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Products that do what Warden does

The only AI governance scanner that tells you the truth

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    Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS…

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    Governance for reliable, long-running AI agents

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    I'm an infrastructure architect who started using AI assistants to write code 3 months ago. After building several systems with Claude, I noticed a pattern: the code always had security issues I could spot from my ops background, but I couldn't fix them myself since I can't actually write code. Why I built this: I needed a way to verify AI-generated code was production-safe. Existing tools either required cloud uploads (privacy concern) or produced output too large for AI context windows. TheAuditor solves both problems - it runs completely offline and chunks findings into 65KB segments that…

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    27 security checks for your AI agent. 2 minutes. Free.

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    Hi HN, I'm one of the creators of HoundDog.ai (https://github.com/hounddogai/hounddog). We currently handle privacy scanning for Replit's 45M+ creators. We built HoundDog because privacy compliance is usually a choice between manual spreadsheets or reactive runtime scanning. While runtime tools are useful for monitoring, they only catch leaks after the code is live and the data has already moved. They can also miss code paths that aren't actively triggered in production. HoundDog traces sensitive data in code during development and helps catch risky flows (e.g., PII…

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    Hey HN, We’re Suchintan and Shu from Skyvern (https://www.skyvern.com). We’re building an open source AI Agent that can browse the web and take actions. Our open source repo can be found at https://github.com/Skyvern-AI/Skyvern. We’ve re-built Skyvern with a Planner-Actor-Validator agent architecture and achieved 85.8% state of the art (SOTA) on the WebVoyager Benchmark. You can see the results for yourself here: https://eval.skyvern.com/ For reference, here were the previous SOTA results: 83.5% - Google Mariner…

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    Your team is shipping AI apps. Do you know what's in it?

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