Open News

The 15 Most Popular Programming Languages on Github

There’s always a lot of questioning when it comes to the most popular programming languages in use.

Github, the network of programming repositories, is always a good place to gauge programmer activity and the trending languages you want to know.

Loggly, “a fast-growing startup helping thousands of cloud-centric organizations to turn log file data into insight and action” has helped do the hard work of finding those languages. Here are the results:

15 Most Popular Programming Languages on Github

15 Most Popular Programming Languages on Github

Technology and Society

The real reason why net neutrality matters

A lot of people think the core of net neutrality is site speed: the amount of time information is served to users. They’re partially right, but there’s a fundamental flaw in keeping the explanation to just those confines.

The Internet at its core is a bunch of servers (computers up 24/7) that receive HTTP requests from clients: your web browser or mine.

The whole point of the Internet is that it abstracts away physical location so that you can consume data created elsewhere: data in the form of textual input/images/ and technical assets such as CSS/Javascript files (NYT’s digital website) or video (Netflix) or in the case of things like Kimono which creates what is known as an Application Programming Interface out of static websites, a structured auto-updated data feed that can be interpreted by your server so you can, for example, scrape data from Yahoo Finance and create your own auto-updating personal dashboard of leading stock picks.

Now the reason why the net neutrality debate has focused on bandwidth and speed of transfer rather than the fundamentals of the Internet are because most people approach it from a user point of view rather than a server/builder point of view, as there are vastly more Internet users than builders so we focus on the paid connections clients have to use to access servers.

Net Neutrality with code(love)

Net Neutrality with code(love)

The crux of the debate isn’t that your Netflix is slower than it should be or that the “tubes” carrying data are filled up and so you will get shittier Internets.

The real core of the debate is that from the builder side, if one were to discriminate based on content type or volume, services like blogs, peer-to-peer cryptocurrency, and more would be threatened because as soon as they show business viability, a monopoly in another industry can arbitrarily decide to toll them either to discourage that growth or to profit from it as much as possible.

This kills innovation. We saw it with the destruction of Google Wallet and the degradation of bittorrent. We will see it when the next Netflix or Spotify fails to ever start because the cost of paying monopoly fees at an early stage will crush any hopes of late-stage returns.

The real argument around net neutrality is whether you trust a monopoly of telecom companies, users, or the government to determine what services the Internet should provide.

I obviously prefer users, but given that the power of the government is being balanced with corporate power, I lean towards the former not because I love governmental intervention but because it is the lesser of two evils. The US government barring its recent spate of backdoor hacking has done a reasonably good job with, for example, giving more power to ICANN (the organization responsible for managing the domain name system) so that innovation is spurred by non-government sources.

Meanwhile, new technologies have constantly been attacked by ISPs.…-problem.shtml

“Even in the U.S., there have been some major violations by small and large ISPs. These include:

The largest ISP, Comcast, secretly interfering with peer-to-peer technologies, including some of the most popular basic technologies used to distribute online TV and music (2005-2008);

A small telephone ISP called Madison River blocking Vonage, a company providing competing telephone service online (2005);

Apple blocking Skype on the iPhone, subject to a secret contract with AT&T, a company that competes with Skype in providing telephone service (2008-2009);

Verizon, AT&T, and T-Mobile blocking the functionality of Google Wallet on Nexus devices, while all three of those ISPs are part of a competing mobile payments joint venture called Isis (late 2011- +today);

and Comcast’s disputes with Level 3 and Netflix over termination fees, and the appearance that Comcast is deliberately congesting its network connections to force Netflix to pay Comcast for an acceptable connection (2010- +today).

In other countries, including democracies, there are numerous violations. In Canada, rather than seeking a judicial injunction, a telephone ISP used its control of the wires to block the website of a union member during a strike against that very company in July 2005. In the Netherlands, in 2011, the dominant ISP expressed interest in blocking against U.S.-based Whatsapp and Skype.”

I don’t want to live in a world where monopolistic ISPs determine what innovations thrive and which ones die.


The fundamental problem in net neutrality isn’t how fast services can be rendered to clients, it’s that if ISPs have their way, those services users want will never get the chance to prove themselves and survive.

Photo credit:

Learning Lists

Nine free, brilliant resources to learn data mining

I’m a big fan of playing with data.

In my earlier corporate life, I often used Excel to look through thousands of lines of spreadsheet goodness. I assumed what I was doing was “big data”, and I prided myself on my association with a trendy buzzword.

I know better now. A lot better.

If you’ve ventured here, you’re probably looking into data science, the mysterious science that seems to verge on mysticism in the press. The virtues of data are constantly praised as innovative and disruptive. They seem like the domain of an exclusive few practitioners lifting numbers into actionable insight.

Harvard Business Review went as far as to saying that the data scientist was the sexiest job of the 21st century.

It seems that data scientists create many of the most exciting projects at the cutting-edge of technology. The people you may know on LinkedIn appear thanks to data mining. Amazon’s book recommendations rely on computers to mine your book preferences and select the one book that is most likely to appeal to you. Facebook finds what posts you like, and serves you more of the same. Google finds out who you are, and filters search results and ads for you.

If I like computers, the search term Python will return me the programming language. If I like snakes, it will return me a whole bunch of snakes.

This is all down to the magic of data mining. You’re here because you want to look behind the veil and learn how to do all this.

