Show HN: My AI Native Resume

Original Article ↗ Hacker News Discussion ↗

Concept and Implementation

  • Thread centers on an “AI-native resume”: a Model Context Protocol (MCP) server plus an llms.txt file exposing structured, LLM-friendly data about a candidate.
  • MCP is described as a standardized way to share tools and context with LLMs, analogous to REST/OpenAPI for web apps.
  • The resume server can:
    • Serve structured background/skills data as “resources”.
    • Offer tools such as: contact-by-email, job-description → cover letter, mock interview generation, and GitHub code walk-throughs (including potentially private repos via hidden tokens).
  • The creator released Node/TypeScript examples and uses JSON Resume under the hood; others mention building MCPs that auto-update resumes from git/editor activity.

Practical Usefulness and Recruiter Workflow

  • Supporters see this as a step up from keyword-filtered PDFs and crude LinkedIn scraping, allowing agents to query richer, structured data and understand skill transfer better.
  • Critics argue recruiters mostly want a LinkedIn export plus a portfolio; they will not jump through MCP setup hoops. Today it mostly serves as a clever demo and personal branding signal.
  • Discovery remains unsolved: no universal way yet for assistants to auto-find candidate MCP endpoints, though proposals like A2A and .well-known plus future directories are mentioned.

Impact on Hiring and Candidate Experience

  • Some see AI agents searching MCP resumes, GitHub MCPs, and role feeds as an inevitable and even desirable evolution, potentially outperforming mediocre human recruiters.
  • Others find the idea dehumanizing: hiring already feels inhumane; this further outsources judgment to “hallucinating” models and rewards performative meta-gaming (trending posts, hype).
  • Multiple commenters say they’d quit rather than be expected to maintain such infrastructure; others counter it’s no worse—and perhaps better—than current ATS keyword gates.

Ethical, Social, and Aesthetic Concerns

  • Deepfaked voice/video responses generated from the MCP data unsettle some hiring managers, who say they’d reject candidates using them even if labeled synthetic.
  • There’s fear of MCP/llms.txt spaces becoming like the failed Semantic Web: spammed, gamed “metadata” and AI job-catfishing.
  • Broader discomfort surfaces around AI intermediating more of human life (hiring, networking, even friendship/dating), with debates over loss of serendipity, social-skill atrophy, and “outsourcing socializing” to bots.