Founder technical toolkit

June 8   Vu Ha

• ML libraries:

  Pytorch

  ,

  Hugging Face

  ,

  spaCy

  . If you need to run ML on a mobile phone or inside a browser, consider

  TensorFlow Lite/JS

  . Definitely check out our guide on machine learning for startups. In particular, there is rarely a need for MLOps or large-scale data labeling efforts at this stage.
• If you do not have engineering backgrounds, you can still build a POC using no-code tools such as

  Bubble.io

  and AI APIs such as

  OpenAI’s

  API

  and

  Hugging Face’s API

  . Paul Jacoubian started copy.ai by tinkering with OpenAI’s GPT-3, for example.
• If you do have engineering backgrounds but have been working at big technology companies (e.g. MAANG), expect a bit of adjustment, as the tech stacks at such companies are not portable to the startup environment. View this as a challenge that you can crush — and at the AI2 incubator we can help! It’s also more enjoyable than practicing binary tree inversion on LeetCode just to pass another MAANG interview.
• If the POC focuses on AI (for example, a novel ML model), consider using Python-based tools such as

  Streamlit

  and

  Gradio

  —no JavaScript is required. They are backed by well-resource companies and thus continue to deliver more features. If you lack front-end/UX experience, consider

  Bubble.io

  if Streamlit/Gradio do not meet the UX requirements.
• If you lack front-end/UX experience but want to pick up some knowledge, that’s great but give yourself a time budget—there are more pressing priorities than mastering the nuances of JavaScript or CSS. The front-end/UX space is always dynamic with new libraries and frameworks popping up regularly. Just pick something on the tried-and-true side and avoid bleeding edge stuff (check out the latest State of JavaScript report for a sense of this).
• Our recommended front-end/UX toolkit for web applications consists of

  ReactJS

  and

  Chakra UI

  . Alternatives such as NextJs and Remix.run can be compelling but unlikely to be worth the added learning curve for a POC. We also heard good things about Svelte, Vue if that's your things.
• REST vs GraphQL. We recommend staying with

  REST

  . The benefit/cost considerations in a pre-seed project heavily favor simplicity. GraphQL is powerful, but not simple.

• Python + JavaScript

  . There are scenarios where other languages such as Swift and Rust are needed, but it’s remarkable how far you can go with just Python and JavaScript. Python is almost required given that you are building an AI-first product, whereas JavaScript is also almost required to build user experiences. Bonus if you choose

  TypeScript

  , but consider turning off Strict Mode for TypeScript—you don’t want to make the TypeScript compiler happy. Focus on finding happy customers instead. It’s worth noting that the venerable computer science textbook Structure and Interpretation of Computer Programs has recently been adapted to JavaScript. Timely decision!
• For the service layer, use either

  Node/Express

  or

  Flask

  . Use Go if you are an expert in it, but don’t try to use Go for the purpose of adding another PL to your arsenal/resume.
• For a data backend, Postgres, Mongo, Dynamo, etc. are all fine options. Pick the one that you are most comfortable with.
• For deployment, use

  AWS

  , specifically the Elastic Container Service (

  ECS

  ). Consequently, some familiarity with

  Docker

  is required.
• At the AI2 incubator we deploy to ECS using

  Pulumi

  , writing our infra-as-code in TypeScript. This allows us to pick up minimal cloud architecture and devops knowledge in a pay-as-you-go fashion. Pulumi’s TypeScript documentation is superb and available at your fingertips inside your favorite IDE. Pulumi is used at AI2 incubator companies such as Ozette and WhyLabs.
• Consider a continuous deployment (CD) strategy with

  GitHub Actions

  . Continuous integration (CI) is definitely optional. Write tests only if you absolutely have to.
• At a high level, aim to optimize for speed and simplicity. Dial up the KISS principle to a pretty high setting, especially compared to the level at a larger organization.

• ML: Pytorch, Hugging Face, spaCy, Tensorflow Lite/JS.
• Rapid prototyping: Bubble.io, Streamlit, Gradio.
• Frontend: React, TypeScript, Chakra UI.
• Services: Express/Flask.
• Cloud/infrastructure: AWS, ECS, Pulumi, Docker, GitHub Actions.
• AI API services: OpenAI, Hugging Face.
• Look for discounts and promotions from major cloud providers. Companies incubated at the AI2 incubator get awesome packages of cloud credits from AWS, GCP, and Azure.

• AI2 Incubator’s webapp template is a full-stack web application with a React/TypeScript frontend and a Flask backend. The app is deployed to AWS’s ECS using Pulumi and GitHub Actions. The deployed HTTPS endpoint URL can be configured by simply specifying a domain name and a subdomain name (e.g. https://demo.example.com). Pulumi takes care of setting up the necessary Route53 and certificates behind the scenes.
• AI2 Incubator’s streamlit template uses the same tools as the webapp template for the purpose of deploying a Streamlit demo to the domain URL of your choice. You may also take a look at Hugging Face’s Spaces as an alternative to host your Streamlit/Gradio apps.