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.

The Problem: Silent Content Truncation

AI models, particularly when generating longer pieces of content, can sometimes be cut short due to token limits or other constraints.

In the rapidly evolving landscape of AI-powered tools, clear communication is paramount. A recent update to the devlog-ist/landing project highlights this necessity, specifically in how these tools interact with platforms like LinkedIn.

The Problem: Markdown Mishaps

AI-generated content, while powerful, often relies on markdown for formatting. However, platforms like LinkedIn don't fully

Introduction

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.

The Challenge

Previously, publishing

When publishing content to LinkedIn, large image sizes can lead to 413 Request Entity Too Large errors, especially when dealing with base64 encoded images. This post discusses how we addressed this issue in the devlog-ist/landing project by implementing server-side caching of images.

The Problem: Large Image Payloads

Directly embedding base64 encoded images into API requests,

Introduction

This post discusses an improvement made to the devlog-ist/landing project, specifically addressing how AI-generated content is formatted for LinkedIn. The issue was that markdown links, which are correctly rendered in most contexts, appear as broken plain text on LinkedIn. The solution involved adjusting the AI's instructions to generate plain URLs instead of markdown.

In the devlog-ist/landing project, which I understand to be a project focused on creating landing pages, a recent effort was made to improve the accuracy of AI-generated banner images. The specific challenge addressed was the frequent misspelling of text within these images, a common issue with many AI image models.

The Problem

AI image models, while powerful, often struggle with accurately

Introduction

Ensuring the quality of AI-generated content is crucial before it reaches the end user. This post details how to implement automated validation checks to detect and prevent the publication of truncated or incomplete AI-generated articles in the devlog-ist/landing project.

The Challenge

AI models, while powerful, can sometimes produce incomplete outputs due to token limits,

This post explores the recent updates to cost tracking and budgeting for AI operations within the devlog-ist/landing project.

The Challenge

As AI models become more integrated into our applications, managing and controlling the costs associated with their usage is crucial. Without proper tracking and budgeting, expenses can quickly spiral out of control, especially when dealing with

Introduction

We recently replaced an AI-powered image generation process for our landing page banners with a programmatic rendering solution. While AI initially seemed promising, inconsistencies and errors in the generated images led us to explore alternative approaches. This post details our journey and the benefits of switching to a more controlled and predictable system.

Introduction

In the realm of web development, ensuring content is accessible and understandable to a global audience is paramount. This post delves into the importance of complete translations within landing page projects, focusing on a recent update to include a missing translation for the 'scheduled_for' field.