Author name: Marcelo Acosta

OpenAI just launched ChatGPT Atlas on October 21, 2025, a browser that brings AI-powered assistance directly into your web experience. There’s one catch: it’s macOS-only at launch, with Windows, iOS, and Android versions coming soon. If you’re on Windows or Linux today and want to try it, you’ll need access to a Mac environment. The […]

You’re working through a CodeCrafters challenge, making real progress, and suddenly realize something frustrating: all your work lives in a private remote repository that CodeCrafters controls. You want your progress on GitHub for your portfolio, for backup, or just to share with colleagues. But CodeCrafters requires you to push to their remote to validate each

Your stakeholder asked a reasonable question: “Will the AI system learn from our users and improve over time?” You said yes, because that sounds right. GenAI systems should learn. That’s what makes them intelligent, adaptive, future-proof. The demo went well. The project got funded. Now you need to deliver a system that actually learns. Six

Consider a typical scenario: A compliance team processes 10,000 transactions in a month. They flag 847 for manual review. Analysts spend three weeks investigating. They find two genuine issues. The other 845 are false positives that cost the team 480 hours and delay legitimate customer transactions. This pattern is not sustainable. Transaction volumes are growing

Your AI model needs to run closer to users. Latency from round-tripping to us-east-1 kills the user experience. You investigate AWS edge options and find two services: IoT Greengrass and Lambda@Edge. Both claim to run code at the edge. Both support AI inference. But they target completely different use cases. IoT Greengrass runs on devices

Your AI model runs great on a GPU instance in development. You deploy to production. Then finance asks why you’re spending $15,000/month on compute when AWS says their custom chips cost 70% less. You investigate Inferentia2, discover it requires model compilation, and the tradeoff analysis becomes complicated fast. AWS offers three hardware paths for AI

Your AI model works perfectly in development. You deploy it to production. Three weeks later, your AWS bill arrives and SageMaker costs are 4x what you budgeted. The model hasn’t changed. Traffic is exactly what you estimated. But you picked the wrong inference option. SageMaker offers four inference modes: real-time endpoints, serverless inference, asynchronous inference,

You have a model. You need to deploy it on AWS. You ask which service to use and get three answers: SageMaker, Bedrock, or Lambda. All three can technically work, but picking the wrong one costs you months of unnecessary complexity or thousands in wasted spend. The decision tree is simpler than AWS’s documentation makes

Your Lambda function responds in 50ms. Great. Except when it takes 3 seconds because AWS had to cold start it. Again. Cold starts are still the top complaint about serverless in 2025. You have three basic options: throw money at provisioned concurrency, accept the latency, or actually optimize. Most teams pick the wrong one. The

A data center consuming the power of ten nuclear reactors. That’s the scale of Nvidia’s $100 billion commitment to OpenAI, announced this month. The partnership will deploy 10 gigawatts of AI computing infrastructure, fundamentally reshaping how we think about artificial intelligence development and deployment. This isn’t just another tech partnership. It’s a strategic bet on