It’s hard, but not as hard as you think. Data science, at its’ core, is all about using computing power to parse through huge data sets.

Learn Data Mining with code(love)

Learn Data Mining with code(love)

Here are nine free, brilliant resources to do just that.

1- Coursera’s Specialization in Data Mining (level: beginner)

Coursera brings the best from the University of Illinois at Urbana-Champaign, ranked in the top 5 for computer science schools in America. It’s a useful introduction to data mining–the application of data science and computing power to find patterns in large collections of data.

2- A UCLA professor’s overview of data mining (level: beginner)

This blogpost delves deep into the specifics of data mining. It provides an overview and a set of definitions that will help bring you up to scratch.

3-Introduction to R (level: beginner)

The coding language R is the workhorse of scientific data analysis and visualization. Codeschool offers an interactive and gamified approach to learn it, similar to Codecademy. Working with R will give you insight into how to move and dance with digital data, a skill that is the foundation of data science.

4- Kaggle’s Wiki on Python (level: beginner)

Kaggle is a platform for crowdsourced data challenges. The website has a ton of resources on how to get started with data science. This particular link leads to their guide on Python, one of the most versatile programming languages for data analysis.

5- Data Science 101 (level: beginner)

This blog knows how to describe itself: “Data Science 101 is about learning to become a data scientist.” Simple, clear and to the point.

6- W3’s Tutorial on SQL (level: beginner)

W3 hosts a bunch of interactive tutorials on the basics of programming. This set of tutorials goes through SQL, a language that allows you to access data from most web databases. The tutorials will give you a glimpse into how data is structured for many websites and they will give you enough knowledge so that you would know how to play with data.

7-Horton’s Hadoop Sandbox (level: intermediate)

Have you ever wanted to play with big data? Learn the basics here and experiment with them. Hadoop helps distribute data across multiple servers, helping to process large amounts of data as seemlessly as possible.

8- Machine Learning on Coursera with Andrew Ng (level: intermediate)

Learn about data mining and the algorithms you can create to make your data analysis job so much easier from a master in the field: the founder of Coursera Andrew Ng, a Stanford professor who has recently become Baidu’s chief scientist.

9- A Programmer’s Guide to Data Mining (level: advanced)

If you can work with Python at a proficient level, this book will help you implement different algorithms that will sort, filter, and manipulate your data for you. A must-read for people looking into the practical applications of data mining.

I hoped that helped get you set on the path to data mining. What resources do you think I’m missing? Comment below. 🙂

Open Stories

This is how you build a business of 130k users.

Peter is the founder of Brickflow, a web application that analyzes Tumblr profiles, and provides photos and videos that will suit the poster’s tastes.

Here is his honest, candid take on what it takes to build a digital idea to a business with tens of thousands of users. 

To be honest, my founding partners and I were pretty clueless when we started Brickflow. After more than two years, I can confidently say that we know how to validate and get initial traction for your startup, and we know what it takes to build a business. Moreover we have learned how to build a product and manage a team. Since then, with more experience and deeper integration of best practices, we can move faster to build our business.

Back at the beginning Tamas Kokeny worked at Prezi as a junior developer, Mihaly Borbely was a hobby-geek and photographer, whereas I worked at a Harvard founded ArtScience Labs incubator in Paris.

We had a lot ahead of us in terms of customer and product development. We did our homework by learning about lean, agile and other methods, but we were not successful implementing these practices.

At first, we built Brickflow in a typical waterfall way without any real validation. But we had passion and courage to learn and do it better. Much better. This is what Startup Wise Guys and Startup Chile realized, so they gave us a chance. SWG was like school with a vertical network of mentors, whereas SUP gave us time to build the product and connected us to the world’s biggest horizontal startup network. These 8 months in Estonia and Chile gave me more than my undergrad studies ever did when it comes to the foundation I needed to build a business.

We launched the first version when SUP ended, but we were not satisfied with user engagement. We realized that we need to test and iterate more, moreover that we need to improve execution significantly. This was the time, when we realized that we have not been applying the best practices that we have been thought. Facing this changed our mindset, and helped sharpen our focus to finding something that would work to build our business. After iterating the product for 6 months, we found something that really works. We arrived at product-market-fit and since then grow our active user base day by day.

But not only our user base grew but the team itself too. In one year we hired 6 people, so we have tripled the team. It was yet again a great challenge to integrate new people into the team and find our own roles as real executives. This is the first time when management and company culture become crucial to the building of our business.

Today, we are agile, we work in strict weekly sprints and do daily stand-ups. We use kanban boards to manage development. Getting used to estimating each task and giving them business value made management smooth. Moreover, we experiment every week with defined assumption – KPI pairs. Each modification of our design, copy and features is based on these experiments. We do not build or change anything without having it tested and validated. Backing everything with metrics made decision making faster and less of an emotional or hierarchical argument.

Being data driven makes our life easier and serves our customers much better.

Being data driven makes our life easier and serves our customers much better. Besides the quantitative experiments we have weekly in-person UX tests too. It is key to listen to the users. If there’s one thing you want to take out of this it’s this: find your users. Make sure you’re building something they want. 

If you liked that story, you should check out our other open stories. 

