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.

The Challenge

AI image models, while powerful, often struggle with accurately rendering text. This can lead to misspellings or nonsensical character combinations, particularly in

Introduction

We recently faced a challenge with our landing page banners on the devlog-ist/landing project. Initially, we relied on AI image generation (Gemini) to create these banners. However, inconsistencies and errors, particularly misspellings, became a recurring issue. To address this, we transitioned to a programmatic approach using Playwright for HTML rendering, leveraging the H7

The devlog-ist/landing project focuses on creating and maintaining a landing page. A recent update involved optimizing the LinkedIn banner image to improve its performance on the platform.

LinkedIn's algorithm favors portrait-oriented images in its feed. By switching the banner image from a landscape to a portrait format, the image occupies more screen real estate, increasing visibility and

The Problem

LinkedIn's aggressive JPEG compression was causing noticeable visual artifacts in the banners of our landing pages, specifically around text elements in the position badge, tech tags, and call-to-action buttons. The solid box-shadows were exacerbating the issue, resulting in a ghosting or duplication effect that degraded the overall visual quality.

The Approach

Improving LinkedIn Banner Text Clarity

The devlog-ist/landing project focuses on creating engaging landing pages. A recent enhancement focused on improving the readability and visual appeal of the LinkedIn banner text. This involved adjusting font sizes and rendering resolution to ensure crisp and clear text output.

The Problem: Pixelated Text

The initial LinkedIn banner text suffered

When creating images for social media, especially banners on platforms like LinkedIn, image quality is paramount. A common pitfall is allowing the platform itself to recompress and resize images, which often leads to unwanted artifacts and pixelation. This post dives into how to optimize image output to prevent LinkedIn from degrading banner quality.

The Problem: Downscaling and Recompression

Introduction

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.

The devlog-ist/landing project focuses on creating a visually appealing landing page. A recent update addressed an issue with the LinkedIn banner image where JPEG compression was causing undesirable pixelation artifacts.

The Problem

LinkedIn's recompression of JPEG images can sometimes lead to visual degradation, especially in areas with high contrast and vibrant colors.

Introduction

When creating diagrams for technical content, especially for sharing on platforms like LinkedIn, image compression can introduce unwanted artifacts. Red tones, in particular, are prone to pixelation due to chroma subsampling during JPEG compression. This post discusses a simple fix to improve the visual quality of diagrams shared online.

The Problem

Diagrams with red borders

Introduction

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.