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Scale AI alum raises M for AI serving critical industries in MENA | TechCrunch

Scale AI alum raises $9M for AI serving critical industries in MENA | TechCrunch

Bilal Abu-Ghazaleh had just moved to London few days before our call, splitting his time between there and Dubai.

After nearly a decade in the U.S., including a stint at Scale AI, he’s bringing that experience to his next venture: 1001 AI , a company creating AI infrastructure for critical industries across the Middle East and North Africa (MENA).

The startup recently raised a $9 million seed round led by CIV, General Catalyst, and Lux Capital. Other backers include global and regional angels such as Chris Ré, Amjad Masad (Replit), Amira Sajwani (DAMAC), Khalid Bin Bader Al Saud (RAED Ventures), and Hisham Alfalih (Lean Technologies).

Abu-Ghazaleh said his two-month-old company promises to cut inefficiencies in high-stakes sectors like aviation, logistics, and oil and gas through an AI-native operating system for decision-making. 

“Just looking at the top three or four industries like airports, ports, construction, and oil and gas, we see more than $10 billion in inefficiencies across the Gulf alone,” the founder and CEO said in an interview with TechCrunch. “That’s just in markets like the UAE, Saudi Arabia, and Qatar. Even without counting other sectors, these industries represent a massive opportunity.”

For example, any efficiencies found in airport operations can compound the savings, impacting both the airport and its airlines. Meanwhile, he said nine out of ten of the regions mega-projects fall behind schedule or go over budget, meaning even small increases in efficiencies can save these projects serious money.

1001 AI hopes to sell its decision-making AI to new projects after it launches its first product, which is scheduled by year’s end. The startup is in talks with some of the Gulf’s largest construction firms and airports, said Abu-Ghazaleh.

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Born and raised in Jordan, Abu-Ghazaleh moved to the U.S. for college and later joined the Bay Area’s startup scene. After an early product role at computer vision startup Hive AI, he joined Scale AI in 2020 during its rapid expansion. There, he rose through the ranks from operations associate to director of the company’s GenAI operations, scaling its contributor network responsible for annotating and labeling training data.

He was later set to join Scale’s international public sector unit, which builds AI solutions for foreign governments. But when Meta invested in Scale, the company shifted direction, and Abu-Ghazaleh left to found 1001 AI.

The Gulf, particularly the UAE and Saudi Arabia, has become one of the world’s most aggressive adopters of AI. From sovereign-backed ventures like G42 in Abu Dhabi to Saudi Arabia’s National Center for AI, governments are investing billions to build local AI infrastructure and attract global talent.

For Abu-Ghazaleh, that mix of appetite, budget, and urgency makes the region a perfect testing ground. But unlike most AI startups focused on software or enterprise tools, 1001 targets real-world physical operations, an area where the company’s investors believe the potential is even greater in the Middle East.

“We’re extremely bullish on AI that solves physical-world problems at scale i.e, optimizing how airports turn around flights, how ports move cargo, how construction sites operate,” said Deena Shakir, partner at Lux Capital. “The MENA region offers significant potential in this space with mission-critical infrastructure that’s under-digitized and ripe for transformation.”

While the product is still under development, Abu-Ghazaleh offered a glimpse into how it works. The system pulls in data from a client’s existing software, models operational workflows, and issues real-time directives to improve efficiency.

“Today, an operations manager might manually call someone to reroute a fuel truck or send a cleaning crew to another gate,” said Abu-Ghazaleh. “With our system, that orchestration happens automatically. The AI orchestrator uses real-time data to reroute vehicles, reassign crews, and adjust operations without human intervention.”

Unlike most early-stage AI startups that target specific industries, Abu-Ghazaleh says 1001 can be accessible by many because operational flows across industries often look the same.

That model borrows from the rigor of consulting and contract work. The team spends weeks embedded with clients, running co-development sprints to tailor its systems to each operation’s realities, the CEO said. 

