AI Opportunity Intelligence Platform
An autonomous intelligence system that watches the entire AI ecosystem 24/7 and turns raw signals into validated, scored, actionable opportunities — my most ambitious build to date.
“Discover AI trends before everyone else. Validate the opportunity. Build faster.”
Role in the platform
The flagship. A standalone, end-to-end autonomous product — the largest system I've architected: a six-service monorepo that ingests the AI ecosystem, clusters and scores it, and delivers decisions, not headlines.
Overview
The AI space moves faster than any human can track. Every day brings new models, tools, repos, launches, and papers — and the signal that matters is buried under noise. This platform is an autonomous intelligence system that monitors the entire AI ecosystem continuously and converts raw signals into validated trends with structured opportunity assessments and concrete recommendations.
It is a decision layer, not a news aggregator: an autonomous pipeline (ingest → dedupe → cluster → score → embed → deliver) pulls from six sources, scores every emerging trend against a consistent 10-dimension rubric, and surfaces only the opportunities worth acting on — each with an action plan you could start building from today.
The problem
Information overload is the tax on everyone building in AI. The trends that matter are drowned out by hype, and by the time an opportunity is obvious it's already crowded. Founders, developers, and creators need to see viable openings early — and know why they're viable.
Aggregators and newsletters surface volume, not judgement. Nothing scores opportunities consistently, attaches an executable plan, or does it continuously and autonomously across the whole ecosystem at once.
What it is
A production monorepo (Turborepo/pnpm) of four apps (web, admin, marketing, docs) and six services (api, ai-service, ingestion, scheduler, notifications, plus supporting packages), deployed across Vercel + Fly.io.
Six connectors — Hacker News, GitHub, Hugging Face, Reddit, Product Hunt, and YouTube — feed a continuous BullMQ scheduler. Signals are deduped, clustered into trends, embedded (pgvector) for semantic discovery, scored on a 10-dimension rubric, and delivered as scorecards, action plans, watchlist alerts, and daily briefs.
Key features
Tech stack
Why it's different
- A decision layer, not a feed — it scores and validates opportunities instead of just listing links.
- Fully autonomous and continuous: six connectors and a 24/7 pipeline, no manual curation.
- Every trend carries an objective 10-dimension score and a concrete, buildable action plan.
- Architected as a real, feature-complete, multi-service product with 200 passing tests — the most ambitious system in my portfolio.