Jambo: The world’s first rideable instrument.

Jambo: The world’s first rideable instrument.

Jambo: The world’s first rideable instrument.

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RESPONSIBILITIES
Sound Interaction
Sound Interaction
Physical Prototyping
Physical Prototyping
DELIVERABLES

Interactive Prototype

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INTRO

Longboard dancing looks cool, can it sound cool, too?

Longboard dancing looks cool, can it sound cool, too?

Longboard dancing looks cool, can it sound cool, too?

Jambo was inspired by our love of longboard dancing - a form of skateboarding best described as flowy, rhythmic, and expressive.

So why can’t longboards actually sing with every move? We feel that with the musical nature of this art, there must be a more immersive, multisensory interface for dancers to express themselves.

We created a working, music instrument that enables riders to express their skating through sound.

Jambo was inspired by our love of longboard dancing - a form of skateboarding best described as flowy, rhythmic, and expressive.

So why can’t longboards actually sing with every move? We feel that with the musical nature of this art, there must be a more immersive, multisensory interface for dancers to express themselves.

We created a working, music instrument that enables riders to express their skating through sound.

Jambo was inspired by our love of longboard dancing - a form of skateboarding best described as flowy, rhythmic, and expressive.

So why can’t longboards actually sing with every move? We feel that with the musical nature of this art, there must be a more immersive, multisensory interface for dancers to express themselves.

We created a working, music instrument that enables riders to express their skating through sound.

Jambo anatomy

Jambo is likely the first time that users are introduced to either longboard dancing, music production, or both. Therefore, making Jambo easy to use for beginners yet versatile enough for maestros was important when developing its interactions. Seymour Papert’s model of low floors, high ceiling, and wide walls model was a guiding principle behind Jambo’s design.

As a MIDI instrument in a longboard, Jambo is at a unique intersection between longboard dancers and music producers. This dual audience would result in users from both backgrounds bringing in expertise from their “worlds”, where skill sets can be combined to co-create new styles of expression.

This is what a Radar look like, but there are more than 20 more them!

This is what a Radar look like, but there are more than 20 more them!

This is what a Radar look like, but there are more than 20 more them!

Concept

A music layer of self-expression.

A music layer of self-expression.

A music layer of self-expression.

  • Instead of following an existing beat, riders can generate their own music.

  • Hands-free, heart-free

  • Instead of following an existing beat, riders can generate their own music.

  • Hands-free, heart-free

  • Instead of following an existing beat, riders can generate their own music.

  • Hands-free, heart-free

Discover Bigger Picture

Discover Bigger Picture

Discover Bigger Picture

Build Depth for Personalized Path

Build Depth for Personalized Path

Build Depth for Personalized Path

Jambo as a MIDI controller...

Jambo as a MIDI controller...

Jambo as a MIDI controller...

Gradient 1 - Blue
Gradient 2 - Purple
Gradient 1 - Blue
Gradient 2 - Purple
Gradient 1 - Blue
Gradient 2 - Purple
Prototyping

High ceiling, wide wall, no floor

High ceiling, wide wall, no floor

High ceiling, wide wall, no floor

Click on each component to explore the sensors on Jambo!

Click on each component to explore the sensors on Jambo!

Click on each component to explore the sensors on Jambo!

Galaxy Visualization: Enables open-ended exploration, revealing hidden connections through an intuitive, visual map.

Galaxy Visualization: Enables open-ended exploration, revealing hidden connections through an intuitive, visual map.

Galaxy Visualization: Enables open-ended exploration, revealing hidden connections through an intuitive, visual map.

Personal Dashboard: Adapts to individual roles and goals, surfacing timely, relevant insights that support day-to-day decisions.

Personal Dashboard: Adapts to individual roles and goals, surfacing timely, relevant insights that support day-to-day decisions.

Personal Dashboard: Adapts to individual roles and goals, surfacing timely, relevant insights that support day-to-day decisions.

Try out the Galaxy Visualization MVP

Try out the Galaxy Visualization MVP

Try out the Galaxy Visualization MVP

Technology View

What it does: Groups projects by the technologies they apply or explore.

