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Comparing Local Tool Calling: Gemma 4 vs. Llama 3 vs. Mistral
17+ hour, 35+ min ago (703+ words) Making developers awesome at machine learning In this article, you will learn how Gemma 4, Llama 3, and Mistral implement tool calling locally, and what trade-offs each model family presents for real-world deployment. Topics we will cover include: Running AI models locally…...
Integrating Agentic AI with Existing Machine Learning Pipelines
1+ day, 17+ hour ago (542+ words) Making developers awesome at machine learning In this article, you will learn how to combine a classical machine learning pipeline with an agentic AI system to build a hybrid, autonomous customer retention workflow. Topics we will cover include: Agentic AI…...
How to Build a Robust RAG System with Minimal Resources
5+ day, 17+ hour ago (962+ words) Making developers awesome at machine learning In this article, you will learn how to design, assemble, and tune a retrieval-augmented generation system that runs entirely on a standard laptop, without cloud infrastructure or paid APIs. Topics we will cover include:…...
Managing Small Context Windows in Language Models
1+ week, 17+ hour ago (343+ words) Making developers awesome at machine learning In this article, you will learn three practical strategies for managing small context windows in large language models, along with working Python examples that demonstrate how two of those strategies are implemented. Topics we…...
7 Regression Tests Every AI Agent Should Pass Before Deploy
1+ week, 1+ day ago (504+ words) Making developers awesome at machine learning In this article, you will learn seven concrete regression tests for catching the orchestration-layer failure modes that matter most before deploying an AI agent to production. Topics we will cover include: Most agent failures…...
Understanding the Role of Latent Space in Machine Learning Models
1+ week, 4+ day ago (347+ words) Making developers awesome at machine learning In this article, you will learn what latent spaces are and how they serve three distinct roles — descriptive, generative, and predictive — across a wide range of machine learning applications. Topics we will cover include:…...
Retrieval vs. Memory in Agentic AI System
1+ week, 6+ day ago (504+ words) Making developers awesome at machine learning In this article, you will learn the conceptual and practical differences between retrieval and memory in agentic AI systems, and how to combine both effectively. Topics we will cover include: We start with why…...
7 Async Patterns for Running Agents Concurrently in Python
2+ week, 17+ hour ago (959+ words) Making developers awesome at machine learning In this article, you will learn seven async patterns for running AI agents concurrently in Python, what each pattern is suited for, and the production-level pitfalls to watch out for with each. Topics we…...
Prompt Caching vs. Fine-Tuning: A Cost and Latency Decision Framework
2+ week, 1+ day ago (321+ words) Making developers awesome at machine learning In this article, you will learn how prompt caching and fine-tuning differ as strategies for reducing cost and latency in agentic AI systems, and how to choose between them. Topics we will cover include:…...
Identifying Token Costs Hiding in Your Agentic Loop
2+ week, 4+ day ago (851+ words) Making developers awesome at machine learning In this article, you will learn how token costs silently compound in agentic AI loops, and what architectural patterns you can use to control them before they escalate. Topics we will cover include: Time…...