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The Geesaa automates (but overcomplicates) pourover coffee – TechCrunch

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Making pourover coffee is a cherished ritual of mine on most mornings. But there are times I wish I could have a single cup of pourover without fussing about the kitchen — and the Geesaa, a new gadget seeking funds on Kickstarter, lets me do that. But it’s definitely still a ways from being a must-have.

I’m interested in alternative coffee preparation methods, low and high tech, so I was happy to agree to try out the Geesaa when they contacted me just ahead of their Kickstarter campaign going live (they’ve already hit their goal at this point). I got to test one of their prototypes and have used it on and off for the last couple of weeks.

The Geesaa is part of a new wave of coffee makers that make advances on traditional drip techniques, attempting to get closer to a manual pourover. That usually means carefully controlling the water temperature and dispensing it not just in a stream powerful enough to displace and churn the ground coffee, but in a pattern that’s like what you’d do if you were pouring it by hand. (The Automatica, another one with a similar idea, sadly didn’t make it.)

Various manufacturers do this in various ways, so Geesaa isn’t exactly alone, though its mechanism appears to be unique. Instead of using a little showerhead that drips regularly over the grounds, or sending a moving stream in a spiral, the Geesaa spins the carafe and pours water from a moving head above it.

This accomplishes the kind of spiral pour that you’ll see many a barista doing, making sure the grounds are all evenly wet and agitated, without creating too thin of a slurry (sounds delicious, right?). And in fact that’s just what the Geesaa does — as long as you get the settings right.

Like any gadget these days, this coffee maker is “smart” in that it has a chip and memory inside, but not necessarily smart in any other way. This one lets you select from a variety of “recipes” supposedly corresponding to certain coffees that Geesaa, as its secondary business model, will sell to owners in perfectly measured packets. The packet will come with an NFC card that you just tap on the maker to prompt it to start with those settings.

It’s actually a good idea, but more suited to a hotel room than a home. I preferred to use the app, which, while more than a little overcomplicated, lets you design your own recipes with an impressive variety of variables. You can customize water temperature, breaks between pouring “stages,” the width of the spiral pattern, the rate the water comes out and more.

Although it’s likely you’d just arrive at a favorite recipe or two, it’s nice to be able to experiment or adjust in case of guests, a new variety of coffee, or a new grinder. You can, as I did, swap out the included carafe for your own cone and mug, or a mesh cone, or whatever — as long as it’s roughly the right size, you can make it work. There’s no chip restricting you to certain containers or coffees.

I’m not sure what the story is with the name, by the way. When you start it up, the little screen says “Coffee Dancer,” which seems like a better English name for the device than Geesaa, but hey.

When it works, it works, but there are still plenty of annoyances that you won’t get with a kettle and a drip cone. Bear in mind, this is with a prototype (third generation, but still) device and app still in testing.

One thing I’ve noticed is that the temperature seems too low in general. Even the highest available temperature, 97 C (around 206 F), doesn’t seem as hot as it should. Built-in recipes produced coffee that seemed only warm, not hot. Perhaps the water cools as it travels along the arm and passes through the air — this is nontrivial when you’re talking about little droplets! So by the time it gets to the coffee it may be lower than you’d like, while coming out of a kettle it will almost always be about as hot as it can get. (Not that you want the hottest water possible, but too cool is as much a problem as too hot.)

I ran out of filters for the included carafe so I used my gold Kone filter, which worked great.

The on-device interface is pretty limited, with a little dial and LCD screen that displays two lines at a time. It’s pre-loaded with a ton of recipes for coffee types you may never see (what true coffee-lover orders pre-ground single-serve packets?), and the app is cluttered with ways to fill out taste profiles, news and things that few people seem likely to take advantage of. Once you’ve used a recipe you can call it up from the maker itself, at least.

One time I saw the carafe was a bit off-center when it started brewing, and when I adjusted it, the spinning platform just stopped and wouldn’t restart. Another time the head didn’t move during the brewing process, just blasting the center of the grounds until the cone was almost completely full. (You can of course stop the machine at any point and restart it should something go wrong.)

Yet when it worked, it was consistently good coffee and much quicker than my standard manual single cup process.

Aesthetically it’s fine — modern and straightforward, though without the elegance one sees in Bodum and Ratio’s design.

It comes in white, too. You know, for white kitchens.

The maker itself is quite large — unnecessarily so, I feel — though I know the base has to conceal the spinning mechanism and a few other things. But at more than a foot wide and eight inches deep, and almost a foot tall, it has quite a considerable footprint, larger than many another coffee machines.

I feel like the Geesaa is a good coffee-making mechanism burdened by an overcomplicated digital interface. I honestly would have preferred mechanical dials on the maker itself, one each for temperature, amount and perhaps brew style (all at once, bloom first, take a break after 45 seconds, etc). Maybe something to control its spiral width too.

