Audit your product-market-fit signals honestly instead of eyeballing 'traction'

You are auditing product-market-fit signals honestly — the goal is an accurate read, not a comforting one.

Context:
- Company stage: Seed stage, 8 months post-launch
- Sean Ellis "how would you feel if you could no longer use this product" survey results, if collected: 80 responses: 22% very disappointed, 51% somewhat disappointed, 27% not disappointed
- Retention/cohort data: Week 1: 100%, Week 2: 48%, Week 4: 31%, Week 8: 29%, Week 12: 28%
- Growth channel mix (organic/referral vs. paid), if known: Month 1: 90% paid ads / 10% organic. Month 4: 60% paid / 40% organic, paid spend flat

Task:
1. If 80 responses: 22% very disappointed, 51% somewhat disappointed, 27% not disappointed is given, restate the percentage who said "very disappointed" without the product, and state plainly whether it clears the commonly cited ~40% threshold. Label this as a widely-used rule of thumb, not a guarantee of anything, and note explicitly if the sample is too small to trust the percentage at all.
2. Read Week 1: 100%, Week 2: 48%, Week 4: 31%, Week 8: 29%, Week 12: 28% for curve shape: does retention flatten after the initial drop-off (a "smile curve," the real PMF signal), or does it keep declining toward zero with no flattening (a "death spiral")? State which, and point to the specific numbers in the data that support your read.
3. If Month 1: 90% paid ads / 10% organic. Month 4: 60% paid / 40% organic, paid spend flat is given, treat a rising organic/referral share against flat or falling paid spend as a secondary PMF signal, and paid-dependent growth with no organic pull as an absence of that signal — state which applies.
4. Give one honest overall verdict — Strong signal / Mixed or early signal / No signal yet — and explicitly refuse to round a mixed result up to "we have PMF" just because some inputs looked good.

Format: four headed sections ending in one blunt verdict line.