Nitin (@nitin_twit) header
Nitin (@nitin_twit) profile picture

Tech | Finance | Spirituality | All my writing and opinions are notes to myself.

South-West India
Joined November 2014
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Finally… I’ve declared war on spammers & repeat callers. BajajFin/Policybazaar/IDFCbank.. You’ve been warned. 😈 I built a small app to fight back. Give it a try e'one and tell me what you think: play.google.com/store/apps/d… Your Call. Your Rules. Your Peace.
🤖 Made with AI
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Nitin retweeted
1977-08-15. 22:16 EDT. 6EQUJ5. Wow! The First Light Ever Spoken
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BREAKING: Memory stocks are getting crushed for a 2nd straight day $SNDK -16.9% $WDC -15.2% $RMBS -14.8% $STX -13.7% $MU -13.0% $SIMO -11.6% $NTAP -7.5% Investors fear the new Democratic Congress will slow AI data center buildouts.
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If this isn't a pump-n-dump, I don't know what is.
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Market and dumbbells This one took quite some effort so even if you don't like it please share 🫤 youtube.com/shorts/6Gv5fUKFG…
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i've reproduced the results from the MIT blog post today on weight-space programming. but one additional result i have to add: this is a new, step-change emerging capability that's appeared in just the latest set of models.
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We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵 Blog: rikiyat.github.io/blog/weigh…
Readers added context they thought people might want to know
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math. rikiyat.github.io/blog/weight-sp… github.com/RikiyaT/lm_und… huggingface.co/Qwen/Qwen3.5-0… huggingface.co/RikiyaT/Qwen3.…
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Fascinating times we live in: people have come to believe that returns from capital (money making money / Stock market) will perpetually outpace returns from labour (people making money).
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A new oral peptide called Icotrokinra completely cleared psoriasis in 27% of patients after 16 weeks. And 50% saw at least 90% improvement. All from a once-daily pill!
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I don't think people realise how big a deal this is. If you have cancer, or somebody you know has cancer, there is a company in Hyderabad that wants to build a personalised cancer vaccine for you. Try approaching any big pharma company and asking for a personal vaccine. If they even reply to your email, this is the response you'll get: we don't do that here. PopVax, on the other hand, takes your tumour sequencing data (or a sequence you've designed), designs candidate neoantigen vaccine sequences with their AI pipeline, produces the mRNA, encapsulates it in their LNP formulation, fills vials, and runs QC, all in 8 to 10 weeks. They’re publicly committing to capacity for 100+ of these over the next 12 months and asking people to email them their cancer type/stage and sequencing details. What a time to be alive.
Launching RNAFoundry.com – we manufacture personalized cancer vaccines.
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Surprise 1: Anthropic acquired a one-person biotech . . . Surprise 2: that person is me Surprise 3: I’m here to tell you all about it Yes, this is what you think it is; after 6 yrs of increasingly loose anonymity: the Professor Oak reveal post …and yes, it has gotten so boring around here that I’m literally doxxing myself to spice up the timeline ------- Ok, allow me to introduce myself. My name is Cody Tranbarger. I mostly tweet, but occasionally find time to dabble in drug dev & starting/building biotechs. My past lives span the VC, crossover, startup continuum; Prof Oak has been my way of keeping a digital foot in those worlds, despite having my physical feet planted elsewhere. The past 18 mo, ‘elsewhere’ was Mithril Medicine, a co. I founded to develop bsAbs for LAMA2-CMD & other ECM-related genetic diseases. Mithril was born out of hands-on building with @MikeNGladstone, @JasonPaulRhodes, @LifeSciVC, & team @atlasventure. Somehow, I made enough half-decent calls along the way (even a broken clock is right twice a day, eh?) to convince them Mithril/I warranted a small seed. While I can hardly rationalize such an outrageously cavalier bet, I’m grateful for it, & I'd partner with Atlas again in a heartbeat. In typical AV fashion, Mithril’s primordial form was virtual, decentralized, & ultra-lean. This stage tends to be short-lived, but for Mithril, it worked surprisingly well, for surprisingly long, thanks in part to the rapid maturation of the AI tools I used liberally. Eventually, Mithril became a bit of an experiment itself - to find the upper bound of how far it could scale as a single operator + a bunch of Claude (+ a stellar bench of part-time consultants). In parallel, I started spending time with @nc_frey, @an1lam, & @CoefficientBio team; and promptly experienced, for the 1st time, AI at the frontier. Soon thereafter, Mithril stopped looking like an experiment, & started looking like a preview of an entirely new operating model. From that spark came “Pipeline-in-a-Person.” The idea immediately resonated when we shared it here back in April; and it was central to the shared vision that led to Anthropic’s acquisition of Mithril, & my joining the team. Why join Anthropic? Three reasons. (1) For Mithril, the programs continue, with more resources & infrastructure behind them, and as much Claude as I can put to work. (2) For me, the chance to start several more programs, & find out how many one person can actually run in parallel - an almost unprecedented luxury