Season 1 · Episode 8

Build the Tool You Wish Existed

Ground School Trainer and NeuroSpicy.

This lesson drops soon — check back here for the video and voiceover.

The most satisfying AI collaboration is the one that ships software. This lesson turns an itch into a deployed app through two real builds: the Ground School Trainer, a study tool with per-user progress, and NeuroSpicy, a self-reflection assessment. The method: start with the user and the moment of use, define the smallest complete loop, write testable acceptance criteria, and make every stage observable.

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The lesson, step by step

Start with the user and the moment of use

Not the framework. Who opens this, in what moment, trying to do what? Ground School: a student practicing before a checkride. NeuroSpicy: someone answering questions to understand themselves. Everything downstream serves that moment.

Define the smallest complete loop

Ground School: sign in, practice, feedback, progress. NeuroSpicy: answer, understand, reflect, revisit. If the loop isn’t complete, it isn’t shippable — it’s a demo with a dead end.

Write acceptance criteria you can actually test

“Saved progress belongs to the right user. A question scores correctly. A result reads well on mobile.” Each criterion is a test waiting to happen — and the lesson runs them.

Decide what’s sensitive before you collect it

Name what data is sensitive and what should never become public analytics. Privacy is a design input, not a post-launch apology.

Scaffold, implement, test, deploy, verify — observably

Each stage produces something you can look at: the scaffold renders, the tests pass or fail visibly, the deploy lands at a URL you open yourself. Nothing advances on “trust me.”

Add analytics only after defining the behavior worth measuring

Metrics follow the loop: what does a successful practice session look like? Instrument that — not a dashboard of vanity counts.

Keep a correction loop

Reproduce the issue, change the smallest surface, rerun the tests, verify the live page. The same surgical discipline as the music lesson, applied to code.

Know what “shipped” means

Public URL, correct auth, real data behavior, mobile check, analytics, and a rollback path. If any one is missing, it isn’t shipped — it’s staged.

Worked example

Acceptance criterion: saved progress belongs to the right user. The test: sign in as User A, complete three practice sets, sign out, sign in as User B — B sees a clean slate, A’s progress intact. Then the adversarial version: A’s session token in B’s browser must not leak A’s data. It passes, on the live URL, on a phone. That’s what “correct auth” means — not a checkbox, a demonstration.

Your takeaway

• Start from the user’s moment of use, not the tech stack.
• Ship the smallest complete loop — every dead end is a broken promise.
• Write testable acceptance criteria and run them on the live URL.
• Decide data sensitivity up front; instrument only meaningful behavior.
• “Shipped” = public URL + correct auth + real data + mobile check + analytics + rollback path.

The BRAVE lens

Every episode runs through the BRAVE method: brief the outcome, reveal relevant context, authorize the next action, verify the evidence, and evolve the system.