Car thefts in San Francisco are down almost 59% for the first eight months of 2026 compared with the same months in 2022, according to analysis of police department statistics by NPR's Planet Money — and car break-ins are down more than 82%. "San Francisco's law enforcement leaders tell us that greater use of surveillance technology — including a controversial, AI-powered license plate camera system made by Flock Safety — has been crucial to this drop in car crime." How much credit does Flock actually deserve...? [In July of 2022, San Francisco District Attorney] Jenkins began taking a more aggressive approach to prosecuting people accused of crimes. Meanwhile, the SFPD beefed up its tactics to fight car thefts and break-ins. In 2023, for example, the police increased patrols in tourist areas known for car break-ins and used "bait cars" to catch and build cases against organized car burglars. But, Jenkins says, "one of the most important factors" behind the drop in crime came when the city introduced new crime-fighting technology. In March 2024, San Francisco voters approved Proposition E, which, among other provisions, made it easier for SFPD to use drones and high-tech surveillance cameras. That same month, the SFPD began installing Flock cameras in strategic locations... In a statement to Planet Money, a spokesperson for the SFPD said, "ALPR has been instrumental in driving down crime in San Francisco. These tools enable our officers to identify crime vehicles with precision, and ALPR is assisting in reducing crimes that were previously very challenging to solve, like auto burglaries." Jenkins described how Flock cameras and drones can work together to catch suspected criminals. In one case the SFPD publicized, the department says Flock cameras spotted a moving vehicle linked to an auto burglary the previous day and alerted officers. Officers deployed a drone, which flew in from above and then began recording suspects in real time as they smashed windows and stole luggage out of a parked SUV. The police then moved in and arrested them. They can do all this, Jenkins says, "without putting innocent people in danger with dangerous police chases." Meanwhile, the surveillance system creates an evidence trail that [Jenkins'] office can use to prosecute suspected criminals... Jenkins also believes high-tech surveillance helps deter crime, although that's harder to prove... The most ambitious study of Flock's system so far is a recent one by University of South Carolina crime researchers Ian Adams and Scott Mourtgos. They analyzed data from 216 law enforcement agencies that introduced Flock cameras, comparing crime trends with places that had not adopted Flock. They found that deploying Flock cameras was associated with an 11% drop in car thefts in the first year. This is only one study, and it has yet to be peer-reviewed. It's also not a randomized controlled trial, the gold standard for finding the actual causal effect of something. Other factors, beyond Flock, could still help explain the decline. That study attempted to control for variables, the article points out — like how San Francisco's decline coincided with a broader drop in crime across American cities as America emerged from the pandemic... But the article also notes another worry shared by "countless citizens" and political leaders. Are these systems handing too much power to law enforcement to "invade our privacy, trace our whereabouts, and stomp on our civil liberties."

Read more of this story at Slashdot.

404 Media reports: "We will not add facial recognition to our devices," Flock CEO Garrett Langley said in a recent video. But other, third-party companies are telling cops that they are willing to add facial recognition to data gathered by Flock cameras in order to "close that gap." A company called VIDIZMO is advertising the capability to export footage from FlockOS to its own platforms that do facial recognition, behavior prediction, and racial and gender analysis on live camera feeds. VIDIZMO is a several decades-old video analysis and database company that has started offering facial recognition technology to cops within the last six months. In a May email to Johnson City, Tennessee, deputy police chief Michael Adams, a salesperson from VIDIZMO wrote that the company's product could do facial recognition on both Flock and Axon [body-worn camera] data in "one searchable platform...." VIDIZMO's CEO, Nadeem Khan, told 404 Media on a video call that the company has not yet started running facial recognition tech on footage from Flock cameras, and that it has not actually built the specific tool that would export data from Flock's system to its own platform (though the company advertises this capability online, and the company already sells other facial recognition products). But Khan said VIDIZMO "would love to do the integration," and that he believes facial recognition "is the way the world is going, the way the world will have to be," and that he thinks Flock should offer the capability itself in a way that "balances privacy, security, and the freedom that we enjoy in this country." The article notes that VIDIZMO says on its website, "You can identify individuals from seven races: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino Hispanic." VIDIZMO adds that stores or cities can also then do "demographic analysis" for marketing purposes by determining the races or ages of people showing up....

Read more of this story at Slashdot.

iOS 27 brings the long-promised Siri AI, but the part that matters most is what happens outside Apple’s apps. If Siri AI is really going to get legs, it needs third-party developer support. I had hoped Apple might use MCP as the bridge between Siri and third-party apps, which could have brought a lot of support along quickly. That’s not the route Apple chose. Given the security and sandboxing constraints of Apple’s operating systems, I’m not even sure it could have worked.

Instead, Apple’s plumbing for Siri AI is App Intents. Developers describe their app’s content and actions in a form Siri, Spotlight, and Shortcuts can understand. The first wave of updates is here, and it gives us a much better idea of what third-party Siri support will look like.

Things 3.24

Things 3.24 lets you speak more naturally when creating a to-do. You can also continue the conversation to add a note, schedule the task, move it to a project, or correct something Siri misunderstood.

