Launched: personalab — I tested it on PostHog, Cal.com, and itself. Every persona said no.

Hey IH 👋

Two months ago I was about to ship a crypto signal product. It "worked technically" but I had zero

signal on whether anyone would subscribe.

So I wrote 12 fictional user personas as markdown files — a burnt veteran trader, a hostile compliance

officer, a YC partner, a noise-allergic fund manager — and built a Python harness that fed each one my

actual product transcripts and asked: "what would you actually do?"

The answers were brutally helpful. They killed features I'd spent weeks on. I open-sourced the harness

as personalab (MIT).

Then I pointed it at three real products to see if it actually generalized:

1. personalab itself — yes, I tested my own tool with my own tool. 0/8 simulated B2B SaaS buyers

said they'd pay $99/mo. The case study became my own roadmap.

2. PostHog — 6/12 personas said "yes I'd pay" after reading a 7-day product transcript. Same 12 over

5-day agentic simulation: 0/12 sustained. The "yes" was first-impression optimism; the "no" was

multi-day reality.

3. Cal.com — 8/12 yes at $5-20/mo. And here's the gold: 75% of complaints converged on ONE thing —

the free-plan "Powered by Cal.com" branding makes recipients suspect spam. 8 distinct personas

independently nailed the same conversion lever.

After 3 case studies, a pattern: the number of dominant friction clusters correlates with PMF stage.

Pre-PMF: 4-5 diffuse complaints. Late-funnel: 1-2 clean levers. If this holds in case study #4+,

personalab becomes a free PMF-stage diagnostic from a $1 LLM run.

Honest disclaimer: default personas accidentally encoded personalab-specific preferences, so some

quotes leak when reused on other products. I kept the bug in the writeup rather than hiding it —

surfaces persona design as a real engineering concern.

Three questions for IH:

1. What product would you point this at first?

2. Real PMF business or just an OSS curiosity?

3. Anyone seen similar tooling work in the wild?