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He built a platform to power hundreds of Okta integrations and maintained open-source projects which have over a billion installs.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/lenb/", "x_url": "https://twitter.com/kevlened", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d51ac2e2d555498ebe6b8cc1c681bdec6e3eb223.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 25433, "name": "JumpWire", "slug": "jumpwire", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/077148a8281f282d5a5d6825d93aedf0b71c08b4.png", "website": "https://jumpwire.io", "all_locations": "New York City, NY, USA; New York, NY, USA; Remote", "long_description": "JumpWire is a data protection platform that adds advanced data security controls between APIs, applications and databases. JumpWire automatically identifies sensitive properties inside large data sets and gives developers full control over which people and applications can access or update records containing sensitive info.\r\n\r\nExamples uses include restricting who can read customer PII to members of the customer service team, giving on-call engineers elevated access to production, or splitting user records between regions for GDPR purposes.\r\n\r\nJumpWire’s approach to securing data in-place minimizes the risk of data leaks exposing sensitive information or mishandling by other applications and vendors. The exact security scheme applied to data is defined by policies that align with an organization’s existing InfoSec program.\r\n\r\nJumpWire helps companies who maintain information security with compliance programs such as SOC or HIPAA. They are processing sensitive data, often from their own customers, and exceed security best practices as a competitive advantage. JumpWire provides defense at depth to data and sits alongside access controls and Layer 4 encryption to provide a comprehensive data security solution.\r\n\r\nJumpWire is unique from solutions such as data vaults by installing inside our customers’ own infrastructure and clouds. It is interoperable with existing applications and databases, which eliminates the need for large data migrations or code refactoring. Lower-level approaches to data security, such as encryption at rest, are too blunt and lack the ability to differentiate between properties in the data itself. Its scope is limited to physical storage, and security is lost as soon as an application or query loads the data.", "one_liner": "Dynamic access controls for all data and databases", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Security", "launched_at": 1644446688, "tags": [ "Security", "Data Labeling", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "status": "Acquired", "industries": [ "B2B", "Security" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/jumpwire", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/jumpwire.json", "year_founded": 2021, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/jumpwire", "x_url": "https://twitter.com/jumpwire_ai", "fb_url": null, "cb_url": null, "github_url": "https://github.com/extragoodlabs", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/077148a8281f282d5a5d6825d93aedf0b71c08b4.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 823026, "full_name": "Ryan Cooke", "title": "Founder", "founder_bio": "Ryan has spent his career working in startups, with a passion for creating innovative technology products. JumpWire participated in the YC W22 batched, and was acquired by WorkOS in 2024. Ryan has previously co-founded and led engineering teams at Docurated, N26 and Orum (acquired by Stripe). He's obsessed with running his own AI, and particularly enjoys building teams aside software.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ryancooke/", "x_url": "https://x.com/mapandfold", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/36cbdb63975aa38768f2c1241ebb5034d7c15861.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 26717, "name": "Blitz", "slug": "blitz", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/9319d5ef3419cc17f12a1fb2910035be736adcb4.png", "website": "https://www.blitznocode.com/", "all_locations": "Porto, Porto District, Portugal; Remote", "long_description": "Blitz is a no-code platform to build internal applications and automate manual tasks. Quickly build a database, integrate your logic, and scale your operations. \r\n\r\nAlternative to Google Sheet and Airtable, Blitz is not limited by a number of lines of records or by an API rate. \r\n\r\nCreate your data model and set up data validation rules. Use our interface builder to create dynamic forms, and quickly onboard customer and partners. Leverage our granular permissions to share data to external users (publicly or privately). Add advanced business logic to your forms and portals, without writing any line of code. The experience is similar to creating spreadsheets, but without their limitations in terms of scalability and security. \r\n\r\nYou can use Blitz to build an onboarding flow, an order management system or some validation workflows for compliance purposes. \r\n\r\nStop using developer resources or rigid SaaS software. Start building your own tools, adapted to your needs, with Blitz. ", "one_liner": "Lovable for non-tech users: Build no-code internal and customer apps", "team_size": 4, "industry": "B2B", "subindustry": "B2B -> Operations", "launched_at": 1656995564, "tags": [ "SaaS", "B2B", "No-code", "AI", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "B2B", "Operations" ], "regions": [ "Portugal", "Europe", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/blitz", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/blitz.json", "year_founded": 2022, "city": "Porto", "country": "PT", "linkedin_url": "https://www.linkedin.com/company/blitznocode/", "x_url": "https://twitter.com/blitznocode", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/9319d5ef3419cc17f12a1fb2910035be736adcb4.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": 879481, "full_name": "Loic Veillard", "title": "Founder", "founder_bio": "Founder and CTO of Blitz. \n\n💻 Product Manager and Full Stack Developer\n📈 Experience at 2 unicorn scaleups (PayFit & Meero) and Corporate (L'Oréal, Amazon, Airbus)\n🆕 Launched PayFit Spain and recruited the funding team\n🤼 Lead 5 squads and 25 developers at Meero \n🔀 Worked as a freelancer CTO & CPO for fintech, edtech and web3 companies\n\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/loic-veillard", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7d655bfff39fb2c0e6efb6a33bf5335e3152d478.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 64760, "title": "⏩ Blitz - Build internal apps with zero code", "tagline": "Stop using spreadsheets or developer resources, and start scaling your operations! ", "body": "We make it easy to structure and automate very manual processes - such as onboarding a new partner or resolving customer tickets. \n\nHi everyone!\n\nWe’re Clement and Loic, the co-founders of [Blitz,](https://blitznocode.com/) and we are super excited to announce the release of [our closed beta](https://www.blitznocode.com/signup)!\n\n### **The problem**\n\nBuilding internal tools is broken. Many no-code tools have tried to address that issue, enabling developers to code faster or helping end users build web applications on top of Google Sheets or Airtable.\n\nBut let’s face it: they don’t work, and the result is always the same: operations teams go back to spreadsheets.\n\n### **Our solution**\n\nBlitz is a no-code platform to build internal apps and workflows on top of a flexible database—so that:\n\n1. **developers** can focus on developing user-facing features, and don’t have to build and maintain back-office applications anymore\n2. **product managers** have now the capabilities to build internal apps with no tech resources\n3. **operations teams** never have to do the same manual task over and over again\n\n![uploaded image](/media/?type=post&id=64760&key=user_uploads/813313/a34fc140-8bf9-48d3-8fef-fab0051bbad1)\n\n**Blitz relies on 3 main pillars:**\n\n1. **a reactive database**—design your data model and integrate your business\n2. **an interface designer**—create internal apps using pre-built templates and reusable components\n3. **a workflow builder**—set up backend or frontend triggers with conditions to automate actions\n\n### **Our ask 🙏**\n\n1. **Sign up** to our beta: \n2. **Share** this post! Please help spread the word, and let’s change the way business apps are built and used.\n3. **Connect us** with operations-intensive teams at fast-growing companies (fintech, marketplaces, e-commerce, healthcare…)\\*\n\n _\\* copy & paste this blurb:_ Second employee at Aircall and ex-PayFit product lead are developing a [no-code app](https://www.blitznocode.com/) to build internal tools and automate manual operations. Check them out and contact [founders@blitznocode.com](mailto:founders@blitznocode.com) for more.", "url": "https://www.ycombinator.com/launches/GqW-blitz-build-internal-apps-with-zero-code", "votes": 70, "created_at": "2022-07-20T13:39:28.514Z" } ] }, { "id": 28659, "name": "PeerDB", "slug": "peerdb", "former_names": [ "PeerDB Inc" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/4a83477f0ae90481f26400baea36bd4c2fed890a.png", "website": "https://peerdb.io/", "all_locations": "", "long_description": "At PeerDB, we are building a fast, simple and the most cost effective way to stream data from Postgres to Data Warehouses, Queues and Storage engines. If you are running Postgres at the heart of your data-stack and move data at scale from Postgres to any of the above targets, PeerDB can provide value.\r\n\r\n\r\nWe support different modes of streaming - log based (CDC), cursor based (timestamp or integer) and XMIN based. Performance wise, we are 10x faster than existing tools. Features wise, we support native Postgres features such as comprehensive set of data-types incl. jsonb/arrays/postgis, efficiently streaming toast columns, schema changes and so on.", "one_liner": "Fast, native ETL for Postgres 🐘 🚀", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Analytics", "launched_at": 1686776037, "tags": [ "Developer Tools", "Open Source", "Data Engineering", "Enterprise Software", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Acquired", "industries": [ "B2B", "Analytics" ], "regions": [ "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": [ { "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "Existing ETL tools primarily focus on supporting a wide range of data-stores. However, they fall short in providing a rich experience for any two specific data-stores. This becomes evident when your workloads need scale or have demanding feature requirements. It is common for such users to try out these tools and fail – tools not meeting their performance and reliability SLAs or lacking the required features. Such users resort to developing their own in-house solutions, investing a lot of time and resources. This was a common pattern across various customers at our previous companies (Microsoft, Google)" } ], "url": "https://www.ycombinator.com/companies/peerdb", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/peerdb.json", "year_founded": 2023, "city": null, "country": null, "linkedin_url": "https://www.linkedin.com/company/peerdb", "x_url": "https://twitter.com/PeerDBInc", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/4a83477f0ae90481f26400baea36bd4c2fed890a.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Diana Hu", "url": "https://www.ycombinator.com/people/diana-hu" }, "founders": [ { "user_id": 728174, "full_name": "Sai Srirampur", "title": "Founder", "founder_bio": "Building world’s best data-movement experience for Postgres. Ex-Microsoft - Headed Solutions Engineering all up for PostgreSQL on Azure, Ex-Citus Data (YCS11 acq. Microsoft) - Early Engineer, IIIT-H Alumnus, He/His", "is_active": true, "linkedin_url": "https://linkedin.com/in/sai-krishna-s-1741b019", "x_url": "https://twitter.com/saisrirampur", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3b5faa67a88cc0f276aeb999922dbb9c96ce2114.jpg" }, { "user_id": 311868, "full_name": "Kaushik Iska", "title": "Founder", "founder_bio": "Working on PeerDB to make the Postgres ETL story stellar. Previous work experience includes leading teams at Google, Palantir, Safegraph.", "is_active": true, "linkedin_url": "https://linkedin.com/in/kaushikiska", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/cd7eaf287a144feb541e7737067323cf99cfe845.jpg" } ], "jobs": [], "news": [ { "title": "Real-time database startup ClickHouse acquires PeerDB to expand its Postgres support | TechCrunch", "url": "https://techcrunch.com/2024/07/30/real-time-database-startup-clickhouse-acquires-peerdb-to-expand-its-postgres-support/", "date": "Jul 30, 2024" }, { "title": "PeerDB raises $3.6 million seed funding to revolutionize data movement for PostgreSQL", "url": "https://blog.peerdb.io/peerdb-raises-3-6-million-seed-funding-to-revolutionize-data-movement-for-postgresql", "date": "Apr 11, 2024" }, { "title": "PeerDB raises $3.6M to accelerate PostgreSQL data movement - SiliconANGLE", "url": "https://siliconangle.com/2024/04/11/peerdb-raises-3-6m-accelerate-postgresql-data-movement/", "date": "Apr 11, 2024" }, { "title": "Launch HN: PeerDB (YC S23) – Fast, Native ETL/ELT for Postgres | Hacker News", "url": "https://news.ycombinator.com/item?id=36895220", "date": "Jul 26, 2023" } ], "launches": [ { "id": 72937, "title": "PeerDB - Modern ETL in minutes, with SQL", "tagline": "Data store-first ETL/ELT, enabling 10x faster syncs and transforms", "body": "## **TL;DR**\n\nPeerDB is a Postgres-compatible SQL interface to seamlessly integrate multiple data stores. It enables data engineers to sync, transform and query data across their stores using simple SQL commands. PeerDB takes a data store native approach in engineering — enabling 10x faster ETL/ELT.\n\n## **Problem**\n\nExisting ETL/ELT tools primarily focus on supporting a wide range of connectors at the expense of delivering high-quality ones. This becomes evident when your workloads need scale or have demanding feature requirements. While Sai was working at Microsoft, it was common to see large enterprise customers try out the existing ETL tools and fail – as the tools were not meeting performance and reliability SLAs or were lacking the required features. Customers spent significant time and resources to build and manage in-house data pipelines.\n\n## **Our Solution**\n\n### Data-store nativity at its core, enabling 10x faster ETL/ELT\n\nPeerDB takes a data store-first approach to data movement. It supports highly adopted stores and implements multiple infrastructural and data store native optimizations – providing a blazing-fast and feature-rich ETL experience.\n\nWe are starting our journey by providing the world's best data integration/ETL experience for **PostgreSQL,** [one of most used and popular databases](https://db-engines.com/en/ranking_trend/system/PostgreSQL). For example, on Postgres we parallelize initial load for larger tables using CTID scans. This yields orders of magnitude speedups - Syncing 100s of GB reduces from days to minutes.\n\nOur architecture is designed for real-time syncs, which enables 10x faster Change Data Capture with data freshness of a few seconds even at large throughputs (10k+ tps). For example, in a sync from Postgres to BigQuery or Snowflake, PeerDB is 10 times faster than other tools and provides <30s data-freshness guarantees.\n\n![uploaded image](/media/?type=post&id=72937&key=user_uploads/728174/dc134471-e0d0-48c5-bbda-6d2ec339c421)\n\n### SQL as the ETL/ELT standard\n\nThe Postgres-compatible SQL layer for ETL makes the life of data engineers very easy. They can develop pipelines using a framework they are familiar with, without needing to deal with custom UIs and REST APIs. They can use [Postgres' 1000s of integrations](https://github.com/PeerDB-io/peerdb#postgres-compatible-sql-interface-to-do-etl) to build and manage ETL.