From GUI to LUI: Lessons in Product Iteration from Last Failed Startup

The Shift Beyond GUI: Adapting Interfaces for LLM and Multimodal Applications

Lessons Learned:

1. Roadmap related: Define if you are infrastructure layer company or product layer company

   1. Three key metrics: Scalability, Latency, Throughput

2. GUI are not as important as API and command any more

3. Make sure to build stable and robust payment/monetization backend, including Stripe API integration and Operational Management System

4. Pricing strategy based on the open source models

5. Decision criteria to decide which open-source model to onboard

6. Close collaboration with sales, business development and founders about the GPU availability, GPU pricing and model pricing

7. Close collaboration with research scientist to evaluate the models to onboard

8. Close collaboration with designer, frontend engineer about MLOps interface, model inference effect

9. Close collaboration with machine learning engineer, backend engineer and infrastructure engineer to make sure the stability of model deployment, model performance, prompting results and cluster management.

10. Constant changing product strategy based on the feedback from founders and investors

11. Lack of solid system design knowledge

12. Lack of internal GPU knowledge

13. Lack of model pre-training, model finetuning and many other deep learning knowledge

14. Competitor Trend & Analysis: Early stage funding does not mean much things. Finish a best practice first is way more important than a rough, macro analysis.

15. Traditional Software Development vs. AI Native Software Development

Core concepts related:

• API vs. Endpoint:

  • API is a set of rules, protocols, and tools that allow different software applications to communicate each other

  • Endpoint is a specific URL within an API that represent a single function or resource, the actual access points that developers use to interact with an API