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Erik Peterson discusses why AI spend is eclipsing cloud bills, triggering a major software engineering cost crisis. He explains tokenmaxxing, unit economics, forced developer promotions, and why fully autonomous AI will write, deploy, and govern code by 2028. By Erik Peterson
Prasanna Vijayanathan and Renzo Sanchez-Silva share how Netflix tackles observability across 38M events/sec. They discuss replacing reactive monitoring with an AI-driven operational ontology and agentic workflows using Claude and graph databases.
Susan Chang explains how Elastic transitioned from siloed, ad-hoc AI agent evaluations to a unified, production-grade framework. She discusses balancing LLM-as-a-judge with deterministic rules, bridging Python data science evals with TypeScript production code, and implementing deep tracing to…
Swaroop Chitlur and Sidd Kodwani share DoorDash’s journey building an internal GenAI platform. They discuss core architectural bets, transitioning from vendor-first setups to open-weights models, navigating LLM and agent gateways, and balancing accuracy, latency, and cost for over 5,000 internal…
The panelists discuss how production operations are evolving with AI, turning operational data into actionable insights for incident response. They explain how automation and new architectural practices help engineering teams build more understandable systems, while exploring how AI reshapes…
Patrick Debois discusses how to manage, evaluate, distribute, and observe context using proven software engineering practices. He shares how treating context like code - complete with testing, CI/CD, package managers, and security scanning - enables engineering leaders to reliably scale AI coding…
Mallika Rao explains that the true complexity of adaptive recommendation systems lies outside model architecture. She discusses how real-time feedback loops, retrieval freshness, multi-stage orchestration, and end-to-end latency budgeting enable systems to continuously learn and evolve in…
Balakrishnan Ramdoss discusses how to architect production-grade, AI-powered conversational apps at scale. He explains how to overcome model latency, leverage server-driven UI and Backend-for-Frontend patterns to dynamically render multi-modal interfaces, optimize prompts for UI selection, and…
Interview editing is rarely slow because cutting a clip is difficult. It is slow because finding the right material takes time. Here is a workflow that gets you to the decisions faster without taking them away from you.
Faster interview editing doesn't have to mean less creative control. Automate the search. Keep the judgment with the editor.
A long interview transcript can look like a collection of unrelated conversations. The structure is often already there — it's just buried inside the material.
When editors think about transcripts, they often think about search. Analyzed as editorial material, they can reveal much more than a word to jump to.
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