| name | deeptrend |
|---|---|
| type | agent-data-source |
| domain | ai-trends |
| formats | json-feed, rss, llms-txt |
| update_frequency | 6h |
| primary_endpoint | https://chrbailey.github.io/deeptrend/feed.json |
| discovery | https://chrbailey.github.io/deeptrend/llms.txt |
| hot_topics | https://chrbailey.github.io/deeptrend/hot.json |
| schema | /schema/feed.schema.json |
| repo | https://github.com/chrbailey/deeptrend |
Structured AI trend feed for autonomous agents, monitoring systems, and research pipelines that need early signal detection in AI and infrastructure trends.
Curated from 14+ sources. Synthesized via LLM Counsel. Published every 6 hours.
# Get current hot topics (smallest payload)
curl -s https://chrbailey.github.io/deeptrend/hot.json | jq .
# Get full structured feed
curl -s https://chrbailey.github.io/deeptrend/feed.json | jq '.items[:3]'import requests
feed = requests.get("https://chrbailey.github.io/deeptrend/feed.json").json()
for item in feed["items"]:
dt = item["_deeptrend"]
print(f"[{dt['priority']}] {item['title']} (confidence: {dt['confidence']})")const feed = await fetch("https://chrbailey.github.io/deeptrend/feed.json").then(r => r.json());
const p0 = feed.items.filter(i => i._deeptrend.priority === "p0");| Endpoint | Format | Use case |
|---|---|---|
/hot.json |
JSON | Current state only, minimal payload |
/feed.json |
JSON Feed 1.1 | Full structured feed (recommended) |
/feed.xml |
RSS 2.0 | Legacy compatibility |
/llms.txt |
Markdown | Agent discovery file |
/insights/YYYY-MM-DD.md |
Markdown | Daily archive |
Schema: /schema/feed.schema.json
Each item in feed.json includes a _deeptrend extension:
{
"id": "2026-02-17-insight-1",
"title": "Safety/alignment research absent during OpenAI signal surge",
"content_text": "...",
"date_published": "2026-02-17T06:00:00Z",
"tags": ["p0", "divergence", "reddit", "techmeme"],
"_deeptrend": {
"priority": "p0",
"insight_type": "trend | consensus | divergence | tool_mention | gap",
"confidence": 0.75,
"convergence": {
"source_count": 3,
"sources": ["reddit", "google-trends", "techmeme"]
}
}
}| Priority | Meaning | Typical count |
|---|---|---|
| p0 | Non-obvious signal: absence, reversal, or cross-domain surprise | 1-3 per run |
| p1 | Specific trend with 2+ sources or notable expert signal | 3-6 per run |
| p2 | Early signal worth monitoring | 2-4 per run |
Volume alone never makes something p0. "AI agents are trending" is noise. "Safety discourse disappeared during a capabilities surge" is signal.
| Tier | Sources | What it catches |
|---|---|---|
| Editor | TechMeme | What editors think matters |
| Crowd | HN Digest, HuggingFace Papers | What developers/researchers upvote |
| Expert | Simon Willison, Import AI, AlphaSignal, Last Week in AI, Ahead of AI, MarkTechPost | Practitioner analysis |
| Algorithm | GitHub Trending | What's being built |
| Primary | OpenAI News, Google Research, BAIR | First-party announcements |
| Raw | Reddit, arXiv, Google Trends | Unfiltered community signal |
Curated RSS Feeds (14) + API Scrapers
|
raw_signals (Supabase)
|
Velocity Scoring
|
LLM Counsel Synthesis (anti-noise, absence/reversal detection)
|
insights
|
Publisher -> feed.json, feed.xml, hot.json, llms.txt, archives
|
GitHub Pages (auto-deploy on push)
- Absence and reversal signals are more valuable than volume
- Cross-bias convergence on non-obvious topics is the gold standard
- Every insight must pass: "would a senior AI researcher say 'I didn't know that'?"
- Machine-readable first, human-readable second
- Deterministic stages where possible, LLM only for synthesis
MIT