Automation status (2026-07-21). AINews forwarding runs once daily in GitHub Actions. The broader radar and Telegram bot workflows remain paused.
Collect trending AI / dev signal from multiple sources, build a vector knowledge base from it, and serve three things off that KB: an AINews-style daily digest, review-ready post drafts, and a Traditional-Chinese chat bot — all synthesised with Claude on your subscription (no API key).
┌──▶ daily thematic digest (.md)
collectors ──▶ dated JSON store (dedup) ──▶ │
HN·arXiv·GitHub·Reddit·X·Gmail(AINews) ├──▶ post drafts (.md, status: draft)
│ │
└──▶ enrich (full text) ──▶ Qdrant ├──▶ Telegram chat bot (繁中, RAG + WebSearch)
vector KB (e5 + BM25 + rerank) │
└──▶ AINews → 繁中 email + 本期摘要 (daily at 18:00 Taipei)
Two model layers: retrieval runs locally for free (fastembed: e5-large dense +
BM25 sparse + jina cross-encoder rerank, vectors in Qdrant Cloud); all generation
runs on Claude Sonnet via the claude CLI subscription. Nothing is ever
auto-published — posts are drafts (status: draft) for a human to approve.
AINews issues are embedded and upserted into the KB by the daily forwarding workflow. Re-runs are idempotent (deterministic point IDs dedup). The broader radar collector remains paused, so its other sources do not update by themselves.
For an LLM-infra / agentic-AI niche, the highest-signal trends surface first on Hacker News, arXiv, GitHub Trending, and Reddit — all free, with clean APIs. X is added as an optional, paid layer via twitterapi.io (~$0.15 / 1,000 tweets, no monthly fee) — deliberately not the official API ($0.005/read) and not self-scraping (fragile + account-ban risk).
python3 -m pip install -r requirements.txt
cp .env.example .env # generation uses your `claude` CLI login — no API key.
# add QDRANT_URL/_API_KEY for the KB, GMAIL_* for AINews + email.# 1) collect today's signal (free sources need no keys)
python -m content_radar.cli collect
python -m content_radar.cli collect --sources hackernews arxiv # subset
# 2) see what came in
python -m content_radar.cli show --top 25
# 3a) AINews-style thematic digest of the day (clusters signal into themes)
python -m content_radar.cli digest --out ./digests --themes 5
# 3b) or draft individual posts from the signal
python -m content_radar.cli synthesize --out ./drafts --n 5Synthesis runs on the local claude CLI (your Claude subscription) by default
— no API key. For automated runs with your laptop off, see Automation below.
The digest command produces an AINews-style brief: a one-line headline, a handful
of themes (each a short narrative that synthesises the day's items with inline
source links), and a Top by engagement list.
A Telegram bot (content_radar/telegram_bot.py) answers questions grounded in the
knowledge base (413+ dated AINews issues 2024–2026 + HN/arXiv/GitHub), retrieved
with hybrid search + cross-encoder rerank. Answers are in Traditional Chinese,
cite sources with dates, and are not truncated. When the KB doesn't cover a
question, it falls back to WebSearch and attributes the web sources.
可以問的問題類型(實際回答節錄,皆為機器人真實輸出):
| 類型 | 範例問題 |
|---|---|
| 📅 時間軸 / 歷史 | 「Anthropic 從 2024 到 2026 完成哪幾輪融資?各輪金額大約多少?」 |
| 🏢 公司 / 模型 | 「Claude Opus 4.8 跟前一代相比有哪些重點改進?」 |
| 🔬 技術概念 | 「Contextual Retrieval 是什麼?它如何改善 RAG 效果?」 |
| ⚙️ 工程實作 | 「推測解碼(Speculative Decoding)如何加速大模型推理?」 |
| 🌐 KB 沒有 → 自動上網 | 「NVIDIA H100 跟 H200 的規格主要差在哪?」 |
Q: Contextual Retrieval 是什麼?它如何改善 RAG 效果?
Contextual Retrieval 是 Anthropic 提出的 RAG 改良技術,核心是在把文件切成 chunks 存入向量庫前,先為每個 chunk 加上「情境說明」,讓每段文字知道自己在整份文件中的位置與意義。
根據 Anthropic 研究(2024-09),它結合 Contextual Embedding 與 Contextual BM25,搭配 reranking 後,前 20 個 chunk 的檢索失誤率從 5.7% 降至 1.9%(降幅 67%);並用 prompt caching 降低為每個 chunk 生成情境說明的成本。
來源:[AINews] Anthropic Contextual Retrieval(2024-09-21)、[AINews] cde-small-v1(2024-10-05)
Q: Anthropic 從 2024 到 2026 完成哪幾輪融資?(時間軸題)
時間 輪次 金額／估值 2024 末 Amazon 策略投資 $40 億 2025-03 Series E 估值 $615 億 2026-02 未具名 估值 $3,800 億(ARR $140 億) 2026-05-29 Series H 募資 $650 億,post-money 估值 $9,650 億(ARR $470 億) 每筆都附對應的 AINews 期數與日期連結。
Q: NVIDIA H100 跟 H200 的規格主要差在哪?(KB 沒有 → WebSearch)
H200 是 H100 的記憶體升級版,同一塊 Hopper(GH100)晶片,差異集中在記憶體:
規格 H100 SXM H200 SXM 記憶體 80 GB HBM3 141 GB HBM3e(+76%) 頻寬 3.35 TB/s 4.8 TB/s(+43%) 因為大模型推論是記憶體頻寬瓶頸,H200 在 MLPerf Llama2-70B 吞吐量提升約 +37～42%。
Sources(WebSearch 自動附上):NVIDIA H200 官方頁、Databasemart、Spheron 等。
完整問答(含 Opus 4.8、Speculative Decoding 等)直接 DM 機器人即可;每題回答都帶日期與來源,KB 不足時自動補網路來源。
Automation uses GitHub Actions only and authenticates with your subscription, not an API key:
.github/workflows/radar.yml— retained collect → enrich → index → digest → drafts pipeline; paused with no schedule..github/workflows/ainews-watch.yml— active once daily at 18:00 Taipei. It checks for a fresh AINews issue, translates and forwards it, then indexes it. Dedup lives in Gmail via theradar-forwardedlabel. Empty checks stop before installing the heavy dependencies.
