LLM

OpenClaw looks simple in demos. In production, it becomes a system.

The quiet loophole that powered a wave of agent experimentation is now closed.

Vane is one of the more pragmatic entries in the “AI search with citations” space: a self-hosted answering engine that mixes live web retrieval with local or cloud LLMs, while keeping the whole stack under your control.

Claude Code is not autocomplete with better marketing. It is an agentic coding tool: it reads your codebase, edits files, runs commands, and integrates with your development tools.

Hermes Agent is a self-hosted, model-agnostic AI assistant that runs on a local machine or low-cost VPS, works through terminal and messaging interfaces, and improves over time by turning repeated tasks into reusable skills.

Text Generation Inference (TGI) has a very specific energy. It is not the newest kid in the inference street, but it is the one that already learned how production breaks -

Here I am comparing speed of several LLMs running on GPU with 16GB of VRAM, and choosing the best one for self-hosting.

Australia has RTX 5090 stock. Barely. And if you find one, you will pay a premium that feels detached from reality.

Ollama is at its happiest when it is treated like a local daemon: the CLI and your apps talk to a loopback HTTP API, and the rest of the network never finds out it exists.

Ollama works great on bare metal. It gets even more interesting when you treat it like a service: a stable endpoint, pinned versions, persistent storage, and a GPU that is either available or it is not.

Running Ollama behind a reverse proxy is the simplest way to get HTTPS, optional access control, and predictable streaming behaviour.

If you are working through retrieval-augmented generation (RAG), this section walks through text embeddings in plain terms — what they are, how they fit search and retrieval, and how to call two common local setups from Python using Ollama or an OpenAI-compatible HTTP API (as many llama.cpp-based servers expose).

SGLang is a high-performance serving framework for large language models and multimodal models, built to deliver low-latency and high-throughput inference across everything from a single GPU to distributed clusters.

Soon you are juggling vLLM, llama.cpp, and more—each stack on its own port. Everything downstream still wants one /v1 base URL; otherwise you keep shuffling ports, profiles, and one-off scripts. llama-swap is the /v1 proxy before those stacks.

Most local AI setups start with a model and a runtime.

Oh My Opencode promises a “virtual AI dev team” — Sisyphus orchestrating specialists, tasks running in parallel, and the magic ultrawork keyword activating all of it.