“Bilal is building the decision engine to automate that complexity with Scale-proven execution and the regional gravity to make 1001 the platform this market builds on,” commented Neeraj Arora, managing director at General Catalyst.

The new funding will accelerate early deployments across aviation, logistics, and infrastructure, while fueling recruitment in engineering, operations, and go-to-market role as it grows its team across Dubai and London.

1001 AI plans to launch its first customer deployment by the end of the year, starting with construction. Over the next five years, Abu-Ghazaleh wants the company to become the Gulf’s go-to orchestration layer for these industries before expanding globally.

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#Scale #alum #raises #serving #critical #industries #MENA #TechCrunch


Johannes Heidecke, the Head of Safety Systems at OpenAI, is leaving. I know what you’re thinking: Hey, didn’t the head of safety at OpenAI just leave?

In fact, it feels like a head of safety at OpenAI is pretty much always leaving. Working in safety leadership—loosely defined—at OpenAI is a little like working as a drummer in the band Spinal Tap; lots of turnover. I’m not the world’s premier OpenAI Kremlinologist, so I might be missing some details and nuance, but here’s my basic timeline:

According to Wired, those previously reporting to Heidecke’s safety teams will be led by Mia Glaese, who is a VP, and also the head of alignment. However, there does seem to be an other replacement for Heidecke, according to Wired. Saachi Jain, former leader of safety teams, will now be an “interim head of safety systems” under Glaese.

What exactly keeps happening inside OpenAI’s offices is anyone’s guess, but OpenAI research chief Mark Chen did at least give Wired a hint, saying, “The demands on safety continue to increase—we are training models at a much faster cadence, and release cycles have come down greatly in turn,” and added, “As a result, we have bigger coordination challenges around safety today than ever before.”

The generous reading is that this is still an immature industry. The points along the chain where safety considerations are needed genuinely may keep jumping around as OpenAI figures out how best to build its products. Perhaps today’s sensible safety test procedure is tomorrow’s unnecessary bottleneck.

And there’s no actual direct evidence for a less generous reading of Heidecke’s departure—for instance, one in which any such consideration is a post-hoc rationalization for a pruning of safety procedures in service of faster product rollouts.

#Safety #Leader #OpenAI #Leftai alignment,AI safety,OpenAI">Yet Another Safety Leader at OpenAI Has Left
                Johannes Heidecke, the Head of Safety Systems at OpenAI, is leaving. I know what you’re thinking: Hey, didn’t the head of safety at OpenAI just leave? In fact, it feels like a head of safety at OpenAI is pretty much always leaving. Working in safety leadership—loosely defined—at OpenAI is a little like working as a drummer in the band Spinal Tap; lots of turnover. I’m not the world’s premier OpenAI Kremlinologist, so I might be missing some details and nuance, but here’s my basic timeline:  According to Wired, those previously reporting to Heidecke’s safety teams will be led by Mia Glaese, who is a VP, and also the head of alignment. However, there does seem to be an other replacement for Heidecke, according to Wired. Saachi Jain, former leader of safety teams, will now be an “interim head of safety systems” under Glaese. What exactly keeps happening inside OpenAI’s offices is anyone’s guess, but OpenAI research chief Mark Chen did at least give Wired a hint, saying, “The demands on safety continue to increase—we are training models at a much faster cadence, and release cycles have come down greatly in turn,” and added, “As a result, we have bigger coordination challenges around safety today than ever before.”

 The generous reading is that this is still an immature industry. The points along the chain where safety considerations are needed genuinely may keep jumping around as OpenAI figures out how best to build its products. Perhaps today’s sensible safety test procedure is tomorrow’s unnecessary bottleneck.

