Guides
Practical guides for reading and using AI company data. Each guide explains what the numbers mean, common pitfalls, and how to make better decisions with developer signals.
How to Evaluate AI Companies Using Developer Data
A framework for assessing AI companies through developer adoption signals: package downloads, GitHub stars, Hacker News mentions, and funding data. Covers what each metric actually measures and common pitfalls.
Understanding the Developer Momentum Index
How the DMI score is calculated, what each component contributes, and how to use it for investment and partnership decisions. Includes worked examples showing score breakdowns.
Reading AI Funding Data: A Practical Guide
How to interpret funding rounds, valuations, and investor patterns in the AI industry. Covers what total funding means vs. recent rounds, and how to spot companies at different stages.
Developer Adoption Metrics Explained
What npm and PyPI download counts actually measure, why GitHub stars can be misleading, and how Hacker News mentions reflect developer mindshare. Practical guidance for reading each metric.
AI Company Comparison: What to Look For
A structured approach to comparing AI companies across multiple dimensions. Covers when to use head-to-head comparisons, what category context matters, and how to weight different signals.