Software Engineering

Parallelizing multiple engineering workstreams

He runs multiple Claude Code sessions and git worktrees so AI can push several tasks forward simultaneously, especially lower-guidance tasks.

Why the human is still essential here

He chooses which tasks can be parallelized, sets up the environments, provides supervision where needed, and prioritizes the work across streams.

How people use this

Parallel frontend and backend branches

AI works in separate worktrees on independent frontend and backend tasks at the same time so progress does not block on a single thread.

Claude Code / git worktree

Concurrent low-risk bug fixes

Multiple AI sessions each handle separate cleanup tasks or minor bug fixes in parallel while the engineer supervises outcomes.

Claude Code / Cursor

Follow-up task batching

After the main feature is planned, AI tackles adjacent work like tests, docs, and polish items across separate branches simultaneously.

Claude Code / GitHub Copilot

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Community stories (1)

Medium

How I Actually Use AI to Ship Production Software in 2026

The discourse around AI software development is stuck between two extremes: people shipping toy apps in a weekend, and people insisting these tools are useless in real codebases. My experience has been neither.

AI doesn’t replace software engineering judgment. It amplifies it best when you use a disciplined workflow built around context, planning, staged execution, and strong verification.

I’m a Staff Software Engineer with 23 years of experience, and this is the workflow I use to ship production software with AI in 2026. I will walk you through how I am leveraging AI tooling to deliver new features into production software products.

Kris WongStaff Software Engineer

Mar 23, 2026