MacStories Weekly: Issue 530
Our Favorite Indie Apps for iOS 27, Vol. 2
It’s time to get back to our favorite indie apps that take advantage of Apple’s latest OS features. Today, we’re highlighting: Bear, Gentler Streak, The Outsiders, DoMarks, and Cheatsheet Notes.
John
Bear
Bear, the note-taking app for iPhone, iPad, Mac, and Apple Watch, just released version 2.10 with several Siri AI features, including actions to:
- Create a new note
- Append text to an existing note
- Append files to a note
- Pin notes
The app also includes onscreen awareness so Siri knows the content of the note you’re looking at without you telling it.
Bear’s new actions make it a compelling way to dictate notes on the go. Simply invoke Siri and start talking. With the ability to start a new note or append to existing notes, you can get quite a lot done with only your voice. One of my favorite uses is annotating screenshots. I take a screenshot, invoke Siri, and in one go, I can ask it to add a new note with my last photo (the screenshot), give it a title, and add a quick note about it. It’s far easier than walking through those steps manually.
Gentler Streak
Our iPhones and the Apple Watch can collect and process a lot of health and fitness data for us. My favorite way of making sense of it all is Gentler Streak, which works on the iPhone, iPad, and Apple Watch, making sense of all that data.
One of Gentler Streak’s greatest strengths is its delightful illustrations and clear, meaningful presentation of fitness data. I love browsing my workouts and other fitness data in Gentler Streak, but with the latest release, I’ve come to appreciate the snippets it offers thanks to App Intents, too.
The update lets you access bite-sized snippets of data without opening Gentler Streak, accessing data like today’s workout, your activity status, and suggested activities all from Spotlight or Siri. It’s handy when you’re in a hurry and just want a quick update on something like how well you slept last night. The app also supports onscreen awareness, allowing Siri to explain features of Gentler Streak to you or interpret onscreen data.
The Outsiders
The Outsiders, from the same team as Gentler Streak, is a great complement to it, trading Gentler Streak’s relaxed approach to workouts for a more intense, performance-driven perspective. I’m a big fan of the iPhone app’s colorful design, which, like Gentler Streak, lets you access data like your training readiness, latest workout, body metrics, and sleep data simply by asking for the data “in The Outsiders.” Onscreen awareness allows Siri to explain metrics to you, too.
If you choose to edit a workout’s title, Apple Intelligence kicks in with a suggestion changing something generic like “Evening Workout” into “Evening Commute Through Davidson College,” which sounds much nicer and adds a little hint about where you were and what you did for that workout.
Cheatsheet Notes
I love how Cheatsheet Notes has evolved over the years from a very basic scratchpad to a beautifully designed and focused note-taking app that adopts all of Apple’s latest technologies. The recent update, which is available for the iPhone, iPad, Apple Watch, and Mac (sold separately) is no exception, incorporating all sorts of Siri AI features, as well as new widgets.
Thanks to Siri AI’s onscreen awareness, you can ask Siri questions about information visible in your notes. You can also append text and images to notes with Siri’s help. The append functionality is especially useful because Cheatsheet is intended for quick notes about things you want to remember, like a parking spot at the airport. Take a quick photo of your parking spot, ask Siri to add it to your Airport Parking note and that’s it.
Cheatsheet has also added extra-large widgets that can display a list of notes, a set of notes tied to action buttons, a shuffling of notes from any folder, or a single note. Of course, the app also supports an extensive list of Shortcuts actions, too.
If you’re looking for a great place to store the sort of information you might otherwise scribble on scraps of paper, check out Cheatsheet Notes. It’s an excellent app and scraps of paper is no way to live.
DoMarks
With the release of Apple’s OS updates, DoMarks reached version 4.0. The app is based on the simple idea that people save links to do something with them later. DoMarks makes that easy by helping you assign an action category to each URL you save whether it’s to buy, read, watch, or do something else with the URL. The app fills a role that’s similar to a read-later app and can be used for reading, but the beauty of it is that what you do with the links you save for later is only limited by your own imagination.
With DoMarks’ Siri AI integration, you can save a webpage from Safari simply by asking Siri to save it to DoMarks. You can also ask Siri to open a link by its name or delete it. DoMarks supports onscreen awareness, allowing you to ask questions about a bookmark, too.
Other actions available via Siri, include keyword searches and opening a bookmark. However my favorite action is “Surprise Me in DoMarks,” which will open one of your saved bookmarks at random in Safari. It’s a great way to rediscover interesting spots on the web that I came across long ago, but didn’t have the time to look at. The app has also added a long list of Shortcuts actions that makes powerful automations possible too.
I’m a link hoarder (shocking, I know), and I’ve always found it hard to know what to do with links for things I want to check out later that aren’t reading material. DoMarks is the perfect way to handle those sorts of links, and now, it’s easier than ever to build your collection of bookmarks with Siri’s help.
