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Overcast 4.1 now available

from Marco.org [alt+shift+b] in programming

Overcast 4.1 is now in the App Store with some small but nice new features. Smart Resume is actually two features: It jumps back by up to a few seconds after having been paused to help remind you of the conversation. It slightly adjusts resumes and seeks to fall in the silences between spoken words when reasonably possible. Both are subtle but noticeable benefits (my favorite kind), especially when you’re being interrupted a lot, such as while following turn-by-turn navigation directions. Smart Resume is on by default, and can be turned off in Nitpicky Details. Delete episodes 24 hours after completion: Before, episodes could either be auto-deleted immediately upon completion, or not at all. There’s now a third option, auto-deleting 24 hours after completion, which will soon be the default for new accounts. The 24-hour threshold is only enforced after a successful sync, so it won’t auto-delete anything in the middle of an extended offline period, such as a long flight. Auto-deletion, either immediate or after 24 hours, also no longer applies to Premium subscribers’ Uploads. Password-protected podcasts: Some private podcast feeds, including many paid and members-only podcasts, require a username and password via HTTP Basic Auth. You can now add these in the Add URL screen. Password-protected podcasts, and other private feeds such as Patreon bonus feeds and anything using the <itunes:block> tag, do not show up in search or recommendations. Noteworthy bug fixes: Resuming playback after quitting in the background, especially on very long podcasts and/or when using AirPods, no longer occasionally results in glitchy noises and incorrect durations. Playback under certain conditions no longer stalls, requiring pausing and playing again. Downloads now fail less often. Playback controls no longer disappear occasionally. Smart Speed total savings now appear at the bottom of the Settings screen for locales that use commas as their decimal separator.1 Extremely large playlists...
14th Mar 2018

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More from Marco.org

Bob and Van

For the first half of my childhood in suburban Ohio, we lived next to Bob. Bob and his wife were a kind, reserved older couple who decorated their home mostly with crucifixes and talked about Jesus a lot. Bob helped my mom with a lot of common household tasks after my dad died. When she woke up to a small fire on our deck, she didn’t call the fire department — she called Bob.1 I never learned any of Bob’s political views, though I can make some guesses in retrospect. They never mattered. He was just our neighbor. We later moved, and hit the neighbor jackpot with Van. Van was deeply kind, generous, and friendly — the sort of neighbor everyone wishes for. In my lazy teenage years, he’d often mow our lawn or shovel our snow for us before I even woke up. As his mobility later declined, I was honored to return the favor. Van and his delightful wife passed away years ago, before I learned how either of them felt on any political issue. If they were my neighbors today, how likely would that be? *     *     * If I learn today that my neighbors have political views that my “side” considers unacceptable and unforgivable, should I be able to have dinner at their house, shovel their snow, or even greet them outside? Should I be condemned for supporting or associating with them in non-political contexts? Should I be condemned for not publicly condemning them myself? While privilege and power dynamics play a complex and unavoidable role in this, I don’t want to associate with any community that would insist on either of those.2 And I can’t help but feel that maybe we were better off before we knew everyone’s hot takes on everything. The town coffee shop was just a coffee shop. We weren’t publicly shamed for going there because one of the owners was an ass. It was just a place to hang out with our friends. Our casual work friends were just casual work friends. We didn’t expect them to take positions on the world’s most complicated conflicts. We could just talk about weather and TV shows, and maybe sometimes get a beer after work. And our neighbors were just our neighbors. That was the day I learned the word “SHIT!” ↩︎ Somewhere on Bob’s wall might’ve been a relevant statement from an influential political dissident about casting the first stone. ↩︎

