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.
By Wei Ming T. on Sep 13, 2025
This complete guide details how to build a sophisticated course recommendation engine using LLMs, vector embeddings, and advanced semantic search. Learn data enrichment, prompt engineering, vector database implementation, and the logic for creating truly personalized learning paths.
By Ryan A. on Jul 12, 2025
Essential GPU and VRAM requirements for running Moonshot AI's Kimi LLM variants. This guide provides the specific hardware setups you need, from base models to Q4 quantized versions, to get started with this powerful AI.
By Ryan A. on Jul 4, 2025
List of the best local LLMs for Apple Silicon Macs, optimized for your specific RAM configuration.
By Ryan A. on Jul 3, 2025
Guide to the specific GPU VRAM requirements for running all variants of Baidu's new Ernie 4.5, from the small 0.3B to the largest 424B model
By Ryan A. on Jun 27, 2025
GPU and RAM requirements for Gemma 3n, Google's cutting-edge on-device AI model. Learn how its innovative architecture redefines efficient AI deployment.
By Wei Ming T. on Jun 23, 2025
The AI Engagement Index ranks nations by their engagement in technical AI content, offering a fresh perspective on the global AI adoption.
By Ryan A. on Jun 20, 2025
List for NVIDIA RTX 50 series GPU for running large language models locally. Discover the best LLMs for every card, master quantization, and optimize performance for privacy and speed.
By Wei Ming T. on Jun 19, 2025
Discover how AI and machine learning priorities differ across the globe. We analyze our user data to reveal the specific tools, techniques, and challenges that engineers from the US, India, China, Germany, and more are focused on right now.
By Jacob M. on Jun 16, 2025
Learn how to critically evaluate LLM benchmarks and choose the right model for your specific coding needs with our step-by-step guide.
By Jacob M. on May 24, 2025
Choosing between PyTorch and TensorFlow? This guide details 5 differences covering API design, graph execution, deployment, and community, helping ML engineers select the optimal framework for their projects.