Have AI coding tools actually made you faster, or just faster at making mistakes?
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AI coding assistants have become part of my daily workflow, especially for repetitive tasks, documentation, and quick prototypes.
At the same time, I've found myself spending more time reviewing generated code than I expected, particularly around security, scalability, and edge cases.
For me, AI has become an accelerator, not a replacement for engineering judgment.
I'm curious how others are using these tools today.
Have they genuinely improved your development process, or do you find yourself rewriting most of what they generate?
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Over the past few months, I've been using AI assistants while designing and building SaaS, fintech, and crypto products.
They've been incredibly useful for generating boilerplate code, explaining unfamiliar libraries, writing documentation, and quickly prototyping ideas.
Where I still slow down is on architecture, authentication, payment flows, security, and performance optimization. Those are areas where I believe human experience and careful review remain essential.
One thing I've started doing is treating AI like a junior developer, it can move work forward quickly, but every suggestion still needs review, testing, and context before it reaches production.
For those building production software, where do you draw the line between trusting AI and relying on your own engineering judgment?
I'm interested to hear how your workflow has evolved.
@daniel_treasure I like the junior developer analogy, I've found the biggest productivity gain comes from letter AI handle implementation while I stay focused on architecture and business logic. The challenge isn't generating code anymore it's knowing what deserves a closer review.