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AI ROI in engineering depends on clean code, smart usage, and outcome metrics—avoid mistaking activity spikes for true gains.
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- Published on
Learn how autonomous AI coding agents can make all technical decisions, verify their work, and orchestrate tasks without burdening the user.- Published on
Static coding tests mislead; dynamic, realistic, and human-centric evals reveal true AI coding capability- Published on
Learn how intentional context compaction keeps AI agents productive in large, messy codebases without degrading quality- Published on
Lessons from autonomous driving reveal how to make AI agents robust: focus on infrastructure, feedback loops, and simulation over model selection.