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My worst workday habit is that I’m a compulsive web page checker. Throughout the day, I’m constantly refreshing the same handful of sites for updates. I’ll check the metrics on my newsletters, swing through a subreddit or two, and click through some tech news sitesand that’s before even getting to email and social media. Every time I do this, it’s hard to refocus. So I was pretty eager to try Aloha Browser’s new “Snips” feature, which uses AI to periodically monitor web pages and notify you when things change. I figured that by having AI check web pages on my behalf, I could avoid the urge to do so myself and be better at staying on task. It’s helped at least a little, but both Aloha and I still have some work to do. [Image: Aloha] How Snips works Snips is currently available in the desktop version of Aloha for Mac and Windows, appearing as a little box-and-scissors icon next to the address bar. Clicking the icon brings up a selector tool for highlighting the part of the page you want to keep track of. [Screenshot: Jared Newman] After selecting a snippet, you’ll see a menu for setting up alerts. Choose how often Aloha should check for updates (the default is once per day, but you can go as frequently as every five minutes), then write a sentence describing what changes it should watch for. For instance, if you wanted to monitor the price on a product page, you could write something like “notify me when the price falls below $300.” [Screenshot: Jared Newman] In my case, I’ve set up a handful of Snips to cut down on compulsive page checking: For the pages where I check on newsletter metrics, I’ve instructed Aloha to only notify me when certain parameters change. I like to check the New York Yankees subreddit, so I’ve asked Aloha to notify me when new posts are created. If I post on social media, I can create a temporary Snip that alerts me if the responses reach a certain threshold. I have alerts set up for when new stories appear on Techmeme, just to make sure I don’t miss anything important. For email, I have Aloha alert me of replies to existing conversation threads. Behind the scenes, Aloha uses on-device AI to analyze page content, then takes routine snapshots of the page to see if things change. For the notification requests, it uses a mix of on-device AI processing and large language models from Grok and OpenAI, but Aloha says no browsing data leaves your device in most cases. (The browser does send some especially complex tasks to a remote server for processing, but requires permission first and deletes the data immediately after.) Once you’ve created some Snips, they’ll appear as screenshots on Aloha’s new tab page. You can tweak the notifications from here, but you can also shuffle and resize the screenshots into a kind of glanceable information dashboard. Why it makes sense There are plenty of other ways to monitor information online. I use CamelCamelCamel for price alerts on Amazon, for instance, and you can always turn on push notifications for email and social media. [Image: Aloha] But Aloha’s Snips feature is a useful alternative because of how granular it can get. You can set up price alerts on any retail site without sharing your contact information, and you can limit social media notifications to specific types of responses or reactions. The alerts come through the Mac or Windows notification tray, so your email inbox and phone notifications stay uncluttered. Room for improvement That’s not to say Aloha’s Snips feature is perfect. It’s subject to the same vagaries as other generative AI tools, which means things may not always work as expected. For instance, I’ve experienced some instances of false positive notifications when nothing changes, or repeat notifications for things I’ve been alerted to already. Aloha’s page refresh capabilities also don’t seem to work 100% of the time. One snippet I set up for the “Newest” section on Techmeme refused to update, and Aloha showed error messages while trying to update standard Reddit pages. (As a work-around, I had to create a snippet on old.reddit.com instead.) If the information you need requires extra clicking or scrolling after reloading the page, it’s not going to work with Snips either. [Photo: Aloha] And even when things are working properly, I still have to provide the appropriate degree of willpower. I don’t need Aloha to check Reddit every five minutes, but if I set the interval to be too infrequent, I’ll likely get antsy and start checking it myself. That’s entirely a me problem. Aloha is not my main browser, and it was not really on my radar until the Snips feature arrived. It’s made by a small team based in Cyprus, and touts an emphasis on privacy, but I still prefer the power-user features in the likes of Vivaldi and Floorp. Even so, it’s easy enough to keep running in the background to discourage my compulsive checking habit. I’m going to keep doing that to take a little of the weight off my mind.
