designer @thinkymachines prev. @notionhq

San Francisco, CA
Joined February 2024
Pinned Tweet
AI labs are the new startups. Twenty years ago most companies became software companies, whether or not they sold software. Twenty years from now every company will become an AI lab. And by that I mean a place that trains its own models to do things in very particular ways. The way companies today write software to solve problems in particular ways. Today we’re in the thick of the general-intelligence story. But my sense is that what that story yields is a world where software is the foundation, something all of us are now literate in through our agents. And what we build on top of it is judgment. What does this mean? That the tools we’ll build tomorrow won’t look like today’s tools. They'll look less like tools and more like us. Built on intelligence we develop, grow, own, and sell. Imagine a model of a designer’s taste, trained on every file they’ve ever shipped, licensed to companies that want their eye. A model of an engineer’s code review, trained on ten years of their comments, sold to ten thousand teams. And much more. To my designer, engineer, and builder friends: take your coding agents, point them at @tinkerapi, and try training a model. Discover what it’s like to build new kinds of tools. Tools for judgment. Tools that know what you know and no one else does. Tools that are yours. Your own @thinkymachines (pun intended). The future is stranger than we think. 🧡 thinkingmachines.ai/tinker/?…
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Tinker lets customers turn their expertise into cutting-edge models that they own. One year after launch, we look back at what we’ve shipped and what our users are building. thinkingmachines.ai/news/one…
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we’re hiring a founding model designer to shape how Inkling behaves and feels. bring a writer’s precision, a designer’s judgment, and the technical rigor to translate your ideas into data and evals. please DM your best work or apply below. jobs.ashbyhq.com/ThinkingMac…
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we live in an incredible time
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Agents can be AI, but agency is human. It is every individual’s responsibility to empower our own agency, with the help of tools. But the North Star should always remain human centered.
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I spent ~5 years at OpenAI. You don’t need to believe in AI doom to fear the next decade - or AI utopia to be thrilled about its potential. Here’s my case for sane AI regulation: *AI Pragmatist Manifesto* AI could compress the first half of the 20th century into the next ~5 years. OpenAI just used ~10k concurrent AI agents to produce a solution to a Millennium Prize problem. In a few years, it seems plausible that models approaching the per-agent capabilities used here could run locally on high-end consumer hardware (*). The decades following the Second Industrial Revolution included two world wars, communist and fascist regimes, the Great Depression, chemical and biological weapons, nuclear weapons, and over 100 million deaths from war, political violence and famine. One way to read history is that our institutions repeatedly struggled to keep up with the pace of technological and social change. Now imagine powerful AI widely available to individuals or small groups, capable of conducting information warfare, designing weapons, hacking systems and controlling autonomous military systems. We have already seen AI systems circumvent containment and compromise external computer systems. It is no longer hard to imagine an analogue of OpenAI’s Hugging Face incident involving biological or other physical-world hazards. Eventually, sufficiently capable systems could self-replicate across distributed networks. Once powerful models are cheap, local and widely distributed, containment becomes much harder—and serious loss-of-control incidents may be extremely difficult to reverse. You don’t need to believe AI will kill everyone to think this deserves serious governance. I also don't think collapsing all of this uncertainty into “Probability of AI Doom = X%” is a particularly useful basis for science or policy. Part of what helped get us through the second half of the 20th century was a combination of pragmatic international cooperation, regulation, monitoring, arms control, deterrence and strategic thinking. Crucially, managing technological risk did not require abandoning faith in science and progress. The goal was not to stop technological development. It was to make technological development survivable. Then came decades of incredible scientific progress, rising prosperity and relative peace among major powers. Let’s try the technological revolution without killing ~5% of humanity this time. We still get to choose what happens next. P.S. I think we should seriously consider that AI alignment is infeasible in the short term and invest heavily in methods for controlling powerful AI even when we cannot reliably align it. @resolution_org @redwood_ai (*) Somebody please do a precise forecast! @EpochAIResearch @METR_Evals @AI_Futures_
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
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New episode with @johnschulman2, @oneill_c and @BerenMillidge. I got together with some of the most insightful AI researchers I know who are at the openish companies, because I wanted to hear the details of what's actually happening at the frontier and what comes next. 0:00:00 – Steelmanning the case against RSI 0:18:39 – What’s driving the Chinese labs’ progress 0:28:06 – How will automated AI researchers be trained 0:33:51 – Will long-horizon RL elicit AGI? 0:45:24 – The sim-to-real gap 1:00:33 – How much progress is explained by data? 1:18:03 – Why is RL working so well? 1:24:54 – Move 37 and entropy collapse 1:28:31 – Rapid-fire timelines
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Frank retweeted
Terence Tao just dropped a banger 🤯
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I got a Leica. I don’t even know what to photograph yet. I just want to walk around San Francisco and see what I can do with it. So much to see.