 And there’s no actual direct evidence for a less generous reading of Heidecke’s departure—for instance, one in which any such consideration is a post-hoc rationalization for a pruning of safety procedures in service of faster product rollouts.      #Safety #Leader #OpenAI #Leftai alignment,AI safety,OpenAI

working as a drummer in the band Spinal Tap; lots of turnover. I’m not the world’s premier OpenAI Kremlinologist, so I might be missing some details and nuance, but here’s my basic timeline:

According to Wired, those previously reporting to Heidecke’s safety teams will be led by Mia Glaese, who is a VP, and also the head of alignment. However, there does seem to be an other replacement for Heidecke, according to Wired. Saachi Jain, former leader of safety teams, will now be an “interim head of safety systems” under Glaese.

What exactly keeps happening inside OpenAI’s offices is anyone’s guess, but OpenAI research chief Mark Chen did at least give Wired a hint, saying, “The demands on safety continue to increase—we are training models at a much faster cadence, and release cycles have come down greatly in turn,” and added, “As a result, we have bigger coordination challenges around safety today than ever before.”

The generous reading is that this is still an immature industry. The points along the chain where safety considerations are needed genuinely may keep jumping around as OpenAI figures out how best to build its products. Perhaps today’s sensible safety test procedure is tomorrow’s unnecessary bottleneck.

And there’s no actual direct evidence for a less generous reading of Heidecke’s departure—for instance, one in which any such consideration is a post-hoc rationalization for a pruning of safety procedures in service of faster product rollouts.

#Safety #Leader #OpenAI #Leftai alignment,AI safety,OpenAI">Yet Another Safety Leader at OpenAI Has LeftYet Another Safety Leader at OpenAI Has Left
                Johannes Heidecke, the Head of Safety Systems at OpenAI, is leaving. I know what you’re thinking: Hey, didn’t the head of safety at OpenAI just leave? In fact, it feels like a head of safety at OpenAI is pretty much always leaving. Working in safety leadership—loosely defined—at OpenAI is a little like working as a drummer in the band Spinal Tap; lots of turnover. I’m not the world’s premier OpenAI Kremlinologist, so I might be missing some details and nuance, but here’s my basic timeline:  According to Wired, those previously reporting to Heidecke’s safety teams will be led by Mia Glaese, who is a VP, and also the head of alignment. However, there does seem to be an other replacement for Heidecke, according to Wired. Saachi Jain, former leader of safety teams, will now be an “interim head of safety systems” under Glaese. What exactly keeps happening inside OpenAI’s offices is anyone’s guess, but OpenAI research chief Mark Chen did at least give Wired a hint, saying, “The demands on safety continue to increase—we are training models at a much faster cadence, and release cycles have come down greatly in turn,” and added, “As a result, we have bigger coordination challenges around safety today than ever before.”

 The generous reading is that this is still an immature industry. The points along the chain where safety considerations are needed genuinely may keep jumping around as OpenAI figures out how best to build its products. Perhaps today’s sensible safety test procedure is tomorrow’s unnecessary bottleneck.

 And there’s no actual direct evidence for a less generous reading of Heidecke’s departure—for instance, one in which any such consideration is a post-hoc rationalization for a pruning of safety procedures in service of faster product rollouts.      #Safety #Leader #OpenAI #Leftai alignment,AI safety,OpenAI

Johannes Heidecke, the Head of Safety Systems at OpenAI, is leaving. I know what you’re thinking: Hey, didn’t the head of safety at OpenAI just leave?

In fact, it feels like a head of safety at OpenAI is pretty much always leaving. Working in safety leadership—loosely defined—at OpenAI is a little like working as a drummer in the band Spinal Tap; lots of turnover. I’m not the world’s premier OpenAI Kremlinologist, so I might be missing some details and nuance, but here’s my basic timeline:

According to Wired, those previously reporting to Heidecke’s safety teams will be led by Mia Glaese, who is a VP, and also the head of alignment. However, there does seem to be an other replacement for Heidecke, according to Wired. Saachi Jain, former leader of safety teams, will now be an “interim head of safety systems” under Glaese.

What exactly keeps happening inside OpenAI’s offices is anyone’s guess, but OpenAI research chief Mark Chen did at least give Wired a hint, saying, “The demands on safety continue to increase—we are training models at a much faster cadence, and release cycles have come down greatly in turn,” and added, “As a result, we have bigger coordination challenges around safety today than ever before.”