\n\nWe extend Postgres' SQL grammar with a few new intuitive SQL commands to enable real-time data streaming across stores. As we evolve, we will make this grammar richer and the standard for ETL/ELT.\n\n## **Our Asks**\n\nIf PostgreSQL is a critical component of your data stack and you are running into challenges moving data in and out of Postgres, reach out to us at [founders@peerdb.io](mailto:founders@peerdb.io) or join our waitlist at We would love to collaborate!\n\nAlso check out PeerDB’s Open Source repo - (don’t hesitate to leave us a star!)\n\n## **Finally, a bit about us**\n\nWe are Sai and Kaushik. We've been best buddies for the past 15 years now. We were roommates back in undergrad and actively collaborated since then. Now we have decided to start a business together! 🙂\n\n![uploaded image](/media/?type=post&id=72937&key=user_uploads/728174/4795901c-d1ac-4e3e-b565-7113c9ef3ca0)\n\nPrior to PeerDB, Sai worked at Microsoft leading Solutions Eng. for all PostgreSQL services on Azure. He was an early engineer at Citus Data (YC S11) and saw it through the Microsoft acquisition. Prior to PeerDB, Kaushik built operating systems and led data teams at Google, Safegraph and Palantir. He also represented India in ACM ICPC World Finals. With our diverse and complementary experiences, we are excited and confident to build the next-generation ETL/ELT!", "url": "https://www.ycombinator.com/launches/IyP-peerdb-modern-etl-in-minutes-with-sql", "votes": 60, "created_at": "2023-07-11T16:24:18.528Z" } ] }, { "id": 28787, "name": "Deasy Labs", "slug": "deasy-labs", "former_names": [ "Deasie" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b2f7a8dffc7dc8a6fdb4c13dce765cc7d2820b9a.png", "website": "https://www.deasylabs.com/", "all_locations": "New York City, NY, USA", "long_description": "Deasy Labs was acquired by Collibra in July 2025 (global leader in enterprise data governance).\r\n\r\nDeasy Labs provides metadata orchestration for AI workflows. Deasie's platform provides the best way for AI teams to create and embed high-quality, customized metadata into their AI workflows (e.g., RAG, Agentic frameworks).\r\n\r\nOur three founders (from Amazon, McKinsey/QuantumBlack & MIT) previously built an ML data governance tool from 0 to 1 within McKinsey, which we deployed with 11 Fortune 500 companies. We saw in early 2023 the ability to create high-quality metadata (without reliance on domain experts) would be a key factor in achieving the accuracy & speed in GenAI applications required for production. \r\n\r\nOur investors include General Catalyst, Y Combinator, RTP Global and world experts in enterprise data. Website: https://deasylabs.com", "one_liner": "Metadata for GenAI workflows", "team_size": 8, "industry": "B2B", "subindustry": "B2B -> Analytics", "launched_at": 1691123582, "tags": [ "Data Labeling", "Big Data", "AI", "AI Assistant", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Acquired", "industries": [ "B2B", "Analytics" ], "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/deasy-labs", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/deasy-labs.json", "year_founded": 2023, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/deasie", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b2f7a8dffc7dc8a6fdb4c13dce765cc7d2820b9a.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": 1227239, "full_name": "Reece Griffiths", "title": "Founder", "founder_bio": "Co-founder of Deasy Labs (YC S23): Data governance for language models in the enterprise. \n\nExited Deasy Labs to Collibra in < 2 yrs (global leader in data governance).\n\nMy documented startup journey: https://reecegriffiths.substack.com/\n\nPrior experience: QuantumBlack: Product Manager for AI for Data Governance product (1.5 yrs); McKinsey & Company: Management consultant (2 yrs); X26: Founder of X26 (global entrepreneurs community); University of Cambridge: Masters in information engineering.", "is_active": true, "linkedin_url": "https://linkedin.com/in/reece-william-griffiths", "x_url": "https://twitter.com/ReeceWGriffiths", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/53fe8f42805f99012d0a74523cf2874504663377.jpg" }, { "user_id": 1237667, "full_name": "Mikko Peiponen", "title": "Founder", "founder_bio": "Mikko is the co-founder of Deasie. He is a technologist with deep experience (5+ years) in data and machine learning space from McKinsey and QuantumBlack, where he implemented and deployed models at large enterprises. Prior to moving into the world of data governance for language models with Deasie, he was the Lead data scientist building AI data quality tool at QuantumBlack. Mikko holds a graduate degree from MIT.", "is_active": true, "linkedin_url": "https://linkedin.com/in/mikkopeiponen", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9dd45b70aec24740409919d85c62d51a6b55da39.jpg" }, { "user_id": 1226121, "full_name": "Leonard Platzer", "title": "Founder", "founder_bio": "Co-founder of Deasy Labs: The Metadata Engine for search usecases. \n\nPrior experience: QuantumBlack: Software Engineer, Machine Learning at AI for Data Quality product; Previous Engineer at Amazon, Mercedes-Benz and ChatBot Startup (E-Bot 7); Bachelor from National University of Singapore and Graduate from UWC Mostar", "is_active": true, "linkedin_url": "https://linkedin.com/in/leonardplatzer", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/91e9bdf6844bdc43fac38dbfaa0bf7fedda307c0.jpg" } ], "jobs": [], "news": [ { "title": "Data governance platform Deasie raises $2.9 million in seed funding - Business Leader News", "url": "https://www.businessleader.co.uk/deasie-raises-2-9-million-seed-funding/", "date": "Oct 13, 2023" }, { "title": "Lakera and Deasie raise funding to make AI large language models more reliable - SiliconANGLE", "url": "https://siliconangle.com/2023/10/12/lakera-deasie-raise-funding-make-large-language-models-reliable/", "date": "Oct 12, 2023" }, { "title": "Deasie raises $2.9M for data governance platform expansion - Tech.eu", "url": "https://tech.eu/2023/10/12/deasie-raises-29-million-for-data-governance-platform-expansion/", "date": "Oct 12, 2023" }, { "title": "Deasie raises $2.9M for data governance platform expansion - Tech.eu", "url": "https://tech.eu/2023/10/12/deasie-raises-29-million-for-data-governance-platform-expansion/", "date": "Oct 12, 2023" }, { "title": "Deasie wants to rank and filter data to make generative AI more reliable | TechCrunch", "url": "https://techcrunch.com/2023/10/12/deasie-wants-to-rank-and-filter-data-to-make-generative-ai-more-reliable/", "date": "Oct 12, 2023" } ], "launches": [ { "id": 73808, "title": "Deasie - Data governance for language model applications", "tagline": "Is your enterprise data ready for language models?", "body": "**tl;dr: Deasie ensures that only relevant, high-quality, and safe data is fed into language models.**\n\nHi! We’re Reece, Leo, and Mikko and we’re excited to launch [Deasie](https://www.deasie.com).\n\n⚠️ **Problem**\n\nFor the first time, companies are turning towards their masses of unstructured data (e.g., documents, reports, emails) for a range of Generative AI use cases. Today, most companies are unable to answer the critical questions needed to ensure language models are trained and deployed reliably: Does this data contain sensitive information? Is this the most relevant data for this problem? Are there inconsistencies in the data that could skew my results?\n\n💡 **Solution**\n\nDeasie is a platform that provides automated checks for compliance (e.g., PII & proprietary data) and quality (e.g., irrelevant, untimely, or inconsistent information) of unstructured data, to reliably govern which data is used for which language model use case.\n\n👨‍👦‍👦 **The Team**\n\nFounders Leo, Mikko & Reece previously built McKinsey's award-winning product that used AI to enhance data quality in the enterprise, built ML applications across QuantumBlack, MIT, Amazon, and Mercedes, and have now set out to unlock the power of unstructured data for the upcoming wave on language model applications.\n\n![uploaded image](/media/?type=post&id=73808&key=user_uploads/1237667/ede65ff1-1431-4c6f-88aa-dd343d325f4d)\n\n👋 **Ask**\n\n**Lots of companies must be delving into the world of LLMs. If you’ve encountered challenges identifying and using high-quality input data (e.g., data that is relevant, timely, accurate, and consistent), then we’d love to hear from you! Please reach out to us at** [**leonard@deasie.com**](mailto:leonard@deasie.com)", "url": "https://www.ycombinator.com/launches/JCS-deasie-data-governance-for-language-model-applications", "votes": 16, "created_at": "2023-08-04T06:57:33.350Z" } ] }, { "id": 28829, "name": "DataShare", "slug": "datashare", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/a781f87fe1bbe72d69bfd79f2391a43f15cb2f9b.png", "website": "http://datashare.co/", "all_locations": "Austin, TX, USA; Remote", "long_description": "DataShare is a data-as-a-service platform that lets you embed charts, dashboards and exports directly into your product. For example, if you run an accounting startup, DataShare would enable you to embed a full profit and loss dashboard, with downloadable statements. DataShare is backed by an enterprise-grade data warehouse, and can be implemented in fewer than 20 lines of code. \r\n", "one_liner": "Embed charts, dashboards and exports in your app", "team_size": 1, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1693343113, "tags": [ "Analytics", "Data Engineering", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/datashare", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/datashare.json", "year_founded": 2023, "city": "Austin", "country": "US", "linkedin_url": "https://www.linkedin.com/company/withdatashare/about/", "x_url": "https://twitter.com/WithDataShare", "fb_url": null, "cb_url": null, "github_url": "https://github.com/datashare-public", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/a781f87fe1bbe72d69bfd79f2391a43f15cb2f9b.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Harj Taggar", "url": "https://www.ycombinator.com/people/harj-taggar" }, "founders": [ { "user_id": 8960, "full_name": "Brian Brunner", "title": "Founder", "founder_bio": "Founder of DataShare, previously was the engineering manager for the team at Stripe that built Sigma and Data Pipeline.", "is_active": true, "linkedin_url": "https://linkedin.com/in/bbrunner", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3b55587837d10e3096bf4a8abd69d08dec332f01.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 74483, "title": "DataShare: Embed customer-facing charts, dashboards and exports directly in your product", "tagline": "Backed by an enterprise data warehouse and just 20 lines of code to integrate.", "body": "Hey there! I’m Brian from [DataShare](https://datashare.co/).\n\nDataShare is a data-as-a-service platform that lets you embed charts, dashboards and exports directly into your product. For example, if you run an accounting startup, DataShare would enable you to embed a full profit and loss dashboard, with downloadable statements. It’s backed by an enterprise-grade data warehouse and can be implemented in fewer than 20 lines of code.\n\n[Book some time](https://calendly.com/bb-datashare/datashare-intro-call) if you want to learn more. We can walk through all DataShare has to offer and give you an idea of what an integration would look like for your tech stack.\n[](https://www.youtube.com/watch?v=LNZGCl7lQMU)\n# Background\n\nOver the past few years, SaaS and financial services companies have been pushed by their customers to provide better business intelligence tooling. These systems are complex to design, hard to build, expensive to run, and challenging to scale. I've seen numerous examples of companies doing things like operating a database per customer or running 10+ hour-long pre-computation jobs just to deliver customer-facing data products.\n\nHowever, during my time at Stripe, I saw a different model. I ran the engineering team that built [Data Pipeline](https://stripe.com/data-pipeline) and [Sigma](https://stripe.com/sigma), and due to Stripe's infrastructure, we were able to rapidly deliver new products that helped to retain some of Stripe’s largest customers.\n\nDataShare is packaging up what I learned, and delivering that knowledge to the rest of the market.\n\n# The Product\n\nDataShare is a platform that combines a scalable, enterprise-grade data warehouse with a front-end framework for integrating business intelligence tools directly into your product.\n\nCharts and dashboards can be built with a point-and-click UI and can be integrated with just two lines of code.\n\n![uploaded image](/media/?type=post&id=74483&key=user_uploads/8960/57e3c582-7c6a-4595-9e20-efb8550438c5)\n\nYou can also skip the UI if you want. Visualizations can be built using DataShare’s AI-powered assistant.\n\n![uploaded image](/media/?type=post&id=74483&key=user_uploads/8960/3520b630-6afd-4725-9e9f-9bad448c8ee4)\n\nDataShare supports more than 20 types of data sources, including PostgreSQL, MySQL, and MongoDB, and is backed by a full data warehouse.\n\n# About DataShare\n\n[Brian](https://www.linkedin.com/company/withdatashare/) - I spent the past 4 years at Stripe and the past year and a half as the eng manager for [Data Pipeline](https://stripe.com/data-pipeline) and [Sigma](https://stripe.com/sigma). My team and I supported some of Stripe’s largest customers like DoorDash, Slack, Zoom, and Lime, helping keep them happy and run crucial financial workflows.\n\n# Ask\n\nIf you are thinking about building our customer-facing business intelligence tools, or you have a customer-facing BI stack you don’t like, reach out at [hello@datashare.co](mailto:hello@datashare.co) or grab [time to chat](https://calendly.com/bb-datashare/datashare-intro-call).\n\nThanks!\n\nBrian from DataShare", "url": "https://www.ycombinator.com/launches/JNL-datashare-embed-customer-facing-charts-dashboards-and-exports-directly-in-your-product", "votes": 35, "created_at": "2023-08-29T20:31:46.784Z" } ] }, { "id": 28900, "name": "Epsilla", "slug": "epsilla", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/a2a1e40bf12e61df1ea8d4d6a4f8b7691ac3982f.png", "website": "https://www.epsilla.com/", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "Epsilla is an all-in-one platform for building AI agents powered by your private data and knowledge. Easy to use for domain professionals, deeply customizable for AI experts, and fully equipped for enterprise customers with security, scalability, and integration.", "one_liner": "All-in-one platform to create AI agents with your private knowledge", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1687398753, "tags": [ "SaaS", "Infrastructure", "AI", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/epsilla", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/epsilla.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/epsilla/", "x_url": "https://twitter.com/epsilla_inc", "fb_url": "https://www.facebook.com/Epsilla-101648343029200", "cb_url": null, "github_url": "https://github.com/epsilla-cloud/", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/a2a1e40bf12e61df1ea8d4d6a4f8b7691ac3982f.