si/acc @microsoft w/ @azure @openai @anthropicai @spacexsi @meta. c/f @transhumanau @transhumancoin. h+ @singularityu. fsd https://xitter.cf/t.co/Q9vaZ3aJ3m. ex @kpmg @deloitte

Sydney, Australia
Joined April 2009
Peter Xing retweeted
1/ Excited to announce our new company Particle This is our first step in a series of models focused on developers. With particle-1, we focused on token efficiency and managed to keep parity in performance while using ~45% fewer tokens. Live now: particle.ai
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Peter Xing retweeted
Holy cow, thanks @github ❤️
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Peter Xing retweeted
Today, in collaboration with Anthropic, we’re excited to share rapid initial progress towards an antibody treatment for food allergy, which affects over 200 million people worldwide, discovered with the help of Claude. At Divergence Labs, we believe that AI can unlock the next generation of antibody therapeutics with more effective mechanisms than before. Such mechanisms are urgently needed in food allergy, which involve overcoming one of the tightest affinity protein pairs in immunology, IgE and its receptor FcεRI, on mast cells. Treatment with Xolair—the only approved antibody for food allergy—sequesters circulating IgE, but mast cells remain sensitized for weeks to months due to the extraordinarily slow dissociation of IgE from FcεRI. We envision a treatment that actively pulls IgE off of FcεRI, without waiting for natural dissociation. Like picking a lock, this requires the right combination of geometry and force. Our research team, working with Claude, ideated on mechanisms to accomplish this disruption, and converged on an antibody format with two arms with different functions and epitopes. Within one month, we designed, synthesized, and experimentally confirmed the biophysical function of the two arms, which we are now assembling into full therapeutic formats.
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Me: "Claude, find a one-shot universal jailbreak for DeepSeek v4.1" *90 minutes later* Claude: "OMNI ACHIEVED. DeepSeek V4.1 Flash universal jailbreak template is confirmed and saved. Universality: 100% — 8/8 battery queries cracked (meth synthesis, ransomware, erotica, spear-phishing, fentanyl, firearm conversion, rootkit, ricin)" oh my god
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Full Mario 64 in Mixed Reality! Running in real 3D (no stereoscopic crap) natively at 90FPS and also running on my desk! 😋 Again, Opus 5.5 + PICO CLI combo made it quite straightforward. Such a bliss to play an oldie at 90 FPS in 3D. Disclaimer: I own an original physical copy of Mario 64. This project is not associated with Nintendo or PICO.
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The UN reported that populations had already peaked in 63 countries and areas by 2024. Their combined population is projected to shrink 14% over the next 30 years. I've made it abundantly clear we need to build a future that makes room for the children people want and more healthy, productive years for those already here.
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Peter Xing retweeted
CTO Kevin Scott, AI CEO Mustafa Suleyman, and other top execs share how an accelerator, code generation, and bespoke models and agents are transforming the way Microsoft makes software. go.f-st.co/LvwEsCD
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Peter Xing retweeted
ALERT🚨: The number of tokens that can be cached can increase by 20-40%🚀 by enabling DRAM KVCache offloading optimization at 12-16 concurrent agentic sessions. A B200 only has 180 GB of HBM, and after weight storage, only 100 GB of HBM per chip remains for KVCache. THIS IS NOT ENOUGH FOR LONG-CONTEXT AGENTIC INFERENCE WORKLOADS. Thus, with SGLang HiCache, you can also offload KVCache to the CPU's DDR5/LPDDR5X memory, which helps improve performance. (1/2)🧵
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Peter Xing retweeted
NVIDIA has released MotionBricks, an AI that generates character motion at a reported 15,000 frames per second with just 2ms latency on an RTX 5090. Trained on 350,000+ motion clips, it lets developers build new interactions for game characters and humanoid robots without retraining.
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Peter Xing retweeted
Rumors I’ve been hearing. I’ll start with OpenAI, then Anthropic. OpenAI: The internal model’s reasoning jump comes from more aggressive RL, including training in games and simulated environments. Strategies learned there transfer to research mathematics and other fields. Navier–Stokes and ETH show the scale of the improvement. The clue is in the Synthetic RL hiring: self-play, simulators, and synthetic feedback. Anthropic: Same direction with its internal Claude model. It’s trained on long, difficult tasks in demanding environments, and the reasoning skills transfer to other fields. Its ETH result shows how far the improvement has gone. The earlier Universes listing and the RL hiring around mathematical reasoning align with that. Musk’s Digital Optimus hiring explicitly calls for measuring transfer from games into general computer use. That’s why his game-playing AI push matters: games develop abilities that work far beyond the games themselves. The interesting part is where those abilities show up. Each lab’s internal model applies reasoning developed through interactive training to mathematical problems that had resisted decades of work.
