Keagan Pang

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On December 1, 2019, Keagan Pang commented on IntelligentX: Changing the world, one beer at a time :

Really interesting article. I’m starting to think science has gone too far! Jokes aside, I would be curious to know what kind of machine learning models IntelligentX uses. Perhaps it is some sort of clustering algorithm that groups users based on their taste preferences. Instead of relying on user generated data, the company might find itself using olfactory sensors to generate its own tasting profiles one day. That being said, I can see how users may actually like submitting their tasting notes because it makes consumption a more social and interactive experience. Cheers.

Great article, especially well researched on the examples! I believe the performance bottleneck of fleet management in industrial/military applications is indeed the ability to capture data. As such, Uptake is in a fantastic position to deliver value to its users. I would be curious to know if the startup intends to continue to develop its own suite of software products to complement the hardware. Perhaps its expertise lies in designing and making sensors, and could be better served if large customers such as the DoD used their own research budgets to develop their own machine learning models.

Thanks for the well-referenced article, Raimu… I mean Short Apple! It certainly seems like credit startups are becoming increasingly common, especially in countries with less than efficient credit markets. After all, the main advantage that these startups have over incumbents is the ability to price credit risk better using machine learning. I would be curious to know why the larger consumer banks are not doing the same thing. Perhaps it is a regulation issue?

On November 12, 2019, Keagan Pang commented on Gauss Control: let data drive your logistics safely :

Super interesting subject. I would hazard a guess that Gauss Control can be used for more than detecting fatigue. Perhaps it can double as a deterrent against drunk driving, vehicle theft and inefficient travel routing. For example, in Australia, most AB double trucks are fitted with GPS trackers and weight sensors to monitor the location, pace and load of the trucks. The downside of this is that truckers feel that they are micromanaged as even the slightest deviation from the standard route can lead to repercussions from management.

On November 12, 2019, Keagan Pang commented on A Brex of Fresh Air in Corporate Credit Access :

Great post, I like the summary of the benefits of Brex. Personally, I tend to be skeptical of over-indexing on the use of data to determine credit limits because correlate until they don’t. With a regime change, perhaps in a overall market down cycle, the financial models used to dynamically adjust credit limits tend to break down. In extreme circumstance, correlations tend to coalesce. Nonetheless, I am all for startups having easier access to credit, especially in markets with inefficient credit markets outside of the US.

On November 12, 2019, Keagan Pang commented on Farm to Data Table: John Deere and Data in Precision Agriculture :

I really liked this post because it touches on an important but often overlooked industry – agribusiness. With the average of a farmer in the US at about 62 yrs old, investing in agricultural productivity makes sense economically and socially speaking. I’d be curious to know if John Deere has reversed its stance on right to repair. If I recall correctly, there was a lawsuit in which the company prohibited farmers from repairing malfunctioning tractors on their own. Perhaps it is in society’s best interest for all these IoT capabilities to be open sourced.

On October 17, 2019, Keagan Pang commented on TikTok: Transforming Video-Sharing :

Thanks for the interesting read. Your post inspired me to download TikTok to see what the hype is about but I have to say, as a crusty old millennial, I found it very hard to get into it. That being said, I think the numbers speak for themselves. TikTok has phenomenal user engagement, which should translate into significant monetization potential. I’d be curious to know how much of TikTok’s success can be attributed to their choice of betting on lip-synced videos. It has been 15 years since the OG of lip syncs came out – GMan250’s Numa Numa video posted on newgrounds. I think its just fascinating to see it has become a the most valuable startup in the world. Reference: https://www.youtube.com/watch?v=KmtzQCSh6xk

On October 17, 2019, Keagan Pang commented on The democratization of fine art: How much for 0.02% of that Picasso? :

Fascinating idea. Thanks for writing about this one. I’ll be keeping an eye on Maecenas to find out how this one plays out. I would be curious to know how Maecenas enforces ownership rules over the pieces of art. With the traditional model, ownership is controlled by physical possession. However, by selling many pieces of equity to a large number of individuals, how does any one shareholder ensure that the he/she will have a fair degree of managerial control over the artwork? Nonetheless, I think if this company gains traction, it would fundamentally change the secondary market for art.

On October 17, 2019, Keagan Pang commented on Kaggle: How a Platform Democratizes AI :

Thanks for the good read. I have found kaggle to have been quite beginner-friendly in the sense that it is easy to get started. The community is very welcoming and as you pointed out, often publish their initial findings to help others get started. Nonetheless, kaggle competitions have enough complexity to attract top data scientists to participate. In this regard, I think kaggle has found the sweet spot in creating a “game” that is easy to learn but hard to master.

Thanks for the interesting read. I think Fashion Nova is a great example of a novel instance of digital transformation. Harnessing the wisdom of the crowd to create popular apparel designs, Fashion Nova is able to minimize operational issues such as over/under-stock because it has the data to more accurately forecast demand. In addition, it has the geography-specific metadata to uncover emerging fashion trends that tend to spread outward from fashion-forward cities such as NYC/Berlin/Tokyo. This could present opportunities to front-run nascent localized trends and in so doing, be a taste-maker by arbitraging the difference in perception of what is considered ‘cool’ across geographies.

On September 24, 2019, Keagan Pang commented on MoNBAll :

Fascinating topic, thanks for the great read. I would curious to know how exactly the optical technology identifies players. I doubt it uses facial recognition because the cameras are probably too far away to get the data granularity needed to classify objects. A quick search turned up an article that the NBA has been experimenting with putting GPS wearable devices under the players’ jerseys.

On September 24, 2019, Keagan Pang commented on Mirror: The Future of Fitness? :

Amazing idea! These things are surprisingly easy to build – all you need is an arduino, LCD/LED screen and a one-way mirror. That being said, I think the application of these mirrors in the fitness space is genius. It serves as a personalized content delivery system and regular mirror for people to check their form during workouts. However, I do not see a tension between Mirror’s and fitness clubs’ business models. I see the fitness clubs as potential customers for Mirror. For instance, 24/7 clubs would love to be able to offer personal-training on demand round-the-clock, without having to staff personal trainers at all hours.