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
Ollama’s default settings are conservative — designed to work on a wide range of hardware without crashing, not to extract peak performance from yours. If you’re getting fewer tokens per second than you expected, or the model is loading slowly, or you’re hitting memory pressure that prevents you from running your preferred model, there’s a … Read more
AI agent frameworks — systems where multiple LLM-backed agents collaborate on tasks, use tools, and produce coordinated outputs — have matured significantly, and both CrewAI and AutoGen work well with Ollama as the local inference backend. Running agents locally means zero API costs for long multi-step agent runs, no rate limits when agents make many … Read more
AI agent frameworks let you build systems where multiple LLM-powered agents collaborate on complex tasks — one agent researches, another writes, a third reviews, and an orchestrator coordinates the whole thing. CrewAI and AutoGen are two of the most popular frameworks for this, and both support Ollama as the local inference backend. Running agents locally … Read more
