I recently completed the "Cloud Fun Facts Generator" project, a full-stack application that leverages Generative AI to deliver witty facts about cloud computing. ☁️🤖 More than just connecting services, this project was a fantastic exercise in serverless architecture and, most importantly, real-world troubleshooting. 🛠️ The Tech Stack: Frontend: React hosted on AWS Amplify for fast and secure deployment. 💻 Backend: AWS Lambda (Python) handling the logic and communicating via API Gateway. 🐍 Database: Amazon DynamoDB for efficient data storage. 📊 GenAI: Amazon Bedrock (Claude Sonnet 4.5) to add that "witty" touch to the generated facts. 🧠 💡 The Real Learning (What diagrams don't always show): It wasn't all "click and run." During initial testing with Bedrock, the Lambda function failed due to token limits and response times. Seeing the app live on the final Amplify domain (as shown at the end of the carousel) is proof that the journey of a Cloud Engineer is built on persistence. ✅ A special thanks to Lucy Wang from zerotocloud for pushing these practical challenges! 🚀 What do you think of this architecture? If you are working with GenAI or AWS, how have you been handling timeout challenges in serverless functions? Let's swap experiences in the comments! 👇 #AWS #CloudEngineering #GenerativeAI #Serverless #AmazonBedrock #Python #DevOps #Amplify #CloudComputing #CaminhoCloud

🚀 Built an AI-Powered Test Orchestrator using Python, MCP, AWS Lambda, and Amazon S3. This project allows natural language commands like: run_tests("local") run_tests("dev") 🔧 What it does: ✅ Runs pytest locally ✅ Invokes AWS Lambda for cloud execution ✅ Stores results in Amazon S3 ✅ Returns structured summaries Github Repo : https://lnkd.in/g33qRYU5 💡 Key Learnings: • AWS Lambda is great for lightweight, event-driven automation workloads. • For enterprise-scale test automation, containers/Kubernetes may be better due to long-running jobs, browser dependencies, and advanced scaling needs. This project helped me understand how AI can integrate with real-world automation systems and where different cloud patterns fit best. #Python #Testing #SDET #AWS #Lambda #S3 #Automation #AI #QA #DevOps 🏗️ Architecture attached below 👇

Just built a Serverless Blog Generator using AWS Bedrock and Llama 3! Repo Link - https://lnkd.in/eCvYm4RZ The Serverless Stack & Workflow: • Entry Point: Amazon API Gateway receives a blog topic via a secure RESTful request. • Processing: AWS Lambda (Python) orchestrates the logic, handles data validation, and manages the model handshake. • Intelligence: AWS Bedrock invokes the Meta Llama 3 model to generate a professional, 200-word blog post. • Persistence: Amazon S3 automatically archives the generated content as a .txt asset for future retrieval. The "Engineering" Highlights: • Layer Optimization: Managed the 250MB Lambda layer constraint by streamlining dependencies—a classic "rite of passage" in serverless development! • Identity-Based Security: Leveraged IAM Execution Roles to enable secure service-to-service communication. No hardcoded API keys, no secrets in code—just pure, identity-based security. • Scalability: Because it's 100% serverless, the architecture scales automatically based on request volume while keeping costs at virtually zero for idle time. This project was a deep dive into how modern cloud infrastructure can act as a force multiplier for Generative AI. #AWS #GenerativeAI #Serverless #Llama3 #CloudArchitecture #Python #Bedrock #MachineLearning

AWS Lambda is a serverless, event-driven compute service that lets you run code for virtually any type of application or backend service without provisioning or managing servers. You simply upload your code (as a .zip file or container image), and Lambda automatically handles everything required to run and scale it with high availability. Key Characteristics 💎 Serverless: You don't have to manage the underlying infrastructure, such as hardware, operating systems, or patching. 💎 Event-Driven: Your code remains idle and costs nothing until it is triggered by an event, such as a file upload to S3, an HTTP request via API Gateway, or a database update in DynamoDB. 💎 Automatic Scaling: Lambda automatically scales from zero to thousands of concurrent executions in seconds to match the rate of incoming requests. 💎 Pay-per-Use: You are billed only for the compute time you consume, measured in milliseconds, and the number of requests made. How it Works ➡️ Upload Code: You write your code in a supported language (Python, Node.js, Java, Go, Ruby, C#, or a custom runtime) and upload it as a Lambda function. ➡️ Set Triggers: You configure an AWS service or HTTP endpoint to trigger your function. ➡️ Execution: When the trigger occurs, AWS Lambda spins up an isolated Firecracker microVM to run your code, then shuts it down once finished. Common Use Cases ➡️ Real-time File Processing: Automatically resizing images or transcoding videos as they are uploaded to Amazon S3. ➡️ Web Backends: Serving as the backend logic for web and mobile apps when paired with Amazon API Gateway. ➡️ Data Streaming: Processing real-time data streams for analytics or monitoring via Amazon Kinesis. ➡️ Automated Tasks: Running scheduled "cron jobs," such as daily report generation or resource cleanup, using Amazon EventBridge. #aws #lambda #cloudcomputing #DevOps #CICD #IT ➡️

