Nathaniel Whittemore
speaker
51,694 appearances
202 recordings
3 series
first heard Oct 2025
last heard yesterday
Nathaniel Whittemore’s voice in public audio — every appearance, attributed to the second.
Trend
recordings per month · last 12 monthsRecordings per month over the last 12 months — 202 in all, peaking in Jun 2026 with 31.
Appearances
If it becomes sufficiently fast and inexpensive, it could run on every incoming request after each draft revision across many candidate documents before an agent takes a consequential step.
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Back in Mike Taylor's article from Every, he gave Jeff the text from all 27 of his articles, alongside 10 deliberately AI-styled counterpoints, then asked the same 21 questions concurrently across all articles to check for AI tells.
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Basically, the check that he was doing with Jev was: does this essay do specific things that indicate to people that it is AI composed?
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Things like, does the text repeat an idea without adding evidence?
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Does it
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Force a symmetrical both sides argument, does it overexplain a straightforward point?
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In less than 0.7 seconds, Mike said, Jeff quote unquote read all 37 documents and answered all 21 questions for each, returning 777 judgments for an estimated quarter of a cent.
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As he points out, that's fast and cheap enough to AI check everything everyone at your company has ever written and get the results back in an instant.
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A comparison that Mike makes.
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Is a code linter for knowledge work.
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He writes, In software development, a code linter is a tool that analyzes your work and almost instantly flags syntax errors, catches bugs, spots bad patterns, and enforces stylistic consistency.
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TypeSafe's model is so fast at turning fuzzy tasks into clear structured answers that it could act as a kind of code linter for knowledge work.
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Give Codex or Claude access to JEV and a list of questions, and it can quickly check its own work for problems you've told it to avoid.
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Peringrat says, Most software is ultimately a giant tree of if this do that, if this root here, if this escalate, if this reject, if this, ask a human.
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Jev is basically asking, what if those if statements could understand messy human context?
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That's a much more interesting framing than another AI model.
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I can see this being very useful for fraud and risk, support routing, moderation, PR and QA automation, lead scoring, compliance, workflow orchestration, and agent routing.
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Early tech, obviously, he says, but the category itself makes a lot of sense.
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Now, interestingly, Matt Stockton points out that in some ways, companies adopting this amount to a post-LLM AI technology, making pre-LLM machine learning techniques a little bit more accessible.
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As he writes,
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Showing 2441–2460 of 51,694 · page 123 of 2585
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