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ycb/README.md

👋 Peter Spannagle

AI product leader | Growth systems | Builder

Designing and delivering AI products for trust and growth

I build AI-driven products that deliver business outcomes. My background includes product management, UX, and front-end engineering. I've led 0→1 at hypergrowth startups (Aurora Solar, FalconX) and delivered impact at scale for global brands (Samsung, Meta, Salesforce). My experience with AI/ML includes building explainable AI at Meta and leading NLP chatbot design for 20M Samsung users. Today, I focus on AI-native product development: designing systems where LLMs are core components of workflow, decision-making, and user value creation.

🔎 What I Build

I'm interested in:

  • AI workflow design • Structured LLM pipelines • Growth instrumentation • Activation & retention systems • UX patterns for trust in AI • Evaluation frameworks for generative systems

My work emphasizes:

  • Clear problem framing • Structured outputs over vague completions • Instrumentation before intuition • Guardrails and failure handling • Shipping fast with measurable iteration loops

🚀 Flagship Projects

🧠 Narrata — AI Career Narrative Engine

AI system for structured cover letter and story generation.

  • Multi-stage prompt pipelines
  • Structured JSON outputs + validation
  • Evaluation loops & quality scoring
  • Streaming architecture
  • CI/CD (main → staging, manual prod deploy)

👉 See: narrata-cover-letter-agent

🎯 Job Finder — Local Tools for Job Intelligence Agent

Turns LinkedIn saved searches into a ranked action queue — built as a Codex MCP agent.

  • Browser-driven LinkedIn intake via named saved searches
  • Deterministic fit scoring against a structured profile
  • De-duped review queue with "Why It Fits" rationale per job
  • Local SQLite — no data leaves your machine
  • Human-in-the-loop review before any high-stakes action

👉 See: job-finder

⚙️ AI Development Playbook

Operational guidelines for working effectively with AI copilots in production codebases.

👉 See: cursor-ai-coding-playbook

🏗 How I Think About AI Product Systems

I view AI product development as four layers:

  1. User Intent Layer — clarify job-to-be-done
  2. Orchestration Layer — structured prompts + routing
  3. Validation Layer — enforce schema, detect failure
  4. Measurement Layer — track behavioral impact

🛠 Stack & Tools

  • Claude, Codex, Cursor and MCP
  • React, TypeScript, Vite, Tailwind
  • Python and SQL
  • Supabase
  • Structured prompt pipelines
  • CI/CD automation
  • Event-driven workflows

📫 Contact

linkedin.com/in/pspan

peterspannagle.com

roadmaps.substack.com

Pinned Loading

  1. cursor-ai-coding-playbook cursor-ai-coding-playbook Public

    Cursor rules to save time and money: lightweight rules to help AI copilots write better code, reduce compute cost, and keep teams moving fast.

    2

  2. free-feedback-tool-google-sheets free-feedback-tool-google-sheets Public

    Free and easy way to collect feedback on your web app. Floating Action Button, "Inspect Tool" selection, pin overlay, sentiment, categorization, email and open text. Google Apps Script instantly po…

    TypeScript 2

  3. narrata-cover-letter-agent narrata-cover-letter-agent Public

    AI cover letter agent that helps PMs land more interviews via intelligent feedback, re-usable content and objective level assessment.

    TypeScript 2

  4. cover-letter-agent-prototype cover-letter-agent-prototype Public

    Cover letter agent with job description parser, dynamic blocks logic, Google Drive and OpenAI integrations

    Python 1

  5. solar-app solar-app Public

    Mobile-first prototype that guides solar installers through a non-linear pre‑inspection capture flow. It enforces photo/video requirements, simulates an auto‑check, and provides a simple inspection…

    TypeScript 1

  6. job-finder job-finder Public

    Job intelligence agent that captures LinkedIn saved searches, de-dupes results, scores fit against a structured profile, and surfaces a ranked action queue.

    JavaScript 1