Benchmark Radar Day 10: AI Briefing, GPT Insight, and Launch Prep

The radar started talking. Hi, Koutian here. Day ten added AI-written briefings, GPT-powered insights, and launch copy for Chinese social platforms.

Author: Koutian Wu; GitHub: ktwu01

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We added a bounded daily AI briefing. It writes a summary of the day’s findings and stays within a token budget, where a token is a small chunk of text the model reads and writes, so it never cuts off mid-sentence.

GPT now generates evidence-rich insights. Each claim links to a specific source. The briefing also says when the data was gathered and what window it covers.

OpenAI’s API has per-minute limits on how many tokens you can send. We now catch rate limit errors, wait, and retry with backoff that grows each time. Truncated error responses get contained instead of crashing the run.

The insight packet is fitted to the token budget, so it does not get chopped at the API boundary.

The homepage briefing now links to its cited evidence. Those details are collapsed by default, which keeps the page easy to scan.

We rank findings by how much you learn, not just by how recent they are. We also name the biggest drops in the selection funnel, which is the path from all findings down to the few we show you.

Records below the recommendation threshold are kept if they meet eligibility rules. That prevents needless data loss.

Rendering observations across the full archive is now bounded, so the page does not slow down.

The scan date filter now shows all dates, not just one at a time. The automatic frontier is kept out of shared URLs so links do not confuse the person who opens them.

We drafted launch posts for Chinese social platforms. The Xiaohongshu draft, a post on a Chinese lifestyle app, is now publishable with honest caveats about the scores.

Why this matters.

The daily briefing was the first output a human could read without opening the dashboard. The radar went from a tool you visit to a tool that visits you. The GPT layer adds interpretation on top of data: not just what was collected, but what changed and why it matters.

The rate limit handling was a must for production. Without retry logic, one rate limit error would kill the entire daily briefing.

Issues addressed:

• #121: bounded daily AI briefing
• #125: sources panel collapses when connectors fail
• #126: fix Semantic Scholar search request (Semantic Scholar is a site that indexes research papers)
• #128: save the daily briefing on the dashboard
• #130: compute daily findings
• #131: rank findings by learning value
• #132: URL and filter fixes (issues 123, 129)
• #133: name the largest funnel drop
• #135: keep eligible records
• #136: name evidence in daily findings
• #138: GPT radar insight
• #139: OpenAI rate limit handling
• #140: fit the token budget
• #141: fix briefing output truncation
• #142: briefing evidence links
• #143: collapse briefing evidence details

Day eleven: KW-Bench capability layer and community launch.

Want to follow Benchmark Radar? Star the repo on GitHub for daily updates, or open the live dashboard to explore the scans. Read the paper: arXiv:2609.11115.