Automation and clipboard utility brings Apple Text Recognition OCR support to additional languages. ($36 new, free update, 36.6 MB, macOS 10.13+)

HomePod room temperature automations turn Apple’s smart speaker into more than a source of music and Siri responses. Its built-in climate sensor can trigger fans, thermostats, smart plugs, shades and other compatible accessories when a room becomes warmer or cooler than a selected temperature.

The automation runs through Apple’s Home app. Once configured, it does not require someone to ask Siri for the current reading or leave an iPhone inside the room. HomePod measures the local conditions while the home hub evaluates the rule and controls the selected accessories.

A bedroom fan could start when the room rises above 24°C, for example. A compatible thermostat could activate heating when an office falls below 18°C, while motorized shades could close before afternoon heat makes a living room uncomfortable.

The same system works with humidity. HomePod can trigger a compatible humidifier, dehumidifier or ventilation accessory when relative humidity crosses a chosen level.

Which HomePod Models Measure Room Temperature

Temperature and humidity sensing is available on HomePod mini and HomePod (2nd generation).

The original full-size HomePod does not contain the supported climate sensor. It can act as a home hub and participate in scenes, but it cannot supply the temperature reading used to start this type of automation.

HomePod mini is sometimes overlooked because Apple did not activate its temperature and humidity sensor when the speaker originally launched in 2020. A later software update enabled the component, allowing existing HomePod mini units to report climate information without a hardware upgrade.

Both supported models should run the latest available HomePod software. The iPhone or iPad used to configure the home should also be updated because older software may not display every sensor or automation option correctly.

HomePod normally updates automatically. Its version can be checked in the Home app by opening the HomePod, selecting its settings and scrolling to the software information.

The Speaker Must Be Assigned to the Correct Room

The room assigned to HomePod determines how its readings appear in the Home app and how Siri interprets questions about local conditions.

A HomePod physically located in the bedroom but assigned to Living Room will report its sensor as part of the living-room climate group. This becomes especially confusing when several HomePods are installed throughout the home.

To verify the room, open the Home app, touch and hold the HomePod tile, open Accessory Settings and check the Room field. Assign a descriptive name to the HomePod as well, particularly when two supported speakers occupy nearby spaces.

The sensor does not measure an entire home. It reports conditions near the HomePod, so the assigned room should correspond to its physical location.

How to Check the HomePod Temperature

The current reading is available through Siri or the Home app.

A person can ask Siri, “What is the temperature in here?” when speaking to the HomePod inside that room. Questions can also identify a different room, such as “What is the temperature in the bedroom?” or “What is the humidity in the basement?”

In the Home app, open the Home tab and select Climate near the top of the screen. Choose Temperature to see readings from available climate sensors. Opening an individual room can make it easier to identify the relevant HomePod when several sensors are installed.

The Home app may display a range rather than one number when multiple temperature sensors belong to the same room or home. Opening the temperature category reveals the individual readings behind that range.

A newly restarted or updated HomePod may not show a result immediately. Apple says the sensor requires time to calibrate after startup.

How to Create a HomePod Room Temperature Automation

The temperature sensor appears as an automation trigger rather than an accessory that can be turned on or off. The user defines a threshold and then selects what another compatible accessory should do when the reading crosses it.

To create the automation:

1. Open the Home app on an iPhone or iPad
2. Select the Automation tab or tap the Add button and choose Add Automation
3. Choose A Sensor Detects Something
4. Select the temperature sensor assigned to the appropriate HomePod and room
5. Choose Rises Above or Drops Below
6. Set the desired temperature threshold
7. Add optional time and occupancy conditions
8. Select the accessories or scene that should run
9. Configure the desired state of each accessory
10. Test the automation and tap Done

The selected action could turn on a fan, change a compatible thermostat, close shades or activate an existing scene containing several accessories.

HomePod does not produce cool or warm air itself. It supplies the measurement and functions as a home hub, while the automation controls other equipment connected to Apple Home.

