Articles about AI Engineering in Technology & AI
Training AI agents often means painstakingly rewriting their software environments. NVIDIA's new Polar framework offers a brilliant solution: a proxy that sits outside the agent, capturing data without any code changes.
Move your LLM app from a messy prototype to a production-ready system. This guide walks you through building a complete Langfuse observability pipeline, from tracing and prompt management to scoring and experiments.
Tired of writing the same prompts over and over? Discover how to build a simple yet powerful framework on top of the Anthropic API to make your interactions with Claude more structured, repeatable, and powerful.
Feeling lost in a complex codebase? Discover how to use Repowise to create a living map of your project, find dead code, and get AI-powered insights in minutes.
Large Language Models are powerful but huge. Let's walk through a practical, hands-on guide to shrinking them with post-training quantization techniques like FP8, GPTQ, and SmoothQuant, and see which one wins.
Ever feel like AI agents have the memory of a goldfish? Let's fix that. We'll walk through, step-by-step, how to build an autonomous agent with a powerful hybrid memory and a modular toolkit, so it can reason, remember, and act like a true assistant.
Are your LLM API bills getting out of hand? Learn how to use NadirClaw, a smart routing layer, to automatically send simple prompts to cheap models and complex ones to powerful models. This hands-on guide will walk you through cutting costs without sacrificing performance.
Stop treating your LLM like a black box. Let's walk through how to build a modular, skill-based agent system in Python—think of it as an operating system for your AI.
Ever had a perfectly good AI prompt suddenly fail for no reason? The culprit is likely tokenization drift, a subtle issue where tiny formatting changes cause big problems. Let's break down why it happens and how to fix it for good.
Ever wonder how AI models learn to be helpful and not just... weird? We'll walk you through the entire process, from basic instruction-following to advanced reasoning, using tools you can run yourself.
Tired of LLM outputs that are *almost* right? Let's move beyond basic prompting and look at five specific, systematic techniques that will give you the reliable, structured, and production-ready results you actually need.
Tired of writing endless unit tests only to find bugs in production? Let's explore property-based testing with Hypothesis, a smarter way to find hidden bugs by testing behaviors, not just examples.