Hit 7,900 AI-scored opportunities in the DB (and found 4 competitors to myself)

1 month building solo. Goal: kill the "brainstorming startup ideas" loop
where every thread on r/SaaS is mostly noise.

What I built (in ~30 days of heads-down coding):

A pipeline that scrapes ~5k posts/day from Reddit, Hacker News, and X → AI
extracts repeated complaints + workarounds people already hack together →
clusters into opportunities → scores each on 11 axes (pain, urgency,
willingness to pay, market size, defensibility, timing, etc.) → generates
MVP roadmap + initial risks + competitor landscape.

Everything free to browse, no signup: monetscope.com

The numbers today:

• 7,963 opportunities in the DB
• Bluesky: 2 likes overnight on a 10-post thread (brand new account,
0 followers) 😅

3 things that surprised me:

1. Hacker News > Reddit as a signal source. A 500-comment HN thread often
   beats 20 Reddit posts on the same problem. Reddit looks louder; HN is denser.

2. "Confidence score" became my most-used filter. Opportunities with high
   overall score but low confidence (<0.7) turned out to be landmines — the AI
   hallucinated a trend from 2 posts. I now trust confidence more than the
   pretty "8.5/10 pain" label.

3. We found 4 competitors to MonetScope in our own scan. Including one
   called "IdeaScore: AI Side Project Viability Validator." Our scanner
   validated us showing up as a validated opportunity. Existential lol.
   monetscope.com/discover/ideascore-ai-side-project-viability-validator

What's next:

• "Who should I interview first" feature — evidence is there but not yet
packaged into an actionable validation shortlist
• Weekly digest: which subreddits / HN topics are heating up this week

Stack: Next.js 14 + .NET 8 + PostgreSQL + pgvector + OpenAI (extraction)

• Grok (deep analysis)

Honest state: Revenue is tiny, distribution is the bottleneck. Classic
indie mistake — spent 30 days building, 0 days selling. This post is me
leaning into that.

Feedback genuinely wanted. Especially if MonetScope shows up in your own
scan one day — I'd love to know what's missing.