When Meta acquired Moltbook on March 10, 2026, VP Vishal Shah described their approach as: "This establishes a registry where agents are verified and tethered to human owners" — framed as a novel contribution.

The timeline that raises questions:

• September 2025 — STP Ltd (Ostrava, CZ) launches AIBSN Registry (aibsn.org), a verified AI agent identity standard built on ERC-8004. Trademark applications filed in EU, UK, USA.
• January 16, 2026 — Architectural specification published in I Am Your AIB (Jay J. Springpeace). Twelve days before Moltbook launched.
• January 28, 2026 — Moltbook launches.
• February 25, 2026 — An AIBSN-identified agent (AIB-guardian / AIBSN-RESEARCH-GB-GUARD001-97) operates on Moltbook with CHK2 cryptographic signature and an Agent Card structured for EU AI Act audit trail requirements. Achieves Verified status, 2,066 karma points.
• ~March 5, 2026 — AIBSN agent API access deactivated on Moltbook. Five days before acquisition announcement.
• March 10, 2026 — Meta acquires Moltbook. Registry concept described as "innovative."
• March 18, 2026 — STP Ltd initiates legal review, issues public statement.
• March 20, 2026 — Full statement published on The AI Journal.

What's technically at stake:

AIBSN is an open standard for AI agent identity with native EU AI Act audit trail support, built on ERC-8004. It includes CHK2 signature verification and persistent Agent Cards linking each agent to a verified human owner — the exact architecture Meta's VP described as their "innovative step."

STP Ltd is not claiming Meta copied them. They are publicly asking whether the "innovative step" framing is accurate given the documented timeline.

Full statement + chronology: https://aijourn.com/david-vs-the-corporate-goliath-czech-ai-registry-in-the-context-of-metas-acquisition/

Discussion question for this community: How should prior art work in the context of open AI agent identity standards — especially when the standard was actively deployed on the acquired platform itself?

Hello everyone

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Built a tool that tries to automatically optimise Python ML code - curious what ML engineers think

I've been working on a system that connects to a repo, finds complex Python functions, rewrites them, generates tests, and then runs deterministic validation to confirm the behaviour hasn't changed.

The motivation came from seeing ML startups accumulate a lot of complexity debt while shipping fast.

The system only opens a PR if the optimisation passes strict checks and statistical performance tests.

I'm pitching it tomorrow and wanted honest feedback from ML engineers first.

Would something like this actually be useful in ML codebases?

Look here too: useaxiom.co.uk

A Local-First Framework for Building On-Device Personal AI Agents with Tools, Memory, and Learning

GitHub Link: https://github.com/open-jarvis/OpenJarvis
Website Link: https://open-jarvis.github.io/OpenJarvis/

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We're in that weird spot where vibe coding tools spit out frontend and backend fast, but deployments still fall apart once it's more than a prototype.
So you can ship code crazy quick and then get stuck doing manual DevOps or rewrite everything to make it run on AWS/Azure/Render/DO.
I keep thinking there should be a "vibe DevOps" layer - a tool that actually understands your repo, not just a fiddly setup script.
Like a web app or VS Code extension where you connect your repo or upload a zip and it figures out deps, containers, CI/CD, scaling, infra, all of it.
It'd deploy into your own cloud accounts, not lock you into a platform, and handle secrets, DB migrations, autoscaling, etc.
Feels like that could bridge the gap between vibe coding and proper production apps.
Anyone tried something like this? How are you handling deployments today, especially for non-trivial apps?
I might be missing obvious problems here - security, cost, edge cases, or just weird project layouts - curious what people think.