Category:
E-Commerce
An empty light truck is cruising along a sun-drenched highway of Qionghai, a city in Hainan Island, the southernmost part of China. As the car that’s filming overtakes it, we can see the truck has no driver. In fact, it doesnt even have a cabin: Its front is just a flat wall crowned by what looks to be sensors and cameras. Its an eerie and surreal view, a Headless Horseman of trucks just as scary as an actual headless horseman. View this post on Instagram A post shared by Ai Revolution (@aitherevolution) The futuristic yet cheap-looking vehicle is part of a fleet of driverless light trucks that can carry 1,000 parcels each completely unattended over a range of more than 110 miles. These vehicles, operated by Chinese logistics giant ZTO Express, are the vanguard of a silent, state-sponsored effort to revolutionize the way China ships goods around the country. Their fleet is already vastly outperforming the efforts of startups in the U.S. They navigate Hainans suburban and rurl routes thanks to an artificial intelligencepowered computer that sees the world in 3D using lasers and high-resolution cameras. The trucks are capable of obeying traffic lights, dodging obstacles, yielding to pedestrians, and “talking” to the road itself and other vehicles. The program began in November 2024 with a single vehicle, followed by three additional trucks as part of a pilot overseen by the Eastern Postal Administration of Hainan Province. Its director Zhang Zhi called it at its launch the beginning of a new intelligent era for the regions courier industry. The initial pilot focused on Qionghais campuses, commercial districts, and residential areas, but ZTO quickly expanded it throughout the island and the rest of China. Beijing turbocharge It’s just another step in Chinas road to automated logistics. The company already had experience with this autonomous technology for last-mile and long-haul logistics. In July 2024, ZTO launched autonomous delivery vans in Taizhousouth of Shanghaieach capable of carrying 600 to 800 parcels per tripdouble the capacity of human couriers. These vans, which started development in 2021, are equipped with 360-degree cameras and AI-trained obstacle detection. They now handle nearly a third of last-mile deliveries in Taizhous industrial zones. The vehicles use V2X (vehicle-to-everything) communication systems, a technology that allows them to talk to traffic lights, road sensors, and other vehicles in real time. Allegedly, V2X reduces collisions and optimizes traffic flow by sharing data like speed, direction, and road conditions. Then, in August, ZTO deployed 400 autonomous heavy-duty trucks across Chinas highway network, developed jointly with Shanghai-based autonomous driving startup Inceptio Technology and Dongfeng Commercial Vehicle, a subsidiary of Chinas state-owned Dongfeng Motor Group. This marked the largest single delivery of intelligent freight trucks globally at the time, each equipped with light detection and ranging sensors that create 3D maps of surroundings, redundant braking systems, and Inceptio autonomous driving software, a proprietary system designed to optimize long-haul freight efficiency by reducing fuel consumption and human error. The company claims the software has driven 124 million miles, a truly impressive record. The key to this success is Beijings aggressive push to fully automate its logistics sector as part of its ambitious 2030 agenda, a national program aimed at building a modern, harmonious, and creative society, according to the World Bank. Hainan was an experiment in which regulatory agility paved the way for this rapid scaling. The province slashed certification requirements to just 1,864 miles of testing, compared to Chinas most populous provinceGuangdongwhich has a 9,32018,600-mile mandate. Since then, 12 provinces have adopted Hainans fast-track certification model, and Beijing has allocated $1.4 billion to retrofit highways with 5G networks and V2X infrastructure. 5Gs ultralow latency provides near-instant data transmission and it ensures autonomous vehicles can process sensor data and communicate with infrastructure without delays, which is a prerequisite for safe operation at high speeds. ZTOs own proprietary unmanned vehicle management platform, launched a year ago, now monitors a 200 autonomous vehicles fleet across 40 cities, tracking everything from battery levels to pedestrian interactions in real time. An army of ghost trucks and bots And its all scaling up this year. As of April 2025, 27 driverless vehicles operate at the companys Laiwu logistics park in Shandong, south of Beijing. Their routes synced with workers handheld scanners. Government officials in this province confirm plans to deploy at least 1,500 such vehicles across Shandong by late 2025, targeting a 50% reduction in labor costs. This shift is driven by necessity: labor costs in Chinas logistics sector have risen 8% annually since 2022, while e-commerce parcel volumes exceeded 130 billion in 2024. Thats why Beijing is so adamant to make this happen. ZTO is not alone in this. Alibaba’s logistics arm