In this blog post, we explore how one of our partners’ projects, which we have been collaborating on since 2016, successfully scaled communication as our team expanded from a small group to a large, distributed team of developers. These strategies offer valuable lessons for any software team navigating similar growth.
The Challenges of Scaling Communication in Distributed Software Teams
Our collaboration with this client started as a modest project with just a few developers from our team. Communication was straightforward, and roles were flexible. However, as the software gained traction, our team expanded from 2 developers to over 25, bringing new challenges, particularly in maintaining effective communication and defining clear roles within a growing, geographically dispersed team.
A recent report by GitLab shows that 84% of software developers believe clear communication is crucial for team success in a distributed environment. Our experience in the project reflects this, as the sudden increase in team size made it difficult to keep everyone aligned and informed.
Effective Strategies for Communication in Growing Teams
To address these challenges, our team rolled out some key strategies to scale communication effectively:
1. Dedicated Communication Channels
We set up new Slack channels to streamline discussions by topic and urgency. This helped team members focus on relevant conversations and avoid information overload.
2. Focused Meetings with a Facilitator
We refined our daily stand-ups to deliver quick, high-level updates, ensuring the whole team stayed informed. For critical tasks, we formed smaller, cross-functional groups that met regularly to dive deeper into the project’s most important aspects.
To keep these meetings efficient, we introduced the role of a facilitator—someone responsible for keeping discussions on track, making sure everyone has a chance to contribute, and clearly define follow-up actions. This approach was essential in keeping the team productive and communication flowing smoothly.
3. Travel Regularly for In-Person Meetings
Recognizing that nothing beats face-to-face interaction, we made it a priority to meet our partner and their team in person. We traveled to the United States to start our partnership in a healthy way, having the chance to dive deeper into their needs and aligning on project goals.
But it wasn’t all work and no play. These trips gave us the chance to bond after hours, sharing good times that helped build a strong team spirit rooted in trust. By closing the physical gap, we not only ensured a more productive collaboration but also made the process more enjoyable for everyone involved. After all, a team that works well together and has fun together is unstoppable!
Developing Roles and Maintaining Clarity in Distributed Teams
As the team grew, it became clear that the informal role distribution that had worked in the past was no longer sufficient. New responsibilities emerged, and specialized roles were necessary to handle them. However, assigning these roles and ensuring that team members were prepared for their new responsibilities required careful planning.
1. Natural Role Evolution
Roles on this project weren’t rigidly assigned. Instead, as new needs emerged, team members who showed both interest and aptitude naturally stepped into these roles. A key figure in this process was the Jedi Master, one of our principal engineers, who had been with the project since the beginning. The Jedi Master played a crucial role in mentoring team members, guiding them through their transitions, and ensuring they felt supported as they took on new responsibilities.
2. Progressive Responsibility
Under the Jedi Master’s guidance, team members gradually took on new roles. As they gained experience, Jedi’s involvement decreased, allowing the new leaders to fully embrace their responsibilities.
3. Structured Onboarding and Training
We implemented a comprehensive onboarding process for new team members, including a set of courses and tasks tailored to the project’s needs. Continuous feedback and opportunities for new members to suggest improvements helped them integrate smoothly and contribute effectively from the start.
Measuring Success: The Impact on SmartBorder
The strategies implemented at this project resulted in a well-coordinated, efficient team capable of handling the complexities of a large-scale software project. Communication became more streamlined, roles were clearly defined, and team members were well-prepared to meet the demands of their positions.
This approach not only maintained the quality of the codebase but also strengthened our team’s relationship with our partner. The quality of the product remained high, with functionalities behaving as expected, thanks to the effective communication and role management strategies in place.
Key Takeaways for Scaling Communication in Software Development
This experience highlights several key takeaways for managing a growing software development team:
Effective communication is vital at every stage of growth. As a team expands, it’s crucial to revisit and refine communication channels to ensure they remain efficient and effective.
Roles should evolve naturally but with guidance. Allow team members to grow into new roles, but provide the necessary mentorship to ensure they succeed.
Training and onboarding are ongoing processes. Continuous improvement in training programs helps new team members integrate smoothly and contribute effectively from the start.
Client relationships benefit from strong internal processes. A well-organized, communicative team translates into a smoother, more transparent relationship with the client, ultimately leading to a better final product.
Conclusion
Scaling a software development team is challenging, but with the right strategies in place, it’s possible to manage growth without sacrificing quality. Our experience with this project demonstrates the importance of evolving communication, defining roles carefully, and investing in team development to ensure long-term success.
In this blog post, we explore how one of our partners’ projects, which we have been collaborating on since 2016, successfully scaled communication as our team expanded from a small group to a large, distributed team of developers. These strategies offer valuable lessons for any software team navigating similar growth.
