Comments URL: https://news.ycombinator.com/item?id=47355468

Points: 3

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=47283155

Points: 15

# Comments: 1

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.

Some have better models that predict with higher accuracy, given the same data.

Comments URL: https://news.ycombinator.com/item?id=43774272

Points: 1

# Comments: 0

Agents can just be viewed as tools, and vice versa. Is this an attempt to save the launch after getting scooped by MCP?

And before going to crowd-workers (maybe you can skip them entirely) try LLMs.

Plug: We just posted a demo of our agent doing sophisticated reasoning over a huge dataset ((JFK assassination files -- 80,000 PDF pages): https://x.com/peterjliu/status/1906711224261464320

Even on small amounts of files, I think there's quite a palpable difference in reliability/accuracy vs the big AI players.

Comments URL: https://news.ycombinator.com/item?id=43535863

Points: 3

# Comments: 0

This dataset was only used to benchmark against other published results. It was first proposed in https://arxiv.org/abs/1509.00685.

As for how does the model deal with co-reference? There's no special logic for that.

We encourage you to try the code and see for yourself.

Comments URL: https://news.ycombinator.com/item?id=12294193

Points: 175

# Comments: 132

Comments URL: https://news.ycombinator.com/item?id=10993919

Points: 179

# Comments: 90

Comments URL: https://news.ycombinator.com/item?id=10978349

Points: 3

# Comments: 1