Self-Hosting

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.

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.

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.

LLM inference looks like “just another API” — until latency spikes, queues back up, and your GPUs sit at 95% memory with no obvious explanation.

OpenClaw is a self-hosted AI assistant designed to run with local LLM runtimes like Ollama or with cloud-based models such as Claude Sonnet.

AWS S3 remains the “default” baseline for object storage: it is fully managed, strongly consistent, and designed for extremely high durability and availability.
Garage and MinIO are self-hosted, S3-compatible alternatives: Garage is designed for lightweight, geo-distributed small-to-medium clusters, while MinIO emphasises broad S3 API feature coverage and high performance in larger deployments.

Garage is an open-source, self-hosted, S3-compatible object storage system designed for small-to-medium deployments, with a strong emphasis on resilience and geo-distribution.

Strategic guide to hosting large language models locally with Ollama, llama.cpp, vLLM, or in the cloud. Compare tools, performance trade-offs, and cost considerations.

Self-hosting LLMs keeps data, models, and inference under your control-a practical path to AI sovereignty for teams, enterprises, nations.

Running large language models locally gives you privacy, offline capability, and zero API costs. This benchmark reveals exactly what one can expect from 14 popular LLMs on Ollama on an RTX 4080.

The Go ecosystem continues to thrive with innovative projects spanning AI tooling, self-hosted applications, and developer infrastructure. This overview analyzes the top trending Go repositories on GitHub this month.