Apple Music Hall Opens in Battersea Power Station
Today, Apple announced the opening of Apple Music Hall, a state-of-the-art live venue in London’s Battersea Power Station. The intimate concert space will offer a unique experience for both artists and fans alike. According to Apple’s press release:
The 600-capacity venue is designed intentionally to close the gap between intimacy and production value and allow for an unprecedented level of creative control. Every seat feels close to the performer, yet the infrastructure offers what an artist requires for an arena tour, with a 38-foot-wide stage that can be reconfigured for traditional front-facing sets or more in-the-round experiences. Additionally, the venue is equipped with a 48-speaker spatial sound system positioned all around the audience. When an artist performs in spatial, it will enable fans to hear that performance in an immersive environment.
In addition, Apple Music Hall will support both traditional video broadcast cameras and iPhone video capture of events.
The new venue looks amazing. I’d love to see a show there myself someday. Fortunately, Apple will also be livestreaming performances from the hall worldwide, so whether you can get into a Battersea show or not, you’ll have a chance to enjoy the performances. I’d love to see Apple Music Hall become a modern version of MTV Unplugged.
M5 Ultra Mac Studio Review: The Dream Mac for Local AI Agents
For the past few days, I’ve been testing the (currently) top-of-the-line M5 Ultra Mac Studio with 256 GB of RAM.
I’ll cut to the chase: the M5 Ultra Mac Studio is a dream machine for local AI agents. This computer makes it possible to run personal assistants powered by local models with great performance and no additional cloud costs. If you’ve been skeptical of testing OpenClaw or Hermes Agent with local models because they’d never be even remotely near the intelligence and speed of cloud ones, this Mac will change your mind about that.
Since last Thursday, I’ve been comparing this Mac Studio to its predecessor, the M3 Ultra with 512 GB of RAM, as well as my own desktop gaming PC with an RTX 5090 inside. For its size, price, thermal performance – not to mention Apple’s approach to unified memory – the M5 Ultra Mac Studio has fundamentally changed how I think about models running locally and what they can enable now. A 5090, of course, still has an edge over the M5 Ultra thanks to its higher memory bandwidth. But considering the sheer size of my PC build, as well as its heat and noise, I would prefer an M5 Ultra Mac Studio any day. It also happens to be a Mac, with an operating system that looks nice and doesn’t suck, plus a vibrant app ecosystem. (Windows fans, I’m sorry, but Microsoft software will never get my sympathy.)
As I’ll explore in this article, running the latest Qwen3.8-Flash-Next model on the M5 Ultra Mac Studio has been so nice and fast, I’ve made it my default in both Open Minis for iOS and Hermes Agent. That’s right: the personal assistants I use the most – more than Siri AI, in fact – are now entirely powered by a model running locally on a Mac Studio. Furthermore, thanks to the M5 Ultra’s faster GPU and higher memory bandwidth, these agents start responding more quickly, stay fast at larger context windows, and can run long, multi-turn loops without slowing to a crawl as the session grows. Because of this, I’ve also been using local models in the Codex app on my Mac – either as main threads or subagents orchestrated by GPT-6 Astra – and I’ve had a great experience doing so.
I should note upfront that I’m not an AI developer by trade: I do not train or fine-tune models. I’m a tinkerer at heart, and I’ve been playing around with local AI models for over a year at this point. This summer, I went all-in on local AI usage for a big project I was working on, which I will explain in the following section.
My goal with this article is to provide you with a mix of two things: numbers and visualizations based on the (many) tests I’ve run over the course of four days, and an explanation of my practical use cases for local AI applied to my workflow and how I get things done for MacStories.
Let’s dive in.
LookAway: The Screen Breaks Your Eyes Deserve [Sponsor]
Hi, I’m Kushagra, the indie developer behind LookAway.
It’s very easy to get into the flow, lose track of time, and end up sitting for hours in front of a screen. By the end of the day, your eyes are tired and your shoulders are stiff.
That’s exactly what was happening to me. I knew I needed to take more breaks, but remembering to take them was difficult. I tried reminder apps, but they kept interrupting me at the wrong moment, so I had to stop using them.
That’s why I built LookAway – a native break reminder app for macOS with a lot of thought put into when to leave you alone.
LookAway automatically waits during meetings, videos, screen recordings, and more. It also waits for you to finish typing, dragging, or dictating so that you are not interrupted mid-thought. It gives you enough heads-up before a break, so you have time to wrap up. Between breaks, there are also gentle blink and posture reminders to help you check in with yourself.
And finally, there’s LookAway Mirror — a free companion app for iPhone and iPad. When your Mac takes a break, it can also block distracting apps and websites on your phone, so you don’t end up doomscrolling another screen.
