For the last couple of days I’ve been thinking about budgets.  Businesses, particularly large ones, have very advanced budgets and therefore tools in place to monitor and analyse cash flows and budgetary concerns, sometimes even in real-time.

You can bet that a company like Facebook has the ability to show ad revenue per hour and probably per minute.  Obviously this is outside the scope of nearly any small business to put together, but the concepts are still valuable.  Tracking your income and expenses as accurately as possible allows for quicker decisions and possibly the ability to identify trends that might disappear in aggregate.

There’s the old adage that What gets measured gets managed.  To that end tools like Mint help with personal finances a bit, but sometimes you need some better real-time feedback on your spending behaviour.

Something that I want to get a better feel for personally is how spending matches up to my budget, if a couple of purchases blow the budget I want to know about it as quickly as possible so that I can adjust before sitting down at the end of the month to review my accounts.

I may try to put together a custom system to create budget alerts for this kind of thing by scraping credit card transactions.  It could be very beneficial.

I’m ramping up a couple of eCommerce websites as part of a strategy to create a home based business that my wife can take charge of when our daughter starts school in 9 months.  Part of building the foundation for a business like that is to get re-acquainted with the world of internet marketing… something that I was deep into several years ago but am now way out of date.

Facebook Ads seems to be an interesting platform for advertising now and is significantly different from Search advertising.  I’ve been reading and learning about various strategies for marketing on Facebook and I’m hearing about some really crazy and convoluted advertising funnels, like leveraging cheaper advertising markets for likes and engagement to help drive down costs and help improve social proof in the target market.

The interesting marketing psychology is that people are not on Facebook to look to buy products and so ads which sell things stand out as an ad and are not engaging. There really needs to be some kind of entertainment or curiosity value.  Like paid advertorials in print media the goal should be to mix in with the rest of a person’s news feed and not stand out as an advertisement.

To that end I’m going to be pumping a fair bit of money into Facebook over the coming months to test ad campaigns and find viable ways to run an eCommerce business.

 

For software developers there is an unhealthy prevailing belief that being a great programmer is some innate skill that others have. Brilliance with developing code is difficult to train for because it either requires some gift you don’t have or years of on the job experience.  There is a large amount of impostor syndrome within the community which is not healthy or productive.

Of course people who are top developers know that it takes a lot of hard work to understand core concepts. It helps to have a mentor and a solid education and access to training.

There is a tactic to getting better which more programmers should be using.  Deliberate practice is the most critical aspect to improving any craft and programming software is not an exception.  Like playing piano or painting or ceramics there is creativity and technical skill which can be improved on with deliberate practice.

If you want to get better at your craft it is not good enough to simply work on job tasks. For work you typically do something once and then it’s done, there’s few opportunities for repetition and critical evaluation.  If you were learning to play piano and you had a sheet of music the equivalent to developer workflow would be to play through the song once, stopping to go back and fix your mistakes then when you finished you’d put the song away move on to a new song.  Practising piano requires playing the same song hundreds of times, you start by playing and focusing on not making mistakes, when that is accomplished there is still practice at making the song sound good with appropriate pedal usage, tempo and dynamic, and finally when that is good enough you can continue to practice the same song and add your own touches – arpeggios, slurs, delays etc.

How many times have you implemented a deck of cards?  Can you write one top to bottom without looking up examples on stack overflow, or querying the documentation or searching through code completion lists? Could you write a deck of cards in a procedural, functional and object oriented styles?  Could you meta program a deck of cards? Could you make a deck that is thread safe? distributed? Web scale? Obfuscated?

Practice. Do it often, and do it deliberately.

Writing code everyday has been an interesting challenge.

In 2015 I started to work towards a long streak on GitHub which eventually capped out at 250 days. The questions I wanted to answer was:

  • Can I apply ‘deliberate practice‘ to programming and get better?
  • Can ‘free coding’ (like free writing) be effective way to push through writers block?
  • How important is memorising to your coding performance?
  • If syntax and API unknowns don’t present bottlenecks to your flow how fast can I translate an idea into code – can it be limited by typing speed?

I started a repository for my daily coding.  It had a simple script to generate a blank file everyday for me to code in and I would try to code something.  Sometimes it would be to explore a new python module, or fiddle with syntax, or challenge myself with a rosetta code example or replicate a previous day’s code from memory.  I wrote dozens of Flask apps, memorised the methods of lots of APIs, and gained a level of confidence with writing Python that I don’t have with any other language.

