim was from /insights (claude code)
Not for everyone, but it radically changed how I build. Senior engineer, 10+ years
Now it's trivial to run multiple projects in parallel across claude sessions (this was not really manageable before using wheat)
Genuinely don't remember the last time I opened a file locally
It sounds like the answer is "No, there is no repeatable eval of the core AI coding productivity claim, definitely not on one of the many AI coding benchmarks in the community used for understanding & comparison, and there will not be"
My data is from Anthropic
Not sure how it works under the hood, probably a better question for them
Perhaps you are misunderstanding the entire premise of this project, this is not an LLM
Maybe there's a fundamental miscommunication here of what evals are?
Evals apply not just to LLMs but to skills, prompts, tools, and most things changing the behavior of compound AI systems, and especially like the productivity claims being put forth in this thread.
The features in the post relate directly to heavily researched areas of agents that are regularly benchmarked and evaluated. They're not obscure, eg, another recent HN frontpage item benchmarked on research and planning.
your question makes sense, it's just not in current scope
we are still benchmarking the compiler at scale and the LLM tools that were made were created as functional prototypes to showcase a single example of the compiler's use case
since much of the unlock here is finding different applications for the compiler itself, we simply don't have the bandwidth to do much benchmarking on these projects on top of maintaining the repos themselves
all the code is open source and there is nothing stopping anyone from running their own benchmarks if they were curious
btw