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Xbox Publishing Kojima's Physint After PlayStation Cancelled It

Hamza — Fri, 11 Sep 2026 01:33:44 +0000

Originally published at TekMag

Hideo Kojima 's stealth action game Physint, long tied to PlayStation since its 2024 reveal, will now be published by Xbox after Sony quietly moved to cancel the project in mid-June. Kojima Productions spent three months looking for a new partner and found one in Microsoft, whose new Xbox chief Asha Sharma called the signing "a big moment for Xbox." The partnership also extends into film and television, and Kojima said development of Physint "is ongoing and will continue moving forward."

The news broke on the night of September 9, 2026, and it unfolded in a way no one in the industry had planned. Sony's official PlayStation account posted a statement saying it had decided to "step away from collaborating with KOJIMA PRODUCTIONS on its project PHYSINT." Less than a minute later, Xbox CEO Asha Sharma confirmed that Physint would be published by Microsoft. Within ten minutes, Kojima himself explained what had actually happened: Sony had told Kojima Productions in mid-June that it was canceling the game, and the studio had spent the last three months quietly negotiating with publishers until it landed back with Xbox, the company that also handles his other game, the horror project OD.

What happened next matters more than the platform swap itself. This is the second time in under a year that Kojima's projects have landed in Microsoft's stable, and it lands at the worst possible moment for Sony's creator relationships. A decade-defining partnership with one of the most recognizable names in game development just went sideways, and the public statements from both companies show exactly how strained it has become.

Key Takeaways

Physint, the action-espionage game from Kojima Productions, is moving from PlayStation to Xbox as its publisher after Sony cancelled the project in mid-June 2026.
Kojima confirmed the cancellation on his own X account, saying he spent three months searching for a new partner and "found in Xbox a partner with whom we share a common vision."
The new Xbox-Kojima deal also covers film and television, not just games, according to an Xbox Wire statement.
PlayStation's public statement said it "stepped away" from the collaboration, framing the split as a mutual decision while praising Kojima's vision.
Xbox CEO Asha Sharma, who took over from Phil Spencer earlier this year, called the signing "a big moment for Xbox." Physint has no release date and was expected to be at least five to six years away.

What is Physint?

Physint is a working title for the next game from Kojima Productions, Hideo Kojima's studio behind Death Stranding and the Metal Gear series. Sony revealed it at a PlayStation showcase in January 2024, alongside a plan for Kojima to build it on PlayStation hardware, and it was widely described as a spiritual successor to Metal Gear Solid. Kojima calls it "a completely new 'Action Espionage' for the next-generation," and he has emphasized that it is meant to work as both a game and a film at the same time, with visuals and production values aimed at the top of what current technology allows.

Very little has been confirmed about the game's content or release window. At Kojima Productions' 10th anniversary event in September 2025, Kojima said development had just started, that he was working on concepts and casting alone at that stage, and that the technology underneath Physint was more advanced than what powered Death Stranding or OD. He also said the game was likely another five to six years away. Three actors have been confirmed: Charlee Fraser (Furiosa: A Mad Max Saga), Don Lee (Eternals), and Japanese actress Minami Hamabe (Godzilla Minus One), each of whom posted a video message for the announcement. Rumors that Robert Pattinson would star in the game have circulated this week, but they are unconfirmed.

One open technical question is which engine the game will run on. Kojima has said the project will use "cutting-edge technology," and at its 2024 reveal it was tied to Sony's technology stack, which included Guerrilla Games' Decima Engine, a technology that has powered Horizon Zero Dawn and other PlayStation titles. Whether Physint continues on that engine, or shifts to something else now that it is a Microsoft project, is not yet known.

How PlayStation cancelled a Kojima game

According to Kojima's own account, the end came quietly. "In mid-June of this year, we unexpectedly received notice from PlayStation Studios that they would cancel the PHYSINT project," he wrote on X on September 9. He described Physint as an important project to him personally and to Kojima Productions, and said the studio then spent the next three months looking for a new publisher.

