Don Sevcik's profile on Product Hunt

About

I am the Founder of Math Celebrity, the fastest math tutor on the planet. Enter a math problem, push the button, and see every step-by-step line of math work in a fraction of a second. We have been featured on Yahoo, Business Insider, CBS, NBC, ABC, and Fox and have helped over 8.1 million people in 233 countries understand their homework and optimise their math skills – all without leaving their home. With the click of a button, we help you vaporise math fears and install confidence in math. Our students, on average, score 150 points higher on the SAT and 3 points higher on the ACT. They finish their math homework and understand it in just 15 minutes. I've written 3 books, which show you how to grow and automate your business. I also host the College Prep Confidential podcast.

Badges

Tastemaker

Gone streaking

Gone streaking 5

Forums

p/inquir

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3d ago

What makes an app worth paying for when you rarely use it?

You open an app to finish one awkward job, get the result, and might not need it again for a month.

A monthly plan can feel expensive for that pattern, even when the app saves real time. The alternatives have their own tradeoffs:

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1mo ago

From 12 years of enterprise sales experience to create my first product

It took me over ten years selling other people s software before I decided to start writing mine. The behaviors translate far less easily than I thought I know how to make a sale, but not how to get someone to voluntarily open your application for the eleventh time.

In making a similar transition from sales or operations to software development, what did you have to unlearn first?

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2mo ago

Every one-line prompt ships with a spec you didn't write

Something I wish I'd understood earlier about working with coding agents: there is no such thing as dispatching without a spec. There's only choosing who writes it.

When you send an agent a one-liner like "fix the login timeout", the unstated parts don't stay undecided. The model fills every blank with defaults from its training data the average project, the average convention, the average meaning of that sentence across every repo it has seen. Where your codebase matches the average, those fill-ins are invisible because they happen to be right. Where it doesn't, you get a diff that answers a question you never asked.

An intern who hits ambiguity tends to come ask. An agent mostly picks, and rarely flags the pick a constraint that never made it into the context might as well not exist.

So we stopped asking "spec or no spec" and started asking two questions before every dispatch. How long would a wrong guess stay invisible? And how far is this task from the average project the model trained on? Fast feedback plus mainstream shape: one line, go. Slow feedback plus weird shape: write the contract, and budget the writing as part of the build.