AI | page 26 of 26

Hands-on guides to LLMs, agents, prompt engineering, and the AI tools Botmonster runs every day for real work, not demos.

Evaluating AGENTS.md: are repository context files actually helpful?

When do repository context files like CLAUDE.md help AI agents? An analysis of documentation quality, structure, and when context files become noise.

Why small language models (SLMs) are better for edge devices

Small Language Models like Phi-4 and Gemma 3 run offline on Raspberry Pi and other edge devices. Why sub-4B parameters beat cloud inference calls.

SDXL 2.0 LoRA: 50-300 MB adapters on 12 GB VRAM

Fine-tune Stable Diffusion XL 2.0 with LoRA adapters: dataset prep with vision-language models, training with kohya_ss, custom styles on 12GB VRAM.

Underground vault library with glowing holographic books arranged in vector space and a robot librarian retrieving relevant volumes

Setup a private local RAG knowledge base

Build a private RAG with Qdrant, BGE-M3 embeddings, and Ollama. Index your documents locally and answer questions with zero data leaving your machine.

Building multi-step AI agents with LangGraph

Build production-grade AI agents with LangGraph's graph architecture. Add self-correction, memory management, and multi-agent patterns to Python apps.

High-end gaming desktop with illuminated NVIDIA GPU visible through a glass side panel, surrounded by floating holographic neural network diagrams and data streams

Run Llama 4 Scout locally: 24GB VRAM, GGUF, real speeds

Run Llama 4 Scout locally with Unsloth dynamic GGUF. Real VRAM by GPU, which quant fits 24GB, Ollama vs llama.cpp, and honest tokens per second.