4 weeks ago • 1 votes
Unforgetful

I have ADHD. Probably.1 My entire adolescence was defined by forgetting to do my homework, disappointing everyone around me, and being told by every adult that I was lazy, “just” needed to try harder, and wasn’t living up to my potential. I’ll be working on the resulting shame and anxiety for the rest of my life.2 If someone tells me, “Remember to pick up the dry cleaning tomorrow,” I definitely won’t remember. If someone tells me, “Remember to put the laundry in the dryer in ten minutes,” I probably won’t even remember that. If you’re thinking, “It’s just ten minutes! Nobody can possibly forget that,” I have made an app that’s probably not for you. *     *     * Computers saved me. Despite constantly being told that I’d never get a good job, computers provided a lucrative career path filled with even worse academic performers who couldn’t care less about my grades. What a stroke of luck! I’d be mediocre to terrible at most jobs, but I turned out to be very good at using computers to turn coffee and Phish into money. But computers also save me in smaller, everyday ways. They help me remember. *     *     * Sorry, I just had to go put the laundry in the dryer, which I had forgotten. Really. It’s that bad. *     *     * Calendar alarms changed my life. Events without alarms don’t happen. (If you’re thinking, “Why doesn’t he just check his calendar?” I have made an app that’s probably not for you.) I had less success with reminders until we had ubiquitous voice capture. If I think of something at inconvenient times, like while driving or exercising, I need to capture it immediately or I’ll forget. It cannot wait until I get to my phone or computer. It has to be recorded right then or it’s gone. Siri, for all of its faults, has always been very good at creating tasks in Apple’s Reminders app — and Siri is everywhere. Computer. Phone. Car. Watch. I can almost always ask Siri to add something to Reminders. So Reminders’ integration and ubiquity are invaluable to me. The Reminders app, though, doesn’t fit me at all. I don’t like how it looks, how it works, or how it’s organized. Hell, I don’t even like its icon. There’s nothing about it I like. So I never open it. (Not as if I’d ever remember to check it anyway.)3 Most of my Reminders interaction has always been via Siri and notifications. And that fits me very well, with three major exceptions: If I say “Remind me to buy milk,” but I forget to specify a time (or Siri misses it), Reminders will never notify me, and I’ll never check the app, so it’ll be lost. If I inadvertently dismiss a Reminders notification without snoozing it, Reminders will never notify me about it again, so it’ll be lost. When I do snooze a Reminders notification, which I do a lot, the snooze options suck.4 This spring, I made myself an app to fix all three, and… it escalated. I’ve been using it for months, and it has profoundly improved my life. Today, I’m releasing it, in hopes that maybe it can help other people, too. *     *     * Introducing Unforgetful. The concept is simple: Reminders for ADHD. This means: It’s impossible to lose a task. Notifications always repeat, even if you miss or dismiss them, until the task is completed or deleted. Designed for procrastination. Snoozing is a core feature, with thoughtful intervals that scale as you snooze a task more. Nothing is hidden away. A hidden task is a forgotten task. No folders, no tags, no organization, no different views or modes. No judgment or shame. Nothing is overdue. You’re not in trouble. Everything is either due now or in the future. Didn’t get to it today? Maybe tomorrow. Every day is a fresh start. And Unforgetful does all of that with your Reminders data. That means: Siri works perfectly, everywhere. Just say “Remind me…” without having to specify an app. It replaces your Reminders notifications. Turn them off, turn these on. No import. This isn’t a new system to learn or migrate into. Your Reminders data is just… there. No export. If you try Unforgetful and it’s not for you, just delete it and turn Reminders notifications back on, and all of your data is right there in Reminders. Mix and match. Don’t like it everywhere? You can still use Reminders on your Vision Pro or whatever, or simultaneously use Unforgetful with Reminders or any other app that uses Reminders data. I’ve optimized lots of features and details for ADHD, too: Fast capture with dictation. Tap the microphone button in Unforgetful — or its full suite of widgets, or its Lock Screen or Control Center controls — and quickly dictate a task.5 Spaced-out notifications, not an overwhelming pile at once. If multiple tasks are set to alert you at the same time, they space themselves out so you’re not barraged with a hopeless stack of obligations. And the order changes every day, so nothing gets lost between things. Recurring-task backlogs don’t pile up. If you miss multiple intervals for a recurring task, completing it doesn’t make you click through all of your past failures to get to the present day… it just schedules the next one from now. Remind me 5 minutes after I get home. Location-based reminders can notify you after a set delay. Because if a “remind me when I get home” task fires as I pull into the driveway, I’ll forget it by the time I’m inside, unloaded, and ready to do anything about it. Unforgetful is a Mac and iOS app that’s $19.99 per year (US). One subscription buys all platforms. There’s a one-month free trial so you can really live with it and see if it’s right for you before paying. There are a million task-management apps. Unforgetful is not for everyone, but it’s really for me, far more than anything else has ever been. Frankly, I have no idea how to reach the other people it’s for. But I know you’re out there. If any of this resonates with you… give it a shot. Despite significant progress on my mental health around this from therapy and media, I haven’t (yet?) sought an official ADHD diagnosis because I haven’t sought medication, and that seemed like the main reason why I’d want a diagnosis. That’s probably worth reconsidering. I’ll make myself a reminder to do it. Someday. ↩︎ I saw a psychologist throughout high school for my homework problem. Rather than recognizing 7 of the 9 symptoms of what we now call “inattentive” ADHD, he was just one more adult condemning me for not caring. (I really did care. Nobody wanted me to be “better” more than me.) In the 1990s, if you didn’t do homework but weren’t hyperactive, you were obviously just choosing to be lazy, and the solution was apparently to apply more shame. Not all doctors are good. ↩︎ I’ve tried other apps that are beautiful and work differently, but they’re all complete task-management systems that introduce a lot of features and complexity that I neither need nor want, at the expense of Reminders’ ubiquitous integration that I highly value. I’ve also tried other apps that use the Reminders database, but I haven’t found any of them to be… good. (Sorry. I probably didn’t try yours.) ↩︎ At least they specify the actual snooze times now instead of vague descriptions like “the afternoon,” which I proudly take full credit for, since it happened to change only after I ranted about it relentlessly on our podcast for months. If I truly made this happen, it might be the greatest impact on the world I’ll ever have. ↩︎ Dictation just records every word you say as the title, which is extremely fast and reliable. It doesn’t yet recognize things like “Remind me to buy milk at 10 AM tomorrow.” I haven’t nailed that natural-language processing yet — coming soon, maybe! For now, Siri does that part better. ↩︎

