This chapter from my upcoming book “From One to Zero” (to appear with NoStarch 2021) teaches you a well-known but still undervalued idea. The idea is to build a minimum viable product (in short: MVP) to test and validate your hypotheses quickly without losing a lot of time in implementation. In particular, you’ll learn how to apply the idea of radically reducing complexity in the software development cycle when creating value through software.
Stealth Mode of Programming
If you’re like me, you know what may be called the “stealth mode” of programming (see Figure 4-1). Many programmers fall victim to it, and it goes as follows: you come up with a wonderful idea of a computer program that will change the world—with the potential to become the next Google. Say you discovered that more and more people start coding, and you want to serve those by creating a machine-learning-enhanced search engine for code discovery. Sounds great? You think so—and you start coding enthusiastically on your idea a few nights in a row.
Figure 4-1: The stealth mode of programming.
But does this strategy work? Here’s a likely outcome of following the stealth mode of programming:
You quickly develop the prototype, but it doesn’t look right. So, you dive into design and optimize the design. Then, you try the search engine, and the recommendation results are not relevant for many search terms. For example, when searching for “Quicksort”, you obtain a “MergeSort” code snippet with a comment
"# This quickly sorts the list". So, you keep tweaking the models. But each time you improve the results for one keyword, you create new problems for other search results. You’re never quite happy with the result, and you don’t feel like you can present your crappy code search engine to the world for three reasons. First, nobody will find it useful. Second, the first users will create negative publicity around your website because it doesn’t feel professional and polished. And third, if competitors see your poorly implemented concept, they’ll steal it and implement it in a better way. These depressing thoughts cause you to lose faith and motivation, and your progress on the app drops significantly.
Let’s analyze what can and will go wrong in the stealth mode of programming shown in Figure 4-2.
Figure 4-2: Common pitfalls in the stealth mode of programming
There are many common pitfalls in the stealth mode of programming. Here are four of the more common ones:
- Losing Motivation: As long as you’re in stealth mode, nobody can see you. Nobody knows about the great tool you’re implementing. You’re alone with your idea, and doubts will pop up regularly. Maybe you’re strong enough to resist the doubts initially—while your initial enthusiasm for the project is big enough. But the longer you’ll work on your project the more doubts will come into your mind. Your subconsciousness is lazy and seeks reasons not to do the work. You may find a similar tool. Or you may even doubt that your tool will be useful in the first place. You may start to believe that it cannot be done. If only one early adopter of your tool would have provided you some encouraging words, you’d probably stayed motivated. But, as you’re in stealth mode, nobody is going to encourage you to keep working. And, yes, nobody is paying you for your work. You have to steal time from your friends, your kids, your wife. Only a minority of people will sustain such a psychological drain. Most will simply lose motivation—the longer the stealth mode, the smaller the motivation to work on the project.
- Getting Distracted: Even if you manage to stay motivated to work on the project for an extended period without any real-world feedback—there’s another powerful enemy: your daily distractions. You don’t live in a vacuum. You work in your day job, you spend time with family and friends, and other ideas will pop into your mind. Today, your attention is a rare good sought by many devices and services. While you work on one project, you’ll have ideas for other projects, and the grass-is-greener effect will kick in: many other projects seem to be so much more attractive! It takes a very disciplined person to manage these distractions, protect their working time, and stay focused on one project until they reach completion.
- Taking Longer: Another powerful enemy is wrong planning. Say you initially plan that the project takes one month if you work on it for two hours every day. That’s 60 hours of estimated working time. Lost motivation and distractions will probably cause you to average only one hour every day, so it already doubles the project’s duration. However, other factors are responsible for underestimating the project duration: unexpected events and bugs take much more time than anticipated. You must learn new things to finish the project—and learning takes time. Especially when you mix learning time with answering Smartphone messages and notifications, emails, and phone calls. It’s tough to estimate how much learning time you need correctly. And even if you already know everything you need to know to finish the project, you likely run into unforeseen problems or bugs in your code. Or other features may pop into your mind that demand to be implemented. An infinite number of factors will increase your anticipated project duration—and hardly any will reduce it. But it is getting worse: if your project takes longer than anticipated, you’ll lose even more motivation, and you’ll face even more distractions causing a negative spiral towards project failure.
