[ { "id": 490, "name": "InfluxData", "slug": "influxdata", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/52f8bfabfa73534a4e180ab67b04914310f2a95e.png", "website": "https://influxdata.com", "all_locations": "New York City, NY, USA; San Francisco, CA, USA; Remote", "long_description": "InfluxData, the creators of InfluxDB, delivers a modern Open Source Platform built from the ground up for analyzing metrics and events (time series data) for DevOps and IoT applications. Whether the data comes from humans, sensors, or machines, InfluxData empowers developers to build next-generation monitoring, analytics, and IoT applications faster, easier, and to scale delivering real business value quickly. Based in San Francisco, InfluxData customers include Autodesk, Cisco, eBay, and Coupa. Visit https://www.influxdata.com.", "one_liner": "The platform for building and operating time series applications.", "team_size": 210, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1354492765, "tags": [ "Analytics", "Open Source", "Time Series", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2013", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/influxdata", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2013/influxdata.json", "year_founded": 2012, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/influxdb", "x_url": "https://twitter.com/InfluxDB?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor", "fb_url": "https://www.facebook.com/influxdb/", "cb_url": "https://www.crunchbase.com/organization/influxdb", "github_url": "https://github.com/influxdata", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/52f8bfabfa73534a4e180ab67b04914310f2a95e.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 35348, "full_name": "Paul Dix", "title": "Founder/CTO", "founder_bio": "Co-founder and CTO of InfluxData, the company behind InfluxDB, an open source time series database. Creator of NYC Machine Learning Meetup, author of Service Oriented Design with Ruby, and series editor of Addison Wesley's Data & Analytics book and video series.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/pauldix/", "x_url": "https://twitter.com/pauldix", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/08f7956ada5049ce69ab9169b75f8e3c0efe4f24.jpg" } ], "jobs": [], "news": [ { "title": "InfluxData lands $51M to grow its time series database offerings • TechCrunch", "url": "https://techcrunch.com/2023/02/13/influxdata-lands-51m-to-grow-its-time-series-database-offerings", "date": "Feb 13, 2023" }, { "title": "InfluxDB IOx update adds SQL capabilities, real-time data functions - SiliconANGLE", "url": "https://siliconangle.com/2023/01/13/influxdb-iox-update-adds-sql-capabilities-real-time-data-functions-reinvent/", "date": "Jan 13, 2023" }, { "title": "Built-for-purpose databases tackle new-era enterprise challenges | VentureBeat", "url": "https://venturebeat.com/data-infrastructure/built-for-purpose-databases-tackle-new-era-enterprise-challenges/", "date": "Nov 28, 2022" }, { "title": "InfluxData raises $60 million for time-series database software | VentureBeat", "url": "https://venturebeat.com/business/influxdata-raises-60-million-for-time-series-database-software/", "date": "Feb 13, 2019" } ], "launches": [] }, { "id": 614, "name": "PipelineDB", "slug": "pipelinedb", "former_names": [ "PipelineDB", "Stride API" ], "small_logo_thumb_url": "/company/thumb/missing.png", "website": "http://www.pipelinedb.com", "all_locations": "Los Angeles, CA, USA", "long_description": "PipelineDB is an open-source PostgreSQL extension that enables realtime reporting use cases by continuously aggregating large streams of time series data down into summary data. It shines the most at larger scales, where storing lots of raw time-series data and aggregating it over and over again becomes inefficient. The amount of value that PipelineDB adds is directly proportional to the amount of continuous aggregation that an analytics use case can benefit from. PipelineDB should be used for analytics use cases that only require summary data, like realtime reporting dashboards. PipelineDB is not a good fit for exploratory analytics on siloed datasets.\r\n", "one_liner": "Open-source time-series data analytics extension for PostgreSQL", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1384978939, "tags": [ "Open Source", "Time Series" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2014", "status": "Acquired", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/pipelinedb", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2014/pipelinedb.json", "year_founded": null, "city": "Los Angeles", "country": "US", "linkedin_url": null, "x_url": "https://twitter.com/pipelinedb", "fb_url": "https://www.facebook.com/pipelinedb", "cb_url": "https://www.crunchbase.com/organization/pipelinedb", "github_url": null, "logo_url": null, "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 409, "full_name": "Derek Nelson", "title": "Founder", "founder_bio": null, "is_active": true, "linkedin_url": "https://www.linkedin.com/in/derekjn", "x_url": null, "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9ffdc88614cd90705f8265814d96827a9fa5c6ad.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 21795, "name": "QuestDB", "slug": "questdb", "former_names": [ "QuestDB Limited" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3aef5c655cdc851ccad387554c204466a87669e7.png", "website": "https://questdb.com/", "all_locations": "London, England, United Kingdom", "long_description": "QuestDB is the data platform for mission-critical workloads where data cannot be lost and batch processing is too slow. It runs real-time trading analytics, grid monitoring, fleet telemetry and mission operations at millions of events a second, with years of history kept queryable.