Sony's official statement took a very different framing. "After careful consideration, we have made the difficult decision to step away from collaborating with KOJIMA PRODUCTIONS on its project PHYSINT," the PlayStation account posted, adding that it "continue[d] to have great admiration for Hideo Kojima's vision for the game" and that the relationship "remains strong after three decades of industry-leading, genre-defining creative cooperation." The word choice does a lot of work here. "Step away" is not the same as "cancel," and Sony is describing a mutual decision while Kojima describes an outright cancellation of his game. Both statements are real, and neither is retracting the other.

The backstory makes the split harder to ignore. When Sony revealed Physint in January 2024, PlayStation Studios head Hermen Hulst appeared onstage with Kojima and said, "I'm excited to announce that together, as part of the PlayStation and Kojima Productions partnership, Hideo's going to take us on an exciting new adventure, one that I've been encouraging him to reinvent for years." The two walked off set and the camera pulled back to reveal a Columbia Pictures sign, a studio Sony owns, sitting just above the word "Physint." That was the image Sony had built the announcement around, and less than three years later the project is heading to Sony's direct competitor.

Why Xbox signed Kojima

Microsoft confirmed the deal in an Xbox Wire post and through Sharma's own X account, where she wrote: "PHYSINT and OD! Proud to build both with @HIDEO_KOJIMA_EN in Japan. A big moment for XBOX." The statement said the partnership "will also extend beyond games, with XBOX and KOJIMA PRODUCTIONS collaborating across film and television to bring new stories to broader audiences."

That film and television angle connects to a separate, recent move from Microsoft. A Puck report said Microsoft was shopping around a $300 million licensing package to adapt its own game franchises, including The Elder Scrolls and Diablo, into movies and TV. Pairing Kojima, who explicitly wants his games to feel like films, with a publisher actively building out that side of the business makes strategic sense for both sides. For Sharma, the signing is also a signal to the industry. She took over from Phil Spencer earlier this year with a stated goal of resetting Xbox, including bringing back true console exclusives, and signing two of the most anticipated Kojima projects is one of the clearest statements yet about what she intends to do with the studio's creative slate.

The timing also matters for OD, Kojima's other project. OD, described as a horror game that some in the industry told Kojima was "something that no one has ever seen before," is being made with Xbox and is expected to release before Physint. Its existence in Microsoft's portfolio, and its reported survival through Microsoft's broader studio restructuring earlier this year, made it a natural home for Physint when Sony's decision came down. As PC Gamer put it, the move is "unlikely to be entirely smooth," mostly because of the open engine question.

What it means for Kojima

The move back to Xbox is not Kojima's first collaboration with Microsoft. His studio already develops OD with Xbox, and that project survived Microsoft's broader studio restructuring earlier this year. What is different this time is scale: two flagship projects at once, a film and television arm on top of the games, and a new publisher actively trying to rebuild its first-party reputation.

For Sony, the situation is the sharper problem. The PlayStation and Kojima partnership has been one of the most reliable creative associations in the industry for roughly thirty years, spanning Metal Gear Solid, P.T., Death Stranding, and now Physint. Losing a flagship title like this, in the middle of the current console cycle, to a direct competitor is a significant public setback, and Sony's statement, which emphasizes that "our relationship remains strong," is doing heavy lifting to keep that association alive for whatever comes next. IGN's Ryan McCaffrey noted that the last time a platform lost a project of this kind to a rival, Final Fantasy left Nintendo for PlayStation in the original console era. Drawing that parallel is not meant to inflate the moment, but it shows how rare these publisher-level shifts still are.

The broader context is that neither company is operating in a vacuum. Microsoft's own studio restructuring has canceled games and laid off staff, and former PlayStation executives have publicly questioned Xbox's publishing strategy. As we covered when id Software's developers called out Xbox after Microsoft's layoffs reshaped the studio, the platform's creative reputation has been under scrutiny. For Kojima, the deal gives him a partner that is rebuilding that reputation, but it also means betting on the same company that has been cutting studios. The two facts sit side by side.

