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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

RedSwan Leads the Way with Real Estate Tokenization and ZirconTech integrates Stellar on its Blockchain Accelerators The iconic Copacabana Palace in Rio de Janeiro set the stage for Stellar’s Meridian 2025, bringing together more than 1,000 attendees from 65 countries, including fintech leaders, policymakers, builders, and innovators shaping the future of open networks. With over

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

OpenAI has officially announced its partnership with Oracle to develop an additional 4.5 gigawatts of Stargate AI data center capacity in the United States. This agreement, widely reported as potentially worth over $300 billion, represents one of the largest known AI infrastructure commitments to date. The partnership advances OpenAI’s broader commitment to build AI infrastructure

Apple’s September 9th announcement of the iPhone 17 lineup introduced several confirmed features alongside significant speculation about technical specifications and capabilities. The launch includes the new iPhone Air model and iOS 26’s Liquid Glass design language, but many circulating details remain unverified. Understanding the difference between confirmed features and industry speculation becomes crucial for enterprise

Meta’s announcement of the Ray-Ban Display glasses with Neural Band control marks a pivotal moment in consumer augmented reality. The $799 package, launching September 30th, represents the first mainstream attempt to combine visual AR displays with gesture-based control through electromyography sensors. The technical approach differs significantly from previous AR attempts. Rather than pursuing complex holographic