Llm
11 posts
POSTS
(11/11)RAG Techniques Compared: A Practical Guide to Retrieval Augmented Generation in 2026— @dylan >>
Compare naive RAG, advanced RAG, agentic RAG, and GraphRAG architectures with real benchmarks, costs, and practical recommendations for production systems.
How Large Language Models Work: The Complete Technical Guide to Transformers, Training, and Inference (2026)— @dylan >>
A deep technical guide to how LLMs actually work — from the transformer architecture and attention mechanism to tokenization, training at scale, KV caching, inference acceleration, fine-tuning, and the modern innovations powering GPT-4o, Claude, Llama 3, and beyond. Backed by 30+ research papers.
Apple Silicon LLM Inference Optimization: The Complete Guide to Maximum Performance— @dylan >>
A comprehensive guide to maximizing LLM inference performance on Apple Silicon — MLX vs llama.cpp benchmarks, quantization formats, RAM requirements, MoE models, speculative decoding, KV cache optimization, and the best models for every Mac configuration.
LLM Model Names Decoded: A Developer's Guide to Parameters, Quantization & Formats— @dylan >>
A practical guide to decoding LLM model names — what B, IT, GGUF, Q4_K_M, MoE, and every other suffix means. Includes comparison tables, links to the best research resources, and real examples from Gemma 4, Qwen 3.5, and Llama 4.
AI Token Throughput Tracking Tools: The Complete Guide for Developers (2026)— @dylan >>
A comprehensive comparison of 30+ tools for tracking AI token usage across Claude Code, Codex CLI, Cursor, Gemini CLI, and more — from lightweight CLI utilities to enterprise observability platforms.
Local LLM Inference in 2026: The Complete Guide to Tools, Hardware & Open-Weight Models— @dylan >>
A comprehensive guide to running LLMs locally — comparing 10 inference tools, quantization formats, hardware compatibility, open-weight models, and the builders behind the ecosystem.
Best Mac Mini for Running Local LLMs and OpenClaw: Complete Pricing & Buying Guide (2026)— @dylan >>
A complete pricing comparison of every Apple Silicon Mac Mini — new vs used — with LLM capability breakdowns by RAM tier. Plus a full guide to running OpenClaw as a local AI agent on your Mac Mini with zero cloud costs.
What is LLMs.txt? Complete Guide to Creating LLMs.txt Files for Your Website— @dylan >>
Complete guide to LLMs.txt files: Learn what llms.txt is, how it helps AI understand your website, and step-by-step instructions for creating and implementing llms.txt files to optimize your site for AI-powered search and content discovery.
Prompt Engineering Guide: System Prompts, Chain-of-Thought, and Structured Outputs (2026)— @dylan >>
Master prompt engineering for GPT-4o, Claude, and Gemini — system prompts, chain-of-thought reasoning, few-shot examples, function calling, structured outputs, and testing strategies.
Transformers Explained: From Attention Mechanism to GPT-4o, Claude, and Open-Source LLMs (2026)— @dylan >>
Understand transformer architecture from self-attention to modern LLMs — covering GPT-4o, Claude, Gemini, Llama 3, Mistral, multimodal models, and practical guidance on which model to use for which task.