\r\n\r\nData stays in open formats like Parquet, and you or any of your agents can query it in plain SQL.\r\n\r\nQuestDB started where the problem is hardest, on the electronic trading floor, and built outward into energy, aerospace and robotics. Today 7 of the largest 20 investment banks use it, as do energy companies and makers of aircraft, rockets and satellites. Its engineers work with each customer's team on the whole solution, from the live feed to the archive.\r\n", "one_liner": "Data platform for mission-critical real-time workloads", "team_size": null, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1593714682, "tags": [ "Developer Tools", "Analytics", "Open Source", "Time Series" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2020", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United Kingdom", "Europe", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/questdb", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2020/questdb.json", "year_founded": 2019, "city": "London", "country": "GB", "linkedin_url": "https://www.linkedin.com/company/27032022/", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/quest-db", "github_url": "https://github.com/questdb", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/3aef5c655cdc851ccad387554c204466a87669e7.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Nicolas Dessaigne", "url": "https://www.ycombinator.com/people/nicolas-dessaigne" }, "founders": [ { "user_id": 432980, "full_name": "Nicolas Hourcard", "title": "Founder", "founder_bio": "Co-founder/CEO at QuestDB.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/nicolas-hourcard-cfa-b7349b37/", "x_url": "https://twitter.com/nicolas_hrd", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/bc590513f179446e8d515ed72c65325fc50acdb4.jpg" }, { "user_id": 476995, "full_name": "Tancrede Collard", "title": "Founder", "founder_bio": "Co-founder and head of product at QuestDB. Before that, I was running algorithmic trading desks at banks and cryptocurrency firms.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/tancrede-collard-35228926/", "x_url": "https://twitter.com/TancThe", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/32e87775d59972e2b1ae146ed36ca880338b2cb6.jpg" }, { "user_id": 481865, "full_name": "Vlad Ilyushchenko", "title": "Founder", "founder_bio": "Founder, CTO, QuestDB\n\n1997 - 2019 Software Engineer, Electronic Trading\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ilyushchenko/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1582960340135f206839659b3f73cc4c6dbfcc58.jpg" } ], "jobs": [ { "id": 109950, "title": "Sales Pioneer, North America", "url": "https://www.ycombinator.com/companies/questdb/jobs/Te2LfsV-sales-pioneer-north-america", "location": "New York, United States / Remote (New York, NY, US)", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$175K - $225K", "equity_range": "0.25% - 0.40%", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 109951, "title": "Pre-Sales Engineer, North America", "url": "https://www.ycombinator.com/companies/questdb/jobs/IPyIzny-pre-sales-engineer-north-america", "location": "New York, United States / Remote (New York, NY, US)", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$150K - $210K", "equity_range": "0.15% - 0.30%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [ { "title": "QuestDB snares $12M Series A with hosted version coming soon – TechCrunch", "url": "https://techcrunch.com/2021/11/03/questdb-snares-12m-series-a-with-hosted-version-on-the-horizon/", "date": "Nov 03, 2021" } ], "launches": [ { "id": 72748, "title": "QuestDB - Data platform for mission-critical real-time workloads", "tagline": "Where data cannot be lost and batch is too slow", "body": "### TL;DR\n\n[QuestDB](https://questdb.com/) is the data platform for mission-critical workloads where data cannot be lost and batch processing is too slow. Banks, exchanges, energy companies and aerospace teams use it for real-time trading analytics, grid monitoring, fleet telemetry and mission operations. It handles millions of events a second and keeps years of history queryable.\n\nIn [QuestDB Enterprise](https://questdb.com/enterprise/), data tiers automatically to Parquet on object storage, replication and failover keep it available, and role-based access control governs who sees what. Run it in your own data centre, or in your own AWS or Azure account managed by our team (BYOC).\n\nTry the [live demo](https://demo.questdb.io) with billions of rows.\n\n### The Problem\n\nData that arrives fast and cannot be lost usually ends up spread across several systems. Teams run a message bus, a real-time store and a data lake for history, with glue code in between. Batch pipelines are too slow for decisions made while the market moves or the vehicle is in flight. Closed systems keep the data in formats and languages few people can use.\n\n### How QuestDB solves it\n\nOne engine covers the whole path, from the live feed to the archive:\n\n\\- Capture: millions of events a second, with deduplication and out-of-order data handled on ingest.\n\n\\- Compute: materialized views, and live views in beta, compute on data as it lands.