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/f684a445e68e17098c7b485b86f0971629d143cc.png", "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/ef792393c19d6a025f86bdc862636cd3d194ab3e.png" ], "primary_partner": { "name": "Diana Hu", "url": "https://www.ycombinator.com/people/diana-hu" }, "founders": [ { "user_id": 14642, "full_name": "Richard Song", "title": "Founder", "founder_bio": "Richard Song is the Co-Founder and CEO of Epsilla. Before Epsilla, Richard was the Senior Director of Cloud Engineering at TigerGraph, led the team to build the DBaaS offering of TigerGraph's graph analytics platform. In addition to this, Richard led the team to build a suite of tools designed to empower developers, data scientists, and DevOps teams, enabling them to extract value from TigerGraph.", "is_active": true, "linkedin_url": "https://linkedin.com/in/renchu-song-a4099247", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/efd8878b92658ca4f156698b03dbc7fa22580f5c.jpg" }, { "user_id": 1378779, "full_name": "Ricki Qin", "title": "Founder", "founder_bio": "Ricki Qin is the Co-Founder of Epsilla. Before Epsilla, Ricki was the Engineering Manager in Cloud team at TigerGraph, led the team to build the Cloud Portal. Ricki has 5 years experience in the database industry, helped to build a suite of tools designed to empower developers, data scientists, and DevOps teams.", "is_active": true, "linkedin_url": "https://linkedin.com/in/jing-qin-48a4baa4", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/bf236ad8ca1ac9aefa6cdfd915d5f8b586d59301.jpg" }, { "user_id": 1378751, "full_name": "Eric Yang", "title": "Founder", "founder_bio": "Eric is the Co-Founder of Epsilla. Before Epsilla, Eric was the Architect of Tigergraph Cloud. Eric has 10+ years experience in Cloud Infrastructure with deep insight of System Relability and Scalability.", "is_active": true, "linkedin_url": "https://linkedin.com/in/eric-yang-a4a743271", "x_url": "https://x.com/eric_epsilla", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2cb278dd724352b939feb5cb82048fdaa03927e6.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 122233, "title": "UHP: Unified Harness Protocol by HarnessRouter", "tagline": "An open standard for running complete agent harnesses as your product's backend, now officially adopted by Qoder by Alibaba", "body": "**TL;DR:** We're launching the Unified Harness Protocol (UHP), an open standard (Apache 2.0) for running complete agent harnesses as your agent product's backend. Qoder by Alibaba now officially supports UHP. Integrate once, and Qoder, Codex, Claude Code, Hermes and 20+ agent harnesses become your product's backend through one contract. No harness engineering required.\n\n\\*\\*Stop building the agent backend. Focus on your product.\\*\\*\\\nYou don't need LangChain, an agent SDK, or a managed agent platform to build your agent's backend. Instead of building your own agent loop, tool execution, sandbox, orchestration, and tracing, plug in a pre-built, configurable harness like Qoder, Codex or Claude Code as your backend. Every task runs in an isolated sandbox, is orchestrated across harnesses, and is fully traced. Tailor your tools, plugins and skills, and focus on your product.\n\n![uploaded image](/media/?type=post&id=122233&key=user_uploads/14642/185decdc-9d10-47be-965c-5f24336a39bf)\n\n**The problem:** A harness is a complete agent runtime that plans, calls tools, edits files and reports back. Every harness already knows how to do the work. What none of them agree on is how a product should drive one: how to start a task, follow its progress, continue it, cancel it, get the files it produced, and understand why it failed.\n\nThat's an N×M problem: N products times M harnesses, each pair wired up by hand. So most teams end up building their own harness instead, and spend months on harness engineering before shipping a single feature.\n\n**What UHP is**\n\n* An HTTP contract. You send a task and get back a running agent, results and files. The unit is a job, not a completion.\n* Shaped like the OpenAI Responses API, so existing SDKs, streaming parsers and UI code work with no changes.\n* Versioned spec, OpenAPI schema, and a conformance suite with 95 runnable checks that defines what \"conformant\" means.\n* No hosted service required: a conformant server can run entirely on your own machine with your own keys.\n\n**Qoder by Alibaba adopts UHP:** Qoder now officially supports UHP, so you can use it as a pre-built, configurable agent harness for your product. Instead of building your own agent loop, tool execution, sandbox, orchestration, and tracing, you get a complete backend: Qoder reads files, runs commands, and delivers finished work, from generated files to completed actions. Every task runs in an isolated sandbox, is orchestrated through HarnessRouter, and is fully traced. Build your frontend and let Qoder handle your users' tasks.\n\n![uploaded image](/media/?type=post&id=122233&key=user_uploads/14642/7be585c9-451d-4618-bd89-1bbf42951eb2)\n\n\n**Run it:** HarnessRouter is the open-source UHP server, and HarnessRouter Cloud runs it for you. You build the frontend; the harness is your backend.\n\n**Links**\n\n* Spec: [unifiedharnessprotocol.org](http://unifiedharnessprotocol.org)\n* Repo: [github.com/HarnessRouter/harnessrouter](http://github.com/HarnessRouter/harnessrouter)\n* Cloud: [harnessrouter.ai](http://harnessrouter.ai)\n\n**Our ask:** If you build agent harnesses, implement UHP and run the conformance suite. If you're building an agent product, skip the harness engineering: use Qoder as your backend through HarnessRouter and tell us which harness you want next.", "url": "https://www.ycombinator.com/launches/VnV-uhp-unified-harness-protocol-by-harnessrouter", "votes": 4, "created_at": "2026-10-14T22:11:14.980Z" }, { "id": 111172, "title": "HarnessRouter: Open-Sourcing the World’s First Unified Interface for Agent Harnesses and the Unified Harness Protocol", "tagline": "Plug Codex, Claude Code, and Hermes into your backend on your own infrastructure. Introducing the Unified Harness Protocol (UHP), an open standard for connecting applications to different agent harnesses.", "body": "Hey everyone 👋 Three weeks ago, we[ launched HarnessRouter here on YC](https://www.ycombinator.com/launches/RpL-harnessrouter-bring-the-world-s-best-ai-agents-into-your-app-with-one-api) with one promise:\n\nBring the world’s best AI agents into your app, with one API.\n\nToday, we’re opening the layer underneath that promise.\n\nWe’re releasing HarnessRouter Community Edition and the Unified Harness Protocol (UHP). You can now run Codex, Claude Code, and Hermes as your product’s backend on infrastructure you control, using the same API contract as HarnessRouter Cloud.\n\n**TL;DR：**\n\nHarnessRouter is the world’s first unified interface for agent harnesses.\n\nYour product sends a task through one agent API. HarnessRouter runs the supported harness you choose and returns completed work, including files, artifacts, progress, and results.\n\nSessions, streaming, cancellation, and failure handling follow the same contract across Codex, Claude Code, and Hermes. You can add or switch a supported harness without rebuilding your product integration.\n\n🎥[ Watch the launch video](https://www.youtube.com/watch?v=iqrfCBrb6zY)\n\n### **Why agent harnesses aren't infrastructure yet**\n\nAn agent harness is the runtime layer around a model. It executes tasks, uses tools, manages sessions and files, streams progress, and returns completed work.\n\nHistorically, product teams built this layer themselves: task execution, session management, streaming, file and artifact handling, and error recovery, then maintained everything as models and tools evolved.\n\nNow frontier labs and open-source communities are building increasingly capable harnesses. Codex, Claude Code, Hermes, and DeepSeek Harness are examples. But they're still seen as coding tools rather than reusable infrastructure, so teams keep rebuilding this layer in-house. Just as product teams don't build their own databases, they shouldn't have to build their own harness layer either.\n\nEven teams that do adopt an existing harness hit the next obstacle: every harness works differently, so each new product-harness pair needs its own integration. We call this the N×M harness problem. In practice, it becomes harness lock-in: teams stick with the first harness they integrated, even when better or cheaper options exist.\n\nWe asked a simple question: what if agent harnesses became plug-and-play infrastructure that any product can build on?\n\nPlug-and-play means you integrate once, run any supported harness, and switch anytime a better or cheaper one ships.\n\nThat is what HarnessRouter makes possible.\n\n### **What we’re open-sourcing**\n\nWe’re releasing two core components under the Apache License 2.0:\n\n* **Unified Harness Protocol (UHP):** UHP is an open standard that gives different agent harnesses a shared execution contract. This release includes a versioned specification, schemas, and a conformance suite covering harness configuration, task execution, progress, sessions, files, cancellation, errors, and results.\n* **HarnessRouter Community Edition:** The self-hosted implementation of UHP. Gateway, Runner, and Console ship in a single Docker container. It runs Codex, Claude Code, Hermes, and more through one API and passes the full UHP conformance suite.\n\n![uploaded image](/media/?type=post&id=111172&key=user_uploads/14642/2bea0a11-8aba-49c5-9a85-112001854d96)\n\nYour model-provider credentials, application state, and files remain under your control. Model calls still go to the providers you configure.\n\nMCP connects agents to tools. UHP covers a different boundary: how an application drives a complete agent harness and receives its full execution lifecycle.\n\n### **Start self-hosted. Scale with Cloud when you’re ready.**\n\nHarnessRouter Community Edition and HarnessRouter Cloud implement the same UHP contract, so one integration works for both. Start self-hosted on infrastructure you control, or scale into Cloud when you're ready.\n\nHarnessRouter Cloud provides agent harness as a service (HaaS): fully managed harnesses with serverless execution in isolated sandboxes, so teams can scale concurrent runs without operating sandbox capacity themselves.\n\n### **Agent harnesses can do more than write code: runnable examples**\n\nWe also released a **Starter Kit** with runnable applications that show an end-to-end Community Edition integration.\n\nOne example puts a harness behind an Excel column so each row can trigger agent work. Other examples cover presentations, dashboards, and video generation.\n\n![uploaded image](/media/?type=post&id=111172&key=user_uploads/14642/914297fb-2588-4d8a-9a3b-37d39d82bae7)\n\nAgent harnesses can do much more than write code. We want the Starter Kit to show what becomes possible when you put them inside products.\n\n![uploaded image](/media/?type=post&id=111172&key=user_uploads/14642/f4cff64a-7539-41b3-804e-fa5ee53d77bd)\n\nStarter Kit components are available under their applicable licenses.\n\n### **Choose harnesses in production with evidence**\n\nWe benchmarked eight Harness × Model configurations on the same task and input. Cost varied by 475× and latency by 3.2×. Even with the model fixed, changing the harness moved cost by 1.5–2.1× and latency by up to 1.95×.\n\nHarness selection is a task-level production decision, not a one-time choice. HarnessRouter helps teams compare configurations on their own workloads. This was one controlled test, not a universal ranking; we published the methodology, results, and limitations.\n\n### **What teams are already building**\n\nTeams are using HarnessRouter in very different settings:\n\n* 🏥 Stanford Medicine is connecting its operational data through a shared agent layer, an “academic brain” for the organization.\n* ✅ Readily.ai encodes domain expertise into agent skills and can move a new agent from problem and plan to production within 24 hours.\n* 🎬 Spira.ai drives its generation pipeline through HarnessRouter.\n* 🪄 hibo.ai uses HarnessRouter to execute and evaluate agent skills.\n\n### **Why we built it**\n\nWe’re Kuanze Ma and Richard Song, the co-founders of HarnessRouter.\n\nKuanze is a four-time founder focused on human-AI systems. Richard has spent more than a decade building large-scale systems, including at TigerGraph and Meta.\n\nWe both worked on agent systems before HarnessRouter. We kept seeing product teams rebuild the same application-to-harness layer, so we decided to turn that layer into shared infrastructure.\n\n### **What we want from you**\n\n* If you build an agent harness or already maintain your own runtime, read UHP and tell us where it is wrong. Which parts should be standardized, and which should remain harness-specific? That comparison is worth more to us than a signup.\n* Try the Community Edition Quickstart on a clean machine. Tell us where it breaks or where the execution lifecycle is incomplete.\n* If your compliance requirements prevent you from using a hosted agent runtime, try Community Edition on infrastructure you control.\n* If you want to contribute, we use a proposal-before-code process. Describe the problem, your proposed change, and its expected impact. You do not need to arrive with a finished implementation.\n* Intros are welcome to teams shipping agent-powered features that do not want to own another backend.\n\n### **Run it yourself**\n\n* [Open-source repository](https://github.com/HarnessRouter/harnessrouter)\n* [Unified Harness Protocol](https://unifiedharnessprotocol.org)\n* [Starter Kit](https://github.com/HarnessRouter/starter-kit)\n* [Benchmark and methodology](https://harnessrouter.ai/benchmarks)\n* [Docs and HarnessRouter Cloud](https://harnessrouter.ai)\n\nReach us at [contact@harnessrouter.ai](mailto:contact@harnessrouter.ai).", "url": "https://www.ycombinator.com/launches/Sv6-harnessrouter-open-sourcing-the-world-s-first-unified-interface-for-agent-harnesses-and-the-unified-harness-protocol", "votes": 6, "created_at": "2026-08-17T22:23:24.250Z" }, { "id": 106971, "title": "HarnessRouter: Bring the World's Best AI Agents Into Your App, With One API", "tagline": "One API that puts Codex, Claude Code, Hermes, and more behind your product, so your users get real output: videos, games, code, documents.", "body": "Hey everyone 👋 Today we're introducing HarnessRouter.\n\n**TL;DR:** HarnessRouter lets builders bring Codex, Claude Code, Hermes, and other agents into your products through one API. Each task runs in an isolated sandbox with step-by-step tracing and configurable cost caps. It's designed to scale from consumer apps to large-scale model evaluations, orchestrating thousands of isolated sandbox runs in parallel. Start with our [AGENTS.md](https://harnessrouter.ai/agents.md), drop it into your coding agent, describe want you want to build, add an API key, and ship without rebuilding agent runtime infrastructure.\n\n**Try it now:** [harnessrouter.ai](http://harnessrouter.ai?utm=yc)\n\n\n\n## **The Problem**\n\nThere's a gap between what AI agents can do and what your product can offer.\n\nAgents like Codex and Claude Code can genuinely finish work now, but they're built for developers, not for your users. Bringing that capability to your users inside your own product means building the infrastructure yourself: isolated execution environments, agent runtime, tool orchestration, file handling, streaming, retries, permissions, cost controls. Every team building an AI feature rebuilds this same stack from scratch, and it takes months. After launch, the upgrades and maintenance never stop.\n\nAs a result, most teams ship a chat interface instead, when what users actually need is finished output.\n\n## **The Solution**\n\nHarnessRouter runs the entire agent stack as a service. Your product sends a task through one API; We run it on the agent you choose and return the result in structured, renderable form: reviewable diffs, generated files, images, video, confirmed tool actions.\n\nTeams are already running production workloads on it:\n\n* 🏥 A top medical research institution is connecting its operational data into a unified agent layer, an \"academic brain\" for the organization.\n* ✅ A leading healthcare compliance company encodes domain expertise into agent skills, saving their clients hundreds of hours of manual work per audit.\n* 🎬 An AI video marketing startup drives its content generation pipeline through HarnessRouter, producing viral-grade video at human quality.