To mark the occasion, everything in the Noodlesoft store is 35% off through Monday night. No code required, the sale price is already showing:

• Hazel 6: $42 → $27.30
• Family Pack, up to five Macs in one household: $65 → $42.25
• Upgrade from an earlier version: $20 → $13.00

For giggles, I’ve also marked the Hazel Field Guide 50% off for the week. Code: HAZELIS20

Happy Birthday, Hazel.

The post Hazel Turns 20 appeared first on MacSparky.

Machine vision is becoming a practical part of Apple’s U.S. factory training strategy, giving smaller manufacturers access to techniques that help production systems inspect what they make. The recent opening of the Houston manufacturing training center extends that effort into a working electronics production campus, while courses developed with Michigan State University introduce businesses to image analysis, machine learning, and automated quality checks.

For a manufacturer, the attraction can be straightforward: finding a missing component before a product leaves the line, identifying a surface defect before more material is added, or recognizing an assembly error while it can still be corrected cheaply.

These are manageable problems with measurable costs. They also offer an accessible starting point for businesses interested in AI but unsure where it belongs inside their operations.

Machine Vision Enters the Manufacturing Curriculum

The Apple Manufacturing Academy’s current course catalog includes Machine Learning with Vision, a course introducing machine learning and deep learning through practical activities using Apple devices. It sits alongside instruction in automation, quality fundamentals, manufacturing data, maintenance, and process improvement.

That placement gives the subject useful context. A camera can produce an image, but a factory needs a decision: accept the part, flag it for review, check its position, or investigate a process that may be drifting out of tolerance.

Machine vision connects image capture to those operational decisions. Some applications use established rules to measure dimensions or identify patterns. Others use machine learning to recognize acceptable variations and defects that are difficult to describe through fixed rules.

The academy’s public description confirms introductory activities with Apple devices. It does not identify a particular iPhone, Mac configuration, software framework, or industrial camera package that every participant must use.

For smaller businesses, the educational value is learning how to define an inspection problem before purchasing equipment. A company that understands what it needs to detect can have a much more productive conversation with an automation supplier.

Houston Brings Training Closer to Production

Apple opened its 20,000-square-foot Advanced Manufacturing Center on August 13. According to the Houston opening announcement, the free program offers small and medium-sized businesses instruction in advanced manufacturing, interactive labs, and experience with representative production equipment.

The first cohort worked with machine learning for quality control and advanced automation. Participants also explored production challenges, used a holographic table, and assembled and laser etched a product.

The location adds context: the center occupies the same Houston facility that builds advanced AI servers and is scheduled to begin Mac mini manufacturing this year. Its teaching program includes final assembly principles and design considerations for printed circuit board assembly.

Houston is the company’s second U.S. manufacturing learning site, following the Detroit academy established with Michigan State University in August 2025. By the Houston opening, Apple said the academy had helped nearly 1,000 workers, students, and entrepreneurs.

That figure measures people reached through the program. It should not be interpreted as 1,000 factories already operating AI inspection systems.

A Useful Inspection Starts With a Specific Problem

Imagine a small business assembling control panels. Each panel needs the same set of connectors, positioned correctly before the enclosure closes. A machine vision station could check for a missing connector while the assembly remains accessible.

The value comes from catching that error at the right moment. Discovering it after packaging creates extra handling and rework. Discovering it after delivery may involve a return, a service visit, and an unhappy customer.

A useful pilot would therefore begin with that single inspection point. The business could compare the existing process with the assisted one, recording missed defects, unnecessary rechecks, inspection time, and the cost of correcting mistakes.

The camera is only part of the exercise. Workers must agree on what constitutes a defect, collect representative examples, and decide what happens when the result is uncertain.

Those decisions draw heavily on experience already present on the factory floor. An operator may know that an unusual mark is harmless, while a barely visible change in alignment predicts a later assembly problem. Turning that knowledge into consistent inspection criteria is valuable work in its own right.