The Challenges of Scaling Communication in Distributed Software Teams
Our collaboration with this client started as a modest project with just a few developers from our team. Communication was straightforward, and roles were flexible. However, as the software gained traction, our team expanded from 2 developers to over 25, bringing new challenges, particularly in maintaining effective communication and defining clear roles within a growing, geographically dispersed team.
A recent report by GitLab shows that 84% of software developers believe clear communication is crucial for team success in a distributed environment. Our experience in the project reflects this, as the sudden increase in team size made it difficult to keep everyone aligned and informed.
Effective Strategies for Communication in Growing Teams
To address these challenges, our team rolled out some key strategies to scale communication effectively:
1. Dedicated Communication Channels
We set up new Slack channels to streamline discussions by topic and urgency. This helped team members focus on relevant conversations and avoid information overload.
2. Focused Meetings with a Facilitator
We refined our daily stand-ups to deliver quick, high-level updates, ensuring the whole team stayed informed. For critical tasks, we formed smaller, cross-functional groups that met regularly to dive deeper into the project’s most important aspects.
To keep these meetings efficient, we introduced the role of a facilitator—someone responsible for keeping discussions on track, making sure everyone has a chance to contribute, and clearly define follow-up actions. This approach was essential in keeping the team productive and communication flowing smoothly.
3. Travel Regularly for In-Person Meetings
Recognizing that nothing beats face-to-face interaction, we made it a priority to meet our partner and their team in person. We traveled to the United States to start our partnership in a healthy way, having the chance to dive deeper into their needs and aligning on project goals.
But it wasn’t all work and no play. These trips gave us the chance to bond after hours, sharing good times that helped build a strong team spirit rooted in trust. By closing the physical gap, we not only ensured a more productive collaboration but also made the process more enjoyable for everyone involved. After all, a team that works well together and has fun together is unstoppable!
Developing Roles and Maintaining Clarity in Distributed Teams
As the team grew, it became clear that the informal role distribution that had worked in the past was no longer sufficient. New responsibilities emerged, and specialized roles were necessary to handle them. However, assigning these roles and ensuring that team members were prepared for their new responsibilities required careful planning.
1. Natural Role Evolution
Roles on this project weren’t rigidly assigned. Instead, as new needs emerged, team members who showed both interest and aptitude naturally stepped into these roles. A key figure in this process was the Jedi Master, one of our principal engineers, who had been with the project since the beginning. The Jedi Master played a crucial role in mentoring team members, guiding them through their transitions, and ensuring they felt supported as they took on new responsibilities.
2. Progressive Responsibility
Under the Jedi Master’s guidance, team members gradually took on new roles. As they gained experience, Jedi’s involvement decreased, allowing the new leaders to fully embrace their responsibilities.
3. Structured Onboarding and Training
We implemented a comprehensive onboarding process for new team members, including a set of courses and tasks tailored to the project’s needs. Continuous feedback and opportunities for new members to suggest improvements helped them integrate smoothly and contribute effectively from the start.
Measuring Success: The Impact on SmartBorder
The strategies implemented at this project resulted in a well-coordinated, efficient team capable of handling the complexities of a large-scale software project. Communication became more streamlined, roles were clearly defined, and team members were well-prepared to meet the demands of their positions.
This approach not only maintained the quality of the codebase but also strengthened our team’s relationship with our partner. The quality of the product remained high, with functionalities behaving as expected, thanks to the effective communication and role management strategies in place.
Key Takeaways for Scaling Communication in Software Development
This experience highlights several key takeaways for managing a growing software development team:
Effective communication is vital at every stage of growth. As a team expands, it’s crucial to revisit and refine communication channels to ensure they remain efficient and effective.
Roles should evolve naturally but with guidance. Allow team members to grow into new roles, but provide the necessary mentorship to ensure they succeed.
Training and onboarding are ongoing processes. Continuous improvement in training programs helps new team members integrate smoothly and contribute effectively from the start.
Client relationships benefit from strong internal processes. A well-organized, communicative team translates into a smoother, more transparent relationship with the client, ultimately leading to a better final product.
Conclusion
Scaling a software development team is challenging, but with the right strategies in place, it’s possible to manage growth without sacrificing quality. Our experience with this project demonstrates the importance of evolving communication, defining roles carefully, and investing in team development to ensure long-term success.
A while ago we noticed something pretty common: everyone wanted to share more knowledge internally, but nobody wanted another heavy corporate ritual.
Internal talks usually start with good intentions and slowly disappear. They take time, preparation, and energy. And at some point people start feeling like they need to be experts before presenting anything.
So we tried the opposite.
15 minute talks.
Small topics.
Low pressure.
And one important rule: every session had to leave something useful behind. A tool, a workflow, an idea, a shortcut, a new way to approach a problem. Something people could actually use after the talk ended.
We didn’t want theory that went nowhere.