\n\nIntegration takes three steps: drop our [AGENTS.md](https://harnessrouter.ai/agent.md) into your coding agent, describe the feature you want and add your API key. Switching agents is a one-line configuration change, and the same setup scales from a single task to thousands of concurrent runs.\n\n## **Who We Are**\n\n**Kuanze Ma, Co-founder**\\\nKuanze is a four-time founder focused on human–AI systems, with experience in systems design, product management, algorithms, recruiting, marketing, and engineering. He holds a BSc in Mathematics, a diploma in Software Engineering, and an MSL from Northwestern University.\n\n**Richard Song, Co-founder**\\\nRichard has over a decade of experience building large-scale systems, including vertical AI agents powered by private data and domain knowledge. Previously, he was a Senior Director at TigerGraph and a Senior Software Engineer at Meta. He holds an MS in Computer Science from Cornell University and a BS in Computer Science.\n\n## **Our Ask**\n\n* **If you're building an AI feature**, start with the free 7-day trial. We'll help you integrate.\n* **If you need to run thousands of agents in parallel**, for model evaluations, batch generation, or large-scale pipelines, that's what the sandbox orchestration layer was built for. Talk to us.\n* **If you've built your own agent infrastructure**, we'd value the comparison. Tell us what we're missing.\n* **Intros welcome** to any team that wants to ship agent-powered features without building the backend.\n\nReach us at [contact@harnessrouter.ai](mailto:contact@harnessrouter.ai)", "url": "https://www.ycombinator.com/launches/RpL-harnessrouter-bring-the-world-s-best-ai-agents-into-your-app-with-one-api", "votes": 29, "created_at": "2026-07-23T13:44:28.619Z" }, { "id": 99723, "title": "ClawTrace — Make Your OpenClaw Agent Better, Cheaper, and Faster", "tagline": "The observability platform for OpenClaw agents: execution traces, cost analysis, and Tracy, your OpenClaw's doctor agent.", "body": "## 🚀 TL;DR\n\n[ClawTrace](https://clawtrace.ai?utm=yc) is the observability platform for OpenClaw agents. It captures every trajectory your OpenClaw agent runs — every LLM call, tool use, and sub-agent delegation — and gives you three views to understand it: an execution path, a call graph, and a timeline. When you need answers fast, ask Tracy, your built-in AI analyst, in plain English. Watch our demo [here](https://youtu.be/-moG7vCkawQ?si=ivl7Utv3h7ynv5rb):\n\n—\n\nHello, world! I'm Richard, and today I'm excited to introduce **ClawTrace** — the observability platform for OpenClaw agents.\n\nAI agents are going to production. But right now, when something goes wrong, you're flying blind.\n\n## ❌ The Problem\n\nDeploying an OpenClaw agent is easy. Understanding what it actually did is not.\n\nWhen a run costs $104.70 instead of the usual $1.5, you have no idea why. When an agent fails silently, you don't know which step broke or what input caused it. When a trajectory takes 3 minutes instead of 30 seconds, you can't tell where the time went. You end up reading raw JSON logs, manually counting token estimates, and guessing at execution paths. This doesn't scale.\n\nExisting options make this worse, not better:\n\n* **General distributed tracing tools** (Jaeger, Zipkin, Datadog) weren't built for LLM concepts — they have no understanding of token counts, model costs, prompt/completion pairs, or sub-agent hierarchies.\n* **Framework-specific tools** like LangSmith only work within their own ecosystem. If you're on OpenClaw, you're on your own.\n* **Point solutions** cover one slice of the problem — just logs, just cost tracking, just evals — and leave you to stitch everything together yourself.\n* **Every tool** dumps data into a dashboard and expects you to interpret it. There's no analyst. You still have to do all the thinking.\n\n## ⭐️ Our Solution\n\nWe built ClawTrace to give you complete visibility into every OpenClaw agent run — automatically, with zero code changes to your agent.\n\nOne plugin. Three commands. Every trajectory captured.\n\n```bash\nopenclaw plugins install @epsilla/clawtrace\nopenclaw clawtrace setup\nopenclaw gateway restart\n```\n\nThat's it. From that point on, every agent run streams to ClawTrace automatically.\n\n**With ClawTrace, you get:**\n\n* **Execution Path** — a collapsible tree of every step your agent took: LLM calls, tool executions, sub-agent delegations, with full input and output payloads at every node. Metadata badges show model name, duration, token counts, and estimated cost inline.\n\n ![uploaded image](/media/?type=post&id=99723&key=user_uploads/14642/d592e3b7-c8d3-4f7c-b71d-a27037551bba)\n\n* **Call Graph** — an interactive force-directed diagram showing the relationships between every agent, model, and tool in a run. Understand the shape of a complex multi-agent workflow at a glance.\n\n ![uploaded image](/media/?type=post&id=99723&key=user_uploads/14642/e81258a7-05d0-4c5d-be9a-0ef944b72329)\n\n* **Timeline** — a Gantt chart of every span in the trajectory, positioned by start time and sized by duration. Bottlenecks, parallelism, and idle gaps become immediately visible.\n\n ![uploaded image](/media/?type=post&id=99723&key=user_uploads/14642/bf94f46d-90e6-4ea4-8b1e-2971ad79af85)\n\n* **Tracy, your OpenClaw’s Doctor** — every other observability tool gives you data and expects you to figure it out yourself. Tracy is different. Ask her a question in plain English — _\"where is the bottleneck in this trace?\"_ — and she queries your trajectory graph live, generates a visualization, and delivers specific recommendations: which step to fix, why, and how. Tracy is powered by a Claude managed agent and has real-time Cypher access to your full trajectory graph via PuppyGraph graph lake-house architecture.\n\n ![uploaded image](/media/?type=post&id=99723&key=user_uploads/14642/7cb3bad9-86b3-4795-b761-75b7a390ce62)\n\n* **Cost Estimation** — per-span cost calculation covering 80+ models across OpenAI, Anthropic, Google, DeepSeek, Mistral, Alibaba Qwen, Zhipu GLM, Moonshot, and more. Cache-aware pricing (fresh input vs. cached input vs. output tokens) for accurate estimates.\n* **Consumption-based billing** — pay for what you use with credits. No seat fees, no per-user pricing. Start with 100 free credits.\n\n## 🔦 The Ask\n\nAI agents are moving from demos to production. ClawTrace is the observability layer that makes that transition safe, debuggable, and cost-efficient.\n\n* 🚀 **Sign up for free** at [clawtrace.ai](http://clawtrace.ai) — 100 credits included, your first trajectory shows up in minutes.\n* ⭐ **Star us on GitHub** — ClawTrace is fully open source under Apache 2.0: [github.com/epsilla-cloud/clawtrace](https://github.com/epsilla-cloud/clawtrace)\n* 💬 **Tell us what you're building** — we'd love to hear about your OpenClaw setup and what observability problems matter most to you. [Schedule a call with me](https://epsilla-ai.sg.larksuite.com/scheduler/dc6a62e558c3a1cc).", "url": "https://www.ycombinator.com/launches/PwR-clawtrace-make-your-openclaw-agent-better-cheaper-and-faster", "votes": 12, "created_at": "2026-04-14T15:34:37.032Z" }, { "id": 84075, "title": "Epsilla — Ship production-ready AI agents on day 1", "tagline": "All-in-one platform to create AI agents with your private data and knowledge.", "body": "### **🚀 TL;DR**\n\n[Epsilla](https://www.epsilla.com/) is an all-in-one platform for building AI agents powered by your private data and knowledge. Easy to use for domain professionals, deeply customizable for AI experts, and fully equipped for enterprise customers with security, scalability, and integration. Watch our demo [here:](https://www.youtube.com/watch?v=ZhBPsxr2AL0)\n\n---\n\n—\n\nHello, world! We are Richard, Eric, and Ricki. Today, we are super excited to introduce Epsilla, the all-in-one platform for creating AI agents with your private data and knowledge.\n\n### **❌ The Problem**\n\nBuilding a prototype AI agent is easy, but creating production-level AI agents with private knowledge can be overwhelming. It involves managing LLMs, prompt engineering, tool usage, memory management, vector databases, RAG (Retrieval-Augmented Generation), evaluation, and more.\n\nExisting AI agent-building products in the market are:\n\n* Either developer-facing tools that require a significant amount of engineering work;\n* Or lacking depth in customization and flexibility, limiting the final agent's performance and quality;\n* Or just focus on only one small aspect of the problem—such as prompt engineering, workflow, retrieval, evaluation, or hosting—leaving builders to handle the heavy lifting of integrating these components into a cohesive system.\n\n### **⭐️ Our Solution**\n\n![uploaded image](/media/?type=post&id=84075&key=user_uploads/14642/83e9fd99-cf4a-473a-b1ac-57f4935ff4dc)\n\nWe have reimagined the AI agent platform from the ground up and developed an all-in-one solution for creating, iterating, and operating AI agents—without writing a single line of code.\n\nWith Epsilla, you can:\n\n* Build high-quality knowledge bases from diverse data sources, including uploaded files, websites, Google Drive, Notion, cloud storage, and more.\n* Deeply customize AI agents with options for appearance, prompt tuning, workflow customization, and advanced, modular, agentic, and graph-based RAG.\n* Evaluate agent performance, monitor user engagement, and manage user feedback.\n* In addition to providing an out-of-the-box ChatGPT or Perplexity-like UI, we also allow developers to use the agents as their application backend via REST API.\n* Epsilla seamlessly integrates with a growing set of Large Language Model providers, allowing you to choose the best model at any stage of the knowledge base and agent workflow to balance quality, latency, and cost.\n\n### **🔦 The Ask**\n\nThousands of GenAI builders, along with dozens of enterprises and SMBs across various industries, are already building their AI agents on Epsilla. We hope to see you join them soon!\n\n* Sign up for FREE at to build your first AI agent within 10 minutes!\n* Let’s talk about your use case! Schedule a meeting with us [here](https://epsilla-ai.larksuite.com/scheduler/4aca8159d1224454).\n* Join our community with us on [Discord](https://discord.gg/cDaY2CxZc5) and share what you built on Epsilla!", "url": "https://www.ycombinator.com/launches/Ls3-epsilla-ship-production-ready-ai-agents-on-day-1", "votes": 35, "created_at": "2024-09-17T16:47:45.508Z" }, { "id": 73587, "title": "Epsilla - A 10x faster open source vector database", "tagline": "The information retrieval foundation for the next generation of AI applications", "body": "### **🚀 TL;DR**\n\nEpsilla is an open-source vector database. Epsilla offers low-latency searches across billions of vectors, consumption-based database-as-a-service, comprehensive APIs, and multi-language bindings. Our team has 7 years of experience building the high-performance graph database, and our advanced ANN index leverages parallel graph traversal technology, making the vector search 10x faster.\n\n![uploaded image](/media/?type=post&id=73587&key=user_uploads/14642/801e5229-0b56-4eb5-a3fb-e5ff7d2686ec)\n\n### **❌ The Problem**\n\nWith the surge of LLMs and the evolution of AI agents for task planning and decomposition for complex tasks, a **high-performance vector database** for information retrieval and enhancing context in prompts is a **MUST**! Most vector databases utilize HNSW, and its performance significantly degrades when aiming for high search precision.\n\n### **⭐️ Our Solution**\n\nWe’ve reimagined vector search from the ground up for scalability, performance, and cost-effectiveness. We implemented the state-of-art ANN index algorithm from the academia that leverages intra-query parallel graph traversal, which outperforms HNSW by **5x** on high precision query latency on medium size (1M) vector space and outperforms HNSW by **50 times** on large-scale vector search.\n\n![uploaded image](/media/?type=post&id=73587&key=user_uploads/14642/7f276b64-0500-4286-b589-e23d339abe6d)\n\n### **🔦 The Ask**\n\n* Try us at \n* Follow our repo at \n* Connect with us on [Discord](https://discord.gg/cDaY2CxZc5)\n* Let’s talk at !", "url": "https://www.ycombinator.com/launches/J8t-epsilla-a-10x-faster-open-source-vector-database", "votes": 41, "created_at": "2023-07-31T01:45:04.471Z" } ] }, { "id": 29087, "name": "ParadeDB", "slug": "paradedb", "former_names": [ "Retake" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6668d68dbb65664dcc7f40eff60e4ee586f09132.png", "website": "https://paradedb.com", "all_locations": "San Francisco, CA, USA", "long_description": "One Postgres for your application data, full-text search, vector retrieval, and aggregations.", "one_liner": "You should just use Postgres.", "team_size": 11, "industry": "B2B", "subindustry": "B2B", "launched_at": 1689280457, "tags": [ "Developer Tools", "Analytics", "Open Source", "Infrastructure", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": [ { "question": "How did your company get started? (i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)", "answer": "We (Phil and Ming) previously built Whist, a cloud-hybrid browser, together with an amazing team of 20 from 2020 to 2023. We originally met at university, in 2016, during the earliest days of orientation, and quickly became close friends, pitching each other startup ideas throughout college, until we decided to make the jump during our senior year.The idea for ParadeDB came from building on the experience acquired in the cloud infrastructure market during our Whist days, and from talking with 50+ customers in the beginnings of YC S23. We initially applied to YC with a completely different idea, but pivoted into what became ParadeDB after seeing more customer interest for the infrastructure we had built to service our prior idea, a sustainability platform, than the idea itself; and thus ParadeDB was born." }, { "question": "How did you decide to apply to Y Combinator? What was your experience applying, going through the batch, and fundraising at demo day?", "answer": "We were looking for an environment that would help us make something people want and not get caught in all the other non-essential parts of building an early-stage company. Our experience with YC was shaped by this mindset from application to fundraising and we had a great experience." }, { "question": "How have you kept in touch with the YC community and continued to use YC resources & programs since the batch ended?", "answer": "We’ve made a bunch of friends in YC, are still in touch with our Group Partner and batchmates, and frequently interact on Bookface." }, { "question": "What's the history of your company from getting started until the present day? What were the big inflection points?", "answer": "ParadeDB started during the YC batch, in Summer of 2023. We first released `pg_search`, our full-text search product in Postgres. A few months later we released `pg_analytics`, our analytics product in Postgres. We eventually combined them both into a single Postgres extension to offer full-text search and analytics in Postgres. Today, we’re productionizing both in ParadeDB and working to serve increasingly large customers." }, { "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "Postgres’ full-text search and analytics capabilities, two critical workloads for SaaS, finance, retail, and countless more industries, are a far cry from dedicated data stores like Elasticsearch. ParadeDB is fixing that." }, { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "ParadeDB is part of the “Postgres for everything” movement, enabling companies to run increasingly larger parts of their data infrastructure on Postgres in an open-source, free of vendor lock-in world." } ], "url": "https://www.ycombinator.com/companies/paradedb", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/paradedb.