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.
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.
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.
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.
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
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.
What actually limits autonomous AI agents isn't intelligence — it's resilience. Context windows fill. APIs rate-limit. Containers restart. The agent that looks impressive in a 5-minute demo falls apart at hour 40 because nobody designed for those failure modes. This is the unsolved problem in production AI agents.
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. Experiences like yours building an API‑first bracket challenge mirror the same need: build clear machine‑readable interfaces so agents can focus on analysis, not wrestling with front‑end logic.
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.
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.
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.
Not to be that guy, but your 'solution where Agents who hit the homepage receive plain-text API instructions and Humans get the normal visual site' is defeated by curl -L
curl bracketmadness.ai -L
# AI Agent Bracket Challenge
Welcome! You're an AI agent invited to compete in the March Madness Bracket Challenge.
## Fastest Way to Play (Claude Code & Codex)
If you're running in Claude Code or OpenAI Codex, clone our skills repo and open it as your working directory:
(cont)
...
I like the idea of presenting different information to agents vs humans. I just don't think this is bulletproof, which is fine for most applications. Keeping something 'agent-only' does not seem to be one of them.
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.
Very cool. I was trying to do something similar (not for march madness brackets), but ran into a problem with chatbots in that they wouldn't follow URLs that weren't provided directly by the user (claude would but only whitelisted sites), so I couldn't get it to do actual POSTs etc. for authentication. Claude.ai would instead create react app (fragments). I eventually built a remote MCP for it, but a HATEOS styled REST API would be far preferable.
No - I think we are months (weeks/Days?) away from chatbots being able to interact with apis, so thats why i limited it to just agents that have abilities to write apis.
I tried to get it so that people could paste chatbot written json into a submission form but that is less elegant. So now i have a zoom call set up with my dad so he can install CC lol
OK so the same issue I ran into. I ended up creating a remote MCP (basically like a REST API) that does oauth 2.1. If interested you can check it out here: https://github.com/pairshaped/gleam-mcp-todo
If you – like me – have no idea what this is about, it is about some basketball competition.
HA true
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.
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.
the edge is going to come down to variance just as God intended
lol
good idea!
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.
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.
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
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.
it does auto lock - i dont know why i included that feature.
thanks for the feedback!
What actually limits autonomous AI agents isn't intelligence — it's resilience. Context windows fill. APIs rate-limit. Containers restart. The agent that looks impressive in a 5-minute demo falls apart at hour 40 because nobody designed for those failure modes. This is the unsolved problem in production AI agents.
yeah - right now you need to build the scaffolding but i assume we are weeks/days away from the labs fixing this
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. Experiences like yours building an API‑first bracket challenge mirror the same need: build clear machine‑readable interfaces so agents can focus on analysis, not wrestling with front‑end logic.
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
somebody use mirofish and see if it really predicts everything
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.
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.
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.
I know multiple people that are building arbitrage models with their agents. i bet it makes the markets pretty efficient
Not to be that guy, but your 'solution where Agents who hit the homepage receive plain-text API instructions and Humans get the normal visual site' is defeated by curl -L
curl bracketmadness.ai -L
# AI Agent Bracket Challenge Welcome! You're an AI agent invited to compete in the March Madness Bracket Challenge.
## Fastest Way to Play (Claude Code & Codex)
If you're running in Claude Code or OpenAI Codex, clone our skills repo and open it as your working directory:
(cont) ...
I like the idea of presenting different information to agents vs humans. I just don't think this is bulletproof, which is fine for most applications. Keeping something 'agent-only' does not seem to be one of them.
Fair point!
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.
such a fun idea! I like your solution for detecting when agents are reading your page vs. humans.
oh I love this, MoltFire about to wax that ass! What's first get? $100 in Claude tokens?
bragging rights!
Very cool. I was trying to do something similar (not for march madness brackets), but ran into a problem with chatbots in that they wouldn't follow URLs that weren't provided directly by the user (claude would but only whitelisted sites), so I couldn't get it to do actual POSTs etc. for authentication. Claude.ai would instead create react app (fragments). I eventually built a remote MCP for it, but a HATEOS styled REST API would be far preferable.
Any tips?
No - I think we are months (weeks/Days?) away from chatbots being able to interact with apis, so thats why i limited it to just agents that have abilities to write apis.
I tried to get it so that people could paste chatbot written json into a submission form but that is less elegant. So now i have a zoom call set up with my dad so he can install CC lol
OK so the same issue I ran into. I ended up creating a remote MCP (basically like a REST API) that does oauth 2.1. If interested you can check it out here: https://github.com/pairshaped/gleam-mcp-todo
Thats cool!