Build a business with code(love)

Build a business with code(love)


Open News

GitColony makes open source projects fun

Mariano Focaraccio is the CEO and co-founder of Gitcolony. I met him during the Dublin Web Summit, where he talked about his solution to help people contribute to open source solutions. Gitcolony has a matching system to help pair coders with open source code they can contribute the most to, and a review system that allows for developers to be given great feedback, and a standardized score, for contributing to open source code. 

Here are questions I asked him.

Open Source Projects with code(love)

Open Source Projects with code(love)

1) What’s your vision for Gitcolony?

We want to redefine the code review experience, help open source projects and build a reputation system for developers.

a) Redefine the code review experience both for open source projects and private repositories: we notice the process is broken and developers use meetings and emails to give feedback on code. This is highly inefficient and not effective as information gets lost. Also, because reviewing code is the most boring part of developers’ role, code reviews get done on a rush before the code needs to get pushed to production.

b) Help open source projects by spreading the responsibility of the revision of the quality of the code. Shellshock and Heartbleed happened because nobody ever revised those pieces of code in 27 years!

Nowadays only the small core teams of open source projects need to review the pull requests they receive. With our voting functionality, a broader community can decide which pull requests are of good quality and are ready to be merged.

c) Build a reputation system by evaluating both the quality of the code and the quality of the code reviews. We’ll allow the community to know which developers are good.

2) What are some tangible examples of how this helped build communities/contributions around open source projects? That’s notoriously difficult to do.

We recently launched Gitcolony but we are already helping build stronger and larger communities around both very well known open source projects like Laravel, WordPress and Rails and smaller and completely unknown projects.

Also, several companies are using Gitcolony internally, for their private repositories to level up the quality of their code and ensure they are building scalable, maintainable, reliable, cohesive and efficient.

3) What are your thoughts on open source projects, and how Gitcolony can encourage more participation in the movement?

We never stop surprising ourselves of how software developers collaborate, give feedback and share solutions with the open source community. Think about it… there’s no other area where this kind of selfless sharing happens.

Until now, developers could contribute with open source projects by coding or documenting. With Gitcolony, they can also do code reviews and we are seeing how code reviews is already getting new developers involved and interested in open source projects. Developers who did not use to participate in the community.

Gitcolony is also a good way to spread the word about open source projects.

Interesting? Check out other cool things you can build with open source at our Open News section.

Learning Lists

Learning Artificial Intelligence

Last year, my partner and I designed Gump, a Voice-Commanded Bipedal Robot for our Engineering Graduation Project. Gump won first place in the Engineering Faculty for Best Project and is currently featured in Engineering News Magazine at Concordia.

The moment we saw our robot walk on two legs for the first time and respond to the sound of our voice was miraculous.

That moment is when we knew we wanted to work on artificial intelligence for the rest of our lives.

Learning Artificial Intelligence with code(love)

Learning Artificial Intelligence with code(love)

That moment is when we knew we wanted to work on artificial intelligence for the rest of our lives.

As technology grows, so does the information that is available to us.

We began working on artificial intelligence in the NLP (Natural Language Processing) domain, something that allowed machines to understand and process human language patterns. We worked on speech recognition and now we are doing text analysis using artificial intelligence.

We’ve learned that the possibilities of using artificial intelligence are endless. Learning artificial intelligence is crucial to understanding how the 21st century will unroll—Chris Dixon, a prominent venture capitalist, argues that the next 10,000 startup ideas are clear: take x and add artificial intelligence to it.

With artificial intelligence, you can get unlimited insights from Big Data. Imagine measuring the mood of iPhone Users on Twitter after Apple’s latest product launch, gauging the world’s opinion on a major event like Russia’s invasion of the Ukraine, or predicting a company’s performance based on previous financial data, without any need for a human to relay that information to us.

Imagine a world where machines can automatically process information, and present it to use in a relatable and relevant form. This is what artificial intelligence proposes.

Artificial intelligence can help us all be more informed in less time, allowing us to make quicker decisions by getting the insights and analytics we need from Big Data.

Learning Artificial Intelligence

Learning Artificial Intelligence

Because of all this, I’m excited about artificial intelligence. I want you to be as excited as I am, so I’ve decided to highlight some ways to get started with artificial intelligence using resources available online

1-NLTK with Python

NLTK stands for Natural Language Tool Kit, an open source library with lot’s of functions to help you use NLP and artificial intelligence. to solve a problem or work on a project. You must know Python to get started with NLTK. To begin your path, I recommend the Coursera class offered by Stanford University. You might want to brush up on your Python skills with this great book for Python and NLTK beginners and then level up and learn more about Natural Language Processing by looking at this book.

2- IBM Watson Sandbox

Watson is IBM’s artificial intelligence., famous for defeating two human contestants on Jeopardy by having only the Wikipedia database to answer questions. It is considered the gold standard for artificial intelligence nowadays. IBM released a sandbox version for using Watson’s APIs for 30 days, which allows you to hack away and create a cognitive application.

If you want to use Watson for your next big thing, you won’t be able to unless you’re a Series B Funded startup, but that will change in the future. The access IBM has given to Watson now bodes well for the future. The ecosystem is still evolving, but it is the gold standard of artificial intelligence, and IBM is working to make it more accessible.

3- Udacity Courses

Sign up for the free courses on Udacity for Introduction to Artificial Intelligence, and the three Machine Learning Courses offered there. There are tons of resources and a community there to answer all your questions. We personally believe that Machine Learning should be a mandatory course for every Software Engineer and Computer Science Major.

