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Integrating Agentic AI with Existing Machine Learning Pipelines
1+ hour, 36+ min 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
4+ day, 1+ 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
6+ day, 1+ 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…...
Understanding the Role of Latent Space in Machine Learning Models
1+ week, 2+ 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:…...
Designing AI Agents That Can Self-Correct
2+ week, 4+ day ago (616+ words) Making developers awesome at machine learning In this article, you will learn how to design AI agents that can reliably self-correct by grounding their feedback loops in external verification rather than the model’s own judgment. Topics we will cover include:…...
Static vs. Dynamic vs. Continuous Batching in LLM Inference
2+ week, 6+ day ago (415+ words) Making developers awesome at machine learning In this article, you will learn how static, dynamic, and continuous batching work in LLM inference, and why the differences between them matter at production scale. Topics we will cover include: Static batching is…...
Using a Transformer Model: From Training to Inference
2+ week, 6+ day ago (1009+ words) Making developers awesome at machine learning If you have implemented a transformer model in PyTorch, you can use the same code for both training and inference, but in very different ways. During training, you usually process a batch of fixed-length…...
Ollama vs. LM Studio vs. llama.cpp: Which Local AI Runtime Should You Use in 2026?
3+ week, 5+ day ago (1083+ words) Making developers awesome at machine learning In this article, you will learn how Ollama, LM Studio, and llama.cpp differ across the dimensions that matter most to practitioners, and how to choose the right one for your workflow. Topics we…...
5 Architectural Patterns for Persistent Memory and State in AI Agents
4+ week, 1+ hour ago (1068+ words) Making developers awesome at machine learning Building an AI agent can be tricky. Keeping it on track over a six-month deployment is incredibly hard. LLMs are stateless by design. Every call starts from scratch, with no memory of what came…...
Stateful vs. Stateless Agent Design: Tradeoffs for Scalable Agentic Systems
1+ mon, 53+ min ago (516+ words) Making developers awesome at machine learning In this article, you will learn how an agent’s approach to managing state — stateless or stateful — shapes both its implementation and the deployment architecture built around it. Topics we will cover include: A previous…...