What we still don't know

There is a gap between the announcement and the game itself. Physint has no release date, no gameplay demonstration, and no confirmed engine. Kojima's "both a game and a movie" description has not been broken down into concrete details, and the casting list so far, three named actors plus a rumored fourth, tells you more about the project's ambitions than its mechanics. The Decima Engine question is the most immediate technical unknown, since it ties directly to whether the game's development environment shifts with its publisher.

The announcement also raises questions about the broader Kojima-to-console relationship. Sony and Microsoft have both been trying to lock in major creators, and the fact that Kojima's two projects both now sit with the same publisher changes how those competition dynamics play out. If you have been tracking the platform wars from the hardware side, the context for why a title moving between consoles matters at all is covered in our piece on GTA 6's extended look and what the reveal signaled about the next console generation and the revival pattern in classic franchise games, both of which sit in the same conversation about how publishers are trying to secure tentpole titles.

Conclusion

Physint is now a Microsoft game. The cancellation was real, Kojima said so directly, and the three-month search for a new partner is the part of the story that tells you how serious the decision was on Sony's side. Xbox gets its first big exclusive signing of the Sharma era, Kojima gets a publisher that is actively building out a film and television arm, and Sony gets a public statement that calls the split a mutual step away. Every party in this story has a version of it. The next real update will come when Kojima shares actual gameplay or a release window, which, by his own estimate, is years out.

Frequently asked questions

Q: Why did PlayStation cancel Physint?

A: Kojima said his studio received notice from PlayStation Studios in mid-June 2026 that the project would be cancelled. Sony's public statement described it instead as a "difficult decision" to "step away from collaborating" with Kojima Productions on the game, and said the relationship "remains strong." The two statements are not in conflict, but they frame the split differently.

Q: Is Physint still coming out?

A: Yes. Kojima said development "is ongoing and will continue moving forward" under Xbox. There is no confirmed release date; last year he said the game was likely five to six years from launch.

Q: Does the Xbox deal change what the game is?

A: Nothing has been announced about gameplay, story, or engine as of the announcement. The open question is whether the game will continue using Sony's Decima Engine or shift to a Microsoft-aligned engine. The deal does extend beyond games into film and television collaborations.

Q: Who is Asha Sharma and why is her involvement significant?

A: Asha Sharma took over Xbox from Phil Spencer earlier in 2026. Her stated priorities have included rebuilding Xbox's reputation around console exclusives. Signing Kojima for Physint is one of the first major publishing moves of her tenure, and she described it as "a big moment for Xbox."

Q: What is the connection between Physint and Metal Gear?

A: Physint is described as a spiritual successor to Metal Gear Solid, Kojima's signature stealth franchise. It is not a new Metal Gear game, but it returns Kojima to the tactical espionage genre he built that series around.

References

Xbox Publishing Kojima's Physint After PlayStation Cancelled It

Hamza — Fri, 11 Sep 2026 01:26:08 +0000

Originally published at https://tekmag.thsite.top/xbox-publishing-kojimas-physint-after-playstation-cancelled-it/

Hideo Kojima's stealth action game Physint, long tied to PlayStation since its 2024 reveal, will now be published by Xbox after Sony quietly moved to cancel the project in mid-June. Kojima Productions spent three months looking for a new partner and found one in Microsoft, whose new Xbox chief Asha Sharma called the signing "a big moment for Xbox." The partnership also extends into film and television, and Kojima said development of Physint "is ongoing and will continue moving forward."

Key Takeaways

Physint, the action-espionage game from Kojima Productions, is moving from PlayStation to Xbox as its publisher after Sony cancelled the project in mid-June 2026.
Kojima confirmed the cancellation on his own X account, saying he spent three months searching for a new partner and "found in Xbox a partner with whom we share a common vision."
The new Xbox-Kojima deal also covers film and television, not just games, according to an Xbox Wire statement.
PlayStation's public statement said it "stepped away" from the collaboration, framing the split as a mutual decision while praising Kojima's vision.
Xbox CEO Asha Sharma, who took over from Phil Spencer earlier this year, called the signing "a big moment for Xbox." Physint has no release date and was expected to be at least five to six years away.

