Personal feed reader that learns your reading taste

What to build

A personal RSS/newsletter reader that learns what you actually open, finish, and dismiss, then re-ranks and filters your incoming feed around that behavior instead of a generic engagement algorithm — built for researchers, analysts, and other power readers drowning in subscriptions.

Power readers already pay for RSS tools, but none of the incumbents re-rank purely on a user's own reading/dismissal history, leaving room for a narrower, taste-learning reader sold as a lightweight personal subscription.

Demand

Researchers and knowledge workers managing dozens of newsletters and feeds want a filter that reflects their own taste rather than a platform's engagement goals, and a competing product (Radaric) is already live on Product Hunt testing this exact pitch.

• Product HuntLaunch

  Radaric launched on Product Hunt as 'AI feed reader that learns your editorial taste,' a directly comparable product validating the pitch in-market.

• Radaric signal summaryMarket

  Target audience defined as power readers, researchers, and professionals overwhelmed by newsletters, blogs, and RSS feeds.

• Radaric signal summaryMonetization

  Proposed monetization is a lightweight subscription aimed at knowledge workers seeking a personal feed curator rather than another algorithmic social timeline.

• Feedly / Inoreader paid tiersWtp

  Established RSS readers already run paid Pro tiers with AI summarization and filtering add-ons, showing knowledge workers will pay for feed curation tools — the gap is a reader that ranks purely on personal read/dismiss history rather than keyword rules or generic AI summaries.

Stack

• RSS/Atom parser (e.g. rss-parser)
• Postgres or Supabase for feed + interaction storage
• pgvector or a hosted vector DB for embedding articles
• OpenAI or Claude API for summarization and embeddings
• Lightweight ranking model (logistic regression or gradient-boosted trees) trained on open/dismiss events
• Next.js + Vercel for the reader UI

Solo + AI difficulty

The reader UI, feed ingestion, and summarization layer are straightforward to ship with AI assistance in a few weeks; the hard part is the ranking model — collecting enough per-user read/dismiss signal to make personalized re-ranking feel better than chronological or keyword filters, which needs weeks of real usage data per user to validate. Realistic time-to-MVP is 4-6 weeks for a single builder, with another 1-2 months of iteration before the ranking actually earns its subscription price.

Entry threshold

A solo builder can ship an MVP in days to a couple weeks using existing feed-parsing libraries and an LLM API for taste modeling and summarization; the hard part is retention and tuning the personalization loop, not the initial build.

Window

3-6 months

Where to find first users

• Product Hunt launch
• r/RSS and r/productivity
• Indie Hackers community
• Newsletter/blogger communities (e.g. Substack creator Discords) where readers already discuss feed overload

Competitors

• Radaric
• Feedly
• Inoreader

Counter-signals & risks

• The signal carries no historical match and only a 0.52 score, indicating weak external validation beyond this single Product Hunt listing.

• Feedly and Inoreader already ship AI summarization and personalization features, raising the differentiation bar for a new entrant.

• Pure personalization-based filtering risks creating filter bubbles or suppressing serendipitous discovery, which can hurt research-oriented users who need to see work outside their established reading pattern.