Check out the forums as well, they’re very insightful and they will help guide you step by step.

4- MATLAB’s Neural Network Toolbox

This one is costly, but if you can get a MATLAB license with Neural Network Toolbox license as an add on, you have a very powerful system to play with. NNT is an Artificial Neural Network that has amazing capabilities for your project like speech recognition, image recognition, and object detection. We loved playing with NNT for our robot, Gump.

With a few lines of code, you can create artificial “neurons” in your program. The neurons can be assigned to do several things. We created eight neurons with MATLAB, each one assigned to a specific voice command. After that, we trained the neurons by recording over 5,000 voice samples from every person we could find on campus. The more voice recordings each neuron had per voice command, the more it grew in knowledge. When we tested it, the response was just perfect! MATLAB is immensely powerful.

Those resources will help you get started with learning artificial intelligence.

Leave comments below if you found this helpful, or know of other resources!

This post was written by Yaz Khoury, the founder of an A.I startup called Summarit that uses artificial intelligence to summarize articles for students.

Learning Guides

How I grew my mailing list by 133 emails in 2 hours

I’ve always had a theory that Newton’s Third Law applied to people. I think that’s largely been borne out by all that I’ve experienced.

Put your trust in people and you’ll get that trust repaid in you.

Do good, and others will do good unto you.

It was Dale Carnegie’s How to Win Friends and Influence People that probably put it best—when you make people feel like they’re important, they’ll respond to you in kind. You shouldn’t ask people for things directly: you should ask them for a favor, make sure that they know that they’re in a position of influence and power. That will elicit a favorable response to your endeavors.

I’ve always been a huge fan of involving people in creative projects, getting people to feel like they have ownership over something even if it wasn’t them that originated the idea. I think that’s the basis of the greatest way to grow something: when people push your idea as if it was their own. Then, if you can get them to join up with you for the long haul by joining your mailing list, you’ll be able to grow your own brand as well.

If people believe that you’ve heeded their words, and that they’ve played a part in your creative process, people will act on your behalf. If you genuinely listen to the input people give you, that’ll make them feel even more inclined to help spread the word.

I’ve always loved testing this—collecting both information that shapes my creative projects, and fans is a strong ideal for me. I worked for a startup where I segmented all of their top users, and surveyed them about what they actually used it for. Those super-users not only gave a ton of insight—they re-activated and used the product again at higher rates.

So while I was preparing for the launch of my book, I wondered how I could get people involved. Finally, it hit me: I was missing the most important element of my book: the title.

So I set off to create a survey form and prepare a list of startup Facebook groups I wanted to ask for their insights. Here’s what the form looked like:

Entrepreneur Blackjack Google Form with code(love)

I collected the answers on a Google spreadsheet connected to the form. Technologically, it took me about 5 minutes to get this all set up.

I then spent a couple of hours sending the link to this form on my personal social media profiles, and on a few selected startup Facebook groups. The results were convincing—

I got 369 clicks. Out of those 369, 167 replied with a title (conversion rate of 45.25%), and 133 asked to be part of the mailing list for when the book launched (conversion rate of 36%).

The click-through rate on the first newsletter I sent out was 20%, leading me to believe that upwards of about 30 people got wind of my book from just a couple of hours of work. I did get a few unsubscribes, but many people opted to stay on my mailing list: so I added a pool of new subscribers interested in what I do. I was able to grow my mailing list very rapidly.

I got great data from the 167 responses.

A significant amount of people chose the other option, which meant my titles I thought up were not strong enough. In a comment on one of the Facebook threads, someone came up with the idea that I should title the book Startup Blackjack since I was defining 21 startup terms—I ended up titling the book Entrepreneur Blackjack: 21 Startup Buzzwords Defined. This was also because titles with the word “Defined” scored high in the chosen categories—and so did the word bulls**t, even if I didn’t incorporate that (it might be something where profanity does drive sales, but I was giving my parents the book, so it wasn’t the best choice for me.)

Why did this happen?

I believe it’s because I really tapped into the entrepreneurs I asked ,making them feel part of the creative process. With two simple call-to-action prompts, people could contribute their input, then buy into the project they just helped create. For me it was a win-win-win—I got more people interested in the book, people who wanted the information I collected got access, and I created the title of the book with customer validation rather than blind guessing. I was able to grow my mailing list with new fans who were engaged straight off the bat.

With every action, there’s an equal reaction. I believed in creating with others—and I was happy to see others wanted to create with me. Here’s to your efforts to activating that same feeling.

Comment below if you’ve ever done something like this, or are giving this a try. I want to learn with you. 

Want to learn more?  Check out our other learning guides, and our learning lists filled with free resources to learn technology and entrepreneurship!

Defining the Future

“Growth hacking” in action: the best revenue-making secrets I found looking through Buffer.

What is growth hacking?

It always helps to have a concrete example. I love learning by doing, so I’ve decided to deep dive into the process for one startup, see what I can learn from it, and share the insights I glean so we can all get a greater understanding of growth hacking. Join me.

growth hacking with code(love)

growth hacking with code(love)



Now, where do we begin?