The generous reading is that this is still an immature industry. The points along the chain where safety considerations are needed genuinely may keep jumping around as OpenAI figures out how best to build its products. Perhaps today’s sensible safety test procedure is tomorrow’s unnecessary bottleneck.

And there’s no actual direct evidence for a less generous reading of Heidecke’s departure—for instance, one in which any such consideration is a post-hoc rationalization for a pruning of safety procedures in service of faster product rollouts.

#Safety #Leader #OpenAI #Leftai alignment,AI safety,OpenAI

Image may contain Adapter Electronics Escooter Transportation and Vehicle

Photograph: Chris Null

Naturally, the AstroRinse also needs a power supply, so if you don’t have a standard electrical outlet near your hose spigot, you’ll need another extension cord solution here. The unit must be level to run properly, and it features adjustable feet and a built-in spirit level to help you achieve that.

All told, you’ll need to carefully consider where you’re going to place the AstroRinse, ensuring you have access to water, power, and drainage—and that the location isn’t too far from the pool. Since the AquaSense X robot itself weighs 29 pounds (and more when freshly pulled from the water), you probably don’t want to haul the thing halfway across the yard to clean and charge it. Unfortunately, given the availability of the above three services in my backyard, that’s exactly what I had to do.

A Familiar Friend in the Water

The Beatbot AquaSense X robot is nearly identical in appearance to the Beatbot AquaSense 2 Ultra except for some changes to the basket design (which is a single piece here instead of two). Setting it up is far simpler than the AstroRinse.

Physical configuration involves installing two side brushes—these are used only by the skimmer function—but this is a fairly quick affair. Once the brushes are attached, the robot must be set on top of the AstroRinse cleaner so the two devices can be wirelessly paired together. (The quick start guide lays out the particular button presses you must do to complete this process; don’t lose it.) Lastly, the system must then be paired to the Beatbot mobile app; you’ll need Bluetooth and a 2.4 GHz or 5 GHz Wi-Fi connection to complete this task. One tiny hiccup I encountered: After completing all this work, both devices downloaded firmware updates, which promptly broke their pairing connection. It was easy to reestablish, however, by simply repeating the pairing process.

Video: Chris Null

After a full charge, I put the cleaner through its paces in the pool on both the floor and the surface, and as expected, I saw no real difference in performance against the AquaSense 2 Ultra. During floor testing with both organic and synthetic debris, the device picked up an average of 97 percent of the test material, doing exceptionally well on steps and platforms. On the surface, the unit was predictably middling to awful, collecting less than half of floating debris and sinking most of the rest. The unit is just too slow to collect much material on the surface, even though its spinning side brushes help, to a small extent, to pull leaves into its maw.

On the floor of the pool, maximum running time is about 41/2 hours, courtesy of a 13,400 mAh battery—the same as the battery on the AquaSense 2 Ultra.

Image may contain Electronics Mobile Phone Phone and Text

ScreenshotBeatbot app via Chris Null

As with other AquaSense robots, a bevy of operating modes are available in the Beatbot app, letting you choose from dozens of potential combinations of floor, wall, waterline, and surface cleaning, each with up to two runs per zone and with various running times. An AI Quick Mode activates the onboard camera to allow the robot to actively search for debris instead of encountering it randomly; it’s good for a quick clean when there’s not much to pick up but more than you can easily fetch with a net.

Again, not much of this is any different from how the AquaSense 2 Ultra behaves, and aside from the poor surface performance, it works outstandingly well.

Charging and Cleaning

On to the main event: cleanup. After each run, the AquaSense X parked itself at the waterline to await retrieval, and I dutifully lugged it across the pool deck to where I had the AstroRinse station set up. While it can take a little trial and error to get the robot seated in just the right spot, once you do, the cleaning system kicks in automatically within a few seconds.

