I treated my CV like a data product-evidence.json,MCP endpoint,llms.txt
Evidence-mapped CV beats PDF for AI recruiter parsing, but applies only to ATS that read these formats.

Resume parser with an API, but LinkedIn and standard PDFs already solve this.
Job seekers and hiring managers/recruiters
LinkedIn · VisualCV · Standard Resume
I've built a better way to handle these pain points (I think). The goal is to onboard companies (probably startups) to use the service, but my GTM is to provide immediate value to anyone who wants to create and maintain their online portfolio/profile.
Would love to hear feedback, bugs, and/or overall impressions. Thanks!
Evidence-mapped CV beats PDF for AI recruiter parsing, but applies only to ATS that read these formats.
Clean inbound email parsing when Mailgun and SendGrid already do this.
GitHub company tech detection is clever, but job boards are saturated.
Turning nested JSON into a live node graph with bidirectional edits actually pays off — double‑click a node to change a value and the raw JSON updates instantly (and vice versa). It’s entirely client‑side for privacy, generates TypeScript interfaces on the fly, and is MIT‑licensed; next steps should focus on UX and performance for massive payloads and clearer import/export flows.
Replaces regex email parsing with AI + schema validation, but Zapier and Make already handle this.
Profile-based resume builder cuts repetition, but crowded space with Rezi and LaTeX.