Choosing Rises Above or Drops Below

The direction of the trigger is important.

Rises Above starts the automation when the measured temperature moves from below the selected value to above it. This is the usual option for starting a fan, lowering compatible shades or changing a thermostat to a cooler setting.

Drops Below works in the opposite direction. It can start heating, turn off a fan or activate another accessory when the room becomes colder than the selected level.

These are threshold-crossing events, not continuous questions asking whether the room is currently above or below a value. If the room is already warmer than 24°C when a new Rises Above 24°C automation is saved, the rule may not run immediately. The reading generally needs to fall below the threshold and then rise across it again.

This behavior also explains why an automation may not repeatedly react while the room remains on the same side of the threshold. Another transition must occur before the trigger fires again.

Use Time and Occupancy Conditions

The Home app can restrict a temperature automation to particular times or household conditions.

A bedroom fan may only need to run overnight. Heating in a home office could remain inactive on weekends. A cooling automation might operate only when someone is home, while another rule protects temperature-sensitive equipment regardless of occupancy.

The People condition can limit an automation to when somebody is home, when nobody is home or when selected household members are present or absent. These conditions depend on location access and the devices associated with members of the Apple Home.

Time restrictions should match the behavior of the controlled accessory. Closing shades during the hottest afternoon hours may be useful, but the same action would provide little benefit after sunset.

An automation can also activate a scene instead of controlling one product. A Hot Afternoon scene could close the west-facing shades, start a fan and turn off heat-producing decorative lights with one sensor trigger.

Useful HomePod Temperature Automations

A smart fan is one of the simplest accessories to control. When the temperature rises above a comfortable level, the Home app can turn on a HomeKit or Matter-compatible fan. A second automation can stop it after the room cools.

A thermostat offers more direct climate control, but the available actions depend on the thermostat manufacturer. Apple Home may expose power, heating, cooling and target-temperature controls, while more advanced scheduling remains inside the manufacturer’s app.

Motorized shades can reduce solar heating before an air conditioner needs to work harder. A temperature trigger can close selected shades when a sun-facing room becomes warm, particularly when the automation is limited to daytime hours.

A ceiling fan or portable fan connected through a smart switch or smart plug can also respond to the HomePod sensor. The connected controller must be rated for the appliance, and the appliance must safely resume operation when power is restored. Products with electronic controls may remain off after a smart plug turns them back on.

Portable heaters and air conditioners require greater caution. Many smart plugs are not designed for their electrical load, and some appliance manufacturers prohibit external switching. A compatible thermostat or manufacturer-supported controller is safer than treating high-power climate equipment like a lamp.

Humidity Can Use the Same Automation System

HomePod exposes temperature and relative humidity as separate sensors.

A bathroom or laundry-room HomePod could trigger ventilation when humidity rises above a chosen level. A dehumidifier with a compatible control could operate in a damp basement, while a humidifier could respond when heated winter air becomes too dry.

The setup process is almost identical to a temperature automation. Select A Sensor Detects Something, choose the HomePod’s humidity sensor, set Rises Above or Drops Below and assign the action.

Humidity automations also benefit from separate starting and stopping thresholds. Turning a dehumidifier on above 65 percent and off below 55 percent creates a useful operating range instead of responding repeatedly around one value.

HomePod Sensor Placement Affects the Reading

HomePod measures the conditions around the speaker, not at the center of the room or inside an air-conditioning return.

Placing it beside a sunny window, heater, fireplace, kitchen appliance or air vent can produce readings that do not represent the space where people spend time. An enclosed shelf may also trap warmer air around the device.

The speaker needs airflow and a stable surface. It should not be covered or placed where condensation, cooking steam or direct moisture can reach it.

Extended audio playback at high volume can reduce sensor accuracy because heat generated by the speaker may influence the local reading. This matters when an automation uses a narrow threshold and the HomePod frequently plays loud music.

