Reflection AI will pay $150 million per month from July 2026 through 2029 for Nvidia GB300 chips and hardware at SpaceX’s Colossus 2 data center in Tennessee, in a contract worth up to $6.3 billion. The open-source-focused startup calls this its first major compute deal and one of the largest infrastructure commitments in the open AI space.

Reflection AI will pay $150 million monthly from July 2026 through 2029 for Nvidia GB300 chips and hardware at SpaceX’s Colossus 2 data center, in a contract worth up to $6.3 billion and cancellable after three months with 90 days’ notice. The startup says this deal underpins its open-weight AI strategy, positioning it against closed labs like Anthropic and OpenAI.

Micron and Anthropic struck a deal covering joint design of AI memory and storage architecture, a multi-year supply agreement, enterprise deployment of Claude, and Micron’s strategic Series H investment. They’ll test HBM, DRAM, and SSD subsystems across AI workloads to boost performance, efficiency, and token economics. Micron is already using Claude for coding and manufacturing tasks.

This edition covers Tesla’s trademark filing for “Megapod,” a turnkey AI data center rack including servers, networking, power, and cooling. It also delves into Apple’s challenge rebuilding its industrial design team after losing influence at the exec level. Finally, it explains how developers can use agent hooks to enforce guardrails and stop AI agents from breaking rules mid-work.

This issue covers Cloudflare’s new real-time WAF rules, Anthropic’s Claude Fable and Mythos 5 models, and HashiCorp Boundary’s agent-aware access controls. It also highlights Microsoft Foundry’s model management, geo-distributed AI training with k0smos, plus tools like MemPalace, whichllm, a Rust Git rewrite, Kubernetes Inference Extension, and Cilium’s CI/CD hardening.

Oracle projects up to $95 billion in capital spending for fiscal 2027 to expand its AI-focused cloud data centers, expecting to recoup $20–25 billion from customer repayments. It plans to raise nearly $40 billion through debt and equity, including a $20 billion at-the-market stock issuance, as it vies with Amazon and Microsoft.

This week’s list ranks the ten GitHub projects that gained the most stars, from agent memory tools like agentmemory to on-device TTS engines like supertonic. The trend shows a focus on persistent AI memory, context-efficient knowledge graphs, and local intelligence.

The S&P 500 rebounded from a 10% drop to a new high in just 11 sessions, marking the quickest V-shaped recovery on record. Big investors warn valuations look stretched, but higher cash supplies and record corporate profits may justify today’s lofty multiples. Meanwhile, semiconductors and AI infrastructure lead gains while software lags, and social media use has peaked globally except in North America.

The article argues that enterprises should measure AI infrastructure economics by cost per token rather than raw compute metrics like FLOPS per dollar. It shows how maximizing delivered tokens—through hardware, software and system optimizations—drives down real-world cost and boosts revenue, citing NVIDIA Blackwell’s 35× lower token cost versus Hopper.

The author argues that modular “Skills”—reusable markdown workflows loaded on demand—outperform standalone AI agents by cutting token bloat and maintenance overhead. A live GEO audit system built with Skills shows how you can turn domain expertise into scalable, service-ready products without managing dozens of agents.