🚨 AWS just changed the game. For years, Amazon Web Services S3 was object storage only — meaning even a tiny edit = full re-upload. Painful. Now with Amazon S3 Files (2026 update), that limitation is gone. 💡 What’s new? • ✏️ Edit files directly in S3 No more download → modify → upload cycles • ⚡ Sub-millisecond latency Thanks to EFS-backed caching • 🔄 No data movement Your S3 bucket is now your file system • 🤖 Built for AI agents Write logs, datasets, and outputs directly using Python/CLI • 📂 Native NFS support Use standard filesystem tools seamlessly ⚠️ Fine print (but important): Changes are committed back to S3 roughly every ~60 seconds. So it feels real-time, but sync happens in the background. 🔥 Why this matters This is more than a feature update — it’s a shift from object storage → hybrid file system Meaning: → Faster pipelines → Simpler architectures → AI workflows just got WAY easier 💭 My take: This might be the biggest S3 evolution in years. If you're building in cloud, data, or AI — you’ll want to pay attention. #AWS #CloudComputing #S3 #AI #DataEngineering #DevOps #MachineLearning #TechTrends #BuildInPublic

Amazon Web Services (AWS) Lambda – Complete Overview (Serverless Computing) Recently, I explored AWS Lambda and created a simple visual guide to understand its working and concepts. 🔹 What is AWS Lambda? @AWS Lambda is a serverless computing service where you can run your code without managing servers. 👉 Upload code → Trigger happens → Code runs → Result returned → Stops 🔹 Key Concept: Trigger A trigger is an event that starts (invokes) your Lambda function. Examples: • API request (via API Gateway) • File upload (S3) • Scheduled time (cron jobs) • Message/Event queues 🔹 How Lambda Works Internally 1️⃣ Trigger occurs 2️⃣ AWS creates a runtime environment 3️⃣ Runtime (Python/Node.js) loads 4️⃣ Your code executes 5️⃣ Response is returned 6️⃣ Environment shuts down 🔹 Event-Driven Architecture Lambda follows an event-driven model: 👉 Event → Lambda runs → Action/Result (No continuous server running) 🔹 Why Runtime Matters? Runtime tells AWS how to execute your code (Python, Node.js, etc.) 💡 Key Benefit: ✔ No server management ✔ Runs only when needed ✔ Cost-efficient & scalable 📌 Serverless. Event-driven. Efficient. #AWS #AWSLambda #CloudComputing #Serverless #DevOps #Learning #TechExplained #Cloud #Programmi

🚀 Just built an AI-Powered DevOps Pipeline on AWS! This project connects Python to AWS, uses Claude AI (via AWS Bedrock)  to analyze cloud infrastructure, and automatically generates + uploads  reports to S3 — all in one pipeline run. Here's what it does: ✅ Connects to AWS and lists all S3 buckets ✅ Creates a dedicated pipeline bucket programmatically ✅ Sends infrastructure data to Claude AI for analysis & recommendations ✅ Auto-generates a report and uploads it to S3 Tech used: Python · AWS S3 · AWS Bedrock · Claude AI · boto3 · Git/GitHub What I learned: Integrating AI into a real cloud workflow isn't just  about writing prompts — it's about connecting services, handling  credentials securely, and automating end-to-end pipelines. 🔗GitHub: https://lnkd.in/gjpGQmQi #DevOps #AWS #CloudComputing #Python #AWSBedrock #AI #BuildingInPublic

Your S3 data just became a file system. 🗂️ Amazon S3 Files launched this week and it changes how startups (and everyone else) work with data on AWS. The problem it solves is one I see constantly with the startups I work with: → Your data lives in S3 because it's cheap and durable → Your tools expect a file system → So you end up building sync pipelines, copying data back and forth, paying for duplicate storage S3 Files eliminates that. You can now mount any S3 bucket as a shared NFS file system on EC2, Lambda, ECS, and EKS. Your Python scripts, ML frameworks, and CLI tools just work with no custom connectors, no new APIs! I published a hands-on walkthrough where I set it up from scratch on EC2, ran into the real gotchas (outdated AL2023 packages, security group timeouts, permission errors), and tested bidirectional sync with a Python script. Some things that stood out: • File system → S3 export completed in under 60 seconds • S3 → file system import was even faster (~15 seconds) • Standard Python open(), os.listdir(), os.makedirs() all work exactly as expected • Up to 25,000 concurrent compute connections per file system • Available in all commercial AWS Regions today If you're currently running S3 + EFS side by side, or writing boto3 get/put calls just to process files, this is worth a peruse. Full article with code examples and setup steps 👇 https://lnkd.in/gY5J-cVr #AmazonS3 #S3Files #AWS #CloudComputing #Startups #MachineLearning #DataEngineering #Python #ServerlessComputing #CloudStorage #BuildOnAWS

AWS Q&A Compare System — I recently completed a Cloud-Native solution that optimizes technical support for AWS documentation. The system uses a Retrieval-Augmented Generation (RAG) architecture to compare a custom fine-tuned T5-Small model against a state-of-the-art Llama 3 (70B) baseline. The system doesn't just answer questions; it semantically searches thousands of pages of AWS docs using FAISS to provide factual, context-aware responses in real-time. Tech Stack:   AI/ML: T5-Small (Fine-tuned), Llama 3.3 (via Groq), FAISS Vector DB, Sentence-Transformers.  Backend: Python & Flask (REST API). Frontend: Modern UI hosted on AWS S3.  Cloud & DevOps: AWS EC2 for model inference, CloudFormation for IaC, GitHub Actions for CI/CD pipelines. Evaluation Metrics (Benchmark Results):  Semantic Similarity: 0.757 (T5) vs 0.882 (Llama 3) F1-Score: 0.391 Avg Latency: 469ms (Llama via Groq) GitHub repo: https://lnkd.in/ddqrQcjU This was a great learning experience working on the course CSET 463. I am grateful to the School of CSET Bennett University, India and Dr. Naween Kumar for his continuous support and mentorship throughout this project. His guidance played an important role in shaping both the idea and its execution. #AWS #GenerativeAI #DevOps #MachineLearning #CloudComputing #RAG #BennettUniversity #AIResearch