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.

Introduction

Ensuring the quality of AI-generated content is crucial before it reaches the end user. This post details how we implemented content validation to detect and prevent truncated or incomplete AI-generated posts from being published in the devlog-ist/landing project.

The Problem: Truncated AI Output

AI models, especially when generating longer content, can sometimes be cut off

Introduction

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.

The Problem: Missing mermaid_diagram

The devlog-ist/landing project

We're constantly striving to improve the content generation process for the devlog-ist/landing project, which aims to create engaging and informative landing pages. Recently, we focused on enhancing the AI's ability to generate relevant diagrams for our technical blog posts. It turns out simply asking for a diagram isn't enough – you need to be specific about its purpose!

The Problem: Generic

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.

The Problem

LinkedIn's API has limits on request size. Sending a

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

This post explores the implementation of cost tracking and budget limits for AI operations within the devlog-ist/landing project. We'll examine how per-request costs are tracked in EUR, how pricing is stored per model, and how monthly cost limits are enforced per plan.

Cost Tracking in EUR

The system now tracks the cost of each AI operation in EUR. This involves:

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.

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.

The Problem

In the ongoing development of devlog-ist/landing, a project focused on creating engaging landing pages, a recent refactoring effort centered on enhancing the clarity and maintainability of our data structures. Specifically, we focused on the PostResource table.

The Problem

The original PostResource table included a column labeled 'Post Reports'. This name was ambiguous and didn't clearly