Somehow, that ended up working much better than we expected.
The idea was to reduce friction
Screenshot of the shared topic pool
Tiny Knowledge Bytes is intentionally simple:
anyone can suggest topics
anyone can end up presenting
you don’t need to master the topic
talks can come from experiments, client problems, tools or random discoveries
sessions should leave something practical behind
if nobody volunteers, the system picks someone for us
The goal was making knowledge sharing feel lightweight instead of exhausting.
Some of the best talks start with:
“I tried this yesterday and it was weird.”
The topic pool started growing on its own
Over time, topics started coming from everywhere.
Sometimes someone took a course and used a Tiny Knowledge Byte as a way to give something back to the team. Other times, a client problem triggered research into new tools, workflows or AI approaches.
A lot of sessions start from curiosity or necessity more than planning.
And honestly, the mix is part of what makes it interesting.
Sometimes a UX session drifts into Computer Vision. Sometimes someone technical shares a visual workflow that half the design team ends up adopting later.
There’s not much curation. It behaves more like a constant exploration system.
Then another problem appeared: choosing who presents
And this is where things became unnecessarily dramatic.
Nobody wanted to be “the person who chooses”. So we started adding absurd layers of randomness until we somehow ended up building a full internal app called 2FS.
Two Factor Sorteo.
Yes, it’s real.
The wheel proposes. The oracle decides.
The logic is simple.
First, a wheel picks someone.
Then a Magic 8 Ball decides whether destiny approves the selection.
If the oracle rejects the person, the process starts again.
That’s it.
The app accidentally became part of the learning loop too
Apps developed for the Tiny Knowledge Bytes.
2FS originally started as an excuse to experiment with:
Claude Code
Claude Design
design systems
editorial interfaces
motion and microinteractions
Eventually those same explorations turned into future Tiny Knowledge Bytes.
The tool we used to select speakers started generating new topics itself.
The system started feeding itself
One of the most interesting side effects is that people started building things outside their usual role because of previous Tiny Knowledge Bytes.
2FS itself is a good example. A designer saw sessions about Claude tooling and AI workflows and thought:
“Maybe I can actually build this.”
What started as a ridiculous speaker selection tool became a real product experiment involving Claude Code, interface systems and interaction design.
Then it came back into the Tiny Knowledge Bytes circuit as a new talk.
That loop became surprisingly valuable:
someone learns something,
tries it,
builds something with it,
and eventually inspires someone else to do the same.
What ended up mattering most
Final Oracle Certificate.
Over time we realized knowledge sharing works much better when:
it doesn’t require huge preparation
it’s allowed to be imperfect
it mixes different disciplines
it leaves something practical behind
and somehow involves a mystical wheel connected to a Magic 8 Ball
At that point, it stops feeling like another internal obligation and starts feeling like something people genuinely want to keep alive.
Just this month, I built a full design system in about 20 hours.
What used to take weeks, sometimes months, is now dramatically faster. So… what actually changed? And more importantly: what didn’t?
Design systems take time. On complex platforms, they can take hundreds of hours.
We were working with a large and complex product where inconsistencies had started to pile up. Different modules had evolved in isolation, teams were making independent decisions, and there were no shared guidelines. The answer was clear: we needed a design system.
AI tools were just starting to emerge back then. They were mostly useful for simple tasks as they tended to hallucinate when things got complex. Developers had started using them earlier than designers, MCP didn't exist yet, and Figma plugins were the best automation we had.
But the context has changed. Fast.
The Manual Era
We did what most teams did. We stopped, and we built it. Manually.
Picture two designers, a mountain of inconsistencies, and no map. We had to cross-reference information manually, digging through the code, detecting what could be merged, agreeing on naming conventions, deciding how to name components. Hours and hours of discussion until we finally landed on a solution.
In the end, we got there. A cleaner system, faster workflows, and for the first time, both teams speaking the same visual language. Hard-won, but it worked.
But now every month a new AI model seems to be released. Design is finally catching up with what developers faced about two years ago. New tools arose, and with that, the scope of our work as designers completely changed.
The Human Factor
For an internal project, I used our Kaizen site as a reference, combined with documentation from industry leaders as a guideline.
I started in v0, which is essentially a chat interface where you can generate UI components through prompts. I fed it the colors, typographies, and a reference image, and from there it was a back-and-forth: the AI generated, I reacted, adjusted, and pushed until the output matched what I had in my head. And just like that, I started prompting my way through a Design System.
Once a component was ready, I used the html.to.design plugin to bring it into Figma (yes, plugins are still alive!). Think of it as a bridge: the plugin exports designs directly from the browser into a Figma file.
Inside Figma, the intervention was more hands-on. First, I checked that everything was visually consistent with what was defined in v0: colors, typography, styles. Then I used Figma's built-in AI to rename all the component layers using BEM convention (something that would have taken a significant amount of time to do so manually).