Apple says temperature sensing is optimized for indoor domestic environments between approximately 15°C and 30°C. Humidity sensing is optimized between about 30 and 70 percent relative humidity. Readings outside those ranges may be less accurate.

HomePod is therefore useful for comfort-oriented home automation, but it should not replace specialized monitoring required for medicine, food storage, animals, greenhouses or other temperature-sensitive environments.

Why a Temperature Automation May Not Work

Begin by confirming that the temperature reading itself appears in the Home app. If it is missing, verify the HomePod model, install current software and allow time for calibration after restarting.

Check that the correct temperature sensor was selected. Homes containing multiple HomePods may present several sensors with similar names, particularly when the speakers were never given descriptive room assignments.

Open the automation and confirm that it is enabled. Review its time and People conditions, because a functioning temperature trigger will still do nothing when an additional condition is not satisfied.

Use the Test This Automation control to confirm that the destination accessory responds. If the test fails, the problem is more likely to involve the accessory, its network connection or the selected action than the HomePod sensor.

If the test succeeds but the rule does not run naturally, remember that the temperature must cross the threshold in the selected direction. Temporarily choosing a value closer to the current room temperature can make testing easier.

Home hubs and accessories should remain connected to the same Apple Home. Thread-based accessories also need a supported Thread-enabled home hub or border router, such as HomePod mini, HomePod (2nd generation) or a compatible Apple TV 4K.

Build Two Automations for Stable Climate Control

A single automation can start a device but may not provide the best way to stop it.

Using the same threshold in opposite directions can cause frequent switching when the measured temperature fluctuates around that value. A room hovering near 24°C, for example, could repeatedly move just above and below the boundary.

A more stable arrangement uses two thresholds. One automation turns the fan on when the temperature rises above 24°C. A separate automation turns it off when the temperature drops below 22°C.

The gap creates a controlled operating range. Once the fan starts, it can continue working until the room has cooled meaningfully rather than stopping after a minor change.

Heating can follow the reverse pattern. One automation activates heat below 18°C, while another stops it above 20°C. Humidifiers and dehumidifiers can use the same paired-rule structure.

This separation also makes troubleshooting easier because every automation performs one clear action in response to one specific sensor transition. Instead of asking one complicated rule to manage the complete climate cycle, HomePod supplies the measurements while two predictable Home app automations define when the connected equipment starts and stops.

The post HomePod Room Temperature Automations in the Home App appeared first on AppleMagazine.

Learn how to set up an automation on your iPhone that turns off Low Power Mode when Adaptive Power Mode automatically enables it.

Since the very beginning, it’s been clear that computers provide incredible power to those who know how to use them to get work done. The challenge has always been how small the group of “those who know how to use them” has been.

The Apple II included support for BASIC, a simplified programming language that was intended to let new computer users write programs. “In my opinion, the real thing [the Apple II] is doing right now is to teach people how to program the computer,” a young Steve Jobs told the New Yorker in 1977. The first computer programs I wrote were in BASIC.

There was also the Logo language, which helped introduce beginners to programming concepts through the manipulation of a virtual robot called a “turtle.” In the ’80s, HyperCard tried to broaden the programming community through its use of HyperTalk, another language designed to speak to beginners.

The (still!) current examples of this on the Mac are AppleScript, which used a format based on English-language sentence structure; and Automator and Shortcuts, two automation tools designed to create flow-chart-based programs.

The goal is always the same: To give regular people the ability to harness the power of computers. And we’ve never, ever been closer to the goal than we are today.

No, I’m not talking about vibe coding apps, mostly because that requires a level of focus and detail that most “regular people” are not going to want to provide. Show a civilian Xcode and watch how their eyes instantly glaze over.

I’m talking about Shortcuts—specifically, Apple’s Describe a Shortcut feature in the forthcoming macOS 27, iOS 27, and iPadOS 27.

