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There's an App for it. 2012 There's an API for it. 2026 scan the list; see what you can integrate for automation or workflow integration need a roadmap? hiddenstatedrift.com

two useful things from today- ctrl+e in Claude Code and... did you know the Wayback machine has a CLI?!?!? The Wayback CDX API is the index/search API for the Internet Archive's Wayback Machine. Instead of manually browsing web.archive.org to see what was captured for a domain, you can query the CDX API programmatically: curl "https://web.archive.org/cdx/search/cdx?url=someweirddomain.com/*&output= json&limit=5" It returns a list of every snapshot (capture) the Wayback Machine has for that domain — timestamps, URLs, status codes, MIME types. This tells us: - How many pages were captured (content richness) - What URLs existed (site structure) - When they were captured (which eras of the site we can reconstruct) - Whether there's enough material to justify a rebuild For the remaining domains, running CDX queries would rank them by content richness so we know which ones are worth rebuilding vs. which are thin enough to just redirect. It's step 1 in the process before you'd need to pull an Archivarix export.

I could blithely say this shouldn't be a surprise, but it is. We now have an intelligence that is purely a creature of mathematics, rather than kludged together (though elegant) synergy of biochem and synaptic networks. I stand to be proven wrong, but as AIs reinvent maths for their own uses, this is THE leverage point. Efficiencies and dimensionality beyond the grasp of humans. I sense that many don't grasp this, even intuitively, particularly among the AI contrarians or ostriches. Simultaneously, this greatly increases the lift and the horizons of interpretability, much less observability.

The AI Literacy Gap: Quantified Ignorance The assertion that AI commentary lacks “fundamental knowledge or familiarity with the research literature” finds strong empirical support. Multiple dimensions of this knowledge gap are measurable: Educational institutions lag profoundly. A 2024 survey commissioned by Actua found that less than 48% of educators interviewed felt equipped to use AI tools in the classroom, 46% felt confident teaching responsible AI use, and only 42% felt ready to teach students how to use artificial intelligence effectively. UNESCO’s 2024-2025 Fluency Report warns that without AI literacy, individuals struggle to distinguish authentic content from synthetic media and lack ability to critically evaluate AI-generated outputs. Even AI experts display concerning knowledge gaps. A 2025 survey of AI experts revealed that only 21% had heard of “instrumental convergence”—a fundamental concept in AI safety predicting that advanced AI systems will pursue certain instrumental goals regardless of their terminal objectives. This represents a shocking level of unfamiliarity with core theoretical frameworks within the expert community itself, suggesting that if credentialed researchers lack grounding in foundational concepts, the broader commentary ecosystem operates at even greater remove from established knowledge. The literacy problem extends asymmetrically across demographics and domains. Stanford research found that less-educated regions adopted AI writing tools faster than highly-educated areas, suggesting enthusiasm outpaces comprehension. Meanwhile, 85% of healthcare professionals expressed interest in introductory AI courses tailored to healthcare, indicating even domain experts recognize their knowledge deficits when AI enters their fields. This creates a dangerous dynamic: those with least technical grounding may be most confident in their AI usage and commentary, while those with deeper expertise recognize the vastness of their ignorance. The result is a marketplace of ideas where confidence and volume substitute for competence.