A

A personalized HN feed that learns from your favorites

Hacker News

A personalized HN feed that learns from your favorites

Hi HN, I've been a daily user here for almost 15 years. Over that time, my interests have shifted. I find I'm now more interested in deep-dive technical posts and personal blogs than the big tech announcements that often dominate the front page. The "top" feed was starting to feel stale, and I was spending more time digging through "new". So, I decided to build what I wanted: a personalized "For You" feed. Link: https://hn.shaped.ai It's a simple concept: you log in with your normal HN credentials, and as you favorite stories, it learns what you're interested in and re-ranks the feed to show you more of that content. How it was built (a 2-day hackathon): The Client: I used an AI coding assistant (lovable.dev) to generate the initial React/Next.js client. It was surprisingly effective at getting a functional baseline up and running quickly. The Backend: Since HN's official API is read-only, I set up a lightweight Supabase backend. It uses edge functions to proxy login/voting requests to HN's unofficial API and a Postgres DB to cache posts and user events (favorites, etc.). The Personalization: The ranking is powered by my own company's platform, Shaped. It ingests the posts and your favorite events in real-time. The core of the ranking logic is a configurable formula. It's essentially the classic HN algorithm with a personalization term multiplied in: (item.score / score_penalty + content_similarity) / (time_decay) The content_similarity is calculated by comparing a post's text embedding to an embedding of your recent favorites. The best part is that you can actually play with the score_penalty in the UI to make the personalization stronger or weaker. This is very much a v1. I'd love to get your feedback. Does the personalization feel right? Any bugs? What's missing that would make you use it daily? Next on my list are things like collaborative filtering (once there's enough data!), semantic search, and a "similar stories" feature. Here's also a more detailed write-up about how it was built: https://www.shaped.ai/blog/building-a-hackernews-for-you-fee... Thanks for checking it out!

Share card

Actual performance

23points
10comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: user, lovable, new · Missing: mac, agents, macos
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

A
A Perceptron Learns56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A Perceptron Learns

Hacker News4
Jo
Joel Learns Copywriting56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Joel Learns Copywriting

Hacker News2
Vi
Visualizing how a NeuralNetwork learns to recognize the MNIST digits61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Visualizing how a NeuralNetwork learns to recognize the MNIST digits

Hacker News8
Fe
Feed the Quine41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Feed the Quine

Hacker News1
Dj
Django Filtered Feed40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Django Filtered Feed

Hacker News1
GT
GTFS (transit) feed normalization41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

GTFS (transit) feed normalization

Hacker News8
Rundown
Rundown32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Your feed, synthesized.

Indie Hackers1ai
AI
AI Feed26%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI Feed

Hacker News1
Tr
Trendly–A Personalized Feed to Cut Through Content Overload39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Trendly–A Personalized Feed to Cut Through Content Overload

Hacker News1
St
Statz – personalized football content into one feed32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Statz – personalized football content into one feed

Hacker News1