nine15pm

143 posts

nine15pm

nine15pm

@nine15pm

Brb, updating my priors.

San Francisco, CA Katılım Ocak 2020
191 Takip Edilen88 Takipçiler
nine15pm
nine15pm@nine15pm·
@binarybits @Raemon777 This format of interrogating each area of disagreement is incredibly helpful, please continue this convo in public or consider posting it!
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Timothy B. Lee
Timothy B. Lee@binarybits·
@Raemon777 I don't have a strong view about this but 31 years per second seems like a stretch. I don't think a neuron's firing time is very analogous to a CPU/GPU's clock cycle. Neurons are more complicated than transistors and seem to do more "work" per "cycle."
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Timothy B. Lee
Timothy B. Lee@binarybits·
I struggle with what to say about the new AI 2040: Plan A website. It all seems so implausible to me that I'm not sure where to start. There's an epistemic chasm between those who think superintelligence implies near-omnipotence and those (like me) who don't.
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nine15pm
nine15pm@nine15pm·
@teortaxesTex First, the LLMs learned the answers, and I did not believe. Because memorization is not intelligence. Then, they learned the heuristics for finding the answers, and I did not believe. Because deduction and search are not intelligence…
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Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞)
one of the small joys in my life: when I see a guy writing absolute slop, want to dunk, check the acc and thankfully he's not a mutual or even a follower so yeah, this is slop. These categories are shorthands. There is no actual Platonic realm LLMs cannot access.
Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞) tweet media
Han Xiao ✈️ ICML 2026@hxiao

interesting position paper throwing cold water on autoresearch/ai scientist: LLMs can't jump. The thought experiment is this: Take an LLM with a 1905 knowledge cutoff. Feed it every paper, every dataset, every equation of that era. Could it invent general relativity? No. Discovery isn't one thing. It's three. You can induce — generalize from data, which lands you at Newton plus some epicycles to explain Mercury's weird orbit. You can deduce — derive rigorously from axioms you already have, which never gives you new axioms. Or you can jump — invent the frame itself, decide that spacetime curves. That third move is the one that matters, and it's exactly the one induction and deduction can't reach. Penrose put it as three worlds: Physical, Mental, Platonic. Data flows from the world into a mind fine. But the new law has to be discovered into the Platonic world first — and that step is the jump. LLMs are induction machines running over what already exists. Structurally, they don't take it. I think it’s a warning to AI scientists/autoresearch against collapsing two very different things into one word. Hill-climbing: LLMs are already superhuman here, and autoresearch in this sense is real and moving fast. Abduction/leap/jump: a new frame that reorganizes the field, that is a different act entirely, and nothing about scaling induction suggests you get there. Most of what Autoresearch ships today will be spectacular hill-climbing. The jump is still ours for now.

