Validating a startup idea doesn’t have to take a ton of effort.

Here are 10 tools you can use to build MVPs in days.

/thread
@bubble | bubble.io

Bubble has a visual programming interface that enables anyone to build a complex web app in days.

I use Bubble to quickly get a working MVP to users after I’ve proven out an initial idea.
@AdaloHQ | adalo.com

Adalo is similar to Bubble, but you can also use it to build native mobile applications.

You can make social apps, marketplaces, task apps, and more.
@streamlit | streamlit.io

If you can write basic Python, Streamlit enables you to build simple dashboard-based frontends in minutes.

I use Streamlit to think through and prototype the earliest iterations of my ideas.
@Replit | replit.com

Replit is a browser-based, collaborative code editor than can also run production code.

I use Replit to prototype APIs and simple web apps, without needing to worry about infrastructure.
@webflow | webflow.com

Webflow is a great way to design a website without coding. Plus, you can turn Webflow sites into full web apps with the help of @airtable and @zapier.

I use Webflow to quickly build engaging sites for my projects.
@Rapid_API | rapidapi.com

RapidAPI is a marketplace where you can find and access thousands of APIs.

I’ve used it for everything from image recognition to speech -> text.
@airtable | airtable.com

Airtable is like excel on steroids. It’s super easy to set up, and you can connect to it directly from code.

I use Airtable as a simple database when I’m prototyping apps.
@typeform | typeform.com

Typeform is a simple survey tool.

I use it to get feedback from users, collect emails, track PMF, and more.
@figmadesign | figma.com

Figma is a collaborative design platform.

Use it to create mockups you can share with users, prototype workflows, and gather feedback on your ideas.
@zapier | zapier.com

Zapier is a great way to power the backend of a MVP. You can connect thousands of other apps together to build valuable tools for your users.

Use it to automate routine tasks, handle data, and so much more.
We used many of these tools to test ideas and build the earliest versions of @OthersideAI’s AI writing assistant, HyperWrite.AI.

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More from @mattshumer_

31 May
A simple overview of the state of massive language models like GPT-3.

/thread
Since 2018, each year has brought new models that are typically 10x+ larger than models from the year prior.
2018:
GPT-1 | 110M Parameters
BERT | 340M Parameters

2019:
GPT-2 | 1.5B Parameters
Megatron | 8.3B Parameters

2020:
Turing-NLG | 17B Parameters
GPT-3 | 175B Parameters

2021:
Google Switch | 1.6T Parameters

What is coming next?
Read 12 tweets
3 Dec 20
How to GPT-3!

A primer thread on GPT-3 prompt structure:
Working with GPT-3 is just a game of figuring out how to structure text to get the results you want.

Here are some methods that work well.

Some of these methods can be used together. There’s an art to figuring out which methods are best for obtaining the results you want.
You can use zero-shot, one-shot, or few-shot methods, depending on the task. Your goal should typically be to zero-shot or one-shot, as latency and costs will be lower.

Here’s a quick primer:
Read 11 tweets

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