This is as close as we’ve come, across 40 years, to the original dream of putting computer power in the hands of everyone. You can literally tell your device what you want it to do, and when—”every morning show me my to-dos and calendar events for the day”—and it will generate a program to do it and a schedule to run it.

Yes, there are lots of limitations. Describe a Shortcut doesn’t work with third-party apps, only Apple’s own stuff (at least, for now). It can sometimes get confused, especially with complex queries. And it has an interesting tendency to kick things to the Use Model action—why am I not surprised that an AI model likes to build Shortcuts that themselves use AI models?

But when it works, which is most of the time, it’s magical. And perhaps its best feature is that you can iterate on your Shortcuts. If there’s something it doesn’t do quite right the first time, you can specify changes you’d like to see—”only show me items from my Personal calendar”—and it’ll rewrite the Shortcut to take those changes into account. In that way, it’s emulating the back-and-forth conversations that can make vibe coding so remarkable.

I was also impressed with the fact that Shortcuts created with this method are… just Shortcuts. With one click, you’re looking at the actual Shortcut blocks that the feature has assembled for you. You can edit them as you see fit. They’re not special in any way, other than that they were built with an actual English-language sentence, not a line of code that resembles one superficially.

I’m sure that future developments will make it even easier for us to tell our devices what to do. But this particular advance feels especially big to me, like we’ve crossed some invisible line of demarcation. We are entering the era where the computers program themselves—and, for the most part, are able to understand what we’re asking them to do.

Update: An earlier version of this article referred to this feature as having been introduced in iPadOS 27, but it was actually introduced in the iPadOS 26 cycle. I missed it. My apologies.

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In iPadOS’ Shortcuts app, the existing ‘Open App’ action was recently updated with the ability to launch an app with a specific window placement parameter. This means you can now automate window positions on iPad by opening a bunch of apps and programmatically selecting where their windows be placed.

To find this feature, use the ‘Open App’ action in Shortcuts and expand it to reveal the new ‘Window Location & Size’ field. Here, you can click the default ‘Full Screen’ value to reveal a host of options:

• Full Screen
• Left
• Right
• Top Bottom
• Top Leading
• Top Trailing
• Bottom Leading
• Bottom Trailing
• Left Third
• Middle Third
• Right Third

You can play around with these options to open any app and resize any window by choosing from the same presets that are otherwise shown by iPadOS when right-clicking the traffic light controls:

That was a neat discovery, but as I was playing around with the action, I realized that its practical utility would be impacted by the fact that you have to manually pre-select the app you want to open as well as its window layout. Wouldn’t it be better if you could turn this Shortcuts action into a reusable technique to programmatically get whichever app is in the foreground with no manual selection upfront, then also give it a pre-selected window preset? As it turns out, you absolutely can.

This is why I created MultiSwitcher, a simple proof-of-concept shortcut for iPadOS users that lets you resize any foreground window to a preset of your choice – no manual input necessary. The idea is fairly straightforward: you can use the ‘Get Current App’ action to get the name of the foreground app, pass it to the ‘Open App’ action, and also pre-configure that action to accept a layout variable.

MultiSwitcher is best used as a shortcut added to your iPad’s dock, so you can click it at any time and instantly resize the foreground window. For MultiSwitcher, I designed a visual menu (with some ASCII characters) that lets you preview window layouts, pick one, and resize the currently frontmost window. It doesn’t have to be this way, though: if you’d rather add multiple variations of MultiSwitcher to your dock, each with a specific window size already selected, you can also do that and skip the menu. The choice is up to you.

While these newfound window-control capabilities for iPadOS are still a far cry from the Mac’s breadth of window managers and multitasking tweaks, I find this implementation in Shortcuts to be quite elegant and intuitive for most users to grasp with no need for a third-party app.

You can download MultiSwitcher for iPadOS below.

Independent practices are losing money at the front desk and most don’t see it on a balance sheet.