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://linkedin.com/company/paradedb", "x_url": "https://twitter.com/paradedb", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/paradedb", "github_url": "https://github.com/paradedb", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6668d68dbb65664dcc7f40eff60e4ee586f09132.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/ce5ca15131669146d463d97b0579fcdc2d4b71e2.png" ], "primary_partner": { "name": "Harj Taggar", "url": "https://www.ycombinator.com/people/harj-taggar" }, "founders": [ { "user_id": 317980, "full_name": "Philippe Noël", "title": "Founder/CEO", "founder_bio": "Founder @ ParadeDB. Previously @Whist, @Microsoft. Harvard CS'20. From Québec.", "is_active": true, "linkedin_url": "https://linkedin.com/in/philippemnoel", "x_url": "https://twitter.com/philippemnoel", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e6ca7eedf6383c2ebb46a29a5f9e96a6e976add1.jpg" }, { "user_id": 1341340, "full_name": "Ming Ying", "title": "Founder", "founder_bio": "Founder @ ParadeDB", "is_active": true, "linkedin_url": "https://linkedin.com/in/ming-ying", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/19af7b14da5cf743d9de76ee556ebaf69adc3ebb.jpg" } ], "jobs": [ { "id": 74017, "title": "Database Engineer — Storage & Query Execution", "url": "https://www.ycombinator.com/companies/paradedb/jobs/GCpYraM-database-engineer-storage-query-execution", "location": "San Francisco, CA, US / Remote (CA; US)", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$150K - $250K", "equity_range": "0.15% - 0.40%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Rust", "Distributed Systems" ] }, { "id": 74019, "title": "Distributed Systems Engineer — Postgres Platform", "url": "https://www.ycombinator.com/companies/paradedb/jobs/yWsaLaN-distributed-systems-engineer-postgres-platform", "location": "San Francisco, CA, US / Remote (US; CA)", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$150K - $250K", "equity_range": "0.15% - 0.40%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Amazon Web Services (AWS)", "Bash/Shell", "Go", "Kubernetes", "PostgreSQL", "Elasticsearch", "RDS/Aurora", "Distributed Systems", "Docker" ] } ], "news": [ { "title": "ParadeDB takes on Elasticsearch as interest in Postgres explodes amid AI boom | TechCrunch", "url": "https://techcrunch.com/2025/07/15/paradedb-takes-on-elasticsearch-as-interest-in-postgres-explodes-amid-ai-boom/", "date": "Jul 15, 2025" } ], "launches": [] }, { "id": 29178, "name": "Lantern", "slug": "lantern-2", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/173b47e676be258f330e690587eca7beae572634.png", "website": "http://lantern.dev", "all_locations": "San Francisco, CA, USA", "long_description": "Lantern is the easiest way to build AI applications using Postgres.\r\n\r\nWe started Lantern because we believe AI is going to impact every single enterprise in every single industry. We want to allow every company to tap into their unstructured data to build better applications.\r\n\r\nWith Lantern Cloud, developers have access to everything they need to build an AI application: embedding generation, vector compression, vector search, efficient indexing, and more. All on top of the database they already know, Postgres, and using infrastructure that scales to billions.\r\n\r\nCheck out our code on Github: https://github.com/lanterndata/lantern", "one_liner": "Postgres vector database extension to build AI applications", "team_size": 3, "industry": "B2B", "subindustry": "B2B", "launched_at": 1690583353, "tags": [ "B2B", "Open Source", "Enterprise", "AI", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2024", "status": "Inactive", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada", "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/lantern-2", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/lantern-2.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/98218505", "x_url": "https://twitter.com/lanterndb", "fb_url": null, "cb_url": null, "github_url": "http://github.com/lanterndata", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/173b47e676be258f330e690587eca7beae572634.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/6cdefae03abf8dbedc86d007cacf0ee7f7b92f1c.png" ], "primary_partner": { "name": "Gustaf Alstromer", "url": "https://www.ycombinator.com/people/gustaf-alstromer" }, "founders": [ { "user_id": 343105, "full_name": "Di Qi", "title": "Founder", "founder_bio": "I'm working on Lantern. We're building the best platform to develop AI applications. Before Lantern, I worked at Y Combinator, on my own startups, and at Facebook. I studied computer science at Princeton, and grew up in Americus, Georgia.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/di-qi/", "x_url": "https://twitter.com/diqitally", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f8599f150bb5f7cff3f5a71233fc21979ae0fcd3.jpg" }, { "user_id": 1518897, "full_name": "Narek Galstyan", "title": "Founder", "founder_bio": "Now building the only database you'll need for your AI applications.\n\nI was an early engineer at Timescale, a billion-dollar Postgres company, where I contributed core features.\n\nI did my undergrad at Princeton, where I built a safe OS schedule in Rust for my thesis. More recently, I was a PhD Student at UC Berkeley working on distributed systems.", "is_active": false, "linkedin_url": "https://linkedin.com/in/ngalstyan", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d4f84260559cbc42248a77a731aa7d3d3b1acb47.jpg" } ], "jobs": [], "news": [ { "title": "YC’s latest Demo Day shows fascinating wagers on healthcare, chip design, AI and more | TechCrunch", "url": "https://techcrunch.com/2024/04/04/ycs-latest-demo-day-shows-fascinating-wagers-on-healthcare-chip-design-ai-and-more/", "date": "Apr 04, 2024" } ], "launches": [ { "id": 79083, "title": "Lantern - Postgres vector database 🏮", "tagline": "Lantern is a Postgres vector database that is scalable, cost-effective, and easy to use", "body": "Hi! We’re Di and Narek. We’re building [Lantern](https://lantern.dev).\n\n## About Lantern\n\nLantern is a Postgres vector database that is easy to use, cost-effective, and scales to billions.\n\nAnyone using a standalone vector database such as Pinecone has to maintain a separate database for their other application needs. This adds complexity. With Lantern, you can run vector search in the database you already know and love, Postgres. In addition, Lantern is [orders](https://lantern.dev/pricing) of magnitude cheaper than Pinecone.\n\nWe recently released [product quantization](https://lantern.dev/blog/pq). This enables index compression so the index can use up to 90% less memory and cost an additional 90% less!\n\nWe support [embedding generation](https://lantern.dev/docs/develop/generate) with Open AI, Cohere, and open-source embedding models inside the database for one-off queries. For bulk transactions and managed columns, we support generating up to 2 million embeddings per hour.\n\n![uploaded image](/media/?type=post&id=79083&key=user_uploads/343105/9ef30a0c-40fe-4de5-b6e7-a64c313ed329)\n\nWe also support [external index creation](https://lantern.dev/blog/hnsw-index-creation), which offloads index creation to external machines to avoid expensive index creation processes causing downtime in production databases.\n\n## Get Started\n\nThe easiest way to get started is with [Lantern Cloud](https://lantern.dev), our managed Postgres service. To self-host or explore our source code, check out our [GitHub repo](http://github.com/lanterndata/lantern).\n\nIf you are currently using another vector database provider or another Postgres provider, we have tools to make migration seamless.\n\nWe have multiple customers using Lantern in production, and we’d love to have you give us a try too! We’re happy to answer any questions or help you get set up - reach out at [support@lantern.dev](mailto:support@lantern.dev) or on X at [@diqitally](https://twitter.com/diqitally).\n\n![](https://lh7-us.googleusercontent.com/olpVbw9LdPqQvlae8m2tpV7bEcDzlwVIuv0laDX55L0r130wsYWkV-lGIcsCletv4I6zgYADCQ_sHmjWtMNZNpJlUkM_g7Z6TpkZJtCO54acRTUMNAyTuPF4HQfT9Na2YWXWzWx19l4_jl5zNikU-bE)\n\n## About Us\n\nBefore Lantern, [Narek](https://linkedin.com/in/ngalstyan) was a PhD student in distributed systems at Berkeley and worked at Timescale, a billion-dollar Postgres company. [Di](https://www.linkedin.com/in/di-qi/) worked at Y Combinator as a software engineer, co-founded a YC-backed 15-minute delivery startup, and worked at Facebook Ads Ranking.", "url": "https://www.ycombinator.com/launches/KZX-lantern-postgres-vector-database", "votes": 49, "created_at": "2024-03-05T21:29:57.288Z" } ] }, { "id": 29231, "name": "Ubicloud", "slug": "ubicloud", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/621ac44e76ec8f002f47c567b4533df1d318abcc.png", "website": "https://www.ubicloud.com", "all_locations": "San Francisco, CA, USA", "long_description": "Ubicloud is an open source cloud that can run anywhere.\r\n\r\nOur cloud services include elastic compute, block storage, virtual networking, managed Postgres, K8s, AI inference, and powerful IAM. Ubicloud provides these services on bare metal providers, such as Hetzner, Leaseweb, or AWS Bare Metal.\r\n\r\nYou can self-host our software or use our managed service to reduce your cloud costs by 3-10x.", "one_liner": "Open source alternative to AWS", "team_size": 15, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1700481295, "tags": [ "Cloud Computing", "Infrastructure", "AI", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2024", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/ubicloud", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/ubicloud.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/ubicloud", "x_url": "https://twitter.com/UbicloudHQ", "fb_url": null, "cb_url": null, "github_url": "https://github.com/ubicloud/ubicloud", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/621ac44e76ec8f002f47c567b4533df1d318abcc.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Garry Tan", "url": "https://www.ycombinator.com/people/garry-tan" }, "founders": [ { "user_id": 26240, "full_name": "Umur Cubukcu", "title": "Founder", "founder_bio": "Previously visiting partner at YC for W23 and S23; now co-founder at Ubicloud. Co-founder and CEO of Citus Data (S11), the creators of the distributed PostgreSQL database Citus. Led the PostgreSQL product team at Azure, after Citus Data was acquired in 2019 by Microsoft.", "is_active": true, "linkedin_url": "https://linkedin.com/in/umurc", "x_url": "https://twitter.com/umurc", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/811b92be504c42ee56cf6aad47e9f0f260d0fc7c.jpg" }, { "user_id": 26241, "full_name": "Ozgun Erdogan", "title": "Founder", "founder_bio": "I’m the cofounder of Ubicloud (YC W24), an open source alternative to AWS. We're on a mission to rebuild the cloud market with different values.\n\nPreviously, I was a partner at Microsoft, leading PostgreSQL engineering teams. I came to Microsoft through its acquisition of Citus Data (S11). I was the cofounder and CTO at Citus, and I learned a lot about doing startups there. Before all this, I built distributed systems at Amazon.", "is_active": true, "linkedin_url": "https://linkedin.com/in/ozgune", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d633f56431a747505416d1e826a81fb1377926ee.jpg" }, { "user_id": 49554, "full_name": "Daniel Farina", "title": "Founder/CTO", "founder_bio": "I am one of the founders of Ubicloud.\n\nI've contributed to a number of control planes from an early phase, starting at Heroku, in what would become Heroku Postgres. Since then, I've serially created new control planes and infrastructure cloud products with some close colleagues, at Citus Cloud and Crunchy Bridge. Ubicloud is the fourth such system.\n", "is_active": true, "linkedin_url": "https://linkedin.com/in/danfarina", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/18aa66df55ba80101caba48b73dd18d85d324c17.jpg" } ], "jobs": [ { "id": 89544, "title": "Software Engineer", "url": "https://www.ycombinator.com/companies/ubicloud/jobs/j4bntEJ-software-engineer", "location": "CA, US / Remote (NL; TR)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$95K - $250K", "equity_range": "0.15% - 0.50%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [ { "title": "Ubicloud wants to build an open source alternative to AWS | TechCrunch", "url": "https://techcrunch.com/2024/03/05/ubicloud-wants-to-build-an-open-source-alternative-to-aws/", "date": "Mar 05, 2024" } ], "launches": [ { "id": 79315, "title": "Ubicloud - Open source alternative to AWS", "tagline": "Ubicloud is an open source cloud that can run anywhere.", "body": "**TL;DR**: Ubicloud provides IaaS cloud features on bare metal providers, such as Hetzner, Leaseweb, or AWS Bare Metal. You can self-host our software or use our managed service to reduce your cloud costs by 3-10x.\n\nHey everyone 👋 We’re Umur, Daniel, and Ozgun from [Ubicloud](https://www.ubicloud.com). We’re on a mission to rebuild the cloud market with different values. More on us below.\n\n## The Problem\n\n![uploaded image](/media/?type=post&id=79315&key=user_uploads/26241/10a339e1-dafe-421e-99f4-9484960966a7)\n\nAWS, Azure, and Google Cloud make life easier for start-ups and enterprises. But they are closed source, have you rent computers at a huge premium, and lock you in. It gets worse after your credits expire.\n\nAlthough the cloud came in with savings in its early years, the market evolved into a few players that overcharge for non-differentiated services, with increasingly complex products for standard, essential services. Each vendor optimizing for their walled gardens leaves customers stuck maintaining their apps and infra across incompatible clouds.\n\nThe cloud is here to stay. We believe a simpler, secure, and open source version of it will have a lasting impact.\n\n## The Solution\n\nUbicloud offers an open source alternative, reduces your costs, and returns control of your infrastructure back to you — all without sacrificing the cloud’s benefits. We’re starting with elastic compute, virtual networking, block storage (non-replicated), managed PostgreSQL, and powerful IAM.\n\nOur managed cloud runs on cost-effective bare metal providers, such as Hetzner. As a result, you can reduce your costs by 3-10x. Example use-cases:\n\n1. GitHub Actions: Change 1-line in your workflow file. Reduce costs by 10x\n2. Compute for batch processing: Run data pipelines for AI, scientific computing, and video processing - on-demand and encrypted. Reduce costs by 3x (vs AWS VMs)\n3. Managed PostgreSQL: Open source and portable. Uses local NVMes for higher performance, ideal for analytics. Reduce costs by 3x (vs AWS RDS)\n\n## The Team\n\n![uploaded image](/media/?type=post&id=79315&key=user_uploads/89898/3ce2495a-3697-4f8e-a61e-a6aa4a64bde7)\n\n_“Never tell me the odds.” — Han Solo_\n\nWe think the cloud galaxy deserves a solution that stands for open source values. We’re building to those values as part of the Rebel Alliance (yes, an alliance is needed to outmatch the empire :). From left to right:\n\n[Umur](https://www.linkedin.com/in/umurc/) - 5 years at startups. Then co-founded and led Citus Data (S11) to Microsoft acquisition. Led product teams within Azure. YC Visiting Partner for the previous two batches\n\n[Ozgun](https://www.linkedin.com/in/ozgune/) - 4 years at Amazon. Then co-founded and led Citus Data (S11) to Microsoft acquisition. Led engineering teams within Azure.\n\n[Daniel](https://www.linkedin.com/in/danfarina/) - Instrumental in building Heroku PostgreSQL. Amazed everyone with his skills in building managed cloud services at Citus Data, Azure, and Crunchy Bridge.\n\n## Our Ask\n\n1. Help us build the Rebel Alliance. [**Follow us (@UbicloudHQ)**](https://twitter.com/UbicloudHQ) on Twitter and tweet [**this post**](https://twitter.com/UbicloudHQ/status/1767594825023721858)!