Local Agentic AI Workflows with Hermes + Ollama
In this article, you will learn how to build a fully local, zero-cost agentic AI workflow using Hermes Agent and Ollama, so that your files,…
In this article, you will learn how to build a fully local, zero-cost agentic AI workflow using Hermes Agent and Ollama, so that your files,…
Theory is easier to trust once it’s running against a real API, so both examples in this article use the same tool — a get_weather function backed by
In this article, you will learn the key differences between AI workflows and agents, and how to decide which approach is right for your use…
In this article, you will learn the mechanical difference between retrieval-augmented generation and fine-tuning, when each technique is the right tool, and how to decide…
In this article, you will learn what embedding drift is, why it matters for production large language models, and how to implement two practical techniques…
In this article, you will learn how LLM inference optimization works and which techniques to apply to make language models faster, cheaper, and more reliable…
Sponsored Content It’s no secret that AI agents burn massive amounts of tokens on search results and file retrievals. They pull in…
In this article, you will learn how a vector database works under the hood by building one from scratch in ten incremental steps using Python…
In this article, you will learn how to build a multilingual text classification pipeline using multilingual large language model (LLM) embeddings and Scikit-learn, without training…
In this article, you will learn how to treat prompt templates as tunable hyperparameters for a language model, using scikit-learn’s grid search to find the…