How Claude Code’s lead designer builds with AI
During Dive Club Live in NYC we got to hear from Claude Code’s lead designer, Meaghan Choi. She shared a demo of how the team at Anthropic uses Claude Code and there are a lot of practical takeaways.
During Dive Club Live in NYC we got to hear from Claude Code’s lead designer, Meaghan Choi. She shared a demo of how the team at Anthropic uses Claude Code and there are a lot of practical takeaways.
For the past two years, the technology industry has raced to make AI agents more capable — teaching them to write code, navigate software interfaces, manage files, and orchestrate multi-step workflows with increasing autonomy. What the industry has not done, at least not with any consistency, is answer the question that keeps chief information security officers awake at night: what happens when an agent goes wrong?
On Tuesday at its annual Build developer conference, Microsoft offered what may become the definitive answer. The company introduced Microsoft Execution Containers, or MXC — a policy-driven execution layer, built into the Windows operating system itself, that lets developers and IT administrators declare exactly what an AI agent can and cannot access, with those boundaries enforced at runtime by the OS kernel.
While many AI open source model providers are pursuing larger and more powerful models, Google is still giving attention to the smaller, more local side of the market. Today, the tech giant released Gemma 4 12B, an 11.95-billion-parameter open-weights model with permissive Apache 2.0 license optimized to execute locally on a standard enterprise laptop using just 16GB of VRAM or unified memory.
That means those enterprise users looking to keep working with AI while on a flight without WiFi, or trying to keep it offline for security reasons, can now do so far more easily and at far less cost (free to download and operate).
Perplexity AI unveiled what it calls the first hybrid local-server inference orchestrator at Computex 2026 on Monday night, demonstrating software that autonomously decides — in real time and mid-task — which AI workloads stay on a user's device and which get routed to frontier models in the cloud.
In this video, I fully test MiniMax M3, the new open-weight frontier model from MiniMax that combines coding, agentic reasoning, multimodal understanding, and long-context capabilities into one model. M3 supports up to a 1 million token context window, is natively multimodal from day one, and delivers some seriously impressive benchmark results across SWE-Bench Pro, BrowseComp, SVG-Bench, KernelBench Hard, OSWorld Verified, and more.
What makes this release even more insane is the pricing. MiniMax M3 is not only competing with models like Opus 4.7 and GPT-5.5, but in several benchmarks it actually beats them while being dramatically cheaper. MiniMax is also offering huge token plans, aggressive API pricing, and open-weight access, making this one of the most accessible frontier-level models available right now.
In this video, we look at running local AI work jobs for LLMs, images and video models, but running it on an AMD GPUs and processors.
Anthropic today released Claude Opus 4.8, an upgrade to its flagship model that ships at the same price as its predecessor, alongside a dramatically cheaper "fast mode" tier and a new feature that lets the model spawn hundreds of parallel subagents for codebase-scale work.