These are examples of how manufacturers could apply the training, rather than projects Apple has announced for individual participants.

Teaching Machine Vision to Handle Ordinary Variation

A demonstration can look impressive when every part arrives clean, centered, and evenly lit. Production introduces variation: different surface finishes, reflections, changing positions, and batches that look slightly different while remaining within specification.

The inspection system must distinguish those acceptable differences from actual failures. Otherwise, it can reject good products or allow defective ones to continue.

Industrial vision supplier Cognex’s deployment guidance recommends validating systems across product models, defect types, and manufacturing lines. It also describes an approach in which automated inspection handles preliminary checks while experienced inspectors review borderline cases.

For a business learning machine vision, that offers a realistic way to introduce the technology. Employees can compare its decisions with their own, investigate disagreements, and determine whether the results justify extending its role.

An inspection system also needs an owner after installation. Someone must understand when a product change requires new examples, when equipment needs attention, and when performance has slipped. Training that responsibility into the workforce helps prevent an initially successful project from becoming an unreliable fixture.

Smaller Manufacturers Are Already Applying the Training

The academy’s reach extends beyond companies producing consumer electronics. In its May update on participating manufacturers, Apple highlighted Block Imaging, a Michigan business that services and refurbishes medical imaging equipment.

The company hosted visitors during the academy’s Spring Forum and described applying techniques learned from Apple engineers and Michigan State experts to its operations. Apple reported that the academy had supported more than 150 American businesses at that point.

The announcement did not identify a particular machine vision deployment at Block Imaging or publish a percentage improvement attributable to visual inspection. It does provide an example of the training reaching businesses whose work differs substantially from building an iPhone.

That wider participation could benefit Apple indirectly. More capable domestic manufacturers can support equipment maintenance, tooling, components, and specialized production services. Participation itself does not establish a supplier relationship, but the skills can be used across customers and industries.

Access Without a Large Training Budget

The academy’s participation information says eligible small and medium-sized U.S. manufacturers can take courses free of charge. Online learning includes on-demand materials and interactive modules, while in-person sessions add workshops and more personalized instruction.

Digital badges are available for different topics, and completed course materials remain accessible for review. That gives a business room to train an employee around existing production commitments and return to the material when an actual project begins.

Free instruction does not remove the eventual cost of cameras, lighting, integration, maintenance, or employee time. It can help a manufacturer understand those costs before committing to a system.

The first proposal brought back to the factory might be modest: inspect one recurring defect at one station and record the results for several weeks. A supervisor can then weigh the time saved against the extra reviews, maintenance, and mistakes. That is the kind of evidence a business owner can use when deciding whether to install a second station.

The post Machine Vision Joins Apple’s U.S. Factory Training Push appeared first on AppleMagazine.

Adam Engst explains why AI software development should be approached as an evolution, starting with safe, verifiable data analysis, advancing to simple scripts, and eventually building applications. He distinguishes programming from development, arguing that non-programmers can act as product managers who direct AI, demand security checks, define requirements, and create software tailored to problems existing apps do not solve. (3) 

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Our conversation with Adam Engst continues with an exploration of  how AI can help non-programmers build sophisticated software by following professional development practices. Adam explains using specifications, architecture documents, and AI-guided decisions to create reliable apps, then examines the approach his app, SetShot, takes to macOS settings, privacy, security, user feedback, Cloudflare, GitHub, and minimizing sensitive data collection. 

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Adam Engst looks back at HyperCard as an early tool that democratized software creation, then connects its legacy to today’s AI-assisted development. He outlines how Claude helped turn an idea for tracking macOS setting changes into a working Mac app, SetShot. Adam discusses Xcode, GitHub, rapid iteration, and the value of visual feedback in his development process, and how AI lowers programming barriers without eliminating the need for thought, judgment, and effort. 