  • Hierarchy: Tech field → Specific methods/models → Projects

  • Why it’s helpful: Ideal for identifying innovation trends, technical overlaps, or evaluating tech adoption maturity.

Technology View

What it does: Groups projects by the technologies they apply or explore.

  • Hierarchy: Tech field → Specific methods/models → Projects

  • Why it’s helpful: Ideal for identifying innovation trends, technical overlaps, or evaluating tech adoption maturity.

Technology View

What it does: Groups projects by the technologies they apply or explore.

  • Hierarchy: Tech field → Specific methods/models → Projects

  • Why it’s helpful: Ideal for identifying innovation trends, technical overlaps, or evaluating tech adoption maturity.

Domain View

What it does: Groups projects based on problem space within SLB, application area, or impact field (e.g., “Subsurface,” “Grid Modernization”).

  • Hierarchy: Domain → Product → Projects

  • Why it’s helpful: Useful for strategists or external partners to see applied impact areas and identify gaps or overlaps in research.

Domain View

What it does: Groups projects based on problem space within SLB, application area, or impact field (e.g., “Subsurface,” “Grid Modernization”).

  • Hierarchy: Domain → Product → Projects

  • Why it’s helpful: Useful for strategists or external partners to see applied impact areas and identify gaps or overlaps in research.

Domain View

What it does: Groups projects based on problem space within SLB, application area, or impact field (e.g., “Subsurface,” “Grid Modernization”).

  • Hierarchy: Domain → Product → Projects

  • Why it’s helpful: Useful for strategists or external partners to see applied impact areas and identify gaps or overlaps in research.

Team View

What it does: Organizes projects by contributing teams (e.g., AI Lab, Frontend, Robotics).

  • Hierarchy: Team → Sub teams within a Lab → Projects

  • Why it’s helpful: Great for internal alignment, performance tracking, and collaboration mapping across the org.

Team View

What it does: Organizes projects by contributing teams (e.g., AI Lab, Frontend, Robotics).

  • Hierarchy: Team → Sub teams within a Lab → Projects

  • Why it’s helpful: Great for internal alignment, performance tracking, and collaboration mapping across the org.

Team View

What it does: Organizes projects by contributing teams (e.g., AI Lab, Frontend, Robotics).

  • Hierarchy: Team → Sub teams within a Lab → Projects

  • Why it’s helpful: Great for internal alignment, performance tracking, and collaboration mapping across the org.

Due to the limitation of time, we didn't get to implement all the features. Below are the proposed features that are possible with available data from radar.

Inspiration Prompt:

  • Inspire user about what question to ask

  • Presentation of popular questions

  • Build mental model of how the system work

Inspiration Prompt:

  • Inspire user about what question to ask

  • Presentation of popular questions

  • Build mental model of how the system work

Inspiration Prompt:

  • Inspire user about what question to ask

  • Presentation of popular questions

  • Build mental model of how the system work

Intelligent Search & Filters:

Help user ask better questions by:

  • Evaluating if the system has enough parameters from user input

    • Ask follow up questions and give suggestions on refinement

Intelligent Search & Filters:

Help user ask better questions by:

  • Evaluating if the system has enough parameters from user input

    • Ask follow up questions and give suggestions on refinement

Intelligent Search & Filters:

Help user ask better questions by:

  • Evaluating if the system has enough parameters from user input

    • Ask follow up questions and give suggestions on refinement

The interactive timeline serves as both a filter and a line graph that indicates change in activity over time.

The interactive timeline serves as both a filter and a line graph that indicates change in activity over time.

The interactive timeline serves as both a filter and a line graph that indicates change in activity over time.

