Here are 5 of the most successful growth hacks we implemented to grow our startup from concept to acquisition in 9 months.

In 2017 I quit my job to join a startup with two of my friends (one of them might be imaginary, still checking on that) that made it easier for developers to use machine learning. It was a pretty technical product and we needed to find ways to get it in the hands of as many people as possible. Partly because we wanted to grow but also because we wanted feedback so we could iterate on the product…

Worked for 6 different companies, wrote 3 patents, published 2 papers, and started (then sold) 1 machine learning startup — all from home.

The last time I worked in an office was late 2008 after a startup I was working for closed its office after an acquisition by a company based elsewhere. There were a few days of packing boxes and organizing equipment (we were a hardware startup), and divvying up the ethernet cables (for some reason) amongst all the employees. …


There has been a lot in the zeitgeist recently about face recognition, and I think some of the better uses of face recognition are getting overlooked. One of my favorite customer use cases of ours is using it to speed up payments and other activities on physical kiosks. Here’s why customers are coming to us to solve this, and how you can try it yourselves.

The problem it solves.

There are a few things that I believe are driving the surprising amount of inbound requests we’ve been getting this last year for Facebox (our easy-to-deploy face recognition tech).

  1. Security. Your face is a biometric…

If I see one more article about Bayesian Forest’s being Random whilst also being Convolutional I’m going to disconnect my cable modem and let all of the internet spill out.

It is frustrating trying to learn about machine learning. Do I use YOLO, Keras, Tensorflow, PyTorch or all of them together somehow? And even if you figure out the PhD stuff, you still have to then master about three other disciplines to get it to work in production; devops, programming, and counting disciplines.

Well I am here for you brothers and sisters in computers, I have good news for your…

Five Aha Moments from VentureBeat Transform

Greg Brockman, Co-founder & Chairman and Ilya Sutskever, Co-founder & Chief Scientist, OpenAI; Kyle Wiggers, Staff Writer, VentureBeat

I just came back from the Venture Beat Transform 2019 conference here in San Francisco, and I have to say, for the first time in many years, I’m starting to feel like the industry is finally starting to get it.

What am I talking about specifically? Well, I’m traditionally always right about stuff, and this time, I’ve been incredibly right about the challenges the market may have had in adopting machine learning. Just check out these excellent blog posts;

  1. Machine Learning is sometimes wrong — how you deal with that is EVERYTHING
  2. How a funny subreddit helps explain machine learning

Imagine if you were using a calculator to do some just fantastic calculations like π^π , !2, or the old standard 2+2, and every once-in-a-while, the answer would came back wrong. Better yet, you’ve implemented a microservice in your stack that occasionally just returns {[“No”]}.

This is what working with machine learning can feel like sometimes. You’re sitting there feeding it pictures of cats, and it is returning the label “cat” consistently until suddenly “guacamole” comes back.



Is this real life?

Yes, this is real life. Machine learning models, especially ones using neural networks, work in mysterious ways. We train them with…

What? What a strange title for a blog post. That is like saying “Don’t not use your telephone to avoid not making phone calls”. But I think it accurately describes the way in which I’m going to show you how to use machine learning to make sure your car is still in your driveway.

Several years ago, someone decided to waltz into my driveway and steal my car in the middle of the night. It wasn’t a pleasant experience, and it of course had me thinking about clever ways to prevent this from ever happening again.

One way was to…

This is it. This is the best goddamn thing I’ve ever done. I don’t normally like to brag but I’m so freak’n proud of myself for this one that I feel like I need to share it. They said it wasn’t possible (no one actually said that), they said it couldn’t be done (lots of people said it could be done), but I did it and it works GREAT!


Its great when you’ve spent 2 hours scanning photos only to suddenly notice you’ve scanned some random number of them upside down. Rather than painfully go through each one and flip them, you might feel like you’d rather just jump in front of a truck. Well, when a customer came to us with a similar problem, I immediately wondered if machine learning might be able to help. And guess what? It did. The end.

If you’re interested to know how I did it in 20 minutes, then please continue to read.

Sometimes machine learning can be more of an art…

OCR, or Optical Character Recognition, is an awesome tool for turning printed text into digital deliciousness. Existing open source libraries and tools work great… on documents. Where they fall down is in just about every other scenario… until now (hopefully).

If you are a lawyer, and have been for 30 or so years, you’ve probably got lots and lots of file cabinets filled to the brim with paper as well as a general malaise about being a lawyer for so long. I’m sure there are contracts, agreements, trusts, memos, letters, and even the occasional random menu from the local Chinese…

Aaron Edell

Co-founder Machine Box (exited)| Entrepreneur | Business Development at Amazon | Agile Product Owner | Author | Father | Amateur Programmer | opinions are mine

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