Video: Chris Null

As the rinsing system starts up, the top-mounted arm swings into place and connects with the mouth the robot uses for surface skimming. Then, a high-pressure stream of water (sounding quite loud) begins blasting from the arm and into the filter basket, which is positioned directly below this opening. The water spray runs uninterrupted for three minutes before the arm swings back and the system shuts off. (A quick mode, which runs for one minute, can also be selected in the app.) After that, the arm retracts and the unit is done. Debris is captured in a net-covered basket built into the base of the cleaning station. Any remaining water drains out through a mesh screen at the very bottom of the unit.

Image may contain Car Transportation and Vehicle

Photograph: Chris Null

#Pool #Robot #Cleans #Pooland #Cleansshopping,review,reviews,robots,home,outdoors">This Pool Robot Cleans the Pool—and Then Cleans ItselfPhotograph: Chris NullNaturally, the AstroRinse also needs a power supply, so if you don’t have a standard electrical outlet near your hose spigot, you’ll need another extension cord solution here. The unit must be level to run properly, and it features adjustable feet and a built-in spirit level to help you achieve that.All told, you’ll need to carefully consider where you’re going to place the AstroRinse, ensuring you have access to water, power, and drainage—and that the location isn’t too far from the pool. Since the AquaSense X robot itself weighs 29 pounds (and more when freshly pulled from the water), you probably don’t want to haul the thing halfway across the yard to clean and charge it. Unfortunately, given the availability of the above three services in my backyard, that’s exactly what I had to do.A Familiar Friend in the WaterThe Beatbot AquaSense X robot is nearly identical in appearance to the Beatbot AquaSense 2 Ultra except for some changes to the basket design (which is a single piece here instead of two). Setting it up is far simpler than the AstroRinse.Physical configuration involves installing two side brushes—these are used only by the skimmer function—but this is a fairly quick affair. Once the brushes are attached, the robot must be set on top of the AstroRinse cleaner so the two devices can be wirelessly paired together. (The quick start guide lays out the particular button presses you must do to complete this process; don’t lose it.) Lastly, the system must then be paired to the Beatbot mobile app; you’ll need Bluetooth and a 2.4 GHz or 5 GHz Wi-Fi connection to complete this task. One tiny hiccup I encountered: After completing all this work, both devices downloaded firmware updates, which promptly broke their pairing connection. It was easy to reestablish, however, by simply repeating the pairing process.Video: Chris NullAfter a full charge, I put the cleaner through its paces in the pool on both the floor and the surface, and as expected, I saw no real difference in performance against the AquaSense 2 Ultra. During floor testing with both organic and synthetic debris, the device picked up an average of 97 percent of the test material, doing exceptionally well on steps and platforms. On the surface, the unit was predictably middling to awful, collecting less than half of floating debris and sinking most of the rest. The unit is just too slow to collect much material on the surface, even though its spinning side brushes help, to a small extent, to pull leaves into its maw.On the floor of the pool, maximum running time is about 41/2 hours, courtesy of a 13,400 mAh battery—the same as the battery on the AquaSense 2 Ultra.ScreenshotBeatbot app via Chris NullAs with other AquaSense robots, a bevy of operating modes are available in the Beatbot app, letting you choose from dozens of potential combinations of floor, wall, waterline, and surface cleaning, each with up to two runs per zone and with various running times. An AI Quick Mode activates the onboard camera to allow the robot to actively search for debris instead of encountering it randomly; it’s good for a quick clean when there’s not much to pick up but more than you can easily fetch with a net.Again, not much of this is any different from how the AquaSense 2 Ultra behaves, and aside from the poor surface performance, it works outstandingly well.Charging and CleaningOn to the main event: cleanup. After each run, the AquaSense X parked itself at the waterline to await retrieval, and I dutifully lugged it across the pool deck to where I had the AstroRinse station set up. While it can take a little trial and error to get the robot seated in just the right spot, once you do, the cleaning system kicks in automatically within a few seconds.Video: Chris NullAs the rinsing system starts up, the top-mounted arm swings into place and connects with the mouth the robot uses for surface skimming. Then, a high-pressure stream of water (sounding quite loud) begins blasting from the arm and into the filter basket, which is positioned directly below this opening. The water spray runs uninterrupted for three minutes before the arm swings back and the system shuts off. (A quick mode, which runs for one minute, can also be selected in the app.) After that, the arm retracts and the unit is done. Debris is captured in a net-covered basket built into the base of the cleaning station. Any remaining water drains out through a mesh screen at the very bottom of the unit.Photograph: Chris Null#Pool #Robot #Cleans #Pooland #Cleansshopping,review,reviews,robots,home,outdoors

