Nicolas Dessaigne (@dessaigne) header
Nicolas Dessaigne (@dessaigne) profile picture

Co-founder of @algolia - Group partner at @ycombinator

San Francisco, CA
Joined July 2009
Photo by Nicolas Dessaigne (@dessaigne)Photo by Nicolas Dessaigne (@dessaigne)Photo by Nicolas Dessaigne (@dessaigne)Photo by Nicolas Dessaigne (@dessaigne)Photo by Nicolas Dessaigne (@dessaigne)Photo by Nicolas Dessaigne (@dessaigne)Photo by Nicolas Dessaigne (@dessaigne)Photo by Nicolas Dessaigne (@dessaigne)
Atoms are the new bits. 25% of YC startups now build hardware, so we're setting up a YC Hardware Lab in Dogpatch. We're hiring the person to run it. If you've built hardware and know your way around a shop, apply or send us someone great: ycombinator.com/careers?job_…
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The bottlenecks in AI are moving beyond the models. We’re excited about startups building the hardware, infra, financial systems, and specialized software to bring AI into the real economy, from factories and data centers to government and space. ycombinator.com/rfs
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Cambridge has always been the best place in the world to be an academic researcher. These days, a lot of cutting edge research also happens in industry, and Cambridge is the home to many amazing research startups, spanning AI research, robotics, biotech, defense, and industrials. We'd love for these two worlds to meet eachother, so we're bringing YC Paper Club to Cambridge! if you're a researcher in industry or academia, request a spot below.
YC Paper Club is coming to Cambridge, MA! YCPC brings together people from academia and industry to discuss the latest research. Our inaugural Cambridge event on November 18 will explore the state of the art in robot learning and control. If you’re a student or faculty at a local university, or a researcher in industry interested in staying up to date with the latest literature, request a spot: events.ycombinator.com/yc-pa…
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Superset is now Paradev: The code editor that helps engineers run armies of coding agents. We just raised an $11.5M Seed round led by @usv with @ycombinator to redefine how software is written.
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We're excited to announce Exa 3.0! A huge number of improvements: - Bigger, better embeddings model driving core search - Higher quality highlights with further reduced token counts - Lower latency, cheaper search type "Lite" - Better reranking, particularly across long documents - Even better real-time news, sports, weather, and market data - Laundry list of other stuff Let us know what you think! 🫡
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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We just solved attribution for AI Agents. Introducing Journeys. AI agents are visiting your product. Now you can see what they’re trying to do, what converts, and what’s making them churn. With Journeys, you can: - See the moment AI agents visit your product - Follow the paths they take - Track purchases, installs, and completed setup - Pinpoint where agents get stuck and whats blocking them - Review fixes prepared for you and publish them with a click - Watch how those changes improve conversion Turn agent traffic into revenue. Try Journeys at tryscope.com
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We are truly legit.
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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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VibeCon SF applications are now open! 🌉 Powered by @MongoDB, VibeCon is coming back to where it all began: San Francisco!!! 200 builders. One weekend. A whole lot of ambitious ideas. And the winner gets an interview opportunity with @ycombinator. October 24–25. Mark your calendar. Apply here: vibecon.com/
🤖 Made with AI
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When you talk to successful founders, they always wish they'd hired fewer top managers from outside. I don't think I've ever met a founder who wished he or she had promoted fewer people from within.
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two decades of friendship with @sirsho29; killed one company; more rejections than we can count and today, @antimattr_one is part of @ycombinator's Fall 2026 batch we're building a voice hardware so that you can do more with your voice, anywhere; we already have close to 1,000 pre-orders this is our way of choosing greatness; this is our "antimattr” story what’s yours?
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Launching Higher-Order Extraction. It deterministically computes the numbers you need from the raw values on the page, even when the final answer isn't printed anywhere.
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🚀 What will the 2027 VC playbook look like? Nicolas @dessaigne, Algolia co-founder turned #YC General Partner, has some thoughts. Catch him on stage at #OneWaySummit, the flagship event for global founders. 📅 Oct 28–29, SF 🔗 luma.com/onewaysummit2026 #SFevents #SFtechweek
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We’re excited to welcome @vivianmshen and @raphaelschaad as YC’s newest General Partners! Vivian and Raphael first came to YC as founders. Vivian co-founded Juni Learning (W18), which teaches kids coding and math, and later founded Acely, an AI tutor for college prep. Raphael founded Cron (W20), the calendar app that became Notion Calendar. They built products used by millions of people, then returned to YC as Visiting Partners to help other founders. Now they're doing it full-time. ycombinator.com/blog/welcome…
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YC said yes ♥️ It started in an IIT Delhi dorm 7 years back, when I first met Pratyush. 9 months ago we took a leap of faith, and today @ByteAsk is part of YC Fall 2026. So what's cooking? The critical infrastructure of the world still runs on legacy C and C++. Trading, banking, exchanges, telecom, energy, automobiles, operating systems, gaming, medical devices, aerospace, and defense all depend on it. ByteAsk brings agentic coding to that code. Developers use it to debug, optimize, and write C and C++ faster than ever, and with confidence. Teams use it on 20-year-old systems with millions of lines and hundreds of dependencies, and see the first change land within hours. It runs fully on-prem, without internet. If your systems run on C or C++, DM me. We are taking 5 more enterprises as design partners, at no cost, and I will fly to your office to get it running. Grateful to our customers, angels and mentors for believing in us, and to @dessaigne , @nemild and @ycombinator team for backing us. PS: Comment "C++" to get access to our early release @pratyush1505
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Two YC companies led the way in igniting a revolution in how the robotics community sees robot control via LLMs. Tune in to hear more about the last five years of research that gave them the conviction that we were heading this way and some predictions on what comes next.
