waitlist's comments

Hi! I'm the author of this blogpost. Thanks for posting it! Happy to answer any questions that anyone may have. If it's of interest, we also published a 2022 version last year, so you can see how things changed: https://getwaitlist.com/blog/solo-dev-startup-stack

TL;DR almost all transactional email services are very expensive, and they're driving most of our increased costs as we scale. Almost all other services we use have good, free/low-cost plans that don't break the bank as we grow.

AWS Activate Credits conventionally run for one or two years, and then expire. Contrary to popular belief, you are eligible for additional credits afterwards, but they have to be greater than your previous grant. So you can't have a $5K grant and then another $5K grant, for example. If you want to stack them effectively, you (for example) get a $5K grant, wait until it runs out, then a $10K grant, and then a $25K grant if you can get one.

Blog author here. I'll be downgrading from Sendgrid to SES in the foreseeable future.

Sentry is a great product, CloudWatch is a real pain to use.

We're much more than a hosted email list. We field over a million requests on a normal day.

Hi All! I built a small Indie SAAS project called Waitlist, designed to help you go viral before you launch. My product provides a simple no-code Waitlist Signup Widget that's easy to integrate on your website, as well as a full API.

Whenever I think about building something new, I always try to first build an audience for the idea -- to validate if people are actually interested, and then to have a group of early users to try it out. I needed a good tool to collect signups, ask people questions, send them emails, etc. so I built Waitlist.

But a Waitlist gets competitive: if people sign up, they want to be first in line. So I gamified the service -- people can move up in line if they refer their friends -- and that's been a really powerful viral marketing service. Referrals have driven over 1.5 million signups via our platform.

It's been a really interesting journey for me so far. I'm happy to answer any questions!

I think data science is a perfect example in favor of types -- the code is often terrible because of the lack of typing. Pandas has notoriously poor developer ergonomics, and I recall painfully poring over type errors across the board -- lists, dataframes, numpy arrays, etc. are all iterables, so they can be interchanged in some contexts, but not in others.

Had I had MyPy back when I was working in data science, I would've saved countless hours and headaches.