Botmonster Tech
Coordinate multiple AI coding agents in parallel using file ownership, iteration caps, and review gates to beat single-agent throughput on real work.
AI | page 22 of 26
Hands-on guides to LLMs, agents, prompt engineering, and the AI tools Botmonster runs every day for real work, not demos.
Route Ollama, vLLM, OpenAI through one LiteLLM API
Unify access to Ollama, vLLM, OpenAI, Anthropic, and Google models behind one endpoint. Routing, load balancing, and rate limiting with LiteLLM.
Gemma 4 vs Qwen 3.5 vs Llama 4: which open model should you actually use? (2026)
Gemma 4, Qwen 3.5, and Llama 4 compared on benchmarks, licensing, speed, and hardware needs so you can pick the right open model for your work fast.
Fine-tuning Gemma 4 with Unsloth on a single GPU: a practical guide
Fine-tune Gemma 4 with Unsloth on a single GPU using QLoRA: dataset prep, the full training workflow, and GGUF export for RTX 4090 or Colab.
Local meeting transcription on Linux with Whisper and Ollama
Local meeting transcription on Linux. Record calls with PipeWire, transcribe with Faster-Whisper, label speakers, and get structured notes from Ollama.
Git worktrees for parallel Claude Code sessions: run 10+ AI agents without file conflicts
Use git worktrees with Claude Code's --worktree flag to run 5-10+ parallel agents per developer, each isolated to its own checked-out branch.