Author name: Marcelo Acosta

The worlds of Artificial Intelligence and Web3 are converging in ways that once existed only in science fiction. This fusion represents more than a simple combination of technologies; it’s fundamentally changing how we think about decentralized systems and machine intelligence. Having worked extensively with both technologies, I’ve witnessed firsthand how this integration creates unprecedented opportunities […]

A couple of years after ChatGPT came out, there’s now a tendency to view Large Language Models (LLMs) as the universal solution to every machine learning problem. While LLMs are undoubtedly revolutionary, there are many scenarios where traditional machine learning models deployed on Amazon SageMaker not only make more sense but deliver superior results. Let’s

React 19 introduces several key updates to its core functionality, with significant enhancements to the Suspense mechanism. These changes aim to optimize how React applications handle asynchronous operations, reduce perceived delays, and improve user experience. Understanding Suspense Enhancements In earlier versions of React, when a component suspended during rendering (e.g., while waiting for asynchronous data),

In web3 a new trend has emerged: AI-themed tokens and projects commanding astronomical valuations. However, beneath the surface of this AI gold rush lies a sobering reality – many of these projects are simply old technology wrapped in new, AI-branded packaging. The Emperor’s New AI The recent drama surrounding the “ELIZA” tokens perfectly illustrates this

Amazon Web Services (AWS) has announced a significant enhancement to Amazon Bedrock with the preview launch of Prompt Optimization. This new feature aims to streamline and improve the way developers interact with foundational models by automatically rewriting prompts for better performance. What is Prompt Optimization? Prompt Optimization is a powerful tool that analyzes and rewrites

Key Considerations When Migrating from On-Premises to AWS The decision to migrate from on-premises infrastructure to AWS marks a significant milestone in an organization’s digital transformation journey. While the promise of improved scalability, reduced costs, and enhanced flexibility is compelling, the path to successful cloud migration requires careful planning and consideration. In this post, I’ll

The question echoes across tech forums, social media, and coding bootcamps: “With AI tools becoming so advanced at generating code, why invest time in learning to program?” It’s a valid concern, especially given the impressive demonstrations of AI coding assistants crafting entire functions or small applications with minimal human input. The traditional path of learning

The Rise of Fractional CTOs: A Modern Approach to Technical Leadership Startups and growing businesses face a critical challenge: accessing high-level technical leadership without the substantial financial commitment of a full-time CTO. Enter the Fractional CTO – a revolutionary approach that’s changing how companies handle their technical strategy and leadership needs. What is a Fractional

When building complex or multi-step generative applications, Amazon Bedrock’s prompt flows provide a robust solution for orchestrating processes that may involve multiple AWS services or conditional logic. This guide demonstrates how to invoke these prompt flows using Python, focusing on integration within AWS Lambda, although the principles apply to any Python-capable execution environment. Understanding Amazon

Machine learning development involves iterative experimentation, model training, and collaboration among data scientists and engineers. Managing this complexity can be challenging, especially when dealing with multiple models, parameters, and datasets. Amazon SageMaker offers a fully managed MLflow service, simplifying the process of tracking experiments and deploying models at scale. Introducing Managed MLflow on Amazon SageMaker