Run agents in parallel

Once the basics are solid, you can stop working one task at a time. Run several agents at once, each on its own feature, each on its own branch so they do not collide. It is the closest thing to having a team while staying a team of one, and it multiplies what a single person ships in a day. This is the frontier of vibe coding, and it is real: the Y Combinator batches now full of near-fully-AI-generated codebases are not solo founders typing prompts one at a time, they are people orchestrating several agents.

A clean pattern for organising them is one orchestrating agent over a set of workers: the orchestrator holds the bigger picture and directs, the workers each grind through a specific piece. It keeps the parallelism from turning into chaos, because something is still holding the overall plan while the pieces get built in parallel underneath it. Where worker agents can reuse the context that is already loaded, you also get the parallelism cheaply rather than paying full freight for each one.

Match the model to the task instead of using your most powerful one for everything. Use a fast, cheap model for the simple, mechanical work, and save the high-reasoning model for architecture and the error-prone parts where getting it wrong is expensive. Think of your models as team members with different strengths, and assign the work accordingly. The cheap model is not a downgrade, it is the right tool for the simple job, and reserving the expensive one for the hard problems is just good allocation, the same logic as the lean AI growth stack cost argument on the marketing side.

The discipline that makes parallel work safe is separation: different branches, clear ownership of who is touching what, so two agents never fight over the same file. Get that right and parallelism is pure upside. Get it wrong and you spend the time you saved untangling a merge conflict three agents created at once.