Ahmad Mujtaba’s Post

AWS just made infrastructure deployment up to 4x faster 🔥 CloudFormation Express mode is here, and it changes how we think about the deploy-test-iterate loop. Instead of waiting minutes for full stabilization checks, Express mode confirms your resource configuration is applied and hands control back to you in seconds while stabilization keeps happening in the background. A few numbers that stood out to me: → SQS queue with a DLQ: 64s → as fast as 10s → Deleting a Lambda with a network interface: 20-30 min → seconds The best part? No template changes required. Just set the deployment config to Express, or use cdk deploy --express if you're on CDK. It's live now in every commercial AWS region, at no extra cost. This is a big deal for two groups specifically: developers iterating on infra locally, and AI agents that need fast feedback loops to build and validate changes autonomously. As agentic coding tools become part of everyday infra work, shaving deployment latency down like this compounds fast. Curious to see how teams start weaving this into their CI/CD and dev workflows. Have you tried Express mode yet? What's your early read game changer for dev environments, or would you still want full stabilization for anything customer-facing? #AWS #CloudFormation #DevOps #InfrastructureAsCode #CloudComputing #AWSCommunity #SoftwareEngineering #AIAgents #TechNews #CloudArchitecture #Cloud #GCP #AI #MachineLearning #Google #Meta #Tesla #Netflix #GenAI #GenerativeAI #DataAnalyst #DataScience #AIML #Dev #kiro #AWSquicks #AWS #Jobs #Hiring #Jobposts #Job

Exploring AI with AWS Today I designed and studied an AI Resume Analyzer architecture using Amazon Bedrock and AWS serverless services. The idea behind this architecture is simple: a user uploads a resume through a web application, AWS Lambda processes the request, Amazon Bedrock analyzes the resume using a foundation model, and the user receives AI-generated insights such as an ATS score, skill analysis, missing skills, and improvement suggestions. While creating this architecture, I also got a better understanding of how services like Amazon S3, CloudFront, API Gateway, Lambda, IAM, CloudWatch, Route 53, ACM, and KMS work together to build a secure, scalable, and serverless AI application. Every architecture I build helps me understand AWS services more deeply and improves my solution design skills. Looking forward to building more AI-powered applications on AWS. Thankyou 😊 #AWS #AmazonBedrock #GenerativeAI #CloudComputing #Serverless #Lambda #APIGateway #AmazonS3 #CloudFront #CloudWatch #IAM #Route53 #AWSArchitecture #SolutionArchitecture #AWSCommunity #LearningEveryDay #claude #anthropic #Claudesonet Amazon Web Services (AWS) #Resume #frontend #backend #AI

🚀 BIG MOVE FROM AWS! 🚀 💰 AWS has announced a $1 BILLION investment in a new Forward Deployed Engineering (FDE) organization — a clear signal that the next AI race is no longer about models alone… it’s about delivering real business outcomes. 🌍⚡ 🔹 Thousands of AWS engineers will work directly with customers. 🔹 Focus on building and deploying Agentic AI solutions in days, not months. 🔹 Engineers will embed with business, engineering, security, and platform teams to solve real-world problems. 🔹 This model follows the growing trend pioneered by companies like Palantir and adopted by leading AI players. (Reuters⁠) 🎯 What does this mean for Cloud, DevOps, Platform Engineering, and SRE professionals? ✅ Less focus on infrastructure management ✅ More focus on AI-enabled platforms ✅ Stronger demand for business-domain expertise ✅ Rise of “Forward Engineers” who combine: • Cloud ☁️ • AI 🤖 • Security 🔐 • Software Engineering 💻 • Customer Engagement 🤝 💡 The biggest lesson? The future belongs to engineers who can bridge the gap between: 📊 Business Problems + 🤖 AI Models + ☁️ Cloud Platforms We’re moving from: ➡️ “Build infrastructure” to ➡️ “Deliver outcomes” AWS isn’t just investing $1B in engineers. They’re investing in the future of how technology teams will work. 🚀 #AWS #ArtificialIntelligence #AgenticAI #CloudComputing #PlatformEngineering #DevOps #SRE #CloudEngineering #GenerativeAI #LLMOps #MLOps #Innovation #FutureOfWork #TechLeadership