- Delivering Too Little Value: Say you manage to overcome the phases of low motivation. You learn what you need, stay focused, and avoid any distraction for as long as it takes to finish the code. You finally launch your project, and—nothing happens. Only a handful of users even check out your project, and they’re not enthusiastic about it. The most likely outcome of any software project is silence—an absence of positive or negative feedback. You’ll wonder why nobody writing in with some constructive or even destructive feedback. Nobody seems to care. There are many reasons for this. A common reason is that your product doesn’t deliver the specific value the users demand. It’s almost impossible to find the so-called product-market-fit in the first shot. Well, even if you’d have found product-market-fit and users would generally value your software, you don’t yet have a marketing machine to sell it. If 5% of your visitors would buy the product, you could consider it a huge success. However, a 5% conversion rate means that 19 out of 20 people won’t buy the product! Did you expect a million-dollar launch? Hardly so; your software sells to one person in the first 20 days leading to an ultimate income of $97. And you’ve spent hundreds of hours implementing it. Discouraged by the results, you quickly give up the idea of creating your own software and keep working for your boss.
The likelihood of failure is high in the stealth mode of programming. There’s a negative feedback loop in place: if you stumble because of any of the discussed reasons, the code project will take you longer to finish—and you’ll lose even more motivation, which increases your chances of stumbling. Don’t underestimate the power of this negative feedback loop. Every programmer knows it very well, and it is why so many code projects never see the light of the day. So much time, effort, value is lost because of it. Individual and even teams of programmers may spend years of their lives working in the stealth mode of programming—only to fail early or find out that nobody wants their software product.
You would think that if programmers spend so much time working on a software project, they’d at least know that their users will find the end product valuable. But this is not the case. When they are sunk in the stealth mode of programming, programmers don’t get any feedback from the real world—a dangerous situation. They start to drift away from reality, working on features nobody asked for, or nobody will use.
You may ask: how can that happen? The reason is simple: your assumptions make it so. If you work on any project, you have a bunch of assumptions such as who the users will be, what they do for a living, what problems they face, or how often they will use your product. Years ago, when I was creating my Finxter.com app to help users learn Python by solving rated code puzzles, I assumed that most users are computer science students because I was one (reality: most users are not computer scientists). I assumed that people would come when I released the app (reality: nobody came initially). I assumed that people would share their successes on Finxter via their social media accounts (reality: only a tiny minority of people shared their coding ranks). I assumed that people would submit their own code puzzles (reality: from hundreds of thousands of users, only a handful submitted code puzzles). I assumed that people wanted a fancy design with colors and images (reality: a simple geeky design lead to improved usage behavior). All those assumptions lead to concrete implementation decisions. Implementing each feature—even the ones nobody wanted—had cost me tens, sometimes hundreds of hours. If I knew better, I could have tested these assumptions before spending lots of time working on them. I could have asked for feedback and prioritized implementing the features valued by the highly engaged users. Instead, I spent one year in stealth mode to develop a prototype with way too many features to test some of those hypotheses or assumptions.
Complexity — A Productivity Killer
There’s another problem with the stealth mode of programming: unnecessary complexity. Say you implement a software product consisting of four features (see Figure 4-3). You’ve been lucky—the market accepted it. You’ve spent considerable time implementing those four features, and you take the positive feedback as a reinforcement for all four features. All future releases of the software product will contain those four features—in addition to the future features you’ll add to the software product.
Figure 4-3: A valuable software product consisting of four features
However, by releasing the package of four features at once, you don’t know whether the market would’ve accepted any subset of features (see Figure 4-4).
Figure 4-4: Which subsets of features would have been accepted by the market?
Feature 1 may be completely irrelevant—even though it took you the most time to implement. At the same time, Feature 4 may be a highly valuable feature that the market demands. There are 2n different combinations of software product packages out of n features. How can you possibly know which is value and which is waste if you release them as feature bundles?
The costs of implementing the wrong features are already high. However, releasing feature bundles leads to cumulative costs of maintaining unnecessary features for all future versions of the product. Why? There are many reasons:
- Every line of code slows down your understanding of the complete project. You need more time to “load” the whole project in your mind, the more features you implement.
- Each feature may introduce a new bug in your project. Think of it this way: a given feature will crash your whole code base with a certain likelihood.
- Each line of code causes the project to open, load, and compile more slowly. It’s a small but certain cost that comes with each new line of code.
- When implementing Feature n, you must go over all previous Features 1, 2, …, n-1 and ensure that Feature n doesn’t interfere with their functionality.