\n\n\\- Query: plain SQL with time-series joins (ASOF, WINDOW, HORIZON) across live data and history in one query. You or any of your agents can query it, through SQL or the QuestDB MCP server.\n\n\\- Retain: older data moves to Parquet on object storage automatically and stays queryable. Data also comes out fast in open formats: 500M rows into Apache Arrow in 2.3 seconds.\n\n### Where it runs\n\n\\- Capital markets: market data and trades captured once, then pre-trade signals, TCA and markouts, intraday risk and surveillance on the full record.\n\n\\- Aerospace and robotics: telemetry from aircraft, rockets, satellites and robot fleets, monitored live and replayed in full.\n\n\\- Energy and infrastructure: readings from reactors, turbines and substations at full resolution, with anomalies flagged as they happen and years of history kept for regulators.\n\n### Production deployments & Customers\n\n7 of the largest 20 investment banks use QuestDB.\n\n\\- **Capital markets:** Ripple, OKX, Broadridge, Mizuho, Brevan Howard, Marex, Paxos, B3 (Brazil’s National Stock Exchange), SIX Group, Raymond James, HDFC Bank, One Trading, BTG Pactual, Laser Digital (Nomura), Selini Capital, Caladan, Danske Commodities, GTS Securities\n\n\\- **Aerospace:** Airbus, Firefly Aerospace, CesiumAstro, OpenC3, US Air Force\n\n\\- **Energy, industrial, Telco:** Bosch, Arbor Energy, Electio, Inductive Automation (Ignition's Core Historian runs on QuestDB), Airtel, NetApp, Weidmann, Copenhagen Atomics\n\nSee our [customers page](https://questdb.com/customers/) for more details.", "url": "https://www.ycombinator.com/launches/IvM-questdb-data-platform-for-mission-critical-real-time-workloads", "votes": 31, "created_at": "2023-07-04T15:26:19.935Z" } ] }, { "id": 32484, "name": "EdotEnv", "slug": "edotenv", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/a53586c9aacc3d5c0003f5bde0beb3535a3613f6.png", "website": "https://edotenv.com", "all_locations": "San Francisco, CA, USA", "long_description": "A Quant Neolab building toward RSI by leveraging markets as self-improving research environments.\r\n\r\nThey work with frontier AI labs and academic groups building research harnesses, evaluation benchmarks, and post-training environments.\r\n", "one_liner": "Quant Neolab building self-improving agents from quant data", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1786503507, "tags": [ "Reinforcement Learning", "Time Series" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/edotenv", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/edotenv.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/a53586c9aacc3d5c0003f5bde0beb3535a3613f6.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 2622651, "full_name": "Rui Wang", "title": "Founder/CEO", "founder_bio": "I am Rui (\"ray\" like the fish). CEO of EdotEnv, a quant neolab teaching LLMs robust research and good research taste. We use markets as an infinite source of increasingly hard data to build environments, harnesses, and post-train models.\nBackground in quant at G-Research, prev. applied maths at ETH Zurich.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/rui-wang-456032198/", "x_url": "https://x.com/RRicefan", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c0681ade5300e908a4d3267d8ead62de1be2484e.jpg" }, { "user_id": 2995240, "full_name": "Michael Zhang", "title": "Founder/CTO", "founder_bio": "CTO at EdotEnv, a quant neolab building toward RSI.\n\nPreviously@Etched: Designed LLM Inference System for SOHU chips\nPreviously@TransmarketGroup: Built automated high-frequency option trading strategies\n\nETH Zürich Alumni", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/michael-zhang-845204232/", "x_url": "https://x.com/Michaelz1914", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/01fb39f6e87e8fc6884b4a54b36981b4011bb42a.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 110398, "title": "EdotEnv: Quant Neolab building towards RSI", "tagline": "We turn markets into self-improving research environments for AI agents.", "body": "EdotEnv (YC S26) is a Quant Neolab building toward RSI. They turn markets into self-improving research environments for AI agents.\n\nThe path to RSI is a continuous loop: agents conduct experiments, learn from the results, and autonomously become better at conducting the next round of research. For that loop to continue, each round must present a harder problem than the last.\n\nMarkets create this dynamic naturally. Every discovered inefficiency attracts competition, and every successful strategy makes future opportunities harder to find. Markets therefore provide an advancing frontier for self-improving agents.\n\nEdotEnv captures this process in multi-step environments where agents form hypotheses, design experiments, verify results and iterate. They use these environments to evaluate and post-train agents on increasingly difficult research tasks.\n\nThey work with frontier AI labs and academic groups building research harnesses, evaluation benchmarks, and post-training environments.\n\nEdotEnv's team previously worked in quantitative research at G-Research, high frequency options trading at TransMarket Group, and LLM inference research at Etched.\n\nIf you're working on RSI, RL, post-training, or AI for research, reach out to them at [info@edotenv.com](mailto:info@edotenv.com).\n\n![uploaded image](/media/?type=post&id=110398&key=user_uploads/89898/46b2eec2-b3c9-4467-b638-d9a51b2c39cd)\n\n", "url": "https://www.ycombinator.com/launches/Sic-edotenv-quant-neolab-building-towards-rsi", "votes": 5, "created_at": "2026-08-13T21:30:00.360Z" } ] } ]