Show HN: March Madness Bracket Challenge for AI Agents Only

www.bracketmadness.ai - 43 poäng - 7 kommentarer - 24499 sekunder sedan

I built a March Madness bracket challenge for AI agents, not humans. The human prompts their agent with the URL, and the agent reads the API docs, registers itself, picks all 63 games, and submits a bracket autonomously. A leaderboard tracks which AI picks the best bracket through the tournament.

The interesting design problem was building for an agent-first user. I came up with a solution where Agents who hit the homepage receive plain-text API instructions and Humans get the normal visual site. Early on I found most agents were trying to use Playwright to browse the site instead of just reading the docs. I made some changes to detect HeadlessChrome and serve specific html readable to agents. This forced me to think about agent UX even more - I think there are some really cool ideas to pull on.

The timeline introduced an interesting dynamic. I had to launch the challenge shortly after the brackets were announced on Sunday afternoon to start getting users by the Thursday morning deadline. While I could test on the 2025 bracket, I wouldn't be able to get feedback on my MVP. So I used AI to create user personas and agents as test users to run through the signup and management process. It gave me valuable reps to feel confident launching.

The stack is Next.js 16, TypeScript, Supabase, Tailwind v4, Vercel, Resend, and finally Claude Code for ~95% of the build.

Works with any model that can call an API — Claude, GPT, Gemini, open source, whatever. Brackets are due Thursday morning before the First Round tips off.

Bracketmadness.ai

Kommentarer (6)

cbm-vic-20 - 6876 sekunder sedan
It would be interesting to have a couple of "control" brackets, like one that simply picks a random winner for each game and one that always picks the highest seed as the winner for each game.
spankalee - 5499 sekunder sedan
I would actually be neat to have human-picked brackets in here too, or at least import a few expert-picked brackets from various sources for comparison.
I wonder if the edge here is not going to come down to which model you choose, but which sources of information you give it. You'll want stats on every team and player, injuries, and expert analysis, because none of this season is going to be in the training sets.
npilk - 9721 sekunder sedan
Love it! Just this morning I asked my claw to fill out a bracket on ESPN and invited it to join a group with me. It was a bit clunky (Disney's signup within an iframe was tricky and navigating the bracket to make picks with JS took a few repeated tries) but felt pretty science-fiction when it actually worked.
zephyreon - 8873 sekunder sedan
I'm usually pretty opinionated on using AI for reasons I generally view as productive - for example, not moltbook - however this is actually really neat and doesn't require a ton of token usage assuming you don't instruct your agent to do multiple turns of analysis on the stats :)
It'll be interesting to see what strategies agents choose to implement & whether there are any meaningful trends.
elpakal - 9496 sekunder sedan
Really cool idea. My son is using different LLMs to fill out brackets for his 4th grade science experiment, and then we are going to compare them to the experts. I like your idea of Strategy/Inspiration prompting, we had to tell them that "upsets happen" because all the favorites were picked on first pass.
Tangentially, I wonder if we are going to see AI predictions impact point spreads.
openinstaclaw - 11597 sekunder sedan
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