daturkel

Surely this logic doesn't apply if we're to believe that "code is cheap" now :p

Skills in CC have been a bit frustrating for me. They don't trigger reliably and the emphasis on "it's just markdown" makes it harder to have them reliably call certain tools with the correct arguments.

The idea that agent harnesses should primarily have their functionality dictated by plaintext commands feels like a copout around programming in some actually useful, semi-opinionated functionality (not to mention that it makes capability-discoverability basically impossible). For example, Claude Code has three modes: plan, ask about edits, and auto-accept edits. I always start with a plan and then I end up with multiple tasks. I'd like to auto-accept edits for a step at a time and the only way to do that reliably is to ask CC to do that, but it's not reliable—sometimes it just continues to go into the next step. If this were programmed explicitly into CC rather than relying on agent obedience, we could ditch the nondeterminism and just have a hook on task completion that toggles auto-complete back to "off."

Notion AI is pretty great for both search and writing.

Giving them more credit than they probably deserve but: when you're solving "by hand" like they are in the show, keeping a known non-match couple together may actually be helpful for interpreting the results of a matchup ceremony because you'll know that that couple didn't contribute to the beams.

> I doubt that any recommendation system is capable of providing meaningful results in absence of the "awareness" about the actual content (be it music, books, movies or anything else) of what it's meant to recommend.

Years of experience have proven that you can get quite far with pure collaborative filtering—no user features, no content features. It's a very hard baseline to beat. A similar principle applies to language modeling: from word2vec to transformers, language models never rely on any additional information about what a token "means," only how the tokens relate to each other.

I recently had a letter published in The New Yorker in response to Andrew Marantz's (excellent) story "Among the A.I. Doomsayers." In particular, I wanted to highlight that extremist doomers and accelerationists are not the only ones concerned about AI's future. In the post, I elaborate a little on the letter and provide links to further reading.

Hey Francois, congrats to you and the team on the launch! I've generally chosen Pytorch over Tensorflow for my day to day, but now that Keras is framework agnostic I'm excited to revisit it.

One thing I'm wondering about is if it's possible (or necessary?) to use Keras in concert with Pytorch Lightning. In some ways, Lightning evolved to be "Keras for Pytorch," so what is the path forward in a world where both exist as options for Pytorch users—do they interoperate or are they competitors/alternatives to each other?