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startup-projects

Show HN: Vaultbase – a self-hosted back end in a single binary

One binary to run and you have a full backend at your fingertips: a database with a REST API, sign-in, realtime, files, server-side hooks and jobs, on SQLite or Postgres. It's MIT licensed. You can try the demo at https://demo.vaultbase.sh (the login is filled in, and it resets every hour). I built it to ship fast. I always tend to write the same logic for any new project, whether it's a side project or a small to medium project for a client. I wanted something my friends and I could work with fast, try new things with, and that's easy to use. Then it became something I really wanted to work on and make as complete as I can for any type of backend. As of writing this post it has 80 releases since May this year and is backed by more than 2,800 tests. I also run security reviews with Claude and fix what they find. It hasn't had an outside audit yet. It was built with the help of Claude, from the codebase to the documentation and fact-checking it against the code. Claude helped speed up the pace, find bugs, and write tests. Vaultbase is pre-1.0, so expect some bugs and issues here and there. I'm working hard to fix each and every one of them. Thank you for taking the time to read this. I'd really appreciate any feedback, even if you didn't like Vaultbase. The demo is yours to try, and it's two commands to run locally. Comments URL: https://news.ycombinator.com/item?id=50448562 Points: 1 # Comments: 0

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Hacker News Show HN
enterprise-apps

The agent evaluation playbook: Built-in, DeepEval, and AutoEval on Amazon Bedrock AgentCore Evaluations

When you deploy an agent, whether it is working is really several questions: did it finish the task, stay on topic, leak data, or skip steps? This post shows how to evaluate agents on Amazon Bedrock AgentCore with built-in, DeepEval, and AutoEval evaluators, and how to match each behavior that matters to the right evaluator at the right level.

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AWS Machine Learning Blog
training-alignment

Which Successes Make Good References? Action-Sequence Alignment for Multi-Turn On-Policy Distillation

arXiv:2611.13135v1 Announce Type: new Abstract: On-policy distillation (OPD) reduces train-inference distribution mismatch by supervising a student on its own trajectories. For multi-turn tool-use agents, conditioning the teacher on a validated same-task successful trajectory as a reference can provide useful hindsight, but different successes may reach incompatible interaction states and offer misleading supervision. We cast successful-reference selection as a free-target optimal transport problem, which reduces to assigning each failed trajectory its most compatible successful reference. Because latent state paths are hidden, we use action-sequence alignment as an observable proxy for compatibility and instantiate it with longest common subsequence (LCS), which identifies a shared ordered backbone of actions while allowing recoverable detours. This perspective makes trajectory compatibility the central criterion for reference selection in multi-turn OPD. Experiments on ALFWorld and the airline domain of $\tau^3$-bench show that LCS is the only evaluated reference selector to improve over reference-free OPD on both ALFWorld overall scores and all three airline metrics. Without further optimization, the airline-trained models also transfer to the retail domain, where LCS improves all three metrics.

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arXiv cs.LG