mattnedrich

Gambyt | Ann Arbor, MI | Remote | https://www.gambyt.com/

* Software Engineer - https://gambyt.bamboohr.com/jobs/view.php?id=53

* Jr. Software Engineer - https://gambyt.bamboohr.com/jobs/view.php?id=57

Gambyt's mission is to make real-money gaming products more relevant and accessible to the world. We are specialists in the lottery industry and create products that deliver life-changing winning moments to players while raising billions of dollars every year for good causes such as public education, nature conservancy, and programs for seniors.

Gambyt | Ann Arbor, MI | Remote | https://www.gambyt.com/

* Software Engineer (Portals and Games) - https://gambyt.bamboohr.com/jobs/view.php?id=54

* Software Engineer (Cloud Services) - https://gambyt.bamboohr.com/jobs/view.php?id=53

Gambyt's mission is to make real-money gaming products more relevant and accessible to the world. We are specialists in the lottery industry and create products that deliver life-changing winning moments to players while raising billions of dollars every year for good causes such as public education, nature conservancy, and programs for seniors.

Gambyt | Multiple Roles | Full-time | Remote / Office Optional in Ann Arbor, MI

Gambyt's mission is to make regulated gaming products more relevant and accessible to the world. We are specialists in the lottery industry and create products that deliver life-changing winning moments to players while driving a meaningful impact on state budgets that benefit good causes.

We embrace an office-optional approach with headquarters in Ann Arbor, Michigan - the only state in the country where all forms of internet wagering (lottery, casino, sports) have been legalized. Our company is on the ground floor of an industry revolution and we are helping to lead the digital transformation of gaming options in the marketplace.

We're currently seeking software engineers in the following areas.

Backend Software Engineer: Tech stack: Node, TypeScript, PostgreSQL, AWS, Heroku https://gambyt.bamboohr.com/jobs/view.php?id=44

Full Stack Software Engineer: Tech stack: React, Node, TypeScript, Postgres, AWS, Heroku https://gambyt.bamboohr.com/jobs/view.php?id=14

Mobile Software Engineer - Android: Tech stack: Mostly Kotlin, some Java https://gambyt.bamboohr.com/jobs/view.php?id=35

Anyone interested please apply through the above links, or reach out to me directly to start a dialogue: matt.nedrich@gambyt.com

More about the development culture at Gambyt: https://www.gambyt.com/development_culture

More about Gambyt: https://www.gambyt.com

I wrote a tool that explores different ways to cluster in the RGB space - https://github.com/mattnedrich/palette-maker

It compares k-means, median-cut, and simple RGB space quantization.

mean-shift is great, but it is unbelievably slow (n^2)

Gambyt | Full Time | Ann Arbor, MI | Onsite or Remote

https://www.gambyt.com

Gambyt develops digital solutions for lottery industry and aspires to enter the casino and sportsbooks markets in the future. Our expertise includes responsive websites, native mobile apps, games of chance, player loyalty programs, and digital promotions.

We're looking to hire:

  * Senior Mobile Developer
  * Senior Full-Stack Developer
  * Backend/DevOps Expert

  Frontend (React, TypeScript)
  Backend (Node, TypeScript, Postgres)
  DevOps (Heroku, AWS)
  iOS (Swift)
  Android (Kotlin)

https://www.gambyt.com/careers

Gambyt | Full Time | Ann Arbor, MI | Onsite (WFH until the pandemic ends)

https://www.gambyt.com

Gambyt develops digital solutions for lottery industry and aspires to enter the casino and sportsbooks markets in the future. Our expertise includes responsive websites, native mobile apps, games of chance, player loyalty programs, and digital promotions.

We're looking to grow our software development team, and looking for folks with any of following skillsets:

* Frontend (React, TypeScript) * Backend (Node, TypeScript, Postgres) * Devops (Heroku, AWS) * iOS (Swift) * Android (Kotlin)

Interested candidates can apply via the links on our careers page:

https://www.gambyt.com/careers

I had the same reaction to the email

I think they'll absolutely do this (assuming there isn't a physical ESC key). But, I think the experience would suck for users that are hitting ESC all day (e.g., vim users)

The MBP is nice b/c of the retina screen. Most laptops have crappy screens. In fact, I would go as far as saying that the MBP has the best screen by a decent margin.

I actually use my laptop keyboard almost exclusively (my setup is MBP + external monitor). I have an external keyboard that I use every now and then, but I love the portability of being able to pick up and move.

I'm kind of in the same boat as you. I'm a heavy vim user, and a weird ESC key would really suck. I may be able to get used to ~ as escape, would take some getting used to though.

I do agree that projects are the most fun when you're building something useful that you want to use. I sometimes fall into the trap of convincing myself that what I'm building isn't very good though, or that is already exists.

Great advice - also applies to college graduates. It applies to everyone, really :)

If the most important quality is being able to think abstractly and solve problems, why does it matter if they have a CS degree or come from a bootcamp?

I do agree with you that CS degrees are very valuable, though it seems like you are saying that they are important because they teach problem solving and abstract thinking - but aren't there many ways to obtain those types of skills?

Additionally, I think passion is something you can develop over time as you become more proficient at something.

Training is like eating your vegetables. It's not always fun, but you eventually come to enjoy it - especially when it starts to pay dividends.

I never really understood the advertising business model. It seems like a reasonable approach for some companies and services, but somehow it has become the cornerstone of the tech industry. Free services with ads.

This always seemed completely unsustainable to me.

Thanks for the link, I'll take a look.

Is it included in sklearn?

Cool, I think you did a great job. Do you have run time data for each algorithm on that data set?

I think the largest challenge with Swift testing at the moment is the lack of easy mocking. You can define inline classes, but it's far inferior to the functionality that something like Kiwi provided for Objective-C.

I wrote an article about mean-shift a while back if anyone is interested in more details about it - https://spin.atomicobject.com/2015/05/26/mean-shift-clusteri...

Some comments on K-Means - one large limitation of K-Means is that it assumes spherical shaped clusters. It will fail terribly for any other cluster shape.

It's interesting that the author compared results on the same data set for the different algorithms. Each clustering approach is going to work best on a specific type of data set. It would be interesting to compare them across several different data sets to get a better feel for strengths/weaknesses, etc.

Some interesting discussion happening here - https://www.reddit.com/r/programming/comments/4givdg/go_home...

While choosing a kernel bandwidth may be arbitrary for certain situations, it does have some nice properties. The bandwidth informally defines how "close" points need to be to be considered similar, which can work well for certain problem domains where this can be easily determined.

Like most clustering problems, if you can't choose a reasonable set of parameter values based on some domain specific information, it is largely trial and error. Of course, there are several metrics that can be used to score certain clusterings (e.g., parameter values) over others, as described in your wikipedia link.

The other nice advantage that mean shift has over k-means is that it does not make any assumptions about the cluster shape. K-means assumes spherical clusters. Mean shift allows for clusters of any shape, since it is driven by density.

Author here. These are all great points. I chose linear regression because it's simple and easy to understand. There is obviously a simple closed form solution to this problem. However, as already mentioned, gradient descent has some nice advantages like scaling. Further, a "close enough" solution/approximation might be sufficient depending on the application.