I Built a Personal AI News Agent That Emails Me Every Morning

Sun, 12 Apr 2026 00:00:00 +0000

I spend too much time scrolling Hacker News, arxiv, and tech Twitter. Most of it is noise. So I built a system that does the filtering for me — every morning at 6 AM, I get an email with the 10 most relevant stories across AI, semiconductors, and space.

Fully automated. Zero cost. Built in an afternoon.

The Problem

My information diet was broken:

Douyin/YouTube recommendations — optimized for engagement, not relevance
Manually checking 10+ sites — time-consuming, easy to miss things
Asking ChatGPT — no context about what I specifically care about

I wanted something that knows my interests (AI hardware, FPGA, space engineering) and filters 200+ daily articles down to the 10 that actually matter.

How It Works

9 RSS Sources → Fetch → LLM Scoring → Summarize (bilingual) → Email

Every day at 6 AM Beijing time, a GitHub Actions workflow:

Fetches ~150 articles from 9 sources (Hacker News, arxiv, Ars Technica Space, SemiAnalysis, EE Times, etc.)
Scores each article 1-10 based on my specific interests using Gemini
Summarizes the top 10 in both English and Chinese
Emails the digest to me with links to every original article

The Scoring Prompt

This is where the personalization lives. The LLM knows I’m an IC student interested in:

AI inference on edge hardware (FPGA/ASIC)
Semiconductor industry moves
Space technology engineering details
Open source AI tools
Contrarian technical takes

A paper about efficient transformer inference on resource-constrained chips scores 9/10. A generic “AI will change everything” opinion piece scores 2/10.

Model Fallback Chain

Free API tiers have rate limits. Instead of failing when one model is exhausted, the system automatically falls back:

gemini-2.5-flash (best quality, 20 req/day)
 ↓ exhausted? auto-switch
gemini-2.0-flash (good quality, 1500 req/day)
 ↓ exhausted? auto-switch
gemini-2.0-flash-lite (basic, unlimited)

The most important items get processed by the best model. Later items gracefully degrade.

Sources

Source	Category	Why
Hacker News	AI & Tech	High signal-to-noise, community-validated
arxiv cs.AI	AI Research	Primary source for papers
arxiv cs.AR	Hardware	Computer architecture papers
SemiAnalysis	Hardware	Best semiconductor industry analysis
EE Times	Hardware	Electronics engineering news
MIT News AI	AI Research	Research breakthroughs
Ars Technica Space	Space	Eric Berger’s space reporting
Everyday Astronaut	Space	Deep engineering explanations
SpaceNews	Space	Industry coverage

Tech Stack

Pipeline: Python (feedparser + google-genai + resend)
Database: SQLite (ephemeral, rebuilt each run)
Scheduling: GitHub Actions cron (free)
LLM: Gemini API (free tier)
Email: Resend (free tier)
Total cost: $0/month

What I Learned from PandaBrief

This project reuses the architecture from PandaBrief, my previous newsletter project. Key improvements:

Model fallback chain — never fail because of rate limits
Bilingual output — Chinese + English in one prompt call
Personalized scoring — not generic “is this important” but “is this important to me”
Private by default — this is a personal tool, not a product

The biggest lesson: the same automation pipeline that couldn’t sustain a public product works perfectly as a personal productivity tool. The difference is that I’m the only user, so I can tune the scoring to exactly what I care about.

Try It Yourself

The code is at github.com/cz-builds/daily-digest. Fork it, change sources.yaml and the scoring prompt to match your interests, add your API keys as GitHub Secrets, and you’ll have your own AI-curated morning briefing.

This is part of my build in public series. I’m an IC student exploring the intersection of AI and hardware.

Productivity on CZ Builds