Documenting code, one commit at a time.
We've recently enhanced our content generation process by introducing a 'random mode' to our manual post generator. This feature aims to accelerate content creation and reduce repetitive tasks. The primary goal is to automate the selection of post examples and prompts, thereby enabling faster and more varied content output.
Improving the auditability of AI interactions is crucial for maintaining security and control. A recent update focuses on preventing the exposure of raw code to AI models, enhancing data security, and providing better insights into flagged code changes.
Previously, raw git diffs were sent to AI models for analysis. This approach, while providing detailed context,
This post discusses recent improvements to our application, focusing on enhanced security measures and smarter code generation capabilities.
We've introduced a 'safe mode' feature, giving tenants more control over security audits during post generation. By default, safe mode is enabled, ensuring all generated content undergoes a thorough security check.
In large projects, running the entire test suite can be a significant time sink. Waiting for all tests to pass before merging changes can slow down development and increase feedback loops.
We recently implemented a system to run only the tests related to files changed in a given commit. This dramatically speeds up our test runs and provides faster feedback to developers.
Motivating users to actively participate in referral programs can significantly boost acquisition and growth. We recently enhanced our application's user dashboard with a new widget designed to provide transparent insights into referral earnings and commission structures.
This widget serves as a central hub for users to understand and leverage their referral potential, directly from their
We've recently enhanced our manual post generator with a new "random mode." This feature streamlines content creation by automating the selection of post examples and additional prompts. This post will discuss the benefits of this new mode and the refactoring involved in its implementation.
The core addition is the ability to toggle a "random mode"
Improving the way we audit code changes is crucial for maintaining security and stability in our applications. Recently, we transitioned from feeding raw Git diffs directly to our AI analysis tools to using structured summaries. This shift significantly enhances auditability and reduces the risk of exposing sensitive information.
Sending raw diffs to AI models
Long test suite run times can significantly slow down development. Running the entire suite for every change, especially in larger projects, becomes inefficient. A focused approach that executes only the tests relevant to the modified code provides quicker feedback and accelerates the development cycle.
The primary challenge lies in accurately
We've recently rolled out enhancements to our post generation process, focusing on flexibility and code accuracy. These updates include a 'safe mode' toggle and intelligent programming language detection.
Recognizing that different tenants have varying security needs, we've introduced a 'safe mode' toggle.
Security audits are crucial for maintaining the integrity of applications. However, overly sensitive rules can lead to false positives, particularly when dealing with illustrative code examples. This post discusses how to refine audit rules to distinguish between genuine security vulnerabilities and intentionally simplified or educational code snippets.
