Documenting code, one commit at a time.
In the "landing" project, we continually seek to enhance user experience and streamline complex processes. A significant feature within this project is CV generation, which initially relied on manual user input for crucial job-related details.
Our original CV generation form required users to manually provide the company name, specific role name, and preferred language for a job
The devlog-ist/landing project focuses on enhancing user experience, and a recent update significantly improves our CV generation feature. This update streamlines how job context is captured and used to create tailored CVs, leveraging AI to extract key details and allowing users to provide additional, nuanced information.
Previously, our CV generation form
Crafting a compelling CV often requires tailoring it precisely to a job description. In the landing project, our goal is to automate this process, allowing users to generate customized CVs with ease. Initially, our AI could extract information, but we discovered that without robust data structures and explicit user control, the generated CVs sometimes missed crucial nuances.
On the landing project, which focuses on providing tools for users, a key feature is our CV generation service. This service relies on external AI models to process and generate content.## The ProblemRecently, our CV generation service encountered persistent issues due to the deprecation of the gemini-2.0-flash AI model.
Managing costs associated with AI-powered features, such as text, image, and video generation, can be complex. Our platform needed a unified system to convert these costs into credits, manage profit margins, and track consumption effectively.
Previously, our credit system lacked a standardized approach. Calculating costs for various AI services (text, image,
The Breniapp project is integrating fal.ai to provide queue-based asynchronous image generation. This enhancement expands the platform's AI capabilities by introducing a new provider for image creation, offering a scalable and efficient solution for content generation.
Traditional synchronous image generation can be slow and resource-intensive,
Breniapp is enhancing its AI capabilities by integrating with Fal.ai to offer a broader range of image and video models. This update focuses on expanding the selection of available models and incorporating their pricing for credit consumption, enabling users to leverage advanced AI functionalities directly within the platform.
The update introduces several new
Campaign emails often require localization for diverse audiences. The landing project recently tackled this challenge by automating the translation process using AI, significantly improving efficiency and reach.
Previously, translating campaign emails was a manual and time-consuming process. Each email subject and body needed to be translated into multiple languages, requiring
In the Breniapp project, we've been focused on improving the user experience through AI-powered content generation. A key part of this is crafting effective prompts for the AI. Recently, we enhanced how the system uses onboarding data to create these prompts, leading to more relevant and personalized outputs.
Initially, the AI prompt generation
The Breniapp project focuses on streamlining brand development. A new feature enhances the onboarding process by incorporating AI-driven analysis of visual references. This allows users to define their brand's visual style through an intuitive, keyword-driven interface.
The new "Visual DNA" step in the onboarding process leverages AI to analyze uploaded
