Pitch

Did you know WA state alone is missing an estimated $929 million worth of fines per year, simply because of an inability to enforce the law that fines drivers who litter? And do you know that the content of litter is a hidden gold-mine of information? e.g. Human waste is a strong indicator for public health risk. An increase in bedding and clothing in litter composition is highly correlated with an increase in the homeless population. Having been collecting road-side litter for almost 2 years, we are determined to improve city infrastructure and urban environment by transforming problems to insights then to solutions. And our approach would also help address the homeless problems which cost CA gov a staging $25 billion in the past five years and $5.3 billion for WA gov in the past 11 years, by offering roadside litter picking as a new type of job - the litter data curators.

For the past two years, we've collected over 120GB of litter data across five cities in the greater Seattle area, developed a 35-class litter classification dataset, built a hardware prototype with a mounted AI camera, and built machine learning prototypes to detect and classify litter. During this process, we brought 7 seemingly unrelated groups of people (roadside workers and volunteers, University Professors, Software / Hardware engineers, Policy Makers, Lawyers, Non Profits and even high schoolers) together and started equipping old-fashion work (litter clean-up) with modern AI technologies. We are on track to completely reinvent the way of road-side litter survey, all while bringing communities closer and creating opportunities for new types of AI empowered workers.

Our business model includes multiple revenue streams:

We believe AI should work alongside people, create new jobs, and make the world a better place by tackling practical problems such as degrading social infra, diminishing meaning of life, and crumbling environment in both urban and wilderness lands.

Intro

The story began in 2019 when I became deeply concerned about the visible degradation of our environment and the lack of resources allocated to address it. For instance, highway ramps became increasingly cluttered with litter during the pandemic, yet no action was taken. Frustrated, I joined a local Adopt-a-Highway volunteer group and began cleaning up litter near my home.

While volunteering, I noticed an untapped potential in the litter itself—it held valuable data. In May 2023, I started taking photos of the litter I collected, and patterns began to emerge. For example, well-maintained areas often had more tissue paper in their litter, while less-managed areas contained abandoned clothing. It was like studying human behavior through the traces we leave behind.

By mid-2024, my friend Autumn Yuan joined as a co-founder, and together we began leveraging these insights to tackle litter control more effectively. Our goal was not just cleaner spaces but smarter, AI-driven solutions that could enhance infrastructure planning, improve environmental outcomes, and even create new types of jobs in an AI-driven era. Through this work, we aim to advance understanding of the physical world and transform how we care for our shared environment.

Work

Since May 2023, we have collected over 120GB of litter survey data across five cities in the greater Seattle area. This effort has engaged a diverse team, including data collectors, curators, machine learning engineers, and hardware prototype designers. Our outputs include litter distribution and composition analyses, as well as a 35-class litter classification dataset that is regularly updated.

This data allows us to identify areas where litter is increasing, highlighting regions in urgent need of litter fine enforcement and intervention. Additionally, by correlating our dataset with public health and demographic data, we can uncover new, previously untapped insights to inform smarter environmental and community strategies.

Here is a sample image taken from our clip-on AI device which can be equipped to any grabber devices on the market.

Here is a sample litter pickup report, consisting of 3k pieces of litter, grouped by 35 categories. This represents litter composition of Kirkland WA near highway 405.

The above example is from highway 405 exit 18-20 near city of Kirkland. The following is from 405 exit 5, near the city of Renton.

About

I'm Lin. the founder of Clip-on.AI and a passionate advocate for community-driven change. My journey has always been fueled by a desire to tackle the challenges that keep me up at night—like litter, mass extinctions, and the growing sense of disconnection in people's lives.

Originally trained as a mechanical engineer, I transitioned into software and machine learning over a decade ago, working at companies like Meta and Microsoft. These experiences gave me the tools to turn complex problems into actionable solutions. With Clip-on.AI, I'm combining my technical expertise and love for my community to reimagine how we approach environmental issues, starting with smarter ways to combat litter problems.

I'm Autumn. co-founder of Clip-on.AI, and I'm deeply inspired by the potential of AI to create positive change in the world around us. My mission is to leverage AI to tackle real-world challenges and empower people to take meaningful action in their communities.

With a background in Finance and Operations, I've spent my career using financial data to drive insights and operational improvements across startups and large corporations. At Clip-on.AI, I'm passionate about turning data into actionable insights that not only enhance infrastructure planning but also inspire individuals to take ownership of their neighborhoods and make a lasting impact.

Together we believe AI can give us unparalleled abilities to see and solve problems in ways we never thought possible, enabling us all to play a part in creating cleaner, more connected communities.

Contact

• LinkedIn
• GitHub
• Email

People

Internship

📦 Serag Internship Summary · 2025-06-07

Intern: Serag
Organization: ClipOn.ai
Internship Period: Spring 2025 (Completed end of May)
Record Location: GitHub

✅ Summary of Contributions

🧠 Performance Evaluation

• Rapid technical growth: Quickly adapted to ClipOn.ai’s hardware-software rhythm
• Excellent communication: Clearly articulated design choices and problem-solving
• High autonomy: Successfully completed project deliverables under pressure

Elements

i = 0;

while (!deck.isInOrder()) {
    print 'Iteration ' + i;
    deck.shuffle();
    i++;
}

print 'It took ' + i + ' iterations to sort the deck.';