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.

The Problem with Raw Diffs

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 Challenge: Identifying Relevant Tests

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.

Safe Mode: Balancing Convenience and Security

Recognizing that different tenants have varying security needs, we've introduced a 'safe mode' toggle.

Introduction

Auditing tools are crucial for maintaining application security and compliance. However, false positives can create unnecessary noise and divert attention from genuine threats. One common source of these false positives is the detection of reserved domain names, such as those under the IANA's example.com, example.net, and example.

This post delves into a practical approach for creating flexible UI components that seamlessly adapt to various themes within an application. We'll explore how to avoid hardcoded styles and instead leverage style variables to ensure consistent visual appearance across different themes.

The Problem: Hardcoded Styles

In many applications, UI components are initially developed with a specific

Introduction

We've recently enhanced the user experience within our application by integrating LinkedIn login and registration, specifically aimed at simplifying the recommendation flow. This post details the changes and the reasoning behind them.

Key Improvements

Simplified Registration and Login

Previously, recommenders needed a GitHub account, which added friction to the process.

Introduction

We've recently enhanced our platform to allow users to solicit professional recommendations for their portfolios, featuring a seamless integration with LinkedIn. This system allows users to request recommendations, and for recommenders to easily provide them while promoting the user's portfolio on their LinkedIn feed.

Key Features

Streamlined Recommendation Requests

Overview

This update introduces an enhanced referral program with tiered commissions, seamless Dev.to integration, and updated terms and conditions to reflect these changes.

Referral Program Enhancements

The referral program has been expanded with the addition of a dedicated landing page, complete with details about the program, commission rates, and automated tools.

Introduction

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.

The Problem

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.