spscream ai-daily
Fetches AI-industry news from RSS feeds, keeps only AI-relevant items (keyword filter), dedups against previously-sent headlines (exact + RapidFuzz), and outputs fresh items as a structured two-block digest for the LLM to format.
When to Use
Use when you need a fresh digest of AI news: daily briefing, channel/feed post, or a roundup of what happened in the last 24–48 hours.
How to Run
python scripts/fetch_news.py [--config config.yaml] [--dry-run] [--no-crawl]
--dry-run prints what a real run would emit but marks nothing as seen, so you
can look at the output without consuming the items.
The script prints two blocks to stdout:
СТАТЬИ (заголовок | источник | дата | url): <-- fresh items
СТАТЬИ-СОДЕРЖАНИЕ (заголовок === текст): <-- short crawled content per item
The agent (LLM) reads these blocks and formats the final human-readable post. Dedup is built in: a repeat run returns only genuinely new items, so it is safe to call every day.
Config
Everything is configurable via config.yaml (or the defaults at the top of the
script). Sample:
feeds:
- name: TechCrunch AI
url: https://techcrunch.com/category/artificial-intelligence/feed/
- name: CNews # Russian-language source
url: https://www.cnews.ru/inc/rss/news.xml
max_items: 40 # a firehose needs a wider window
max_items: 10 # default window per feed
feed_category_rules: # applied before the keywords
TechCrunch AI:
require_any: ["ai"] # keep only items tagged AI
Ведомости:
deny_top: ["политика", "общество"] # drop whole sections
full_ai_feeds: ["TechCrunch AI", "HN LLM"] # feeds assumed fully AI-relevant
# Matched as a whole word only. Vendor names go here when the name is also the
# start of an ordinary word: meta/metal, cohere/coherent.
ai_acronyms: ["ии", "ai", "llm", "gpt", "meta", "cohere", "gemini", "llama", ...]
# Matched at the start of a word, so endings may vary: "модел" -> "модель".
# Vendors, labs and model names live here too.
ai_keywords: ["искусственн", "модел", "claude", "openai", "deepseek", "anthropic",
"nvidia", "mistral", "midjourney", "qwen", "gigachat", ...]
db_path: ./seen_ai_news.db # SQLite "seen" store (dedup)
max_hours: 48 # freshness window
crawl_max_chars: 1600
max_crawl: 14
tavily_api_key_env: TAVILY_API_KEY # optional, for /extract content
Requires: python -m pip install feedparser requests rapidfuzz pyyaml
What the editor must do with this output
The keyword filter in the collector is deliberately generous: it is better to carry one item too many than to drop real news, because an item it rejects is never seen again. So the list you receive can contain something off topic, and dropping it is your job, not the collector's.
- Judge by meaning, not by the words in the headline.
- Drop anything that is not about AI. Say nothing about it — do not apologise for a short digest and do not list what you removed.
- A short post beats a post with a stray item in it. Fewer items than you were aiming for is a normal outcome.
- These belong in the digest even when the headline never says "AI": robotics, autonomous vehicles and drones, chips and hardware for training or inference, datacenters built for AI, funding rounds and valuations of AI companies, regulation and court cases about AI.
The last point matters. Told only to drop what is off topic, a model starts cutting anything whose headline lacks the word "AI" — a robotics funding round, for instance. Naming the profile of the channel is what keeps it in.
Output format (what the LLM turns into a post)
СТАТЬИ (заголовок | источник | дата | url):
Title | TechCrunch AI | 16.08 | https://...
СТАТЬИ-СОДЕРЖАНИЕ (заголовок === текст):
[Title]
<short text>
[КОНЕЦ]