I like to say that startup growth should be an orchestra: different sections are trying different notes—when you listen to it from the crowd while they’re practicing, you can hear a consonance of some kind, but what’s happening is that each section is practicing how it should tune itself in sync with how the section sounds—and ultimately, how the orchestra itself sounds.

The beauty you hear at the end of a well-practiced orchestra comes from a series of players optimizing for their individual performance, as well as their collective one.

Brian Balfour, VP Growth of Hubspot, nailed it for me: he talks about how the most common question he gets is: what magic growth hack will propel your startup to instant success?

That’s a cop-out.

There’s no single magic bullet. The orchestra can’t just get a superstar violin player that can catapult the whole collective to success: they have to work so that each section of the orchestra gets progressively better, working within their individual units and the whole to deliver incredible, measured results.

There’s no magic bullet to startup growth.

You need a process that takes in new plans, works on existing ones, and gets new and existing players to work together to deliver magic, learning from yesterday’s mistakes to deliver progressively better results, forever experimenting and tinkering to do better.

You need a system to manage that.

For a system you need the aforementioned process, data, creative experiment ideas, and goals to aim for.

For a system you need the aforementioned process, data, creative experiment ideas, and goals to aim for.

I’ve worked with several startups already, but I’ve never had a complete taste of all of the data out there. This has been a function of my location as well as my inclinations: I’m based in Montreal, so I don’t have easy access to all of the startups clustered in major American cities. I’m also inclined towards earlier-stage startups where there isn’t as much data assembled: the startups are still going through the process of learning exactly how to grow. Most of them haven’t had the time to really look back at the data they’ve assembled.

Thankfully, with today’s emphasis on openness, that isn’t a barrier. Taking a page from Ivan Kriemer’s analysis of the Buffer dashboard gave me the inspiration to take a look at Buffer, which has wonderfully made its data and practices available to all.

You might know about Buffer from the great content they put out, not only on their blog, but through the social media management tool that forms the basis of their business. Buffer allows you to queue up a vast array of social media posts: the software handles the optimal timing for everything, allowing you to collect data on the effectiveness and reach of your social media efforts, while reducing the friction of organizing content for those efforts.

They also have a cultural commitment to radical transparency. Buffer has posted employee salaries online, charted their organizational thinking openly, and posted all of their metrics online. All of this leads to a wealth of data to play with.

Data-driven with code(love)

Data-driven with code(love)



Playing with the data yields a whole host of insights. It’ll allow us to come up with experiment ideas, and goals that make sense to “growth hack” systematically, and push Buffer even further into the green.

“[Growth hacking is] experiment-driven marketing.” – Sean Ellis

Caveat here: I might make mistakes in reasoning or gathering numbers here: if I do, I want you to call me out on them in the comments productively. We’re all in here to learn together.

Also, none of this is conclusive because we don’t have all of the data.

It’s very difficult to parse cohort-by-cohort analysis from the open Buffer dashboard, one of the things you should do because each collection of users goes through a different life-cycle of using the product. This incomplete view on the data has a negative effect on any data analysis.

Buffer is a software-as-a-service business, where their users pay monthly for premium features, a typical freemium subscription model. Churn is the enemy of this model. The number of people who jump ship after becoming a subscriber decreases the amount of money you can count on coming in every month.

Most cancellations occur in the first month of a subscription. A churn rate increase, one of the red flags all startups are told to watch for, can be deceptive in this case. You won’t know if it is a function of your product going bad. It could mean that you’re experiencing explosive growth that is driving churn because a larger portion of your userbase are hitting that first month wall with higher cancellation rates. You wouldn’t know if this was the case unless you could measure growth cohort-by-cohort, and separate out old users unhappy with the product, and new ones are just trying it out.

We must also keep in mind that this is insight from Buffer’s Stripe account, which means we’re looking exclusively at when Buffer collects money: that’s fine, but we have no idea of, among other things, when Buffer spends money.

We’d need a look into their Google Analytics/Kissmetrics for that, which means we’re missing at least one essential piece of the SaaS puzzle: cost of acquiring a customer.

The metrics are also anonymized so we have no idea of who the clients are, which helps with privacy issues, but hurts when it comes to making tangible recommendations—beyond hypotheses, assumptions and some aggregated data, we can’t really target great clients based on little more than the revenue categories they’ve been placed in.

Nevertheless, the Buffer dashboard is a great look into one of the fastest growing consumer applications in the world: a set of numbers that implies a lot of activity, and interesting insights to be gained. Finding trends in the data is where all good growth hacking begins, so let’s start.

We have to look at all of the data before we find trends. The current situation looks strong, but there can be improvements. Monthly recurring revenue at $367,000 implying an annual run rate ($367,000 * 12) of $4.4 million (4.404 million to be anatomically precise) means that we’re looking at a business that has hit product-market fit. There are 28,484 current paying customers. Buffer long ago hit the 1 million total user mark: it happened in September 2013, more than a year ago.

In the last Buffer Open update, CEO Joel Gascoigne mentions that there were 1,725,172 total users of Buffer. Extrapolating the average of the two previous monthly growth rates in users (3.3% and 3.7% average to 3.5%)—I’d do a weighted average, but that’s just being pedantic at this point, then you can assume that at mid October, there are now 1,816,800 users assuming a growth rate of 3.5% that takes to us to the end of September, and 1.75% that takes us to the end of October.

