Jenn Choi is a New York-based product designer and NYU graduate who brings technical curiosity into both her main projects and her experiments.

You might recognize Dandi, the Figma Buildathon project she worked on with teammates including Beverly and Queenie, whom I have featured before. But Jenn’s portfolio offers more than another look at a familiar project.

She tells the story from her own perspective and shows a willingness to explore how software, AI, and physical interactions work together. Let’s look at what comes through well, and where she could make that profile clearer.

Jenn’s Dandi case study gave me something new, even after seeing the project in two other portfolios.

Some of the material is familiar, which makes sense. Teammates share deliverables, research, and a finished concept. Jenn still finds her own way through that material, emphasizing the parts that help explain her understanding of the problem and the experience.

That matters beyond the unusual situation of me reviewing several people from the same team.

A group project needs to become a story you can discuss from your own position. Shared outcomes can establish that the team made something compelling, but an interview will eventually turn toward your decisions, contribution, and reasoning.

Jenn’s account feels like someone explaining work she was involved in. It does not read like a detached report assembled to cover every deliverable.

She focuses on the problem, the proposed experience, and what that experience could change for someone using it.

Her treatment of the personas is a good example.

Rather than expecting me to study a complete persona document, she draws out the useful information: the person’s situation before using the concept and the intended change afterward.

That makes the material relevant to the product story.

Because Dandi is a concept, those imagined outcomes need to be understood as scenarios rather than measured results. They still help explain the team’s intention and give the reader something concrete to assess.

The project’s Most Impactful award at the Figma Buildathon provides a separate form of recognition. Jenn can show that achievement without making the scenarios carry more weight than they should.

Her writing also works better than I might expect from the amount of text on the page.

I often recommend cutting copy, and there are places where Jenn could still do that. But word count alone does not determine whether a case study is easy to follow.

Her headings are visible and communicate useful information while scrolling. Larger visuals establish context, and highlighting helps draw attention to parts of the supporting copy.

I could follow the overall story without stopping to read every paragraph.

She also avoids giving every part of the project equal attention. The physical ear cuff receives relatively little detail compared with other aspects of the experience.

I cannot infer her exact responsibilities from that choice alone, but the focus works. A case study does not need to explain every part of a team project to demonstrate what its author can contribute.

Jenn would still need to make her own responsibilities clear in a hiring conversation. What I appreciate here is that she has already made editorial choices instead of trying to fit the entire team’s work into one page.

Even with familiar material, I came away feeling that I had heard Jenn’s version of the project.

Jenn’s Play section gives useful evidence of how she learns.

There are small coding experiments, work involving hardware, an interactive cat toy, and explorations that reach into AR and physical interaction. Some pieces are modest. Others have enough substance to support a longer project story.

Their value comes partly from seeing that she made them at all.

Jenn has taken an idea far enough to encounter what happens when it becomes interactive. A sketch can suggest how something should behave; building it introduces questions that are much harder to anticipate from a static screen.

How does the interaction respond? What happens between states? Which parts are difficult to implement? Does the result feel the way you expected?

Her experiments give me reasons to believe she is willing to work through those questions.

The p5 experiment is a good example. Its motion could become more convincing with additional attention to the physics, but I can already see that Jenn sat down, explored the tool, and made something respond.

I would rather see that attempt than hear a broad claim about being curious.

This does not mean every rough experiment deserves a permanent place in a portfolio. Presentation and execution still matter, and the collection should evolve as Jenn’s skills develop.

But an experiment does not need to resemble a finished commercial product before it can reveal something useful about its maker.

Links to repositories can add another layer, particularly for someone with Jenn’s technical interests. A short explanation of what she built, what she explored, and where the implementation lives gives a visitor a way to investigate further.

Her thesis exploring AI as a creative partner reinforces that picture.

I was less concerned with how current the underlying models were than with her understanding of how to connect the parts into an experience. A proof of concept can demonstrate technical judgment even when its interface or outputs would change if built again today.

The ability to construct those pipelines and think through the interaction around them deserves attention.

I also found the projects connected with hardware compelling. Across the collection, Jenn seems comfortable exploring experiences that reach beyond a conventional screen.

