Blogs on 'dot net programming' tag
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API Content Negotiation: A Production-Grade Step-by-Step Guide
Content negotiation allows a client and server to agree on the most suitable representation of a resource without changing the underlying endpoint.
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Implement CORS in .NET Core the Production-Way
Cross-Origin Resource Sharing (CORS) is one of the most commonly misconfigured features in modern APIs. Many developers enable AllowAnyOrigin() during developme
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Implement Common Middleware for Standardized Success & Failure Response Format in .NET Core
Whether your API succeeds or fails, clients should always receive predictable and structured responses. This improves frontend integration, debugging, logging,
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Sentiment Analysis Using ML.NET – Step-by-Step Guide
Machine Learning is transforming how applications understand user feedback, reviews, and social media conversations. One of the most common Natural Language Pro
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Implement a Common API Response Structure in ASP.NET Core
Building APIs in ASP.NET Core becomes much easier to maintain when all endpoints return a consistent response structure.
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How to Implement Rate Limiting in .NET Core API
In modern web applications, APIs are constantly exposed to high traffic, bots, and malicious requests. Without protection, excessive requests can overload your
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Implement Blob Storage Using Presigned URL in .NET Core and JavaScript
Uploading files through your backend server can create performance bottlenecks, especially for large files like images, videos, PDFs, or backups.
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Implement Azure Blob Storage with .NET Core API (Production-Ready Guide)
Whether you're building an e-commerce platform, document management system, or media service, Azure Blob Storage provides secure and cost-effective storage for
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How to Implement “Send SMS on Mobile” Using .NET
Sending SMS messages from a .NET application is a common requirement for: OTP verification Login authentication Notifications Alerts Marketing campaigns
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Machine Learning with .NET – A Complete Guide for Developers
Machine Learning is no longer limited to Python ecosystems. With the power of the .NET platform, developers can build, train, and deploy ML models directly with