Exploring what's next | prev at Data Platform @Airbnb & Data @Twitter

San Francisco, CA
Joined June 2011
I left Airbnb a few weeks ago, a decade after joining in early 2016. I wrote down some reflections on my time there. This post is part about what I think makes Airbnb unique, and part a recollection of lessons learned over the years. medium.com/p/reflections-on-…
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Robert Chang retweeted
I've been traveling the world a bit. One of the most incredible experiences was getting to visit Taiwan w/ @JensenHuang. It was wild 🤯.. huge crowds of engineers, insanely good street food at night markets, and of course a lot of good conversations across many days. I caught some of it on video, see below. Alright, that's it from me, love you all ❤️ Timestamps: 0:00 - Highlights 0:46 - Intro 3:25 - Computex: crowds, engineers, tech, future of AI 9:12 - Chat with Jensen: keynote prep & nerves 13:03 - Chat with Jensen: Taiwan's tech history 19:47 - Raohe night market 29:37 - Chat with Jensen: memories from childhood 33:44 - NVIDIA party 40:29 - Lunch: braised pig's feet 43:09 - Chat with Jensen: GPUs, AI, and competition 55:07 - Ningxia night market 1:10:14 - Taiwanese breakfast 1:29:54 - Outro
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Robert Chang retweeted
We gave our AI system a spec, and in under 2 weeks, it designed, verified, and deployed a chip that beats NVIDIA. It’s built for low-power physical AI workloads. We’re running live inference on >B+ parameter models like Llama, Qwen, and Kimi, serving at 3.4x better perf/watt than NVIDIA Jetson. From just a specification, our AI system autonomously generated all of the RTL Design, UVM verification, formal proofs, firmware, drivers, and kernels, co-designing the model, software and silicon as one optimization loop. Better AI can now design better chips to run AI, leading to a loop of recursive-self improvement towards our path to abundant intelligence.
Readers added context they thought people might want to know
Architect Labs did not manufacture a physical chip; the design was tested on a commercial AMD Versal FPGA board. Furthermore, the 3.4x perf/watt gain over NVIDIA Jetson is a simulation-based projection for a future 8nm ASIC, not a physical benchmark. businessinsider.com/architect-labs… architectlabs.com/architect-labs…
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Crisp post on how AI is bringing a renaissance to the semantic layer. Having worked on Airbnb's semantic layer for years and seen firsthand how it enabled our internal data agent, I can confirm this resurgence is real. georgexing.substack.com/p/ho… by @g_xing
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Every once in a while, you meet someone so obsessed with their craft that their passion becomes mesmerizing. @dhh is one of those people. After his 6-hour interview with Lex Fridman last year, I am glad to watch his new 2-hour deep dive with @GergelyOrosz youtube.com/watch?v=JiWgKRgd…
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Expansion of Ambition: AI agents allow developers to pursue "hunches" and micro-optimizations (e.g., the "P1" performance project) that were previously cost-prohibitive.
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Seniority and Validation: Senior developers are experiencing massive acceleration (5x–10x), while junior roles are becoming more tenuous as the need for human validation of AI-generated code becomes a critical security and quality gate.
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I use Claude Code every day, but hadn’t fully appreciated the complexity involved in building an agent harness. This blog post (magazine.sebastianraschka.co…) from @rasbt is a great introduction that highlights many of the key components.
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We spent all night wondering what you were "actually" waiting for, and we finally have it: Images as sources!!! Whether it's a photo of handwritten notes, a screenshot of a textbook or graphs on a web page, @NotebookLM can synthesize the information and produce outputs from it.
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Oh, you thought that was it? In addition to our new look we've got some new tricks too: 🎉 VIDEO OVERVIEWS are starting rollout TODAY 🥳 (English + desktop only to start) Create visually engaging slide summaries of your notebook content. We can't wait to see what you make!
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Robert Chang retweeted
We launched a Claude Code learning mode! Claude Code not only makes you more productive, but can now also help you get better at coding. Whether you're a CS student or seasoned programmer, it will push you to think deeper about the code you're generating.
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Andrej Karpathy strikes again! If you appreciated his 2017 post on Software 2.0, you’ll definitely enjoy his latest talk on Software 3.0. youtu.be/LCEmiRjPEtQ?si=qWDY…
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tweet from the man himself: xitter.cf/karpathy/status/193551…
Nice - my AI startup school talk is now up! Chapters: 0:00 Imo fair to say that software is changing quite fundamentally again. LLMs are a new kind of computer, and you program them *in English*. Hence I think they are well deserving of a major version upgrade in terms of software. 6:06 LLMs have properties of utilities, of fabs, and of operating systems => New LLM OS, fabbed by labs, and distributed like utilities (for now). Many historical analogies apply - imo we are computing circa ~1960s. 14:39 LLM psychology: LLMs = "people spirits", stochastic simulations of people, where the simulator is an autoregressive Transformer. Since they are trained on human data, they have a kind of emergent psychology, and are simultaneously superhuman in some ways, but also fallible in many others. Given this, how do we productively work with them hand in hand? Switching gears to opportunities... 18:16 LLMs are "people spirits" => can build partially autonomous products. 29:05 LLMs are programmed in English => make software highly accessible! (yes, vibe coding) 33:36 LLMs are new primary consumer/manipulator of digital information (adding to GUIs/humans and APIs/programs) => Build for agents! Thank you again for the invite @ycombinator and congrats again on an awesome events! I'll post some links/references in the reply.
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