tools
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A Guide to MCP: Providing Context for AI Models Model Context Protocol (MCP) is an open-source framework that gives AI models context and integrates them with external tools and data sources via a client-server architecture. |
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Exploring the Features of CodeWhisperer and Its Role in the Future of Coding Learn how Amazon CodeWhisperer is uniquely designed to help you develop better code faster, improve security, protect your privacy, and strengthen—not threaten—your career as a coder. |
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Using Pipelines for CI/CD Continuous integration and continuous deployment have become very important in the daily workflow of building a product to production. We take a look at the stages necessary to have a well-functioning CI/CD pipeline. |
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Comparing 4 Top Cross-Browser Testing FrameworksThe market is flooded with cross-browser testing frameworks, with more options than ever before. How should you decide which option is best to test your application for compatibility with different web browsers? Let’s take a look at four of the top open source solutions today and compare their benefits and drawbacks. | 
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5 Pitfalls to Avoid When Developing AI ToolsDeveloping a tool that runs on artificial intelligence is mostly about training a machine with data. But you can’t just feed it information and expect AI to wave a magic wand and produce results. The type of data sets you use and how you use them to train the tool are important. Here are five pitfalls to be wary of. | 
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Benefits of Using Columnar Storage in Relational Database Management SystemsRelational database management systems (RDBMS) store data in rows and columns. Most relational databases store data row-wise by default, but a few RDBMS provide the option to store data column-wise, which is a useful feature. Let’s look at the benefits of being able to use columnar storage for data and when you'd want to. |
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Choosing the Right Threat Modeling MethodologyThreat modeling has transitioned from a theoretical concept into an IT security best practice. Choosing the right methodology is a combination of finding what works for your SDLC maturity and ensuring it results in the desired outputs. Let’s look at four different methodologies and assess their strengths and weaknesses. | 
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Comparing Apache Sqoop, Flume, and KafkaApache Sqoop, Flume, and Kafka are tools used in data science. All three are open source, distributed platforms designed to move data and operate on unstructured data. Each also supports big data in the scale of petabytes and exabytes, and all are written in Java. But there are some differences between these platforms. |
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