anothertraveler

Great points.

In my opinion, it is easy to feel like an indentured servant as an employee, especially if that employee lives paycheck to paycheck or is servicing a lot of debt (mortgage, car payment, health insurance premium and bills) that I see a lot in the American economy.

There is also a lot of pro-management literature that defines a work culture where employees should be loyal to their company, boss, etc., give two weeks notice when quitting, among other things that psychologically disenfranchise employees.

In reality though, at least right now in the tech industry, if you don't like your salary, job or work environment, it isn't hard to re-tool and find a new job...

In blue-collar/unionized industries, it might be totally different. I don't totally understand the work cultures of other industries, especially lower paying ones.

Good point. Owners can become employees, but there are much more benefits to being an owner that employees never really have access to, so unless the owner doesnt have the skill or capital, I don't see why an owner would want to be an employee. Sometimes employees can't even fathom making 1MM+ on a business deal because employees think in terms of their salaries.

Owners become employees when they accept too much outside investment. They become employees of investors...and investors buy ownership.

I think the balance of power is primarily affected by two things: 1) Barrier of entry into starting a business, such as laws that inhibit/regulate small businesses, or independent workers (owners) 2) Regulation that gives bigger businesses an inherent advantage over smaller businesses, such as limited liability, indemnification, bnakruptcy, mandated group health insurance plans, etc.

Sure, an owner can make 1MM per year and only pay the employee 100K.

But I think the question we really need to understand is, what's stopping the employee from quitting and starting his own company? I don't know the answer.

I think the key point here is that - if a founder is raising money for a company from investors, and the founder simultaneously buys a mansion - the founder is implicitly communicating that s/he doesn't believe that investing that capital in the company is as valuable as a real-estate investment in the mansion.

Founders have a very specific job to investors: to return their money with a hefty return on investment. If the expected ROI is higher buying a mansion, then maybe the investors should invest in mansions rather than the tech start ups.

There are platitudes that money doesn't really matter. Money doesn't buy happiness, etc... But in the US, money is very important. If you have to go to work every day to survive, you are working class. If you get laid off, and you have to find a new job to make a mortgage payment, you are working class.

Having money is simply a means to an end. For some people, that end is the freedom to not have to go to work every day. To focus on things they care about. To have new experiences that don't involve sitting at a desk for 14 hours straight, 5 days a week. But there are many ends. For me, I respect those who don't have to work to survive as having made it, because I know the guy driving the BMW probably financed it and is driving to work.

Indeed...

> every dollar the boss keeps _is_ a dollar that the employees don't get and the boss is the one deciding how much both sides get.

I've thought a lot about this over the past few years. There's definitely a difference between being an owner and an employee. An owner lives with a whole different level of stress and risk. An employee has a choice to work for an owner, or go work somewhere else, or become an owner by starting a company.

I'm not sure where the balance of power between business owner and employee is determined...it's fairly complex.

4TB of RAM is pretty impressive, especially if they are actually making money on this offering. It must be pretty expensive to have them sit idle at all.

echo "export EDITOR=nano" >> ~/.bashrc

nvidia-docker is just docker that configures the docker to see the physical gpu device, and also mounts the run-time drivers that match the hardware driver that is installed on the host. It makes life way easier if you need portability across different Linux hosts for GPU-driven software. The Nvidia container images also have some of the "proprietary" free software packages like cuDNN which accelerate some popular deep learning packages with optimized CUDA implementations.

If you run on a bare-metal platform, like your laptop booting linux or a bare-metal cloud, there's a very small amount of overhead for using nvidia-docker, mainly just the same overhead as running a regular docker container (a container is just chroot + Linux kernel cgroups + cgroup kernel namespaces).

If you're in an AMI on AWS, it's a virtual machine anyway, so there's virtualization overhead which is quite a bit higher than container overhead, but there's other baggage as well such as shared tenancy/noisy neighbors, and possibly oversubscription of hardware to the virtualized environment.

