Tag Startups

Posts: 5

Startups and Open Source

This is my third startup. Or maybe my fourth, I'm not sure but it gets hazy after a while. What I do know is that if I ever do another startup, I'll use open source to make it happen.

Let me explain with a bit of back history.

My most recent startup was a consulting business that I started in mid 2017. I had just resigned from another startup in the machine learning space and with their software I did Data Science and Engineering consulting for a handful of clients. As luck would have it an opportunity at H2O.ai came a long and I closed down my consultancy to come on board there.

There was another, rather short lived startup during that time centered around autonomous drones that never quite got off the ground. Pun intended.

So, what do I mean by doing a startup with open source?

Doing a startup with open source

Right now the ability to begin a startup is the easiest it's ever been. The ability for one person or a group of people to show up and change the way we live has never been greater. All this because of open source software.

There are so many open source startups out there the it boggles the mind. Do you like Python? Well Python is open source. You can build anything you want with that.

How about web development? NodeJS is open source and you can create any front end you want.

What about trading? R is open source and you can analyze your rate of returns using the quantmod package.

Got an idea for a new machine learning startup? Check out open source H2O or Scikit-learn.

Do you have an idea for a new content related website? Download Wordpress and start writing.

Of course you'll need a place to host all the digital assets you run. That will require some $ on your part especially to buy hosting or spin up an EC2 instance on AWS, but it's really small when you think about it.

The rest of it is YOUR time.

Invest your time

It's said that time = money, and that's true. If you spend your time working on code, a front end or content, it's like writing a check to someone for their services, except that someone is you.

Sometimes that check will bounce and sometimes it will pay more than what it was written for. Not all your endeavors you spend time on will come to fruition. Some will crash and burn. They will fail. That's ebb and flow of creating something.

The trick is to keep going. Cut your losses quickly and move on to the next idea. Roll up your sleeves again and get to work. Be a Thomas Edison or Marie Curie!

After all, what would you rather be doing? Goofing off or creating?

ABC - Always Be Creating

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VC's Killing Startups

While VC's are a necessary part of growing a startup, they can kill it too. I've seen this happen too many times where the VC's kill the goose that laid the golden egg. They're so driven by financial goals that they interfere with great startups that just need a bit more time to 'gel.'

A recent study commissioned by Eric Paley at Founder Collective found that by pressuring companies to scale prematurely, venture capitalists are indirectly responsible for more startup deaths than founder infighting, technical debt and slow customer adoption — combined. via Techcrunch

Quite honestly, if you create a startup and start making money from it, why really scale? With scale comes so many other headaches. The reason I say that is Mike Carson (aka Uglychart) was recently interviewed by Indie Hackers about his company Park.IO. He's a one man shop that runs everything on AWS and allegedly makes a small yearly fortune. The one takeaway I got from this interview was that he wants to be a one man shop. With growth comes so many other headaches and the danger of losing control.

The same goes for Engineering firms. My buddy in CA says the same thing, he never hired people and grew his consulting business because he hates managing people. He's always too busy but makes a great six figure income.

Where's the line? Hard to tell but it really depends on the Founders. If it were me personally, I'd do what Mike and CA buddy have done, keep it small but profitable.

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What works; What Doesn't Work

An important lesson I've learned while working at a Startup is to do more of what works and jettison what doesn't work, quickly. That's the way to success, the rest is just noise and a waste of time. This lesson can be applied to everything in life.

Data is your friend

We generate data all the time, whether it's captured in a database or spreadsheet, just by being alive you throw of data points. The trick is to take notice of it, capture it, and then do something with it. It's the "do something with it" that matters to your success or not.  Your success can be anything that is of value to you. Time, money, weight loss, stock trading, whatever. You just need to start capturing data, evaluate it, and take action on it.

This is where you fail

Many people fail by taking no action on the data they captured and evaluated. They hope that things are going to get better or that things are going to change. Maybe they will, maybe they won't but you must act on what the data is telling you now. NOW!

My Examples, what Works/Doesn't Work

  1. My $100 Forex experiment worked really well for a time, then it started to flag. The data was telling me that my trading method was no longer working. Did I listen? Nope. I blew up that account. This didn't work for me.
  2. Writing RapidMiner Tutorials on this blog ended up getting me a job at RapidMiner. This lead to an amazing career in Data Science. Writing and taking an interest in things works.
  3. Day trading doesn't work for me. I blow up all the time. What works for me is swing and trend trading. Do more of that and no day trading.

Keep it simple, stupid

The one thing I've also learned working at a startup is to keep things simple and stupid. You're running so fast trying to make your quarter that you have no time for complex processes. Strip things down to their minimum and go as light as you can. This way you can adjust your strategy and make changes quickly, you can do more of what works and jettison what doesn't.

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Startup Funding

ICYMI the Startup markets are getting hotter in the Data Science space. Every time I turn around, some small company got millions of dollars in startup funding. It used to be a company with an algorithm or data science library but now it’s Data Science platforms. These platforms are suddenly all the rage and many new entrants are racing to gain market and mind share.

The above image from FundersandFounders.com really captures a successful startup from inception to IPO. Most interesting for me is how the ownership “pie” is cut over time. If you’re the Founder, you first start out with a 50/50 share with your Co-Founder. Then you get some Seed money, say from an Angel Investor like Howard, which takes a small % of ownership.

Startup Growth

As the Startup grows and matures it should attract more VC money and the ownership pie changes. With every VC investor, you sell parts of your company. This is incredibly important if you want to maintain control of your company and should be carefully analyzed.

My personal opinion is that you can do all this without VC money but it will be harder and take a longer time. It could take decades and in this industry time is not your friend. The market is so hot that your competitors will fill your weaknesses in the market within a quarter or shorter. So in essence you really need startup funding from VC’s to be agile and build/keep your market share. Just keep an eye on those term sheets and make sure that the “pie” is big enough for everyone.


Originally published at Neural Market Trends.

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Machine Learning on a Raspberry Pi

It looks like Google is catching up to the idea of machine learning on a Raspberry Pi! Someone put RapidMiner on a Pi back in 2013 but it was slow because the Pi was underpowered.

The Pi has been a great thin client and a small, but capable server. I’ve used it for my Personal Weather Station project and as an FTP server. Based on the news, things are about to get interesting for both Google and Raspberry Pi.

I don’t know what Google is planning to release to the Pi and Maker community but based on the survey I filled out, they haven’t decided yet. They’re looking at C#, Javascript, Go, Swift, Python, and all the other usual suspects.

Raspberry Pi

The problem is optimizing the machine learning libraries for the Pi and having enough available to make it worthwhile for the community.  My guess is that they’ll go with Python, TensorFlow, and Go (Grumpy).

Whatever they decide, I consider this big news for Tinkerers and Makers everywhere. There will be an explosion of innovation if the Google toolkit is comprehensive. The Startup barrier to entry has been lowered, all you need is Pi ($40), a domain, some sweat equity, and a dream.

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