Documenting code, one commit at a time.
We've been working on the landing page project which serves as the main marketing site. A key aspect of this project involves streamlining content publishing across multiple platforms. We've recently integrated Dev.to publishing capabilities directly into our Filament admin panel, mirroring our existing LinkedIn publishing workflow.
Previously, publishing
The devlog-ist/landing project focuses on creating landing pages, and a key feature is generating images for sharing on social media platforms like LinkedIn.
Image generation for LinkedIn previews was failing due to an outdated Gemini model ID. The previous model, gemini-2.0-flash-preview-image-generation, was returning a 404 error, preventing the creation of these preview
When integrating AI into software development workflows, it's crucial to handle potential inconsistencies in the AI's responses. This post addresses a scenario where an AI service, designed to provide structured data, occasionally omits a specific key, leading to errors in the consuming application.
mermaid_diagramThe devlog-ist/landing project
The devlog-ist/landing project, which provides a landing page experience, recently underwent an internationalization enhancement to broaden its accessibility. Ensuring a consistent user experience across different locales is critical for global projects.
A significant update involved the addition of missing translation keys for German (de), Spanish (es), and
In the devlog-ist/landing project, which likely serves as a landing page or blog platform, we recently tackled an interesting problem: duplicate tags appearing in the LinkedIn banner. Here's how we addressed it.
When generating banners for LinkedIn, tags like "HTML", "html", and "Html" were being treated as distinct entities, resulting in duplicates
In the ever-evolving realm of AI-driven content creation, details matter. Even seemingly minor aspects like text accuracy in generated images can significantly impact the final product's quality and usability.
AI image models, while powerful, often struggle with accurately rendering text. This can lead to misspellings or nonsensical character combinations, particularly in
When creating marketing materials for LinkedIn, image quality matters. However, LinkedIn's automatic image processing can sometimes degrade the quality of uploaded banners, especially those with sharp text. This post details how we optimized banner generation in the devlog-ist/landing project to minimize quality loss during LinkedIn's recompression process.
In the devlog-ist/landing project, which focuses on creating engaging landing pages, we encountered an interesting challenge: ensuring the accuracy of AI-generated content, specifically for LinkedIn posts. The issue arose when the AI model, while drafting the post text, rephrased the job position title, leading to inconsistencies with the banner image associated with the post.
The devlog-ist/landing project focuses on creating engaging landing pages for developers. A recent enhancement addresses how a user's position is displayed in the banner section of their profile. When a user's current position is not explicitly set, the system now leverages AI to infer a relevant title from their context, such as technologies used or posts made.
In the dynamic world of web development, understanding the competitive landscape is crucial. Our recent endeavor focused on a detailed analysis of landing page strategies employed by key players in the developer community. This involved scrutinizing the approaches of platforms like Dev.to, GitBook, daily.dev, Hashnode, and ReadMe.