5 Things I Learned from Top AI Coding Founders to Improve Productivity

Show Notes

Parker shares practical lessons from AI-focused founders to boost productivity, emphasizing planning, context, and a phase-by-phase workflow over endless tool-hopping. He ties these lessons to a real-world build and shows how to turn ideas into a maintainable product with less chaos.

Core thesis: plan, then build, with context

• Don’t chase every new tool. Reserve tool hopping for nights and weekends; for real work, maximize what you already use.

• Learn from top builders to craft a repeatable process: define the vision, map data, plan architecture, and execute in phases.

• Context is king: you must understand the domain deeply so the AI can operate with meaningful context.

• Actionable takeaways

  • Start every project with a clear vision and a simple data model.
  • Plan before coding; let the data model drive the code structure.

Influences and key thinkers

• Terraform founder: planning and phased work

• Ghosty (community/CI ideas) and Boris (Claude Code) notes

• Augment founder: contextual planning and prompts as a workflow

• What Parker took away: plan in phases, use context engines to guide decision-making, and sanity-check AI outputs with your own knowledge

• Actionable takeaways

  • Build a mental model of the system first, then translate to code.
  • Use expert prompts or frameworks from respected practitioners to shape your workflow.

The planning framework: vision, data, flow, state, structure

• Start with the vision of the product (e.g., a community site with posts, comments, road-map, and profiles).

• Flesh out data structures on a whiteboard or notebook:

  • What entities exist? (Users, Posts, Comments, Profiles, etc.)
  • How do they relate? Are comments nested or flat? What tables/pages exist?

• Decide the code structure and patterns you’re comfortable with (e.g., hexagonal architecture, specific factories or utilities).

• Map the data flow and UI state:

  • Where does data live? What needs optimistic updates? How is sharing URL handled?
  • What are the minimal pieces required to render a page?

• Iterate the plan with rough diagrams and notes before writing code.

• Actionable takeaways

  • Diagram data relationships first; translate them into a database/data-model plan.
  • Sketch the user flows and state boundaries before touching UI code.

From vision to execution: a phased planning approach

• Phase 1: Capture the vision and define the feature tree

  • Post, Comments, User/Profile, Roadmap, Activity feed
  • Define data relationships and basic UI screens

• Phase 2: Decide on code structure and patterns

  • Pick patterns you know well; avoid over-architecting for unknowns

• Phase 3: Detail data flow and state

  • How do actions cascade? What should be optimistic vs. server-validated?

• Phase 4: Create planning and research checklists

  • Planning checklist (vision, data model, architecture)
  • Research checklist (unknowns, patterns, tools)

• Phase 5: Break into tasks and execute phase-by-phase

  • Use prompts to generate task lists and track progress
  • Move through phases with a focused toolchain (see below)

• Actionable takeaways

  • Break work into clearly defined phases and complete one phase before starting the next.
  • Maintain a living planning doc (planning.md) and a research doc (research.md).

The practical workflow and toolchain

• Planning and research prompts

  • Create a planning doc with concrete tasks, milestones, and a feature breakdown.
  • If gaps exist, use a research checklist to fill in unknowns.

• Task lists and phase execution

  • Generate a task list from your plan; refine it with prompts to fit real work.
  • Use a phase-by-phase runner (e.g., Opus 4) to execute one phase at a time.

• Context engines to guide planning

  • Augment and Claude-style prompts help shape the plan, but you still verify the outputs yourself.
  • Use prompts to surface alternative approaches or missing edge cases.

• Git and environment workflow

  • Use Git worktrees to keep contexts isolated for each phase or feature.
  • Keep a master branch and multiple worktrees to avoid cross-contamination of context.

• Sample commands

  • Create and manage worktrees (high-level examples):

    # List worktrees
    git worktree list
    
    # Add a new worktree for a phase/feature
    git worktree add ../phase-1 feature/initial-setup
    
    # Prune unused worktrees
    git worktree prune
    

• Claude Code and local setup

  • Claude Code helps manage context and phase-specific tasks; keep root config clean to avoid issues.
  • If Claude Code misbehaves, reset its local state in your home directory (e.g., remove ~/.cloud and reinitialize).

• Integrating tools into your IDE

  • Augment can turbocharge planning, but Vim/IDE integration may vary—adjust setup to your workflow.
  • Playwright (for UX tests) can be integrated into the plan for end-to-end checks.

• Actionable takeaways

  • Use a dedicated worktree per phase to prevent context spillover.
  • Run one phase at a time; don’t try to do everything in a single draft.
  • Keep a clear root path and config for Claude Code to avoid environment issues.

Handling bugs and debugging like a pro

• Always ask: why does this issue exist?

• Try to solve it yourself first; don’t rely on AI alone.

• Use multiple worktrees to isolate experiments and avoid cascading failures.

• For code review and quality, leverage two-line comments and pseudo-code to reason about blocks of code (rubber ducking).

• If you hit a blocking bug, commit thoughtfully with conventional commits (feat, fix, chore, UI, etc.) and update the plan accordingly.

• Actionable takeaways

  • If a bug arises, isolate it in a separate worktree and reason about root cause with a small, focused prompt.
  • Document reasoning with simple pseudo-code comments to surface assumptions.

UI/UX, data, and design considerations for a living product

• Balancing features vs. simplicity

  • Progressive disclosure: show only essential features first; reveal more as users engage.

• Data-driven UX

  • Profiles, activity, and posts should reinforce engagement without overwhelming users.

• Using prompts to critique design

  • Use design prompts (e.g., "Steve Jobs" style prompts) to surface UX issues, then refine iteratively.

• Actionable takeaways

  • Start with essential UI paths; layer in additional features as the product gains momentum.
  • Use context-driven prompts to challenge design decisions and catch UX problems early.

Final reflections and practical stance

• The big takeaway: ship with a disciplined planning process, use context engines to inform you, and execute in well-scoped phases with isolated work contexts.

• Tools are supportive, not a substitute for thinking. Build with your own domain understanding and verify AI outputs against reality.

• Actionable takeaways

  • Add a planning-and-research checklist to every new project.
  • Embrace phase-based execution and guard against excessive tool hopping.

Links

• Terraform
• Augment
• Claude (Anthropic)
• Playwright (UX tests)
• Claude Code
• Git worktree docs

If you found these approaches useful, tell me what phase you’re starting with in your next project and what data model you plan to sketch first.