At the end of the streak I had a repository with hundreds of small scripts.  Only a handful of them were multi-day efforts or had any real value.  The variety of this collection proved to be useful on it’s own too – several times I have referred back to these examples to help with my actual work and to copy/paste snippets from.  Some of them started me down a path of exploration – like calculating the return on investment for solar panels.

Part of what enabled me to maintain this streak as long as I did was a simple script I wrote to check GitHub for daily activity and email me if I hadn’t yet committed any code.  This simple hack was enough of a reminder to keep me focused even when I was otherwise distracted.

This past week I turned that script into a web service anyone can use.  CodeStreak.io will watch your public github activity and email or SMS you if you haven’t yet pushed any code for the day.  This is the first project of 2017 that I plan on building to expand on my previous streak.

In 2017 I want to build 12 projects.  Each should be roughly 10-20 hours of effort and result in something that provides value for other people.  CodeStreak.io is an example of the kind of project that I want to undertake this coming year, but it is also a tool to help ensure that the momentum is sustained for 12 months.  Blocking out 4 hour chunks of time is a helpful way to really focus and be productive, but 4 hours once per week has been (for me) too sparse to maintain interest in something long enough to finish it.  A little bit everyday keeps a project on your mind.  Attempting to maintain a streak will be a tool to power through the bits that are otherwise uninteresting or difficult.  CodeStreak.io is a foundational tool necessary to accomplish my 2017 goal.

The questions I want to explore with this new goal are:

  1. Without a concern for generating revenue can I just write cool things and get them out there?
  2. Can I get deeper into something new and create something useful out of it with less than 20 hours of effort?
  3. Can you get good at seeing a project from start to finish – what skills or traits will improve the odds?

Hopefully, I’ll have some answers at the end of 2017.

One of the welcome additions to Amazon’s AWS offerings is a simplified server provisioning service to compete directly with Digital Ocean called Lightsail.  Lightsail provides a nicer web UI for launching instances, many quick launch options for common apps like WordPress or GitLab and simplified billing (yay!).  With Lightsail you don’t need to pre-pay for Reserved Instances to get a good price on an EC2 server.

Dokku is mini heroku you can run on your own servers.  It uses the same buildpacks that Heroku does to enable git push deployments. By building on top of Docker a collection of available dokku plugins make it easy to start up databases, caching or tie in other services.  In this tutorial I add Postgresql and get an SSL cert using Let’s Encrypt

Together, Lightsail and Dokku create an easy way to manage your application deployment on an inexpensive server.

Get started on Lightsail by starting up a new virtual server:

And then selecting an Ubuntu Image:

There’s a spot here for ‘Add launch script’ where you can drop in these commands to automatically install dokku on first boot:

wget https://raw.githubusercontent.com/dokku/dokku/v0.7.2/bootstrap.sh
sudo DOKKU_TAG=v0.7.2 bash bootstrap.sh

Give it a name and press Create to start booting up the server. You should be able to SSH to the new server very quickly though you can connect before dokku and package updates have been applied (it’ll take a couple minutes for the dokku command to become available)

After a couple of minutes have passed and things are installed and running visit your server in a web browser:

For the public key you’ll want to grab the key on your computer.  if you have linux or macOS you can grab the contents of ~/.ssh/id_rsa.pub.  If you need to generate a key there’s a good How-To on Github about generating them.

Set the hostname you’ll use for the server if you have one and Finish Setup.

Next step is to SSH to the server and fiddle with it there using the private key you can download from Lightsail:

ssh -i LightsailDefaultPrivateKey.pem ubuntu@<YOUR PUBLIC IP ADDRESS>

And create the app you will be deploying:

dokku apps:create your-app

Add a postgres database (there are other great plugins available for dokku too)

sudo dokku plugin:install https://github.com/dokku/dokku-postgres.git
dokku postgres:create database-name
dokku postgres:link database-name your-app

Now, back to your local app add a git remote to this new server and deploy it:

git remote add dokku dokku@<PUBLIC IP OR HOSTNAME> your-app
git push dokku master

If that is successful then the project should be visible online. Yay!

Then there are some next steps to help complete the app. Set any environment variables you need for the app:

dokku config:set your-app ENV=prod

You can install an SSL cert using Let’s Encrypt very easily:

sudo dokku plugin:install https://github.com/dokku/dokku-letsencrypt.git
dokku letsencrypt your-app

You can configure some pre and post deploy hooks inside the app.yaml file in your project repository to run checks or execute database migrations.

