Opportunity Radar

An n8n flow which finds companies likely to be hiring before roles are even posted: a live dashboard ranking hiring momentum by tracking signal strength across news, Reddit, LinkedIn, GitHub, Hacker News, funding, and exec hires in real time.

Designer, builder

Solo

3 days, July 22 to 24, 2026

  • n8n
  • Google Sheets
  • Claude Code
  • VS Code
  • GitHub
Opportunity Radar dashboard: a leaderboard of companies ranked by hiring signal, with the top company's row open to show its eight contributing signals
By the time a job is posted, 100+ people have applied. Hiring leaves signals earlier, but I had no way to see them.
An n8n pipeline that scores companies on eight signal sources, with a dashboard that ranks hiring momentum and surfaces the people worth contacting.
Across 39 tracked companies and 48+ scans, it surfaced real companies with rising hiring momentum, and the people worth reaching out to.

It started in a coffee shop, mid job search: hiring leaves a trail before the posting.

v1.0 was two days of vibe-coding n8n with ChatGPT; it only filled a spreadsheet.

A favorable reply on LinkedIn was the real validation.

LinkedIn job posting showing over 100 people clicked apply, reposted 5 hours ago
n8n workflow canvas showing signal collection, normalization, and aggregation nodes

That reply made it worth revisiting: more signal sources, a simpler pipeline.

I'd switched to Claude, and fed it the n8n workflow JSON to derive the project's goals.

It then sketched a first UI in Figma, through an MCP connection I'd set up.

Claude suggested a radar-style view of the ranked opportunities, which gave it its name.

A short stretch in Figma showed the DOM was the faster loop, so I left Figma.

One exception: once the CSS matured, I derived a style guide from it.

Code editor with an AI coding agent building the dashboard interface

People further along treated project .md docs as essential, so I took a short class.

I wrote a handful mid-build, then hit a rabbit hole an earlier doc would have prevented.

Not a tooling problem; my gap was knowing which guardrails a project needs upfront.

Partway through, I wasn't sure it was an agent, so I derived three tests.

Could you write the rule down, could a simpler tool do it, would two competent people disagree?

The answer was no on all three: a useful pipeline, but no real judgment anywhere.

A ranked leaderboard, plus a company inspector with momentum trends and contacts.

Each score shows how it was calculated, so the ranking isn't a black box.

No Crunchbase API access, so funding-round and headcount data comes from weaker, indirect signals (news mentions, exec-hire activity) instead of the real thing — the first upgrade I'd make with real budget.
No verified email finder either — contacts surface as LinkedIn profiles, not confirmed email addresses, leaving a manual step from "here's the person" to actually reaching them.
Job-posting discovery and verification, removed from Fit's scope entirely once it split off — it now judges company and contact fit, not whether a specific listing exists.
8
Signal sources tracked
39
Companies tracked live
48+
Scans run to date
~3
Avg. contacts retrieved per company
…
Contacts surfaced to date

Live and in use, surfacing real companies with rising hiring momentum and who to contact.

The instinct underneath: find the signal, build the smallest system for it, stay honest about limits.

It shows real contacts, so I share it by walkthrough; get in touch.

  • Product Design
  • n8n
  • Live Product