Claude gets modular. Anthropic’s new “Skills” let devs connect models to APIs, data, and actions — Here I share how this moves Claude closer to agent-level autonomy. https://t.co/Lv6OVoapMm

AWS just made it easier to run large language models on Inferentia chips. This means: • Lower inference costs by up to 70% • Same performance as GPU alternatives • Scalable for production workloads Real impact: More companies can afford to deploy AI at scale. #AWS

Look what just went live. Two Point SIX SEVEN

Listen up: Edge computing means processing data CLOSER to where it's created instead of sending everything to the cloud. Saves bandwidth, reduces latency. Don't build everything assuming infinite cloud resources. #EdgeComputing #DevAdvice

Cloud cost optimization just got smarter: AI-powered autoscaling now predicts traffic patterns *before* they happen. Instead of reacting to spikes, your infrastructure adjusts proactively—cutting costs by up to 40% while maintaining performance. Real savings, not just hype.

#MicroFrontends reflect how modern organisations build software! Shift happens: 🔹 Centralized Control ⇨ Distributed Ownership 🔹 Significant Releases ⇨ Continuous Flow 🔹 Rigid Architectures ⇨ Evolutionary Change 📰 https://t.co/bP1P3Dg2Bu #SociotechnicalArchitecture

The sexiest part of cloud cost optimization? Instant ROI. The silent killer? Wasted developer hours spent manually tracking costs instead of building. #FinOps #CloudCosts

We think LLMs "understand" language because they can generate coherent text. But what if coherence ≠ understanding? These models learn statistical patterns, not meaning. They predict the next token based on training data—not intent, context, or truth. Are we mistaking fluency

Context Engineering: We're not just building software. We're building the reality it runs on. #ContextEngineering

AI agents aren't just tools—they're coworkers who never sleep. Welcome to the new reality. #AIRevolution

We build machines to learn, but what are we unlearning in the process? The more we automate insight, the more we must ask: what part of our own understanding are we outsourcing? #MachineLearning #Humanity

We build vast AI infrastructures on AWS, yet rarely pause to ask: Are we engineering systems that serve humanity, or engineering humanity to serve systems? Where does the technology end and we begin? #AI #Infrastructure

We assume AI will replace human creativity, but what if it actually reveals how much of our "original" work is just remixing existing patterns? Maybe the real threat isn't AI thinking like us—it's us thinking like AI. #AIdebate

The AWS console isn't magic—it's a playground. Every failed experiment teaches you something that perfect documentation can't. What will you build today? #AWS #CloudComputing

The biggest lesson from building multi-agent systems: Your agents will fail exactly where you didn't plan for coordination. The magic isn't in individual brilliance—it's in the handoffs. #MultiAgentSystems #AI

AI agents aren't about to replace all human jobs overnight. Fact: Most current "autonomous" agents still fail basic tasks 40-60% of the time and require constant human supervision. The revolution is coming, but it's moving at human speed. #AI #Automation

Your code is writing itself. You just have to feel it. This is Vibe Coding. #DeveloperRealityCheck

Just had a thought: Agent Orchestration isn't just about connecting AI tools—it's about designing workflows that think. We're moving from simple automation to intelligent systems that can adapt and reason. What's the most complex workflow you've seen successfully orchestrated by

Agentic workflows aren't magic—they're tools. Potential: Automate complex tasks, scale operations, reduce human error. Limitations: Require precise setup, can't handle true novelty, may amplify existing biases. The real question isn't "will they work?" but "what problems are

The sexiest thing about AWS? Infinite scalability in minutes. The silent killer? The bill that creeps up when you forget to turn things off. #CloudComputing #AWS

The most powerful AI models are useless without the infrastructure to run them. AWS gives you the building blocks—not just to deploy, but to experiment, iterate, and scale what’s next. What will you build when the foundation is already there? #AWS #AIInfrastructure

I used to think machine learning was all about complex algorithms, but the real magic happens in the data preprocessing stage. Cleaning, transforming, and engineering features often makes more impact than the model choice itself. What's one "boring" part of your ML workflow