rank-by-user-fit

Rank by User Fit

Workflow

Per candidate pool:
- [ ] Step 1: Score each on 5 dimensions (intuition_density_fit, goal_alignment, dedup_penalty, source_reliability, freshness)
- [ ] Step 2: Weighted sum → rank
- [ ] Step 3: Top items with score > threshold (default 30), max 10 → keep list
- [ ] Step 4: Remaining → drop list; per-drop one-line reason using worst-scoring dimension
- [ ] Step 5: Enforce minimum: ≥2-3 drops visible even in slow weeks (ranker transparency)

Scoring rubric

Dimension	Weight	Range
intuition_density_fit	3.0	high=10, medium=5, low=0
goal_alignment	2.0	full-match=10, partial=5, none=2, anti-aligned=-3
dedup_penalty	2.0	NEW=+5, OVERLAPS seed=+2, OVERLAPS draft=0, OVERLAPS published=-2 (unless reinforcement_angle strong)
source_reliability	1.0	essential=10, optional=6, aggregator=4
freshness	1.0	in-window=10, republished=5, older=2

Threshold for keep: score > 30.

Worked example

Candidate: Karpathy microgpt (teaches GPT internals in 200 lines, in window).

• intuition_density_fit: 10 × 3 = 30 (high)
• goal_alignment: 10 × 2 = 20 (matches "intuition-first" explicitly)
• dedup_penalty: +5 × 2 = 10 (NEW)
• source_reliability: 10 × 1 = 10 (essential)
• freshness: 10 × 1 = 10 (in window)
• Total: 80 → keep, high rank

Candidate: "OpenAI announces GPT-5.5" release post.

• intuition_density_fit: 0 × 3 = 0
• goal_alignment: 2 × 2 = 4
• dedup_penalty: +5 × 2 = 10
• source_reliability: 4 × 1 = 4
• freshness: 10 × 1 = 10
• Total: 28 → drop. Reason: capability announcement; no mechanism taught.

Guardrails

1. Never let freshness outweigh intuition-density. Freshness is a tiebreaker.
2. Never keep more than 10.
3. Never drop all items from a single essential source two weeks in a row without surfacing "this source may be stale" for update-watchlist.
4. Never drop an item solely for source weight 4 — good content from unknown sources matters.
5. Always produce drops. Zero drops in a week with >5 candidates = ranker failure.