Anthropic’s Claude skills feature lets you define file pointers to tool descriptions and load them only when needed, cutting token usage by over 98%.

Build a microservices platform that dynamically spins up services to resolve pointer-based context references for LLMs, enabling scalable on-demand co...Show more

Without MCP-enabled context loading, multi-step AI tasks become impractical and the model is effectively worthless for complex workflows.

Use pointers to external tool documentation instead of embedding full tool dictionaries in the prompt to drastically reduce token usage.

"Tool definitions occupy context window space, increasing response time and costs.",

A real-world MCP server listing 52 tools consumed about 32,000 tokens just to describe them, illustrating severe context waste.

Loading full tool definitions into the context can consume tens of thousands of tokens and cause diminishing returns in multicall protocols.

Cloudflare’s code mode provides a similar approach by having the model generate code against an API rather than loading static tool metadata into the ...Show more

Anthropic’s new code execution mode lets an LLM inspect available tools and emit code to call them dynamically, avoiding the heavy token cost of full ...Show more

Instead of bulk-loading all tool definitions via MCP, generate and execute code snippets on the fly to reduce context overhead and improve invocation ...Show more

Use DGX OS blueprints as a model for building a distributed AI operating system supporting edge and cloud to simplify deployment pipelines.

Standard Windows Remote Desktop Protocol struggled when connecting to DGX for blueprint deployment, indicating a need for improved remote tooling.

DGX OS provides a distributed AI operating system for running workloads on DGX hardware or in DGX Cloud with seamless portability.

Prebuilt AI deployment templates enabling one-click setup on DGX, cloud, or edge with blueprints for world models, analytics, and custom pipelines.

ChatGPT 5.1 could blow fine-tuned experimental models out of the water, calling into question the value of incremental model tweaks.

Nvidia’s Omniverse world models aren’t simple or easy enough for experimentation to drive widespread adoption, making them less appealing.

Cameron struggled to get Nvidia’s sample blueprints like PDF-to-podcast working on DGX without extensive finagling and still doubts it’s operational.

Nvidia’s 100 million-splat world-scale digital twin demo suggests opportunities to build applications around city-scale simulations and urban analytic...Show more

There’s high appeal in dynamically moving AI workloads between local clusters and cloud for flexible, low-complexity operations.

Use Nvidia Omniverse Blueprints for one-click AI pipeline deployment on DGX or cloud to lower complexity of running advanced models.