Improving AI-Generated Content for LinkedIn: Fixing Markdown Links

When generating content for different platforms, it's crucial to consider how each platform renders text and formatting. This post discusses a recent fix in the devlog-ist/landing project to improve how AI-generated content is displayed on LinkedIn, specifically addressing the issue of markdown links.

The Problem: Broken

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

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

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.

Introduction

In the development of web applications, maintaining consistency across different themes and ensuring all features are accessible can be a challenge. This post discusses how we addressed an oversight in the landing page project where LinkedIn share buttons were missing from the desktop navigation in certain custom themes.

The Problem

The devlog-ist/landing project uses

This post focuses on improvements to the integration between devlog-ist/landing and Dev.to, specifically simplifying the process of setting up the necessary API key.

The Need for Clearer Instructions

Previously, integrating devlog-ist/landing with Dev.to required users to manually configure an API key. The existing documentation lacked sufficient detail, leading to confusion and a higher

Introduction

When generating content for multiple platforms, the need for tailored approaches becomes apparent. A one-size-fits-all strategy often falls short, especially when dealing with platforms like LinkedIn that thrive on specific content types. We addressed this by introducing independent random modes for post generation, allowing customized content creation for each platform.

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 Goal

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