Opportunity Radar

Job seekers see roles only after they're posted, after every other applicant has too.

LinkedIn shows "over 100 people clicked apply" before you've even opened the posting.

The real advantage is spotting hiring momentum before a listing ever exists.

Built for my own job search, to reach the right person before the applicant queue forms.

Hiring momentum leaves a trail across public signals: news, community activity, funding, exec hires.

That trail can be tracked and scored automatically, well before a role is ever posted.

Weighting enough independent sources beats watching any single one alone.

No formal study. The research was already sitting in plain sight.

That signal alone was enough to validate the problem before any code was written.

If the problem starts there, the fix has to happen earlier, before the listing exists at all.

No design tool, no mockups. This one started as an idea and went straight to a working pipeline.

An n8n workflow pulls and normalizes signals from eight sources, writes the results to Google Sheets.

An AI coding agent turned that data model directly into the interface: leaderboard, company detail views, and the scoring breakdown all generated from the real schema, not a static comp.

A leaderboard ranked by weighted opportunity index, a company inspector panel with momentum trends and suggested contacts, and a per-company detail view breaking down exactly how each score was calculated, so the ranking is legible, not a black box.

Live and in use: it's surfaced real companies with rising hiring momentum and the people at each one worth reaching out to directly.

Just as valuable was what building it taught me: three days deep in Claude Code, VS Code, and GitHub, going from an idea to a working, hosted product using tools I'd only touched at the edges before.