Llms

Building uReview, Uber’s Multi-Agent Code Review Engine — Will Bond & Ameya Ketkar, Uber

Building uReview, Uber’s Multi-Agent Code Review Engine — Will Bond & Ameya Ketkar, Uber

Uber's Will Bond and Ameya Ketkar present uReview, an in-house automated code review system developed to combat rising review times (3 to 9 hours for first review). They detail why Uber built it over buying—due to Phabricator, agentic SDLC integration, and distributed ownership—and its architecture, including comment deduplication. The presentation highlights their iterative approach using advanced observability (sentiment, addressal rate, agent trajectory) to tune model performance, acknowledging that "the model never knows that it is wrong." They discuss empowering hundreds of teams with custom review agents and skills, sharing impressive results: 25,000 comments weekly, 67% addressal rate, and 60% cost reduction. Finally, they explore the evolving role of human engineers in an agentic SDLC, predicting an "expanded outer loop" focused on architecture and domain expertise rather than direct code review.

The Death of Developer Advocates — Stephanie Jarmak, Sourcegraph

The Death of Developer Advocates — Stephanie Jarmak, Sourcegraph

Stephanie Jarmak argues that Developer Relations (DevRel) isn't dead, but its audience has shifted to include AI agents. She introduces the "Agent Advocate" role, focusing on measuring agent experience (e.g., through CodeScaleBench), optimizing for "Generative Engine Optimization" (GEO), and adapting traditional DevRel principles like enablement and community for this new user. The core message is that designing for agents, like a curb cut, ultimately improves the experience for all human developers.

Parallel’s Parag Agrawal: Building a New Web for AI Agents

Parallel’s Parag Agrawal: Building a New Web for AI Agents

Parag Agrawal, CEO of Parallel Web Systems, outlines his vision for an agent-centric web where AI agents query the internet 1000x more than humans. He details how Parallel is reinventing search infrastructure, dismissing human click data as a "bug," and tackling the economic crisis of the ad-supported internet with a novel monetization model based on Shapley values to pay content creators.

Supabase: Cash Does Not Equal Success

Supabase: Cash Does Not Equal Success

Paul Copplestone, CEO of Supabase, discusses the company's rapid rise as a leading dev tool. He explains their strategic bet on open-source Postgres, the pivotal "Open Source Firebase" rebrand, and how three distinct "chapters" of AI — from vector databases and code generation to advanced AI agents — have dramatically accelerated Supabase's growth and forced a re-evaluation of developer experience and internal operations.

Every Exponential Ends — Silicon Valley Forgot — Adam Becker

Every Exponential Ends — Silicon Valley Forgot — Adam Becker

Adam Becker, author of *More Everything Forever*, challenges Silicon Valley's pervasive techno-utopian narratives, including the 2045 singularity, mind uploading, Mars colonization, and the AI apocalypse. Drawing on his physics background, Becker deconstructs the scientific flaws in these ideas, arguing that exponential growth always ends, space is inherently hostile, and current AI models are sophisticated "pocket calculators for language" rather than pathways to sentient AGI. He contends that these influential, evidence-free concepts are bankrolled by tech billionaires seeking a narrative of perpetual growth and serve to distract from pressing social and political problems. Becker advocates for serious engagement with social sciences, aggressive tech regulation, and wealth redistribution.

Why Most AI Agents Fail Horribly

Why Most AI Agents Fail Horribly

Maarten Grootendorst discusses the foundational understanding developers need for modern AI tools, emphasizing core LLM concepts like tokens, embeddings, and attention. He provides a pragmatic view on AI agents, distinguishing hype from practical applications like coding assistants, and explores the role of memory, guardrails, and the growing importance of open-weight models for control and efficiency in AI infrastructure.