@tecoker

venture @felicis SF based, ATL native free time // 📷: https://xitter.cf/t.co/kcntgPWauQ

San Francisco, CA
Joined April 2011
VCs as rappers – no explainers: Sequoia: Jay-Z Greylock: Future Felicis: Gunna Amplify: Young Thug Benchmark: Kendrick Index: Skepta Lux: Tyler the Creator Tiger: Kanye Khosla: Eminem a16z: Drake
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Tobi Coker retweeted
Introducing Exa 3.0: our largest ever improvement to search quality. Exa is now #1 on every benchmark we tested, from public evals to verticals like coding and GTM intelligence. It's the result of multiple research breakthroughs 🧵
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Exclusive: Instinct is using Exa for web search We gave Instinct URLs for nonexistent pages on a website we administer. Since the requests were for fake pages and the timing matched our conversation with Instinct, the requests couldn’t plausibly have come from anything else. In total, it made about 12 requests over an hour and a half. We checked Cloudflare analytics and the server’s raw request logs to get the full picture. A few things stood out: - Instinct used Exa to fetch pages. Every direct fetch arrived with the user agent Mozilla/5.0 (compatible; ExaUser/1.0; +crawler.exa.ai/exauser), from a different IP each time. - Exa signs its requests (Web Bot Auth, Signature-Agent: "crawler.exa.ai"). But nothing in the requests identifies Instinct. They are indistinguishable from those of any other product built on Exa. - In one test, after the fetch returned a 404, Instinct loaded the page in its browser without being asked. - Instinct’s browser had no self-identifying features. It looked like standard Chrome on desktop Linux from a residential IP address, with no bot user agent or signature. We directly asked it to use the browser in two other tests. All three browser visits came from the same residential IP over about 80 minutes. The browser kept its cache between visits, consistent with a persistent browser behind a residential proxy. We can’t say whether that IP is stable over longer periods or shared with other users, so it’s not a reliable way to identify Instinct. This presents unique challenges for marketers hoping to track Instinct traffic to their site. Unlike ChatGPT, Claude, or Meta, Instinct doesn’t send its own user agent or signature. Its fetches look like generic Exa traffic, and its browser visits look like a normal user’s. All of this means you can’t track, allow, or block Instinct specifically. The only reason we could link these requests to Instinct was that we controlled the URLs and the timing. For normal traffic, you don’t have those luxuries. Link to it on sunbeam: sunbeam.ai/thoughts/instinct…
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Tobi Coker retweeted
In 2012 I said machines would do 80% of what doctors do. Many were furious. Now AI out-diagnoses doctors in study after study. Is printing organs on demand next? Having your own stem cell repository is next generation "planning". @cellinobio is building autonomous foundries that make personalized stem cells with AI and lasers. Your cells age every day. Preserve your immune cells with @withreservoir. Excited to back @nabihasaklayen, @madridmarinna & team.
In partnership with Vinod Khosla (@vkhosla), I’m proud to introduce Reservoir. Billionaires have owned their cells for years. Starting today, you can too. Sign up today: withreservoir.com
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How do you own your cells? > One 15-minute blood draw. > We isolate your immune cells. > We cool them below −150°C and store them in an FDA-registered facility, in your name.
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In partnership with Vinod Khosla (@vkhosla), I’m proud to introduce Reservoir. Billionaires have owned their cells for years. Starting today, you can too. Sign up today: withreservoir.com
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Tobi Coker retweeted
Today we’re launching Datology Labs: a new commitment to open and collaborative research into the science of data. Research has been a core pillar of DatologyAI from the start. Now we’re partnering with researchers at universities and across industry to take it further.
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🚀🚀🚀
Exa just passed $100m in annualized revenue. We've grown almost exactly 10x per year for 3 years. We now handle 2 billion requests a month from 200k+ teams. And it’s still so early: We predict agents will search 1000x more than humans by 2030. Agents don't search like humans. They write long, complex queries and want all the world's data, not just the web. We've spent 5 years building a search engine designed for them. We now serve personal agents, AI labs, coding agents, GTM agents, really any agent. Exa gives these agents access to 100s of billions of web documents and tons of proprietary data. Our customers are increasingly noticing that information is the bottleneck to agent performance. While the world seems close to perfect intelligence with models like Fable and Astra, we are very far from perfect information. To our small but legendary team, thank you for getting us here, from engineering to research to GTM to ops. To our customers, it is an honor to serve you. We have some exciting releases coming soon and can't wait to share them 🫡
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Tobi Coker retweeted
defining the science of data. together. 10.14.26
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a large part of the promise of software is democratizing access and capabilities. @datologyai's curation capabilities enable frontier level model quality for every company. frontier intelligence for the masses!
Today, we’re excited to launch the Datology Curation Studio, customizable frontier data curation for every team training its own models. Data quality is the ultimate compute multiplier, and Curation Studio makes it easy for everyone.
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fun to discuss everything from AGI to namesake origins with some of the most technically gifted operators in the valley! massive thank you for hosting @AndreaShuyuWang & @pxd cc: @MilesKWang, @TheRealRPuri, @turtlesoupy, @joannejang, @khoomeik, @realchillben, @reah_ai, @harinsrikanth, @gstsdn, and @lindensli
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Tobi Coker retweeted
We built ATLAS, a web search benchmark for how agents actually search today. Existing agentic search benchmarks are often outdated, contaminated, or saturated. ATLAS is designed to separate strong search from weak search. Learn more: exa.ai/blog/atlas-benchmark
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Tobi Coker retweeted
New from DatologyAI: Zephon, an open-source data loader for text and multimodal training. Resume a run on a different number of GPUs and most loaders quietly change the data. In our tests, GPU count alone moved eval scores by up to 0.82 points, about 4x the gap used to choose between data curation methods. tl;dr: same training batches on 8, 16, 32 or 64 GPUs, even while tokenizing, packing and mixing on the fly.
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This shows saturation of coding advancements. Would be interested to see this broken out by application. My hunch is pre-training for Bio + Robotics is flipped here
~90% of frontier lab compute now goes to post-training and inference.
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Tobi Coker retweeted
Search has left the search box for good. @ExaAILabs is building the crawling, indexing, retrieval, and evaluation systems that give agents the information they need to be more useful and informative. @felicis investors @tecoker and @pxd dig into the future of search with Exa co-founder @WilliamBryk 🎧 felicis.link/l19w0nm
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Agent search is easy to demo and hard to evaluate. @ExaAILabs is building the index, retrieval systems, and eval discipline agents need to rely on the web in production. We at @Felicis are true believers in @WilliamBryk, @jeffzwang, and the Exa team. 🎧 felicis.link/cs1kM9W cc: @pxd
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Runway and 60 Minutes: a behind-the-scenes look at how we made the intro for the new episode with Jon Wertheim.
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AI is making biological hypotheses abundant. But every AI-generated drug, protein or molecule eventually has to confront the physical world. That creates a new bottleneck: experimentation. I think automated labs become a critical layer of AI for science.
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This is where robotics + AI converge. Automated labs turn biology into programmable infrastructure: 24/7 operation. Higher throughput. Lower labor costs. Standardized experiments. Reproducible results.
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But verification is the part I find most interesting. Models can propose millions of answers. Biology ultimately determines which are true. Whoever owns that experimentation layer sits at a critical control point between AI-generated science and the physical world.
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