AI is making developers faster. No debate there.
But speed is only one side of the story.
A lot of AI-generated code looks correct at first glance. It passes the “seems fine” test. Then later, teams run into logic gaps, requirement mismatches, missed edge cases, or code that works technically but doesn’t fully solve what the ticket asked for.
That’s the part we’ve been thinking about deeply.
We have built Sniffr.ai around a simple belief:
The next challenge in engineering is not just writing code faster. *It’s shipping with more confidence.
*
A few questions I think teams should be asking more often:
Is the code correct, or just convincing?
Does the PR match the requirement, or only the implementation?
Are we reviewing code quality alone, or actual delivery intent?
As AI-generated output increases, how should code review evolve?
We’d love to hear from this community:
What is the biggest problem you’ve seen with AI-assisted coding so far? speed, trust, maintainability, requirement mismatch, or something else?
We're here to learn, share, and contribute more around this space.
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