On January 23, a Waymo autonomous vehicle hit a child in Santa Monica, California. The age and identity of the child are not public, but "minor injuries" were reported. Waymo responded with this blog post where they essentially argued that "if this had been a human driver, the accident would have been worse."
The event occurred when the pedestrian suddenly entered the roadway from behind a tall SUV, moving directly into our vehicle's path. Our technology immediately detected the individual as soon as they began to emerge from behind the stopped vehicle. The Waymo Driver braked hard, reducing speed from approximately 17 mph to under 6 mph [~9.7 km/h] before contact was made.
To put this in perspective, our peer-reviewed model shows that a fully attentive human driver in this same situation would have made contact with the pedestrian at approximately 14 mph. This significant reduction in impact speed and severity is a demonstration of the material safety benefit of the Waymo Driver.
All car accidents causing human injury are unfortunate, but car accidents involving AVs are obviously more noteworthy right now. In my mind, it makes sense that a Waymo should be more responsive than a human driver in the face of a pedestrian jumping out into a roadway.
But being "less bad" is not going to win everyone over. The accident is being investigated to ensure "the Waymo AV exercised appropriate caution given, among other things, its proximity to the elementary school during drop off hours, and the presence of young pedestrians and other potential vulnerable road users.”
The headline is suboptimal for AVs, but it's very possible the Waymo did everything it could, and did it better than any one of us could ever do. We shall see.
Cover photo by Andri Aeschlimann on Unsplash
Towards the end of last year, Meta released SAM 3, which stands for the third version of its Segment Anything Model. The way it generally works is that it allows you to detect, edit, and experiment with things in images and videos. For example, if you were looking at a video of a street, you could ask it to find all the scooters (which I did below), count the number of pedestrians wearing black pants, blur all the license plates on the cars, and so on.
Prediction markets have become a big deal, presumably because a lot of people like betting. But functionally and economically, prediction markets are also supposed to be about information discovery. If you get enough people researching, analyzing, and thinking about something, eventually the "wisdom of the crowds" should prevail and something resembling the truth should, in theory, emerge. The stereotypical use for a prediction market (also referred to as an event market) is a binary bet. Will this happen? Yes or no.
But now, you can also bet on real estate prices:
Parcl, the real-time housing data and onchain real estate platform, and Polymarket, the world’s largest prediction market, today announced a partnership to bring Parcl’s daily housing price indices to a new suite of real estate prediction markets on Polymarket.
The partnership will introduce housing-focused markets that settle against Parcl’s published price indices, giving traders and analysts an objective, data-driven reference point for forecasting where home prices are headed. Polymarket will list and operate the markets; Parcl will provide independent index data and settlement reference values designed for transparent verification.
Housing is the largest asset class in the world, but it’s still hard to express a clean view on price direction without taking on property-level complexity, leverage, or long timelines. By combining Parcl’s daily indices with Polymarket’s event-market structure, the partnership offers a simpler way to trade housing outcomes, with clear settlement rules and public, auditable resolution data.
Here's a specific example: What will the median home value in Miami be on February 1?
On January 23, a Waymo autonomous vehicle hit a child in Santa Monica, California. The age and identity of the child are not public, but "minor injuries" were reported. Waymo responded with this blog post where they essentially argued that "if this had been a human driver, the accident would have been worse."
The event occurred when the pedestrian suddenly entered the roadway from behind a tall SUV, moving directly into our vehicle's path. Our technology immediately detected the individual as soon as they began to emerge from behind the stopped vehicle. The Waymo Driver braked hard, reducing speed from approximately 17 mph to under 6 mph [~9.7 km/h] before contact was made.
To put this in perspective, our peer-reviewed model shows that a fully attentive human driver in this same situation would have made contact with the pedestrian at approximately 14 mph. This significant reduction in impact speed and severity is a demonstration of the material safety benefit of the Waymo Driver.
All car accidents causing human injury are unfortunate, but car accidents involving AVs are obviously more noteworthy right now. In my mind, it makes sense that a Waymo should be more responsive than a human driver in the face of a pedestrian jumping out into a roadway.
But being "less bad" is not going to win everyone over. The accident is being investigated to ensure "the Waymo AV exercised appropriate caution given, among other things, its proximity to the elementary school during drop off hours, and the presence of young pedestrians and other potential vulnerable road users.”
The headline is suboptimal for AVs, but it's very possible the Waymo did everything it could, and did it better than any one of us could ever do. We shall see.
Cover photo by Andri Aeschlimann on Unsplash
Towards the end of last year, Meta released SAM 3, which stands for the third version of its Segment Anything Model. The way it generally works is that it allows you to detect, edit, and experiment with things in images and videos. For example, if you were looking at a video of a street, you could ask it to find all the scooters (which I did below), count the number of pedestrians wearing black pants, blur all the license plates on the cars, and so on.