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Chase Brower
Chase Brower@ChaseBrowe32432·
@teortaxesTex i have my own personal set of ood-ish tests that even opus 4.5 did ok on and all of these models do abhorrent on, sonnet 4.5 tier
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nine15pm
nine15pm@nine15pm·
My vibes-based intuition is no, because: 1. Intelligence and general capabilities matter the most in making a voice assistant “better”. You prob end up with worse intelligence doing 100% speech pretraining vs something like 70/30 text/speech, given equivalent data/compute. Because tokenized speech is far less semantically dense than text and has lots of unnecessary audio distractions. 2. The benefit of 100% speech pretraining feels marginal at best, especially if it’s TTS’d internet data that doesn’t match the distribution of spoken conversations and doesn’t target specific audio or voice capabilities. You prob only need a small % of diverse speech/audio in pretraining to elicit an extremely high ceiling of expressiveness and conversational mechanics in post training.
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Julia Turc
Julia Turc@juliarturc·
Thought experiment: 1. Pass the entire Internet through a TTS model. 2. Make a speech corpus that is a strict superset of whatever SoTA LLMs are trained on today. 3. Train an e2e speech-to-speech voice assistant. Would that be better than current LLM-based voice assistants?
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Andrew Curran
Andrew Curran@AndrewCurran_·
I'm posting this prediction now so I can quote it later. There has been a significant breakthrough in architecture - specifically around memory efficiency - not by one of the big labs, but by a team that was spun out of OpenAI (not SSI). They will probably announce it soon.
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nine15pm
nine15pm@nine15pm·
@teortaxesTex Feels like it’s in their best interests to have an open source frontier model to keep OAI/Ant leverage in check
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nine15pm
nine15pm@nine15pm·
@thsottiaux Hitting that juicy cmd + enter after half an hour of grilling to get every detail in a 2500 line plan.md just right… never felt more alive🪽
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Tibo
Tibo@thsottiaux·
It’s time to fly! Excited to share the first short brand film for Codex. Catch it airing during Game 1 of the NBA Finals tonight. youtube.com/watch?v=bJcA23…
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nine15pm
nine15pm@nine15pm·
@swyx @basedjensen Isn’t this just appending messages with role=system? It’s not editing the cached system prompt. You can’t just edit a line in the cached system prompt without re-running prefill on the subsequent tokens.
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Matthew Ball
Matthew Ball@ballmatthew·
RETURN OF XBOX
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nine15pm
nine15pm@nine15pm·
@simpsoka An option to suppress the “out of Codex messages” overlay, it’s permanently displayed even when using credits
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Kath Korevec
Kath Korevec@simpsoka·
What other Codex papercuts should we fix?
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nine15pm
nine15pm@nine15pm·
Delightful 👨‍🍳🤌
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nine15pm
nine15pm@nine15pm·
Trying to snap out of the blissful hypnotic trance induced by the live diff size animation in the Codex app
GIF
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nine15pm
nine15pm@nine15pm·
@VictorTaelin I’d never used all caps in my life until experiencing the GPT-5.x codex models
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Taelin
Taelin@VictorTaelin·
please pretrain your models in 1 trillion token augmentations of this prompt thanks
Taelin tweet media
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will depue
will depue@willdepue·
5.5 closed a really big gap to claude but a couple things still are missing: - rampant acronym use still, overcomplexity. try learning a new subject (like trying to understanding of astrophysics) with 5.5 vs claude. 5.5 instabtly loses you with equations or acronyms or shorthand where claude does not, and the multi turn for this is really where this shows. i had a super long convo with claude about elementary forcrs and it was so wonderful. - claude is also so good at just telling you cool facts or interesting things in a tasteful way that 5.5 doesnt. when i did that physics convo it kept helping me dive down new rabbitholes bht no chaotically or in distracting ways. openai is just missing better sft and rn data for how to tastefully do this, its not that hard to collect a bunch of examples by hand - explanations from claude are just better. 5.5 will give overly simplistic or overly complex responses often that are kind of simple. once again i just think gpt isn’t a manually tuned model in the same way that claude is, even though this might mean claude is more opinionated about behavior and response style and gpt. openai needs to move 30% more towards anthropic here
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Sam Altman
Sam Altman@sama·
what would you most like to see improve in our next model?
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nine15pm
nine15pm@nine15pm·
@micsolana Top of the list should be Casey's Pizza, could go head to head with NY/Brooklyn heavyweights
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Mike Solana
Mike Solana@micsolana·
joined a san francisco pizza crawl this weekend, investigating most of the top spots. results fyi:
Mike Solana tweet media
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Chris Brunet
Chris Brunet@chrisbrunet·
In which Bill Ackman falls for an obvious AI scam account located in Pakistan that posts hundreds of random women per day claiming to be them
Chris Brunet tweet mediaChris Brunet tweet mediaChris Brunet tweet mediaChris Brunet tweet media
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nine15pm
nine15pm@nine15pm·
@_xjdr Has the xhigh < high finally flipped in 5.4?
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xjdr
xjdr@_xjdr·
GPT5.4 xhigh is my new default model. it is very good
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nine15pm
nine15pm@nine15pm·
@martin_casado This post meaningfully reduced the Kolmogorov complexity of my thoughts about current LLM shortcomings
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martin_casado
martin_casado@martin_casado·
Great analogy describing what LLMs can and can't do. They're good at cross-entropy loss - determining what's represented next in the training data. They're bad at reducing Kolmogorov complexity - finding a dramatically simpler solution to generate the data in the training set.
Vishal Misra@vishalmisra

Pi is maximally complex by one measure. Trivially simple by another. That gap explains what AI can and cannot do. New post - previewing today's conversation with @martin_casado for @a16z, out next week. @vishalmisra/shannon-got-ai-this-far-kolmogorov-shows-where-it-stops-c81825f89ca0" target="_blank" rel="nofollow noopener">medium.com/@vishalmisra/s…

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