Botmonster Tech
Real-ESRGAN is free and fast. Topaz Photo AI wins on quality and face recovery. SUPIR makes the most detail but needs 12GB VRAM. Speed and cost too.
AI
Hands-on guides to LLMs, agents, prompt engineering, and the AI tools Botmonster runs every day for real work, not demos.
Gemma 4 architecture explained: per-layer embeddings, shared KV cache, and dual RoPE
Gemma 4 uses per-layer embeddings, a shared KV cache to cut memory, and dual RoPE for mixed local-global attention. Activates 3.8B of 26B params.
Running Gemma 4 26B MoE on 8GB VRAM: three strategies that work
Run Google Gemma 4 26B MoE on a budget 8GB GPU using aggressive quantization, GPU-CPU layer offloading, and tensor parallelism.
AI coding agents are insider threats: prompt injection, MCP exploits, and supply chain attacks
Study of 78 coding agents including Claude Code, Copilot, and Cursor: all vulnerable to prompt injection, which succeeds 85% of the time.
Claude Code skills ecosystem: 1,340+ installable agent skills for AI coding assistants
Explore Claude Code's 1,340+ agentic skills ecosystem: major repositories, top skills by category, building custom skills, and future directions.
Running Gemma 4 locally with Ollama: all four model sizes compared
Google's Gemma 4 spans 2.3B to 31B parameters. Compare hardware needs, speed, and quality across all variants when running locally through Ollama.