🛠️ Building data infra for AI/ML. Ex-Data Scientist @Microsoft. Created DVC, now DataChain. PhD in CS. Serious about data. Less serious about everything else.

San Francisco, CA
Joined October 2011
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Replying to @ajs6888
Exactly 😄 this is where the fun begins (Glad X can translate in one click)
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Claude Code and Codex were built for weightless data. Physical AI data has mass. My talk on the other physics of agents: youtube.com/watch?v=bUJgirn4…
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This made me check my own citations. Turns out DVC has 40+ citations which is more than my PhD research Apparently, open source is a better academic strategy than academia 😄 @shcheklein @rkuprieiev @isidentical congrats, we’re academics now. Who knew?
You don’t have to publish papers to have academic citations or even an h-index. I dropped out of uni years ago 🤦‍♂️, and always wanted to contribute in different ways: open-source libraries, datasets, benchmarks, and overly long blog posts. I finally created a Google Scholar profile and started linking my GitHub repositories and articles. It turns out some academics are kind enough to cite good ideas regardless of where they come from 🫶 Scholar’s attribution machinery is hilariously basic: it split citations between GitHub and Zenodo versions, missed entire projects, and failed to connect several papers to the software or articles they explicitly reference. Nevertheless, I have already amassed a monumental h-index of 3 😂 Open-source devs, your software may already be part of the academic literature. You might even have an h-index without knowing it. Go check Google Scholar, you may be pleasantly surprised! P.S. Geoffrey Hinton’s h-index is 192. Only 189 to go.
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Replying to @dexhorthy
Strongly agree. Today's coding RLVR mostly targets functional correctness, not design or maintainability. The real leap comes when RLVR adds multi-objective evaluation for design, code quality, and maintainability. Then comes the sequel, @dexhorthy: why great software still needs humans 😄
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Replying to @signulll
My ex-teammate made the same choice. A US PhD and Big Tech experience made it much easier for him to start a company in China.
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Coding-agent intuition breaks down on physical-world data That’s my AI Engineer World’s Fair Online Track talk this week. See you there! I’ll be the guy in the Villain-Con shirt 😄
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Claude and ChatGPT are both down. Tokenmaxing seems to work.
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Replying to @ChiefScientist
This is how we vibe coded in 2000s
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Replying to @fantopy
It’s about Data Agents over S3. Semantic Layer for Files.
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Talked about data agents beyond SQL at @pydatalondon today. Files, video, sensors, metadata, Python workflows - all the messy stuff where real data work happens. Thanks everyone who came by 🙌
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Replying to @trikcode
5 —> 128 Another proof that 10x productivity isn’t a limit
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Replying to @ArchiveExplorer
Is it because Claude code, I guess?
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Excited to speak at @PyData London this Sunday! I’ll be talking about data context for agents beyond SQL: video, sensors, files, metadata, and Python workflows. 10:15 · Hardwick Hub Conference Room Come say hi!
Tomorrow, PyData London 2026 begins. If you have your ticket, we'll see you there. If you don't have yours yet — there's still time: hubs.la/Q04jpJ3R0 - - - PyData London 2026: Convene Sancroft, St. Paul's Tutorials: Friday, June 5-7 Talks: Saturday and Sunday, June 6-7 Keynote Speakers all 3 days Tickets: hubs.la/Q04jpJ3R0
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Replying to @Hans365days
Depends on the problem but yes, context engineering usually takes majority of the time. In data likely 90%
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Replying to @BlackDotttt
Find 5 differences 🙂
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