4 4DA docs
How it works

The 5 axes

Every item is scored on five independent axes. This is the core of what 4DA calls PASIFA scoring — the reason a keyword match alone can't buy its way into your feed.

Axis What it measures
Context Semantic similarity to your active codebase
Interest Alignment with your declared and learned topics
ACE Real-time signals from your Git commits and file edits
Dependency Direct matches against your installed packages
Learned Save / dismiss feedback that boosts or suppresses future scores

The 2-of-5 gate

An item must pass 2 or more axes to surface. Single-axis matches are hard-capped at 28% — one strong signal, no matter how strong, cannot pass alone.

This is deliberate. A single axis is easy to trigger by accident (or on purpose). Requiring corroboration across independent signals — semantic relevance and a dependency you actually installed, say — is what separates "mentions your tech" from "you need to see this."

Quality multipliers

Passing the gate is necessary, not sufficient. Survivors run through 12 multipliers:

  • Content depth — thin content is demoted
  • Novelty detection — introductory posts down; new releases and security advisories up
  • Title–body coherence — a title has to deliver on its promise
  • Competing-tech penalties — content pushing alternatives to your stack is discounted
  • Intent scoring — recent Git and file activity nudges what surfaces toward what you're working on now

Calibration

None of these constants are guesses. The pipeline is benchmarked against 9 simulated developer personas (Rust systems, Python ML, fullstack TypeScript, DevOps/SRE, mobile, first-run, power user, stack switcher, niche specialist) with 215 labeled test items scored as relevant or noise.

Measured result across those personas: 92% of content filtered as noise, 98% of actual noise correctly rejected. Your own rejection rate — computed from your data, not ours — is shown in the app.

Accurate first. 4DA never shows intelligence the system can't stand behind. Correct results from a capable model beat fast results from a weak one.

Next: Privacy & BYOK.