Can we trust AI to debug code without making things up?

AI coding tools are getting better at writing code. But when a bug appears, how do we know whether the AI’s explanation is actually correct?

A debugging assistant can sound convincing and still point developers in the wrong direction. That’s why I’m interested in a different approach: evidence-first debugging.

With Meeba Brain, we’re working on helping developers:

• Investigate bugs using actual code and repository evidence.

• Understand relationships between files, functions, and components through a code knowledge graph.

• Separate verified facts from inferences and unknowns.

• Explore potential fixes without pretending every hypothesis is a confirmed diagnosis.

Our principle is simple: if the AI cannot verify a claim, it should say so rather than guess.

We’re early in this journey, and I’d love to hear from developers here:

What frustrates you most about AI-assisted debugging today?

1. Hallucinated explanations

2. Missing context across a large codebase

3. Fixes that introduce new bugs

4. Difficulty tracing the root cause

I’m building Meeba Brain and would genuinely value your experiences and criticism.

If you’re curious, you can explore it here: https://meeba.ai/brain

Looking forward to the discussion!