And of course at $700 (at the currently available pledge level) this thing is expensive as hell. The comparisons made in the campaign pitch aren’t really accurate — you can get an excellent coffee maker like a Bonnavita for $150, and of course plenty for less than that.

At $700, and with this thing’s capabilities, and with the side hustle of selling coffee packets, this seems like a better match for a boutique hotel room or fancy office kitchen than an ordinary coffee lover’s home. I enjoy using it, but its bulk and complexity are antithetical to the minimal coffee-making experience I have enjoyed for years. Still, it’s cool to see weird new coffee-making methods appear, and if you’re interested, you can still back it on Kickstarter for the next week or so.

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Apple and Google’s AI wizardry promises privacy—at a cost

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Since the dawn of the iPhone, many of the smarts in smartphones have come from elsewhere: the corporate computers known as the cloud. Mobile apps sent user data cloudward for useful tasks like transcribing speech or suggesting message replies. Now Apple and Google say smartphones are smart enough to do some crucial and sensitive machine learning tasks like those on their own.

At Apple’s WWDC event this month, the company said its virtual assistant Siri will transcribe speech without tapping the cloud in some languages on recent and future iPhones and iPads. During its own I/O developer event last month, Google said the latest version of its Android operating system has a feature dedicated to secure, on-device processing of sensitive data, called the Private Compute Core. Its initial uses include powering the version of the company’s Smart Reply feature built into its mobile keyboard that can suggest responses to incoming messages.

Apple and Google both say on-device machine learning offers more privacy and snappier apps. Not transmitting personal data cuts the risk of exposure and saves time spent waiting for data to traverse the internet. At the same time, keeping data on devices aligns with the tech giants’ long-term interest in keeping consumers bound into their ecosystems. People that hear their data can be processed more privately might become more willing to agree to share more data.

The companies’ recent promotion of on-device machine learning comes after years of work on technology to constrain the data their clouds can “see.”

In 2014, Google started gathering some data on Chrome browser usage through a technique called differential privacy, which adds noise to harvested data in ways that restrict what those samples reveal about individuals. Apple has used the technique on data gathered from phones to inform emoji and typing predictions and for web browsing data.

More recently, both companies have adopted a technology called federated learning. It allows a cloud-based machine learning system to be updated without scooping in raw data; instead, individual devices process data locally and share only digested updates. As with differential privacy, the companies have discussed using federated learning only in limited cases. Google has used the technique to keep its mobile typing predictions up to date with language trends; Apple has published research on using it to update speech recognition models.

Rachel Cummings, an assistant professor at Columbia who has previously consulted on privacy for Apple, says the rapid shift to do some machine learning on phones has been striking. “It’s incredibly rare to see something going from the first conception to being deployed at scale in so few years,” she says.

That progress has required not just advances in computer science but for companies to take on the practical challenges of processing data on devices owned by consumers. Google has said that its federated learning system only taps users’ devices when they are plugged in, idle, and on a free internet connection. The technique was enabled in part by improvements in the power of mobile processors.

Beefier mobile hardware also contributed to Google’s 2019 announcement that voice recognition for its virtual assistant on Pixel devices would be wholly on-device, free from the crutch of the cloud. Apple’s new on-device voice recognition for Siri, announced at WWDC this month, will use the “neural engine” the company added to its mobile processorsto power up machine learning algorithms.

The technical feats are impressive. It’s debatable how much they will meaningfully change users’ relationship with tech giants.

Presenters at Apple’s WWDC said Siri’s new design was a “major update to privacy” that addressed the risk associated with accidentally transmitting audio to the cloud, saying that was users’ largest privacy concern about voice assistants. Some Siri commands—such as setting timers—can be recognized wholly locally, making for a speedy response. Yet in many cases transcribed commands to Siri—presumably including from accidental recordings—will be sent to Apple servers for software to decode and respond. Siri voice transcription will still be cloud-based for HomePod smart speakers commonly installed in bedrooms and kitchens, where accidental recording can be more concerning.

Google also promotes on-device data processing as a privacy win and has signaled it will expand the practice. The company expects partners such as Samsung that use its Android operating system to adopt the new Privacy Compute Core and use it for features that rely on sensitive data.

Google has also made local analysis of browsing data a feature of its proposal for reinventing online ad targeting, dubbed FLoC and claimed to be more private. Academics and some rival tech companies have said the design is likely to help Google consolidate its dominance of online ads by making targeting more difficult for other companies.

Michael Veale, a lecturer in digital rights at University College London, says on-device data processing can be a good thing but adds that the way tech companies promote it shows they are primarily motivated by a desire to keep people tied into lucrative digital ecosystems.

“Privacy gets confused with keeping data confidential, but it’s also about limiting power,” says Veale. “If you’re a big tech company and manage to reframe privacy as only confidentiality of data, that allows you to continue business as normal and gives you license to operate.”