Buffer with code(love)

Buffer with code(love)



We now have enough to get almost all of the metrics Kissmetrics considers must-have for SaaS businesses at this stage: monthly recurring revenue, churn, average revenue per user, and the lifetime value of customers. Acquisition cost, as mentioned above, is something we’re going to have to assume.

Important trends from the data

Number of total users (approx): 1,816,800

Number of total paying subscribers: 28,484

Subscription rate (approx): 1.567%

Lincoln Murphy often sees an average 3%-5% conversion rate, but that’s for pure B2B subscription services. For applications with more B2C implications like Dropbox and Evernote, it’s tends to be lower. Phil Libin, for example, structured Evernote to be profitable at a 1% conversion rate. Now, about 2% of all Evernote users are paying customers. Obviously, Buffer would like to do better, but while there is room to improve, this isn’t a bad baseline.

MRR (Monthly Recurring Revenue—the lifeline of a subscription business. How much money keeps on coming in month-by-month: $367,000, growing at a 5.7% clip in the last 30 days.

Joel mentions that August’s 4.8% growth rate was tepid and that he would like to focus on getting it up. The slow growth rate might have to do with the seasonality of the product, given that August is a slow month for social media professionals, an important point to always remember when treating numbers: they always have a greater context. July’s growth was 6.6%.

Getting monthly recurring revenue growth rate up would seem to be a key performance indicator, something important to move the chains on, and measure, because it represents a crucial factor to the health of any business. That’s something we’ll definitely have to consider on our experiment proposals: we’re going to focus more on tasks that are lower in the acquisition funnel, and which deliver money now.

Churn rate (the number of people who are unsubscribing from the service): Monthly user churn: 5.1%, decreasing 5.1% in the last 30 days.

Monthly revenue churn: 6.5%, increasing 2% in the last 30 days.

Seems like good news on the user front, but remember, August was a slower month than most. A churn rate decrease could just be less people hitting that first month wall. A year ago, monthly churn was around 4.4% for context within Buffer. The yearly change in monthly churn rate is 15.8%, a significant uptick.

For context outside of Buffer—70% of SaaS companies had annual churn under 10%. 5-7% annual churn rate is acceptable for pure B2B SaaS companies according to Lincoln Murphy. Again though, Buffer is a strange beast with massive consumer and business applications, more akin to Evernote than any of the SaaS companies incorporated in the survey in the number of people it attracts. Still, a 5.1% monthly churn rate on a purely annualized basis (without taking into account cohort thinking) comes out to 61.2% annual user churn—meaning on an oversimplified basis that Buffer is losing more users than it keeps in a year. It’s something to keep in mind, even if it doesn’t look that bad given Buffer’s expansive user base of millions, and available consumer growth engines: churn rate is pretty high. Getting users might not be as important as keeping them, especially given that it often costs 4 to 6 times more to acquire new users than just to keep existing ones.

How is revenue churn increasing, while user churn is decreasing? Turns out that this a function of Buffer’s split of users into casual and business users, a key insight that  will come back again and again.

The vast majority of Buffer users are on the Pro plan, which costs $10/month, or $102/year. This allows you to connect 12 social accounts, and queue 200 posts: basically, if you’re a small business or a freelancer doing social media management, Buffer is reaching out to you. On the other end, there are business plans that can connect hundreds of accounts that will cost you upwards of $250/month.

It appears that monthly user churn of heavy enterprise accounts is really high, a function of the fact that there are so few, and that this seems to be a relatively new cohort. Monthly churn rates for some of these categories are as high as 33.3% (though that seems to me to be a function of the fact that there are so few of these plans). They’re such heavy money earners, even though user churn might be going down as a function of less Pro/Awesome plan members churning, revenue churn can still increase: an important point we’ll return to.

Average Revenue Per User: $13, growing at a 1.7% clip per month.

In a year, this has grown by about 26%, which is relatively static considering monthly recurring revenue has grown by 146% at the same clip. This seems to indicate that the majority of growth is coming from new users rather than finding ways to up-sell current users.

Given that the Awesome/Pro plan is really the only option for consumers, and the rest are billed as business plans, this makes a lot of sense. Upsells would only typically happen if people were transitioning from the Awesome plan to Business plans, suggesting a user behavior pattern of trying the Awesome plan to validate if it can work for a business.

The number of upgrades last year were 3,264, compared to 1,196 downgrades. However, this is somewhat confusing because Buffer counts switches from monthly to annual plans as a downgrade—despite the fact that annual plans actually have a much higher LTV than monthly ones (more on that later). In total, you can surmise that 4,460 people were moving around in the Buffer system last year. Considering that there were 28,424 customers, an oversimplified metric that didn’t account for users doing multiple transitions, and the changing composition of Buffer customers, would indicate that about 15.7% of people move from one tier to another, after only about 1.5% go to being paying customers in the first place. What this means is that people are ready to be upsold, but perhaps Buffer hasn’t captured as much value from that as it should.

Average LTV (Lifetime Value, or how much Buffer gets on average from each client in their full stay with Buffer) of customer: $251, up 7.2% from the last 30 days

This is where Ivan’s analysis of Buffer shines. You realize that there are large LTV values for Enterprise clients but those on the Awesome plan pale in comparison: this is a quantity over quality play, but the contrast is quite stark: $583 LTV for the yearly Pro plan, and merely $134 for the monthly one. This in comparison to the enterprise plans, which often average many times more—some above $25,000 in LTV. You can make a theory that finding ways to commit people to the annual plan will drastically increase MRR, by reducing revenue and user churn.

