I'm 15. I mass published 134K lines to hold AI agents accountable
Software makes commitments all of the time. "I won't transfer more than $500," "I'll only access these three APIs," "I won't modify production data." But there is truly no way to verify it actually kept those commitments after the fact. All you do is just trust the logs and which the software itself wrote. I built Nobulex to fix this. It is open-source middleware that does three things: 1. Lets you define behavioral rules in a simple DSL (permit, forbid, require) 2. Intercepts all actions at runtime and blocks anything that will violate the rules 3. Logs everything in a hash-chained audit…
I'm 15. I mass published 134K lines to hold AI agents accountable launched on March 1, 2026 with 6 votes, #1,631 of 23,380 launches that month and more than 75% of that year's launches.
What it does
Prior direction, kept rather than deleted. Signed, offline-verifiable receipts for AI agent actions, and a reference implementation of the OWASP Agentic Skills Top 10 AST09 receipt pattern. Nobulex is now the independent reliability registry for agent tools: github.com/arian-gogani/nobulex-registry - arian-gogani/nobulex
In the maker’s words, at launch
Software makes commitments all of the time. "I won't transfer more than $500," "I'll only access these three APIs," "I won't modify production data." But there is truly no way to verify it actually kept those commitments after the fact. All you do is just trust the logs and which the software itself wrote. I built…
Does a similar job
all alternatives →- Agent 37Mar 2026 · agent37.com · ▲401
Hey HN, I noticed there are 1,000+ MCP skills on GitHub with zero way for creators to charge for them. So I built Agent37. Selling Claude skills today means asking customers to download your skill, set up Claude Code on their laptops, configure MCP servers, and pray it works. No trial, no updates, often you're just handing over the source. Agent37 lets creators upload a skill, share a link, and anyone can try it instantly (no Claude account needed). Built-in Stripe, creators keep 80%. Looking for feedback on the approach. Would you use something like this?
ReplicasJun 2026 · replicas.dev · ▲239Hey HN! I'm Connor, creator of Replicas. I'm a heavy Codex/Claude Code user, and I wanted to hand off tasks to agents like I would to an engineer - kick something off before work, let it scaffold while I'm at lunch, even come back to a ready PR. The problem: existing alternatives use workspace VMs that are black boxes. I have no access to any of them - they can't run tests, read logs, or use tools like the Playwright MCP (which I use to verify agents actually fixed what they claim to fix). Replicas gives agents real cloud workspaces where they can: - Run your test suite and read the…
Progress AI ObservabilityAug 2026 · telerik.com · ▲168Hi HN! We’re the team behind Fiddler, and we recently built an Agent Cache feature. It works by capturing an LLM request/response pair and, once caching is enabled, serving subsequent matching calls locally so duplicate requests never hit the provider. It’s meant to reduce three pains that show up together in agent development: token cost, feedback-loop latency, and output non-determinism. We’d love to hear your thoughts.
delegareApr 2026 · delegare.dev · ▲70Hi guys, am building SecureLend.ai and when working on our underwriting agents (free trial, paid after) I had issues with seamless payment options. Of course I looked at x402 which I believe is a great protocol but not a fan of a) sharing private keys with the agent b) having a human in the loop or c) similar workaround e.g. pre-funded wallets So I started building out a solution. For my use case it was important to not only have stablecoins but also a fiat rail which is now implemented using Stripe. Initially I build a mandate system internally but am very glad and big shoutout to the guys…
Oodle.ai – $10 per million agent tracesJul 2026 · oodle.ai · ▲31Hi 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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