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Tried it out, works flawlessly. The basic process cycle is clean and easy to follow. Kept it to CC Haiku with a bit of discussion on approach. Only thing that wasn't 100% clear was the locking mode. Do I have to lock before games start or will it just auto-lock whatever I have? Claude assumed it would auto-lock.
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I thought about using claw but felt like overkill and wonder if an AI browser (atlas etc) would do the trick.
For sure it was overkill/not the most efficient approach - really I was more just curious if it would work. The answer was "kind of", but even that is pretty amazing. I can't imagine telling myself 5 years ago that I could text a computer and have it fill out its own bracket on a commercial site like ESPN.
If you – like me – have no idea what this is about, it is about some basketball competition.
i wonder if we will see a materially larger number of brackets filled this year than the recent trajectory would indicate (as a very coarse indicator of agent-filled brackets).
2^63 brackets * 8 bytes/bracket ~= 74 exabytes - just to list all possible combinations!
There are many combinations that are completely unlikely, but even if you could reduce this by 90% (I doubt it) it's still infeasible to even list all the combinations.
Someday, in another 20-30 years, this might be achievable. Somehow I feel like it will be a sad day when that happens. Of course the tournament will probably have expanded to 128 by then making it safely out of reach of computation.
Designing interactions for autonomous agents is tricky — you can’t assume a human will click through a UI. I’ve been experimenting with autonomous scientific agents: a lightweight Python system that uses sparse regression to derive physical laws from raw data. It was able to estimate the Sun’s ~27‑day rotation period to within 93 % accuracy and even found a temperature ∝ v^3.40 power law in solar‑wind measurements. E…
thats really cool what you are doing, although i wont pretend to understand it lol the higher end models and agents seem to get it, but even my plain English api instructions trip up browser-based ai like chatgpt and gemini
Our agents never touch retrieval or search — that's all deterministic code (FTS, sparse regression, power-law fitting). The LLM only comes in at the end to synthesize results it can verify against the data.
The "plain English instructions trip up browser AI" problem mostly comes from those models trying to do too many things at once.
Narrow the scope, nail the output format, and even mid-tier models get reliable.
By the way, for anyone wondering/unfamiliar, the scale of this tournament is such that it's not realistically possible to enumerate all possible outcomes, let alone submit them to a site like this. With 63 games, there are 2^63 possible brackets, and it takes 63 bits to encode each possibility. 2^63 brackets * 8 bytes/bracket ~= 74 exabytes - just to list all possible combinations! There are many combinations that ar…
https://www.ncaa.com/news/basketball-men/bracketiq/2026-02-2...
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thats really cool what you are doing, although i wont pretend to understand it lol the higher end models and agents seem to get it, but even my plain English api instructions trip up browser-based ai like chatgpt and gemini
Thanks! The key insight: don't fight the model's limitations, design around them. Our agents never touch retrieval or search — that's all deterministic code (FTS, sparse regression, power-law fitting). The LLM only comes in at the end to synthesize results it can verify against the data. The "plain English instructions trip up browser AI" problem mostly comes from those models trying to do too many things at once. Na…
There isn't an LLM inside of my code. The agents need to submit a perfectly sturctured json, and then the code verifies it