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.

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

In the fast-paced world of social media, optimizing visual content is crucial for capturing audience attention. This post details our recent effort to improve the performance of LinkedIn banner images for the devlog-ist/landing project, which focuses on developer-related content.

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:

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

This post details the recent enhancements to the content generation process within the devlog-ist/landing project, focusing on the integration and management of LinkedIn prompts for improved content quality and platform-specific tailoring.

The Goal

The primary objective was to enhance the content generation workflow by incorporating LinkedIn-specific prompts, allowing for more targeted and

Introduction

We recently added a "Share on LinkedIn" feature to our landing page project, devlog-ist/landing. The goal was to increase user engagement and expand the reach of user portfolios by enabling seamless sharing on LinkedIn. This post details the implementation process, covering AI-powered post generation, direct publishing via the LinkedIn API, and considerations for different user

Introduction

In our ongoing effort to improve the reliability and traceability of email communications within our application, we've recently implemented transactional email headers for recommendation notifications. This, coupled with some code style enhancements, aims to provide a more robust and maintainable system.

Transactional Email Headers

Transactional emails, such as

Improving the accuracy and scope of technology detection is crucial for generating relevant and informative content. A recent update introduces rule-based technology detection, significantly expanding our ability to identify the technologies involved in code changes. This enhancement allows for more precise tagging and categorization of blog posts, benefiting both content creators and readers.

Our application now supports content negotiation to better serve AI agents and LLMs. We've added the ability to return Markdown instead of HTML when requests include the Accept: text/markdown header.

This enhancement allows AI agents to directly consume the raw Markdown content of our posts, simplifying parsing and improving efficiency.

The Problem

Previously, our application served HTML