Hey X, Iโm Paul ๐
- Intern turned โAI guyโ at a sports tech startup in NYC
- Our clients: FIFA, NBA teams, Premier League clubs..
- Building the AI system our revenue team runs on
- Posting my agent setup (Claude Code, Hermes, OpenClaw, Grokbot), what works and what breaks
Also: half-French, half-Taiwanese, but my Spanish beats my Chinese ๐ฅด (sorry mom), used to work in fashion, love photography and (cuban) salsa
Come say hi if you relate to any of the above!
As an GTM ops that had never written a line of code before march, I asked my (cracked) CTO to help me structure the repo behind the system finding, enriching, scoring and routing our leads.
Here are the files he handed me as references:
- CLAUDE.md: a sticky note telling Claude โyour instructions are in the other file.โ Itโs just a redirect.
- AGENTS.md: the house rules for the AI working on this project. The main one is that the AI can prepare changes but never publishes them live without the repo's owner approval.
- AGENTS.wmill.md: the standard user manual from Windmill, the automation tool, explaining how to build and test workflows correctly. Itโs maintained by Windmill, not by us, and gets updated automatically.
- README.md: the guide for humans on how a change goes live. You build and test in a sandbox, a colleague reviews it, and only after approval does it reach the live system our revenue uses.
- Beads: the AIโs to-do list and project logbook. It tracks whatโs done, in progress and next, so anyone in the repo can ask โwhere are we?โ and get a factual answer even weeks later, instead of relying on someoneโs memory.
@IAmAaronWill I swear
Just started using the app, and it feels so much more TRUE than any other social media
The fact that it's way more based on writing makes people put way more thoughts into what they post
And there are genuinely helpful people and community out here
@yongfook this has always been the problem of social media & algorithm
but if you find the right niche with poeple actually trying to grow and share, you will learn so much!!
okay now that we're past that tired discussion, let's talk about how we get agents to write more correct code (note: this won't mean the code will be Good, but it should at least work)
this is the entrypoint into your most basic agentic loop: verification. it means giving your agent the ability to verify its own work, most commonly by actually running and interacting with it like a real user would. whether your interface is a cli/api/ui/whatever, you want to start here so that your agents can close the loop. if you want you can even have your agents model the problem in TLA+ and attempt to verify if you have things like state machine bugs.
the point is to give yourself confidence in the agents' work by them producing some kind of artifact that proves that 1) that the work is correct, 2) that it matches what you intended, and 3) is easier to review than reading the whole PR. most commonly this would be a video and/or screenshots for UI work.
everything else like skill packs are really bonus and optional, all you need is a way for your agents to verify their own work to start using them more autonomously. some caveats are that not all domains can be verified easily yet, and that verification is just approximation and can still be wrong. it's no different than trusting that a human actually ran the code they wrote and trusting it
so start here!
To all my GTME fellows, I built the best inbound engine EVER in only 2 steps:
1. Get invited to #Sculpt2026 by @clay
2. Wear a https://t.co/xJldcpEAdO red mohair sweater
This is f*king insane. Google Brain founder Andrew Ng:
"Prompting will die in 6 months.
99% will keep writing prompts.
The 1% will build loops and graphs."
In a 2-hour lecture, he breaks down the path from prompts to agents, loops, and graphs
Prompts โ Agents โ Loops โ Graphs
A prompt gives you an answer
The first 15 minutes are worth more than most $500 agent courses
Bookmark and watch it today
Then read my full guide to loops and graphs below โ
turning your past life into marketable experiences is not new though. 10 yrs ago success was getting into GAFA, 5 yrs ago into MBB, now itโs getting into YC and exiting. it's the same playbook and the same people, just under a different logo.
what AI actually changed: building is cheaper, and the prospect of an exit promises MUCH MORE wealth x status than those old golden paths.
but scaling isnโt easier.
the filter might not be investors (bluffable), but customers are, and usage even more.
a lot of the AI deals signed right now are FOMO pilots no one opens.
the larpers can exit before the lights come on, but their products wonโt.