Building SummarAIzeIT
A periodical technical build log about turning SummarAIzeIT from a product idea into a Rails AI application that can ingest sources, summarize them, recover from unreliable APIs, and send useful digests without constant babysitting.
The series is for anyone interested in the technical side of building a real AI product: architecture, tradeoffs, background jobs, data modeling, and the practical edges that show up after the prototype works.
One product, one focused engineering story at a time.
No giant architecture dump. The roadmap shows the direction, and each article gets written when it is the next story worth telling.
Roadmap
The list below is the publishing direction. I will add new articles as each part is ready.
1. Building SummarAIzeIT: from information overload to a daily AI digest
The product problem, the first architecture boundary, and why the app is more than a scrape-and-summarize script.
2. The data model behind SummarAIzeIT
Projects, sources, snapshots, posts, newsletters, fetch runs, and cached YouTube summaries.
3. Designing ingestion around strategy objects
How source-specific fetchers keep RSS, pages, YouTube videos, and channels out of one giant service object.
4. Fetching web pages without pretending to be Google
Nokogiri cleanup, main-content heuristics, change detection, and honest limits around web extraction.
5. RSS and Atom ingestion in Rails
Feed discovery, item parsing, duplicate protection, import windows, and idempotent persistence.
6. YouTube transcripts: why I tried yt-dlp and moved to an API pipeline
The operational tradeoff behind moving from local extraction experiments to a provider-based transcript pipeline.
7. Fallbacks are product decisions, not just error handling
Transcript summaries, metadata fallbacks, content origin labels, and upgrade paths when better data appears later.
8. YouTube channels: videos.xml first, Data API when needed
Channel feeds, Data API fallback, URL parsing, shorts filtering, and bounded batch processing.
9. Rate limits, retries, and making external APIs boring
Local rate-limit records, provider failures, retry policy, and why some errors should wait instead of fallback.
10. Scheduling daily AI digests with Rails jobs
Schedules, slots, time zones, GoodJob concurrency, catch-up windows, and digest delivery.
11. Newsletter ingestion: Gmail, IMAP, Mailgun, and messy email bodies
Email import paths, body extraction, sender resolution, threading, and import limits.
12. Shipping a solo Rails AI product
Subscriptions, webhook recovery, deployment, monitoring, runbooks, and the lessons from operating it.