I’ve put a lot of care into making LookAway something you’ll want to keep using. I’d love for you to try it. This week, MacStories readers can get LookAway for 30% off with the code MACSTORIES30 when you purchase it directly from the website.
Our thanks to LookAway for sponsoring MacStories this week.
MacStories Weekly: Issue 529
The iPhone 18 Pro Camera: Apple Reference Image and Tyler Stalman’s Review
In the wake of last week’s Surprise and Shine event, Apple published a post on its Security Research blog about Apple Reference Image, a way to verify that a photograph taken with an iPhone hasn’t been altered. As Apple explains, AI offers helpful features like one-touch removal of distractions from an image, but that can make knowing what is real difficult.
This is not a simple problem to address. Modern cameras rely on sophisticated image-processing algorithms to produce the final viewable image, so certifying that an image accurately reflects what a real camera sensor captured requires a chain of trust covering the sensor as well as the computational photography software that interpreted the capture. Industry approaches to this problem, based on the C2PA standard, attach provenance metadata after capture and certify the history of image edits from that point forward. This approach, however, is vulnerable to compromise at any point in the editing chain, and a viewer has no way to detect such a failure. It can also create privacy risks for photographers working in dangerous conditions by tying the image to a public identity, either to a particular device or to an individual.
…new, opt-in camera mode lets a photographer create a securely timestamped reference image that accurately reflects what was captured by the iPhone’s camera sensor. Dedicated secure hardware on the device protects the integrity of this reference image, and Private Cloud Compute protects the privacy of the image data during processing.
The entire post is well worth reading because the solution is both clever and a great showcase of what Apple’s control over its chipset and Private Cloud Compute architecture can achieve for its customers. Apple also supports Google DeepMind’s SynthID watermarking technology.
Also, don’t miss Tyler Stalman’s review of the iPhone 18 Pro and Pro Max on YouTube. Tyler always does a fantastic job with his reviews, and this year’s review doesn’t disappoint, covering features like the camera hardware’s new variable aperture and what this year’s software updates mean for iPhone 18 Pro and Pro Max users.
Chance Miller on the Apple Watch Series 12 and Ultra 4→
Apple Watch reviews are out and Chance Miller at 9to5Mac has an excellent, in-depth look at both the Series 12 and Ultra 4. One of the things that Chance points out that might not be immediately obvious to many people is that the inclusion of an always-on chip as part of the S11 system on a chip allows Apple to fill meaningful gaps in both Watches’ data collection:
They will capture your heart rate every five seconds, regardless of whether you’re moving or stationary. Having such frequent and accurate heart rate readings unlocks new levels of data and insight.
When measuring your heart rate once every five minutes while stationary, previous Apple Watch models had an inherent blind spot. They either didn’t capture moments where your heart rate increased while not working out, or they captured one or two arbitrary readings somewhere in the middle of those moments.
That always-on component, combined with the Secure Exclave, is what Apple will also use to keep microphone data private and secure for features like Live Rewind and Siri Recap that are coming later this year.
It was these more frequent heart rate and HRV readings, along with the larger, more accurate sensor on the rear of the Apple Watches, that led me to order an Ultra 4. So I was also glad to see this observation from Chance after his first week with the new Watches:
In my one week with the Apple Watch Series 12 and Apple Watch Ultra 4, I’ve found that added data very insightful. I’m learning more about how little things I do throughout the day impact my heart rate. For example, I can visualize the impact of a late afternoon cup of coffee, or the impact of a stressful moment at work.
That’s the sort of feedback that you can act on and the kind of data that I expect developers will be able to use to make even more meaningful apps across the health and wellness spectrum.
ChatGPT Is Surprisingly Thorough at Planning Running Routes→
Every now and then, I go looking for new running routes to mix things up, which was why I was intrigued by this post by Simon Willison, who gave ChatGPT Work his address and asked it to find 5K and 10K routes, using OpenStreetMap data:
It worked for 27 minutes and produced exactly what I’d asked for, as both an embedded visualization and downloadable GPX file and GeoJSON files. Here’s that 5K route:
](https://cdn.macstories.net/images/uploads/2026/09/17/5k-route-1789644737884-49c7f49629.webp)
Source: simonwillison.com
I decided to give the same thing a try and was impressed by the results. ChatGPT spent a similar amount of time, poring over OpenStreetMap data, but it didn’t stop there. The model consulted my town’s website to account for road construction and the availability of sidewalks. Then, it visited the county’s website to review maps of greenways that aren’t part of the local road system. The final results were within 100m of exact 5K and 10K routes with notes on where to be careful to verify safe pathways and road crossings.
What I love about this sort of experiment is that it’s not something I’d ever have thought to ask ChatGPT myself. In hindsight, it’s obvious that all the data is there, ready to assemble. However, it just goes to show the many ways that I, and I’m sure many others, are still just scratching the surface of what LLMs make possible.