Prediction markets have become a big deal, presumably because a lot of people like betting. But functionally and economically, prediction markets are also supposed to be about information discovery. If you get enough people researching, analyzing, and thinking about something, eventually the "wisdom of the crowds" should prevail and something resembling the truth should, in theory, emerge. The stereotypical use for a prediction market (also referred to as an event market) is a binary bet. Will this happen? Yes or no.
But now, you can also bet on real estate prices:
Parcl, the real-time housing data and onchain real estate platform, and Polymarket, the world’s largest prediction market, today announced a partnership to bring Parcl’s daily housing price indices to a new suite of real estate prediction markets on Polymarket.
The partnership will introduce housing-focused markets that settle against Parcl’s published price indices, giving traders and analysts an objective, data-driven reference point for forecasting where home prices are headed. Polymarket will list and operate the markets; Parcl will provide independent index data and settlement reference values designed for transparent verification.
Housing is the largest asset class in the world, but it’s still hard to express a clean view on price direction without taking on property-level complexity, leverage, or long timelines. By combining Parcl’s daily indices with Polymarket’s event-market structure, the partnership offers a simpler way to trade housing outcomes, with clear settlement rules and public, auditable resolution data.
Here's a specific example: What will the median home value in Miami be on February 1?
This is immediately useful for a company like Meta because it allows for object-level modifications across its content creation platforms. So if you took a video of someone dancing and you desperately wanted to give them a bobblehead, SAM 3, I'm told, would allow you to quickly do that. Other AI models, such as Gemini, can also segment, but supposedly the SAM models are better and more precise at this specific task.
Beyond bobblehead videos, the potential of this model seems enormous for real estate, cities, and, of course, many other things. Using the above image as an example, you can quickly imagine SAM 3 being used to count and track modal splits across a city, and then make planning decisions based on real-time data.
People are also using it for real estate purposes. Pair the model with satellite images, and you can ask it to tell you how many houses have a pool, which houses recently had their roof replaced (and have solar panels), how many cars are parked on a street, how many cars are parked at Canadian Tire, and the average building lot coverage in an area.
You could also use it to swap out finishes in a real estate listing (including in videos), and get material/area takeoffs ahead of a construction project. I don't know for sure, but I would also imagine that this model would make a great building condition inspector. Come to think of it, I'd love a SAM 3 that could walk our construction sites and document every little detail!
Of course, a lot of these use cases are already being tackled. But the models are getting that much better. And that will lead to even more innovation.
Cover photo by Above Horizon on Unsplash
Right now, the market seems to believe it will be greater than $1.1 million. This is fascinating. Among many other things, it gives us a clear and real-time sense of market sentiment. But as Matt Levine wrote in Money Stuff, it also gives homeowners the ability to hedge and diversify their housing market risk. If you live in a cold, high-tax place and you're super envious of everyone moving to Miami, you could, of course, just sell your house and move there too. But if you don't want to do that and you still want to participate in its growth, now you can just bet on its home prices using this derivatives market.
Cover photo by Cody Board on Unsplash
This is immediately useful for a company like Meta because it allows for object-level modifications across its content creation platforms. So if you took a video of someone dancing and you desperately wanted to give them a bobblehead, SAM 3, I'm told, would allow you to quickly do that. Other AI models, such as Gemini, can also segment, but supposedly the SAM models are better and more precise at this specific task.
Beyond bobblehead videos, the potential of this model seems enormous for real estate, cities, and, of course, many other things. Using the above image as an example, you can quickly imagine SAM 3 being used to count and track modal splits across a city, and then make planning decisions based on real-time data.
People are also using it for real estate purposes. Pair the model with satellite images, and you can ask it to tell you how many houses have a pool, which houses recently had their roof replaced (and have solar panels), how many cars are parked on a street, how many cars are parked at Canadian Tire, and the average building lot coverage in an area.
You could also use it to swap out finishes in a real estate listing (including in videos), and get material/area takeoffs ahead of a construction project. I don't know for sure, but I would also imagine that this model would make a great building condition inspector. Come to think of it, I'd love a SAM 3 that could walk our construction sites and document every little detail!
Of course, a lot of these use cases are already being tackled. But the models are getting that much better. And that will lead to even more innovation.
Cover photo by Above Horizon on Unsplash
Right now, the market seems to believe it will be greater than $1.1 million. This is fascinating. Among many other things, it gives us a clear and real-time sense of market sentiment. But as Matt Levine wrote in Money Stuff, it also gives homeowners the ability to hedge and diversify their housing market risk. If you live in a cold, high-tax place and you're super envious of everyone moving to Miami, you could, of course, just sell your house and move there too. But if you don't want to do that and you still want to participate in its growth, now you can just bet on its home prices using this derivatives market.
Cover photo by Cody Board on Unsplash
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