Not every piece needs to use AI or prove the same capability. Together, they show someone trying tools, building interactions, and making room for work outside a standard case-study format.

That is a worthwhile part of her profile, and I would keep giving it space.

Jenn’s storytelling is already clear enough to follow at a glance. A further editing pass could remove the places where the reader has to work harder than necessary.

Diagrams are one example.

A large collection of notes may show that a team completed research or synthesis, but it asks a hiring manager to decode the artifact before understanding why it matters.

Even when the text is technically readable, that is a considerable request during a portfolio review.

I would pull out the finding or relationship the reader needs and give that the visual emphasis. The complete artifact can remain available elsewhere if it adds useful depth.

Jenn already applies this thinking to parts of the persona presentation. I would carry it further.

If the important finding is summarized beneath an image, the image itself may only need a portrait or a cropped detail. Showing the entire document at a size where its text becomes visual noise adds little.

A reader should not have to decide whether they are expected to read something that has been made too small to absorb comfortably.

Repetition creates a similar issue.

I noticed visuals appearing again after a short scroll. The later appearance sometimes helped explain the story, which made me question whether the earlier instance was needed.

Repeating a visual can be useful when its role changes. It might introduce an experience first and support a detailed explanation later. But each appearance needs a purpose.

I would review the case studies with a few questions in mind: what does this image explain, what should someone notice, and what would be lost if it were removed?

The same applies to dense blocks of copy. Jenn can preserve the depth while moving the central point into a heading, a highlighted sentence, or a smaller visual explanation.

Visitors need to grasp the story before they can decide which parts deserve a closer read.

Body copy can reward that attention once it has been earned. Diagrams and headings should make the first pass easier.

This is a refinement of an approach Jenn already uses well. Removing the remaining friction would make her strongest decisions easier to see.

Jenn introduces herself as a “Product Designer who ships the code.”

I believe that claim. The portfolio contains evidence that she can build, and her technical understanding appears to go beyond producing something with an AI tool and accepting the first result.

What remains less clear is what she wants that capability to mean for a hiring team.

One possible direction is product design with the ability to carry an idea into a working implementation. In that case, I would want the portfolio to make ownership and delivery more apparent: what Jenn took from concept to implementation, what she handled herself, and what she learned once it worked.

Another possible direction places more weight on design engineering.

For that, I would want stronger examples of technical depth and carefully resolved interactions. A small number of pieces taken further could help demonstrate how she moves beyond an initial implementation and refines its behavior.

Her current work contains beginnings of both, but the introduction does not help me understand which capability she most wants to foreground.

Giving the positioning more space would help its visibility. The larger task is connecting the promise to the evidence a visitor encounters afterward.

A more prominent sentence will only do part of that job. Project selection, descriptions, and the depth given to individual experiments need to reinforce it.

There is also another direction I see in the work: the connection between software and physical experiences.

The cat toy, hardware explorations, and AR work suggest an interest that could become a recognizable part of Jenn’s profile. Dandi sits near that territory too, although her case study gives the physical device less attention.

For a role involving AR, connected objects, or interactions that move between hardware and software, I would want to hear more from her.

That is my reading of the portfolio. I do not know whether it matches the work Jenn wants next.

Her preference matters here. I would not recommend reorganizing the portfolio around physical interaction if she wants to spend her time on a different kind of product.

But once she chooses a direction, she has material that could support a clearer account of it.

She could bring relevant experiments forward, explain the technical decisions behind selected projects, and give those pieces enough depth to demonstrate the capability she is naming.

Jenn does not need to invent a new identity. She needs to decide which parts of the existing work should lead, and help the introduction and projects tell the same story.

Jenn’s portfolio gives me a clear sense of someone who is willing to investigate how things work and build enough to learn from the result.

Her Dandi case study brings a personal perspective to shared material. The Play section broadens that picture through coding, hardware, and interaction experiments, while her AI work shows technical curiosity with more substance than a passing interest in the tools.

I would focus the next round of improvements on editing the remaining dense visuals and making her technical positioning easier to interpret.

The software-and-hardware connection stood out to me, but I would want to ask Jenn whether that is where she hopes to take her work. Her answer would change which projects I would bring forward and which experiments I would encourage her to develop further.

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