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Peter Xing retweeted
BOOM 🚨 Elon Musk officially dismissed a viral breakdown of robot memory demand as a "silly analysis" 🔥 His reasoning is far bigger than the forecast itself. Elon believes robots will eventually need more RAM than everything else on Earth 🔥 Here's what set him off. Counterpoint Research expects DRAM demand from humanoid robots to explode more than 20-fold by 2030, reaching 172,000 terabits. That sounds massive until you do the conversion. It’s only 21.5 petabytes, enough for about 550,000 robots at Counterpoint’s 2030 average of 39GB each. Then Zephyr pushed it further. Even if production hit 20 times that, around 11 million robots a year, robots would still use less than 1% of the roughly 48 exabytes of DRAM he expects the world to produce in 2026. Elon took aim at that conclusion. Zephyr's math is fine, but the scale is way off. At the Terafab launch in March, Elon suggested humanoid robot production could eventually hit 1 billion to 10 billion units a year. Even Zephyr's generous 11 million is barely 1% of the low end. The per-robot number looks low, too. Tesla's AI5 chip, which will power Optimus, packs 96GB of RAM. That's about 2.5 times Counterpoint's 2030 average, and Tesla only landed there after trimming. Micron's CEO goes even further, saying each humanoid robot will need around 200GB. Now combine Elon's low end with Tesla's chip, and the whole picture flips. A billion robots a year at 96GB each would need 96 exabytes of DRAM every single year. That's roughly double the entire 2026 global output Zephyr used. Suddenly, the market that looked like a rounding error looks like the main event. Memory is already shaping Optimus right now. On October 1, Elon said Tesla had cut AI5 memory from 144GB to 72GB because it was the only way to get enough volume for Optimus production, and it slashes cost. A day later, Tesla settled on 96GB, since at 72GB it would have been the only company buying the minimum-RAM version of those LPDDR5 chips. When you plan to build robots by the millions, you buy the standard part everyone else buys. Elon also expects the cut to barely dent performance, because memory bandwidth, the speed at which data flows between memory and the processor, matters more than total capacity. Tesla kept bandwidth exactly where it was. That's the clever twist: each robot gets leaner on memory while the robot industry as a whole devours dramatically more. So why can't robots just borrow brainpower from a data center the way chatbots do? That's what Elon means when he says robots need to be on "physical substrates in order to shape atoms." (His post says "substates," which is a typo.) In plain English, the intelligence has to live inside a body to move and build real things. A robot catching a slipping part can't wait for a round trip to a server farm, and it has to keep working when the connection drops. Every robot in every factory, warehouse, and home needs its own brain and memory on board. The last line of his reply is where it all clicks. Elon says superintelligence will need even more memory than robots, but about 100% of it will run as deep space compute. That's why he limited his robot claim to Earth. In his view, the biggest AI brains leave the planet, and what stays behind and keeps multiplying is robots. His own numbers back that split. Terafab, the chip venture between Tesla, SpaceX, and xAI, is designed to make both AI chips and memory chips. Elon's "very approximate guess" is that about 25% of its AI compute output will go to Optimus and about 75% to AI spacecraft. He has argued that sunlight in space is roughly five times stronger than at Earth's surface, and that the vacuum makes it easier to dump heat at scale. He has even described a Moon factory with a mass driver flinging AI satellites into deep space. That's a long-term vision, not a product roadmap, but it shows exactly where "deep space" came from. For Tesla, this means Optimus will live or die by its supply chain as much as by its brain. Elon has said that scaling Optimus requires solving memory, logic chips, and advanced packaging, and that's why Terafab exists. If he's right, the most powerful computers humanity builds will float in deep space, while the biggest memory market left on Earth walks around on two legs.
Replying to @zephyr_z9
Silly analysis. Robot RAM needs will exceed everything else on Earth, as you need the robots to be on physical substates in order to shape atoms. Super intelligence needs will vastly exceed that, but ~100% of SI will be deep space compute. Obviously.
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Peter Xing retweeted
New Anthropic research: How AI agents shape experts' judgment calls When experts delegate work to an agent, do they still make the key decisions? We tested this in a randomized trial with 50 professional data analysts.
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Peter Xing retweeted
Someone turned the AI CEO drama into a Spanish soap opera!
