A community of technologists helping each other go from -1 to 0.
San Francisco, NYC, Bangalore
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Why you’re more likely to find something worth building in a group than alone.
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South Park Commons retweeted
I had a fun conversation with @adityaag at SPC a few weeks ago.
“The universe is really big, and almost none of it is inside computers.”
When a problem is hard, make it bigger. @svlevine put a room full of robots in a repurposed Google conference room and had them grasp objects all day.
It worked. Then it hit a wall: lots of experience, no prior knowledge.
That wall became @physical_int.
This conversation at @spc was awesome. I learned a lot!
(00:28) From Video Games to Robotics: Sergey Levine's Journey
(04:24) Founding Physical Intelligence: Why the Timing and Team Mattered
(06:30) Inside Pi Zero: Building a Foundation Model for Robots
(09:37) From Pi 0.5 to Pi 0.7: Teaching Robots to Reason and Generalize
(16:45) What Makes a Great Robotics Researcher?
(21:10) Solving Robotics' Data Problem and Building Learning Feedback Loops
(28:04) The Future of Robotics: General-Purpose AI vs. Specialized Robots
(33:24) World Models, Data Strategy, and Building a Robotics Research Culture
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With open models, we can build safety tooling directly into the inference layer.
Our CEO and co-founder @tuhinone sat down with @kr00ney at @cnbc to discuss Project Beacon, and our work with @GoodfireAI to make safety the default for open intelligence.
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SPC alum @ScottWu46 has always been a math savant.
Here's his take on the moneyball-ization of startups:
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South Park Commons retweeted
Some of the biggest advances in AI have come from solving many problems together, rather than training specialized systems for individual tasks.
@svlevine (@physical_int) talks about the same principle for robotics. Make the problem bigger.
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South Park Commons retweeted
Robotics is going through the same shift LLMs did: from fine-tuning, to prompting, to just telling the model what you want.
@svlevine talked about how π0.7 learns new skills from plain-language coaching.
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South Park Commons retweeted
A PhD from a top university doesn't necessarily make someone a great robotics researcher.
“The universe is really big, and almost none of it is inside computers.”
When a problem is hard, make it bigger. @svlevine put a room full of robots in a repurposed Google conference room and had them grasp objects all day.
It worked. Then it hit a wall: lots of experience, no prior knowledge.
That wall became @physical_int.
This conversation at @spc was awesome. I learned a lot!
(00:28) From Video Games to Robotics: Sergey Levine's Journey
(04:24) Founding Physical Intelligence: Why the Timing and Team Mattered
(06:30) Inside Pi Zero: Building a Foundation Model for Robots
(09:37) From Pi 0.5 to Pi 0.7: Teaching Robots to Reason and Generalize
(16:45) What Makes a Great Robotics Researcher?
(21:10) Solving Robotics' Data Problem and Building Learning Feedback Loops
(28:04) The Future of Robotics: General-Purpose AI vs. Specialized Robots
(33:24) World Models, Data Strategy, and Building a Robotics Research Culture
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Watch the talk with @physical_int co-founder @svlevine now ⬇
“The universe is really big, and almost none of it is inside computers.”
When a problem is hard, make it bigger. @svlevine put a room full of robots in a repurposed Google conference room and had them grasp objects all day.
It worked. Then it hit a wall: lots of experience, no prior knowledge.
That wall became @physical_int.
This conversation at @spc was awesome. I learned a lot!
(00:28) From Video Games to Robotics: Sergey Levine's Journey
(04:24) Founding Physical Intelligence: Why the Timing and Team Mattered
(06:30) Inside Pi Zero: Building a Foundation Model for Robots
(09:37) From Pi 0.5 to Pi 0.7: Teaching Robots to Reason and Generalize
(16:45) What Makes a Great Robotics Researcher?
(21:10) Solving Robotics' Data Problem and Building Learning Feedback Loops
(28:04) The Future of Robotics: General-Purpose AI vs. Specialized Robots
(33:24) World Models, Data Strategy, and Building a Robotics Research Culture
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South Park Commons retweeted
We’re betting our $5.5M seed round that the next leap in AI coding won’t come from OpenAI or Anthropic.
It’ll come from a new feedback loop that learns from you.
Introducing Beacon: open-source, self-improving memory for coding agents.
Your sessions capture how you debug and solve problems. Today, that context gets lost and you have to re-explain things every time you switch tools.
Beacon continuously distills your sessions into reusable skills across Claude Code, Cursor, Codex, and 20+ harnesses, so your next agent can learn from the last.
Beacon is free and OSS 🌟 github.com/Asymptote-Labs/ag…
Why should the frontier labs be the only ones winning with your sessions?
You already did the work. Now let your coding agents learn from you.
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South Park Commons retweeted
Early @PalantirTech. Cofounder of @anduriltech. GP at @foundersfund.
Last time @traestephens was at @spc was back in 2022.
The world looks a little different now.
Wednesday. RSVP below.
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SF Design Chats at @SPC are BACK:
Wed Nov 4 @iansilber
Tue Nov 10 @alexcornell
Tue Dec 8 @johnbai
Tue Jan 12 @mschoening
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Demos on Sunday: luma.com/data-hackathon-demo…
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South Park Commons retweeted
Very excited to welcome a dear friend to @spc next week.
@sriramk is joining us for a fireside chat and Q&A.
He helped build Azure, scaled Meta’s mobile ads, and led a16z’s international expansion.
Then he went to the White House and helped write the U.S. AI Action Plan.
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"The entire way that humans contribute to the economy is going to move.”
@mercor Co-founder & CEO @BrendanFoody explains what he believes will replace repetitive human work.
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Watch the full conversation hosted by member @nickmecklenburg at SPC: piped.video/watch?v=kmyxPcoK…
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South Park Commons retweeted
Building for national security means understanding how to sell to the Pentagon.
Alan Estevez, former senior Pentagon official and Under Secretary of Commerce, joined @spc NYC founders to share stories and practical advice on the importance of building dual-use technology
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SPC company @GoodfireAI just made the field’s best argument that AI models are more interpretable than we thought.
@nickcammarata, who helped build interpretability’s early assumptions, published the case that the field had been too pessimistic. Neural networks are already clean.
We're proud to back them. Important read ⬇️
For years, neural networks looked inscrutable, like tangled knots that needed to be unwound. We no longer think that's the case. AI models are far more interpretable than we thought. We think this is great news for interpretability.
Goodfire's @nickcammarata co-authored Zoom In, the paper that introduced polysemanticity and superposition. He spent months revisiting the vision model organisms that much of interpretability grew out of and studying modern language models up to trillions of parameters. His new essay explains what the early field, him included, got wrong, why there's reason for optimism today, and why there might be a bitter lesson for interpretability.
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If you missed the sign up for our Bio-Data Hackathon this weekend...
You can still catch demos of the projects this Sunday!
RSVP below ⬇️
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We're excited to collaborate with partners like @AnthropicAI, @awscloud, @Stanford, @OwkinScience, @biohub, @phylo_bio, @gxl_ai, @centai_health, & more.
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