Hypothesis Driven Development · Alex Cowan · 2022
This book explains how small, interdisciplinary teams achieve high-impact work by combining agile methods with a test-and-learn mindset. It emphasises that good ideas must be testable, and that generalists—those willing to cross disciplinary boundaries—thrive when they frame product decisions as falsifiable hypotheses. The approach seamlessly integrates design thinking, Lean Startup, agile, and A/B testing to measure every new idea against concrete user behaviours.
Key Points
Hypothesis-Driven Development (HDD) provides a structured approach for small, autonomous teams to achieve big outcomes through rapid experimentation and learning. It integrates principles from product design, lean startup, and agile to help interdisciplinary "traction" teams make smart bets on what to build.
HDD relies on a continuous cycle of generating and testing hypotheses about user behaviour. Teams focus on clear intent, measurable outcomes, and crisp decisions made in small batches. This disciplined process is crucial when operating in fast-moving, unpredictable markets.
HDD pairs well with agile, empowered teams and time-boxed sprints. It adds rigor to the four key areas of the product pipeline:
- Continuous Design - discovering the right features to build
- Application Development - building those features efficiently
- Continuous Delivery - releasing quickly and frequently
- Hypothesis Testing - validating assumptions about user behaviour
Focusing the entire team on a single metric (F) - representing the total cost per successful feature - provides a north star for assessing their overall innovation performance.
In the Continuous Design phase, teams must adopt a growth mindset - embracing the learning journey with humility and managing risk through small bets. They use the "double diamond" model to first diverge and converge on the right user problem, then repeat the process for the optimal solution.
HDD translates this into four testable hypotheses:
- Personas: Who are we building for?
- Jobs-to-be-Done: What do they need to accomplish?
- Demand: Do they actually want our proposed solution?
- Usability: Can they figure out how to use it?
Teams explore these assumptions through user interviews, observations, and lightweight MVP experiments like "Concierge", "Wizard of Oz" and "Smoke Tests." Design Sprints allow them to rapidly iterate toward a problem definition that is "right enough" to begin building.
As the team shifts into development, the same hypothesis-driven principles apply. Model-View-Controller architectures help separate concerns. User stories and prototypes guide implementation of what's truly needed. Unit, integration and UI tests, combined with small batch sizes, enable fast debugging while keeping the codebase clean.
DevOps and Continuous Delivery practices make the path from development to release far more automated and standardised. Version control, containerisation, feature flags, and CI/CD pipelines dramatically accelerate the pace of delivery while reducing errors in production. High performers achieve elite DORA metrics - deploying more often, recovering faster, and introducing fewer defects.
Experimentation is essential for turning new releases into validated learning. Teams frame each launch with a clear hypothesis and target metrics. They structure their tests thoughtfully, considering techniques ranging from case-control studies to fully randomised trials. Bayesian statistical approaches are well-suited to digital products, enabling dynamic multi-armed bandits that identify winning variants quickly.
Organisations build a true "experimentation engine" by measuring what matters and ensuring all results are actionable. Explicitly pairing user stories with a quantitative definition of success keeps the entire team focused on meaningful outcomes over arbitrary output.
Finally, teams decide where to run the next experiment by mapping the customer experience (CX) end-to-end and looking for steps with low conversion or high dropoff. Each touchpoint has a "line in the sand" - a target threshold that separates success from failure. When metrics fall short, fast follow-up tests isolate whether the issue lies in problem definition, solution design, or actual demand. Frequent, low-risk iterations are key to uncovering brighter spots with minimal waste.
Mastering Hypothesis-Driven Development takes patience and collaboration. But by testing relentlessly, learning humbly, and deciding prudently, even small teams can deliver outsize value in an increasingly digital world.
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Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning
Sebastian Raschka. 2018. (View Paper → )
The correct use of model evaluation, model selection, and algorithm selection techniques is vital in academic machine learning research as well as in many industrial settings. This article reviews different techniques that can be used for each of these three subtasks and discusses the main advantages and disadvantages of each technique with references to theoretical and empirical studies.
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Book Highlights
Think of how many things in your life just happen without you noticing: water when you turn on the faucet, the lights when you flip a switch, the realization that a friend is online given by a green indicator in Skype or Google Chat. All of these are technologies that are calm because they have evolved to work with us in our everyday lives with the least amount of friction. Amber Case · Calm Technology
In general, the further into the future one looks, the more unpredictable technology becomes. The somewhat predictable technological future seems to be about five to seven years off. Beyond that, the strategist has to take a portfolio point of view, making bets on a variety of possibilities, some of which will conflict or compete with one another. Richard Rumelt · The Crux
Energy is a scalar, which in physics is a quantity described only by its magnitude; volume, mass, density and time are other ubiquitous scalars. Power measures energy per unit of time and hence it is a rate (in physics, a rate measures change, commonly per time). Vaclav Smil · How the World Really Works
Whether that hope ends in disappointment or excitement ultimately boils down to how well you understand what exactly they’re hoping for. Wes Bush · Product-Led Onboarding
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Quotes & Tweets
Set aside two hours everyday to focus on your most important goal. Never skip a day. Unknown
I’ve been progressing through my current book about three times faster than my pace on the last one, by internalizing that four deep focus hours of work every weekday compiles into incredible long-term progress. So I begin the day with a timer set to four hours. I start the timer when I start work and pause the timer anytime I’m not working. If I wake up and knock out the four hours like a grown man, I can be free all afternoon. If I dick around all day like a moron, I have to suffer and stay up late. But the four hours has to get done. Tim Urban