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A few months ago I added a “Report a Typo” feature to Six Colors. The goal was to give people a simple place to go — sixcolors.com/typo — if they found a mistake on the website. That Google form inserts the information into a Google Sheet, and a script attached to the Google Sheet kicks off a simple automation that sends me a notification in a couple different places.

This works pretty well. I’m using a very simple Google Apps script to fire off a webhook when there’s a new form submission. The webhook is running on Make.com, which we use for a bunch of simple web automations, but that’s an implementation detail. Make.com posts an item in a private Discord channel and also crafts a custom push notification to my phone using Pushover.

However, the moment I turned this feature on, I discovered the problem with asking people to report errors or typos: Some people see any open channel of communication as an open channel for any sort of communication. I immediately started getting more “this is more a comment than a question” items about articles than actual typo submissions. Commentary included personal preferences for writing style, web design, political references (real or imagined), and much more.

I’m going to make an admission: This sort of feedback is maddening. I want to be alerted about mistakes on the site so I can fix them promptly, but I am not interested in receiving push notifications regarding your opinion of the professionalism and appropriateness of my writing style.

Unfortunately, it’s an open form. There’s nothing to be done. Or is there?

This is a challenge that can’t really be solved by looking for a bunch of keywords. You need wholesale sentiment analysis of the entire submission. This is—brace yourself—actually a good use of a large language model.

I solved this problem by using a plug-in within Make.com that lets me get a reply back from a cloud LLM that uses a simple model with an API cost of pennies per use, but you could also just use Shortcuts to build a version of this automation that runs on a Mac (Tahoe or Golden Gate) using Apple’s built-in models. It works just fine in Shortcuts on my Mac, via the Use Model action. (See screenshot above.)

Here’s the prompt I’m currently using:

Your task is binary classification.

Input: a user-submitted comment about an article, claiming to report an error.

Output: one token only: ALLOW or REJECT.

Definition of ALLOW:
A concrete, objective, verifiable issue in the article. This includes:
* factual mistakes
* spelling, grammar, formatting mistakes
* broken or incorrect links
* wrong names, dates, numbers
* technical inaccuracies
* omissions of material consequences of a feature or change (e.g., failing to mention that a feature deletes stored Wi-Fi passwords)

Definition of REJECT:
Anything subjective. This includes:
* political disagreement
* tone or style complaints
* metaphor or comparison preferences
* ‘you should have covered X more’
* speculation about motives
* emotional reactions or rants
* admissions that it’s not actually a mistake

Classification rules:
1. If the comment identifies a specific factual, technical, or materially consequential issue in the article, return ALLOW.
2. If the issue would meaningfully affect reader understanding or use of a product/feature, treat it as an omission and return ALLOW.
3. If the comment is opinion, taste, politics, vibe, personal criticism, or preference, return REJECT.
4. If there is no concrete, verifiable claim about the article being wrong or incomplete, return REJECT.
5. If it’s off-topic or spam, return REJECT.
6. If it’s a short but simple phrase like “there not their” or “r/sheild/shield”, those are nerdier folks reporting typos and that’s fine, ALLOW.

Respond with exactly one of these two tokens: ALLOW or REJECT. No explanation.

Submission: [[insert report text here]]

This has worked surprisingly well. And, yes, all messages are logged in the original Google Sheet, so I can occasionally check to monitor the quality of the filtering and make sure that the system isn’t failing to pass on actual mistakes.

Back in March I took possession of an Aqara UWB Smart Lock, which allowed me to live out my Star Trek dreams by using Ultra Wideband technology to sense my arrival and unlock the door without any interaction from me. Now a second UWB lock has arrived on the scene, Schlage’s Sense Pro UWB smart lock. I’ve spent the last month locking and unlocking my front door with it, and it’s got some definite strengths over the Aqara lock—as well as some definite weaknesses.

Let’s start with the prices. The Aqara is $269, and the Schlage lock is $400. While it can be hard to justify a smart lock at any price, it’s extremely difficult to recommend one lock with much the same feature set over another when there’s this kind of price disparity.