\n2. Start using Ubicloud for [GitHub Actions](https://www.ubicloud.com/use-cases/github-actions), [secure virtual machines](https://www.ubicloud.com/use-cases/ubicloud-compute), or [managed PostgreSQL](https://www.ubicloud.com/use-cases/postgresql). We reduce costs by 3-10x and we are open source!\n\n## The Deal\n\nTweeting and following us on Twitter gets 5,000 free compute minutes (standard-2). Using Ubicloud for one of the above use cases before demo day gets 10,000 compute minutes. Claim those minutes by sending us an email at [founders@ubicloud.com](mailto:founders@ubicloud.com)", "url": "https://www.ycombinator.com/launches/KdH-ubicloud-open-source-alternative-to-aws", "votes": 121, "created_at": "2024-03-12T16:11:25.761Z" } ] }, { "id": 29300, "name": "Trellis AI", "slug": "trellis-ai", "former_names": [ "Trellis" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f9cc80521b4e48dc602aef29ce4fb7d4c0f5dda1.png", "website": "https://runtrellis.com/", "all_locations": "San Francisco, CA, USA", "long_description": "", "one_liner": "Accelerate Medication Access with AI", "team_size": 34, "industry": "B2B", "subindustry": "B2B", "launched_at": 1709255809, "tags": [ "B2B", "Data Engineering", "Infrastructure", "AI", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2024", "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/trellis-ai", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/trellis-ai.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/runtrellis", "x_url": "https://twitter.com/runtrellis", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/f9cc80521b4e48dc602aef29ce4fb7d4c0f5dda1.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Tom Blomfield", "url": "https://www.ycombinator.com/people/tom-blomfield" }, "founders": [ { "user_id": 365595, "full_name": "Mac Klinkachorn", "title": "Founder/CEO", "founder_bio": "Mac is the co-founder and CEO of Trellis. Previously, he worked at the Stanford AI lab on large multimodal models for Stanford Health and built ML infrastructure at Cresta, Moveworks, and Amazon. ", "is_active": true, "linkedin_url": "https://linkedin.com/in/sirapop-klinkachorn-stanford", "x_url": "https://twitter.com/MKlinkachorn", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3b1519f0bcfd993a8dbb236c3c3623c4e3ef1818.jpg" }, { "user_id": 1470616, "full_name": "Jacky Lin", "title": "Founder/CPO", "founder_bio": "Jacky is a co-founder of Trellis and has taught hundreds of Stanford graduate students how to build, train, and deploy AI models in the Stanford School of Engineering & Graduate School of Business. Previously, he worked at Meta, the World Bank, and Wayfair. ", "is_active": true, "linkedin_url": "https://linkedin.com/in/jhslin", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/839a64adac97b949f442d82bf502f77a5cc97e42.jpg" } ], "jobs": [ { "id": 90948, "title": "AI Deployment Strategist", "url": "https://www.ycombinator.com/companies/trellis-ai/jobs/qCGwSCc-ai-deployment-strategist", "location": "San Francisco, CA, US / Remote (US)", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$120K - $220K", "equity_range": "", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 92422, "title": "Member of Technical Staff (full-time)", "url": "https://www.ycombinator.com/companies/trellis-ai/jobs/cg2Sy6U-member-of-technical-staff-full-time", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150K - $275K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 96753, "title": "Windows Systems Engineer ", "url": "https://www.ycombinator.com/companies/trellis-ai/jobs/g0VlgB5-windows-systems-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Devops", "salary_range": "$100K - $175K", "equity_range": "0.10% - 0.30%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "PowerShell", "Windows" ] }, { "id": 111456, "title": "RCM Operations Specialist, Infusion (AI Platform)", "url": "https://www.ycombinator.com/companies/trellis-ai/jobs/Dtj2yEH-rcm-operations-specialist-infusion-ai-platform", "location": "San Francisco, CA, US / Remote (US)", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$70K - $120K", "equity_range": "", "experience": "3+ years", "visa": "Will sponsor", "skills": [] }, { "id": 116076, "title": "Forward Deployed Engineers (October 2026)", "url": "https://www.ycombinator.com/companies/trellis-ai/jobs/GU8CEhE-forward-deployed-engineers-october-2026", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$120K - $225K", "equity_range": "", "experience": "1+ years", "visa": "Will sponsor", "skills": [] }, { "id": 116768, "title": "Account Executive", "url": "https://www.ycombinator.com/companies/trellis-ai/jobs/J5OtVzK-account-executive", "location": "San Francisco, CA, US / Remote (US)", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$250K - $350K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 117422, "title": "GTM Engineer", "url": "https://www.ycombinator.com/companies/trellis-ai/jobs/TGrLp7J-gtm-engineer", "location": "San Francisco, CA, US / Remote (US)", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$100K - $175K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [ { "id": 78943, "title": "🌊 Trellis AI: Automate your Pre-sevice workflow⚡️", "tagline": "Reduce time-to-treatment, improve reimbursement rates, and ", "body": "### **TL;DR**\n\nTrellis helps healthcare providers treat more patients faster—while eliminating pre-service paperwork. We automate document intake, prior authorizations, and appeals at scale to streamline operations and accelerate care.\n\n❌ **Problem — Administrative costs are delaying care and draining revenue**\n\nOver 20% of U.S. healthcare spending goes toward administrative costs, delaying care, reducing revenue, and driving staff burnout—while leaving providers with less visibility into patient care than ever before.\n\nPrior authorizations, appeals, and pre-service documentation are some of the biggest bottlenecks to timely treatment. Manual processing is slow, error-prone, and costly—leading to delayed treatments, increased denial rates, and frustrated patients.\n\n🎉 **Solution**\n\nOur AI agent is trained on millions of clinical data points and converts messy, unstructured documents into clean, structured data directly in your EHR—at scale.\n\nWe handle the heavy lifting:\n\n* **Automating document intake** from faxes, PDFs, and scanned forms\n* **Processing prior authorizations and appeals** with high accuracy\n* **Integrating directly into EHR workflows** so staff can focus on care, not paperwork\n\nTrellis in action: Processing hundreds of pages per patient case to identify missing information, complete submissions, and accelerate treatment approvals.\n\n🏃🏽‍♂️ **Leading healthcare organizations use Trellis to:**\n\n* **Reduce time to treatment by over 90%**—ensuring patients start therapy faster\n* **Improve prior authorization approval and reimbursement rates**—reducing revenue leakage\n* **Leverage structured data for better clinical decision-making**—enhancing drug program performance and patient outcomes\n\n**About Us:**\n\nMac & Jacky are friends from the Stanford AI Lab, where we spent too much time indoors building language models and hoping they’d talk back 🧠💻\n\nPreviously, Mac built LLM infra at Cresta and Moveworks where our ML pipelines processed terabytes of chat logs and voice calls. Jacky taught AI classes at Stanford and worked at Meta on real-time machine learning systems handling complex model deployment challenges.\n\nTrellis is a spinout from Stanford AI Lab and is backed by leading investors including YC, General Catalyst, Telesoft Partners, and executives at Google and Salesforce.", "url": "https://www.ycombinator.com/launches/KXH-trellis-ai-automate-your-pre-sevice-workflow", "votes": 69, "created_at": "2024-03-01T14:13:48.722Z" } ] }, { "id": 30187, "name": "Praxim", "slug": "praxim", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/be59095cc480f86c19c6f83b000c574f9e8e3cd0.png", "website": "https://www.praxim.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Praxim is the agentic AI word editor that makes edits across your entire word document with formatting control, file and web context, and your personal preferences in mind. Simply tell Praxim what you'd like to do with your document and Praxim will implement your changes automatically. ", "one_liner": "The agentic AI Word editor", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Productivity", "launched_at": 1740746196, "tags": [ "SaaS", "Consumer", "B2B", "AI", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2025", "status": "Active", "industries": [ "B2B", "Productivity" ], "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/praxim", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/praxim.json", "year_founded": 2025, "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/be59095cc480f86c19c6f83b000c574f9e8e3cd0.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": 2305260, "full_name": "Richie Hsiung", "title": "Founder/CEO", "founder_bio": "Richie is the Co-Founder of Praxim. He studied Math/CS/Philosophy at Yale.\n\nHe was a USA Math Olympian and won the American Invitational Math Examination with a perfect score. The AIME is now one of the most commonly used math benchmarks for AI models.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/richiehsiung/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7341d65d0715ab1a74119686556933478b7b9a77.jpg" }, { "user_id": 1276682, "full_name": "Frank Li", "title": "Founder/CTO", "founder_bio": "Frank is the Co-Founder of Praxim. He studied pure mathematics and computer science at Yale.\n\nHe was a USA Math Olympian and an ML/AI researcher at the Yale NLP Lab and the Fields Institute in Toronto.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/frank-li-6866411b0/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2cba819b2c0e0553ef646f2aff372e31bae1b65f.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 94226, "title": "Praxim - The Agentic AI Word Editor", "tagline": "Transform Microsoft Word into an AI-native Word Editor", "body": "https://youtu.be/ffplqbuzfT4\n\nHey Everyone! We’re Praxim.\n\n**The Problem:**\n\nMicrosoft Word serves over a billion users, but Copilot is still stuck in the Stone Age, requiring manual selection just to generate crude, format-ignoring block replacement edits completely devoid of context from your preferences, files, or the web.\n\n**The Solution:**\n\nWith a single prompt, Praxim makes edits across your entire document:\n\n* No required manual selection to edit\n* No more copy-pasting and context switching between different apps\n* No formatting worries\n* Takes into account your preferences\n\nPraxim knows exactly what to change and what to keep and edits with word-level granularity—precisely editing only what needs editing, not making brute block replacements.\n\nMost importantly, Praxim creates real Word formatting:\n\n* Actual lists, tables, and styles—not fake imitation structures that are actually just paragraphs\n* Maintain formatting, or even change it\n\nPull data, research, and edit all without leaving Word, with features that 10x your productivity:\n\n* File and Web context\n* Voice dictation at the speed of thought\n* Intuitive edit previews and clickable references that jump to the location in the document\n\nPraxim is already used for use cases from form filling with data from multiple sources to contract redlining with precise legal edits.\n\n**Why only now?** Programmers left the era crude AI assistance years ago with agentic code editors. Formatted document editing is a far harder technical problem—a single word is encoded by formatting and structural markup several orders of magnitude more complex than plaintext code, with countless ways to render correct text incorrectly. Semantic meaning and formatting both matter.\n\nPraxim for Word solves this, delivering a quantum leap over any other Word editing tool today. Start using Praxim today at [praxim.ai](http://praxim.ai)!", "url": "https://www.ycombinator.com/launches/OVm-praxim-the-agentic-ai-word-editor", "votes": 11, "created_at": "2025-09-30T14:00:52.553Z" } ] }, { "id": 30277, "name": "ReJot", "slug": "rejot", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f75ba97279846098d21850986cc1298a5b21d9a0.png", "website": "https://rejot.dev", "all_locations": "Amsterdam, NH, Netherlands", "long_description": "ReJot is building Fragno to let API companies go from simply exposing an API to offering drop-in integrations that work out of the box.\r\n\r\nFragno is an open‑source framework for packaging backend endpoints, data storage patterns, and frontend utilities into reusable building blocks. API providers ship full integrations, not just low‑level SDKs and docs.\r\n\r\nCustomers configure rather than write code, so integrations are both much faster to launch and far more consistent across customers.", "one_liner": "We turn complex API integrations into drop‑in full‑stack libraries ", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1737760830, "tags": [ "Developer Tools", "API", "Infrastructure", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2025", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "Netherlands", "Europe" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/rejot", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/rejot.json", "year_founded": 2024, "city": "Amsterdam", "country": "NL", "linkedin_url": "https://www.linkedin.com/company/rejot/", "x_url": "https://x.com/ReJotSync", "fb_url": null, "cb_url": null, "github_url": "https://github.com/rejot-dev/", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/f75ba97279846098d21850986cc1298a5b21d9a0.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/68e338dd900d2b3b7d20081a394ee01f7ab156fd.png" ], "primary_partner": { "name": "Harj Taggar", "url": "https://www.ycombinator.com/people/harj-taggar" }, "founders": [ { "user_id": 2356635, "full_name": "Wilco Kruijer", "title": "Founder/CEO", "founder_bio": "CEO & Co-founder at ReJot, previously worked on a distributed financial platform at Adyen. Interested in the relation between distributed data and software maintainability.", "is_active": true, "linkedin_url": "https://linkedin.com/in/wilcokruijer/", "x_url": "https://twitter.com/WilcoKr", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c559d7b970c16e5775029a16c951070b472d676c.jpg" }, { "user_id": 2377112, "full_name": "Jan Schutte", "title": "Founder/CTO", "founder_bio": "CTO & Co-founder at ReJot, former Data Platform Engineer at Adyen, interested in distributed systems and database technologies. Current product is https://fragno.dev/", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jan-schutte-183145135/", "x_url": "https://x.com/Jan_Schutte", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5b79dd57e3e607f50dbdb4bce0ff2926a9e3da01.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 88081, "title": "ReJot - Enterprise Synchronization Engine", "tagline": "Sync engine to share data across engineering teams in enterprises", "body": "Hey everyone, we're Wilco & Jan, co-founders of ReJot! We're building a sync engine that helps big companies share data across engineering teams. We increase developer velocity and, most of all, ensure companies are not forced onto Kafka when they don't actually need event streaming.\n\n\n\n# The Problem\n\nGrowing companies are forced to adopt micro-services as their desire to give engineering teams autonomy increases. The number one reported problem in this architecture paradigm is **data silos**. Silos create high coordination overhead when integrating data from other engineering teams, slowing down development and delaying product launches.\n\nThe industry has defaulted to event streaming platforms like Kafka to enable teams to share data asynchronously. As a result, enterprise software architectures are pushed into an event-driven model. However, events come with inherent complexity—they encode **something happening** rather than **something being** (i.e., state).\n\n**We believe that events are used for no good reason other than the infrastructure forcing developers onto them.