for a curious drug hunter. (3) For our mission, a path to institutionalize & scale this new way of working, then put it in the hands of all of biopharma, all over the world - which, candidly, I find the most energizing. Will AI x Bio cure diseases? Unequivocally yes, imo… but not by the route that gets the most airtime. Rather than timeline acceleration & compression, I see orchestration & parallelization as today's low-hanging fruit. Most drug hunters see a handful of go/no-go calls in a career, & spend entirely too much of the rest trading emails (my inbox can attest). Give them the bandwidth to run several programs at once, and the time to focus on scientific judgment, and I’m willing to bet they’ll make better calls. And at scale, better calls translate to more drugs, higher hit rates, broader patient impact, & likely, a disease or 10 or 100 cured. I couldn't be more excited to build toward that future @AnthropicAI. Finally, a heartfelt thank you to BioTwitter/X. The end of anonymity is bittersweet, but if anyone’s earned a proper introduction, you all have. It’s difficult to overstate how much this app has done for me: a decade of education, plenty of friends, many job opportunities, several mentors, a few investors, even a co-founder (fmr). It was the generosity of this community that inspired me to start posting 6 years ago, after just as many years lurking. I hope I’ve been able to pay it forward, in my own small way. But, to be clear, Prof Oak isn’t going anywhere. Same beat, same themes, and now, a front-row seat to drug development at the frontier. Stay tuned for dispatches from the trenches; and remember... Gotta Cure 'Em All
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Many of you don't understand how serious this is, so let me break it down. In computer science there is a famous kind of puzzle called 3-SAT. You are given a long list of yes-or-no switches and a set of rules about how they must be arranged, and you have to find a combination that satisfies every rule. The obvious way to solve it is to try every combination, but each new switch doubles the number of possibilities, so a puzzle with a few hundred switches would take longer than the age of the universe to brute force. For decades, the smartest people in the field found clever tricks that made this a little faster, but every trick still hit the same wall: the time required kept exploding as the puzzle grew. About 25 years ago, two computer scientists, Russell Impagliazzo and Ramamohan Paturi, working with Francis Zane, turned that wall into a formal idea. They proposed the Exponential Time Hypothesis, which says this explosion is not a failure of human cleverness but a law of nature. No algorithm, however brilliant, will ever solve 3-SAT without the time blowing up exponentially. Nobody could prove it, but almost everyone believed it, and it became one of the foundations of modern computer science. Researchers used it as a measuring stick. If you could show that solving some other problem quickly would also crack 3-SAT quickly, you had shown that problem was hopelessly hard too. Hundreds of papers, across scheduling, graphs, logic and much more, rest on this one assumption. Now an internal AI model at OpenAI claims to have broken it. It says it found an algorithm that solves these puzzles meaningfully faster than the hypothesis allows, and it has checked the proof in a computer verification system called Lean. And here is the remarkable part: the very people who proposed the hypothesis, Impagliazzo and Paturi, along with MIT's Ryan Williams, are now leading the public review of whether a machine has disproved their own idea. So what happens next? If the disproof holds, a large part of theoretical computer science has to be revisited. Every result that said "this problem cannot be solved faster, assuming ETH" loses its foundation, and researchers will have to work out which of those walls were real and which were simply where we stopped looking. It does not mean everything becomes easy. The new algorithm is still slow in absolute terms and this is not the famous P versus NP problem being solved. But it means our map of what is possible was wrong in a place we were very confident about, and it was an AI that redrew it. If the disproof fails, the hypothesis survives and the field breathes out, but we will still have watched an AI produce serious, checkable work on one of the deepest questions in the discipline, closely enough that the founders of the idea had to take it seriously. Either way, something has shifted.
An internal model at OpenAI has produced a claimed refutation of ETH! Russell Impagliazzo, Mohan Paturi, and I have been asked to help organize an open and independent examination of this claim. 1/4
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Is the end of LLMs near? arxiv.org/html/2610.14162v1
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Let’s break down this paper in simple terms. teapaw.org/nitin_twit/status/2114…
The main idea behind this paper is that AI don't need to generate text 1 token at a time and while doing so, it shows the significant improvement in training efficiency and inference speed..
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The main idea behind this paper is that AI don't need to generate text 1 token at a time and while doing so, it shows the significant improvement in training efficiency and inference speed..
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At each level, it predicts many pieces of text at the same time, instead of one by one. Then it moves to the next level and adds more detail. Think of it as building text layer by layer, in parallel.
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So how much improvement reported? see below:
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