Latvia
Joined February 2012
Kristaps Bude retweeted
GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in making cholesterol. GPT-Rosalind suggested a possible explanation: cholesterol may not be reaching where neurons need it. That gives scientists a specific mechanism to test and, if confirmed, a direction for developing new treatments for neurodegenerative disorders. With @perturbai_tx and @scrippsresearch, a team of thousands of GPT-Rosalind agents worked in parallel, connecting gene-expression results from millions of mouse brain cells with scientific literature in just 32 minutes. We’re now expanding this collaboration to identify new drug targets. openai.com/index/scaling-ai-…
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This might be the pinnacle moment of the strongest El Niño in history. Pacific Super Typhoon Champi is unbelievable by every metric. A buoy just measured a ~909 MBAR pressure at the edge of her 40+ mile-wide eye, which has now reached a blistering dryness of -10.94 C. Her eyewall is a 180+ MPH (290+ kph) buzzsaw of cyclonic violence, and her CDO is a perfect Saturnian ring of icy, -110 F (-80 C) convective thunderstorms. And she is still. Getting. Stronger.
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Kristaps Bude retweeted
Google DeepMind proved you can boost an LLM’s reasoning without adding a single parameter. They proposed a 4th scaling dimension beyond depth, width, and params. For years, the entire AI industry has been operating on a single assumption: to make an AI smarter, you have to make it bigger. More parameters. More GPUs. More money. If you wanted elite reasoning, you had to pay the massive enterprise API tax. But, DeepMind published a paper that completely breaks the scaling laws of artificial intelligence. They proved you can exponentially boost an LLM's reasoning capability without adding a single new parameter. Zero extra size. Zero extra memory footprint. How? They introduced a concept called "Virtual Logic Depth". Instead of building a massive tower of new layers, they just take the existing layers and run the data through them in a loop. They force the same parameters to think about the problem multiple times before answering. DeepMind showed that the number of parameters dictates a model's knowledge capacity, but not its reasoning capability. This fundamentally changes the economics of AI. Small businesses, operators, and developers don't need to rely on trillion-parameter monopolies to solve complex problems anymore. We can take tiny, cheap, local models and loop them until they out-reason the giants. The future of AI isn't about building a bigger brain. It's about teaching a small brain how to think twice.
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Kristaps Bude retweeted
1/4 Today, we’re releasing Mercury 3. Mercury 3 is by far our most powerful model and the most intelligent diffusion LLM ever released. It outperforms GPT-6 Luna and Haiku 5.5 on quality while running at 1000+ tok/sec.
🤖 Made with AI
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Kristaps Bude retweeted
We have a huge pivot underway here at Epic, moving developers around to retool for maximum AI acceleration. The aim isn't to do the same stuff with fewer people, but to enable everyone here to build MORE and BETTER stuff, and to bring the benefits to all teams using Unreal.
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Kristaps Bude retweeted
gpt-6.1 sol examined nearly 800 mathematics papers, finding major errors in 6% and minor mistakes in another 30%. researchers estimate that 90% of its major findings are correct i'm glad AI is now cleaning up decades of human slop
How often do ArXiv papers contain errors in the main theorems they claim to prove? Using GPT-6.1 Sol to identify such mistakes, it finds: - 6% of papers before 08/2026 contain main theorems that are false. - Only 1.5% contain similar mistakes in the last two months.
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This graph is all you need to know
The cost is dropping faster than any transformative technology in history, 54 times faster than electricity, and it's been dropping at this same rate since GPT-3.
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HERMES CAN NOW CONTROL YOUR PHONE. 🤯📱🔥 A new Phone Harness plugin gives Hermes the ability to actually operate an iPhone or Android. We're talking real phone control: • See what's on the screen • Open apps and navigate menus • Tap, swipe, scroll, and type • Read the results and continue working • Handle multi-step tasks inside mobile apps And here's what makes this so interesting... Hermes doesn't need a dedicated API integration for every app. It can interact with the same interface you use. Supports iPhone through Mac mirroring, Android through ADB, and cloud phones. Think about how many new workflows this opens up for Hermes. Repo + setup in the first reply. 🔥 @phone_harness
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Kristaps Bude retweeted
GPT-4 was the undisputed best model in the world for a full year and we all debated whether anyone could catch up or if OpenAI had some secret formula that nobody else knew about. Now there are are 19 AI models that can run on a modern smartphone that are better than GPT-4!
