Tag: llm

RAGAS: Evaluating RAG Without Vibes

Stop guessing if your RAG pipeline works. RAGAS gives you reproducible metrics: faithfulness, answer relevance, context precision and recall.

KV Cache Quantization: Free LLM Context, Almost

KV cache eats your VRAM at long context, not the weights. Q8/Q4 KV quantization in llama.cpp and vLLM cuts it 2-4x with almost no quality hit.

Aider & Cline: Terminal AI Coding That Actually Ships

Aider vs Cline: two agentic AI coding tools that go beyond autocomplete. Which terminal AI coding agent ships cleaner work?

Mixture of Experts (MoE) for Self-Hosters, Demystified

MoE LLMs like Mixtral and DeepSeek-V3 run 70B-class quality on 7B-ish active params. Here's how sparse activation works and how to run it at home.

Speculative Decoding: Faster LLMs With a Tiny Sidekick

Speculative decoding, Gemma 4 MTP, and DeepSeek DSpark all make LLMs 2-6x faster losslessly. How each works, and which to use for local vs. serving.

Karakeep: Self-Hosted Bookmarks With AI Tagging

Karakeep (formerly Hoarder) is a self-hosted Pocket alternative with AI auto-tagging, full-text search, and Docker Compose deploy in under 10 minutes.

Stop Feeding the AI Your Whole Repo

Comparing context-mode, code-review-graph, token-savior, and claude-context, MCP tools that stop AI coding agents from reading your whole codebase blind.

RTK vs snip vs lean-ctx: Token Killers

RTK, snip, and lean-ctx filter your AI coding agent's output before it burns context tokens. I run RTK daily, here's the one I'd actually switch to.

AI Swarm Audited My 840-Post Blog

I pointed a parallel swarm of AI agents at 840 technical posts to fact-check and refresh them, the architecture, the token bill, and the guardrails.

Used GPU Buying Guide for Home Lab LLMs

Your no-BS guide to buying a used GPU for local LLM inference in 2026. Budget tiers from $200 to $1500 plus, real VRAM math, and tips to dodge the scams.

Claude Code in a Homelab Workflow

Claude Code puts an agentic AI assistant in your terminal for real homelab work, compose files, bash, Ansible, systemd. The honest take on cost and data.

Self-Host a Local AI Coding Workhorse

Self-host Gemma 4 or Qwen3-Coder via Ollama or llama.cpp in Docker, then let Claude delegate the grunt work to it. Free tokens, zero code leakage.