🤖 Operating real hardware with Gemini AI
Learn how Gemini AI was used to build a 3D web controller, enabling real-time operation of a physical robot arm from 1,700 miles away by transmitting joint coordinates.
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How MasterClass Builds Multi-Agent AI That Learners Can Trust
MasterClass details its multi-agent AI system, NCX, and the advanced observability stack built on CoreWeave. Learn how tracing, MCP servers, and an AgentOps loop enable self-improvement in complex AI applications.
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From Context to Memory: Your Agents Need a Real Memory Layer — Anders Swanson, Oracle
Anders Swanson details how to build a real memory layer for AI agents, moving beyond context windows and scratch files. He outlines a system using multi-model databases, hybrid search, and governance controls for persistent, scalable recall.
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Three Questions Every Agent Engineer Should Be Able To Answer
To own your agent's intelligence, focus on three questions: where it fails, how to reproduce it, and how to fix it. This requires building robust 'worlds' and 'tasks' that mimic production.
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Designing CLIs for Agents, Not Humans — Pedro Lopez, Airbyte
Learn how Airbyte designed two interfaces—MCP and CLI—to make data actionable for AI agents. Discover best practices for building agent-friendly CLIs and understanding the trade-offs between MCP and CLI architectures.
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High-Performance Engineering: Forward Deployed at Aston Martin F1
CoreWeave details its AI partnership with Aston Martin F1, showcasing how advanced data platforms and ML models are used to process everything from live telemetry to noisy radio chatter, enabling critical decisions in a high-stakes environment.
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Building low-latency remote robotics with Gemini
Learn how to build low-latency remote robotics controls over 1,700 miles using Gemini and custom 3D web controllers. See how AI handles motor position scripting.
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Building on the Codex Harness — Dominik Kundel, OpenAI
Learn how to build sophisticated AI agents using the Codex App Server. This open-source JSON-RPC protocol provides deep control over the Codex harness, enabling custom tools and multi-threaded operations.
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Keep It Secret, Keep It Safe: How IBM Deploys Sensitive Data and Workloads on CoreWeave
IBM details its journey deploying sensitive AI workloads on CoreWeave, focusing on scaling H100/GB200 clusters and implementing 'federation first' identity controls to meet stringent security and compliance needs.
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Managed Deep Agents | Deploying mission critical agents to production
Managed Deep Agents simplifies deploying mission-critical AI agents. It bundles the Deep Agents harness with managed LangSmith infrastructure, allowing developers to define, test, and deploy agents using the mda CLI.
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Stop Prompting — Greg Pstrucha, Sentry
Stop re-prompting your coding agent. Instead, codify the rules you keep repeating into deterministic checks using linting. Learn how to keep APIs synced with the OpenAPI schema and manage qualitative code policies.
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Post-Training at the Frontier: How You.com, NVIDIA, and CoreWeave Improved Nemotron’s Agentic Search
The key goal was within this three-week sprint being able to have a measurable lift in performance and accuracy on this benchmark using the Neotron model postrained with the core weave dedicated inference platforms and .com web search team.
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If you thought the speed of writing code was your problem, you have bigger problems - Andrew Murphy
If you think the speed of writing code is your problem, you have bigger problems. The true bottleneck in software is rarely the keyboard; it's usually Discovery, Review, or Deployment.
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Optimize capacity, control cost, and scale economically
Learn how to optimize AI compute capacity, control costs, and scale economically using CoreWeave's advanced capacity plans, Spot Instances, and the Mission Control platform.
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OpenAI's always-on agents, 700+ math manuscripts & HackerRank's AI interviewer
The podcast analyzes advanced AI topics, covering persistent agents (Dots), the implications of 722 AI-generated math manuscripts, and the technical architecture of Mixture of Experts (MoE) models.
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AI Writes More PRs. Who Validates Them? — Ali-Reza Adl-Tabatabai, Sonar
AI is making PRs bigger and more numerous, turning CI/Code Review into a bottleneck. Discover agentic validation: an automated system that reviews, autofixes, and auto-merges PRs, keeping your development velocity high.
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Why 80% Reliability Isn't Good Enough — Felipe Blanes, Amazon AGI Lab
Why are static benchmarks failing? Felipe Blanes details the 'benchmark illusion' in AI agent development and introduces the 'eval flywheel'—a customer-driven process for building truly reliable products.
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How a Remote Company Builds AI Fluency — Em Shreve, Automattic
Automattic shares its strategy for building AI fluency in a fully remote company. They advocate for two-week immersive programs, focusing on real project work and advanced topics like autonomous agents and MCPs.
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Building with Gemma 4
Discover how Gemma 4 enables powerful, private AI builds that run entirely on-device. Learn about deploying open models for robotics, remote data analysis, and offline coding.
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Brains vs Hands: How to Run AI Agents Safely in Production — Viren Baraiya
Running AI agents in production requires separating the 'brain' (LLM planning) from the 'hands' (deterministic harness). This approach treats agent harnesses as late-bound sagas, ensuring reliability and durability for complex, long-running workflows.
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