
🛠️ 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)
Physical AI data breaks the usual agent loop.
Tomorrow: Claude Code over raw video, metadata, and expensive compute in S3.
Live demo: 8am San Francisco / 4pm London / 5pm CET
Join us: luma.com/3jjb56bx
Going deeper this Thursday - with a live Claude Code demo over raw Physical AI data in S3.
8am San Francisco / 11am NY / 4pm London / 5pm CET
luma.com/3jjb56bx
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…
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.
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 😄
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.
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 😄
Replying to @fantopy
It’s about Data Agents over S3. Semantic Layer for Files.
Call for Proposals for AGNTCon + MCPCon North America 2026 is open on Sessionize and I've just submitted a session! sessionize.com/agntcon-mcpco…
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 🙌
Replying to @trikcode
5 —> 128 Another proof that 10x productivity isn’t a limit
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
Replying to @Hans365days
Depends on the problem but yes, context engineering usually takes majority of the time. In data likely 90%