BEM, which stands for Block Element Modifier, is a widely adopted naming convention in CSS. It structures layer names hierarchically and predictably, for example: button__label--disabled.
Using it keeps the code clean, readable, and consistent, especially when you're working alongside a developer who needs to understand what came out the other side.
Beyond naming, I also made sure the layer structure would generate the right properties when building component sets in Figma, so that all the variants would be correctly exposed and usable. My team also pointed out that adding descriptions to components and variants was key as context for any agent using them through an MCP.
The last step was connecting everything to Windsurf via MCP. With a frame selected in Dev Mode, Windsurf could read the Figma file and use the components to build more complex screens.
We worked closely with a developer throughout this phase. Not just for the technical knowledge, but because having someone who reads code fluently meant catching things we wouldn't have spotted otherwise. The design role here was direction and supervision: making sure the AI used the components correctly and didn't invent solutions where context was missing.
Every step of the process had a human decision behind it.
An Unexpected Discovery
At one point, before we had any of the naming conventions figured out, I selected a frame and asked Windsurf to build a form using the components inside it, styled to match a specific card. The developer next to me was skeptical until he saw the result, and then he was just as surprised as I was.
What we realized is that the MCP wasn't reading layer names to understand context. It was reading everything inside the frame, even the loose text sitting alongside the components. Good naming is still worth doing. But the MCP doesn't need it to understand what it's looking at.
Learning to Talk to an AI
The more specific and contained your prompt, the better the outcome. We started with the most atomic component: the button, and worked outward from there. Each approved component became context for the next one, so the system gradually picked up the visual language we were building.
At some point I got ambitious and asked for five cards in a single prompt: blog card, service card, testimonial card, stats card, feature card… structures, states and all. The AI delivered.
Visually, everything looked fine. Then the developer looked at the code and pointed out that all five cards were independent components instead of variants of one. For a design system, that breaks everything.
One correction prompt fixed it. But it was a good reminder: the AI does exactly what you ask, not what you mean. And fixing it after the fact can cost more than getting it right from the start.
Some Things Learned Along the Way
Precision is key. Natural language is fine when you're asking for a cooking recipe, but when referring to a component, if you say things like "create" instead of "add", you'll probably end up with a whole new set of components instead of additional variants of an existing one.
The "Frame" is the context: MCPs can read everything inside the frame you select. This is a game-changer. It means the "naming conventions" debate might be shifting. If the AI understands the context visually and structurally, will we still spend hours discussing nomenclature in 2027?
No matter what happens, you can always roll back in less than 5 minutes and start over.
Work closely with a developer: they can help you understand MCPs and clear up any code-related doubts. Once you start to grasp their logic, you'll learn very quickly how to prompt in ways that AI actually understands.
There's nothing to lose by asking the AI to follow a specific naming convention for the code. It keeps everything clean and readable, and it takes no extra effort.
The AI covers roughly 80% of the work (generation, variations, exploration...), but the remaining 20% is where quality lives, and that part is not delegable. The AI executes. The judgment is still yours. And if you skip the review, you're not saving time: you'll spend it later.
Context matters more than tooling. What you don't define, the AI will invent. Small components may be resolved well, but large interfaces require more definition from the start. A well-defined system scales. An undefined one generates inconsistencies faster than you can fix them.
Figma is no longer the mandatory starting point. It's useful as a visual reference, a QA space, or a consolidation layer. But the AI doesn't need it. We still do.
There's no single right workflow yet. What you do depends on the project. We're in a transition moment where the tools change faster than the standards. The best thing you can do right now is experiment.
What AI Still Can’t Replace
Through all of this, a few things became very clear. These are the parts that didn’t change:
Knowing when something looks off. The AI generates, but it doesn't notice when the result doesn't feel right. That eye is yours.
Direction and supervision. The AI used the components we gave it, but without someone supervising it, it invents solutions where there is no context to work from.
The definition of done is still a human call, whether it's a conversation with a PO, a stakeholder, or just the designer's criteria. There's no prompt for that.
The context: knowing why certain decisions matter, what a component should communicate, what the user will actually feel. Business knowledge, stakeholder dynamics, unwritten rules, empathy for the end user. These take years to build and live in the people doing the work, not in the tools they use.
My Two Cents
The tools changed, and that gave me the chills, but throughout this experience I found that the designer's role is more alive than ever.
What once took a team weeks can now be prototyped in hours. That’s not a threat; it’s an invitation to get curious.
I'm still figuring a lot of this out, and I suspect most of us are. There's no right workflow yet, and honestly, that's fine. We are in a transition where tools change faster than standards. The best thing you can do is experiment. Don't wait for a "definitive" workflow, it might be obsolete by next month.
Go ahead, try prompting your way through a component. You might be surprised how fast the system starts to take shape.
