Documenting code, one commit at a time.
In the devlog-ist/landing project, we're focused on delivering high-quality content. A crucial part of this is ensuring that AI-generated content meets our standards before it's published.
AI models, particularly when generating longer pieces of content, can sometimes be cut short due to token limits or other constraints.
In the ongoing development of devlog-ist/landing, a project focused on creating engaging landing pages, a recent refactoring effort centered on enhancing the clarity and maintainability of our data structures. Specifically, we focused on the PostResource table.
The original PostResource table included a column labeled 'Post Reports'. This name was ambiguous and didn't clearly
This post delves into a recent refactoring effort within the devlog-ist/landing project, focusing on enhancing the structure and clarity of the PostResource table. The primary goal was to replace the 'Post Reports' column with 'Scheduled For', aiming for a more intuitive and maintainable data model.
Initially, the PostResource table included a column named 'Post Reports'.
This post details the recent enhancements to the content generation process within the devlog-ist/landing project, focusing on the integration and management of LinkedIn prompts for improved content quality and platform-specific tailoring.
The primary objective was to enhance the content generation workflow by incorporating LinkedIn-specific prompts, allowing for more targeted and
Sometimes, the most challenging bugs are not in the core logic, but in the tooling around it. Recently, while working on the diagram rendering service for a project, I encountered an issue where invalid Mermaid syntax would cause the entire process to hang. Here's how I tackled it.
The diagram rendering service, used for generating visualizations, relied on an external script to
Ensuring consistent code quality and documentation can be a challenge. One area where consistency is crucial is in the generation of diagrams for technical documentation. This post discusses how we've improved the consistency and validity of Mermaid diagrams generated as part of our technical writing process for the devlog-ist/landing project, which provides a landing page for
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.
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
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.
