Tryardent
Hey HN! We’re Vikram and Evan from Ardent (https://tryardent.com). We're building database sandboxes for you and your coding agents. In the last two years coding agents have gotten dramatically more capable at handling complex engineering tasks. But without access to a realistic sandbox at the DB layer for testing, they ship garbage that can take down production databases. I spent over a year building an AI Data Engineer that failed for this exact reason. Evan spent the last 12 years in data engineering and hit this wall building agents at his last company. Ardent was built to make it possible for coding agents to get near instant access to production-like sandboxes so they can test their work. To do this we write a replication stream out of the target DB, scaling with kafka onto a read replica with copy on write enabled and autoscaling compute (we currently prefer neon as a primary branching engine due to their implementation of these properties). Our replication stream uses logical replication + ddl triggers to enable usage on any hosted postgres DB since most platforms do not allow physical replication which is traditionally used for creating replicas. This provides a few primary benefits: 1. Does not require a platform migration to a DB provider like neon, allowing strong separation of production and development concerns. 2. Minimal impact on the production database while allowing clones to spin up in <6s, even at TB scale with copy-on-write Security matters a lot with cloning production so we run a proxy layer to generate custom postgres URLs and route all connections to allow more granular access control to clones, prevent credential leak, and follow a split plane architecture to allow full data residency on your cloud through BYOC. We also support anonymization through the ability to register SQL that runs on branches before they are returned. This has been used for PII redaction and branch modification. Our goal is to make every data infrastructure platform “cloneable” in one place so agents can fully test the impact of their changes on production like data environments without risk. Here's a demo of it: https://youtu.be/5S1kwPtiRU0 We’d love to understand how you work with coding agents on the DB and if you try Ardent (it's free to get started) what worked, what broke and what’s missing.
AI-анализ
Анализ скоро появится.
Похожие продукты
Capgo
Мгновенные обновления для Capacitor-приложений. Выпускайте исправления за минуты, а не недели. Отправляйте OTA-обновления пользователям без задержек App Store.
OpenAlternative
OpenAlternative — каталог open-source альтернатив проприетарному софту. На сайте собраны проекты из разных категорий с информацией о возможностях, стеке технологий и метриках GitHub. Платформа монетизируется через платные размещения и партнёрские ссылки.
Ancestree
Every time I talk to my older family members, especially my grandparents, I find out a new super interesting fact about them. Last week: my Croatian grandfather served in the French marine corps... Not worth mentioning I guess. I realized that these stories are passed down only by re-telling them. Just think about how much you don't know about your family from 2 generations ago. For this reason, I created ancestree.marindedic.com It's a really cool family tree creation app, where each person gets their own book. Inside it, write what they did, what happened to them, what they were like... whatever you want. Little biographies of your loved ones. Apart from exporting normal and detailed versions of your family tree, you can even export someone's chapters as one long biography. Of course, open-sourced, no account, no server. Nothing you draw or write ever leaves your browser.
Keenable
Hey HN! We built https://keenable.ai, a different web search API for AI agents. Keenable searches our own 100B+ page index. We are focused on low cost and latency (p95 <250ms from us-east). We don’t believe in benchmaxxing, so we open-sourced our internal benchmarking suite, NEEDLE (available at https://keenableai.github.io/needle): a live benchmark that compares Keenable with other search APIs on fresh agent-like queries. I spent seven years at Amazon as a scientist working on web grounding for Alexa/AGI, and my co-founder Andrey previously led search at Yandex. We started Keenable because agents search differently from humans, and we wanted to build around those patterns directly. The API is available now and we provide a free allowance of 100,000 requests a month. It also exposes a novel SQL-like interface to the web, which is useful for structured extraction and agent workflows. Happy to answer questions about the index, crawl, ranking, latency, or benchmarking.
Structural code grep across public GitHub repositories
Codemod grep searches public code by syntax and structure, not just text or regex. Use ast-grep patterns to find code by shape across repositories.