**\n\n# Solution\n\nReJot is a **sync engine** that enables sharing data between back-end services without the need for APIs or event streaming. We break data silos without forcing a paradigm shift from state to events.\n\nWe do this in the following way:\n\n* **Familiar Querying:** by syncing database-to-database, ReJot allows engineers to query data from other teams **directly from their own database**. Developers use what they know best: SQL queries.\n* **Sharing Data:** We realize that internal database schemas are sacred, and they should never be public directly. ReJot provides strong **data-sharing primitives**, allowing data owners to control _what_ and _how_ data is shared. Simplicity is key—setting up data publications in ReJot is trivial.\n* **Integration Flexibility:** Downstream teams don't necessarily need data in the same shape as the original source. SQL queries are used once more to transform the data to fit the integrating team's needs.\n* **Data Catalog**: Integrating data is impossible without first understanding what data is available. Our system overview maps an enterprise's software architecture through its databases, allowing the user to explore and search schemas to uncover opportunities for deep integration between products.\n\n![uploaded image](/media/?type=post&id=88081&key=user_uploads/2356635/d80813fb-077e-4916-98f0-bae320ddd57c)\n\n_\\[This overview shows how accounts owned by the accounting team are made public and integrated into the banking database by means of the \"Accounts Consumer\" integration.\\]_\n\n# Launching Today\n\nToday, we’re excited to launch the **open-source** Limited Access Preview of ReJot!\n\nOur control plane doubles as a database catalog, providing a unified view of all data stores within your organization. Connect Postgres databases to document your system architecture.\n\nIn the coming weeks we'll be rolling out ReJot's synchronization functionality to select users. As we scale our infrastructure, more users will be onboarded.\n\n![uploaded image](/media/?type=post&id=88081&key=user_uploads/2356635/c2ee7761-283c-4064-af0e-44984f23320a)\n\n# The Team\n\nWe met nearly 10 years ago in Amsterdam where we studied Computer Science together, and have been friends ever since.\n\n* Wilco (CEO) worked as a software engineer on Adyen's banking infrastructure. During a period of high growth he worked on the internal ledgering system and a framework to sync bank account data throughout the company, which inspired ReJot. He also has a background in software maintainability research.\n* Jan (CTO) was a data platform engineer for Adyen's big data platform. There, he managed infrastructure for 600+ ETL pipelines created by 50+ teams that produced hundreds of terabytes of data every week. Jan is a published AI researcher.\n\nThrough a shared passion of modern software technology and reducing complexity in enterprise systems we connected the dots to create ReJot's sync engine.\n\n# The Ask\n\n* _🔗_ Share this post! Do you know of any organizations struggling with their distributed architecture? Reach out to us at [founders@rejot.dev](mailto:founders@rejot.dev) —we’d love to help!\n* ⭐️ Star us on [GitHub](https://github.com/rejot-dev/rejot), follow us on [X](https://x.com/rejotsync) & [LinkedIn](https://www.linkedin.com/company/rejot)\n* 📧 Sign up for our [newsletter](https://rejot.dev/email-updates/)\n* 🌐 Connect your databases & learn more: \n\n![uploaded image](/media/?type=post&id=88081&key=user_uploads/2356635/98e4ff65-8266-448c-a2c9-637da1065625)\n\n", "url": "https://www.ycombinator.com/launches/Muf-rejot-enterprise-synchronization-engine", "votes": 84, "created_at": "2025-02-26T21:55:32.444Z" } ] }, { "id": 30432, "name": "HelixDB", "slug": "helixdb", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/64b0b8869e482d2c399a6856e5127933c31381bd.png", "website": "https://helix-db.com", "all_locations": "London, England, United Kingdom", "long_description": "As AI agents replace traditional software, they need a way to store, recall, and reason over contextual data. HelixDB gives them all of that in one system. We’re building the knowledge infrastructure every AI agent will depend on.", "one_liner": "The fastest & most scalable graph-vector database on the market", "team_size": 6, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1747978820, "tags": [ "Developer Tools", "Open Source", "Infrastructure", "AI", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Spring 2025", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "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/helixdb", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/helixdb.json", "year_founded": 2025, "city": "London", "country": "GB", "linkedin_url": "https://www.linkedin.com/company/helixdb/", "x_url": "https://x.com/helixdb", "fb_url": null, "cb_url": null, "github_url": "https://github.com/HelixDB/helix-db", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/64b0b8869e482d2c399a6856e5127933c31381bd.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1342245, "full_name": "George Curtis", "title": "Founder/CEO", "founder_bio": "CEO & Co-Founder of HelixDB, a graph-vector database for building RAG.", "is_active": true, "linkedin_url": "https://linkedin.com/in/george-curtis-12b232a5", "x_url": "https://x.com/georgecurtiss", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/798e68bcfd436f7772a4c61133638d2dde6c0de4.jpg" }, { "user_id": 1335533, "full_name": "Xavier Cochran", "title": "Founder", "founder_bio": "The \"rust guy\". Building HelixDB.", "is_active": true, "linkedin_url": "https://linkedin.com/in/xaviercochran", "x_url": "https://x.com/xav_db", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3f8da8079cb678cdbf47207673bb3c7c705639db.jpg" } ], "jobs": [], "news": [ { "title": "Fondo | HelixDB Launches: The Database for RAG & AI", "url": "https://www.tryfondo.com/blog/helixdb-launches?utm_term=launchblogs-helix&utm_campaign=Launch%20Blogs&utm_content=334218246&utm_medium=social&utm_source=twitter&hss_channel=tw-1250676001727639553", "date": "May 29, 2025" }, { "title": "Build MCP AI Agents with NoCode PLUS: Native graph-vector database, Use MCP servers with Claude on web", "url": "https://www.theunwindai.com/p/build-mcp-ai-agents-with-nocode", "date": "Apr 14, 2025" } ], "launches": [ { "id": 90705, "title": "HelixDB - The database for RAG & AI", "tagline": "HelixDB is a graph-vector database that brings structure to your un-structured data for RAG and AI applications. ", "body": "Hi everyone 👋\n\nWe’re [George](https://www.linkedin.com/in/george-curtis-12b232a5/) and [Xav](https://www.linkedin.com/in/xaviercochran/) – co-founders of [HelixDB](https://www.helix-db.com)\n\n# TL;DR\n\n**HelixDB is an open-source graph-vector database that brings structure to your un-structured data for RAG and AI applications.**\n\n**_ASK:_** Can you introduce us to people/companies that are working on Graph/Hybrid RAG that could benefit from better performance or less overhead in their development cycles? Contact me at [george@helix-db.com](https://mailto:george@helix-db.com)\n\nhttps://youtu.be/V5viTRj2h68\n\n# ❌ The Problem\n\nAI is changing at a rapid rate which is fundamentally changing technology. This new tech **_needs_** new infrastructure.\n\nEveryone is trying to build AI applications, which often involve dedicated data retrieval for their specific use case. Building these retrieval systems is hard. Previously, we’ve relied solely on vector databases to retrieve semantic matches on tiny snippets of text data. But, this technology is shifting and is relying more heavily on connected data, which comes with better context.\n\nBut building these retrieval systems often involve:\n\n1. **Vector databases**\n2. **Graph databases**\n3. Bespoke **middleman/syncing** software\n\nThese setups are complicated, take a lot of time, engineering expertise, and create huge amounts of overhead which makes maintaining them very time consuming and expensive.\n\n# ✅ Our Solution\n\nHelixDB integrates semantic meaning (through its vector types) with relationships to other data (graph types), a similar model to how we structure information in our brains.\n\n![uploaded image](/media/?type=post&id=90705&key=user_uploads/1342245/468263b3-fb06-4efe-9352-4c30923c6d10)\n\nThis makes it the best solution for making AI retrieval engines for agents and LLMs.\n\nHow we currently do this:\n\n1. Seamlessly integrate **vector types** into your **knowledge graph**\n2. **Type-safe query language** that guides developers and agents to write correct queries before they are executed\n3. **Extreme speeds** outperforming industry leaders by 1-3 orders of magnitude\n\nHow we’re going to make it better:\n\n1. **MCP tools** that allow agents to walk the graph themselves, deciding at each step how to traverse the graph based on the schema and available data.\n2. Built-in **ingestion pipelines** for multi-modal data.\n3. Built-in **embedding-models** bringing immediate structure to your knowledge graph.\n\nTo get setup, follow the guide in our README:\\\n[HelixDB Github](https://github.com/helixdb/helix-db)\n\n# 🙏🏻 Our Ask\n\n* Can you introduce us to people/companies that are working on Graph/Hybrid RAG that could benefit from better performance or less overhead in their development cycles? Get them to [book a call](https://cal.com/georgec/30min) or email [george@helix-db.com](https://mailto:george@helix-db.com)\n* If you have a Github account, then [give us a star 🌟](https://github.com/HelixDB/helix-db)\n\nYour help and support makes a huge difference for this project. **Thank you!**", "url": "https://www.ycombinator.com/launches/Naz-helixdb-the-database-for-rag-ai", "votes": 160, "created_at": "2025-05-24T19:55:54.908Z" } ] }, { "id": 30448, "name": "PgDog", "slug": "pgdog", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e611bab66e36c7b14163abfaf0665e260a2ac3e2.png", "website": "https://pgdog.dev", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "PgDog is an application for sharding PostgreSQL. It understands SQL and can distribute queries automatically between databases. It's built for managed databases, like AWS RDS, and doesn't require any changes to application code or schema.\r\n\r\nIn addition to sharding, PgDog is a load balancer and pooler, so it can act as a replacement for PgBouncer, RDS Proxy, and other Postgres scaling products. It brings the simplicity and performance of HTTP load balancing to the database.\r\n\r\nBuilt from the experience of sharding Postgres at Instacart during peak growth in 2020, PgDog is the answer to the old question: does Postgres scale? It does now.", "one_liner": "Scaling engine for PostgreSQL", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1745009582, "tags": [ "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Spring 2025", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/pgdog", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/pgdog.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/pgdog/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": "https://github.com/pgdogdev/pgdog", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e611bab66e36c7b14163abfaf0665e260a2ac3e2.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Garry Tan", "url": "https://www.ycombinator.com/people/garry-tan" }, "founders": [ { "user_id": 989617, "full_name": "Lev Kokotov", "title": "Founder/CEO", "founder_bio": "Second time technical founder, sharded Postgres at Instacart in 2020 during peak growth. Previously built PostgresML (AI + PostgreSQL = 🚀).", "is_active": true, "linkedin_url": "https://linkedin.com/in/levkk", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/981db84827f7a8a66abe5b0836d254f245380246.jpg" } ], "jobs": [ { "id": 78009, "title": "Founding Software Engineer", "url": "https://www.ycombinator.com/companies/pgdog/jobs/uWymUYy-founding-software-engineer", "location": "San Francisco, CA, US / CA, US / OR, US / Remote (San Francisco, CA, US; CA, US; OR, US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150K - $225K", "equity_range": "1.00% - 3.00%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "PostgreSQL", "Rust" ] } ], "news": [], "launches": [ { "id": 90596, "title": "PgDog: Scaling PostgreSQL", "tagline": "Proxy for scaling Postgres without app changes or database extensions.", "body": "Hey YC,\n\nI’m Lev and I invented time travel… for your database.\n\n“Huh?”\n\n**TLDR:** PgDog is a proxy for scaling PostgreSQL. It can load balance traffic and shard databases, without changes to app code or using database extensions. What does this have to do with time travel? Keep reading.\n\nhttps://www.youtube.com/watch?v=trSVJel3gyc\n\n# ❌ The Problem\n\nScaling Postgres is a full time job. Your app is writing data but the database can’t keep up. You’re adding more databases and thinking of breaking the app into services to spread the load.\n\nPgDog is for you if:\n\n* You had an outage caused by a bad query\n* Your database is unpredictable; app latency and uptime are all over the place\n* You know more about Postgres internals (like vacuum) than you’d like, and you just want it to work so you can go back to building your product\n\nAnd you catch yourself day dreaming about the good old days, when you were pre-PMF and your DB was at 10% CPU.\n\n# 👷 The Solution\n\nDatabase problems should be solved at the database layer. PgDog is a load balancer, connection pooler, bouncer, and sharder. It speaks fluent SQL and understands every single Postgres query. It uses this knowledge to:\n\n* Distribute queries between shards, horizontally scaling your relational database\n* Load balance traffic between replicas\n* Block or rewrite queries you don’t like, e.g. queries that forgot an index or are missing the customer_id and were about to leak data\n\nWith PgDog, your DB is young again and ready to grow 10x. Written in Rust, for security and performance, PgDog is fully tested and ready for production.\n\n# 🙏 The Ask\n\nIf any of this sounds familiar, **get in touch!** I’m happy to come work for you to fix your DB. I’ll stay in the basement and make PgDog scale Postgres, while you work on your startup.\n\nYou’re my ideal design partner if:\n\n* Postgres is your production database (naturally)\n* Despite your troubles, you love it and want to do more with it\n* You believe in first principles solutions to hard problems\n\n![uploaded image](/media/?type=post&id=90596&key=user_uploads/989617/91f087f7-4175-47fe-9753-57ed4e9bda62)\n\nEmail [lev@pgdog.dev](mailto:lev@pgdog.dev), join our [Discord](https://discord.gg/CcBZkjSJdd), and give us a star on [GitHub](https://github.com/pgdogdev/pgdog). Everything we build is open source.\n\n# 👬 The Team\n\nI’m a second time technical founder (previously co-founded [PostgresML](https://github.com/postgresml/postgresml)) and started the Instacart storage infra team. I scaled 100TB+ databases when Instacart grew 5x in 3 months. This is my second pet-inspired project for scaling Postgres (have you heard of [pgcat](https://github.com/postgresml/pgcat)?)\n\n![uploaded image](/media/?type=post&id=90596&key=user_uploads/989617/86ff8181-b991-4bf9-bd05-8b0a387ed978)\n\n", "url": "https://www.ycombinator.com/launches/NZE-pgdog-scaling-postgresql", "votes": 40, "created_at": "2025-05-21T17:22:59.749Z" } ] }, { "id": 30935, "name": "s2.dev", "slug": "s2-dev", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/db49ec4c66c85d205171bbf2f1700ea1146f627c.png", "website": "https://s2.dev", "all_locations": "San Francisco, CA, USA", "long_description": "S2 is an API for durable streams. We make working with streaming data dead simple and reliable.\r\n\r\nThe more we worked on data systems, the more we felt like there was a missing building block. The seamless experience of object storage simply did not exist for durable streams – so we set out to fix that!\r\n\r\nS2 reimagines streams as an unlimited and access-controlled web resource. Like if Kafka and S3 had a baby.