Arctic_fly

Interesting post. Did the difference in wall clock training time take the reduction in cold start time into account? Seems like that could be a significant factor for small jobs and negligible for large ones.

Does the IRS get to decide that? Seems like something that the a judge should rule on.

Seems relatively benign, seeing as he'll continue leading design on Apple Watch for some time. Also, I had no idea that COOs did design work.

I hover somewhere between AI doomer and boomer. For a long time I thought all the economic gains and power produced by AI would be concentrated in a company like OpenAI and that the quality of our lives would hinge on decisions made by its stakeholders. Fortunately, that future no longer appears inevitable.

This is an excellent question. My very unscientific suspicion is that the decreases in average attention span and ability to concentrate zero out the theoretical possible increases in productivity that computers allow.

Definitely not the case for coding. I'm a capable senior engineer, and I know many other very experienced senior engineers who are all benefitting immensely from AI, both in the code editor and chat interfaces.

My company just redid our landing page. It would probably have taken a decent developer two weeks to build it out. Using AI to create the initial drafts, it took two days.

New PII redaction library that's SOTA and runs locally on a variety of devices. Kind of slow when I tested on my M3 Max macbook, but works pretty well!

We've looked into this tool a couple times, haven't adopted it yet. The main issue is compatibility with prisma, which we use for defining our db's schema and automating migrations. If there were a slick integration between the two, that would go a long way to convincing us to adopt. In the meantime, we'll go with convenience and (slightly, almost immeasurably) lower reliability. We haven't had downtime due to a migration in months!

> What’s the benefit of generating embeddings for such large chunks?

Not an expert, but I believe now that we can fit more tokens into an LLM's context window, we can avoid a number of problems by providing additional context around any chunk of text that might be useful to the LLM. Solves the problem of misinterpretation of the important bit by the LLM.

> But in 2015 there is a stark discontinuity, where the number of stories (with text) shoots up by >10x, and the average score drops by 5x! Is this some kind of eternal September?

Based on the later analysis in the post (which I agree with), the total score of a comment is disproportionately tied to whether it hits the front page, and of course how long it stays there. Regardless of the quality of the average post starting in 2015, the sheer quantity would make it impossible for all but a few to stay on the front page for very long. Hacker News got more popular, so each story got less prime time.

I remember hearing about Dragon when I was in elementary school. It's cool to reflect on how far things have progressed in the last decade and a half.