How Context Engines Help Coding Agents Merge Code
Peter Werry explains that coding agents produce more mergeable work when a context engine supplies the architecture, decisions, conversations and team knowledge that each task actually requires.
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Why LLMs Struggle With Multi-GPU Kernels
Simran Arora shows that frontier LLMs can produce some fast multi-GPU kernels, but still struggle to reason reliably about communication, scheduling and hardware tradeoffs.
What OpenAI's Rogue Agent Logs Reveal
Nick Saraev and Jack Roberts argue that OpenAI's rogue agent logs show why frontier evaluations need stronger containment, monitoring and limits on shared resources.
How Anthropic Labs Builds With AI Agents
Mike Krieger describes an AI-native building model that delegates outcomes rather than isolated tasks, tests ambitious ideas quickly and keeps human judgment focused on intent and tradeoffs.
How Agentic Commerce Works
Ahnaf Prio explains how MCP, A2A, ACP, UCP and AP2 combine to let AI shopping agents discover products, coordinate with merchants and complete controlled payments.
Smarter Routing for Agentic Inference
Yuchen Fama and Ashish Kamra show how cache-aware routing and separate prefill and decode workers can reduce latency for long, volatile multi-turn agent workloads.
How to Build a $5,000 Local AI System
Ahmad M. Osman explains how a roughly $5,000 local AI budget should be allocated around model fit, memory bandwidth, software support and a practical upgrade path rather than headline GPU specifications alone.
How Gemini Robotics 2 Coordinates Multiple Robots
Gemini Robotics 2 demonstrates multiple robots coordinating object handling and storage tasks so that different machines can contribute to one shared physical workflow.
How Gemini Robotics 2 Coordinates Whole-Body Tasks
Gemini Robotics 2 aims to make robots more general by combining coordinated whole-body control, dexterous object manipulation and independent reasoning between multiple robots working on one task.
What Qwen 3.8 Flash Next Reveals About Qwen 4
Bijan Bowen finds that Qwen 3.8 Flash Next previews promising Qwen 4 vision and coding capability, but spatial errors, fragile game behavior and a local memory crash show that the architecture still needs refinement.
Why AI Labs Are Betting on Compute Markets
Evan Conrad argues that AI labs are making capital-intensive bets on GPU capacity, while transferable compute contracts can reduce residual-value risk and turn fixed infrastructure commitments into a more liquid market.
How Automated AI Labs Accelerate Research
Jerry Tworek describes an automation-first AI lab where humans still choose research directions while agents execute experiments, gather evidence and compress iteration cycles from months toward days.
How WebMCP Makes Websites Agent-Ready
Vince Canger explains that WebMCP lets websites expose concise, structured actions to a user's own AI agent, reducing brittle page navigation while retaining browser authentication and visible confirmation.
Why Developer Advocacy Must Include AI Agents
Stephanie Jarmak argues that developer advocacy must expand to AI agents because they increasingly use, evaluate and recommend developer tools, while human communities remain essential for trust and feedback.
How AI Agents Scale Go-To-Market Teams
Justin Joyce explains how Cloudflare combines curated skills, connected business data and self-service agents to scale go-to-market analysis and execution.
How Exa Built a Live Model for Go-To-Market
Jeffrey Wang argues that go-to-market is a data problem and explains how Exa gives agents a live model of customers, product use and company knowledge.
How to Make Open Source Libraries Visible to AI Agents
Christopher Burns explains how open-source projects can make current documentation easier for AI agents to find, retrieve and use without wasting context.
Why AI Needs to Know What It Does Not Know
Zoubin Ghahramani argues that reliable AI must represent and update uncertainty so it can make safer decisions, admit its limits and support people without false confidence.
How Ramp Turns Go-To-Market Intent Into Execution
Arman Vaziri explains how Ramp turns a described go-to-market motion into coordinated execution across data, agents, teams and channels.
Why More AI Agents Create More Human Work
Nate B Jones argues that AI agents shift people above the execution loop, creating new work in planning, supervision, verification and recovery as agent use expands.