That’s about it! git push deploys to update your project whenever you want.

Recently I’ve been interested in finding a business investment – something like a B&B that allows me to put some of my retirement savings into a business that I have some control over its success.  The normal process for something like this would be to write a business plan or at least do some back of the envelop estimations for how much revenue is expected from the property.

The usual tool of choice is a spreadsheet.  And those are excellent ways to work through the numbers and visually see things.  However, the flexibility of a spreadsheet is somewhat limited for even more advanced analysis.

I wanted to take things to a different level.

What information could I get from looking at the market and scraping webpages that I could feed into a bigger model to see how other owners of similar businesses do.  By pulling in 1000+ comparables and running them all through a similar model to estimate each of their profitability it becomes possible to identify the traits of a successful business.

Applying this sort of ‘big data’ analysis is proving interesting.  There is an amazing amount of information freely available on the internet, but much of it exists in different silos.

In the example of running a B&B, there are lots of them listed on booking.com and similar travel booking sites.  These provide a partial picture of how popular a place is (from it’s availability) and the revenue (from the cost to stay there). Another big piece of the picture is the costs – which you can estimate by checking real-estate listings.  By putting all this information you can see many interesting things.

If your model is accurate then you can get answers to these questions:

  • What percentage of B&Bs turn a profit each year
  • Is there an optimal size / number of rooms
  • which attributes of the property correlate most to it’s profitability

You can take a deeper dive into the best performing properties to see if they do something unique – do they have nicer websites / photos? Do they do aggressive advertising? Are they active on social media?  Answers to these questions can help you find the strategies that are working best in the market – and perhaps things that are a waste of time.

This type of analysis is something I think more people should be doing.  It provides some competitive advantage in terms of the information that you bring with you into a potentially big investment, and reduces the risk that you inadvertently buy a lemon.

 

There’s nothing quite like the feeling of starting a new project idea and seeing it all the way through to finished and published.  It’s a feather in your cap that you can look back on and say “I built that”.  Regardless of if it is a big hit or not, it will make you a stand out – very few people get something all the way to done on their own.

Ambition can act against you in this.  The larger the project the more opportunties there are to hit roadblocks which derail it. The size of a project is a risk that should be minimized.

That’s why I believe it’s important to create momentum with smaller projects.  A small win still gives you a great amount of confidence.

This applies both to home projects, or code projects or hobbies.

Small is a relative term.  You may be able to handle a small 40 hour project, while someone else cannot yet tackle something that big.  Small may be as simple as fixing a wall hook or creating a pull request to fix a typo in the documentation of an open source project.

By putting a lot of these small projects together you create something bigger than the sum of them.  Fixing all the small things around your house can turn it into a relaxing home, Contributing to Open Source projects could gain you some notoriety and help you get a dream job.

Derek Sivers said “the best option is the one with the most options” and doing many small projects gives more options than one big one.

37 signals (now basecamp) started out with 6-10 individual products. When starting they didn’t know which would be a success so creating many smaller ones diversified their risk and helped them succeed.

Small projects are going to be a core part of my strategy for 2017.  Launching micro-sites, simple tools, or open-source libraries that can be finished in 8-10 hours of effort.

Think small, get out there, and finish it.  It’s a step to something bigger.

Lately I’ve been thinking back on the past business opportunities that have been explored and which ones have been more effective than others.  This story about the confessions of a Google spammer reminded me of a project I attempted to build back in 2012 to build and control and automate lots of blogs. My project was much more whitehat than spamming google but it landed at the same time that Google cracked down on similar blog networks with their penguin update. It made me think about what ideas have worked best and worst of the things I’ve tried over the years.

By far, the easiest and most straight forward business ventures I’ve done have been consulting and freelance work.  People understand how this works so it’s easy to sell. “oh you need a website built? I know how to do that – will you pay me to do it for you?” That’s a great base for business and been the largest single source of income for Halotis. Companies are comfortable paying for your time and expertise to create new things and they’ll pay as long as you can convince them of your time availablility and expertise.

iPhone Apps were the next biggest success.  Getting in early and leveraging the distribution of the iTunes App Store was a big win.  Lack of time to maintain and improve the apps and the influx of AAA quality competition eventually killed things for me.  Now it’s too hard for a single developer to do without a lot of luck.