And yes, these locks are very much playing by the same playbook: Both use UWB and Apple’s HomeKey technology to detect when your iPhone (11 or newer) or Apple Watch (Series 6 or newer) approaches your door, unlocking without having to wait or tap your device against the keypad. You can read my previous review for more about how this works, but it’s a huge improvement on both NFC tap-to-unlock and Bluetooth proximity detection, because it can wirelessly detect if you’re inside or outside the door—and only unlocks when you’re approaching from the outside.

The Schlage Sense Pro trades versatility for compactness and attractiveness. The Aqara lock is a big black slab, compared to a relatively squat keypad that comes in either black or satin nickel (a perfect match for my front door hardware). Schlage’s lock is undeniably better looking. It also has a light-up keypad so you can unlock it with a code, and you can program additional codes via the Schlage app or directly via the Apple Home app. And it supports NFC unlock, so if someone in your house doesn’t have a UWB device, they still have an avenue to electronically unlock the door via a tap.

Past Schlage smart locks—I used one for several years before buying the Aqara UWB model—offered a little 9-volt battery nubbin at the bottom of the outside to provide emergency power if the device’s batteries died and you needed to get into the house. This model has a USB-C port to do the same thing, and I don’t know about you, but I’m far more likely to source a USB-C cable (and an iPhone to provide power) than I am to find a 9-volt battery laying around outside my house.

Unlike the Aqara lock, the Schlage Sense Pro doesn’t have a thumbprint scanner or a physical key. The Aqara’s thumbprint scanner works but always seemed a bit pointless to me; the key, on the other hand, might have value if you like the idea of using a hide-a-key or giving a physical spare key to a friend. Alternately, it might seem unnecessary or even a security flaw. Trade-offs.

The Schlage Sense Pro is powered by four AA batteries that slide into the lock in a plastic sled; I found that if I was too forceful sliding the sled in, a battery occasionally popped out. I strongly prefer Aqara’s battery solution—a power-bank-style rechargeable battery pack that offers long life and a relatively easy USB charging path—to swapping AA batteries every so often.

My front door is not quite perfectly aligned, so automatic locks sometimes fail to lock it properly. The Schlage’s lock motor is forceful enough to do the job, but a lot less forceful than the Aqara’s, which would often have enough oomph to get the door closed even if it had to push really hard. In truth, though, both locks were equal in their ability to lock my weird door, though Aqara is much more aggressive in making a noise when it fails to lock the door, which I appreciated more than Schlage’s somewhat muted alert.

I also appreciated Aqara’s gesture to lock the door when leaving the house—you basically tap the pad anywhere—to be a lot easier to use than the Schlage lock, which requires you tap the top portion of the touch pad twice. I got used to the double-tap, but Aqara’s was faster and less fussy.

When testing the Schlage Sense Pro, I also ran into a very odd quirk where the lock became completely unresponsive until I pulled the battery sled out and put it back in—essentially, power cycling the lock. According to Schlage, this was likely due to a faulty lock and the company supplied me with a replacement that hasn’t shown any signs of the same issue.

Both locks do a great job of unlocking automatically. This is what the whole show is really about: If I walk up to a door with my watch on my wrist or my phone in my pocket, does the door unlock by the time I put my hand on the latch? Yes, almost all the time. Very occasionally there would be a slight pause when I reached the door, but almost never. It really is a magical feeling to have your door know that it’s you and unlock as you approach, and if you connect it to other HomeKit automations, you can have that unlock trigger other devices, like turning on your entryway lights.

In the end, the Schlage Sense Pro is a very nice lock, with a restrained look that I prefer to the Aqara’s industrial black block. Aqara’s extra locking methods (fingerprint, traditional key) are not really ones I feel are necessary. Yes, Aqara’s battery approach is better than Schlage’s, but I’ve been using AA-powered locks for ages, and it was fine, if a little annoying.