Dozens of new robot arms have been installed at General Motors’ flagship electric vehicle factory in Detroit—even as 1,300 workers remain out of work following what was supposed to be a temporary layoff. The latest automation push has spurred union pushback over a potentially existential issue for automakers and their workers.

General Motors installed approximately 50 robot arms at GM’s Factory Zero plant in Detroit, Michigan, according to reporting by Crain’s Detroit Business. Made by the Japanese robotics company FANUC, the robots are designed to help attach various components to vehicles during the assembly line process. But leaders at United Auto Workers (UAW), the primary US union for autoworkers, reacted with anger to the new robotic presence, given how GM has not yet called back any of the workers affected by supposedly temporary layoffs in March.

More than 1,000 union members are still “laid off indefinitely,” James Cotton, president of UAW Local 22, told The Detroit News. He said that the company could bring some of those members back to work instead of installing the 50 robots.

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Researchers built Sashimi-Bot to master one of robotics’ slipperiest challenges.

In a pioneering stride towards maintaining the integrity of scientific literature, MDPI has fully implemented its proprietary artificial intelligence-powered system called Ethicality. This sophisticated tool is now embedded within the publishing workflow to automatically screen all submitted manuscripts, revolutionizing the approach to safeguarding the scientific record. Ethicality is engineered to operate continuously throughout the editorial process, offering a proactive framework that enhances the evaluation and decision-making capabilities of editors globally. Presently, it processes around 2,000 submissions daily, handling a vast and diverse array of academic contributions from across the world.

The development of Ethicality reflects the pressing need for automated solutions in the face of surging scholarly output and increasing demands for transparency, speed, and quality in academic publishing. Traditional manual screening methods, once the norm, are no longer adequate to detect sophisticated manipulations or emerging threats posed by advanced generative AI techniques. Its design integrates an end-to-end integrity layer that scrutinizes manuscripts from the moment they enter the editorial system, ensuring continuous vigilance rather than episodic checks. This paradigm shift marks a new era in editorial practices, combining the precision of AI with human expertise to set robust guardrails around the publication pipeline.

Dr. Milos Cuculovic, MDPI’s head of technology innovation, emphasizes that the role of AI within this context is not to replace editors but to augment their capabilities. Ethicality functions as a vigilant assistant, flagging potential ethical issues and inconsistencies while leaving final judgment to experienced professionals. This human-in-the-loop approach enables a balance where automation streamlines routine tasks without compromising critical oversight. Such a model helps preserve transparency and trust at scale, combating increasingly sophisticated tactics that jeopardize scientific authenticity, such as synthetic content generation, fabricated submissions, and manipulated peer review reports.

Ethicality’s analytical engine examines each submission comprehensively by dissecting fundamental manuscript components, including the title, abstract, author metadata, main body, and reference lists. Beyond textual elements, it also reviews peer review reports sent with the manuscripts, employing natural language processing techniques and machine learning algorithms tailored for scholarly content. This holistic evaluation identifies a wide spectrum of potential integrity breaches, ranging from paper mill-generated manuscripts and spurious authorship claims to subtle citation manipulations and irregular referencing patterns that may indicate gaming of metrics or unethical publication practices.

One of the system’s remarkable features lies in its ability to detect AI-generated or altered text, a rapidly emerging threat in the digital publication ecosystem. Leveraging large language models fine-tuned to recognize nuances and inconsistencies characteristic of synthetic content, Ethicality can pinpoint sections likely created or tampered with by artificial intelligence. This capability is crucial amid widespread concerns that generative AI tools could be exploited to fabricate scientific results or obfuscate fraudulent data, undermining trust in scholarly communication. The AI’s detection mechanisms, however, are continuously refined against real-world data to minimize false positives and maintain editorial efficiency.

