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Efficient scaleups in 2024 vs 2021: Sourcegraph (with CEO & Co-founder Quinn Slack)

The Pragmatic Engineer

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AI Advancement Requires Ground Truth and Collaboration

Achieving near-complete AI capabilities in software development involves a dual approach: refining AI autonomy through a bottom-up methodology and establishing a reliable ground truth for its output verification. AI must not be tasked with self-reviewing code, as this may serve only as a temporary aid for novice developers. It's essential to comprehensively validate code through type checks, testing, and deployment oversight, ensuring minimal errors, and ultimately measuring the impact of code changes on revenue growth. Currently, elite software engineering teams struggle to reach this level of accountability, highlighting a significant gap in the industry that AI tools may inspire companies to bridge.

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