Career

Distilling vault lessons into career capital — what works with AI, what doesn’t, and how to demonstrate it.

Links: Vault Index, The Cyborg Model, Level 6 — Direct Execution, Working With Claude

Purpose

This vault has accumulated real, tested knowledge about working with AI — not hype, not tutorials, but hard-won lessons from building projects, running agent teams, debugging failures, and developing frameworks. The career section turns that into something demonstrable:

• What works — patterns, workflows, and mental models that consistently produce results
• What doesn’t — failure modes, antipatterns, and where the hype diverges from reality
• How to show it — translating vault knowledge into portfolio pieces, talks, writing, and project showcases

Relevant Vault Context

The vault already contains significant career-relevant material scattered across sections:

• AI capabilities and limits: Claude Opus 4.6, H-Neurons, LLM Grounding Problem
• AI as economic actor: LLMs as Praxeological Actors, Agent Teams vs. Theory
• Collaboration model: The Cyborg Model, Level 6 — Direct Execution
• Practical tooling: Claude Code on Cloud VM, Working With Claude
• Projects built with AI: Slay, CyborgDJ, YouTube Migration, OOP Neurons

Sub-Topics

• The Role Landscape — how the industry describes AI-native development roles; title analysis, the API boundary, the level gap
• Career Strategy — The Performative Loop — the job search IS the portfolio; director-level positioning; the Gödel loop
• Tools Roadmap — 9 tools to build, each one both a career tool and a portfolio piece; build order and existing resources
• Active Postings — director-level AI positions organized by lane; tracking table for applications
• Presentation Generator Spec — live slide generation: benchmarks, dual-output approach, build plan

Tags

ai, career