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AI coding agents make it easier to produce code, but they also create more work that is difficult to see: reviewing generated changes, correcting assumptions, debugging unexpected behavior, and deciding what should ship.
Meridian is built to capture that hidden work locally and turn it into reviewable updates when you choose.
Which part of working with AI coding tools is hardest to reflect in your tickets, commits, or standups: prompting, review, debugging, or the product decisions behind the changes?
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Debugging is the hardest to reflect. The final fix may look small, but the failed approaches, incorrect assumptions, and investigation behind it rarely make it into a commit or ticket. How are others capturing that work without adding more reporting overhead?
@adityaharish2002 I usually keep a short investigation trail with the failed approaches and why they failed, then link it to the final ticket or PR. The challenge is capturing that context automatically without interrupting the debugging flow.
One thing that helps is keeping an AI decision log. For every major AI-generated change, what the AI suggested, what I rejected, why I changed it, and what I learned. It takes less than 2 minutes but makes code reviews and retrospectives much more meaningful. And when it comes to Prompt iterations, they are also invisible. Sometimes the final prompt looks simple, but it took 15 failed attempts to get there. That experimentation is valuable engineering work that rarely gets documented.
Maybe IDEs should automatically record the engineering journey—prompts, diffs, test runs, and debugging checkpoints—and generate a concise summary instead of expecting developers to remember everything at the end.
@adarsh_nayak_sr The decision log is a practical approach, especially for prompt iterations that disappear once the final result works. Automatic capture of prompts, diffs, test runs, and debugging checkpoints could preserve that context without turning it into another reporting task.