Cainiao claims to have deployed “thousands” of autonomous delivery robots and vehicles during its 2024 Cainiao Smart Global Logistics Summit. Chinese retail giant JDs logistics division has 600 autonomous vehicles in operation, making millions of deliveries. And food delivery titan Meituan has been deploying hundreds of fully driverless delivery vehicles in major urban centers like Beijing and Shenzhen, according to its Q3 2024 earnings call. Neolix has been deploying thousands of its homegrown autonomous vehicles for various commercial delivery applications since 2021. Its a stark contrast with whats happening in the U.S., where theres a patchwork of state- and city-led policies. Companies like Kodiak Robotics and Gatik are testing autonomous trucks for middle-mile delivery, with Gatik operating a small fleet of box trucks for Walmart in Arkansas. However, deployments remain constrained by fragmented state regulations and a lack of centralized infrastructure investment. For example, California requires permits from both the DMV and Public Utilities Commission for commercial autonomous operations, while Texas allows driver-out testing in the state: In May 2024, Pittsburgh-based autonomous truck technology company Aurora Innovation announced that its first commercial trucksdeveloped with Volvoare now driving between Dallas and Houston. The company said that, to date, its self-driving tech has completed 1,200 miles without a driver. Compare that to Inceptio’s 124 million miles. A for smaller vehicles, Waymo Via’s publicly acknowledged deployment of fully driverless delivery vans is likely in the dozens, primarily within pilot programs. Waymo-powered trucks were in trial runs until 2022, but the company stopped its efforts in 2023. Nuro claims it has expanded its autonomous vehicle operations in a handful of cities: Mountain View, Palo Alto, and Houston. Notably, Amazon has not disclosed large-scale deployment numbers for fully driverless road vehicles in commercial operation, and its nutty air delivery system is just not flying as Jeff Bezos probably expected. By 2030, S&P Global Mobility estimates that China will dominate autonomous freight, with 250,000 Level 4 logistics vehicles in operation, compared to 230,000 in the U.S.most of which will remain focused on ride-hailing, not freight. Seems optimistic for the U.S. side. China’s strong push for automated driving, bolstered by significant government support and regulatory frameworks, positions it as a potential leader in the development of autonomous vehicle technology and relative to commercialization of the autonomous vehicle industry, the report says. A centralized strategy, which has resulted in 28,000 miles of roads that are now open for autonomous vehicles with 16,000 licenses issued nationwide. Only time will tell if the U.S. can overtake Beijing, but for now, I can only see a formidable army of Chinese ghost trucks amassing beyond the Great Wall and shaping the future of roads, while we are still playing with cars in geofenced Disneyland rides.
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E-Commerce
This fall, NASA scientist Kris Bedka flew into Hurricane Helene to test a device that uses lasers to create ultradetailed wind measurements. It could be the key to unlocking better storm predictions. The new device is called the Aerosol Wind Profiler (AWP), and its been in the works at NASA for about four years. The AWP uses the Doppler effect to create real-time, 3D maps of wind patterns above the Earths surfacedata that, before now, has been much more difficult to capture. Bedka is the AWPs principal investigator at NASAs Langley Research Center, and he has spent more than 100 hours in the air testing the device in collaboration with the National Oceanic and Atmospheric Administration (NOAA), which runs the National Weather Service (NWS). He believes the AWP could be the missing link in weather agencies abilities to accurately map severe weather events like hurricanes and thunderstorms. [Photo: NASA/David C. Bowman] The shortcomings to current wind data collection To create predictions for severe weather phenomena, agencies like the NWS collate a vast swath of data including atmospheric temperature, moisture levels, and pressure patterns, typically pulled from satellite readings. Wind patterns, both on the ground and above ground level, are another piece of assembling the overall puzzle. But when it comes to fitting wind patterns into the model, there are a few limitations. If forecasters need wind measurements close to the Earth, Bedka says, its fairly simple to take readings using sensors that can be mounted on the ground. But what’s most important for weather forecasting is having a sense of the three-dimensional picture of the windso winds not just at the ground, but many miles above us, which all combine to drive the weather that we experience at the ground, he says. Currently, to get a sense of the 3D wind picture, forecasters mainly use weather balloons. These balloons give accurate readings, Bedka says, but there are only around 1,300 launch sites across the globe, meaning their data