n8n is an open-source workflow automation tool — think Zapier or Make, but self-hosted, and with first-class support for AI nodes that can call local LLMs. Connecting it to Ollama gives you a powerful combination: visual workflow automation with local AI processing, running entirely on your own infrastructure. You can build pipelines that classify emails, … Read more
The Ollama library contains a lot more than the models that get written about most. While everyone knows Llama 3.1, Mistral, and Qwen 2.5, there’s a tier of excellent models that rarely comes up in conversations — models that either launched quietly, serve a niche particularly well, or have been overshadowed by newer releases despite … Read more
Zed is a fast, GPU-accelerated code editor built in Rust that’s been gaining serious traction among developers who want VS Code’s capabilities without its memory overhead. Its AI assistant feature — Zed Assistant — supports custom AI backends including Ollama, which means you can get AI-assisted coding in one of the fastest editors available, backed … Read more
Not every image analysis task needs a 7B+ model. Moondream and BakLLaVA are small, fast vision models that run on hardware where larger vision models struggle — including CPU-only machines and machines with 4–6GB RAM. If you need local image understanding without the overhead of Gemma 3 or LLaVA 7B, these are worth knowing about. … Read more
Ollama’s model library has more coding-specific models than most people realise, and choosing the right one for your hardware and workflow makes a bigger difference than it might seem. General-purpose models like Llama 3.1 or Mistral handle code adequately, but dedicated coding models — trained on much larger and more diverse code corpora — produce … Read more
Qwen 2.5 72B is Alibaba’s largest open-weight model and one of the most capable you can run locally. At 72 billion parameters it’s in the same tier as Llama 3.1 70B and Mixtral 8x22B, but it benchmarks ahead of both on several tasks — particularly coding, mathematics, and multilingual work. If your hardware can handle … Read more
LlamaIndex is one of the most capable frameworks for building RAG systems — retrieval-augmented generation pipelines that let your LLM answer questions grounded in your own documents. Connecting it to Ollama means you get the full LlamaIndex feature set (document loading, chunking, vector indexing, query engines, agents) backed by a local model with no API … Read more
Ollama exposes a clean REST API that lets you interact with any locally running model from your Python code — no paid API keys, no rate limits, nothing leaving your machine. Whether you’re building a chatbot, automating document processing, or integrating LLM capabilities into an existing application, the Ollama API is your bridge between Python … Read more
Command R+ is Cohere’s flagship open-weight model and one of the most capable models you can run locally for RAG (retrieval-augmented generation) and tool use. It was specifically trained with these workflows in mind — it handles long documents better than most models its size, supports structured tool calling natively, and performs well in multi-turn … Read more
LLaVA (Large Language and Vision Assistant) was one of the first open-source multimodal models that could meaningfully analyse images locally. You give it an image alongside a text prompt and it responds based on what it sees. While newer models like Gemma 3 have surpassed it on benchmarks, LLaVA remains widely used and is one … Read more
