Career Opportunity Radar · Case Study

Turning job-search uncertainty into an executable system

The problem is rarely a lack of listings. It is deciding which role is worth acting on, whether personal experience forms credible evidence, and what to do next.

Data: the July 17, 2026 full refresh and qualification queue. Tests rerun on July 21, 2026.

47source executions
1,667aggregated records
1,666deduplicated and assessed
10currently actionable roles
204automated checks passed

These are engineering-state metrics for a private, single-user system—not user counts, placement rates, or commercial conversion.

01 THE PROBLEM

More information can make action slower

Most job aggregators leave the real work to the candidate: open every site, reread each JD, assess location and seniority, recall which experience proves the fit, then rebuild the resume, application note, and interview stories.

None of these decisions is especially difficult alone. Compressed together, they create a persistent feeling that something has been missed.

02 MY JUDGMENT

Hard gates before relevance

I defined the product as a local-first personal decision system, not a public job board. Policy signals and market heat may help discover directions, but they cannot override location, work authorization, minimum experience, seniority, or required skills.

The core product unit became a traceable action chain: full JD, qualification decision, personal evidence, risk, role-specific materials, and a next step.

03 HOW IT WORKS

Absorb information density in the back; deliver action in the front

Public roles and full official JDs
Normalization, deduplication, failure retention
Location, experience, seniority, and other hard gates
Personal experience and portfolio evidence
Quality gates for role-specific materials
Human confirmation and outcome feedback
04 CORE CAPABILITIES

The system must know when to say “no”

Official-role convergence

A role needs a full JD, location, and official entry point before qualification. Failed sources retain historical records.

Hard gates first

Location, authorization, experience, seniority, and required skills take precedence over preferences and semantic similarity.

Traceable evidence

Direct experience, transferable ability, and unresolved risk stay separate. “Related to” cannot become “did.”

Consistent materials

Recommendation rationale, resume, application note, and interview preparation share the same verified facts.

Safe human–AI division

The system prepares materials but never handles CAPTCHAs, accepts privacy terms, or performs final submission.

Feedback within the gate

Outcomes reorder qualified roles; they never restore a role blocked by a hard requirement.

05 VERIFICATION

Successes, failures, and boundaries remain visible

The latest full refresh produced 47 source executions: 43 succeeded and 4 failed. It aggregated 1,667 records and completed deduplication and qualification assessment for 1,666. All 10 currently actionable roles have reviewed materials.

204 automated checks passed 183 Python tests, plus 6 feedback-learning checks, 8 state API checks, and 7 client-sync checks.

Desktop and mobile critical paths were reverified. Failed sources were not hidden or replaced with new sources for the portfolio.

06 PRIVACY BOUNDARY

A public portfolio is not a public private-job-search system

This case study uses only public job fields, redacted verified metrics, and system structure. It does not read the working version that contains private job-search information.

Safe to show

  • Public company, role, and JD fields
  • Workflow, decision framework, and test methodology
  • Redacted screenshots and verified aggregate metrics

Never public

  • Name, email, phone, resume, or application materials
  • Personal model, fit rationale, or real application status
  • Private company judgments, secrets, or private builds
07 NEXT

Validate ranking with real outcomes—not more sources

Next: repair and reverify the four failed sources. Once enough application and interview outcomes exist, evaluate whether the ranking actually improves action efficiency.