- Every new feature results in new (unit) tests that must compile and run before you can release the next version of the code.
- Every added feature makes it more complicated for a new coder to understand the codebase, which increases learning time for new coders that join the growing project.
This is not an exhaustive list, but you get the point. If each feature increases your future implementation costs by X percent, maintaining unnecessary features can result in orders of magnitude difference in coding productivity. You cannot afford to systematically keep unnecessary features in your code projects!
So, you may ask: How do you overcome all these problems? If the stealth mode of programming is unlikely to succeed—then what is?
Minimum Viable Product — Release Early and Often
The solution is simple—quite literally. Think about how you can simplify the software, how you can get rid of all features but one, and how you can build a minimum viable product that accomplishes the same validation of your hypotheses as the “full” implementation of your ideas would have accomplished. Only if you know what features the marketplace accepts—and which hypotheses are true—should you add more features and more complexity. But at all costs, avoid complexity. Formulate an explicit hypothesis—such as users enjoy solving Python puzzles—and create a product that validates only this hypothesis. Remove all features that don’t help you validate this hypothesis. After all, if users don’t enjoy solving Python puzzles, why even proceed with implementing the Finxter.com website? What would have been the minimum viable product for Finxter? Well, I’ve thought about this, and I’d say it would have been a simple Instagram account that shares code puzzles and checks if the Python community enjoys solving them. Instead of spending one year writing the Finxter app without validation, I should’ve spent a few weeks or even months sharing puzzles on a social network. Then, I should’ve taken the learnings from interacting with the community and build a second MVP (the first one being the social media account) with slightly more functionality. Gradually, I’d built the Finxter app in a fraction of the time and with a fraction of the unnecessary features I’ve implemented and removed again in a painful process of figuring out which features are valuable and which are waste. The lesson of building a minimum viable product stripped from all unnecessary features is one I’ve learned the hard way.
Figure 4-5 sketches this gold standard of software development and product creation. First, you find product-market-fit through iteratively launching minimum viable products until users love it. The chained launches of MVPs build interest over time and allow you to incorporate user feedback to gradually improve the core idea of your software. As soon as you’ve reached product-market fit, you add new features—one at a time. Only if a feature can prove that it improves key user metrics, it remains in the product.
Figure 4-5: Two phases of software development: (1) Find product-market-fit through iterative MVP creation & build interest over time. (2) Scale-up by adding and validating new features through carefully designed split tests.
The term minimum viable product (MVP) was coined by Frank Robinson in 2001. Eric Ries popularized the term in his best-selling book Lean Startup. Since then, the concept has been tested by thousands of very successful companies in the software industry (and beyond). A famous example is the billion-dollar company Dropbox. Instead of spending lots of time and effort on an untested idea to implement the complicated Dropbox functionality of synchronizing folder structures into the cloud—that requires a tight integration in different operating systems and a thorough implementation of burdersome distributed systems concepts such as replica synchronization—the founders validated the idea with a simple product video even though the product they made a video about didn’t even exist at the time. Countless iterations followed on top of the validated Dropbox MVP to add more helpful features to the core project that simplify the lives of their users.
Let’s have a more in-depth look at the MVP concept next, shall we?
A minimum viable product in the software sense is code that is stripped from all features to focus on the core functionality. For Finxter, it would have been a social media account centered around code puzzles. After that validation was successful, the next MVP would have been a simple app that does nothing but present code puzzles. You’d successively add new features such as videos and puzzle selection techniques extending the MVP functionality based on user need and early adopters’ feedback. For Dropbox, the first MVP was the video—and after successful validation, the second MVP was created building on the customer insight from the first MVP (e.g., a cloud storage folder for Windows but no more). For our code search engine example, the MVP could be a video shared via paid advertisement channels. I know you want to start coding right away on the search engine—but don’t do it until you have a clear concept that differentiates itself from other code search engines and you have a clear plan on how to focus. By working on your MVP concept before you dive into the code, you’ll not only save lots of time, but you stay nimble enough to find product-market-fit. Even the minimal form of your software will already satisfy your market’s needs and desires if you find product-market-fit. The market signals that they love and value your product, and people tell each other about your software product. Yes, you can achieve product-market-fit with a simple, well-crafted MVP—and by iteratively building and refining your MVPs. The term to describe this strategy of searching for the right product via a series of MVPs is called rapid prototyping. Instead of spending one year to prepare your big one-time launch, you launch 12 prototypes in 12 months. Each prototype builds on the learnings from the previous launches, and each is designed to bring you maximal learning in minimal time and with minimum effort. You release early and often!
