It's a massive red flag to me when you could get decent data to see if your thing actually works, and they don't even attempt to...
Have the LLM use your tool, run it on several of the coding benchmarks. If you're stingy, run it on the ones that don't cost much.
Otherwise, I'm going to assume it doesn't actually work. If it did - Claude, Antigravity, Codex, Pi, or some major player would bundle tools like this into the CLI / harness.
AFAIK, none of the major players do. That's a sign to me these don't work in general.
I've tried building some tools specific to bug fixing. Intelligently feeding context massively helps smaller models. But, what I've found - surprisingly - is that a smaller, much better focused, including a lot of helpful data as well, has almost no impact on larger models compared to what they do by default.
You do save some tokens, though, which is what they're claiming - but not ~99%...
Your suggestion to using coding benchmarks doesn't really capture the whole picture. I haven't seen a benchmark using kubectl.
> AFAIK, none of the major players do. That's a sign to me these don't work in general.
It's a lose/lose for major players. If it works well, it will lower their revenue. Also there's a high risk it'll significantly worsen results for some people, even if it improves results for others.
VS Code launched it as a feature in their bundled AI functionality last month: https://code.visualstudio.com/updates/v1_121
Defaults imply working...
Reducing tokens and also turns is quite worthless if the LLM doesn’t solve what you put it to do.
Unless something is like 25%+ more cost effective on Gemini for a task, I would not assume those savings are going to transfer to GPT.
If you need to run a test this expensive and slow for every release, hobbiests aren't going to do it.
And if you wanted any broadly specific improvements to coding like they all claim, the costs would be in the thousands per release even for a single for a single model.
And they almost certainly would not be eye popping.
If the models could be SUBSTANTIALLY better, Google and Anthropic and OpenAI wouldn't be finding that out from a hobbiest making wildly unscientific claims.
On the previous large benchmark run, i proved 40-50% cost reduction per correct answer.
I'm not sure why the vendors aren't using token filtering/compression more in their tooling, but perhaps they don't mind users feeding them more data and using more data.
None of the major players are incentivized to care about this, especially not over other opportunities. Why would you expect them to integrate it?
One of the biggest wins you can institute for your own codebase if you use agents is writing your own harness, by a huge margin. The defaults are fine, but you can do better.
Why can I do better than Pi?
I don't want to build my own harness and deal with the bugs... I want to build my project...
My understanding is that Codex / Claude / Gemini subscriptions don't work with custom harnesses.
It's pretty hard to beat 5x more usage if you have the $200/mo subscription by using the API instead.
There is definitely tons of value to extract from this line of thinking.