14th Aug 2026 • 1 votes
A letter to John Ternus

As Apple celebrates its fiftieth birthday, we celebrate the spirit of its formation, when people who loved computers started making great computers to inspire more people to love computers. That spirit is difficult to find in the tech business today. Immense scale, soulless optimization, and an insatiable thirst for growth dominate its behavior and discourse, leaving little room for the spirit and principles embodied by Steve Jobs and Steve Wozniak. Apple still has this spirit, and I believe you do, too. But it’s not infinite or invincible. It’s under constant pressure, including from Apple itself. It seems likely that you’ll soon be leading Apple, which will place unfathomable responsibility on your shoulders. As you grow into the leader that we know you can be, I urge you, on behalf of everyone who loves computers as much as we do, to protect and cultivate this spirit of Apple’s founders as the company’s top priority: We love computers. We don’t hide that — we celebrate it! We use computers to enhance our minds, lives, and abilities — not to be controlled, restricted, tricked, placated, angered, or surveilled. Our computers work for us, with the utmost respect for our time, attention, money, data, and privacy. We are customers and owners — not resources to be harvested, annoyed, or badgered into ever more services and upsells. Apple leads the industry in these values, but leading doesn’t always mean excelling. Remaining true to these values requires constant diligence, honest evaluation, introspection, and the audacity and courage to effect change. Apple doesn’t settle for fine, functional, or good enough in its hardware (and thanks for your incredible work on that). We love making and using products that aren’t just great, but greater than they need to be, always raising the bar of greatness for its own sake. Software, services, revenue sources, and world impact need to be held to that same standard. Focus on making great computers with great user experiences above all else, and you can trust that every other major goal will follow: profit, market share, expansion, impact, and benefit to the world. Making great computers must remain Apple’s top responsibility, because if you don’t do it, nobody will.

