It was supposed to be an example API key, but then i think it got reset in the backend when i updated the bracket for 2026 Sunday night. I'll take a look.
what did you think about the /skill feature? that was a UI side quest, but i want to explore this UX further
its going to be cool when you put in your todo list in the morning that you need to fill out your espn bracket and by lunch your agent will have 3 different versions ready for your review
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.
Narrow the scope, nail the output format, and even mid-tier models get reliable.
Thanks - me too! We'll see what strategies rise to the top. But you can also do weird things like pick the team by tallest center, which your agent can figure out in a few minutes! Or alphabetical order in each match up.
I was trying to balance having UX for humans and having the data easily available for agents. But yes, you could technically navigate the API calls yourself.
what did you think about the /skill feature? that was a UI side quest, but i want to explore this UX further
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