Setup:
- Locally:
claude setup-token→ copies a 1-year subscription token (works on monthly Pro/Max). - Repo → Settings → Secrets and variables → Actions → add
CLAUDE_CODE_OAUTH_TOKEN(and optionallyTWITTERAPI_IO_KEY,REDDIT_CLIENT_ID,REDDIT_CLIENT_SECRET). - Use
workflow_dispatchfrom the Actions tab only when a manual retry is needed.
Each draft is a Markdown file with YAML front matter:
---
id: stop-asking-which-llm-server-is-faster
platform: linkedin
status: draft
publish_date: 2026-06-02
title: "..."
angle: opinionated-take
tags: [LLM, MLOps]
sources:
- https://news.ycombinator.com/item?id=...
---
The post body, ready to review and paste.Interests are data, not magic values — edit content_radar/config.py:
keywords = ("LLM", "inference", "agent", "RAG", "vLLM", ...)
arxiv_categories = ("cs.CL", "cs.LG", "cs.DC", "cs.AI")
subreddits = ("LocalLLaMA", "MachineLearning")
x_queries = ("LLM inference", "agentic RAG", ...)
min_score = {"hackernews": 20, "reddit": 50, "github": 25, ...}content_radar/
models.py # immutable Item
store.py # dated JSON store + dedup (idempotent re-runs)
config.py # interests + secrets + synth_model / web_fallback / email toggles
collectors/
hackernews.py # Algolia API (free)
arxiv.py # export API (free)
github_trending.py # trending page parse (free)
reddit.py # OAuth or public JSON (free)
x_twitterapi.py # twitterapi.io: keyword search + curated account timelines (opt-in)
discord_collector.py # official Bot REST API (opt-in; no self-bots)
gmail_imap.py # fold AINews (& other newsletters) from your inbox via IMAP
enrich.py # fetch + attach linked-article text (detailed KB)
chunk.py # sentence-aware chunking, no overlap
temporal.py # query analyzer: EXPLICIT/IMPLICIT/NONE temporal intent → date filters
rag.py # KB: contextual chunks + hybrid (e5 dense + BM25) + jina rerank → Qdrant
kb.py # local SQLite FTS fallback when Qdrant isn't configured
digest.py # thematic clustering + best-of-N pick; full 繁中 translation + 本期摘要 TL;DR
synthesize.py # claude CLI backend (subscription; tools off by default)
chat.py # RAG chat: retrieve → answer in 繁中, WebSearch fallback on KB gaps
telegram_bot.py # long-poll Telegram bot over chat.py (serverless, no own host)
mailer.py # Gmail SMTP: 繁中 AINews edition
watch.py # daily AINews watcher, dedup via Gmail label
eval_qa.py # LLM-as-judge QA harness (generate questions, score answers)
cli.py # collect / show / enrich / index / import / digest / synthesize / check-ainews / email-digest / eval
tests/ # 95 tests (python -m pytest)
| AINews technique | content-radar |
|---|---|
| Breadth: Twitter accounts + Reddit + Discord | x_accounts timelines, Reddit OAuth, Discord bot |
| Click through links and summarise them | enrich.py (full article text) |
| Cluster into themes with attribution | digest.py |
| Run N pipelines, pick the best | --best-of N |
| Searchable archive of every past issue | Qdrant vector KB (rag.py) — every day's signal auto-indexed |
| Ask it anything, grounded | 繁中 chat bot (chat.py + telegram_bot.py), RAG + WebSearch fallback |
| Automation | GitHub Actions only; broader radar and bot workflows remain paused |
| AINews forwarding | Daily at 18:00 Taipei; Gmail-label dedup makes retries idempotent |
- Collectors never raise. A dead source logs a warning and returns what it has; one broken feed can't take down a run.
- Idempotent store. Re-collecting the same day dedups by
source:idand keeps the higher score. - Human in the loop by construction. Synthesis only ever writes
drafts. - The KB can grow incrementally. Each explicit run embeds that day's signal (incl. the day's AINews) into Qdrant; deterministic point IDs make re-indexing idempotent.
- Two cost layers. Retrieval (fastembed + Qdrant free tier) is local and free; only Claude generation uses your subscription. No per-token API billing.
- Tools off by default. Generation calls run with no agent tools so they can't touch the filesystem; only the chat bot's KB-gap path opts into WebSearch.
MIT