Documenting code, one commit at a time.
The devlog-ist/landing project focuses on creating landing pages. Recently, we tackled a performance bottleneck during the LinkedIn publishing process. The issue arose from sending large base64 encoded images directly within the publish request, leading to 413 errors from Nginx due to exceeding the maximum request size.
LinkedIn's API has limits on request size. Sending a
This post details a small but important change in how we generate content for sharing on LinkedIn, focusing on aligning the tone with the platform's user experience.
The primary goal was to shift the voice of automatically generated LinkedIn share posts to a first-person perspective. Since the posts are published from the user's profile, using "I" instead of third-person references
In the ongoing development of the devlog-ist/landing project, a critical focus has been placed on fortifying security and reliability. Recent efforts have addressed several code review findings, enhancing the overall robustness of the application.
Several key areas were identified and improved during the code review process:
The devlog-ist/landing project focuses on creating landing pages and managing content for various platforms. This post details the addition of an independent LinkedIn random mode for post generation.
Previously, the application used a single random mode toggle that controlled whether random post examples and prompts were used across all platforms.
When developing tools for content generation, flexibility is key. A recent update to the devlog-ist/landing project focuses on providing more granular control over how content is generated for different platforms. Specifically, we've decoupled the random mode setting for LinkedIn from the portfolio's random mode.
Previously, a single "random mode"
We recently added a "Share on LinkedIn" feature to our landing page project, devlog-ist/landing. The goal was to increase user engagement and expand the reach of user portfolios by enabling seamless sharing on LinkedIn. This post details the implementation process, covering AI-powered post generation, direct publishing via the LinkedIn API, and considerations for different user
Recent code reviews have highlighted several opportunities to improve the robustness, performance, and maintainability of our application's reporting queries. These changes focus on ensuring data consistency, optimizing query execution, and adhering to coding standards.
We addressed an issue where facades (like File
This post summarizes recent code review findings and improvements made to a database migration script within our application. The focus is on enhancing both performance and code quality through addressing issues ranging from index usage to data consistency and coding style.
A critical issue identified was the absence of explicit facade imports.
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
This update introduces an enhanced referral program with tiered commissions, seamless Dev.to integration, and updated terms and conditions to reflect these changes.
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.
