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I found this Wired article on AI fact-checking frustrating. It could have been about why we continue to need human fact checkers (talk to people, use judgement, resolve conflict). Instead it is full of old info & stuff about free models GPT-5.5 Pro checked it (& I checked GPT)
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The hard part is that "what matters" is domain-specific and the LLM has no way to know that on its own. That is where typed schemas earn their keep. You encode what matters once, and every future ingestion inherits that judgment without the model having to rediscover it.
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The key idea: SkillOpt does not make the deployed agent larger. It trains a reusable external skill artifact offline, then ships only the improved procedure. That is a very different adaptation layer than fine-tuning, prompting, or adding more inference-time calls.
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Ontology-constrained memory is a great way to frame it. The key insight that clicked for me is that the schema is not just a filter on what gets stored, it is also a reasoning boundary. What the agent cannot represent, it cannot hallucinate connections about.
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YES, Optimization keeps moving up the stack. Weights, then prompts/harness, now skill files.
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One year later with Omni and this test can pass. I saw it getting pretty close, so I tweaked the prompt:
— fofr (@fofrAI) 26 mai 2026
> A video of a man counting to 10 on his fingers, show the number in the corner. A new number every 1s, no dialogue other than the numbers he says. He uses two hands for… https://t.co/TPd8HEl97W pic.twitter.com/09M1eYw0qV
One year later with Omni and this test can pass. I saw it getting pretty close, so I tweaked the prompt: > A video of a man counting to 10 on his fingers, show the number in the corner. A new number every 1s, no dialogue other than the numbers he says. He uses two hands for
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It will look through your past sessions and turn repeated prompts into reusable skills + subagents
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If you find this useful, also check this out. Turn Claude into 20+ different specialists for marketing & business. Install real expertise, not just prompts. Get my Claude skills bundle
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The text version of the prompt: "Look back over my recent work from the last 30 days using all available context. Use available evidence in this order:
– Session Memory summaries and MEMORY. md entries
– Git log and recent commit history across branches
– CLAUDE. md and
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One catch though. The bright future goes to people who can direct an agent and check its work. Direction on its own just builds a faster mess.