John Fite

Lab

AI systems, under examination.

Experiments in what AI can do, how to test it, and where human judgment belongs. Start with Sentinel’s public code and local demo, then explore the work still taking shape.

01 Agent security · Testnet lab · Apache-2.0

Sentinel

Does the action match the authorization?

An open-source testnet lab for checking an agent’s proposed blockchain transaction against a human-signed mandate. It connects the evaluation to execution of one exact call.

Testnet lab; no production security guarantee.

A transaction can call an allowed contract and stay under a spending limit while sending the purchase to the wrong person. Sentinel gives that failure a reproducible test: does this proposed action conform to what the owner signed?

The proposer has no signing authority. An evaluator checks the call’s decoded arguments and simulated effects against the mandate. A signer in a separate process attests the decision, and an execution contract checks that the submitted call matches the signed record.

You can follow that sequence through the source code, run the failure cases, and verify signed decision records. The repository includes the tests, adversarial findings and corrections behind its claims, so you can examine both the mechanism and the evidence for it.

Human authorization. Exact calls. Inspectable evidence.

Run the demo
One authorized purchase executes. Seven negative controls verify refusals for a changed recipient, altered calldata, a wrong deployment authority and receipt replay. The demo runs locally with Node.js, Foundry and Python; it needs no LLM account, funded wallet or remote RPC service.
Inspect the record
Solidity contracts, a TypeScript evaluator and isolated signer, signed receipts, and Python verification tools. The architecture guide explains the trust boundaries; the review index maps the findings and decisions.
Know the limits
The evaluator is trusted to decode the call. Offline verification cannot establish live chain state, and per-action limits do not cap cumulative losses. Testnet-only use is documented, not enforced. Read the full boundaries.
Status
Public on GitHub under Apache-2.0. Source, documentation and a runnable local demo are available for technical evaluation.

Tiny LLM Lab and Project Euclid remain in design and early build. Their public demos are still to come.

02 Tiny LLM Lab AI Engineering · Educational · Coming soon Build a small language model to understand how it works and where it fails. Then explore a local, retrieval-assisted workflow with each step open to inspection. Pending
03 Project Euclid Mathematical Discovery · Process · In design An AI-assisted mathematics research process combining language models, symbolic tools, search, and formal verification. Progress must leave a checkable trail from conjecture to proof. Pending
Learning labs
Inspectable builds that teach the machine and the workflow.
Bounded prototypes
Narrow demos at real trust boundaries — not general platforms.
Reference fixtures
Stable, linkable artifacts other work can point at later.

Sentinel is available to explore now. More project code and write-ups will be linked here as they are released.