AI Web App: Prompts With Different ML Models and Connections

AI Electron Prompt Mixer

AI Web App: Prompts With Different ML Models and Connections
Client
AI startup USA
Services
React, Typescript, Electron, IndexedDB, LLM
Project Timeline
2023
2024
Platforms
Front-end

Project Overview

Our client is a startup based in the United States, specializing in artificial intelligence, specifically Large Language Models (LLM). LLM models are deep learning language models pre-trained on massive datasets. To provide a familiar example, ChatGPT is one such instance of an LLM model. The client's primary need arises from the multitude of requests to these models, necessitating an efficient storage solution.

Project Execution

Startup. Frontend part from scratch for AI based markdown editor app. Client’s goal is to develop a cutting-edge Electron Markdown Editor application that provides a seamless, intuitive, and feature-rich experience for writing and managing markdown texts.

The challenge presented was to develop functionality for their startup where they could input queries and test the performance of their AI and LLM models. Additionally, we were tasked with creating an authentication panel, a theme-switching panel, and features such as login and screen size adjustment.

The project involved tackling complex aspects both in terms of comprehension and development. Addressing the extensive range of client requests, our team worked diligently to design and implement features that aligned with the startup's goals. The frontend business logic posed numerous non-trivial challenges, with a particular focus on creating a flexible layout, data storage on the frontend (indexed DB), and incorporating dark/light themes. The integration of the Electron library was also a key aspect of the development process.

Our commitment to understanding the client's needs and going the extra mile to comprehend areas where knowledge was lacking has been well-received. The client expressed satisfaction with our efforts to incorporate their requirements and preferences into the project.

In conclusion, the project has not only met the intricate demands of the client but has also introduced innovative features that enhance the usability and functionality of their startup's platform. The collaborative efforts of our team, coupled with a commitment to understanding and addressing challenges, have resulted in a successful and satisfying outcome for both parties involved.

Project results

The delivered project includes a robust system for inputting and testing AI and LLM model queries. The additional features such as the authentication panel, theme-switching functionality, and screen size adjustment capabilities have been successfully implemented. The frontend boasts a flexible layout, allowing users to modify the position of elements and save the layout state. Data storage on the frontend through indexed DB ensures efficient query handling.

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