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Future

Where I'm heading and why. These aren't vague aspirations — they're directions I'm actively investing in. For the full timeline of how I got here, see About.

AI hasn't replaced software engineers — it has expanded what we can do. The scope of engineering is growing, and so is the impact a single engineer can create. I want to earn the Senior title not just by tenure, but by consistently shipping systems that matter at scale.

  • Own and deliver end-to-end projects with measurable business impact
  • Design systems that other engineers can build on top of
  • Mentor teammates and raise the quality bar across the team
  • Build a track record of reliability — zero incidents isn't luck, it's discipline

Being good at code is table stakes. I want to understand the internet industry at a deeper level — how businesses grow, how products find market fit, and how technology decisions ripple into user experience and revenue. The goal is to bridge the gap between engineering execution and business intuition.

  • Develop sharp product sense — understand why features succeed or fail
  • Learn to read business metrics and connect them to technical decisions
  • Study how great internet companies scale beyond their first product
  • Build the vocabulary to talk with PMs, designers, and leadership fluently

AI is reshaping everything — how we code, how we think, how we create. I don't want to just use AI tools passively. I want to deeply understand what each new wave of models and products can do, how they change my daily workflow, and where the real leverage is. The engineers who thrive will be the ones who treat AI fluency as a core skill, not a side hobby.

  • Continuously evaluate new AI products and integrate the best into my workflow
  • Understand model capabilities at a technical level, not just surface features
  • Build AI-native tools and systems that create compounding value
  • Share learnings publicly to sharpen thinking and help others navigate the shift

Current Focus

What I'm working on and learning right now.

Working on

  • Digital twin experiment: distilled my Obsidian knowledge base into an AI that maintains this site.
  • AI-native quality engineering — knowledge-base-driven test generation, pipeline gates, and agent-assisted regression.
  • Weekly blog posts on AI + engineering + cognitive systems, with the digital twin as co-author.

Learning

  • Coding agent internals — read Claude Code's full 1884-file source and built a tutorial site.
  • MCP (Model Context Protocol) and how it enables tool integration for AI agents.
  • First-principles frameworks for career navigation and expertise redefinition in the AI era.

Side interests

  • NBA playoffs, DCA investing, geographic/career arbitrage analysis (CN vs SG vs US), and building compounding systems.