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Skeet: Towards a Lightweight Serverless Framework Supporting Modern AI-Driven App Development

Kawasaki Fumitake, Shota Kishi, James Neve

TL;DR

The paper identifies a gap where traditional MVC-based web frameworks struggle to support cloud-native AI integrations. It introduces Skeet, a lightweight, TypeScript-driven serverless framework grounded in Domain-Driven Design, with CLI tooling and multi-datasource support to streamline AI features (e.g., chatbots) and cloud deployments on GCP. The architecture covers cloud infrastructure, back-end function organization, and AI integration (including ChatGPT and Vertex AI), along with Prisma ORM-powered data modeling. An initial survey with 136 participants indicates favorable usability, faster project and chatbot setup, and positive reception of Skeet’s AI tooling, suggesting it can accelerate modern AI-driven app development for non-specialist developers. The work presents Skeet as a practical step toward accessible, scalable AI-enabled app development, with future work focusing on rigorous validation and broader adoption.

Abstract

The field of web and mobile software frameworks is relatively mature, with a large variety of tools in different languages that facilitate traditional app development where data in a relational database is displayed and modified. Our position is that many current frameworks became popular during single server deployment of MVC architecture apps, and do not facilitate modern aspects of app development such as cloud computing and the incorporation of emerging technologies such as AI. We present a novel framework which accomplishes these purposes, Skeet, which was recently released to general use, alongside an initial evaluation. Skeet provides an app structure that reflects current trends in architecture, and tool suites that allow developers with minimal knowledge of AI internals to easily incorporate such technologies into their apps and deploy them.

Skeet: Towards a Lightweight Serverless Framework Supporting Modern AI-Driven App Development

TL;DR

The paper identifies a gap where traditional MVC-based web frameworks struggle to support cloud-native AI integrations. It introduces Skeet, a lightweight, TypeScript-driven serverless framework grounded in Domain-Driven Design, with CLI tooling and multi-datasource support to streamline AI features (e.g., chatbots) and cloud deployments on GCP. The architecture covers cloud infrastructure, back-end function organization, and AI integration (including ChatGPT and Vertex AI), along with Prisma ORM-powered data modeling. An initial survey with 136 participants indicates favorable usability, faster project and chatbot setup, and positive reception of Skeet’s AI tooling, suggesting it can accelerate modern AI-driven app development for non-specialist developers. The work presents Skeet as a practical step toward accessible, scalable AI-enabled app development, with future work focusing on rigorous validation and broader adoption.

Abstract

The field of web and mobile software frameworks is relatively mature, with a large variety of tools in different languages that facilitate traditional app development where data in a relational database is displayed and modified. Our position is that many current frameworks became popular during single server deployment of MVC architecture apps, and do not facilitate modern aspects of app development such as cloud computing and the incorporation of emerging technologies such as AI. We present a novel framework which accomplishes these purposes, Skeet, which was recently released to general use, alongside an initial evaluation. Skeet provides an app structure that reflects current trends in architecture, and tool suites that allow developers with minimal knowledge of AI internals to easily incorporate such technologies into their apps and deploy them.
Paper Structure (13 sections, 4 figures)

This paper contains 13 sections, 4 figures.

Table of Contents

  1. INTRODUCTION
  2. VISION AND DESIGN GOALS
  3. Web Frameworks
  4. The Vision
  5. Design Goals
  6. Framework Design
  7. Cloud Infrastructure Architecture
  8. Back-End Architecture
  9. Skeet AI
  10. EVALUATION
  11. Survey Design
  12. Results
  13. CONCLUSIONS

Figures (4)

  • Figure 1: An overview of the Skeet infrastructure architecture.
  • Figure 2: Question 4 - How experienced are you with AI technologies?
  • Figure 3: Question 6 - How easy was it to create and run a new project in Skeet compared to other frameworks?
  • Figure 4: Question 8 - Compared to other frameworks you’ve used in the past, how easy was it to create a chatbot using Skeet?