I Built an AI Dating Assistant Because Swiping Is Statistically Inefficient

Amelie Li· Sep 22, 2026 · 3:50 AM UTC ·14 min read · 0 reactions · 0 comments · 6 views

More from Medium ai Compare coverage Trending Talk Blindspots Daily Sources Live wire

#ai #data science #dating apps #machine learning #productivity

TL;DR · WeSearch summary

Data scientist Amelie Li developed an AI-powered dating assistant to address the inefficiencies and poor filtering algorithms of mainstream dating apps. The system utilizes machine learning to extract, analyze, and rank over 1,100 profiles, aiming to improve compatibility matching beyond standard engagement-driven metrics. This project combines a custom data science pipeline with an AI agent to automate the reverse engineering of app APIs for better user experience.

Key facts

▪Amelie Li, a data scientist, built an AI assistant to analyze and rank more than 1,100 dating profiles using clustering algorithms.
▪The author identified that dating apps prioritize user engagement over effective matching, resulting in poor filtering and irrelevant suggestions.
▪The solution includes a data science pipeline for profile analysis and an AI agent skill designed to automate API reverse engineering.
▪Li's initial experience with dating apps revealed significant issues with distance filters and an overwhelming volume of low-quality matches.
▪The project aims to solve the 'data visibility problem' in modern dating by providing a ranked list of potential matches with highlighted red flags.

About this source

Hacker News (AI / LLM) files mainly under ai. We currently carry 5,919 of its stories.

All Hacker News (AI / LLM) coverage →

Original article

Medium · Amelie Li

Read full at Medium →

Story provenance

Source · retrieval · rights · ranking — open for full record

inspect →

Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.

Record

Original publisher	Medium
Canonical URL	https://medium.com/@mgcblee/i-built-an-ai-dating-assistant-because-swiping-is-statistically-inefficient-bdafd31c0738
Publication time	Tue, 22 Sep 2026 03:50:15 +0000
Retrieval time	2026-09-22T03:53:49.282Z
Last seen	2026-09-22T03:53:49.282Z
Headline source	Publisher (no WeSearch rewrite)
Excerpt source	publisher body
Excerpt method	First ~120 words (~800 chars) of extracted publisher body, fair-use limited.
Summary	WeSearch · cerebras-chat (WeSearch summarizer)
Summary source text	contentText
Citation coverage	Summary is a WeSearch-generated derivative; primary citation is the original publisher URL.
Cluster	DGvW0BH-BFCr · 1 stories
Cluster logic	Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison.
Ranking reason	Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking.
Publisher visit	Yes — open original
Substitutes article?	No — link-out required for full text

Rights status (four layers)

Publisher-declared

No publisher-confirmed rights record for this source yet.

Machine-readable

No source-specific machine-readable restriction detected beyond the public feed.

WeSearch interpretation

WeSearch declared handling (basis: Derived from the published RSS/Atom feed). This is WeSearch policy, not a legal grant on the publisher's behalf.

Unknown

Retrieval and training permissions are not asserted unless the publisher confirms them.

WeSearch handling by dimension

Indexing	May the item be indexed (stored, ranked, made findable)?	Allowed
Snippet	May a short excerpt of the publisher's text be shown?	Allowed
AI summary	May WeSearch generate its own short summary of the article?	Limited
Retrieval / RAG	May the content be exposed for third-party retrieval-augmented generation?	Not asserted
Model training	May the content be used to train AI models?	Not asserted
Commercial reuse	May the content be reused commercially?	Not permitted

Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

Open original publisher → Source rights JSON → Machine-readable provenance → Rights framework → WESEARCH-STORY-PROVENANCE-V1

Opening excerpt (first ~120 words) tap to expand

I Built an AI Dating Assistant Because Swiping is Statistically InefficientAmelie Li14 min read·21 hours ago--ListenShareA dating app newbie’s journey from “What’s a sapiosexual?” to clustering 1,158 profiles with machine learningReading time: 10 min | Tags: Data Science, Python, Machine Learning, Dating Apps, API Reverse EngineeringConfession time: Before August 2026, I had never used a dating app in my life. Not Tinder. Not Hinge. Not Bumble. Nothing.I know, I know. In the year of our Lord 2026, that’s basically admitting I’ve been living under a rock.

…

Excerpt limited to ~120 words for fair-use compliance. The full article is at Medium.

Anonymous · no account needed

Discussion

0 comments