# The Jaseci Programming Paradigm and Runtime Stack: Building Scale-out Production Applications Easy and Fast

arXiv: https://arxiv.org/abs/2305.09864 · HTML: https://mlsanity.com/paper/2305.09864/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [TinyBERT: Distilling BERT for Natural Language Understanding](https://mlsanity.com/paper/1909.10351/) (2019)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)

## Built on by (most cited first, 4 of 4)

- [MTP: A Meaning-Typed Language Abstraction for AI-Integrated Programming](https://mlsanity.com/paper/2405.08965/) (2024)
- [GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2](https://mlsanity.com/paper/2509.16248/) (2025)
- [Prompt Less, Smile More: MTP with Semantic Engineering in Lieu of Prompt Engineering](https://mlsanity.com/paper/2511.19427/) (2025)
- [ContextRender: From Execution Dependencies to Agent Context](https://mlsanity.com/paper/2609.37743/) (2026)

## Abstract (arXiv)

Today's production scale-out applications include many sub-application components, such as storage backends, logging infrastructure and AI models. These components have drastically different characteristics, are required to work in collaboration, and interface with each other as microservices. This leads to increasingly high complexity in developing, optimizing, configuring, and deploying scale-out applications, raising the barrier to entry for most individuals and small teams. We developed a novel co-designed runtime system, Jaseci, and programming language, Jac, which aims to reduce this complexity. The key design principle throughout Jaseci's design is to raise the level of abstraction by moving as much of the scale-out data management, microservice componentization, and live update complexity into the runtime stack to be automated and optimized automatically. We use real-world AI applications to demonstrate Jaseci's benefit for application performance and developer productivity.