Implement a hybrid architecture combining cloud APIs for scaling and local models managed by tools like Azure Model Router for latency, control, and c...Show more

Use auto-routing between heavy and lightweight models—such as autocompletes mini-models or base cursor subscriptions—to balance API cost and performan...Show more

Apply a simple per-interaction and session-length calculation to estimate average token consumption, enabling better API usage planning and cost forec...Show more

Use back-of-the-envelope estimates of parameter activations (e.g., 3.2B in a 20B parameter model) to optimize model selection for efficiency and resou...Show more

Systematically test tool calling and multiparty computation (MCP) integrations in AI models to map out their capabilities and limitations before produ...Show more

Use a unified benchmark like the “artificial intelligence benchmark” as a blended metric to compare open source models on speed, cost, and accuracy ra...Show more

Enable desktop applications to download and locally store AI models, ensuring fast, offline inference and user control over model versions.

Implement on-device model downloads and local storage in mobile apps to support offline inference while minimizing download sizes and storage overhead...Show more

Use the operating system’s Activity Monitor to track CPU, memory, and GPU usage of AI services in real time for rapid performance troubleshooting.

A step-by-step approach to download, install, and configure desktop AI applications with background service integration for seamless local inference.

Adopt the structured OpenCode methodology outlined in the referenced blog post to integrate open-development frameworks and accelerate community-drive...Show more

Pivot your entire AI stack to agent-based architectures, enabling modular components to autonomously collaborate and streamline complex workflows.

Tom Spencer outlines a method for breaking down complex tasks in Claude into sub-agents focused on discrete functions like trend analysis and team bui...Show more

Cameron Rohn stresses thoughtful prompt design and introduces meta prompting as a way to structure multi-step AI interactions more effectively.

Tom Spencer demonstrates key AI development concepts through a concrete example, highlighting practical application over abstract theory.

Cameron Rohn recommends explicitly mapping inputs, outputs, and workflows to optimize AI task management and ensure clarity at each pipeline stage.

Use a SQL database to ingest and index all generated agent files for fast querying, retrieval, and version control.

Cap the number of active sub-agents at around five to prevent resource overuse and reduce system complexity during experiments.

Define the number of AI sub-agents dynamically to tailor your research team's scope and balance granularity with manageability.

Organize AI development by creating a structured project folder that encapsulates data, code, and configs for easier collaboration and sharing.