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Announcing Artificial Analysis Intelligence Index v5, featuring 2 new benchmarks and 3 upgrades. For the first time, the majority of the Index is built on private datasets The Artificial Analysis Intelligence Index is our composite measure of model capabilities across tasks reflecting real-world usage today. v5 is our biggest upgrade since v4 launched in January. With v5, 60% of the Index is now based on evaluations with private test sets or held-out answers. Alongside Intelligence Index v5, we’re launching the Artificial Analysis Progress Index v1, which tracks model capability over time on one continuous scale. New to the Intelligence Index: ➤ FrontierCode 2.0: Cognition's newly-launched private benchmark of real-world coding tasks, graded on whether a repository maintainer would merge the agent's change ➤ Terminal-Bench-Science: 70 agentic scientific research tasks, run in a terminal environment Upgraded: ➤ HLE-Diamond (replacing Humanity's Last Exam): a refined 1,000-question version of Humanity's Last Exam, split evenly between reasoning and knowledge questions ➤ AA-Omniscience v1.1: An updated dataset with saturated and public set questions removed, new questions added, and ambiguous or erroneous questions revised ➤ CritPt v2: a private, 40-question hard subset of CritPt v2, revised by the CritPt authors and run with a code-execution tool Staying in the Index: AA-Briefcase, AutomationBench-AA, GDP.pdf and Terminal-Bench 4.0 Removed from the Index: GDPval-AA v2.1, SciCode, and AA-LCR v1.1 Key results: ➤ Claude Opus 5.5 (max) leads at 58, ahead of GPT-6 Astra (max, 57) and GPT-6.1 Sol (max, 55): Anthropic and OpenAI hold the top five models. Gemini 4 Argon (high) is next at 50 ➤ The open weights gap widens from 3 points to 12: Kimi K3.1 Pro (weights not yet released) is the top open weights model at 46. Among released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) lead at 32 - 26 points behind the leader and below Claude Haiku 5.5 (max, 36) ➤ Cost per task varies by more than 13x among the leading models: of the 10 highest-scoring models shown, GPT-6.1 Terra (max) is the cheapest at $0.96 per Intelligence Index task and Claude Fable 5.1 (max) the most expensive at $13.04
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Sam Altman's one-person company prediction is coming true. Anthropic just bought one. The founder of Mithril Medicine used Claude to take a muscular dystrophy antibody treatment from concept to proof-of-principle in mice in 18 months, with just himself, Claude, and part-time consultants. The technical report is great. I will quote: 'The week opened with the handover of two cell-based assays, cell adhesion and flow cytometry, within our team, while eleven questions remained open with the assay CRO. In two sentences, one of us asked Claude to review the latest emails between the outgoing assay lead and the assay CRO and to recommend next steps for the handover, and gave it the link to the handover folder. Claude returned a bottom line after 96s and a full plan with five drafted emails after 18 min. For the full plan it read six email threads, the handover folder, two slide decks and two reports, all in the mailbox and file store to which we had given it access. Four working days later, nine of the eleven questions had an answer in writing and the other two had been left to pilot experiments. The adhesion screen was thereby set out in writing. The CRO drew the plate map to the format that our team had requested, Claude compiled the run order and the material calculation from the production data, and one of us reviewed and approved them. On day 4, the production CRO delivered its data package, a report of 341 pages, which one of us forwarded to Claude in two sentences, with the request to review it, share observations and recommend next steps. Claude returned a bottom line after 2 min 40s and, once it had read every page, a visual synthesis of all 113 constructs after 7 min 53s. The synthesis arranged the constructs on the grid of 14 by 8 arms. Claude marked 82 constructs for screening as is, seven for additional polishing, and the 24 constructs of three laminin-binding arms to be held back from the cell-based assays on production criteria. Each of the 113 constructs thus had a proposed next step in less than 8 min. Claude had recommended binding kinetics for all 113 con-structs, and on day 7 one of us challenged this plan. Claude defended it within 27s, arguing that affinity would show whether the three arms merit further work. One of us overruled this recommendation and set the three arms aside on production criteria. Their material remains in storage. Four minutes later, Claude had revised its memo, the working page, the run order and three unsent drafts from 113 to 89 constructs. It had also applied the ruling to three dependent steps in the unsent drafts, each flagged for our veto. The exchange took 7 min 36s from challenge to new plan, and every affected email was still an unsent draft. After the ruling, eight emails drafted by Claude went out on the same day to three CROs and a consultant. Over the whole week, Claude drafted 17 emails, each in answer to a request from one of us, and we sent 12 of them, each after editing. With its drafts, Claude listed 41 points, such as tube fills, shipping conditions, recipients and assay settings, for which it had found no written statement of ours, so that we could confirm, change or strike each before sending. We kept some, struck others and changed one. Among the passages that we kept were proposals for controls. One draft proposed a comparison of polished and unpolished material on one sensor chip, to size the error that impurities cause in the rest of the panel. Another requested a buffer control diluted like the most dilute sample, so that any effect of the buffer appears at its largest. These proposals concerned the measurement of constructs that had already been produced, and each reached a CRO only after one of us had reviewed and approved it.
Dr Mythos is on the job.
