Old Claude Sonnet model explains why 90s American music was so angsty
Replying to @VeryBadLlama
I had actually made an AI benchmark where in one test convo I asked various models to opine on this github.com/firasd/vibesbench…
Blinkit exec saying that they don’t compete on their platform with a private label because, “We would rather avoid that conflict unless the growth or margin case [is] clearly compelling” is a rare example of someone admitting that their stated ideals is also ‘talking their book’
🚨🚨Blinkit is not convinced that private labels are a profitable long term strategy
Meanwhile Swiggy has gone all-in to scale its in-house brand Noice
thearcweb.com/article/swiggy…
Blinkit also has its own private label — Whole Farm & Savemore
The zoning/permitting doesn’t really allow for it … George Lucas spent over a decade trying to build a Museum of Narrative Art in San Francisco and gave up
Seems when it comes to ‘genre prestige’ in movies, crime gets a pass. Like ‘Jurassic Park’ was never going to be Oscar-worthy, but ‘Silence of the Lambs’ or ‘The Godfather’ are shoo-ins (despite their source material not being any more ‘respectable’ in the literary world than JP)
It’s a little awkward that US political opinion on the conflict is so socially constructed—20 years ago you’d be jeopardizing a public career to be critical of Israel, and now it’s like ‘hey you’re not to hip to Free Palestine yet?’
Well it’s how politics works I guess
Seems this is due to the overloading (ambiguity) of the word ‘spicy’ in English ie. it doesn’t distinguish between what in Hindi would be called ‘tez mirchi/teekha’ (chilli pepper) vs masala
What kind of comparison is this? It’s like comparing a random Britney B-side to Hung Up or something. Fatal Attraction?! The bunny boiler lady?! Not in the same league
Doesn’t ring true. In fact a lot of American public figures (Will Smith, business people etc) say the exact opposite — that they decided to overcome any disadvantages vs the competition by working harder than everyone else
“You don’t study for the spelling bee” is a deep and important aspect of American culture. I was at a baseball game discussing some young players with a buddy one time and he said to me offhandedly, “hard work is fine, but nothing beats talent.”
Americans have this sort of sense that there’s something a little off about working really hard to get good at something that you’re not naturally good at. You’re never really going to be on the same level as the guy who has a natural talent. We have room in our hearts for scrappy little guys who work hard, but we are by default suspicious of grinders and strivers.
If you were really meant to do it, then you’d be naturally good at it. You sign your kids up for little league, but you don’t take it too seriously unless they show remarkable early talent.
Yes, this sounds sort of ridiculous if you take it too literally. No, nobody is saying that there’s any kind of practical test for this or that anybody should be excluded because we found out that they secretly studied too hard.
But this is a part of American culture. And if you can’t understand it, then that just marks you out as foreign to that culture to an even greater degree.
This is a category error. In-context learning happens when the weights already have a compressed representation of something
But chess’ whole property that makes it unsolved is that we don’t have a compression beyond ‘welp I guess we gotta try this combo and see what happens’
I have a Continuous Learning benchmark where models attempt to learn to play chess. They are given a /goal of learning and improving playing against a Stockfish opponent in 200 games. They can choose the difficulty, take notes, whatever they like - except cheating (e.g. using a chess engine) of course.
So far the improvement in Elo has been negative for Astra. Tiny bit positive for Opus, but could also be random. I've started Astra off sooner, so it finished its 200 games already, Opus is still playing.
Site here to watch how they are doing: ai-learning-to-play-chess.su…
The reason why this is interesting is that while we obviously don't have continuous learning, at the back of my mind I was thinking that maybe models can simulate it through self-scaffolding. Turns out not so much at least in this context. Perhaps it's a solvable problem and we don't need 'true' self-learning for models to learn in some way.
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Just a note, the idea for the benchmarks belongs to someone else, but I don't want to use their name to give this more weight without permission.
Liveclip in ChatGPT… beautiful
I just said sort by likes and the LLM just called a table_sort tool .. no need for the AI to write ad-hoc code
I’m thinking of making this a open source MCP server so you can just run it on your localhost and have Claude Code, Codex etc use it
Web publishing extension xitter.cf/firasd/status/21031538…
Emoji picker using Liveclip's built in vector search features against keyword descriptions of emojis #AppsOnDemand



























