How I Built My First AI Startup (With No Experience)

🌈 Abstract

The article discusses the author's journey of building a startup as a data scientist, including advice on how others can do the same. It covers topics such as the benefits of building a startup, the process of validating ideas, finding a co-founder, and building the frontend and backend of the startup.

🙋 Q&A

[01] Why Build a Startup?

1. What are the key reasons the author provides for building a startup?

• The author gained a lot of practical knowledge and skills by building their own product, beyond what they learned from courses and projects
• Building a startup is great experience to add to your resume, as it counts as commercial work experience
• Building a SaaS (Software as a Service) startup can provide a source of passive income

[02] Validating Ideas

1. What process does the author recommend for validating startup ideas?

• The author introduced their startup, https://validator.yazero.io, which helps with the idea validation process using AI, web scraping, APIs, and quantifiable feedback
• The author also recommends using your "personal advantage" - leveraging your existing knowledge or connections to identify problems you can solve

[03] Finding a Co-founder

1. Why does the author recommend finding a co-founder?

• A co-founder can take on parts of the startup that the author doesn't want to do themselves, like marketing, frontend, or sales
• A co-founder provides additional validation and feedback on ideas, and helps get the startup running faster
• The author found their co-founder on the YCombinator co-founder matching platform

[04] Building the Frontend

1. What advice does the author give for building the frontend?

• Use the frontend framework you are most familiar with, even if it's not the latest trend
• This allows you to develop faster and fix bugs more easily
• The author recommends checking their previous articles on top Python frontend libraries

[05] Building the Backend

1. Why does the author recommend having a backend?

• A backend provides better security, observability, control over pipelines, logging, and the ability to leverage more computational power
• The author used tools like FastAPI, Gunicorn, Docker, and Nginx to build the backend
• Monitoring is important to track API performance and identify issues

2. Why does the author argue that Python is a suitable language for backend development?

• The speed difference between Python and other languages is often negligible, especially for user-facing applications
• Many Python libraries are optimized and use lower-level languages like C/Fortran under the hood
• For AI-heavy applications, the response time is more dependent on the computational power than the programming language

[06] Next Steps

1. What additional topics does the author suggest covering in a potential second part of the article?

• AI in production, MLOps, marketing, publishing, sales, advertisements, and pricing models
• More practical guides and examples on productionalizing the backend