Kumar Prateek

           Better habits of Copilot usage help in two ways: Higher quality code output Reduced credit waste The objective isn’t to limit our use of the tool. The goal is simply to be more precise. Context becomes part of the cost Under usage-based billing, context length directly impacts cost. Vague prompts often lead to repeated attempts, longer answers and more token usage. Effective prompting provides the AI with essential details immediately: The specific logic to modify All necessary source files Mandatory architectural constraints Code areas to remain untouched Required testing and validation The desired response format Weak prompt example : “Fix this code” Better prompt example : “Refactor the selected logic for clarity while maintaining the existing API. Do not modify the method signature. Provide unit tests covering edge cases like null inputs. Output the refactored code with a brief summary” This approach is more professional. It significantly min...

API Response Compression in .Net Core If you are here, I am sure you must have used (consumed/ produced) APIs and indeed its been the case in modern web/ mobile application where APIs are used very frequently.  Data transfer size in case of APIs is a very big concern as the volume of data which can be transferred via APIs are not really meant to have huge data as because of following reasons: Data transfer time over network can be a big constraint if the size of data is huge APIs response time are expected to be fast and quick and if the volume of the data is huge obviously the response time of the API will be impacted as well, and no client application consuming the APIs will expect the response time of an API to be beyond a few seconds (at max). So how do we really solve this problem. Well we all understand that API is not really meant to perform huge data transfers but just in case we still have to do how to do it effectively. And there comes API compression frameworks at our re...

List of target databases or tools applications where data can be moved using Azure Data Factory  (aka. Azure Data Factory Connectors)   Azure Data Factory as we know is an PaaS (Platform as a Service) offering by Microsoft Azure. Which is known for its fully managed integration services. Which can also be called as serverless integration service as most of the other PaaS offering. With use of Azure Data Factory one can seamlessly integrate the source data source, perform the data transformation (if any) and load the data into a difference data store. Basically, it's a fully-managed ETL (Extract-Transform-Load) process/ tool in other words.     But most of us don’t know which are the data sources where Azure Data Factory can make connection with using there in built connectors. Well, there are more than 90 built-in,  maintenace -free connectors (as per Azure official documentation). And in this article, we will try to understand what are  those lists of conn...

What is Cloud Computing?     I am sure you much have heard tons of time about Cloud Computing and as few among us might be even aware of aware of this term as well. And one must not be surprised by that as this term had blown up the internet industry and everyone in past decade or so has been taking about it even if they know about it or not.     So, what exactly is cloud computing?      Well to understand that we have to understand how do internet work.     How internet works   Well internet is nothing but millions and millions of systems connected with each other and sharing data among themself. So, where do we need cloud computing in this. Internet is controller/ managed by multiple data centers and each data centers use to have numerous machines/ hardware. And anyone who need to consume the resource available in these data center need to pay for a complete machine up till a few decades ago, even though you need the complete machine c...