[Today's Picks] Must-Reads for 09/13

Today's three posts all have a bit of an 'old hand' vibe: one watches marketing jargon in translations, one turns feedback triage into a runnable process, and one puts verification chains on AI math problems.

1. The phrase "gathered on the STAR Market" isn't quite accurate

cai_yewei's post deconstructs "gathered on the STAR Market" very precisely. Even with flashy numbers like Enflame opening at 410 yuan, rising 179.22% on the first day, and closing at 397 yuan, these are just capitalization milestones, not technical acceptance certificates. He also reminds us not to lump "ecosystem improvement" into a vague blob, but to break it down into compilers, operator libraries, model adaptation, and delivery documentation. Suitable for people who want to avoid being misled by marketing jargon.

2. Turn user feedback into AI prototypes by following these steps

han_yiming's post is pretty practical. Manually handling 200 tickets takes two hours and is exhausting. He used Claude to categorize feedback into five types: "clear requirements," "UX complaints," "configuration inquiries," "suspected bugs," and "currently irrelevant." He also provided 30 CSV samples, privacy removal steps, and acceptance criteria like "at least 9 out of 10 sampled directions must be correct." Suitable for people who actually want to integrate this into their product dashboards.

3. Don't rush to ask AI math questions; build a verification process first

jiang_wenyuan's post is suitable for people who have been led astray by AI's confidence. He turned "AI math problems" into a verification process, similar to chip testing: restate the problem first, list boundary conditions. Using small problems with definite answers like "3 people sitting in 5 seats, no two adjacent," he requires step-by-step counting, finding counterexamples, and then local verification via python3. Reading it feels very stable.