Joined September 2023
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Hi all, thanks for checking out my page! Wanted to share a bit more about my background than the X bio allows: - Finished school at 16 - Started as an AI researcher at JetBrains at 18 - By 21 I was coordinating a research direction there and published 5 papers in one year - Then I joined Google DeepMind Game Theory Group - Published a paper in Science at 23 (I honestly thought that milestone would come closer to 40) - Later went to Google X to work on an exoskeleton project (wanted to see what the product side looked like) Now I'm building something new. Stick around ✨
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vitamin and supplement bars at offices would be a better perk than snack bars, just saying
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Sunday reset before another heavy week. The view helps.
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Is it me or AI is literally everywhere now?
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CVPR best paper, congrats @DrJimFan and the team! worth reading.
NitroGen just won CVPR Best Paper Honorable Mention!! We are making strides towards general-purpose embodied agents that master not only the real world physics, but also all possible physics across a multiverse of simulations. It’s been 4 years since MineDojo, our first embodied agent in Minecraft, won NeurIPS Best Paper. Congrats to everyone on the team!!
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Now I get it why Pope works with Anthropic! God asked for more fable tokens, because people on x say it is good at game engines
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what @axionlabsinc is building matters so much restoring independence to people who've lost it is one of the most meaningful things technology can do 🙏
Imagine trying to type an email by blowing into a straw. For 8 million people in the US & Canada with upper limb impairment, this is their reality. @axionlabsinc has built a solution that restores dignity and independence with Click, a non-invasive BCI. Watch my full chat with @Reddy_XX and @TinTunjic at @southpkcommons below.
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1k followers in one month of actually posting thank you to everyone who finds this useful! much more coming 🚀🚀🚀
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many products today are built on AI pricing that may not stay this low forever, and some startups have no backup plan if the economics change three things that help: (1) open-source models, (2) smaller cheaper models, and (3) smarter model routing in practice this means: - start using OSS now even though it's worse - for simple tasks, use small models - for complex tasks, use a constellation of models
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So excited to work together with Ana, will be sharing some updates soon! 🚀
I've been posting more actively lately and wanted to properly introduce myself for people who don't know me personally: • At 16 I finished school, and at 20 got my BSc in ML • At 21 moved to the Bay Area: I sent 200 applications after graduating, and got exactly one offer there 😅 • At 22 I came across @80000Hours and decided I needed to work on AI Policy • At 23 I got into the AI Governance Masters at @TU_Muenchen (top-20 CS unis in the world) • At 25 I volunteered on responsible AI reports with @AllTechIsHuman and AI Standards Lab • At 26 I joined @farairesearch, a leading technical AI Safety research org Now back to fundamental ML research and working on something new. More soon 😉
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Tamaz is running a series on fundamental texts for AI researchers, worth following! This one landed for me: if generative training is just learning increasingly precise approximations of a shared reality structure, then interpretability research has a cleaner target than most people assume. So the research direction could be to characterize that platonic space directly.
day 2/n The Platonic Representation Hypothesis by @minyoung_huh, @thisismyhat, @ssnl_tz, and @phillip_isola this one keeps generating research questions the more you sit with it the central claim is that any model trained on enough data essentially approximates some shared platonic space of meanings representations from different models (not necessarily from the one domain so could be LLMs, vision models, whatever) converge toward this space and preserve the same relational structure between concepts so training a generative model is really just learning increasingly precise approximations of this underlying space (and different architectures produce same destination) very philosophical, very worth reading!
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last frontier labs batch for now bio and world models coming next - @_inception_ai (@adityagrover_, @StefanoErmon) - @ElorianAI (@AndrewDai, @yinfeiy) - @si_pbc (@_neelr_, @devanshpandey) - @adaption_ai (@sarahookr) - Sooth Labs (@rsalakhu) x.com/i/lists/20617784890369…
been building this list for a week now to follow the people behind the labs (as these are two very different feeds) this batch is particularly interesting because almost all of them are betting on something architecturally non-standard: - @SakanaAILabs (@hardmaru) - @liquidai (@helloiamleonie, @maximelabonne, @xanamini) - @flappyairplanes (@aidanmantine) - @magicailabs (@EricSteinb) - @ndea (@mikeknoop, @fchollet) - @_doubleAI_ (@YoavLevine) full list: x.com/i/lists/20617784890369…
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almost any task can be improved with an actor-critic setup the question is always whether the gains are worth the added complexity and compute
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my dream researcher: - works relentlessly - does design / roadmap - lots of exposure to latest and classic papers - deeply curious - strong network, great with people - thinks at scale - sharp on details - has a vision for where the field is going - can visualize ideas clearly - always mentions problems & limitations - writes good full stack code - autonomous
my dream angel investor: - human (or fund, no ego) - check size: $25,000 to $1,000,000 - says yes and actually wires - signs the safe without redlining it - doesn't want a board seat - makes the intro before i ask - doesn't ghost after "keep me posted" - reads my monthly updates - writes the check this quarter, not "next fund" - already a paying aesty subscriber (annual, obviously!)
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been building this list for a week now to follow the people behind the labs (as these are two very different feeds) this batch is particularly interesting because almost all of them are betting on something architecturally non-standard: - @SakanaAILabs (@hardmaru) - @liquidai (@helloiamleonie, @maximelabonne, @xanamini) - @flappyairplanes (@aidanmantine) - @magicailabs (@EricSteinb) - @ndea (@mikeknoop, @fchollet) - @_doubleAI_ (@YoavLevine) full list: x.com/i/lists/20617784890369…
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been building this list for a week now to follow the people behind the labs (as these are two very different feeds) this batch is particularly interesting because almost all of them are betting on something architecturally non-standard: - @SakanaAILabs (@hardmaru) - @liquidai (@helloiamleonie, @maximelabonne, @xanamini) - @flappyairplanes (@aidanmantine) - @magicailabs (@EricSteinb) - @ndea (@mikeknoop, @fchollet) - @_doubleAI_ (@YoavLevine) full list: x.com/i/lists/20617784890369…
continuing with robotics, a field close to my heart 🤖 - @SkildAI (@pathak2206) - @physical_int (@hausman_k) - @RhodaAI (@startupjag, @GordonWetzstein)
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good resource if you're building with AI and want working examples to learn from 50 open-source AI engineering projects covering agents, RAG, multimodal, and OCR each with full code and setup instructions, production-ready and free
Hands on AI Engineering! I open-sourced a collection of 50 hands-on AI engineering tutorials. It features step-by-step projects and tutorials on: • AI Agents and Multi-agents • RAG (Agentic, Vision, and Local) • MCP AI Agents • OCR Apps • Voice AI Agents • & so much more 100% free and open source. 1k Github stars I've shared the link in the comments!
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agent rule #1: never change the code and the tests at the same time
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researcher mode: off
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