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Hello world !

 The story of "Hello, World!" is deeply tied to the history of programming and computer science education. Here's a quick rundown of its origins and significance: 1. Origins in Early Programming The phrase "Hello, World!" first appeared in programming literature in the 1970s. It was popularized by Brian Kernighan in his book The C Programming Language (1978), co-authored with Dennis Ritchie , the creator of the C language. However, Kernighan had already used it in an earlier 1972 internal Bell Labs tutorial for the B programming language, a precursor to C. The first recorded "Hello, World!" example in B looked like this: main() { printf("hello, world\n"); } 2. Why "Hello, World!"? Simplicity : It's a small, easy-to-understand program that demonstrates basic syntax. Testing : It's often the first thing programmers write when learning a new language. Debugging : It ensures that the compiler and en...
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Understanding Google Cloud IAM, with a step-by-step gcloud walkthrough

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Cloud Engineering · Infrastructure as Code : TERRAFORM GUIDE with GCP

Cloud Engineering · Infrastructure as Code · ~28 min read Provisioning Google Cloud APIs the Right Way: A Complete Terraform Walkthrough (Modules, IAM/WIF Deep-Dive, and Tests) Most Terraform tutorials for Google Cloud jump straight into resources — a bucket here, a Cloud Run service there — and quietly skip the step that breaks everything on a fresh project: enabling the underlying APIs first , and understanding how those APIs actually trust each other through IAM . If you've ever seen Error 403: API [x] not enabled on project [y] , or spent an afternoon debugging why a GitHub Actions job can't authenticate to GCP, this post is for you. We're going to build a real, working use case — a serverless event-driven data pipeline — entirely with Terraform, structured as reusable modules rather than one flat file, enabling every API it needs, explaining the parameters on every resource we write, and mapping out exactly how IAM and Workload Identity ...
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Kaggle Tutorial · Data Science in Retail

c Kaggle Tutorial · Data Science in Retail Retail Data Science — From Data to Revenues A complete, hands-on guide to mastering KPIs, forecasting, customer segmentation, and machine learning in retail — using fully synthetic data you can run today. 6 Phases · 3–6 Months · Python + SQL · Portfolio-Ready €€€ Business impact driven by DS 12 Core retail KPIs explained 8 ML use cases with code 5 Portfolio projects to build Introduction Value Chain KPIs ML Use Cases Skills Projects Datasets Roadmap Why This Guide Why Retail is One of the Best Playgrounds for Data Science Retail generates some of the richest, most varied, and most immediately actionable data of any industry. Every pu...
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