AWS Lambda MicroVMs are here! AWS has introduced Lambda MicroVMs — a new serverless compute primitive that combines: 🔒 VM-level isolation ⚡ Near-instant launch & resume speeds 💾 Stateful execution with suspend/resume for up to 8 hours Built on Firecracker, the technology behind 15+ trillion Lambda invocations per month, MicroVMs are ideal for AI agents, coding assistants, interactive development environments, data analytics, and multi-tenant applications. Key benefits: ✅ Dedicated compute environment per user or job ✅ HTTPS endpoints with HTTP/2, gRPC & WebSocket support ✅ Dockerfile-based image creation ✅ No infrastructure or virtualization management required A significant step forward for developers building secure, scalable, and AI-powered applications on AWS. #AWS #Lambda #MicroVM #Serverless #CloudComputing #DevOps #GenerativeAI #PlatformEngineering

AWS has introduced AWS Lambda MicroVMs — a new serverless compute primitive designed for isolated execution of user-generated and AI-generated code. This is a strong step forward for teams building AI agents, coding assistants, interactive sandboxes, data platforms, security scanners, and multi-tenant applications where every user or job needs its own secure execution environment. Key benefits: ✅ VM-level isolation Run untrusted or dynamic code inside isolated MicroVM environments without managing virtualization infrastructure. ✅ Near-instant launch and resume Start and resume environments quickly, making it suitable for interactive and session-based workloads. ✅ State preservation Suspend and resume execution while preserving memory and disk state, which is useful for long-running or interactive sessions. ✅ Full lifecycle control Control MicroVM lifecycle with run, suspend, resume, and terminate operations. ✅ Built for modern AI workloads A practical option for AI-generated code execution, agent sandboxes, developer environments, and secure multi-tenant platforms. In short: AWS Lambda MicroVMs brings stronger isolation, fast startup, and stateful execution into a managed serverless model — without the operational burden of running your own Firecracker/KVM-based sandbox platform. Official AWS announcement: https://lnkd.in/dPam-5vm #AWS #AWSLambda #Serverless #LambdaMicroVMs #CloudComputing #DevOps #CloudSecurity #MicroVMs #Firecracker #AIInfrastructure #AIAgents #Sandboxing #PlatformEngineering #CloudNative #SecureCodeExecution

I set out to run an open-source project and ended up contributing to it. I was exploring the Amazon Web Services (AWS) Intelligent Document Processing (IDP) pipeline, a reference architecture, to understand how AWS structures a full IDP system end to end. Getting it running surfaced issues I had to work through, and resolving them meant reading the whole system: - How Cognito and IAM are provisioned through CDK, - How the frontend, backend, Lambdas, and AI agents are separated, and - How the tooling ties it all together. That work turned into a fix I contributed back. Engaging with unfamiliar, production-grade code improves your judgment on architecture and tradeoffs far faster than staying inside your own stack. A model is a small part of a real system. The infrastructure, auth, and pipelines around it are what decide whether it ships, and open source is one of the best ways to study how strong teams get that right. #OpenSource #AWS #MachineLearning #MLEngineering #SoftwareEngineering #AI

🚀 What if your AWS logs could explain the problem instead of just reporting it? Imagine an AI-powered log analyzer that doesn't stop at detecting errors—it tells you: ✅ What went wrong ✅ The root cause ✅ The business impact ✅ Recommended fixes ✅ Who should be notified automatically 🏗️ Proposed AWS Architecture CloudWatch Logs → EventBridge → AWS Lambda → Amazon Bedrock → Amazon SNS → Slack/Email/Teams Optionally, the solution can leverage Amazon S3 and Amazon OpenSearch as a knowledge base to provide even more accurate troubleshooting and recommendations. 💡 Why build this? - Reduce Mean Time to Detect (MTTD) - Reduce Mean Time to Resolve (MTTR) - Automate root cause analysis - Eliminate manual log investigation - Deliver AI-generated incident summaries in seconds - Improve DevOps and Cloud Operations productivity This is one of the AI + AWS solutions I'm planning to build and open-source. It combines Serverless Architecture, Amazon Bedrock, CloudWatch, Lambda, EventBridge, and SNS to create an intelligent operations assistant for AWS environments. 💬 Would you like a complete step-by-step implementation with source code, architecture, and deployment guide? Comment "AI Log Analyzer" below, and I'll build the complete project and share it with the community. #AWS #AmazonBedrock #GenerativeAI #ArtificialIntelligence #CloudComputing #AWSDeveloper #DevOps #CloudWatch #Lambda #Serverless #Observability #MLOps #Python #Automation #OpenSource #sarotechhub

