Documenting code, one commit at a time.
This article discusses a common issue in applications using GitHub OAuth for authentication: inadvertently overwriting a user's existing token with one that has fewer permissions. We'll explore how to ensure the application preserves the user's original, broader-scoped token when requesting narrower scopes during subsequent logins.
Many applications use GitHub
When auditing file system operations within an application, a common challenge arises when dealing with generic or placeholder file paths. These paths, often used during testing or initial setup, can trigger false positive alerts in audit logs, obscuring genuine security concerns. A recent update addresses this issue, enhancing the accuracy and reliability of our auditing process.
Sometimes, during development, placeholder values can inadvertently slip into the final application. This post discusses how we addressed such an issue in our vlog index page, specifically focusing on unresolved :app_name placeholders.
Unresolved placeholders are problematic because they present a poor user experience and can sometimes expose internal
We've all been there: a quick fix for an apparent issue, like an unresolved placeholder in a user-facing section. But what happens when the fix itself introduces more complexity than it resolves?
Recently, we addressed an issue where the :app_name placeholder wasn't being correctly rendered on the /earn hero section of our application.
Ensuring the security and integrity of published content is paramount. We've recently integrated a mandatory security audit step into our content generation and publishing pipeline to bolster these efforts.
AI-generated content offers numerous benefits, but also introduces potential security risks. It's crucial to implement safeguards to prevent
We've recently refactored and optimized our AI token usage tracking to improve performance and maintainability. This involved extracting duplicated queries and enhancing user filtering.
The primary focus was on improving the efficiency of retrieving daily and monthly tenant AI usage data. This was achieved through two key changes:
Developers often strive for efficiency, but sometimes, optimizing too early can lead to more problems than solutions.
Optimizing code before understanding its performance bottlenecks can waste time and introduce unnecessary complexity. It's tempting to micro-optimize, but it's crucial to identify the actual performance-critical sections first.
This post delves into optimizing SQL queries for product ranking and data aggregation, focusing on common pitfalls and effective strategies to enhance performance and accuracy. We'll explore techniques to address memory errors, improve query speed, and ensure data integrity when dealing with complex relationships and large datasets.
