Practical Handbook for Product Developers – First Steps: Idea, Validation, MVP – Simple Solutions First
The goal of an MVP isn’t completeness. It’s to learn if your solution works for the problem you’ve identified. Every feature should be simplest thing that solves that problem and enable learning.
Hello, developers! 🚀
Welcome back to the Learn Agile Practices newsletter, your weekly dose of insights to power up your software development journey through Agile Technical Practices and Methodologies!
📚 Previous on this series
⏩ Section 1 - First steps: Idea, Validation, MVP
1️⃣ Chapter 1 – How to Decide What Goes In and What Stays Out of the MVP
2️⃣ Chapter 2 – Simple Solutions First → You’re here.
3️⃣ Chapter 3 – Fake it ‘till You make it → Coming next week!
🧭 Follow the journey: each issue is a micro-chapter of the series: Practical Handbook for Product Developers — released weekly.
🔎 Scenario / Pain Point
Most startups die of over-building.
Teams design for scale before they have a single user. They worry about performance, permissions, or micro-services when what they really need is a working product that validates one clear solution. The codebase becomes a cathedral for imaginary traffic.
This happens because developers and founders often confuse a product with a project. A project is finished when the spec is implemented. A product is never finished — it evolves through feedback, validation, and learning.
The goal of an MVP isn’t completeness. It’s to learn if your solution works for the problem you’ve identified.
That means starting from the simplest thing that solves that problem, not from the most elegant, extensible, or scalable design.
Yet many engineers still believe that “simple” means “cheap” or “temporary.” It doesn’t. Simple means purpose-driven: just enough to solve the current problem and to let you learn from real use.
If your app only needs addition and subtraction, don’t build multiplication and square roots “for later.” If you only have two types of users, don’t design a full permission system with 14 roles. Every extra abstraction delays learning and adds useless complexity.
In the early phase, speed equals survival. The rest of the company — product, marketing, sales — is moving fast, experimenting daily. Engineering must match that rhythm, not hide behind over-architecture. Otherwise, by the time you finish your perfect foundation, the market has already moved on.
And when startups grow, this slowness often becomes invisible. The rest of the company slows down too, and technical inertia feels “normal.” But that doesn’t make it right — it just turns lack of agility into culture.
⚡ Why it matters
One of the fundamentals elements of a product mindset is the ability to distinguish between what must be decided now and what can safely wait.
Amazon calls this the difference between one-way-door and two-way-door decisions.
One-way doors are irreversible choices (e.g., foundational tech stack, data model design). Delay them until you have enough information.
Two-way doors are reversible (e.g., naming conventions, API structure, UI flows). Move through them fast. Learn, adjust, repeat.
In MVPs, almost every decision is a two-way door. Optimize for speed and learning. Don’t over-plan what can be easily changed.
Software development is continuous discovery: learning what users need, what scales, and what to drop. Every line of code should teach you something about your product or your problem space.
That’s why we talk about building to learn, not building to complete.
Each slice you build should:
Deliver a tiny piece of real value.
Be simple enough to replace or evolve.
Generate feedback you can act on.
Techniques like Thin-Slice Development help. Build vertical features, not horizontal layers — an entire “slice of cake,” not a spoonful of frosting. Deliver a full end-to-end experience, however small, instead of shipping disconnected technical components.
Another useful approach is what Alistair Cockburn called a Walking Skeleton — a minimal, end-to-end version of the system that connects all layers, however crudely. It’s not a slice of backend here and a mockup there; it’s a thin vertical cut that actually works from UI to persistence. Once that skeleton is alive, you can evolve it.
That’s where Evolutionary Architecture comes in: the idea that architecture itself should start as an MVP. It must be small, understandable, and adjustable by the team that writes the code. Complexity grows only when the product earns it.
When you design in thin slices, you avoid premature optimization. You can add complexity only when data justifies it.
AI tools make this mindset even more powerful. With generative design and code assistants, you can prototype in hours. Build quick sketches in Figma, generate first UI drafts, and let AI create boilerplate code so your energy goes into validation — not scaffolding.
And the classics still hold true: TDD keeps your code simple by forcing you to write only what’s necessary to make the next test pass. Continuous Integration gives you the confidence to evolve architecture safely, one change at a time.
🛠️ How we solve it
Start with a Walking Skeleton.
Build the simplest vertical slice that proves the system can run end-to-end. One button, one API, one database call. Ship it.
Adopt an Evolutionary Architecture mindset.
Your architecture has its own MVP. It should evolve with the product — not be designed once and frozen. The only constant is the ability to change safely.
Focus on the main pain point.
Every feature must map directly to the problem you’re solving. If it doesn’t, it’s out.
Simplify the implementation, minimize the scope.
Two user roles may be enough. Two workflows may be enough. Remember: you can add complexity later, but you can’t easily remove it.
Prototype fast, with tools you know.
If speed is the goal, build with technologies your team masters. Experiment with new tech only through isolated spikes.
Decide through doors.
For every choice, ask: Is this reversible? If yes, decide fast. If not, delay until data informs you.
Build to learn.
Ship small, measure impact, refactor. Every iteration should reduce uncertainty.
⚖️ Trade-offs
Too simple: you’ll refactor later — acceptable to learn fast as soon as it’s tested and under control.
Too complex: you’ll never learn, and you’ll have to live with a lot of wrong choices — fatal.
Fake solutions: good short-term validation, replace them quickly as soon as they start to be unsustainable.
Evolutionary design requires discipline, or it can bring to chaos if unmanaged. At the same time, it’s the only way to have a “good enough” architecture under control.
The goal isn’t perfection — it’s momentum with intention. It’s having intentional choices instead of unexpected ones.
🚀 Next Steps (tomorrow morning)
List your current architectural decisions. Mark which are one-way and which are two-way doors. Commit only to what you can reverse.
Draw your Walking Skeleton. One flow, one data path, one deployment pipeline. Visualize it end-to-end.
Ship a thin slice. Deliver one feature that a real user can touch, even if it’s ugly. Learn from it before building anything else.
📎 Primary Resources
Building Evolutionary Architectures — Neal Ford, Rebecca Parsons, Patrick Kua
A Walking Skeleton - a minimal initial implementation of an application’s architecture that includes and connects the basic components of the system
Minimum Viable Product revisited - an article by Kent Beck
Where do you see your team over-engineering right now?
Reply to this email — I’d love to hear one place where “simpler” could get you learning faster.