Based on our record, NumPy seems to be a lot more popular than Brakeman. While we know about 107 links to NumPy, we've tracked only 7 mentions of Brakeman. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
My team and I released Bearer a couple of weeks ago, a newer open and free alternative to Brakeman to check your code for security and privacy risks. In addition to Ruby/Rails, we also cover your JS/TS code, which allows you to use a single solution for your whole Rails application. Source: 10 months ago
Brakeman is a static analysis security vulnerability scanner for Ruby on Rails applications. It finds potential security issues in Rails applications by examining the Ruby code. Brakeman helps find and fix security holes before deploying your Rails app. - Source: dev.to / 10 months ago
Brakeman is another useful Ruby gem that is a static analysis security vulnerability scanner for Ruby on Rails applications. - Source: dev.to / 12 months ago
A while ago, I came across a Brakeman false positive that I wanted to fix. - Source: dev.to / almost 3 years ago
In order to prevent this issue, your organization needs to implement regular checks of your dependencies against the CVE database for known vulnerabilities, as well as establishing a process for keeping all dependencies up-to-date. Fortunately, much of this can be automated using vulnerability scanning tools, such as the OWASP Dependency Check, RetireJS, or Brakeman. Additional tools, such as WhiteSource's... - Source: dev.to / almost 3 years ago
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / about 2 months ago
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / about 2 months ago
Numpy: A library for scientific computing in Python. - Source: dev.to / 5 months ago
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy. - Source: dev.to / 6 months ago
A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / 7 months ago
RuboCop - A Ruby static code analyzer, based on the community Ruby style guide.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
SonarQube - SonarQube, a core component of the Sonar solution, is an open source, self-managed tool that systematically helps developers and organizations deliver Clean Code.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Reek - Code smell detector for Ruby
OpenCV - OpenCV is the world's biggest computer vision library