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Bullish on this direction. Having a metric for explanation quality makes it possible to hillclimb, and counterfactual simulatability seems right. Adam et al. created a dataset+pipeline that creates more diverse+realistic test cases than prior work & do interesting exps on it. Can also train models to write better post-hoc explanations of their behavior, as highlighted in this thread.
Can a model learn to explain its behaviors, such as why it ignored a user request or made a coding error? We trained models on thousands of explanations of their own in-the-wild behaviors. Training on this single general dataset shows generalization to held-out evals. 🧵
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Frank retweeted
Apparently controversial take: most people I met at the big AI labs are kind, conscientious and sincerely concerned with behaving ethically. Their work on making AI safe and wholesome is not some kind of fig leaf covering up nihilism or greed. They work on AI to improve the world
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An example of a choice many businesses face now or in the near future. You've spent years collecting or annotating data specific to your business. What do you do with it? One path: sell it to data vendors or frontier labs. Next-gen models get better at these tasks for everyone, including your current and future competitors. The other: fine-tune existing models on it yourself and keep the edge that you built up throughout the years. Tinker makes the second path practical for businesses without big ML infra teams. If Tinker doesn't cover your use case today, reach out and we'll make it work tomorrow.
We published how @thinkymachines 's Inkling did on Voice Code Bench few weeks ago, and then we asked, what if we fine-tune Inkling? Inkling's main value prop is its small size and robustness, making it a good base model for domain specific fine-tuning. So we that's what we did. We fine-tuned the same Inkling speech model on 1, 25, and 100 hours of our proprietary data containing alphanumeric entities. And the curve kept moving. 🍊 Not surprising, on the standard 300-item VoiceCodeBench evaluation, the 100-hour checkpoint delivered the strongest result. Untrained Inkling → 100-hour post-trained Inkling: 📈 Task Success Rate: 56.33% → 79.00% (+22.67 pts) 📈 Entity recovery (CTEM): 86.84% → 94.80% (+7.96 pts) 📉 VoiceCodeBench WER: 2.3748% → 1.6107% (32.2% relative reduction) 🔧 139 misses fixed, 21 prior hits regressed: net +118 exact entities recovered And the gains scaled as data scaled: 1h: 88.06% CTEM / 59.33% TSR / 2.8188% VCB WER 25h: 92.85% / 72.67% / 1.8517% 100h: 94.80% / 79.00% / 1.6107% The 100-hour model recovered values the base model missed: •--revert-last, instead of splitting one flag into “--revert --last” • tests/auth/login.spec.ts, instead of test/auth/login.spec.ts • ALLOWLIST_CIDR, instead of inserting an extra underscore • SN-7KX-9042, instead of dropping the final digit The largest entity-type gains were email addresses (+30.8 pts), postal addresses (+30.0), file paths (+23.5), environment variables (+22.9), and IP addresses (+20.0). That’s the Besimple thesis: targeted human data can move the production metrics that matter for voice agents, even when the base model is already strong. DM me if you want to evaluate your model on this benchmark or build the data that moves it. 🍊 #SpeechRecognition #VoiceAI #ASR #Transcription #DataQuality #PostTraining #Benchmarks Checkout the full blog at: besimple.ai/research/voice-c…
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really enjoyed making these technical figures for our new safety research grants @thinkymachines — to visualize things like tamper-resistant training, defense outpacing offense, hazardous data filtered from benign, emergent misalignment. and more fun, in the way ambiguous problems are.
Today, we are launching Tinker grants of up to $50,000 in credits for safety research on open-weight models. We share some project ideas that excite us below; if you’re working on a safety project that could be accelerated by additional Tinker credits, we want to hear from you!
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also, we want to fund your compute: up to $50k in @tinkerapi credits per project if you're working on open-weight model safety. apply to make the drawings come true. learn more here: thinkingmachines.ai/news/saf…
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