Notion AI is convenient but it sends every piece of text you ask it to process to Anthropic’s or OpenAI’s servers. For personal notes, confidential work documents, client data, or anything sensitive, that’s a meaningful privacy trade-off. With Ollama and a few tools, you can replicate most of what Notion AI does — writing assistance, … Read more
Logseq is an open-source knowledge management tool built around bidirectional linking, daily journals, and a graph view of connected notes. Its AI features — query answering, content generation, summarisation — can be backed by any OpenAI-compatible API, which means you can connect them to Ollama for fully local, private knowledge management. Your notes never leave … Read more
Benchmarking intro text. This guide covers how to measure Ollama performance precisely, what to look for in the numbers, how to compare models and settings systematically, and how Flash Attention improves both speed and memory usage. The goal is to give you concrete numbers rather than vague impressions about what is fast or slow. Built-in … Read more
Ollama is designed around interactive single-user inference, but many real workflows need to process dozens, hundreds, or thousands of items through a model — classifying records, generating descriptions, summarising documents, extracting structured data. Getting this right requires understanding how Ollama queues requests, when to use parallel processing, how to structure batch jobs for throughput, and … Read more
Microsoft’s Semantic Kernel is an open-source SDK for building AI-powered applications in .NET, Python, and Java. It provides abstractions for LLM services, memory, plugins, and agents that let you build production-grade AI applications without being locked to any specific model provider. Because it supports OpenAI-compatible endpoints, connecting it to Ollama requires minimal configuration — and … Read more
Oobabooga’s text-generation-webui (often called “ooba”) is one of the most feature-rich local LLM frontends available — a browser-based interface with extensive model loading options, sampling controls, prompt templates, extensions, and API access. It predates many newer tools and has accumulated years of features and community extensions. While Ollama and ooba can both run local LLMs, … Read more
Ollama has a large set of parameters that control how models behave, how much memory they use, and how they run on your hardware. Most users never touch these and get reasonable results — but knowing what’s available and what each setting does unlocks significantly better outcomes for specific use cases. This guide covers every … Read more
Connecting Ollama to Slack gives your team an AI assistant inside the tool you already use — no switching to a browser tab, no sharing API keys, no data leaving your infrastructure. When someone messages your bot, their message goes to your server, Ollama generates a response, and the reply appears in Slack. The architecture … Read more
A Slack bot backed by Ollama gives your team an AI assistant that lives in the tools they already use, costs nothing per message, and keeps all conversation data on your infrastructure. Users can tag the bot in any channel or DM it directly, and it responds using your local Ollama model. This guide walks … Read more
If your Ollama inference feels slow, there are usually several things you can do about it. The gains available vary enormously by hardware and configuration — some users get 2–3x faster inference from a single setting change, others find they’re already close to their hardware ceiling. This guide covers every meaningful optimisation, starting with the … Read more
