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CodeRemedy- we'll clean your codebase from technical debt

Hacker News

CodeRemedy- we'll clean your codebase from technical debt

Today we're launching CodeRemedy, http://.coderemedy.co/ a service that focuses on improving your existing codebase by eliminating technical debt. The process is very simple- you add our developers to your repository and they will go line-by-line through your code and improve it. We have a big team of developers with almost two years of training experience in improving codebases and who keep up-to-date with the latest technologies and best practices. Most of software development agencies run into a lot of legacy code, especially with big enterprise applications. According to a 2018 Stripe report on The Developer Coefficient, developers waste almost half of their working time (17 hours/week) on code maintenance (i.e. dealing with bad code/errors, debugging, refactoring, modifying). Just thinking about that makes one dizzy. Almost half of the developers’ time is spent working on code maintenance: not developing new features, improving the look and feel of the application, or making it more secure, but dealing with technical debt and bad code. This is when we decided to make it our mission to improve the codebase of our clients. After working with them for some time, we saw the results and they were so good that we decided to change our business model and focus mainly on that- improve the "internet's code" and eliminate code debt from as many web products as we can. The process is very simple- you pick a plan that works for you (depending on how big the codebase is and how fast you need it to be improved) and then our developers will jump right into it and start improving your code line-by-line, file by file. We will document everything and any change will be in form of a pull-request that can be reviewed by your team. This is a big day for us and we hope to get a lot of feedback from the community. Help us get rid of code debt!

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nativeThis product was originally launched on this platform.
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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