Mike Krieger, a former CPO at Anthropic, details his transition to an IC role to build directly with AI, advocating for "unreasonable" asks like porting entire codebases. He shares lessons from Instagram on scaling and highlights Anthropic's internal use of Claude as a proactive teammate, flexible lab structure, and critical insights on AI product design, vertical applications, and mental health in the fast-paced AI industry.
Dan Feng outlines Maven Clinic's transformation into an AI-native company, driven by the realization that "building is cheap and arguing is expensive" in the AI era. This shift has reshaped planning to short, iterative sprints, revolutionized software development with AI coding tools, forced adaptive changes in code review processes, and necessitated a nuanced, multi-layered approach to ensuring reliability in generative AI systems.
Ankit Jain argues that traditional line-by-line code review is defunct, with AI reviewing code that humans no longer read. He asserts that code review's vital, often overlooked, purpose is 'alignment'—knowledge sharing, mentorship, and architectural feedback—which current AI-driven workflows fail to capture. His proposal involves capturing user-AI interaction prompts as acceptance criteria, generating test plans with an 'AI Slop Registry' (codified recurring review comments), and verifying changes against live previews. The new review surface becomes the 'intent and evidence' rather than the code diff, preserving collaboration and ensuring semantic accuracy.
Ali Haghani, co-founder of CircleBack, shares his unique hardware setup and delves into how his AI notetaker streamlines business operations, from interview tracking to customer support. He discusses the shift from manual coding to AI-orchestrated development, the nuances of prompt engineering, and his vision for why recording meetings with AI is becoming essential for leveraging LLMs and agents effectively in enterprises, ultimately reshaping the future of software engineering.
Justin Smith from Resolve AI discusses how AI agents address the increasing operational burden on engineers, highlighting that 70% of an engineer's time is spent running code. He introduces Resolve AI's background agents, which autonomously monitor deployments, perform health checks, generate reports, and answer engineering questions by leveraging deep production context and a self-learning knowledge system, effectively reducing the "on-call tax" and managing system complexity.
Denys Linkov details Wisedocs' journey of refactoring a complex, distributed ML pipeline into a monorepo, benchmarking the process against evolving AI coding tools. He honestly audits whether the six-month effort was justified, or if waiting for more advanced AI would have been better, ultimately concluding that the significant social and technical benefits made the refactor a successful strategic move despite current LLM limitations.