Hualin Luan Cloud Native · Quant Trading · AI Engineering

Topic

AI native application architecture

Paradigm innovation of software architecture in the AI ​​era, exploring AI First design principles and new models of human-machine collaboration.

AI native application architecture focuses on the fundamental shift in the software design paradigm in the AI ​​era, from “AI as a function” to “AI as the core”.

paradigm shift

  • From deterministic to probabilistic: Accept the uncertainty of AI and design corresponding fault-tolerant mechanisms
  • From Imperative to Intentional: The user expresses the intention, and the AI ​​decides how to execute it
  • From Static to Dynamic: Interface and behavior are dynamically generated based on context

design principles

  1. Progressive Enhancement: Even if the AI ​​fails, core functionality remains available
  2. Transparent and controllable: Users understand what the AI ​​is doing and can take over at any time
  3. Feedback Closed Loop: User feedback continuously improves AI performance
  4. Clear Boundaries: Clearly define the boundaries between AI and human responsibilities

Index

Knowledge Index

Core subtopics and learning directions for this topic.

AI First design principlesAI-based reconstruction of traditional applicationsHuman-computer collaboration interaction designEmbedding AI capabilities into architectural decisionsIntelligent interface design

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AI native application architecture

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Paradigm innovation of software architecture in the AI ​​era, exploring AI First design principles and new models of human-machine collaboration.

  1. 1. Original interpretation: How Coding Agent reconstructs the collaboration paradigm of the EPD team

    post

    Explore the profound impact of AI coding agents on engineering, product, and design roles, as well as fundamental changes in the way teams are organized

  2. 2. Original interpretation: Engineering practice of data preparation - from raw data to AI-ready training set

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    In-depth exploration of the engineering methodology of LLM data preparation, from IBM Data Prep Kit tool analysis to enterprise-level data pipeline construction, revealing the systematic engineering practices behind high-quality training data

  3. 3. Original interpretation: The art of LLM fine-tuning—from data preparation to model refinement

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    In-depth exploration of the complete practical path of fine-tuning large language models, from engineering thinking in data preparation to detailed control of model training, reveals the key methodologies that turn general AI into domain experts.

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Resources

Resources

External references and project resources for this topic.

Vercel AI SDK

https://sdk.vercel.ai/

Copilot Patterns

https://github.com/features/copilot