The update also adds Spotlight indexing, which gives Siri a way to find to-dos and projects already in your database. This is much closer to talking with a task manager than dictating into one.

OmniFocus 4.9

OmniFocus 4.9 goes deeper. Siri treats OmniFocus actions as reminders, projects as lists, and folders as groups. It can create and edit actions, set due dates, add flags and repeat schedules, and work with notes and image attachments.

The visual piece is especially interesting. The Omni Group says you can point Visual Intelligence at a handwritten list and move those items into OmniFocus. Siri can also use Spotlight’s index to answer questions about your remaining and recently completed tasks.

Fantastical 4.2

Fantastical 4.2 connects Siri AI to Fantastical calendars. You can ask about your schedule, create an event, or move an existing event through a follow-up request. Those conversations also appear in the new Siri app.

Calendar work is exactly the sort of thing a voice assistant should handle quickly and reliably.

Fantastical is a MacSparky sponsor.

Dark Noise 3.6

Dark Noise 3.6 is a nice demonstration of Siri’s on-screen awareness. You can ask Siri to play a particular sound or one of your favorites without building a shortcut first. While looking at a sound in the app, you can say “Play this” or “Favorite this,” and Siri knows what “this” means.

Ulysses 41

Ulysses 41 adds on-screen awareness, Ask Siri, and Write with Siri. On the Mac, there is also a toolbar button for calling up Siri’s writing features while you work. If you would rather keep Siri out of your writing app, Ulysses lets you switch the feature off on the Mac.

The Ulysses implementation is less about commanding the app and more about giving Siri useful context while you write.

The Real Siri AI Test

Siri AI is still a beta. It requires supported hardware, language, and region settings, and the first wave of app support is uneven. The operating system also needs time to index an app’s actions and content, so a newly installed integration may not work immediately.

It’s nice to see apps showing up in multiple categories. If this is to get momentum, I think Apple needs to do everything in its power to really push the gas down on third party support. That means further available actions, code support, and they should continue to promote Siri AI-enabled apps.

The post Apps Adding Siri AI Support for iOS 27 appeared first on MacSparky.

There is a world of difference between asking a robot, “Help me do this,” and asking, “What should I do?” AI can be remarkably good at the first question but lead you astray with the second. New users often learn that distinction the expensive way.

I’ve taught thousands of people how to use agentic AI, and I’ve seen a similar pattern. People get into trouble after they have some success with AI and try to push it further. Quite often, the thing they want on the other side just isn’t there.

At first, you’re skeptical of AI’s ability to provide any real help. With a little time and training, you figure out how to give it context, build skills, and assign the kind of donkey work it can reliably do unsupervised.

The moment that sinks in is liberating. Suddenly, you’ve got more time for creative work while the AI handles the grunt work.

As you get more reps in and the AI becomes reliable on this kind of work, your confidence in it grows. Then, somewhere along the way, you start giving it more credit than it’s due.

That’s where the drift begins.

The robot really isn’t equipped for the why. Put simply, the robot can’t exercise judgment and taste like you can. I’ve seen a lot of new models come out, and they keep getting smarter. Yet they still fail miserably in matters of taste and judgment.

I think the reason is context. The robot hasn’t lived your life. It doesn’t truly know your preferences. No matter how smart the model gets, its assertions of judgment are based on incomplete and inadequate context. It may have read a staggering amount of material, but it hasn’t been you.

I understand why people fall into this trap. The AI is fast, and it’s very confident in its answers. It feels authoritative. And yet, when you get beyond donkey work, these robots can be full of bullshit.

It doesn’t help that vendors of these frontier models keep promising the technology will change your life. Artificial intelligence is a transformational technology. It just isn’t nearly as transformational as the guy trying to pump up his company’s stock price claims.

People still give it more authority than they should. They ask it to do harder things than it can handle. After all that early success with donkey work, they start trying to make the AI smart enough to make those calls for them.

The fact is, it can’t make that judgment for you.

Trying to outsource judgment and taste to AI is an expensive way to get bad results. It’s like asking your toaster to pick the best bread for your turkey sandwich.

Also, even if it could exercise judgment and taste, do you really want it to? Isn’t that your job? Shouldn’t you get to decide what bread goes on your sandwich?

I see this often with people going through my coursework. They reach a point of frustration and give up on the utility of artificial intelligence because they pushed it beyond its capabilities.

My advice is always the same. Scale back. Get back to the useful zone.

Give the robot work where you know the outcome and can tell whether it’s any good. Limit its scope to jobs where you can provide enough context.

AI is great for grunt work that a computer can do. Stay in that space, and you’ll spend remarkably little time managing the AI. Draw clear lines in your head about how far you can take it and where it needs to step aside so humans can take over.

The reason I like the term “donkey work” so much is that it captures what AI does best. Granted, that doesn’t justify a trillion-dollar stock valuation, but it can make a real difference in your life.

You’re the human. You make the map. Hand the robot the shovel.

When you go beyond that, friends, there be dragons.

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