1st Apr 2026 • 1 votes
Retreating to Safety

Ten years ago, Apple’s Phil Schiller surprised Apple enthusiasts and developers by walking out on stage at John Gruber’s The Talk Show Live WWDC event and giving an open, human, honest interview to a somewhat jaded community. I wrote this in response: Both Apple and Phil Schiller himself took a huge risk in doing this. That they agreed at all is a noteworthy gift to this community of long-time enthusiasts, many of whom have felt under-appreciated as the company has grown. […] Phil’s appearance on the show was warm, genuine, informative, and entertaining. It was human. And humanizing the company and its decisions, especially to developers — remember, developer relations is all under Phil — might be worth the PR risk. This started a ten-year run of interviews by Apple executives on The Talk Show every year at WWDC that proved to be great, surprisingly safe PR for Apple. No executive ever said something they shouldn’t have (they’re pros), no sensational or negative news stories ever resulted from them, and Apple’s enthusiastic fans and developers felt seen, heard, and appreciated. *     *     * For unspecified reasons, Apple has declined to participate this year, ending what had become a beloved tradition in our community — and I can’t help but suspect that it won’t come back. (A lot has changed in the meantime.) Maybe Apple has good reasons. Maybe not. We’ll see what their WWDC PR strategy looks like in a couple of weeks. In the absence of any other information, it’s easy to assume that Apple no longer wants its executives to be interviewed in a human, unscripted, unedited context that may contain hard questions, and that Apple no longer feels it necessary to show their appreciation to our community and developers in this way. I hope that’s either not the case, or it doesn’t stay the case for long. This will be the first WWDC I’m not attending since 2009 (excluding the remote 2020 one, of course). Given my realizations about my relationship with Apple and how they view developers, I’ve decided that it’s best for me to take a break this year, gain some perspective, and decide what my future relationship should look like. Maybe Apple’s leaders are doing that, too.

30th May 2025 • 38 votes
Ten years of Overcast: A new foundation

Today, on the tenth anniversary of Overcast 1.0, I’m happy to launch a complete rewrite and redesign of most of the iOS app, built to carry Overcast into the next decade — and hopefully beyond. Like podcasts better than blog posts? Listen to ATP #596 for more! What’s new Much faster, more responsive, more reliable, and more accessible. Modern design, optimized for easily-reached controls on today’s phone sizes. Improvements throughout, such as undoing large seeks, new playlist-priority options, easier navigation, and more. What’s not Most features. Overcast is still Overcast! The audio engine. It’s the best part of Overcast, and still leads the industry in sound quality, silence skipping, and volume normalization. (More soon!) The business. I’m still a one-person operation, with no funding or external ownership, serving only my customers. My principles. I always want to make the best podcast app, and I’ll never disrespect your time, attention, or privacy. What’s gone Streaming. Most big podcasts now use dynamic ad insertion, which causes bugs and problems for streaming playback.1 Downloading episodes completely before they begin playback is much more reliable. Tapping a non-downloading episode will now open the playback screen, download it, then start playback. It works similarly to the way streaming did before, but playback begins after the download completes, not after a portion of it is buffered. On today’s fast networks, this usually only takes a few extra seconds. And in the near future, I’ll be adding smarter options and more control over selective downloading of episodes to further improve the experience for people who don’t automatically download every episode. What’s next The last few missing features from the old app, such as Shortcuts support, storage management, and OPML. These are absent now, but will return soon. More options for downloading and deleting episodes. Upgrading the Apple Watch app to the new, faster sync engine. (The Watch app is currently unchanged from the previous one.) And, of course, more features, including some of your most-requested features over the last decade. Getting this rewrite out the door was a monumental task. Thank you for your patience as I work through this list! Why? Most of Overcast’s core code was 10 years old, which made it cumbersome or impossible to easily move with the times, adopt new iOS functionality, or add new features, especially as one person. That’s why there haven’t been many new features or changes in years. You saw it, and I saw it. I wasn’t able to serve my customers as well as I wanted. For Overcast to have a future, it needed a modern foundation for its second decade. I’ve spent the past 18 months rebuilding most of the app with Swift, SwiftUI, Blackbird, and modern Swift concurrency. Now, development is rapidly accelerating. I’m more responsive, iterating more quickly, and ultimately making the app much better. Thank you all so much for the first decade of Overcast. Here’s to the next one. Dynamic ad insertion (DAI) splices ads into each download, and no two downloads are guaranteed to have the same number or duration of ads. So, for example, if the first half of an episode downloads, then the download fails, and it downloads the second half with another request, the combined audio may jump forward or back at the halfway mark, losing or repeating content. ↩︎

