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Developer judgment for AI-assisted engineering

Trust Forward

What would you do in my shoes?

What to trust, what to verify, what to delegate, what to promise, and when to take the wheel back. Authored professional situations that require you to commit your judgment before you see what Ben actually did.

Continue to Trust Forward →Start Trust Forward Lite →

Finishing Lite earns you a coupon toward Trust Forward on Studio.

The YY Method™ sequence

  1. Capture
  2. Why
  3. Why-Not
  4. Commit
  5. Timestamp

You commit first. Ben's historical judgment comes afterward.

40+
real cases from lived professional experience
42
canonical case families
3
passes per underlying problem
90
days, AI-assisted curriculum

Two doors

Trust Forward Lite helps you hear your own signal. Trust Forward tests whether it survives.

Trust Forward LiteDeterministic

The deterministic introduction.

It gives you authored professional situations, asks what you would do in Ben's shoes, and requires you to commit your judgment before you see what Ben actually did. It preserves an auditable record of your decisions.

  • 5 fixed cases
  • About 30–45 minutes
  • Stays in your browser

It is deliberately bounded:

  • No AI interprets your free text.
  • No personality score is produced.
  • No AI gets to decide what you believe.

Free · earns a coupon

Finishing Lite earns you a coupon toward Trust Forward on Studio.

It costs nothing, it stays in your browser, and it is the cheapest way to find out whether the full curriculum is worth your ninety days.

Start Trust Forward Lite →

Trust Forward90 days

The 90-day curriculum.

The deeper 90-day AI-assisted judgment curriculum, built from 42 canonical case families grounded in Ben Chan's lived professional experience. You encounter each case across multiple rounds.

  1. Pass 1Draw From the Well

    Make an independent call before seeing Ben's historical decision and outcome.

  2. Pass 2Study the Map

    Revisit the same underlying problem after a meaningful condition, incentive, perspective, or role changes.

  3. Pass 3Build Your Compass

    Transfer the pattern into a sufficiently different situation where simply copying Ben — or your own earlier answer — can fail.

Selected cases also include STORM recovery exercises, source inspection, perspective inversions, and spaced retrieval of your own earlier judgments.

Continue to Trust Forward →

The rule

Every consequential decision follows the YY Method™.

  1. Capture
  2. Why
  3. Why-Not
  4. Commit
  5. Timestamp

You commit first. Ben's historical judgment comes afterward. Ben's current judgment may disagree with his past judgment. The AI Coach can retrieve, compare, challenge, and pressure-test, but it does not make your decision for you.

The goal is not to teach you to copy Ben. It is to help you accumulate enough real judgment evidence, corrections, disagreements, verification rules, delegation boundaries, and recovery principles that you build a Developer Judgment Playbook of your own.

40+ real cases drawn from Ben Chan’s actual professional experience.

Cases may be anonymized or composited where necessary to protect clients, employers, colleagues, confidential information, or identifying details while preserving the underlying decision pressure.

Developer judgment, answered

What to trust, what to verify, what to delegate.

  • Can you trust AI-generated code?

    AI-generated code can be trusted only to the extent that the parts that matter have been verified. The required evidence should scale with the consequence of being wrong, and the developer should understand enough of the implementation to know what still needs checking.

  • How do you verify AI-generated code?

    Start with the actual promise the code has to keep, then test the consequential paths against that promise. Review assumptions, inspect the parts that carry meaningful risk, and use deterministic evidence such as tests and observed behavior rather than treating a plausible implementation or an AI claim of completion as proof.

  • How much work can AI safely own?

    AI can carry a large share of execution. Accountability still needs an explicit human owner. More work can be delegated when scope is clear, consequences are bounded, verification is available, and someone remains responsible for deciding what evidence is enough before the work ships.

  • Is my engineering team ready for AI?

    AI readiness is less about access to AI tools than the judgment surrounding their use. A ready team can clarify ambiguous work, decide what to delegate, verify according to consequence, surface uncertainty, keep ownership visible, and recognize when generated work exceeds the team's ability to judge responsibly.

  • AI readiness assessment

    An AI readiness assessment should examine how a team handles scope, verification, promises, risk, delegation, and accountability as AI carries more execution. The goal is to expose where AI creates real leverage and where faster implementation could outrun the judgment needed to stand behind the result.

  • AI fluency and judgment

    AI fluency is more than knowing how to prompt or generate code. It includes knowing what to ask AI to do, what should remain human, how to test what comes back, when to challenge a plausible answer, and what responsibility still belongs to the person or team using the tool.

Commit your judgment first.

Five cases. About 30–45 minutes. Everything stays in your browser.

Start Trust Forward Lite →Continue to Trust Forward →

Finishing Lite earns you a coupon toward Trust Forward on Studio. Ask for your coupon

Finish Lite, then send a note. There is no account and nothing to sign up for.

You're not talking to AI anywhere on this site. No chatbot, no coach, no generated answers. AI executes inside boundaries; human judgment sets them.