Thanks to everyone who’s taken the time to comment on the Boost.SQLite draft. The feedback has been sharp and helpful, and it also points to a broader challenge: accuracy in AI-assisted contributions. Sooner or later, someone will say that “hallucinations” make this kind of work impossible. But if we look at domains like law or medicine, the bar is already clear: no degree of inaccuracy is acceptable. That’s true whether the source is human or machine. In both cases, what matters is the process we put around the work—fact-checking, red-teaming, and review. Believe it or not (“low effort AI slop”) quite a lot of HITL (human in the loop) took place before the decision to publish made the call to publish. Not enough — on this occasion — we can agree. I should have phrased this better in the draft. My aim isn’t to excuse errors, but to emphasize that a useful workflow combines research with adversarial review. Trusted agents won’t emerge from a single prompt—they’ll be shaped over time, with heavy human-in-the-loop checks at the start and automated cross-checks as things mature. Reuters, for example, developed a legal research assistant this way: a year of iterative review by lawyers until its answers were consistently trustworthy. I’ll continue refining AI research generation with this in mind: less “AI produced,” more “AI assisted, human reviewed, evidence logged.” The goal is to get closer to the accuracy standard that applies to all contributions here, whether typed by a person or assisted by a tool.