SelfHosting

A self-hosted AI agent needs more than a long context window. It needs a way to retain useful facts, retrieve earlier decisions, and revise what it thinks it knows as projects evolve.

Virt-Manager (the libvirt GUI) copies a KVM disk and assigns the clone a UUID and MAC. The guest keeps its machine-id and SSH host keys until virt-sysprep clears them.

Amazon S3 has become the de facto API for object storage. After the open source MinIO repository was archived in April 2026, seven credible self-hosted alternatives remain.

WordPress SEO plugins add the metadata, sitemaps, schema, and AI layer above WordPress core, and in 2026 that layer includes plugins that run inference on your own hardware.

Self-hosted SEO now covers crawlers, Search Console dashboards, rank trackers, MCP servers, and local AI. This guide compares the main open-source platforms and which features work without paid SEO APIs.

Deep Research has become its own category of software, not just a model pointed at a search box. This article compares twelve self-hosted systems and the research architectures behind them.

Mnemosyne is a local-first memory provider for Hermes Agent, storing working memory, structured facts, temporal data, and episodic history in local SQLite — no hosted service, no mandatory network calls, and unusually granular write control.

Persistent memory turns an agent from a re-explained tool into one that carries context forward — but it opens a failure mode stateless chat avoids: an interpretation can become memory, retrieved as fact, and justify a stronger version of itself.

Migrating an AI assistant is not the same as copying an application config. The hard part is preserving identity, memory, tool behavior, scheduled work, and messaging access without two gateways acting as the same bot.

Ollama and llama.cpp are often compared as if they were rival inference engines. The real choice is between a managed model service and a toolkit you operate directly.

ROCm and Vulkan both accelerate AMD GPUs for local LLM hosting, but they are not interchangeable. The right choice depends on the engine, GPU, and workload.

A model can advertise a 128K context window and still fail at 40K tokens on a 16 GB GPU. The architecture ceiling never promised that weights, KV cache, compute buffers, and the desktop compositor would fit on your card at the same time.

Every API call feels like a simple transaction - until enough of them accumulate that your fine-tuning data, evaluation harnesses, and tool schemas are all shaped around one vendor, and switching stops being a routing change.

An LLM Wiki fails when old facts remain plausible, contradictions become polished, and generated summaries drift from their sources.

Syncthing keeps files synchronized across devices you control, making it one of the most practical tools for a self-hosted knowledge infrastructure that avoids cloud lock-in.

The AI hardware landscape has shifted significantly in 2026, with NVIDIA, AMD, and Intel all competing for developers who need GPUs capable of running local large language models and AI inference workloads.