Beatbot AquaSense 2 Ultra except for some changes to the basket design (which is a single piece here instead of two). Setting it up is far simpler than the AstroRinse.

Physical configuration involves installing two side brushes—these are used only by the skimmer function—but this is a fairly quick affair. Once the brushes are attached, the robot must be set on top of the AstroRinse cleaner so the two devices can be wirelessly paired together. (The quick start guide lays out the particular button presses you must do to complete this process; don’t lose it.) Lastly, the system must then be paired to the Beatbot mobile app; you’ll need Bluetooth and a 2.4 GHz or 5 GHz Wi-Fi connection to complete this task. One tiny hiccup I encountered: After completing all this work, both devices downloaded firmware updates, which promptly broke their pairing connection. It was easy to reestablish, however, by simply repeating the pairing process.

Video: Chris Null

After a full charge, I put the cleaner through its paces in the pool on both the floor and the surface, and as expected, I saw no real difference in performance against the AquaSense 2 Ultra. During floor testing with both organic and synthetic debris, the device picked up an average of 97 percent of the test material, doing exceptionally well on steps and platforms. On the surface, the unit was predictably middling to awful, collecting less than half of floating debris and sinking most of the rest. The unit is just too slow to collect much material on the surface, even though its spinning side brushes help, to a small extent, to pull leaves into its maw.

On the floor of the pool, maximum running time is about 41/2 hours, courtesy of a 13,400 mAh battery—the same as the battery on the AquaSense 2 Ultra.

Image may contain Electronics Mobile Phone Phone and Text

ScreenshotBeatbot app via Chris Null

As with other AquaSense robots, a bevy of operating modes are available in the Beatbot app, letting you choose from dozens of potential combinations of floor, wall, waterline, and surface cleaning, each with up to two runs per zone and with various running times. An AI Quick Mode activates the onboard camera to allow the robot to actively search for debris instead of encountering it randomly; it’s good for a quick clean when there’s not much to pick up but more than you can easily fetch with a net.

Again, not much of this is any different from how the AquaSense 2 Ultra behaves, and aside from the poor surface performance, it works outstandingly well.

Charging and Cleaning

On to the main event: cleanup. After each run, the AquaSense X parked itself at the waterline to await retrieval, and I dutifully lugged it across the pool deck to where I had the AstroRinse station set up. While it can take a little trial and error to get the robot seated in just the right spot, once you do, the cleaning system kicks in automatically within a few seconds.

Video: Chris Null

As the rinsing system starts up, the top-mounted arm swings into place and connects with the mouth the robot uses for surface skimming. Then, a high-pressure stream of water (sounding quite loud) begins blasting from the arm and into the filter basket, which is positioned directly below this opening. The water spray runs uninterrupted for three minutes before the arm swings back and the system shuts off. (A quick mode, which runs for one minute, can also be selected in the app.) After that, the arm retracts and the unit is done. Debris is captured in a net-covered basket built into the base of the cleaning station. Any remaining water drains out through a mesh screen at the very bottom of the unit.

Image may contain Car Transportation and Vehicle

Photograph: Chris Null

#Pool #Robot #Cleans #Pooland #Cleansshopping,review,reviews,robots,home,outdoors">This Pool Robot Cleans the Pool—and Then Cleans Itself
Image may contain Adapter Electronics Escooter Transportation and Vehicle

Photograph: Chris Null

Naturally, the AstroRinse also needs a power supply, so if you don’t have a standard electrical outlet near your hose spigot, you’ll need another extension cord solution here. The unit must be level to run properly, and it features adjustable feet and a built-in spirit level to help you achieve that.

