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).

Netlify is one of the most developer-friendly ways to ship Hugo sites and modern web apps with a production-grade workflow: preview URLs for every pull request, atomic deploys, a global CDN, and optional serverless and edge capabilities.

Apache Flink is a framework for stateful computations over unbounded and bounded data streams.

Neo4j is what you reach for when the relationships are the data. If your domain looks like a whiteboard of circles and arrows, forcing it into tables is painful.

Static sites and blogs change whenever you deploy. Search engines that support IndexNow can learn about those changes without waiting for the next blind crawl.

Putting email on your own domain sounds like a weekend DNS task. In practice it is a small distributed system with a twenty-year legacy.

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.

Apache Kafka 4.2.0 is the current supported release line, and it’s the best baseline for a modern Quickstart because Kafka 4.x is fully ZooKeeper-free and built around KRaft by default.

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.

I have tested how OpenCode works with several locally hosted on Ollama LLMs, and for comparison added some Free models from OpenCode Zen.

The biggest capability jump in OpenCode comes from specialised agents: deliberate separation of orchestration, planning, execution, and research.

OpenHands is an open-source, model-agnostic platform for AI-driven software development agents. It lets an agent behave more like a coding partner than a simple autocomplete tool.

LocalAI is a self-hosted, local-first inference server designed to behave like a drop-in OpenAI API for running AI workloads on your own hardware (laptop, workstation, or on-prem server).

Oh My Opencode turns OpenCode into a multi-agent coding harness: an orchestrator delegates work to specialist agents that run in parallel.

I keep coming back to llama.cpp for local inference—it gives you control that Ollama and others abstract away, and it just works. Easy to run GGUF models interactively with llama-cli or expose an OpenAI-compatible HTTP API with llama-server.