Creating real products and services were another profitable option for me. I used Product Launch Formula to help strategize a couple of sales which were a big success.  Big launches attracted a bunch of sales that ended up becoming a real product business.

Ad supported and affiliate sales based websites have been break even for me.  They cover the hosting costs – which is to say almost no money.

Those have been my biggest lessons.  Hopefully that is helpful to readers who are thinking about their own businesses.

 

The more that I learn about Deep Learning and other Machine Learning concepts the more intrigued by the idea that we could apply some of things we learn about how these ML models behave back onto human psychology.  This is not something I have heard discussed yet. They were, after all, roughly modelled on how our own neurons work and could be considered a crude model of how we work.

What are some of the behaviours and lessons we’ve learned from training AIs that could be applicable to how we learn for example.  AIs are obviously dramatic simplifications of our own minds but they learn in similar ways.

Machine learning algorithms can be divided into supervised and unsupervised learning models.  They are not equivalent and the things you can do with one are not possible with the other approach. Would it be helpful to identify topics in school that can be associated with each approach so that we can optimise our teaching approaches?

A concrete example of this is how we learn a new language.  A common suggestion for language learning is to immerse yourself in it.  To that end people will listen to radio and music in their target language. Is that an effective way to improve your understanding?  This would be considered mostly unsupervised since we have no answers for what a particular sound we hear might mean (unless we can guess from a context of other words we already know).  If we fed 10,000 hours of voice recordings into an unsupervised machine learning algorithm what kind of things would we be able to learn?  It might be able to pick up some common words or phrases that are used, it might be able to find words that are often used close together.  It would get a feel for the ‘sound’ of a language.  But that is likely as deep of an understanding as it could make.

Given this insight we could hypothesis that immersing yourself with just recorded audio is not particular effective at learning what words mean.

If we wanted to teach a computer to hear a word and turn it into text we need to have the sounds and the matching text.  This is a supervised approach and can be quite effective. However, we know that this is much more effective if we have lots and lots of training data.  For a particular word it helps to have the word spoken by many different people, spoken quickly and slowly, varying pitches and accents.  The more examples we have to train on the better the accuracy is going to be.  You’ve probably experienced listening to a song and hearing a word you can’t quite make out. You listen over and over but still can’t get it. Then someone else tells you what the lyric is or you hear a different recording of the song and suddenly it becomes crystal clear.  Now you can hear it.

Given this, perhaps we could ensure that training programs on a computer don’t just replay the same recorded words over and over again but instead give lots of variations.  It would be interesting experiment to have a 1 page story in your target language recorded by 10-20 different people. Would listening and reading along to all the recordings help with your listening comprehension?  How much better would you learn listening to 1 recording 20 times vs 20 different recordings?

Several studies have looked at the efficacy of same-language closed captioning to reading and listening comprehension and prove that it can help.  Similar application of supervised learning applied to people.

Another area that generates much concern in machine learning is how to identify and prevent over-training.  Over training happens when the algorithm essentially memorises the answers and has difficulty applying to new input it hasn’t seen yet.  There are techniques for testing that are used to help diagnose over training. One such approach is to separate the training data from the testing data.  Trying to determine if students have memorised the answers or really understand a concept is critical to their ability to move forward and build on those lessons. Could we apply our machine approaches to humans to help identify memorisation vs understanding?

I’m sure there are more fascinating ways we could take what we have learned from teaching machines and apply it to how we teach people.

Time is our one major limited resource and so it’s important how we decide to use it.  One of the biggest questions we ask ourselves as developers is often what kind of cool stuff could we do on evenings and weekends.

Here are my considerations for any side projects that I feel are worth saying ‘yes’ to.

  • It should align with your broader goals
  • It should ideally provide an incremental step towards those goals
  • It should be fun and interesting
  • It should have a number of quick wins
  • It should be something that you can’t or won’t get experience with during work hours

One thing to consider is conflicts with your job.  It would suck to be working on a passion project at night and find the need to hide it from co-workers and your boss or risk some sort of discipline. Co-workers can be great sounding boards and strong supporters of your success with these personal projects and it’s awesome to be able to take advantage of that.

The key to being one of those people who seems to get a lot of stuff done is to be continually walking in the same direction rather than running scattered from place to place.  It’s not as easy as it sounds.  There’s always a new hot idea to pursue or a reason to give up on the project you haven’t yet finished.  For that reason it is good to pick a project that hits as many checkboxes as possible for you and that you believe you can see through to the end.