All told, you’ll need to carefully consider where you’re going to place the AstroRinse, ensuring you have access to water, power, and drainage—and that the location isn’t too far from the pool. Since the AquaSense X robot itself weighs 29 pounds (and more when freshly pulled from the water), you probably don’t want to haul the thing halfway across the yard to clean and charge it. Unfortunately, given the availability of the above three services in my backyard, that’s exactly what I had to do.

A Familiar Friend in the Water

The Beatbot AquaSense X robot is nearly identical in appearance to the Beatbot AquaSense 2 Ultra except for some changes to the basket design (which is a single piece here instead of two). Setting it up is far simpler than the AstroRinse.

Physical configuration involves installing two side brushes—these are used only by the skimmer function—but this is a fairly quick affair. Once the brushes are attached, the robot must be set on top of the AstroRinse cleaner so the two devices can be wirelessly paired together. (The quick start guide lays out the particular button presses you must do to complete this process; don’t lose it.) Lastly, the system must then be paired to the Beatbot mobile app; you’ll need Bluetooth and a 2.4 GHz or 5 GHz Wi-Fi connection to complete this task. One tiny hiccup I encountered: After completing all this work, both devices downloaded firmware updates, which promptly broke their pairing connection. It was easy to reestablish, however, by simply repeating the pairing process.

Video: Chris Null

After a full charge, I put the cleaner through its paces in the pool on both the floor and the surface, and as expected, I saw no real difference in performance against the AquaSense 2 Ultra. During floor testing with both organic and synthetic debris, the device picked up an average of 97 percent of the test material, doing exceptionally well on steps and platforms. On the surface, the unit was predictably middling to awful, collecting less than half of floating debris and sinking most of the rest. The unit is just too slow to collect much material on the surface, even though its spinning side brushes help, to a small extent, to pull leaves into its maw.

On the floor of the pool, maximum running time is about 41/2 hours, courtesy of a 13,400 mAh battery—the same as the battery on the AquaSense 2 Ultra.

Image may contain Electronics Mobile Phone Phone and Text

ScreenshotBeatbot app via Chris Null

As with other AquaSense robots, a bevy of operating modes are available in the Beatbot app, letting you choose from dozens of potential combinations of floor, wall, waterline, and surface cleaning, each with up to two runs per zone and with various running times. An AI Quick Mode activates the onboard camera to allow the robot to actively search for debris instead of encountering it randomly; it’s good for a quick clean when there’s not much to pick up but more than you can easily fetch with a net.

Again, not much of this is any different from how the AquaSense 2 Ultra behaves, and aside from the poor surface performance, it works outstandingly well.

Charging and Cleaning

On to the main event: cleanup. After each run, the AquaSense X parked itself at the waterline to await retrieval, and I dutifully lugged it across the pool deck to where I had the AstroRinse station set up. While it can take a little trial and error to get the robot seated in just the right spot, once you do, the cleaning system kicks in automatically within a few seconds.

Video: Chris Null

As the rinsing system starts up, the top-mounted arm swings into place and connects with the mouth the robot uses for surface skimming. Then, a high-pressure stream of water (sounding quite loud) begins blasting from the arm and into the filter basket, which is positioned directly below this opening. The water spray runs uninterrupted for three minutes before the arm swings back and the system shuts off. (A quick mode, which runs for one minute, can also be selected in the app.) After that, the arm retracts and the unit is done. Debris is captured in a net-covered basket built into the base of the cleaning station. Any remaining water drains out through a mesh screen at the very bottom of the unit.

Image may contain Car Transportation and Vehicle

Photograph: Chris Null

#Pool #Robot #Cleans #Pooland #Cleansshopping,review,reviews,robots,home,outdoors

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