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40 articles tagged Engineering Leadership.
Superhuman CTO Loic Goussier discusses the architectural and organizational challenges of integrating LLMs into a product defined by sub-100ms latency. Key strategies include a 'smart-to-cheap' model optimization pipeline, dimension-based evaluation frameworks, and maintaining human-in-the-loop controls to mitigate agent risk.
Former Uber CTO Tuan Pam shares insights on navigating hyper-growth, managing complex system rewrites, and the accidental evolution of thousands of microservices. He discusses the critical role of engineering culture, reputation-based career progression, and the program vs. platform organizational structure. The analysis extends to current trends, highlighting how AI agents and swarm coding are reshaping developer productivity while core engineering traits remain constant.
James Everingham of guild.ai shares how Meta’s DevInfra team shifted from AI autocomplete to agentic infrastructure. Learn why centralized control planes are essential for governing agent workflows, reducing onboarding time, and eliminating code freezes through organic, challenge-driven adoption.
Coinbase's Senior Director of Engineering details the strategic framework for driving AI adoption across 1,000+ engineers. Learn how to shift from skepticism to velocity using hands-on leadership, speed-run events, and automated feedback loops.
Executives must stop treating engineering as a cost center. This analysis clarifies the critical distinction between product ownership and engineering execution, offering strategies to eliminate middleman inefficiencies and restore quality accountability.
Grady Booch argues AI is a new abstraction layer, not a replacement for human creativity. This analysis explores the strategic implications of the 'Third Golden Age' of software, emphasizing human accountability and the risks of de-skilling in AI-assisted development.
DORA research and industry experts analyze how GenAI acts as an amplifier for software delivery. This brief covers the shift from code writing to context engineering, the strategic value of specs, and actionable steps for leaders to manage AI-driven throughput and risk.