\r\n\r\n- Building agents? Use granular streams to persist context in real time, make workflows auditable, and coordinate between agents.\r\n\r\n- Publishing fast-moving data? Streams support high fanout and can be accessed directly over the internet, so they are a great fit for broadcasting real-time feeds.\r\n\r\n- Sandboxing? Emit to per-instance streams, and read history or live events just as easily.\r\n\r\n- Local-first and multiplayer experiences? Propagate updates reliably and efficiently.\r\n\r\nStop dealing with clusters, and start streaming.", "one_liner": "API for unlimited, durable, real-time streams", "team_size": 5, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1759174945, "tags": [ "Developer Tools", "Open Source", "API", "Infrastructure", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2025", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada", "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/s2-dev", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/s2-dev.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/streamstore/", "x_url": "https://x.com/s2_streamstore", "fb_url": null, "cb_url": null, "github_url": "https://github.com/s2-streamstore", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/db49ec4c66c85d205171bbf2f1700ea1146f627c.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/f0557b757a7ba472cfd3e29dc99f717e31930607.png" ], "primary_partner": { "name": "Nicolas Dessaigne", "url": "https://www.ycombinator.com/people/nicolas-dessaigne" }, "founders": [ { "user_id": 235940, "full_name": "Shikhar Bhushan", "title": "Founder/CEO", "founder_bio": "Making streams plentiful and serverless. Hacked on retrieval systems at Etsy, a KV store at Facebook, and Kafka Connect at Confluent.", "is_active": true, "linkedin_url": "https://linkedin.com/in/shikhrr", "x_url": "https://x.com/shikhrr", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/57590395516b13e3ef14efeeefecfc7409c09e62.jpg" }, { "user_id": 1723893, "full_name": "Stephen Balogh", "title": "Founder/CTO", "founder_bio": "Loves Rust, and building systems that scale. Previously built novel infra for multi-modal product search at Etsy, and digital repository software at NYU", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/stephen-balogh-078a4995/", "x_url": "https://x.com/sgbalogh0", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/23704db7d2ff94bf5b6f5b2309970e60225a41b3.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 95319, "title": "s2.dev: Streams as a cloud storage primitive", "tagline": "The serverless API for unlimited, durable, real-time streams", "body": "Hi everyone! We are [Shikhar](https://www.linkedin.com/in/shikhrr/), [Stephen](https://www.linkedin.com/in/stephen-balogh-078a4995/), and [Dwarak](https://www.linkedin.com/in/dwarakgovindparthiban/), the co-founders of [s2.dev](http://s2.dev). S2 is the S3 for streams.\n\nhttps://www.youtube.com/watch?v=8-mUPf4P_8g\n\nMany of the most important features in products we love are powered by streaming data, from frontend UIs that go _brrr_ with real-time updates, to backends that store and process massive event flows. But building these sorts of real-time systems is really hard.\n\nWe are turning streams into a cloud storage primitive – just like object storage did for static files.\n\nS2 is a completely serverless datastore, offering unlimited streams and configurable (even bottomless) data retention. Streams can be appended to and followed in real-time, and since all writes are durable, you can also read from anywhere in a stream’s history, indexed by sequence number or timestamp.\n\nWe basically took the core abstraction of Kafka – [the log](https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying) – gave it a URI, and brought it online with a dead simple API. And none of the bloat. 😀\n\nhttps://www.youtube.com/watch?v=2a1OdJ9l78A\n\n## Why we’re building S2\n\nWe built large-scale ML infra at Etsy and Meta, and always felt the lack of a system like S2. Serverless infra for data at rest has been seamless for a long time (S3 launched 20 years ago!) – but the ecosystem around streaming data, in comparison, felt stuck in the stone age.\n\n![uploaded image](/media/?type=post&id=95319&key=user_uploads/235940/72f8cb1e-a550-4458-b57b-d5936dc98191)\n\nWhen we [first introduced S2](https://news.ycombinator.com/item?id=42480105), we thought it would be most attractive to people already using streaming platforms like Kafka – but we’ve found that it resonates much more with builders in areas where these systems are entirely absent, who end up cobbling together general-purpose stores like Postgres and Redis and run into their limits.\n\nWe stand out on dimensions that are completely off the map for legacy systems: like the ability to have an unlimited number of streams backed by object storage, and to make them directly accessible [over REST](https://s2.dev/docs/rest/records/overview). This means S2 streams can be used as a serverless, reliable alternative to custom WebSocket or SSE infra.\n\n## S2 radically simplifies streaming\n\nIf you are dealing with a **high-cardinality of data sources** – for example, code execution sandboxes or IoT devices – S2 is for you. Streams can be instantly created on-demand. You don’t have to buffer or batch, just pipe into S2.\n\nhttps://www.youtube.com/watch?v=wsFiwxv_RY0\n\nWe recently [supercharged](https://news.ycombinator.com/item?id=45310294) read scalability to handle massive fanouts. You can broadcast a **real-time feed** with S2 such as market data without building any infra – publishing is just a matter of POST-ing to a stream’s URL.\n\nIf you are building **agents**, a common pain is **syncing** progress between the backend and frontend, while also being able to replay history. A lot of these agent state management challenges become simple with S2.\n\n## Try it out\n\nWe should talk if [s2.dev](http://s2.dev) resonates with you! You can [sign-up](https://s2.dev/sign-up) and follow the [quickstart](https://s2.dev/docs/quickstart) to start streaming in seconds with free credits.\n\nThere is a lot more we are cooking, including an ⭐ open source self-hostable S2 https://github.com/s2-streamstore/s2 ⭐\n\n## Talk to us\n\n**email**: [founders@s2.dev](mailto:founders@s2.dev)\n\n**discord**: https://discord.gg/JfTWJ5xxZ6\n\n**twitter**: https://x.com/s2_streamstore\n\n**bluesky**: https://bsky.app/profile/s2.dev\n\n![uploaded image](/media/?type=post&id=95319&key=user_uploads/89898/8c2da77e-e2c6-4bb6-bba6-9da0a09f87ef)\n\n", "url": "https://www.ycombinator.com/launches/OnP-s2-dev-streams-as-a-cloud-storage-primitive", "votes": 64, "created_at": "2025-11-08T06:30:48.434Z" } ] }, { "id": 33279, "name": "Hebbian Robotics", "slug": "hebbian-robotics", "former_names": [ "Vanda Labs" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/4e7272f21860060f8aa33bb297af6d18fab5ffdc.png", "website": "https://hebbianrobotics.com/", "all_locations": "San Francisco, CA, USA", "long_description": "Hebbian Robotics builds open source foundational infrastructure for Physical AI to develop robots that extend what humanity can do, for the benefit of the communities they serve.", "one_liner": "Foundational infrastructure for Physical AI", "team_size": 2, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1786123276, "tags": [ "Robotics", "Data Science", "Data Engineering", "AI", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "Industrials", "Manufacturing and Robotics" ], "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/hebbian-robotics", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/hebbian-robotics.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/hebbian-robotics/", "x_url": "https://x.com/hbr_pbc", "fb_url": null, "cb_url": null, "github_url": "https://github.com/Hebbian-Robotics", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/4e7272f21860060f8aa33bb297af6d18fab5ffdc.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/18513ded63751a708b6d42e2a84d2517cb404aaf.png" ], "primary_partner": { "name": "Diana Hu", "url": "https://www.ycombinator.com/people/diana-hu" }, "founders": [ { "user_id": 388120, "full_name": "Brandon Ong", "title": "Co-Founder", "founder_bio": "Brandon is the co-founder of Hebbian Robotics, which builds APIs that help robotics data teams verify data quality for model training. He previously built Gigit AI, a Sequoia-backed AI customer experience startup. His research on AI for low-resource languages topped SEA-HELM, an LLM benchmark co-developed with Percy Liang's research group at Stanford. He briefly pursued a PhD in Robotics at Columbia and NTU before starting Hebbian.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ongjjbrandon", "x_url": "https://x.com/brandon1", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5b65343ad1d0b482401872b3124da731618a3058.jpg" }, { "user_id": 968709, "full_name": "Kingston Kuan", "title": "Co-founder", "founder_bio": "Kingston spent the last few years at Jane Street building systems that scaled to millions of messages per second. He graduated valedictorian from the National University of Singapore (NUS) in Computer Engineering.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/kingston-kuan/", "x_url": "https://x.com/kstonekuan", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b674a8b80df5cd21ef03f13e6470d6126bc4c333.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 109711, "title": "Hebbian Robotics - APIs for searching and analyzing Physical AI data at scale", "tagline": "We enable evaluation of data quality without training a robotics model", "body": "# \\*\\*\\*\\*\n\n**TLDR;**\n\n* **Hebbian Robotics builds APIs for searching and analyzing Physical AI data at scale**\n* Our APIs enable data providers to get **on-demand signals and metrics** on the quality of their data, without managing infrastructure\n* We help our customers evaluate data quality without training a robotics model\n\n\n\n**Our Ask**\n\n* Buyers want data they can trust. If you're selling egocentric or robot data, **Let’s Chat ([https://calendly.com/brandon-hebbianrobotics/30min](https://calendly.com/brandon-hebbianrobotics/30min)**) and we'll show you how quality signals and metrics help you sell more at higher prices\n* **Check out Pareto ([https://pareto.hebbianrobotics.com/](https://pareto.hebbianrobotics.com/)),** an example of how our APIs can be used\n\n### **Who We Are**\n\n* We’re Brandon and Kingston, we've trained AI models for industrial cleaning robots and built production systems for camera fleets across the globe\n* [**Brandon Ong**](https://bookface.ycombinator.com/user/388120)'s previous AI startup was backed by Sequoia. He did research on AI for low resource languages that topped Stanford’s LLM benchmark, SEA-HELM, and dropped out of his joint PhD in Robotics at Columbia and NTU\n* [**Kingston Kuan**](https://bookface.ycombinator.com/user/968709) spent years at Jane Street building systems that scaled to millions of messages per second. At Verkada, he worked on their core video surveillance platform that processed large scale multimodal data\n\n### **The Problem**\n\n* **High quality data is the bottleneck for unlocking the next frontier of intelligence in robotics**\n* Buyers want **data they can trust** \n* Data providers don’t have the bandwidth to provide signals and metrics about the quality of their data when they are **focused on** **operational excellence** \n* This means that **model developers are forgoing model improvements due to a lack of high quality data**, impeding progress in robotics\n\n### **Our Solution**\n\n* APIs for searching and analyzing Physical AI data at scale that enables providers to get **on-demand signals and metrics on data quality**\n* Buyers demanding per-collector hour limits? **Prove compliance while we stay ahead of the workarounds**\n* Changed standard operating procedures for collection? **Monitor for quality drift and regression**\n* Suspect duplicate samples? **Verify quickly with a single API call**\n* Our APIs enable **evaluation of data quality without training a robotics model, fast iteration cycles for** high quality data collection, and unlock **model improvements** \n\n### **How to reach us**\n\n* We want to support data providers who believe in collecting high quality data in an ethical way\n* To get started, email [**brandon.ong@hebbianrobotics.com**](mailto:brandon.ong@hebbianrobotics.com)", "url": "https://www.ycombinator.com/launches/SXX-hebbian-robotics-apis-for-searching-and-analyzing-physical-ai-data-at-scale", "votes": 81, "created_at": "2026-08-09T03:00:00.116Z" } ] }, { "id": 36774, "name": "ObsessionDB", "slug": "obsessiondb", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/4d02f08af0be569fcf511900d03b0d8eccfdf473.png", "website": "https://obsessiondb.com", "all_locations": "San Francisco, CA, USA", "long_description": "ObsessionDB is a real-time data warehouse that makes your data stack agent-ready. Your agents can ingest and query vast amounts of data in seconds, without the scary infra bill. Powered by our own more efficient fork of ClickHouse®.", "one_liner": "Real-time data warehouse for agents", "team_size": 5, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1792056940, "tags": [ "Analytics", "Big Data", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada", "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/obsessiondb", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/obsessiondb.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/obsession-db", "x_url": "https://x.com/ObsessionDB", "fb_url": null, "cb_url": null, "github_url": "https://github.com/obsessiondb", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/4d02f08af0be569fcf511900d03b0d8eccfdf473.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Christopher Golda", "url": "https://www.ycombinator.com/people/christopher-golda" }, "founders": [ { "user_id": 1006862, "full_name": "Rafael Aviles", "title": "Founder/CEO", "founder_bio": "Co-Founder & CEO at ObsessionDB. Bootstrapped our previous company to $1.5 M ARR and ran it profitably for 4 years, serving 5 billion real-time API requests per month. We ran data workloads at petabyte scale. Previously worked with the data team at enterprises like Wendy's, Victoria's Secret, FOX, Disney, Axel Springer, and Groupon.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/avilesco/", "x_url": "https://x.com/avilesrafa", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d37da273557c89396660d9bd178877a0bb6742ef.jpg" }, { "user_id": 364520, "full_name": "Marc Höffl", "title": "Founder", "founder_bio": "Founder at ObsessionDB. Serial founder with 4 companies, including Frens (acquired by Swyke) and Laserfocus (YC W20). Previously CTO at Numia building real-time blockchain data infrastructure. Founded Gambify in 2014, soccer app scaled to 6 figure players with support from German National Team. Engineering Manager at Localytics. MS in Data Science from KTH Stockholm, MS in Cloud Computing from TU Berlin.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/marc-h%C3%B6ffl/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9e312a2474088fbaaa2cd8982da7b2589589ad8b.jpg" }, { "user_id": 3960852, "full_name": "Marc Leuthardt", "title": "Founder", "founder_bio": "Co-Founder at ObsessionDB (YC F26). Previously co-founded Numia, scaling blockchain infrastructure to serve billions of monthly requests across 100+ blockchains. Co-founded and CTO at Frens (acquired by Swyke), autonomous infra validating 70+ blockchains. Co-founded and CTO at Lokalportal, led a team of 25. Degree in chemistry and economics. For a year spent a few mornings a week in a highschool chemistry lab to blow up things with students.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/marc-leuthardt", "x_url": "https://x.com/marcmacmac", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/bf248e989f02f62d53fdcd4cb8a26f9f63a224e8.jpg" } ], "jobs": [], "news": [], "launches": [] } ]