Jev and AI Decision Models: A Practical Getting Started Guide— @dylan >>
Learn what Jev and AI decision models do, compare hosted and local alternatives, and build your first workflow with practical examples and research resources.
16 posts
Learn what Jev and AI decision models do, compare hosted and local alternatives, and build your first workflow with practical examples and research resources.
Answer Engine Optimization and Generative Engine Optimization are the new SEO frontier. Learn how to get your site cited by ChatGPT, Perplexity, and Google AI Overviews with JSON-LD, llms.txt, robots.txt, and content restructuring backed by peer-reviewed research.
Compare naive RAG, advanced RAG, agentic RAG, and GraphRAG architectures with real benchmarks, costs, and practical recommendations for production systems.
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.
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.
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.
A comprehensive guide to running LLMs locally — comparing 10 inference tools, quantization formats, hardware compatibility, open-weight models, and the builders behind the ecosystem.
Choose the best Mac mini for local LLMs: compare M6, M5 Pro and refurbished M4, dated US prices, RAM tiers, model fit and OpenClaw requirements.
A complete guide to Yazi — the async, Rust-powered terminal file manager with image previews, Lua plugins, and vim-style keybindings. Covers installation, commands, plugins, and practical workflows for development, Linux administration, and AI-assisted coding.
A complete guide to installing and using 10 terminal tools that pair perfectly with AI coding agents like Claude Code — from LazyGit for reviewing AI-generated diffs to Zoxide for lightning-fast directory navigation.
How real teams use Claude Code at scale: case studies from incident.io, Nx, Anthropic, Every, Y Combinator startups, Treasure Data, and best practices for parallel agents, git worktrees, and verification loops.
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.
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.
Learn how embeddings, vector databases, and RAG architecture work together to build AI systems with custom knowledge — with working code examples using OpenAI, pgvector, and the Vercel AI SDK.
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.
Master Midjourney v6+ with current parameters, prompt structure, style references (--sref), character references (--cref), model options, and real prompt examples for consistent, high-quality AI artwork.