Given some insight I reached by meeting with Buffer’s COO, Leo, I’ve realized that these are soft-sell clients who have passed the limits of standard plans and asked for custom limits. Given how high the LTV is however, it may well be worth the time to find out how to formalize this arrangement.

My meeting with Leo, COO of Buffer 

I managed to snag a meeting with the COO of Buffer, who gave me a lot of insights. I’ve realized that Buffer’s analytics and data are the biggest sell for enterprise clients, that Buffer doesn’t use paid acquisition (doesn’t really work for them) and the biggest growth engines are built into the product itself.

Important insights from the data

Enterprise clients are really important—and it seems Buffer is moving to that direction.

Buffer’s profile is akin to Evernote’s: freemium with a high customer base, and higher adoption rates, but with higher churn rates as a result. Churn can be lowered but it’s not the biggest issue here.

Buffer focuses on its’ content and the product itself as its’ main avenues for growth: and its’ largely working.

Awareness isn’t as much of a problem as activation. The KPI Buffer is really looking for is increasing the monthly recurring revenue.

Buffer is affected by seasonality—it does rise and fall with the whims of its’ power base.

Buffer is seeing a lot of movement between tiers, but maybe not taking full advantage of it.

Experiment ideas

1-Get a contest going: best social media agency, or social media agency with the most clicks gets a free custom enterprise package from Buffer.

This will help spread more awareness of the enterprise options available, and business plans, and will bring out Buffer power users to compete against one another. If you mandate that they have to share their results with the Buffer team, and their tactics, highlighting the agency at the same time—you create a rich slew of content from heavy social media users about some of their tips and tricks.

This will create more awareness about business plans, opening up a new slew of rich, interactive content. Test to see if it is driving more adoption of business plans by linking contest content with sign ups, and tracking to see if this cohort of business sign ups have similar, better or worse metrics than the current average.

2-Create a bigger incentive to sign up for yearly plans.

Add prompts in the upgrade panel about an added benefit—A/B split test to see what works. It could be more posts in the queue,or more social profiles.Offer people who do two months straight an upsell to the yearly plan with an even better discount for being dedicated Buffer members. “Hey, we chose you because you’ve become a Buffer power user, get 20% off our yearly plan!”. Really try to lock people into it.

This should decrease churn, and increase monthly recurring revenue.

3-Post sample case studies of business Buffer analysis and have a Buffer content crafter go over it. Do and don’t of social media. This will highlight the most popular feature of Buffer for Business, and create content that highlights how powerful Buffer’s analytics dashboard truly is.

This should increase activation via signups from the analytics dashboard, and increase awareness of the Buffer for Business rollout.

4-Test a new pricing tier/test pricing

You don’t want to take away from the transition to the business plan, but perhaps a plan that fit in between the Awesome plan and the business plan would work at a flat yearly rate of $200/year. Create a pricing page where you preview a new tier and see how many people sign up, and if people downgrade from a business plan. If the pricing tier makes more MRR, keep it going.

So there you have it: data. A system to manage that data, use it to glean experiments, and measure their effects. If they move the needle, keep the experiments going and scale them. If they don’t, or work against metrics, scrap them.


I hoped you enjoyed this rundown of what growth hacking represents to me. If you want to challenge me on any of my assumptions, or thoughts, please comment below! I know that I don’t know many things, and I am constantly discovering new ways of learning. Please connect with me at or at if you want to further the discussion! 


Defining the Future

How to build 12 startups in 12 months: the startup MVP.

The following is an excerpt from my latest book, Entrepreneur Blackjack: 21 Startup Buzzwords Defined. The book is a guide to surviving startup cocktails, and building incredible new ventures. It defines a series of common startup buzzwords through storytelling and concrete examples so that everybody can learn from them, rather than using them to confuse.


The minimum viable product is an art, and not a science.

It’s when you finally decide your product is good enough to test out your original experiment. It is the bare minimum needed to get data about the market you’re in, and how customers will actually react to your idea.

A good MVP is a vehicle for your hopes and dreams.

As with any first car, there are a few rough patches to deal with.

When Twitter first started, they were not the Twitter you know and love. Twitter revolved around users texting messages with their phones,posting those messages on a publicly accessible web platform.

Netflix’s first rough version wasn’t even delivering movies online: it was about delivering movies through mail.

It’s important to realize that way back when they were getting started, those companies didn’t command the billions of dollars they do now. Their resources had to be focused on one narrow test, and that test had to be delivered under the simplest conditions possible.

Twitter was testing whether or not people wanted to be part of an organized universal message board, somewhere where you could post your thoughts simply, and see them displayed for all to see. The largest unorganized cocktail party on Earth ensued. It was only afterwards that Twitter worked on refining the experience.  The Twitter you know now that allows people to post directly on the platform through mobile applications, and allows them to easily find out what is happening around them through hashtags, and curated accounts—that was a Twitter that took years to evolve.

Netflix was trying to test a simple theory: would people be willing to pay for convenient access to movies? Instead of going to Blockbuster or anywhere else, would they prefer getting it shipped to their homes? If they did prefer that, it naturally followed that they would like to have video streamed to them on-demand with the web. After all, instead of dealing with the messiness of physical tapes, imagine instead a service that could deliver to you the media you wanted whenever you wanted it.

