Latest Posts
By Wei Ming T. on Dec 11, 2025
An evolutionary algorithm that compresses JSON specifically for LLM tokenization. Learn how 32 generations of AI self-improvement resulted in a 62.2% reduction in payload size.
By Aditya S. on Dec 9, 2025
The most common errors engineers make when transitioning to Machine Learning, from neglecting data cleaning to chasing state-of-the-art models, and learn actionable fixes to accelerate your progress.
By Wei Ming T. on Nov 28, 2025
Information on the mathematics behind estimating Time to First Token. We break down prefill dynamics, hardware scaling, and attention mechanisms to help you predict model latency without running the code.
By Wei Ming T. on Oct 21, 2025
Step-by-step guide to walk you through the exact endpoints and response specs OpenAI expects, including the undocumented OIDC location and token exchange.
By Aaron T. on Oct 8, 2025
The Model Context Protocol (MCP) promises to unify AI tools, saving you money on subscriptions. So why has it failed to gain traction? We'll look at the technical and market hurdles holding it back.
By Jacob M. on Oct 6, 2025
How to stop saying 'AI' for everything. This guide gives you 30 specific machine learning terms that you can use to demonstrate proficiency.
By Aaron T. on Sep 26, 2025
Learn what the Model Context Protocol (MCP) is and follow our step-by-step guide to connect Claude to an external MCP server, giving it access to live data and powerful tools.
By Wei Ming T. on Sep 25, 2025
Learn to build a secure and scalable Model Context Protocol (MCP) server using the fastapi_mcp library. This step-by-step guide covers setup, authentication, and integration with AI tools like Claude, turning your APIs into a powerful toolkit for large language models.
By Wei Ming T. on Sep 18, 2025
Before you spend a fortune fine-tuning an LLM, discover faster, cheaper, and often more effective methods: prompt engineering and RAG. Learn why fine-tuning should be your last resort.
By Jack N. on Sep 18, 2025
Learn which gradient boosting model to choose for speed, accuracy, and handling categorical data, with code examples and diagrams to guide you.