For the last 20 years, we have been playing a game called “Keyword Bingo.” You write a resume (a static PDF). You guess what keywords the ATS wants. You pray a human reads it.

I am deleting that game.

I am currently building Hidden Market OS, an Agentic Operating System that replaces the “Resume” with a “Win Locker.”

The Core Innovation: “Predictive Discovery”

Most AI tools are “Wrappers.” They wait for you to find a job, then they write a cover letter.

Hidden Market OS is Predictive. It doesn’t wait for you. It scans the market 24/7, finds roles that match your specific achievements, and delivers them to you.

But to do that, it needs to know the truth about what you’ve done.

Feature 1: The “Hostile Biographer” (The Input)

I built an AI Agent whose only job is to interrogate you (nicely).

  • The Interaction: You don’t write bullets. You just talk. You leave a voice note: “I led a cloud migration last year.”

  • The Agent: It analyzes your voice using OpenAI Whisper and then challenges you: “That’s vague. What was the baseline cost? What was the savings percentage? Did you own the P&L?”

  • The Result: It forces you to quantify your impact. It turns your vague story into a Verified Asset (e.g., “$5M Cloud Cost Reduction”) and stores it in your Win Locker..

Feature 2: The “Arbitrage Engine” (The Match)

This is where the magic happens. Once we have your “Win Locker” (your verified assets), we don’t look for “Job Titles.” We look for Problem/Solution Fits.

  • The Scan: My agents scrape the market for companies signaling specific pain points (e.g., “Earnings call mentioned high cloud costs”).

  • The Match: It maps your “$5M Cloud Cost Reduction” asset directly to their “High OpEx” problem.

  • The Alert: You get a notification: “Google Cloud is hiring. You are a 94% Match based on your Win Locker. Arbitrage Gap: +$42k.”

The Vision: SaaS to DaaS (2030)

Right now, this is a SaaS platform to help you win today. But the long game is DaaS (Data as a Service).

In 2030, hiring won’t be human. It will be API-to-API.

  • The Buyer Agent (Recruiter): “Query: Find candidates with >$25M ARR scaling experience.”

  • Your Agent (Hidden Market): “Response: Verified Asset Found. User #1492. Interview accepted.”

I am building the infrastructure for that future.

Building in Public

I am architecting this in public.

I am coding the entire stack myself, Python Agents, Vector Databases, and API Layers, to prove that a modern Product Leader must understand the code to own the P&L.

I will be documenting the architecture, the agent logic, and the unit economics of the product right here.

This is not just a tool. It is a Cyber-Weapon for your career.

The Beta is coming. If you want to stop applying and start closing, stay tuned.

Richard Ewing is a Product Executive and the creator of The Product Economist framework. He serves as a Strategic Advisor to B2B SaaS organizations, helping leaders audit their roadmaps for capital efficiency and prevent “model collapse” in their business models.

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