Run a Local AI Model with Ollama in 15 Minutes
In this article, you will learn how to get a small language model running locally on your own machine in under 15 minutes using Ollama….
Scikit-Ollama for Scikit-LLM/Ollama Integration
In this article, you will learn how scikit-ollama bridges the scikit-learn interface with locally running Ollama models to perform zero-shot text classification; no cloud API…
LLM Evaluation Frameworks Compared: How to Actually Measure What Your Model Does
In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS, DeepEval, and Promptfoo — and why…
Building AI Agents? Here Are Some Anti-Patterns to Avoid.
Agent systems change constantly in production.
Choosing the Right AI Agent Memory Strategy: A Decision-Tree Approach
In this article, you will learn how to choose the right memory strategy for an AI agent by working through a simple decision tree, one…
LLM Orchestration Frameworks Compared: LangChain vs. LlamaIndex vs. Raw API Calls
The default assumption in most LLM developer communities is that you start with raw API calls and graduate to a framework as your project grows.
Tools vs. Subagents: Building Effective AI Agents Without Over-Engineering
Tools execute code.
Intelligence is Free, Now What? Data Systems for, of, and by Agents
… government of the people, by the people, for the people … — Abraham Lincoln, Gettysburg Address (1863) The cost of AI is dropping rapidly. GPT-4-class capabilities cost roughly $30 per million tokens in early 2023; today the same runs under $1, and some providers are pushing costs below $0.10. Across benchmarks, inference prices have …
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The Complete Guide to Tool Selection in AI Agents
You build an agent with five tools.
Context vs. Memory Engineering in Agentic AI Systems
Compression on Arrival Tool outputs should be compressed after a call returns, not after the window fills.