Making workspace navigation faster in Postman
How a simple "remove workspace from list" request uncovered a deeper navigation problem and led to starring, peek, and faster workspace switching.
Snapshot
Postman users switch workspaces multiple times a day, yet the experience had turned into guesswork. Similar names, unpredictable ordering, and irrelevant entries made a basic action feel heavier than it should. Blunt feedback from users made this impossible to ignore: they wanted more control, and many asked for the ability to delete workspaces from the list.
As the lead designer, I reframed the problem. The real issue wasn’t clutter. It was relevance and recognition. Users couldn’t reliably tell which workspace was which, and the system wasn’t surfacing the ones that mattered.
I led the redesign around three pillars: starring for control, peek for recognition, and improved sorting for predictability. Behind the scenes, this required untangling data gaps, aligning on a shared activity signal, revisiting old logic, and using AI-led prototyping to validate ideas quickly and start engineering work early. The result is a workspace navigation that matches how people actually work: fast, clear, and contextual.
The Core Problem
A highly upvoted GitHub thread pushed this issue to the top of the backlog. Developers didn’t hold back:
“My switcher is full of workspaces I never needed.” “I visited a workspace once by accident and now it is stuck there forever.” “The names don’t help at all. I keep opening the wrong one.”
Quotes from users on GitHub
Many asked for a delete button. But once we looked deeper through interviews and usage patterns, it became clear that deletion was only a symptom of something bigger. The switcher wasn’t aligned with the workspaces people actually depended on, and workspace names alone weren’t enough to recognize anything. Teams often reused the same names or used vague labels, so users ended up guessing their way through navigation multiple times a day.
Users needed clarity, not cleanup tools
People weren’t asking for deletion because they wanted to curate lists. They were asking because the system wasn’t helping them. Once that clicked, the underlying issue came into focus: the real problem was relevance and recognition, not cleanup.
The Solution
We rebuilt workspace navigation around clarity and user control.
Starring for relevance
Starring gives users a simple way to elevate the workspaces they rely on most. Starred workspaces now appear first in the switcher and receive priority in search and forking flows.
Peek for recognition
To address ambiguous naming, we introduced peek. Peek appears on hover anywhere a workspace name is shown and includes essential context:
- quick actions like starring or copy link
- summary when available
- owner
- last activity
- workspace ID
Peek turns “Is this the right workspace?” into a quick, confident check instead of trial and error.
Ordering users can trust
The previous list leaned heavily on recency, causing workspaces to jump around in ways that were hard to anticipate. We shifted toward relevance and ownership, so the list behaves consistently and aligns with how people mentally organize their work.
How We Worked: AI Accelerated and Prototype Led
AI shaped how this project came together. We started with an interactive prototype built in Cursor and Lovable that included the new switcher, starring, deletion, and peek. Seeing these flows in context helped us validate what worked, what didn’t, and what technical challenges we would face.
Engineering began drafting implementation details before final designs existed. Product leaders aligned faster because they could interact with real flows instead of imagining them.
Keeping momentum high
As the deadline approached, I used Cursor to handle some light UI polish directly in code. This let engineering focus on the more complex work like full end-to-end testing, edge cases, and the activity signal alignment. It was a small contribution that kept momentum high and helped us ship a cleaner experience on time.
This AI-supported workflow has become my new default. It speeds up exploration, de-risks decisions, and keeps teams aligned.
Key Decisions
Decision 1
Do not build a delete function. Solve relevance instead
The loudest user request was deletion. But removing items from an unreliable list doesn’t make the list better. Starring and peek solved the underlying issue and removed the need for manual cleanup entirely.
| Criteria | Delete from list | Starring + peek |
|---|---|---|
| Solves root problem | ❌ No | ✅ Yes |
| Ongoing user effort | ⚠️ High | ✅ Low |
| Scales well | ❌ No | ✅ Yes |
| Outcome | ❌ Rejected | ✅ Chosen |
Decision 2
Choose “last activity” instead of “last updated”
Early interviews pointed toward last updated, but deeper conversations showed the nuance. Most users judge relevance by usage, not edits. Activity became the stronger signal. I initially leaned toward last updated, but after revisiting users with more focused questions, I realized I was wrong and shifted direction. Engineering was building an activity endpoint in parallel, so we aligned around that and ensured it captured the necessary signals.
“Probably like most users, I judge relevance by usage, not edits; if people keep visiting it, that’s a strong signal.”
Internal user quote
Decision 3
Pause AI generated summaries
Given the scope of foundational work around metadata, activity signals, and peek itself, we decided to ship a dependable foundation first and treat AI summaries as a follow-up.
AI-generated summaries would significantly improve recognition, especially in teams where workspace names are reused or vague. They would reduce the burden on authors and help collaborators quickly understand whether a workspace is relevant.
Impact and Reflection
Early internal signal
Internal testing showed immediate improvement. Colleagues adopted starring quickly and relied on peek to get context without opening multiple workspaces. One teammate even used a screenshot of peek when filing a bug, because it neatly included all the information needed to find the relevant workspace for reproducing the issue. A small detail, but a strong signal that peek had already become the fastest way to gauge a workspace.
Pattern adoption
Beyond the switcher itself, this work began to ripple outward. Starring now influences search relevance, helping surface the most important workspaces across the product. Peek has also proven itself as a reusable pattern. Other teams have started adopting it for collections and related resources, using the same interaction to reduce unnecessary navigation and provide lightweight context wherever users need it.
Immediate impact
[Placeholder: external feedback, starring adoption rates, navigation speed improvements, reduction in repeated workspace switching]
Next steps
Looking ahead, AI generated summaries remain a clear next step. With the foundation now in place, summaries could further improve recognition for similarly named workspaces and reduce the burden on authors, while helping collaborators quickly understand whether a workspace is relevant.
Final Takeaway
Even when users strongly request a specific feature, it’s important to understand what motivates them.
Fifty developers on GitHub gave us a strong signal, that we could not ignore, but designing for 35 million users requires solving the underlying problem, not the literal ask.
If you want to discuss this work, feel free to reach out.