is fairly limited. Another tool, called geostationary satellites, can use snapshots of cloud cover and atmospheric moisture patterns to calculate wind vectors, but only at the cloud top, meaning that the 3D wind picture is still missing. Many experts believe that tools like the AWP are the “missing link” to address this problem. [Photo: NASA/David C. Bowman] How the AWP uses lasers to make a 3D wind map Before making a detailed 3D wind map, scientists need to understand two main factors: how fast wind currents are moving and in what direction. The AWP does that by tracking the movement of particulatesincluding tiny pieces of cloud matter, dust, smoke, pollution, and sea salt that are all floating in the atmosphereto see how wind is buffeting them at a given moment in time. To capture the movement of those particulates, the AWP is mounted to an aircraft with viewing ports underneath it. From there, the instrument emits 200 pulses of laser energy per second toward the atmosphere in two opposite directions, where they scatter and reflect off the particulate matter. This scattering causes a measurable change in the laser pulse wavelength, also known as the Doppler effect. You’ve probably heard of the Doppler effect before, and youve experienced it yourself, Bedka says. You hear an ambulance coming towards you, and at one particular distance, it sounds very high pitched, and then as it comes by you and then goes away from you, you hear the pitch changethat’s due to the Doppler effect. A Doppler wind lidar kind of behaves in an analogous way. In simple terms, the altered frequencies of laser light that bounce back from particulates give the AWP the information needed to calculate wind speed and direction, even measuring conditions at different altitudes in the atmosphere simultaneously. All of those details can then be stitched together to create a complete 3D wind map. [Photo: NASA/Maurice Cross] The AWP flies through Hurricane Helene In 2022, Bedka says, NOAA solicited new technologies for accurate wind measurement, which had been an ongoing challege for the agency when trying to predict severe weather. Since Bedkas team had just wrapped up their AWP prototype, they proposed an aircraft flight campaign that would validate the tools effectiveness. NOAA agreed to fund the proposal, and last fall, Bedka took flight for over 100 hours in a kind of flying laboratory, installed inside a 1970s-era DC-8 aircraft. The lab came outfitted with NASAs AWP and its High-Altitude Lidar Observatory (HALO), another tool built to measure water vapor, aerosols, and cloud properties. Over the course of the flight campaign, AWP and HALO worked together to create ultradetailed 3D maps of wind patterns and aerosol layers. Bedka was aiming to collect data from as wide a range of weather conditions as possibleand, as it happened, that included Hurricane Helene. Because Helene was a relatively well-predicted storm, Bedkas team had time to plan a flight route that would allow the AWP to measure as close to the storm center and the highest winds that were available to us. Given the planes limited six-hour flight range, Bedka and the crew flew through the edges of the hurricane in several legs on September 26, traveling down the western edge of the storm, going around the eye in the Gulf of Mexico, and heading back up the East Coast. In all, it took about nine hours. Bedka, who has flown in several NASA aircrafts through intense thunderstorms, says the conditions were choppy but not too severe. During the hurricane flight, his team was able to collect a rich database of wind measurements that proved the AWPs potential effectiveness during severe weather. Whats next for the AWP For now, the AWP is just in testing phases, but NASA is currently working to make it more widely available. That would involve bringing on an agency or commercial partner, like NOAA, willing to invest further in the technologyideally, by adapting it for use on smaller satellites rather than flying it up on planes. Currently, the AWP is about the size of a coffee table, but to fit on a vessel set for space, researchers would need to shrink it to about one-tenth its current size, Bedka says. (According to a NASA spokesperson, the AWP project hasn’t been impacted by federal budget and staff cuts at NASA and NOAA.) Ideally, NASA would be able to create a constellation of AWPs orbiting the Earth that could measure winds simultaneously all across the globe. With such a wide swath of data, prediction models for extreme weather would become significantly more accurate. Severe storms don’t just pop up just out of the clear blue sky on a random day, Bedka says. They form because all the ingredients align in order to make them become as intense as they are. What we’re trying to do with this technology is to measure the winds with as much spatial and vertical detail as permitted by laser technology. We’ve already found that when this data is put into weather prediction models, it has a really big impact.
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E-Commerce
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