See projects with related domains, tech, team easily

See projects with related domains, tech, team easily

See projects with related domains, tech, team easily

First working prototype in a month

First working prototype in a month

First working prototype in a month

visual parameter

Mapped to

What’s needed to implement

Planet size

effort invested (time or money)

Actual effort metrics based on e.g. time and money

Star Brightness

Impact or visibility

quantitive impact score, or stakeholder priority

Rotation speed (within the trail or self-rotation)

Update frequency within stages, major updates

More granular way of documenting updates in projects

Split off trails

if multiple spin-off projects emerged from an original one

More relationship data to indicate relationships between projects

Satellite (Level 3 Category)

work (tech evaluation, partner, spin off) that supported that specific projects

Evaluation of DataOS

Planet shape

Entity type

Project, tech evaluation, partner

Domain star cluster

Product sub concepts

product line-features

Fabrication

using an ESP-32 microcontroller, homemade pressure sensors, and plenty of impromptu testing.

using an ESP-32 microcontroller, homemade pressure sensors, and plenty of impromptu testing.

using an ESP-32 microcontroller, homemade pressure sensors, and plenty of impromptu testing.

Our approach enables adaptive interfaces through conversational feedback. Users can directly express what they want to track or explore.

By entering their goal or question into a prompt input box, the system performs intent tagging to categorize the query and automatically surfaces the most relevant UI components.

Step 1: Intent Tagging: Understanding the ask

On interpreting the user input side, I defined 5 parameters to tag a questions.

On interpreting the user input side, I defined 5 parameters to tag a questions.

On interpreting the user input side, I defined 5 parameters to tag a questions.

For example ☝️

How did I come up with these parameters? Why?

How did I come up with these parameters? Why?

How did I come up with these parameters? Why?

During the user interview, I asked each interviewee: " If you can ask the galaxy page any question, what would you ask?" and looked for recurring patterns in what they were fundamentally asking for.

While an LLM could infer UI responses directly, defining a parameter tagging framework adds a structured, interpretable layer between user intent and system response. Putting LLM on rails as oppose to letting it range free.

During the user interview, I asked each interviewee: " If you can ask the galaxy page any question, what would you ask?" and looked for recurring patterns in what they were fundamentally asking for.

While an LLM could infer UI responses directly, defining a parameter tagging framework adds a structured, interpretable layer between user intent and system response. Putting LLM on rails as oppose to letting it range free.

During the user interview, I asked each interviewee: " If you can ask the galaxy page any question, what would you ask?" and looked for recurring patterns in what they were fundamentally asking for.

While an LLM could infer UI responses directly, defining a parameter tagging framework adds a structured, interpretable layer between user intent and system response. Putting LLM on rails as oppose to letting it range free.

Step 2: UI Matching: How to Answer with Different UI Components

On the system output side, for each of the potential intent, I have a set of corresponding UI components to answer.

On the system output side, for each of the potential intent, I have a set of corresponding UI components to answer.

On the system output side, for each of the potential intent, I have a set of corresponding UI components to answer.

A white and gray background with a gradient of black dots

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A white and gray background with a gradient of black dots

01

A white and gray background with a gradient of black dots

01

A white and gray background with a gradient of black dots

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A white and gray background with a gradient of black dots

02

A white and gray background with a gradient of black dots

02

A white and gray background with a gradient of black dots

03

A white and gray background with a gradient of black dots

03

A white and gray background with a gradient of black dots

03

A white and gray background with a gradient of black dots

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A white and gray background with a gradient of black dots

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A white and gray background with a gradient of black dots

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For example, for "Discover" intent, an UI component like this be fetched☝️

Step 3: Curate Dashboard Space with Feedback

Improvement area 2: More Compact & Secure Case

Improvement area 2: More Compact & Secure Case

Improvement area 2: More Compact & Secure Case

Fabrication Process

Fabrication Process

Fabrication Process

During the process of brainstorming how to leverage the power of LLM to empower better search, I talked to a start up company called glean, which provides a software that combines all source of information across platforms and domain and fuse them into smart search that's specific to the company.

Reflection

The launch of the productivity dashboard has yielded impressive results for [Client’s Name]. Users have reported significant improvements in task management and overall efficiency, with increased visibility into project progress and performance metrics. The intuitive design and powerful features have streamlined workflows, reduced administrative overhead, and enhanced collaboration within teams.

The launch of the productivity dashboard has yielded impressive results for [Client’s Name]. Users have reported significant improvements in task management and overall efficiency, with increased visibility into project progress and performance metrics. The intuitive design and powerful features have streamlined workflows, reduced administrative overhead, and enhanced collaboration within teams.