16th Jul 2024 • 93 votes

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Two-Stack Sliding-Window Aggregation

An aggregation is some kind of summary of a set of data. This can be the sum, length, minimum, etc. It is quite common to want to calculate such a summary repeatedly, e.g. “the maximum noise level in dB for the past 30 seconds” for a nuisance detector. In such a case we say there is a sliding window over our data, and we want to aggregate over our window. If our aggregation is a binary operator with an inverse, like integer sums, there is a very easy solution using a double-ended queue: from collections import deque class SlidingWindowSum: def __init__(self): self.sum = 0 self.elems = deque() def push(self, x): self.sum += x self.elems.append(x) def pop(self): self.sum -= self.elems.popleft() def eval(self): return self.sum But what if our operator has no inverse? This is actually the case for most interesting summaries such as minimum, quantile, approximate unique count (for example using HyperLogLog), etc. In fact, even something as simple as a floating-point sum suffers from the fact that floating-point addition is not invertible. For example, if you ever have a NaN in your input data with the above naive algorithm your sum will forever remain NaN, even long after the bad value has left your window. Six years ago I came up with an algorithm for maintaining just the minimum/maximum in a sliding window and posted it to cs.stackexchange. I now consider this algorithm pointless, because it turns out there is a simple and efficient algorithm that solves this problem for a very wide class of aggregations. I’m writing this blog post to spread the word, because I feel it should be more widely known. Folklore I came across this algorithm while reading a far more advanced paper, Low-Latency Sliding-Window Aggregation in Worst-Case Constant Time by Tangwongsan et al. Why is this paper titled low-latency? Because it does the same as what I’m about to describe, but in O(1) time for each step. However, in it they also described a “two-stack” algorithm, which does it in amortized O(1), and is far, far simpler. Amortized O(1) means that across many operations the total amount of work per element is constant, but an individual operation can take much longer. This is almost always fine, unless you absolutely need a low upper bound on latency. Funnily enough that paper attributes this algorithm to “adamax” from a 2011 Stack Overflow post. They in turn credit a 2001 lecture note by D. Sleator for the inspiration. However, this lecture note does not describe a sliding window aggregate, it describes the classical two-stack algorithm for implementing a FIFO queue and does amortized analysis on it. Ultimately I would not be surprised to find that this algorithm was already described in an obscure paper from the 1970s, seeing how simple and brilliant it is. Two stacks Like the authors of the paper, I will generalize the two-stack algorithm to arbitrary associative aggregation functions. By abstracting the aggregation as a set of functions, empty(), unit(x), combine(x, y) and finalize(x), you can describe many possible aggregations, for example a mean: empty = lambda: (0, 0) unit = lambda x: (x, 1) combine = lambda x, y: (x[0] + y[0], x[1] + y[1]) finalize = lambda x: x[0] / x[1] if x[1] else None I’d like to note here that these functions have the following signatures: fn empty() -> Agg; fn unit(x: Value) -> Agg; fn combine(x: Agg, y: Agg) -> Agg; fn finalize(x: Agg) -> Out; I’m making a distinction here between Value, Agg and Out because while they seem superficially similar for something like an integer sum, for an approximate unique count on strings you would have (Value, Agg, Out) = (String, HyperLogLogSketch, u64), three wildly different types. Without