As AI becomes a bigger part of cloud operations, moving beyond simple alerting to intelligent incident analysis is the next logical step. This architecture is a great example of combining AWS serverless services with Amazon Bedrock to transform raw logs into actionable insights—helping teams understand not just what failed, but why it failed and what to do next. Reducing MTTR through AI-assisted root cause analysis has huge potential for modern DevOps and SRE teams. Looking forward to seeing this project evolve and the open-source implementation. 🚀 #AWS #DevOps #CloudComputing #GenAI #AmazonBedrock #Serverless #SRE #Observability #CloudOps

A founder thought they had an infrastructure problem. They had a decision problem. AWS was costing them over $11,000 every month, and the obvious answer was to buy more servers. Before touching the infrastructure, I looked at something cheaper. The code. One endpoint was triggering hundreds of identical database queries on every request. No cache. No proper indexes. The database was doing the same work thousands of times a day. I didn't redesign the platform. I added the right index. Cached what didn't need recalculating. Later... The monthly infrastructure bill dropped to $1,900. Nothing changed for the users. The only thing that changed was that someone finally looked where everyone else assumed the problem wasn't. That's what I've learned building backend systems and AI platforms. Most companies don't have an AI problem. They have a systems problem. Everyone wants a smarter model. Very few ask whether the architecture deserves one. That's where I spend my time. Building AI platforms that don't just work in demos—they continue working when thousands of real users show up. #SeniorbackendDeveloper #RAG #AIengineer #DevOpsEngineer #MLOPS

𝗛𝗼𝘄 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗦𝗲𝗹𝗳-𝗛𝗲𝗮𝗹𝗶𝗻𝗴 𝗔𝗪𝗦 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘄𝗶𝘁𝗵 𝗔𝗜. 𝗛𝗲𝗿𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗲𝘅𝗮𝗰𝘁 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝘁𝗼 𝗲𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗲 𝟯 𝗔𝗠 𝗽𝗮𝗴𝗲𝗿 𝗰𝗮𝗹𝗹𝘀 𝗳𝗼𝗿𝗲𝘃𝗲𝗿. Production outages cost companies thousands of dollars per minute. But what if your infrastructure could fix itself before your team even wakes up? I recently designed an architecture that merges 𝗔𝗜𝗢𝗽𝘀, cloud native engineering, and intelligent automation to create a completely autonomous cloud environment. Here is exactly how the lifecycle works: 1️⃣ Detection: Amazon CloudWatch spots an anomaly (e.g., a sudden CPU spike or memory leak in Amazon EKS). 2️⃣ Automation Trigger: Amazon EventBridge routes the alert instantly to an AWS Lambda function 3️⃣ AI Analysis: Lambda extracts metrics and feeds them to Amazon Bedrock. The AI scans historical incident logs stored in Amazon S3 to find the exact root cause. 4️⃣ Remediation: The AI Decision Engine triggers AWS Systems Manager to execute a precise runbook (like scaling pods or restarting a leaking service). 5️⃣ Verification & Audit: The system validates the fix, stores the case data in Amazon DynamoDB, and logs the victory in an operational dashboard while notifying the DevOps team via Amazon SNS. 🎯 Real-World Scenario: Instead of a memory leak crashing a production database at midnight, the AI identifies the pattern, restarts the specific microservice container, scales the cluster temporarily, and texts the team: "Issue resolved in 42 seconds." 🛠️ The Full Tech Stack Used: • Compute & Orchestration: Amazon EKS, AWS Lambda • Monitoring & Routing: CloudWatch, EventBridge • Intelligence Engine: Amazon Bedrock (GenAI) & S3 • Execution & Storage: AWS Systems Manager, DynamoDB, SNS By moving from reactive troubleshooting to proactive self-healing, we don't just reduce downtime—we eliminate manual intervention. 𝗟𝗲𝘁’𝘀 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗼 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝘆𝗼𝘂𝗿 𝗰𝗹𝗼𝘂𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀. — Muhammad Tayyab Ghaffar #AWS #DevOps #CloudComputing #ArtificialIntelligence #AIOps #Kubernetes #CloudArchitecture #Automation #SRE #PlatformEngineering