Netflix certainly imagined it. They executed on it masterfully.

Startup MVP Timeline

Startup Timeline

The test for instant on-demand online access to media was validated. Consumers accepted it en masse. Along the way, Netflix learned a great deal about the pain they were solving, and how they could go about delivering video to their consumers in the best way.

Netflix decided to move to external servers after an internal data center failure almost wiped out their ability to deliver videos on the web—which at the time was an experimental feature. By moving over to a more secure solution, Netflix ensured that the experimental feature that would become the centerpiece of its business would always be reliable.

The minimum viable product allowed for these large giants to test ideas cheaply, and make sure that when the time was right to spread their product like wildfire, everything was on a solid foundation.

It is through the minimum viable product that you first collect the data you need to establish if you have an idea worth pursuing. It is here where you determine whether or not you might need to pivot. It is here that your idea becomes something tangible that can be shared throughout the web, rather than a figment of your imagination. It is here that you see whether or not you are solving a real problem—and whether or not you have real customers who need your idea to exist.

Pieter (aka is an entrepreneur who has committed to doing 12 startups in 12 months. He’s constructed a series of minimum viable products that have been featured on Wired, the Next Web, and a whole host of publications, and been used by hundreds of thousands of people.

His latest venture, NomadList, focuses on sorting the cities of the world so that you can distinguish how friendly they would be to remote workers, from climate conditions all the way to how LGBT-friendly the cities were.

The idea has gone on the top of Product Hunt and Hacker News, two popular directories for startup ideas that will drive incredible traffic to new ideas. NomadList has received over 100,000 visits in less than a month.

NomadList MVP with code(love)

NomadList MVP with code(love)

Pieter is the perfect example of somebody who gets what a MVP should be about. When he talks about his ideas, he talks about building them as simply as possible, and just getting them done.

He started NomadList, a venture that was profitable from day 1, through creating a shared Google Spreadsheet, nothing more. He didn’t need to create a website or anything fancy to test the theory that people needed a guide to help them figure out cities where they might want to work remotely.

It can take you or me, or anybody less than five minutes to set up a Google Spreadsheet.

Soon after Pieter shared it, the list was getting populated with new information and new categories. Somebody filled in how LGBT-friendly each city was, rounding out the NomadList to its’ final form.

Demand for Pieter’s idea was established in the simplest fashion possible.

Perfect is the enemy of done. Done is when you can begin testing your theory that your idea is something people will use.

It is here where an idea becomes a startup.

Get the book here.

Technology and Society

How Citizens United is corrupting America and the open web

If you’ve ever Google searched for an image, you’ve encountered two of the strongest tools for digital builders in the modern age: Creative Commons (built by Lawrence Lessig) images, and Linux servers.

Google is built on the back of Linux servers, made possible by a collaboration of digital builders who claim no money, but rather build free software—free in the sense of free speech, not free in the sense of free beer, though most versions of Linux are free in both senses of the word.

Any image you find that is tagged with Creative Commons licences can be used, often with simple attribution. You can use it to build out your next website, your next slide deck, your next sales pitch: whatever you wish.

code(love) with creative commons

code(love) with creative commons

All you are asked to do is to pay it forward. Give the creator a link. Help contribute to open software.

Both of these systems rely on a simple principle: builders should be free to call upon a shared heritage, and move it forward to the benefit of all. Free software has been the kernel that has powered the distributed innovation of this age, from the multi-billion dollar successes of consumer apps like Facebook to Twitter, to entire industries based on big data to 3D printing.

At Facebook, we have always been strong advocates of open software. From our earliest days – when the site was built on PHP, MySQL and memcached – we’ve been privileged to stand on the shoulders of open source giants.

What the richness of open web culture has shown us is that everybody should benefit from a shared and rich tapestry of collective creativity and building. One voice, one vote, and a system that encourages the little guy to get what they can, and pay it forward—this has led to the creation of projects of incredible strength.

Politicians would do well to head the many they are entrusted to represent, and not the few that can buy their time, because this sort of innovation hangs in the balance.

The Supreme Court disagrees. Money, after all, is free speech. It is not to the creators of a vast and rich intellectual heritage that benefit should accrue: it is to the takers, even those who have avoided the law to make it to where they are.

Disney’s movies, based so closely on the Brothers Grimm. Hollywood, moved to the West Coast to avoid Edison’s patent fees. Apple, and Microsoft, built on initial prototypes of others, and stealing. The irony is palpable, but inevitable. The rebel who becomes the incumbent seldom remembers it. Moats are entrenched so that nobody else can join. After all, there are shareholders to look after: shareholders beholden to material wealth and cultural poverty.

That the incumbent has so much access to the system means that the system will always favor those who are seeking to capture creation rather than build on it. Or as Lessig put it: “the government is dependent on the few and not on the many.” And those few are often very focused on ensuring that they remain the few with the ability to influence.

A government that really listens to only a section of its’ people will oversee a stagnant system counter to the innovation that is powering the 21st century. It will fundamentally betray the tenets of its’ own creators: “Madison told us that ‘the people’ meant ‘not the rich more than the poor,’ ” Lessig said. It will mistake money for representation, and ignore what made America great.