AI
Hide your slop PR descriptions behind
Publication: Simon Willison's Weblog
Author: Not available in collected metadata
Source published: 09/11/2026, 04:25:05 UTC
Briefing created: 09/11/2026, 04:29:31 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/11/2026, 04:27:24 UTC.
Briefings
A developer using AI coding assistants for pull requests discovered that AI-generated descriptions are accurate but lack human editorial value, causing immediate reader fatigue. The author implemented a 'details-summary' HTML pattern to hide the full AI-generated content while keeping a concise, human-curated summary that explains the PR's purpose and includes screenshots. This method allows readers to quickly grasp the high-level intent without scanning the verbose technical details, significantly improving the perceived quality of AI-assisted PRs. The author cites specific GitHub PR examples where this technique was applied, such as a redesign of the row page and a new plugin hook, to demonstrate the practical implementation and its immediate positive effect on reader engagement.
Key points
- The author uses an HTML 'details-summary' pattern to hide the full AI-generated PR description, displaying only a concise summary that includes a screenshot to explain the change.
- Specific GitHub PRs were cited as examples where this technique was applied, including a redesign of the row page and a new plugin hook for agent reference types.
Context
This finding addresses the growing trend of AI-assisted coding, where agents generate comprehensive but often unreadable pull request descriptions. The author's solution leverages GitHub's native 'details-summary' feature to create a human-readable summary that acts as a high-level introduction, effectively filtering out the technical verbosity of AI output while preserving all necessary information for developers.
Limitations
The evidence is limited to a single developer's personal experience and specific GitHub PR examples. The author does not provide quantitative data on user adoption rates, the effectiveness of this pattern across different coding agents, or long-term impact on code review workflows. The implementation relies on the availability of the 'details-summary' feature in GitHub's Markdown editor, which may not be supported in all environments or configurations.
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