Building With AI: The Promise and Friction of Using Lovable
Lasting Embers - My first project built using Lovable.
There is a moment when you realize something extraordinary is happening in software. Not in the abstract sense that technology is advancing, but in the very real moment where you watch an idea that lived quietly in your head begin to take shape on a screen in front of you. That’s the first feeling you get when using platforms like Lovable.
It feels almost magical.
You type a prompt describing something that would normally take a developer days or weeks to build, and moments later the system returns with functioning code, components, and structure that resembles what you imagined. For anyone who has spent years working with developers, trying to translate ideas into tickets, specs, and wireframes, I can imagine that the experience is both exhilarating and disorienting. Suddenly the barrier between idea and execution collapses. But after that initial excitement wears off, something else begins to emerge.
The reality of building with AI is far more nuanced than the early demos suggest. Lovable is incredibly powerful. But it is also incredibly demanding.
And learning how to work with a system like this requires an entirely new set of skills—skills that have less to do with coding and far more to do with systems thinking, patience, and emotional resilience.
For context, I’ve been using Lovable pretty heavily over the past few months and at L5 for usage.
The Promise: Why Tools Like Lovable Are So Powerful
The core promise of platforms like Lovable is simple. They dramatically accelerate the speed at which ideas can become real products. In traditional development environments, building software typically requires:
researchers
product managers
designers
engineers
QA testers
project managers
sprint planning
multiple feedback cycles
Even relatively simple features can take weeks to fully implement. Lovable collapses many of these layers into a single interaction. Instead of coordinating a team, you begin by describing the system you want to build. In many cases, the platform can:
generate working UI components
write backend logic
connect database models
create API endpoints
structure navigation and layouts
For founders, creators, and operators who have always had ideas but lacked the resources to execute them, this feels revolutionary. You are no longer limited by your ability to code. You are limited by your ability to think clearly about systems. And that shift is incredibly empowering. But it also introduces a new set of challenges. But to be fair here, this is really only half the story…
The Friction: Where Reality Begins to Show
The biggest misconception about AI development platforms is that they eliminate complexity. They don’t. What they do is move complexity somewhere else. Instead of writing code manually, you are now responsible for:
managing system architecture
preventing duplication of logic
ensuring consistency across components
maintaining a coherent data model
avoiding regressions when making changes (emphasis on this one)
The AI can generate code quickly. But it does not always maintain long-term architectural integrity unless you actively guide it. And that is where many builders—including myself—begin to encounter moments of deep frustration. You might ask the platform to fix a small UI issue, only to discover that the change breaks a previously working feature somewhere else in the system. You might attempt to simplify a workflow, and suddenly the navigation structure changes in unexpected ways.
At times, the experience can feel like trying to maintain balance while standing on shifting ground. The platform is incredibly capable. But it also requires constant stewardship.
The Emotional Side of Building With AI
One thing that is rarely discussed in conversations about AI development platforms is the emotional experience of using them. When everything works, the process feels exhilarating. You move quickly. Ideas materialize rapidly. Progress feels tangible. But when things break—and they inevitably will—the emotional swing can be dramatic.
You can spend hours trying to diagnose a problem that didn’t exist the day before. You may feel moments of doubt about whether the system will stabilize or whether you’ve unintentionally created something too complex to manage. And because these platforms move so quickly, the feedback loops are intense. Success and frustration can occur within minutes of each other.
For builders who are deeply invested in their projects, this dynamic can take a toll on personal wellbeing. It becomes important to recognize that the platform itself isn’t the source of stress. The stress comes from the pace of iteration and the emotional investment in the outcome. Learning to manage that emotional rhythm becomes just as important as learning the technical aspects of the system.
The First Lesson: Architecture Matters More Than Ever
One of the biggest lessons I’ve learned while building with Lovable is that architecture matters even more than it does in traditional development. When humans write software, architectural mistakes often emerge gradually. When AI writes software, those mistakes can multiply quickly. A single duplicated piece of logic can appear across multiple files within minutes. A navigation system can develop multiple conflicting permission maps. Route guards, access controls, and layout logic can begin to drift if the system is not carefully guided.
The temptation is to focus on speed. But long-term success depends on discipline in how the system is structured from the beginning. Before building large features, it’s worth spending time defining:
a single source of truth for permissions
a clear data model
shared access primitives
consistent routing structures
predictable layout hierarchies
Without these foundations, complexity accumulates rapidly.
Practical Steps for Starting a Project With Lovable
If I were starting a new project from scratch today, there are several things I would do differently.
1. Define system primitives early
Before building features, establish the core building blocks of the system:
user roles
access categories
route permissions
shared UI components
database entities
This prevents duplication later.
2. Enforce single sources of truth
Any logic that determines how the system behaves should exist in exactly one place. Examples include:
route permissions
subscription tiers
navigation visibility
feature access
When these rules appear in multiple files, drift becomes inevitable.
3. Document system intent
AI platforms are extremely powerful, but they benefit greatly from context. Maintaining documentation that explains the following is critical:
why the system is structured a certain way
how components should interact
what patterns should be avoided
Having clear documentation can dramatically improve the quality of future prompts.
4. Introduce changes incrementally
Large sweeping changes are risky in AI-generated systems. Instead, smaller targeted updates help maintain stability. Think of it less like rewriting a system and more like guiding its evolution.
The Importance of Patience
Perhaps the most important realization I’ve had while working with Lovable is that these tools reward patience. They are not magic. They are incredibly capable collaborators. But like any collaborator, they perform best when given:
clear direction
thoughtful feedback
well-defined boundaries
refactor, refactor, refactor
When approached this way, the platform becomes a powerful extension of your thinking. When rushed, it can create complexity faster than it can be managed.
A New Kind of Builder
Platforms like Lovable are quietly reshaping what it means to build software. The role of the builder is shifting. Less time is spent writing code directly. More time is spent designing systems, guiding architecture, and thinking deeply about how pieces interact. In many ways, the future of building may look less like programming and more like conducting an orchestra of intelligent tools. And like any orchestra, the quality of the outcome depends on the clarity of the conductor.
The Long-Term Perspective
Despite the challenges, I remain incredibly optimistic about where tools like Lovable are heading. They represent a profound shift in how ideas can move from imagination to reality. The friction we experience today is part of the learning curve of an entirely new creative medium.
And just as filmmakers once had to learn how to work with cameras, lighting, and editing tools, builders today are learning how to work with AI systems that behave more like collaborators than machines.
The process is imperfect. But it is also full of possibility. And for those willing to learn how to navigate both the power and the friction, the opportunity to build meaningful systems has never been greater.
The Project Behind the Lessons
Much of what I’ve learned through this process has come from building my own platform called Lasting Embers. The project began as a personal exploration of how people can live more intentional, sustainable lives while pursuing creative work. Over time, that exploration evolved into a platform that brings together tools for reflection, accountability, learning, and community.
Building it with Lovable has been both exhilarating and humbling. There have been moments where the platform moved forward faster than I thought possible, and other moments where the complexity of the system forced me to slow down and rethink how everything fits together. In many ways, Lasting Embers has become both the product I’m building and the environment where I’m learning what it truly means to create software alongside AI.
The lessons in this article aren’t theoretical—they’ve been shaped directly through the process of building and refining this system in real time.