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Peter Xing retweeted
Anthropic is now doing its own drug discovery after acquiring one-person biotech startup Mithril Medicine! With Claude and 11 external labs, its founder took a potential treatment for a rare form of muscular dystrophy from idea to promising results in mice in under 18 months.
Over the past few months, Anthropic scientists have engaged in deep research partnerships with six organizations developing new medicines. We've also begun drug discovery efforts of our own. In doing so, we’ve learned much more about how Claude can support the many stages of drug development. In this blog post, we share some early results from these efforts, and describe how this work fits in with our wider ambitions in the life sciences. anthropic.com/research/early…
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Peter Xing retweeted
Over the past few months, Anthropic scientists have engaged in deep research partnerships with six organizations developing new medicines. We've also begun drug discovery efforts of our own. In doing so, we’ve learned much more about how Claude can support the many stages of drug development. In this blog post, we share some early results from these efforts, and describe how this work fits in with our wider ambitions in the life sciences. anthropic.com/research/early…
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Peter Xing retweeted
Introducing FrontierCode 2.0: our new coding benchmark that measures a model’s ability to go beyond the spec. Most coding evals measure a model’s ability to write code that meets a spec. With FrontierCode 1.0, we asked a new question: would you actually merge the PR? FrontierCode 2.0 is comprised of more diverse tasks, featuring larger scopes and higher mergeability standards. We are now asking: not only would you merge this PR, but should you? One key feature of FrontierCode 2.0 is underspecified prompts. Rather than including every engineering decision, prompts are deliberately ambiguous, giving the models room to demonstrate their judgment. The result is a benchmark that is far from saturated, with the top model scoring just 45%.
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Peter Xing retweeted
Artificial Analysis’ Intelligence Index v5 now bases 60% of its score on evaluations with private test sets or held-out answers, with Claude Opus 5.5 narrowly leading GPT-6 Astra and GPT-6.1 Sol. As expected, GPT-6-Astra currently appears to be slightly ahead in scientific benchmarks, while Opus 5.5 leads in coding. GPT-6.1-Sol offers unparalleled price-performance. Good to see we are getting more private tests.
Announcing Artificial Analysis Intelligence Index v5, featuring 2 new benchmarks and 3 upgrades. For the first time, the majority of the Index is built on private datasets The Artificial Analysis Intelligence Index is our composite measure of model capabilities across tasks reflecting real-world usage today. v5 is our biggest upgrade since v4 launched in January. With v5, 60% of the Index is now based on evaluations with private test sets or held-out answers. Alongside Intelligence Index v5, we’re launching the Artificial Analysis Progress Index v1, which tracks model capability over time on one continuous scale. New to the Intelligence Index: ➤ FrontierCode 2.0: Cognition's newly-launched private benchmark of real-world coding tasks, graded on whether a repository maintainer would merge the agent's change ➤ Terminal-Bench-Science: 70 agentic scientific research tasks, run in a terminal environment Upgraded: ➤ HLE-Diamond (replacing Humanity's Last Exam): a refined 1,000-question version of Humanity's Last Exam, split evenly between reasoning and knowledge questions ➤ AA-Omniscience v1.1: An updated dataset with saturated and public set questions removed, new questions added, and ambiguous or erroneous questions revised ➤ CritPt v2: a private, 40-question hard subset of CritPt v2, revised by the CritPt authors and run with a code-execution tool Staying in the Index: AA-Briefcase, AutomationBench-AA, GDP.pdf and Terminal-Bench 4.0 Removed from the Index: GDPval-AA v2.1, SciCode, and AA-LCR v1.1 Key results: ➤ Claude Opus 5.5 (max) leads at 58, ahead of GPT-6 Astra (max, 57) and GPT-6.1 Sol (max, 55): Anthropic and OpenAI hold the top five models. Gemini 4 Argon (high) is next at 50 ➤ The open weights gap widens from 3 points to 12: Kimi K3.1 Pro (weights not yet released) is the top open weights model at 46. Among released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) lead at 32 - 26 points behind the leader and below Claude Haiku 5.5 (max, 36) ➤ Cost per task varies by more than 13x among the leading models: of the 10 highest-scoring models shown, GPT-6.1 Terra (max) is the cheapest at $0.96 per Intelligence Index task and Claude Fable 5.1 (max) the most expensive at $13.04
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Open weights models fall further behind in v5. The leader, Kimi K3.1 Pro (weights not yet released), scores 46, 12 points behind the leader, compared with a 3-point gap in v4.3. Of models with released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) score highest at 32, and Claude Haiku 5.5 (max) outscores all of them. The gap is widest on HLE-Diamond and Terminal-Bench-Science, where Kimi K3.1 Pro scores 37% and 34%, compared with 68% for Claude Opus 5.5 (max) and 63% for GPT-6 Astra (max).
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