Launch Intel predicts product launch success probability from text alone: your product name, tagline, and description. No surveys, no founder interviews, no manual scoring. The system is trained on 256,000+ real launch outcomes across seven platforms, each with its own independent model. This article explains how it works and what it can and cannot see.
Most launch advice is anecdotal. "Launch on Tuesday." "Get a top hunter." "Write a catchy tagline." These heuristics come from individual founder experiences, not systematic analysis of thousands of launches.
Launch Intel replaces heuristics with measured patterns. We collected 256,000+ products from seven platforms, labeled each with its actual outcome, and trained ML models to identify which text patterns correlate with success on each platform.
Each platform has its own data pipeline that collects product name, tagline, description, topics, engagement metrics, revenue (where publicly available), and launch date. Data is refreshed periodically to keep the models current.
Success labels are platform-specific. Product Hunt: daily leaderboard placement. Indie Hackers: revenue-generating (MRR > $0). AppSumo: above-median review count. Hacker News: high-traction vs. low-traction based on upvote distribution. TrustMRR: MRR above zero. BetaList: listing-ready vs. not. Acquire.com: revenue-generating vs. pre-revenue.
Your product description is converted into a numeric representation where each dimension reflects how characteristic a word or phrase is in your text relative to the full training corpus. Words that appear everywhere carry little signal; words that appear in winning launches but rarely elsewhere carry high signal.
The vocabulary is built once from training data and frozen at inference time - so the model compares your description against the same patterns it learned from historical launches.
See how your description scores across Product Hunt, Hacker News, Indie Hackers, AppSumo, BetaList, TrustMRR, and Acquire.com.
Try it on your product descriptionEach platform has an independent ML classifier trained to separate successful from unsuccessful launches. Models are trained with balanced class weights to handle the natural imbalance - on Product Hunt, 44% of posts reach the leaderboard; without balancing, a naive model would predict "non-leaderboard" for everything and achieve 56% accuracy while being useless.
Raw scores are post-processed to align with empirical success rates, so the output probability is calibrated against historical outcomes rather than being an arbitrary model score.
For platforms with quantitative outcomes, secondary regression models estimate magnitude. Product Hunt: comment count. Indie Hackers: MRR. TrustMRR: MRR and customer count. These train only on the successful subset.
Counts use log scaling during training because launch outcomes are heavily right-skewed. Estimates are presented as percentile-based ranges, not point predictions.
The model evaluates text only. It cannot see product quality, founder reputation, pricing, timing, existing audience, hunter identity, or visual assets. A mediocre product with excellent copy will score high. An excellent product with vague copy will score low.
This is a feature, not a bug. Description quality is the one launch variable founders control completely before launch day. The model isolates that variable and gives actionable feedback.
The Description Optimizer at /optimize uses beam search to rewrite your description. Starting from your input text, it generates word-level variations, scores each against the target platform model, and keeps the top candidates at each depth level.
Typical improvements range from 9-21 percentage points across our published examples. The optimizer adds platform-specific signal words while preserving your core message. Beam width and depth are configurable: higher values explore more variations but take longer.
Score your description across Product Hunt, Hacker News, Indie Hackers, AppSumo, BetaList, TrustMRR, and Acquire.com. Trained on 256,000+ real launches.
Try it on your product descriptionAcademic analysis of 49,748 PH launches for Series A conversion: API 2.98%, Payments 2.42% by topic
Annual data report on indie product launches: 208,072 real outcomes across 7 platforms, success rates, and what winning descriptions have in common.
Product Hunt description writing guide: problem/audience/solution formula, ML-backed word lists, and 15-21% score improvement from the Optimizer.
Product Hunt algorithm analysis for 2026: day-1 velocity, upvote quality signals, comment weight, and what 25,636 launches reveal about ranking.
