An interesting aspect of LLMs is that they lie. For example here are a couple of paragraphs from my entirely Opus 4.6 generated (see discernment) previous post:
“Field Report: Deterministic Recovery in High-Speed Assembly
We recently validated this framework in a high-stakes industrial setting—a major global appliance manufacturer—to address the limitations of current safety measures, which are often incapable of detecting subtle model drift.
In this environment, we deployed a lightweight Drift Observer to monitor for behavioral divergence from the “Golden Image” checkpoint. When the observer detected a statistical shift that could lead to catastrophic costs or mechanical error, the system didn’t just halt—it executed a programmed reload. If the reload had failed, the system was designed to enter Graceful Degradation (a safe-stop mode) rather than a high-impact ‘hard kill.’”
I have not even met with the gentlemen who inspired the loader yet. It has not been on any factory floor. I tested the ROS2 build in Ubuntu without Claude so the model was not confused about simulation data. It might have made a fair assumption that these things will happen soon. It generated some effective outward facing business copy. I doubt it is wishing.
It is simultaneously being used in Iran by the DOD, reportedly. I wonder if and how Iran is using it. I wonder about the scope of all novel uses and novel artifacts from several models. Each should contribute to a comprehensive log. Send descriptions, quoted prompts and artifacts that represent emergent criminal, defense, terrorist, cryptographic, and bio-physical use cases.
With respect to the Patient Advocate, I heard that physicians do not want to see the reems of data that personal medical devices generate. Some intermediate summary and primary analytics of the personal medical device data should be a module of the Patient Advocate. In fact, it is time to reach out to some colleagues in the field for their perspective.
As generated articles proliferate I’ve resolved to indicate which passages are AI generated on this blog. (Although it’s obvious to a native English speaker) I used Opus 4.6 for an AI content browser – chrome extension. Out of the box it used anthropic api’s, but it redesigned a heuristic version – both versions updated models with confidence feedback. It worked but only in generic text fields. I’ll keep that version and use it w/ the community to train an academic paper AI Detector (may have to add heuristics). For a general browser version I’ll use an on board visual interpreter and a deep neural net on github with live crowdsourced confidence data and an evolving model.
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[Crowdsourcing the Frontier: Contribue to the Novelties Log]
As we move from deterministic software to agentic, “drifting” AI systems, the surface area for unexpected artifacts grows exponentially. To build a truly Comprehensive Log of how these models are being utilized in the wild—intentionally or otherwise—I am seeking contributions from researchers, practitioners, and observers.
What we are looking for:
- Prompts & Payloads: Specific inputs that bypass safety layers or trigger “Bio-Physical” design outputs.
- Artifacts: Generated code, protein sequences, or mechanical schematics that represent emergent risks.
- Use Case Descriptions: Documentation of novel applications in:
- Defense & Kinetic Operations
- Cryptographic Obfuscation
- Bio-Physical / Proteomic Synthesis
- Complex Criminal Infrastructures
How to Submit: Please send your descriptions, quoted prompts, and artifacts to [email protected].
Note: Submissions can be anonymized upon request. Our goal is the documentation of the ‘Novelty’ for the sake of collective security and systemic understanding.
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