Tag: llm

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.

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 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 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.

Stop burning expensive AI tokens on boring grunt work. The overseer/workhorse pattern routes mechanical tasks to a cheap model and saves more than you'd think.

Wire a self-hosted SearXNG instance into Claude Code via a Bash wrapper for private, scriptable web search — and when to use it vs the built-in tool.

Dify is an open-source LLM-app builder you can self-host. Visual workflow editor, RAG, agents, tool use — without writing 500 lines of LangChain glue.

CUDA vs ROCm for AI on Linux: NVIDIA's easy path, AMD's emotional journey, and why CPU inference isn't dead yet. Real Docker setups included.

LLaMA, Mistral, Falcon, GPT — the LLM landscape is crowded. Compare model families, sizes, licensing, and what each is actually good for.

Temperature, top-p, top-k, context length — LLM inference parameters explained so you stop guessing why the model gives weird output.

Confused by AI agent frameworks? Compare LangGraph, CrewAI, and AutoGen with real Python examples, a no-nonsense breakdown, and zero hype. Pick the right one.

GGUF, GGML, AWQ, GPTQ — LLM file formats and quantization levels explained: trade-offs between model quality, size, and inference speed.