If you're in a docker container in an AMI, there's the slight container overhead plus the virtualization overhead and baggage and benefits that come with it. Virtualization overhead is probably an order of magnitude higher than container overhead. Natively with an AMI is not so native (though Amazon is trying to improve that with their C5).

I am a Debian sid user, so I know well what you mean, but as long as you don't do a kernel upgrade, Linux has been extremely stable for me. This wasn't always the case. 10 years ago, Debian sid would break all the time, but I have probably had only one or two non-kernel/driver related issues in the past 5 years.

Another subtle difference is that on Linux platforms, you have more control over when/how to update, as well as visibility into exactly what is updating. I'm sure it's visible on other platforms, but I don't have to worry about rebooting my machine and having to wait 2 hours for it to come up.

Perhaps another point worth mentioning, LTS versions of Linux distros (Debian stable, or Ubuntu LTS versions) mainly get security patches, and, optionally, back ported software. That makes your OS stack super stable!

I'd put kernel upgrades into a different bucket... but most users don't need to update their kernel on a daily or even yearly basis except for security patches.

Great point about coreutils too! No need to update ls and grep...

They're probably similar software-wise...but the Nvidia GPU Cloud is a docker container registry, so you can pull the docker image and use nvidia-docker to run it. Containers are easier to move around across cloud providers (AWS, Azure, Nimbix, etc...), or even to your local machine, so you don't need to manually configure (and sometimes compile from scratch) a whole slew of complex software packages. The Nvidia GPU cloud is maintained by NVIDIA themselves, so you know they have the configuration just right for optimal performance on their GPU hardware, wherever you choose to use it.

I gave up on Windows about 10 years ago. I've had to use it for work sometimes, but the Windows Updates are actually destructive sometimes, and often take hours to install during business hours (especially if you happen to need to reboot before an important meeting...). I can't handle uncertainty in Software, and I think that many business are losing productivity because of this. Or maybe IT support departments are happy to keep themselves employed.

Whoa! I was just talking about this text book the other day! I think it's out of print these days, but it's a hidden gem!

This list is solid, and could keep you busy for a few years.

1. Start with the fast.ai courses. It's an applied deep learning course using state of the art techniques. 2. For classical machine learning (regression, etc...), Andrew Ng's course on Coursera is widely considered "the basics" 3. As you progress, check out CS231 and CS224 from Stanford for state of the art image processing and natural language processing techniques. The lecture videos are on YouTube and the course assignments are available online. The third course I recommend is Geoffrey Hinton's neural networks course on Coursera (he is one of the most important researchers in the field). 4. If you're an application engineer, focus on using existing tooling to build cool projects. Keras and scikit-learn are great out of the box tools. 5. If you are more research oriented, you can start reading papers. In Silicon Valley, there's a meet up group that reads papers every Monday and tries to implement the algorithms in the paper. It takes a while to get to this level, but try not to get overwhelmed. Experts spend 7 years studying this stuff full time to get a PhD. 6. You really don't need much math to get started with ML. A high school understanding of calculus and some basic understanding of numerical optimization are the two main concepts you need to know. If you want to get into the research, there'll come a time when you will need more advanced math, but in my experience you can pick that up as you go along if you are curious.

Maybe you could start an AI study group online? The Silicon Valley study group was great, but I was just visiting.

I thought the sunglasses would've been a nice transition into a larger AR play, but they fumbled...and had no plan for how to compete having a product that is otherwise easy for other social networks with a larger userbase to copy.

The best editor is the one you know how to use. This is sage advice!

If you study a bit of corporate finance, you can learn the mechanics of valuation. At the end of the day, though, how do you determine the market value of any asset? You auction it off. If you keep it, you are saying it is worth more than $50K. If you sell it, you are agreeing that the market value is whatever the other party is offering you.

Rather than trying to calculate explicitly what it's worth, have you considered trying to find other buyers?

Edit: Another thing to consider is, if this isn't a life changing amount of money to you, you could treat this like an empirical experiment to learn the process of selling your company. This will be invaluable experience in your next start up/side project, or for any future business endeavor where you engage as an owner or potential owner of a company in a transaction like this.