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Kristaps Bude retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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Kristaps Bude retweeted
We've partnered with @OpenAI to bring the new Sign in with ChatGPT experience to Nous Portal. Log into Nous Portal with ChatGPT to use your plan in Hermes Agent, with full visibility and controls in ChatGPT settings.
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Kristaps Bude retweeted
🚨 Claude Opus 5.5 on the AI-2027 chart same formula the chart uses , just fed it Epoch's ECI as of Oct 10 > Opus 5.5 : 11.7h > GPT-6 Astra : 10.0h , not 16h (its ECI got revised 169.2 → 166.45) both sit 7-8 weeks behind the curve , pacing the frontier.
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Kristaps Bude retweeted
Forget lidar. One single camera. Runs in real time & is open source: A streaming 3D model that reconstructs scenes live, at ~20 FPS, over long sequences. End-to-end. Optimization tricks, cleanup steps? Nope. And it beats both streaming and even some offline methods. Perception is becoming software-first. Closer to machines that see and understand the world as it unfolds. Thanks for sharing, @YinghaoXu1 📍Models: huggingface.co/robbyant/ling… Project page: technology.robbyant.com/ling… Code: github.com/Robbyant/lingbot-… Paper: arxiv.org/abs/2604.14141 —— Weekly robotics and AI insights. Subscribe free: 22astronauts.com
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Kristaps Bude retweeted
Cool eval. Simply ask an LLM “Land or Water?” and give it a latitude and longitude coordinate as text. Ask 16,200 times, plot as image. The models know. From compressing the internet.
Replying to @celestepoasts
results for all claudes
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Kristaps Bude retweeted
Claude Opus 5.5 just took a big drop on NerfBench. Yesterday it was scoring above launch. Today it's at 94.2%. GPT 6 Astra: 98.0% Sonnet 5.5: 100.9% GPT 6.1 Sol: 106.7% 94.2% is still inside normal variance, so we can't call it a nerf yet. But we're watching Opus 5.5 very closely.
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Kristaps Bude retweeted
Very excited to announce Gemini 4 Argon, our new Frontier AI model, and a huge step forward across key capabilities. We’re focused on rolling it out responsibly starting with government and trusted cyber defenders through our Fairwind Program today, before wider availability soon
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Kristaps Bude retweeted
Here are some of the behind the scenes for the F.02 Decommission To be clear: we actually trained our robots to jump autonomously, shipped them to Finland, and had them leap into a vat of molten steel
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Kristaps Bude retweeted
Leaving aside the arguments over the reasons why this has happened, it is shocking that Europe does not have a single frontier AI lab, nor even a near-frontier lab nor even an effort that could likely lead to building up a frontier lab in the future.
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Vācija, pēc mūsu lūguma, pastiprināti sargās mūsu gaisa telpu vēlēšanu laikā 1/2/3 oktobrī. Thanks to @_FriedrichMerz for support Mūsu sabiedrotie ir kopā ar Latviju. Tas ir konkrēts NATO solidaritātes apliecinājums un mūsu kopīgās apņemšanās sargāt Latvijas gaisa telpu un cilvēku drošību. Paldies Vācijai par atsaucību un ciešo sadarbību!
Pēc Latvijas valdības lūguma Vācija aptuveni uz nedēļu izvietos Lielvārdē četrus Eurofighter iznīcinātājus un savus speciālistus, lai pastiprinātu Latvijas gaisa telpas uzraudzību pirms 3. oktobra Saeimas vēlēšanām un vēlēšanu laikā. 📸 Thorsten Jakobs, Bundesvērs (Bundeswehr)
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