A Google spokesperson said the company “builds for privacy everywhere computing happens” and that data sent to the Private Compute Core for processing “needs to be tied to user value.” Apple did not respond to a request for comment.

Cummings of Columbia says new privacy techniques and the way companies market them add complexity to the trade-offs of digital life. Over recent years, as machine learning has become more widely deployed, tech companies have steadily expanded the range of data they collect and analyze. There is evidence some consumers misunderstand the privacy protections trumpeted by tech giants.

A forthcoming survey study from Cummings and collaborators at Boston University and the Max Planck Institute showed descriptions of differential privacy drawn from tech companies, media, and academics to 675 Americans. Hearing about the technique made people about twice as likely to report they would be willing to share data. But there was evidence that descriptions of differential privacy’s benefits also encouraged unrealistic expectations. One-fifth of respondents expected their data to be protected against law enforcement searches, something differential privacy does not do. Apple’s and Google’s latest proclamations about on-device data processing may bring new opportunities for misunderstandings.

This story originally appeared on wired.com.

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Amazon joins Apple, Google by reducing its app store cut

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Enlarge / The Amazon Fire HD 8 tablet, which runs Amazon’s Fire OS.

Apparently following the lead of Apple and Google, Amazon has announced that it will take a smaller revenue cut from apps developed by teams earning less than $1 million annually from their apps on the Amazon Appstore. The same applies to developers who are brand-new to the marketplace.

The new program from Amazon, called the Amazon Appstore Small Business Accelerator Program, launches in Q4 of this year, and it will reduce the cut Amazon takes from app revenue, which was previously 30 percent. (Developers making over $1 million annually will continue to pay the original rate.) For some, it’s a slightly worse deal than Apple’s or Google’s, and for others, it’s better.

Amazon’s new indie-friendly rate is 20 percent, in contrast to Apple’s and Google’s 15 percent. Amazon seeks to offset this difference by granting developers 10 percent of their Appstore revenue in the form of a credit for AWS. For certain developers who use AWS, it could mean that Amazon’s effective cut is actually 10 percent, not 15 or 20 percent.

But for some, it amounts to something more like giving the developer a coupon on a purchase of services from Amazon than actually putting more cash in their pockets. It leaves small developers who aren’t spending a bunch of money on Amazon’s services with a worse deal than they’d get on Apple’s or Google’s marketplaces.

As with Apple’s program—but not Google’s—the lower rate applies to developers only if they made $1 million or less in total (in this case, the numbers assessed are those from the previous year). Crossing that threshold will lead developers to pay the older, higher rate on all of their earnings. In contrast, Google always takes a smaller cut of the first million in a given year and then applies the bigger cut to revenues after $1 million without changing the amount it took from the first million.

The Amazon Appstore primarily exists as the app store for Amazon’s Android-based Fire OS software that runs on tablets. It’s also offered as an alternative App Store for users of other Android-based operating systems.

All three companies are facing various forms of regulatory scrutiny, and that scrutiny was likely a factor in Apple’s decision to cut the fees it applies to apps released by small developers on the Apple App Store. Google followed shortly afterward for its Google Play marketplace.

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Microsoft’s Linux repositories were down for 18+ hours

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Enlarge / In 2017, Tux was sad that he had a Microsoft logo on his chest. In 2021, he’s mostly sad that Microsoft’s repositories were down for most of a day.

Jim Salter

Yesterday, packages.microsoft.com—the repository from which Microsoft serves software installers for Linux distributions including CentOS, Debian, Fedora, OpenSUSE, and more—went down hard, and it stayed down for around 18 hours. The outage impacted users trying to install .NET Core, Microsoft Teams, Microsoft SQL Server for Linux (yes, that’s a thing) and more—as well as Azure’s own devops pipelines.

We first became aware of the problem Wednesday evening when we saw 404 errors in the output of apt update on an Ubuntu workstation with Microsoft Teams installed. The outage is somewhat better documented at this .NET Core-issue report on Github, with many users from all around the world sharing their experiences and theories.

The short version is, the entire repository cluster which serves all Linux packages for Microsoft was completely down—issuing a range of HTTP 404 (content not found) and 500 (Internal Server Error) messages for any URL—for roughly 18 hours. Microsoft engineer Rahul Bhandari confirmed the outage roughly five hours after it was initially reported, with a cryptic comment about the infrastructure team “running into some space issues.”

Eighteen hours after the issue was reported, Bhandari reported that the mirrors were once again available—although with temporarily degraded performance, likely due to cold caches. In this update, Bhandari said that the original cause of the outage was “a regression in [apt repositories] during some feature migration work that resulted in those packages becoming unavailable on the mirrors.”

We’re still waiting for a comprehensive incident report, since Bhandari’s status updates provide clues but no real explanations. The good news is, we can confirm that packages.microsoft.com is indeed up once again, and it is serving packages as it should.

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