All in all, these locks are pretty close to even, with Schlage having the advantage in aesthetics. The problem is that the Schlage lock costs $130 more than the Aqara. If you don’t mind the extra charge for a more attractive lock, I won’t fault you, but for most people a smart lock is a bit of a splurge purchase as it is. It’s not the kind of product that can be snuck by household members who are less enthusiastic about technology, and sometimes the price tag is all it takes for the household to decide that it’s not worth doing.

The Schlage Sense Pro is a great product that brings that Star Trek feeling to my front door—but at a price that makes me wince. The first UWB lock that arrived on the scene, Aqara’s U400, is equally capable—and a lot cheaper.

When I was visiting my pal Myke Hurley in London last year, I noticed that he had a second display to the right of his main one. It was large and mounted vertically, and when he recorded a podcast, he kept all his recording information over there. I liked the idea of monitoring my recordings while they were going on, just for extra peace of mind, and a second display seemed like the right way to do it.

If only I had a second display! But I do, of course, because my main Mac is now a MacBook Pro. So I’ve taken to recording podcasts using the “Hurley Method,” with my Audio Hijack windows positioned on the laptop display, off to my left.

It’s nice, but if you know me, I quickly became frustrated by the number of clicks and drags I needed to perform every single time I record a podcast: Open up my laptop, open Audio Hijack, open the list of recording sessions, double-click on the one I’m using right now, then drag the window over to the laptop display.

So today I automated it.

None of my automations work if I haven’t opened the laptop, and while it sits next to my Mac in a nice stand from Twelve South, it’s closed most of the time. So my first step before moving windows to the laptop display is to make sure that the display is available! Fortunately, there’s a command-line command for that:

system_profiler SPDisplaysDataType

If you want, you can simplify the output by piping it through a grep command like system_profiler SPDisplaysDataType | grep -B1 "Resolution", but all I really want to do is search for the text string “Built-in”, which indicates that the laptop is open and its built-in display is running.

I automate most of my recording stuff using Keyboard Maestro macros triggered by an Elgato Stream Deck. So I built my automation into an existing macro in Keyboard Maestro, just adding a new if/then statement: If the calculation `SCREENCOUNT() > 1 returns true.

Then the macro runs the shell script, saves that to a local variable called LocalScreens, and it’s time for one more if/then statement, to ensure that this part of the macro only runs when more than one screen is attached and when the internal MacBook Pro display is active.

Unfortunately, the next step requires automating Audio Hijack, and while that app is very scriptable via JavaScript, and controllable via Shortcuts (or a plain text file?!), there’s no direct method of automating it from within Keyboard Maestro. So I created a basic Shortcut containing the JavaScript required to tell Audio Hijack to open a specific session by name, with the name passed to the Shortcut as input:

Then it’s a one-step item in Keyboard Maestro to run that shortcut with the name of my Audio Hijack session as the input:

(I wish this was cleaner and I didn’t need to run a shortcut. The alternative is to literally write out a text file containing the JavaScript and run it via a shell script. Is that cleaner? Doesn’t feel like it, but it’s more self-contained and doesn’t rely on the good graces of Shortcuts.)

Now that I know that my MacBook Pro display is open and my Audio Hijack session window is open, it’s a one-step command in Keyboard Maestro to make the move:

This one’s a little tricky. I have to use the Move and Resize Window command, but instead of standard numerical values, I need to use custom “smart” values that Keyboard Maestro supports. In this case, horizontally it’s SCREENVISIBLE(Internal,MidX)-600 —essentially, 600 pixels to the left of the midpoint of the internal display—and vertically it’s SCREENVISIBLE(Internal,MidY)-250—250 pixels up from the midpoint of the display. You can play around with the dimensions until the window ends up in the right spot, and of course, at the right size.

And that was it. Now, when I press my recording button on my Stream Deck, if my laptop display is open, the session window will automatically pop open on my laptop display. I sure do like it when things happen on my computer automatically.