nbosse

I'm curious to learn whether people think the anthropic era will last

I think a fundamental problem is that the customer of a prediction market is the trader (gambler?), not the public. If you want accurate forecasts, you need sharp traders. If you want sharp traders, you need to pay them a lot. As a platform, the straightforward way to do that is to attract a large number of uninformed gamblers. And ultimately, the accuracy is not determined by volume, but by the fraction of informed and uninformed capital that is trading for idiosyncratic reasons uncorrelated to the "true" probability. Someone has to put in the effort to make the markets accurate, and that someone has to be paid and that money has to come from somewhere.

What are people's thoughts on the role of a social media company in the age of AGI?

oh, what a shame! Also interesting that they stopped that after a couple of years... You'd think that it would either flop from the beginning or just work

One of the underrated ways of reducing meat consumption, imo, would be to mix a certain percentage of plant-based meat to regular meat products. Imagine a world in which McDonald's would just mix 20% plant-based meet to their patties. I can see some risk for them, but honestly, long-term, I don't think people would actually mind much.

We researched 5,298 award winners spanning 26 years of ceremony data (2000–2025) and 15 different awards leading up to the Oscars.

> Auto-review on routine PRs produced too much noise. A one-line config change doesn't need a 12-point review

Shouldn't you prompt the review to just be a one-line review then? I see real danger in having humans review "the easy fixes" manually, but then rely on Claude for the complicated stuff.

I'd be curious to learn whether you have similar prompts and skills for producing code, as opposed to just code review.

We built this after too many rounds of deduplication on messy data. Each technique in the deduplication funnel solves what the previous one can't, but the real pain is orchestrating all three together at scale: chunking to avoid O(n²), batching LLM calls (accuracy degrades past ~25 items), rate limiting across embedding and completion APIs simultaneously. We packaged the pipeline into a Python SDK. Here's a 500-row CRM dataset that cost $0.74, ~100 sec to dedupe: https://everyrow.io/docs/resolve-entities-python

I'm confused by this... It seems to me like the relevant part is "playing computer games is good" not "the type of sitting you do matters". Playing computer games while standing might be even better