[evara-ai]

The comment about having "3 to 6 hours per day" to work directly with code is the key insight here. I run a small AI consultancy and use Claude Code daily to deliver client projects — chatbots, automation pipelines, API integrations — and the spec-driven approach described in this post is what makes it actually work at scale.

The pattern I've converged on: spend the first 30 minutes writing detailed markdown specs (inputs, outputs, edge cases, integration points), then let Claude Code chew through the implementation while I review, test, and iterate. For a typical automation project — say a WhatsApp bot that handles booking flows and integrates with a client's CRM — this cuts delivery time roughly in half compared to writing everything manually.

The biggest practical lesson: the spec quality is everything. A vague spec produces code you'll spend more time debugging than you saved. A good spec with explicit error handling expectations, API response formats, and state transitions produces code that's 80-90% production-ready on the first pass.

Where I disagree slightly with the parallel agent approach: for client-facing work where correctness matters more than speed, I've found 2-3 focused agents (one on backend, one on frontend, one on tests) more reliable than 6-8 competing agents that create merge conflicts. The overhead of resolving conflicts and ensuring consistency across parallel outputs eats into the productivity gains fast.

[theshrike79]

I've recently started adding a PROJECT.md to all my own projects to keep the direction consistent.

Just something that tells the LLM (and me, as I tend to forget) what is the actual purpose of the project and what are the next features to be added.

In many cases the direction tends to get lost and the AI starts adding features like it's doing a multi-user SaaS or helfully adding things that aren't in the scope for the project because I have another project doing that already.