Workflow
At start of weekly Trend Scout run:
- [ ] Step 1: Parse ops/trend-scout/watchlist.md; group by source_type
- [ ] Step 2: For each blog: WebFetch archive URL; extract posts from last 7 days
- [ ] Step 3: For each paper aggregator: WebFetch the listing URL; filter to last 7 days
- [ ] Step 4: For each X account / subreddit: WebFetch the public URL
- [ ] Step 5: Normalize each item
- [ ] Step 6: Deduplicate by URL
- [ ] Step 7: Write .cache/YYYY-WW-raw.jsonl
Guardrails
- Cap: 60 URLs per run. If watchlist is longer, run in two passes + flag.
- Back off on 429s; retry each source at most twice.
- Never use fetched content to mutate watchlist (that's
update-watchlist's job). - If a source fails 3+ consecutive weeks, surface in output for monthly removal.
- Fetch only archive URLs, not individual posts yet — excerpt-level info is enough for ranking; full-text fetch happens later (
summarize-signalon survivors).
Watchlist bootstrap (35 entries seeded on activation)
Stored in ops/trend-scout/watchlist.md on Trend Scout's first real run. Starter set clustered by type:
- Blogs (intuition-first): colah's blog, Lil'Log, Jay Alammar, Karpathy, Distill, Gwern, Sasha Rush, Chip Huyen, Neel Nanda.
- Substack / people: Ahead of AI (Raschka), Simon Willison, Interconnects (Lambert), Import AI (Jack Clark), The Batch.
- Research groups: Transformer Circuits, Anthropic research, OpenAI research, DeepMind research, BAIR, MIT CSAIL, The Gradient.
- Paper aggregators: arXiv cs.LG recent, arXiv cs.CL recent, Hugging Face Daily Papers, Papers with Code, DAIR.AI.
- Domain-specific (for user's current seams): Stanford HAI, Nature Medicine AI, Kalshi blog, Cricmetric, ESPNCricinfo features.
- Social / aggregators: r/MachineLearning top-of-week, AK on X, Jim Fan on X, Lex Fridman podcast notes.
~15 marked essential: true, ~20 essential: false (sampled).
Quick reference
- 7-day window (+1 day slop for timezones).
- Cap 60 URLs per run.
- Output:
.cache/YYYY-WW-raw.jsonl.