One of the biggest surprises in AI over the last few years has been how well coding agents generalize beyond software. In a recent essay, MIT professor Philip Isola argued that we may be entering the era of robot-use agents: general-purpose models that can control different robots, write policies, and learn new physical tasks with little or no robot-specific training. In this episode of Decoded, we're joined by the founders of @theWaddleLabs and @Robocurve, two of the startups whose work helped drive this realization. They're working at the frontier of using general-purpose models to control robots, and their recent demos helped inspire the growing conversation around robot-use agents. Together, we dig into the research behind that idea, from code-as-policies and vision-language-action models to the harnesses and evals needed to make these systems work in the real world. 00:00 — What Are Robot-Use Agents? 02:11 — From Vision-Language-Action Models to General LLMs 04:07 — The Bitter Lesson for Robotics 07:04 — How Coding Agents Learned to Control Robots 10:41 — How Robots Learn From Experience 14:22 — The Harness as a Form of Robot Intelligence 16:14 — Watching Astra Control a Robot 20:55 — Why General Models May Win in Robotics 26:05 — How Close Are General-Purpose Robots?
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🤯
Introducing Agent Ultra - a step change in deep research Agent Ultra orchestrates swarms of agents to perform exhaustive research, build comprehensive lists, and answer questions requiring thousands of sources. In both evals and vibes, it's state of the art
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Introducing GLiNER2.5-Decide, our new 340M parameter open weight, encoder-based decision model. GLiNER2.5-Decide is built for fast, deterministic classification. The model evaluates a set of user-defined typed questions and rules, and jointly decodes their answers, returning structured decisions with probability distributions and confidence scores. We evaluated the model’s performance on Fast Decisions, an unseen, internally generated classification suite based on 17 datasets testing real-world use cases across routing, triage, classification, sentiment, and content understanding. Measured against similar decision models, GLiNER2.5-Decide leads in 9 of the 17 datasets, achieving the highest average score: - GLiNER2.5-Decide: 60.1% - SemIf: 56.4% - JevK5: 57.5% - Laya: 46.6% This performance makes the model a strong fit for use cases like tool calling, model routing, browser and computer use, and LLM-as-a-judge. GLiNER2.5-Decide’s lightweight encoder architecture makes it easy to fine-tune the model for specific tasks, while being efficient enough to run locally on consumer-grade CPUs or in air-gapped environments, giving users greater control over where their data goes and where the model runs. To make building and experimenting with GLiNER2.5-Decide as easy as possible, we're also offering hosted inference. You can now use our API to run inference and fine-tune GLiNER models on specialized tasks right inside your own coding agent: agent.fastino.ai As with previous models, we’re also releasing the model weights on @huggingface under the Apache 2.0 license: huggingface.co/fastino/GLiNE…
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Replying to @ycombinator
@ycombinator's @dessaigne is joining us at Flower AI Day 2026. Nicolas is a General Partner at YC and co-founder of Algolia, the search API company that went through YC’s Winter 2014 batch. As CEO, he grew @algolia to a team of 350 before handing over the role in 2020. Today, he works with founders at Y Combinator, bringing experience from both building a developer-focused company and helping others build theirs. His background also includes a PhD in Computer Science from the @NantesUniv. Join Nicolas and the researchers, engineers, and founders at Flower AI Day on September 30 at The Pearl in San Francisco. We’ll spend the afternoon and evening exploring open, collaborative AI through technical talks, demos, firesides, and conversations with the people building it. Register: luma.com/flwrlabs-uygg
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There's something deeply unsafe about safetyism. We wish people a safe flight—yet flying commercially is almost perfectly safe. We don't wish people a safe drive—yet US roads are the equivalent of a 737 crashing every 48h, killing everyone aboard. But we scare people when they get in an airplane but not when they get in a car. The biggest opportunity we have to improve road safety is self-driving, yet there's lots of anti-self-driving safety talk, and self-driving is held to a *dramatically* superhuman safety bar. The din about AI safety has reached extreme tenor—yet there have been zero material safety incidents. And the positive benefits of AI could improve and save many lives. But the safetyists say we must slow down. We destroyed the nuclear power industry in this country nominally out of safety concerns, yet joule for joule nuclear power is *by far* the safest form of energy, even accounting for Chernobyl (a genuine disaster) and Three Mile Island (generally a non-event). Building and fire codes are supposedly there to protect us from unsafe buildings. But actually the red tape makes development slow and sometimes impossible. So the safetyism protects us from jobs, innovation, and reindustrialization. TSA was there nominally to protect us from terrorist threats—yet to the extent it pushes passengers at the margins from safe airplanes to dangerous roads, it actually kills not just time and money but actual humans. Safetyism is deeply unsafe.
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