Pov Lyhoung

AI Practice

AI is in the loop, not in charge.

How AI actually sits inside my delivery cycle — from framing a problem to verifying what ships. Models draft; I own the architecture and the merge.

  1. 01 · Discovery

    Frame the problem before writing code

    Turn a vague operational request from the business into a written spec — user, flow, data, and edge cases. I use models as a thinking partner to pressure-test the shape of a feature before it costs a sprint.

    ToolsClaude MaxChatGPT ProGemini ProSuper Grok

  2. 02 · Design

    Model the data and the interface together

    Schema, API contract, and screen flow get designed in one pass so the client and the service agree from day one. AI accelerates the exploration of alternatives; the architecture call stays mine.

    ToolsClaude DesignFigmaGoogle StitchAdobe XD

  3. 03 · Build

    AI pair programming in the real repo

    Since the MDMS v1 sessions with GPT-4 in 2024, AI pair programming has been part of daily delivery — scaffolding, refactors, migrations, and test coverage across Flutter, .NET, and Next.js codebases, now on Claude Max and Codex Premium.

    ToolsClaude CodeCodex PremiumGitHub Copilot ProCursor

  4. 04 · Review

    Verify before it reaches production

    Model output is a draft, not a merge. Every AI-assisted change goes through the same review bar as hand-written code — read it, run it, and check it against live data before release.

    ToolsCode ReviewGitLabSentryManual QA

  5. 05 · Ship

    Release, watch, and iterate

    Ship behind a controlled rollout, watch the error surface, and fold what production teaches back into the next cycle. The Condo updater service exists because safe rollout is part of the product.

    ToolsDockerGitLab CIRelease ManagementSentry

Model output is a draft, not a merge.

Every AI-assisted change goes through the same review bar as hand-written code.

See my work