In addition to content analysis, Ethicality collaborates with external software suites to cover domains beyond textual scrutiny. For instance, image manipulation detection is managed via integration with specialized tools like Proofig, which applies forensic algorithms to uncover alterations in scientific figures and illustrations. For plagiarism and text duplication, the system incorporates iThenticate services to cross-reference submissions with an extensive repository of published works. This multi-layered approach strengthens MDPI’s ability to uphold ethical standards by combining internal AI capabilities with trusted third-party solutions to comprehensively monitor manuscript authenticity.

The advent of such an automated system answers growing concerns about the sustainability of peer review in an era characterized by exponential growth in submission volumes. By automating repetitive and technical screening tasks, Ethicality frees up valuable editorial resources, allowing human experts to concentrate on evaluating the scientific merit and originality of manuscripts rather than being overwhelmed by manual integrity checks. This redistribution of effort ensures that editorial workflows can keep pace with demand without compromising quality or publication timelines, addressing one of the major bottlenecks in modern scholarly publishing.

Dr. Enric Sayas, the product owner of Ethicality, highlights that the publishing sector is entering a technological race where adversaries leverage advanced AI to generate deceptive manuscripts and fake review reports. Traditional detection tools alone are insufficient to counteract such sophistication. Only by deploying equally advanced AI systems, capable of multi-modal analysis encompassing textual, image, and metadata anomalies, can publishers effectively defend the credibility of scholarly records. Ethicality embodies this next-generation defense mechanism, combining deep learning models with domain-specific heuristics to expose fraudulent submissions before they can impact the scientific corpus.

Ethicality’s role extends beyond mere detection; it facilitates informed decision-making by generating risk signals that prioritize editorial scrutiny. This nuanced output classifies flagged cases according to severity and type of ethical concern, enabling editors to apply their judgment where most critical while bypassing lower-risk items. Such targeted intervention reduces unnecessary delays in the publication process and improves the overall efficiency of manuscript handling. The system’s iterative design also permits continuous learning from editor feedback, enhancing accuracy and adaptability as new manipulation techniques emerge.

The timing of Ethicality’s deployment coincides with an inflection point in scholarly publishing, where generative AI tools have made it increasingly feasible to produce lifelike but fraudulent scientific content. This proliferation necessitates automated defenses that not only identify outright fraud but also detect subtler anomalies such as inconsistent author data, suspicious referencing behavior, and peer review irregularities. By integrating these multifaceted analyses, MDPI demonstrates a comprehensive and forward-looking strategy aimed at preserving the integrity and credibility of academic knowledge dissemination.

Crucially, Ethicality exemplifies how technology can be harnessed responsibly within complex human ecosystems. It respects the indispensable role of expert editors by operating as a sentinel that brings potential issues to attention while leaving interpretive authority intact. This model upholds core values of transparency, accountability, and collaborative oversight, providing a blueprint for other publishers grappling with integrity challenges in the AI era. Moreover, it underscores the broader imperative for publishers to innovate continuously in response to evolving threats and technological opportunities, ensuring that scientific publishing remains a trusted pillar of global knowledge advancement.

As MDPI’s journey with Ethicality moves forward, continuous enhancements and iterations are planned, driven by real-world data and editorial collaboration. This adaptive approach ensures the system evolves alongside the changing landscape of academic publishing and the advent of new manipulative tactics. Ultimately, Ethicality stands as a testament to the potential of AI-powered tools to reinforce scientific integrity, protect the scholarly record, and empower editorial professionals in an increasingly complex and dynamic environment.

Subject of Research: Artificial intelligence applications in scholarly publishing and research integrity
News Publication Date: 2024
Image Credits: MDPI

Keywords

Academic publishing, research integrity, artificial intelligence, editorial workflow automation, AI-generated scientific content detection, paper mill detection, peer review screening, scholarly publishing technology, automated manuscript analysis, scientific record safeguarding

Unlike humanoid robots designed around a fixed form — think Boston Dynamics — Theker's machines are built to be reconfigured.