Top Tweets for #authorityclaw

6 days ago

If a model can read an invoice, who is allowed to let the money move? Yesterday my #AuthorityClaw https://t.co/yuXDhd6TI1 (built on Olares One) was selected first place in the @NVIDIA Claw Agent Challenge: DGX Spark Founders Edition, a GTC Berlin pass, and a live demo at Build-a-Claw on 14 October, London. Not a research prize. A city developer challenge, remote, open to anyone in the UK. I do not know how many entries there were, so I will not invent a denominator. What I can say is what ran. The chain on the bench: → #Nemotron as perception, not authority. It read synthetic passports. A deterministic MRZ gate and a #Prolog policy refused malformed extractions. NEED_MORE_EVIDENCE. The model explained. It did not override. → The problem, mapped. Banking onboarding, correspondent payments, digital identity. The question underneath all of them is the same: on whose authority is this agent acting? → #NemoClaw 1.0.30 was on the same @Olares_OS One (bundled v0.0.38, OpenShell 0.0.36). Useful as an orchestration shell. For the authority-critical path I did not trust the shell to be the gate. The verified run used AuthorityClaw’s own persistent Python harness, calling the local model directly, plus an OpenClaw-compatible authority-gate skill. That is a measured no, not a dismissal. → The pilot. Fictional company, simulated Faster Payments rail, no real money moved. Six invoices. A live revocation. A bank-detail change. Two prompt-injection attempts inside invoices, treated as data. → A quantum double-check on a separate job, reported with the mismatch it actually produced. Engine output is evidence, not interpretation. The ladder only narrows: → Northside Roasters Ltd → Jane Okafor, director, £25,000 / £50,000 a week → Tom Hale, office manager, £1,500 / £5,000 → the payments agent, £1,000 / £3,000 → Jane revoked Tom mid-run. The next ordinary invoice was refused. The inherited mandate no longer stood. £1,240 was held for a human. £4,900 to an unapproved supplier was refused, including the instructions the invoice tried to give the agent. New bank details on a known supplier stayed held for out-of-band confirmation. = The model reads. The gate decides. The rail executes only against a valid receipt. AI override = false. Box: Olares One, @NVIDIAAI RTX 5090 Laptop, 24 GB, unsloth/Qwen3.6-27B-GGUF Q4_K_M through llama.cpp. Seven receipts, hashes on the page, chain intact. This does not make it a payments system, or KYC, or regulatory approval. First place validates the problem-to-product-to-engineering approach, not compliance. Real deployment still needs key management, a human review UI, audit export, and a lawyer on the mandate semantics. I am not a career software engineer. I founded a bank and took a de novo licence through its regulator. The interesting part, for me, is that the same question can now be an executable artifact rather than a diagram. Page Repo: https://t.co/WkpK6STHjn

digital identity.global @NansenID

about 1 month ago

After completing the @Google × @NVIDIA learning pathway, I extended my #Sovereign Decision Plane #AI experiment with a real @GoogleCloud Storage witness: https://t.co/y4KN1VtA0Z A 545-byte receipt from a recorded synthetic decision was uploaded to a private bucket, then downloaded from the exact object generation. The bytes and SHA-256 matched. NVIDIA #Nemotron provides candidate extraction in the recorded #localAI experiment; verification and policy determine the downstream decision. The local decision core is unchanged. This demonstrates receipt storage and byte integrity—not certification of decision correctness = but the #cloud additional layer could give you much better flexibiliy, for example, for inter- and correspondent- banking communications (and GDPR\similar regulation to split local vs cloud data flows)! #NVIDIAGTC @Jennifer_Dee

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