One idea of building your MVPs to find product-market-fit is based on the theory that your product’s early adopters are more forgiving than the general market. Those people love new and unfinished products because it makes them feel special—they’re part of a new and emerging technology. They value products more based on their potential than the actual implementation. After all, they identify with being early adopters, so they must accept half-baked products. This is what you’re providing them with: rough, sketchy products with a great story on what this product could be. You reduce functionality, sometimes even fake the existence of a specific feature. Jeff Bezos, the founder of Amazon, initially faked to have individual books in stock to satisfy his customers and start the learning loop. When people ordered these books, he bought them manually from his local book publisher and forwarded them to his customers. True MVP-thinking!
If you’re building your first software based on MVP thinking, consider these four pillars: functionality, design, reliability, and usability.
- Functionality: The product provides a clearly-formulated function to the user, and it does it well. The function doesn’t have to be provided with great economic efficiency. If you sold a chat bot that was really you chatting with the user yourself, you’d still provide the functionality of high-quality chatting to the user—even though you haven’t figured out how to provide this functionality in an economically feasible way.
- Design: The product is well-designed and focused, and it supports the value proposition of the product. This is one of the common mistakes in MVP generation—you create a poorly-designed MVP website and wonder why you never achieve product-market-fit. The design can be straightforward, but it must support the value proposition. Think Google search—they certainly didn’t spend lots of effort on design when releasing their first version of the search engine. Yet, the design was well-suited for the product they offered: distraction-free search.
- Reliability: Only because the product is supposed to be minimal; this doesn’t mean it can be unreliable. Make sure to write test cases and test all functions in your code rigorously. Otherwise, your learnings from the MVP will be diluted by the negative user experience that comes from bad reliability. Remember: you want to maximize learning with minimal effort. But if your software product is full of bugs—how can you learn anything from the user feedback? The negative emotions could’ve all come from the error messages popping up in their web browsers.
- Usability: The MVP is easy to use. The functionality is clearly articulated, and the design supports it. Users don’t need a lot of time figuring out what to do or on which buttons to click. The MVP is responsive and fast enough to allow fluent interactions. It is usually simpler to achieve superb usability with a focused, minimalistic product because a page with one button and one input field is easy to use. Again, the Google search engine’s initial prototype is so usable that it lasted for more than two decades.
A great MVP is well-designed, has great functionality (from the user’s perspective), is reliable and well-tested, and provides good usability. It’s not a crappy product that doesn’t communicate and provide unique value. Many people frequently misunderstand this characteristic of MVPs: they wrongly assume that an MVP provides little value, bad usability, or a lazy design. However, the minimalist knows that the reduced effort comes from a rigorous focus on one core functionality rather than from lazy product creation. For Dropbox, it was easier to create a stunning video than to implement the stunning service. The MVP was a high-quality product with great functionality, design, reliability, and usability nonetheless. It was only easier to accomplish these pillars in a video than in a software product!
Advantages of MVP-driven software design are manifold. You can test your hypotheses as cheaply as possible. Sometimes, you can avoid writing code for a long time—and even if you do have to write code, you minimize the amount of work before gathering real-world feedback. This not only gives you clues on which features provide the best value for your users, but it also reduces waste and provides you with fast learning and a clear strategy for continuous improvement. You need much less time writing code and finding bugs—and if you do, you’ll know that this activity is highly valuable for your users. Any new feature you ship to users provides instant feedback, and the continuous progress keeps you and your team motivated to crank out feature after feature. This dramatically minimizes the risks you’re exposed to in the stealth mode of programming. Furthermore, you reduce the maintenance costs in the future because it reduces the complexity of your code base by a long shot—and all future features will be easier and less error prone. You’ll make faster progress, and implementation will be easier throughout the life of your software—which keeps you in a motivated state and on the road to success. Last but not least, you’ll ship products faster, earn money from your software faster, and build your brand in a more predictable, more reliable manner.
