AI

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

Cross-section of a translucent crystal brain threaded by red, gold, and teal attention ribbons resting on a doubly-stochastic matrix pedestal beside a guitar-tuning lab figure.

DeepSeek V4 tech report: 3 tricks that cut compute 73%

DeepSeek V4 ships 1.6T parameters and 1M context on 27% of V3.2's inference FLOPs. Inside the hybrid attention, mHC residuals, and Muon optimizer.

The 80% coverage trap: why AI-generated tests create a false sense of security

AI test generators hit 90%+ line coverage fast, but coverage doesn't measure defect detection. Mutation testing reveals the real gap and the fix.

Why AI is killing the internet: model collapse and the knowledge commons

AI eroded the web: Stack Overflow dropped 78% since ChatGPT. Sources lose traffic to AI answers, which breaks the incentive to share anything.

Run DeepSeek R1 locally: reasoning models on consumer hardware

Run DeepSeek R1 reasoning models locally on consumer GPUs with 16 GB VRAM using Ollama or llama.cpp. Chain-of-thought without cloud API costs.

Generate conventional commits locally with Ollama and Git hooks

Wire a local LLM into Git hooks to automatically generate well-structured conventional commit messages from staged diffs. Excellent drafts for review.

RAG vs. long context: choosing the best approach for your LLM

Compare RAG and long context windows: different tools for different problems. Understand tradeoffs, costs, and scalability for production LLM systems.