Oodle.ai
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 Lambda. Since we process each span of every trace, instead of running LLM-based evals on each, we first analyze them using deterministic techniques. We detect tool failures, retries, loops, abnormal token usage, latency regressions, schema violations, sentiment, and other production signals. We've written more about the approach here: https://blog.oodle.ai/you-cant-sample-your-way-to-reliable-a... The combination of our own engine, no sampling, and deterministic processing before LLM-for-evals allows us to price at $10 per million traces, provide sub-second p99 query latency, and have healthy margins. Before building this, we used Langfuse for our own agent observability, which was 6x more expensive. Still super early, and rough around some edges, we would love your questions and feedback!
Opening lines of famous literary works
This came from an idea that had been knocking around in my head for several years. I had been collecting opening lines of famous works and thought it would be cool to see one everyday as I opened the browser. I tried different styles but landed on the simple background with the text, let the words speak for themselves. Over time i've added more quotes I believe now there are close to 60, so hopefully you can refresh a few times and get a fresh one every time. I hope you guys like it, enjoy!
ZenStack
Hi HN, I'm Jiasheng, co-creator of ZenStack. Access control usually ends up scattered across app code instead of living with the data model — and that's riskier when the code is written by an agent, since it's easy to ship a query with a missing or incomplete authorization check. ZenStack enforces policy (RBAC/ABAC/relation-based) directly at the ORM layer, on top of Kysely, so every query gets checked the same way regardless of who wrote it and who is calling it. Postgres RLS is an option too, but it's hard to maintain and scale, and it's Postgres-only — ZenStack's approach is database-agnostic. One of our users, MermaidChart, put it well after launching their team feature on ZenStack: "much cleaner and easier to maintain than writing RLS policies or application-level checks that will surely leak after some time." Happy to answer anything.
Benchmark your eng team's AI agent maturity in 5 minutes
we had hundreds of discussions with engineering leaders over the past few months, and everyone's trying to understand where they are in the AI journey. we collected all this data into a benchmark and built a free grader to let you know where you stand. you answer on a 1–5 scale (e.g., autonomy runs from "suggestions only" to "agents own multi-hour workflows across code, infra, and external systems") - takes about 5 minutes. https://agent-benchmarks.com/software-factory/ waiting for your results!
Microphone
If you are an aspiring founder, any VC will ask you this question: “why are you the only person who could solve this”. If you want to generate passive income with your side idea, get ready to enter a crowded market as everyone and their mother is shipping. Unless you have an active X account or you’re a TikTok sensation distribution is going to be tough. I just launched the trie.dev microphone beta to help folks find their edge. You yap into your phone about your ideas; Trie turns the rambling into hypotheses, then prioritizes them based on your experience and your realistic ability to distribute in that idea space — surfacing the problems only you can solve. From there you can generate creative and run Meta ads against your hypotheses straight from your phone, with zero setup, to see how real people respond. I built it initially for myself and friends as an “intake form” for running paid ads to help validate our side gig ideas. Happy to chat about how it works or the stack. Joining the waitlist will send you an email to join via TestFlight.
I built a one-prompt hackathon platform, free entry, sponsored prizes
Show HN: I built a one-prompt hackathon platform, free entry, sponsored prizes
Nobie
Show HN: Nobie – an Excel-compatible runtime for agents and humans
Adaptive Recall, persistent memory for AI assistants over MCP
Show HN: Adaptive Recall, persistent memory for AI assistants over MCP
Kurvengefahr
A few years ago I made a pen plotter attachment for Prusa MK4 (https://www.printables.com/model/827264-pen-plotter-attachme...) and at the time I didn't have a good way to turn artwork into G-code for it, and I put the project on ice for a while. I recently wanted to dabble in line art again and made a small browser app to make it easier. As agentic AI tools of 2026 are quite addictive, it rather quickly grew into something quite a bit more - an integrated browser CAD/CAM for pen plotters that covers everything from importing existing artwork, creating artwork from scratch, preparing for plotting and hardware integration. It includes some off-beat features like a Logo interpreter for turtle art and Graves RNN for handwriting synthesis and in addition to 3D printer pretending to be pen plotters it now also supports actual pen plotters based on EBB (AxiDraw) and GRBL firmwares through Web Serial. If you own an AxiDraw or a GRBL plotter, I'd very much appreciate it you gave it a try and give feedback. As I don't own those, I did all the testing with a hardware mock on STM32, so I am not sure how well it works attached to an actual plotter. Source code and docs are on GitHub: https://github.com/tibordp/kurvengefahr
Dev
"How 500 HN users crashed my 2GB server in 60 seconds – postmortem
English
Construction workers, electricians, couriers: ICE disguises to detain migrants
Ant
Hello HN! I'm the author of Ant, a JavaScript ecosystem built around a runtime with its own JavaScript engine. Ant also includes a package manager, the ants.land package registry, a platform for deploying and hosting applications, and Ant Desktop for building native desktop apps with web technologies, similar to Electron. The goal is for these pieces to work as one coherent platform while remaining compatible with the wider JavaScript ecosystem. It's still early, and I'd appreciate any feedback on the overall direction or what you'd like to see from an e2e alternative to the existing JavaScript stacks. P.S. I’ve shared Ant here before as a runtime; since then, it has grown into the broader ecosystem you see today.