The final step of the software creation process is split testing: you not simply launch a product to the user base and hope that it delivers the value. Instead, you launch the new product with the new feature to a fraction of your users (e.g., 50%) and observe the implicit and explicit response. Only if you like what you see—for example, the average time spent on your website increases—you keep the feature. Otherwise, you reject it and stay with the simpler product without the feature. This is a sacrifice because you spend much time and energy developing the feature. However, it’s for the greater good because your product will remain as simple as possible, and you remain agile, flexible, and efficient when developing new features in the future—without the baggage of older features that nobody needs. By using split tests, you engage in data-driven software development. If your test is successful, you’ll ship more value to more people. You add one feature at a time if adding this feature leads to your vision—You’re on a path to progress with incremental improvements by doing less.
Low-Hanging Fruits & Rapid Greedy Progress
Figure 4-6: Two different ways of creating a software project by implementing a set of features: (Good) High-value low-effort features first; (Bad) Low-value, high-effort features first
Figure 4-6 shows two different ways of approaching a software project. Given is a fixed set of features—the horizontal length of a feature defines the time duration of implementing the feature, and the vertical length defines the value the feature delivers to the user. You can now either prioritize the high-value, low-effort features or prioritize the low-value, high-effort features. The former leads to rapid progress at the beginning of the project phase. The latter leads to rapid progress towards the end of the project phase. Theoretically, both lead to the same resulting software product delivering the same value to users. However, life is what happens if you plan—it’ll play out differently: the team that prioritizes the low-value, high-effort features won’t get any encouragement or feedback from the real world for an extended period. Motivation drops, progress comes to a halt, the project will likely die. The team that prioritizes high-value, low-effort features develops a significant momentum towards more value, gets user feedback quickly, and is far more likely to push the project to completion. They may also decide to skip the low-value, high-effort features altogether, replacing them with new high-value features obtained from the feedback of early adopters. It is surprising how far you can go by reaping only the low-hanging fruits!
Is Your Idea Special? You May Not Like The Truth
A common counterarguments against rapid prototyping and for the stealth mode of programming is that people assume their idea is so special and unique that if they release it in the raw form, as a minimum viable product, it will get stolen by larger and more powerful companies—that implement it in a better way. Frankly, this is such a poor way of thinking. Ideas are cheap; execution is king. Any given idea is unlikely to be unique. There are billions of people with trillions of ideas in their collective minds. And you can be quite sure that your idea has already been thought of by some other person. The ideas are out there, and nobody can stop their spread. Instead of reducing competition, the fact that you engage in the stealth mode of programming may even encourage others to work on the idea as well—because they assume like you that nobody else has already thought of it. For an idea to succeed, it takes a person to push it into reality. If you fast forward a few years, the person that will have succeeded will be the one who took quick and decisive action, who released early and often, incorporated feedback from real users and gradually improved their software by building on the momentum of previous releases. Keeping the idea “secret”—even if you could accomplish this in the first place—would simply restrict its growth potential and reduces its chances for success because it cannot be polished by dynamic execution and real-world feedback.
Envision your end product and think about the need of your users before you write any line of code. Work on your MVP and make it valuable, well-designed, responsive, and usable. Remove all features but the ones that are absolutely necessary to maximize your learnings. Focus on one thing at a time. Then, release an MVP quickly and often—improve it over time by gradually testing and adding more features. Less is more! Spend more time thinking about the next feature to implement than actually implementing each feature. Every feature incurs not only direct but also indirect implementation costs for all features to come in the future. Use split testing to test the response to two product variants at a time and quickly discard features that don’t lead to an improvement in your key user metrics such as retention, time on page, or activity. This leads to a more holistic approach to business—acknowledging that software development is only one step in the whole product creation and value delivery process.
In the next chapter, you’ll learn why and how to write clean and simple code—but remember: not writing unnecessary code is the surest way to clean and simple code!
Where to Go From Here
Do you want to develop the skills of a well-rounded Python professional—while getting paid in the process? Become a Python freelancer and order your book Leaving the Rat Race with Python on Amazon (Kindle/Print)!
While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.
To help students reach higher levels of Python success, he founded the programming education website Finxter.com. He’s author of the popular programming book Python One-Liners (NoStarch 2020), coauthor of the Coffee Break Python series of self-published books, computer science enthusiast, freelancer, and owner of one of the top 10 largest Python blogs worldwide.
His passions are writing, reading, and coding. But his greatest passion is to serve aspiring coders through Finxter and help them to boost their skills. You can join his free email academy here.