Author name: Marcelo Acosta

If you want to build a production-grade voice agent, there are some things you will need: A way to connect phone networks. Speech-to-text processing. Understanding requests and executing actions. Text-to-speech output. Observability so it’s not a black box. The hard part is not any single component. Managing voice streams, converting speech to text for processing, […]

Your healthcare AI chatbot passed security review. It has Amazon Bedrock guardrails configured to block PII and sensitive medical topics. The web client connects directly to the Bedrock runtime endpoint. Everything works in testing. Then a patient asks: “I’m John Smith, SSN 123–45–6789, and I have stage 4 pancreatic cancer. What are my treatment options?”

AWS just released a new certification: Certified Generative AI Developer – Professional. If you work with GenAI on AWS or plan to, this exam outline doubles as a surprisingly useful roadmap for what you need to learn, regardless of whether you ever sit for the test. The blueprint maps what separates proof-of-concept GenAI from production-grade

Werner Vogels stepped on stage for what he announced would be his final re:Invent keynote after roughly 14 years. After more than a decade of defining how AWS thinks about building software, he handed developers a framework for the next era: the Renaissance Developer. The timing makes sense. AI coding assistants are everywhere. Developers are

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