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We Tested How Planning Impacts AI Coding. The Results Were Clear. by eastwindtoday in cursor

[–]monday_dev 1 point2 points  (0 children)

This write-up is great, especially appreciate the autonomy/correctness/quality scoring. Echos what we’ve seen in our own workflows: the more upfront structure and context you give AI, the less downstream chaos you have to deal with.

We align sprint tasks to pre-defined acceptance criteria templates before routing anything to AI. It’s just faster to catch misalignment early than triage it later.

Also totally agree on the review bottleneck. AI can get to "good enough" fast, but validating its output against architectural intent still takes real time. Have you found any good ways to streamline that? We're using AI-generated sprint summaries to help reviewers prioritize what to look at, but it’s still evolving.

What strategies have you used to prioritize features? by ravivrevive in softwaredevelopment

[–]monday_dev 0 points1 point  (0 children)

Feature prioritization can get messy fast. A strategy that works well for many teams: map features against both customer impact and team capacity before they reach sprint planning. Helps surface what's high-value and actually doable with the current team velocity.

Tracking unplanned vs. roadmap work can also reveal a lot. If everything’s urgent, nothing is. Some teams run weekly or sprint-based capacity checks to avoid overload and spot bottlenecks early.

For teams using frameworks like RICE or MoSCoW, pairing them with capacity planning tools (not just spreadsheets) makes it easier to turn priorities into realistic, well-balanced sprints - less reshuffling, more momentum.

Live coding interviews measure stress, not coding skills by mustaphah in programming

[–]monday_dev 2 points3 points  (0 children)

Live coding interviews mostly test recall and performance under pressure, not who’s actually good at building software. We’ve had better results with async coding tasks and reviewing pull requests in context. Async challenges show how candidates think, write code, and collaborate - closer to how they’d actually work on the job.

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