I mentioned a potential OpenAI insider in https://x.com/peterjliu/status/2024901585806225723, that was from 5 minutes of investigation. There are probably more. And then there's a lot of other companies.
Post author here: To clarify, this is not a post from Polymarket.
This is talking about using Compound AI (product I'm working on) to query Polymarket data, including finding insiders, just as a fun example analysis you could do.
Often you need a well-calibrated probability of a future event to feed into some other analysis, and Polymarket is pretty great for that. An example is how much insurance (hedge) to buy for some disastrous event.
I would start by making the examples yourself initially, assuming you have a good sense for what that real-world task is. If you can't articulate what a good task is and what a good output is, it is not ready for out-sourcing to crowd-workers.
And before going to crowd-workers (maybe you can skip them entirely) try LLMs.
We've (ex Google Deepmind researchers) been doing research in increasing the reliability of agents and realized it is pretty non-trivial, but there are a lot of techniques to improve it. The most important thing is doing rigorous evals that are representative of what your users do in your product. Often this is not the same as academic benchmarks. We made our own benchmarks to measure progress.
Author of post here. I'd say most of the examples generated from the best model were good. However we chose examples that were not too gruesome, as news can be :)
We encourage you to try the code and see for yourself.