Field
notes.
Some thoughts are best understood in the context of a real project. These are the ideas I return to when building.
18 essays ↓How I build AI-native products: SHIP + OPERATE
A practical operating model for moving from idea to trusted launch.
Sketch
Decide whether the idea deserves a build.
Helm
Set enough direction that the work can be built without drifting from the product intent.
Iterate
Build the fastest proof that the core value can be felt.
Perfect
Turn a validated path into a launch-ready product.
Operate
Keep the product learning after launch.
AI collaboration model
How I use AI collaborators while keeping judgment human-owned.
Working backwards
Start from the customer promise before the internal solution.
Four product risks plus trust
A case-study lens for value, usability, feasibility, viability, and trust.
Jobs to be Done
Name the moment where a user hires the product to make progress.
Strategy choice cascade
Clarify aspiration, where to play, how to win, capabilities, and management systems.
Evidence-guided work
Use evidence without pretending every early signal is product-market fit.
Product Ops three pillars
Business/data insight, customer/market insight, and process/practices.
AI product-team translation
How this work maps to AI product environments without becoming a job application.
AI is leverage, not authorship
How I use AI collaborators while keeping human judgment explicit.
Trust is a product surface
Payments, privacy, compliance, access, and residual risk as product work.
The artifact ladder
When the right artifact is a doc, prototype, PR, eval-like check, or operating loop.
From scaled product systems to independent building
What scaled product systems taught me about independent product development.