- /
- Topics/
Topics
Topics is the raw-gathering layer: a set of independently-tracked domains — AI’s societal impact, IP and training rights, open vs. closed ecosystems, vibe coding, and others — each searched on its own cycle, filtered against what’s already been seen, and written up as a dated entry with links and commentary.
It works by running keyword and watched-author searches per topic each cycle, discarding anything already covered, then asking an LLM to synthesize what’s new — including cross-topic connections it notices and meta-observations flagging emerging themes, useful new keywords, or gaps in coverage.
Internally this is “Column A,” with a deliberately self-aware working name: Confirmation Bias. Actively searching for content on a topic inherently biases what gets found toward that topic’s own framing — Topics doesn’t hide that limitation, it names it. Signals exists specifically to look past it.
Claude-Specific Expertise
AI Agent Accountability
Applications of Vibe Coding
Open vs Closed AI Ecosystems
Team & Org Use of Claude
claude-expertise for that).