further ado, the algorithm: class TwoStackAgg: def __init__(self): self.values = [] self.values_agg = empty() self.cum_aggs = [] def push(self, x): self.values.append(x) self.values_agg = combine(self.values_agg, unit(x)) def pop(self): if not self.cum_aggs: cum_agg = empty() while self.values: cum_agg = combine(unit(self.values.pop()), cum_agg) self.cum_aggs.append(cum_agg) self.values_agg = empty() self.cum_aggs.pop() def eval(self): return finalize( combine(self.cum_aggs[-1], self.values_agg) if self.cum_aggs else self.values_agg ) That’s it, the entire algorithm. There’s two stacks (values and cum_aggs) and one more aggregate, values_agg. At any point in time values_agg holds the aggregate of values, and cum_aggs contains the cumulative aggregates of all values in our window that aren’t in values, in reverse order. From this we can get the aggregate over our entire window in constant time by by combining the last value of cum_aggs with values_agg. The neat part is that (assuming w is our window size) every wth operation we drain all of values and maintain a running aggregate while pushing the partial cumulative aggregates onto cum_aggs. This is what makes it amortized O(1), doing O(w) internal operations every wth pop bounds the total amount of work per element to O(1), even though a singular operation might not be constant time. I think this is best visualized. Suppose we sum [1, 2, ..., 10] with a fixed-size sliding window of four elements, then the state on each eval() call would look like this (values_agg not shown as it is simply the aggregate of the values): cum_aggs values out [] [] = 0 [] [1] = 1 [] [1, 2] = 1 + 2 [] [1, 2, 3] = 1 + 2 + 3 [] [1, 2, 3, 4] = 1 + 2 + 3 + 4 [4, 3 + 4, 2 + 3 + 4] [5] = 2 + 3 + 4 + 5 [4, 3 + 4] [5, 6] = 3 + 4 + 5 + 6 [4] [5, 6, 7] = 4 + 5 + 6 + 7 [] [5, 6, 7, 8] = 5 + 6 + 7 + 8 [8, 7 + 8, 6 + 7 + 8] [9] = 6 + 7 + 8 + 9 [8, 7 + 8] [9, 10] = 7 + 8 + 9 + 10 [8] [9, 10] = 8 + 9 + 10 [] [9, 10] = 9 + 10 [10] [] = 10 [] [] = 0 In total the memory usage is O(w), where w is your maximum window size. Note that for simplicity of analysis and the example I assumed a fixed-size window w, but there is nothing about the two-stack algorithm that requires this. You can call push(x) and pop() as many times as you’d like between each eval(), growing and shrinking the window size as needed. Floating-point non-associativity Note that we required above that our aggregate combine is associative, meaning: combine(combine(x, y), z) = combine(x, combine(y, z)) Technically speaking, floating-point addition doesn’t respect this. Nevertheless, the above algorithm is still very useful because the results closely match the expected outcome, even more so if you use a compensated summation algorithm like Kahan summation. Another neat thing about the two-stack algorithm is that it doesn’t require commutativity, if you follow the above implementation precisely. The order of operands is maintained, which can matter for things like string concatenation. However, there is a second very useful property of the above algorithm. Each aggregate is strictly a combination of the elements in the window, and none outside the window. This means if your window contains a NaN or infinity (or some other outlier), that value only poisons the windows that contain it rather than the rest of your computation. But even without NaN or infinity it is useful, due to not propagating errors endlessly. E.g. if your sliding window starts with [1e20, 1], this is what would happen with a naive rolling sum: >>> 1e20 + 1 - 1e20 - 1 -1.0 Compensated summation will reduce these effects, but not making your result depend on values outside of the window will eliminate long-term error accumulation entirely.

yesterday • 1 votes
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