
Hermes Agent + LM Studio Bionic: Running Gemma 4 Locally
A Windows walkthrough of Hermes Agent connected to LM Studio Bionic with Gemma 4 E4B: local inference, custom endpoint settings, tool use, and the checks that confirm it works.
$ vishctl get notes -l topic=ai

A Windows walkthrough of Hermes Agent connected to LM Studio Bionic with Gemma 4 E4B: local inference, custom endpoint settings, tool use, and the checks that confirm it works.

My early-access look at TypeSafe’s Jev: Noul, Choice, and Score in the playground, the coding-agent skill, and what the performance claims do and do not establish.

My local LLM picks for Apple Silicon: oMLX first, Ollama second, vMLX third, and official MLX-LM fourth. Setup commands and the benefits of each.

A beginner-friendly AI and LLM glossary with short definitions, everyday examples, and a clickable table of contents for every term.

A practical explanation of agentic AI, agent harnesses, tool loops, context growth, and why agents cost more to run.

My visual notes on how LLMs work: tokens, parameters, attention, reasoning, KV cache, quantization, and serving models on GPUs.

A practical guide to running vLLM on Kubernetes in WSL2 with Minikube, configuring NVIDIA GPU passthrough, and serving an OpenAI-compatible API.

Testing FreeToken on an RTX 4070 Laptop GPU: running Qwen 3.6 35B-A3B NVFP4 across 8 GB VRAM and 32 GB RAM with hierarchical memory offloading.

Compile llama.cpp with Metal support on Apple Silicon, serve local GGUF models, and benchmark prompt eval and generation speeds head-to-head against Ollama.

Install Ollama, pull and run local models, and call the local API on a 32 GB Apple-silicon MacBook Air.