What is Physint?

How PlayStation cancelled a Kojima game

Why Xbox signed Kojima

What it means for Kojima

What we still don't know

Conclusion

Frequently asked questions

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  <h3>Q: Why did PlayStation cancel Physint?</h3>

    <p>A: Kojima said his studio received notice from PlayStation Studios in mid-June 2026 that the project would be cancelled. Sony's public statement described it instead as a "difficult decision" to "step away from collaborating" with Kojima Productions on the game, and said the relationship "remains strong." The two statements are not in conflict, but they frame the split differently.</p>



  <h3>Q: Is Physint still coming out?</h3>

    <p>A: Yes. Kojima said development "is ongoing and will continue moving forward" under Xbox. There is no confirmed release date; last year he said the game was likely five to six years from launch.</p>



  <h3>Q: Does the Xbox deal change what the game is?</h3>

    <p>A: Nothing has been announced about gameplay, story, or engine as of the announcement. The open question is whether the game will continue using Sony's Decima Engine or shift to a Microsoft-aligned engine. The deal does extend beyond games into film and television collaborations.</p>



  <h3>Q: Who is Asha Sharma and why is her involvement significant?</h3>

    <p>A: Asha Sharma took over Xbox from Phil Spencer earlier in 2026. Her stated priorities have included rebuilding Xbox's reputation around console exclusives. Signing Kojima for Physint is one of the first major publishing moves of her tenure, and she described it as "a big moment for Xbox."</p>



  <h3>Q: What is the connection between Physint and Metal Gear?</h3>

    <p>A: Physint is described as a spiritual successor to Metal Gear Solid, Kojima's signature stealth franchise. It is not a new Metal Gear game, but it returns Kojima to the tactical espionage genre he built that series around.</p>

References

Apple iPhone Duo Hands-On Review: Apple's First Foldable Phone Is Finally Here

Hamza — Thu, 10 Sep 2026 05:25:21 +0000

Apple's long-awaited foldable iPhone is finally here. The iPhone Duo, announced at Apple's September 9, 2026 "Surprise and Shine" event, brings a passport-shaped design with a 5.4-inch outer display and 7.6-inch inner foldable panel. Starting at $1,999, it positions Apple squarely in the premium foldable market alongside Samsung and Google — but with one crucial difference: Apple waited until the technology was truly ready.

After years of speculation and rumors, Apple has officially joined the foldable phone revolution. The iPhone Duo represents the company's first foray into the form factor that Samsung has dominated since 2019. Under new CEO John Ternus, Apple has taken a different approach — rather than following Samsung's lead with a square-shaped tablet-phone hybrid, Apple chose a wider, passport-like design that prioritizes media consumption over multitasking.

Design & Display: Passport Shape, Crease-Free Claims

The iPhone Duo adopts a distinctive passport-shaped form factor when closed, with a 5.4-inch asymmetric outer display that features pointy corners on the hinge side and rounded ones on the other. Apple designed this asymmetry to "invite you to open" the phone.

When unfolded, the device reveals a 7.6-inch Super Retina XDR foldable panel with a 1:1.4 aspect ratio — slightly wider than a classic 4:3 monitor. Both screens support ProMotion, always-on displays, and up to 3,000 nits of peak brightness.

The standout feature is Apple's claim of a creaseless display. Using multiple layers of flexible glass, a titanium base, and a custom polymer top layer with nano-texture finish, Apple says the Duo eliminates the visible fold line that plagues most competitors. During hands-on sessions at The Verge, reviewers confirmed they "couldn't spot a display crease on the demo units" and described the fold as "almost impossible to find with my finger."

The titanium frame includes internal ceramic fiber reinforcement for durability, with over 100 hinge components providing "custom variable torque" for smooth opening and closing. Magnets in the frame ensure the device snaps shut satisfyingly. The phone carries an IP68 rating for water and dust resistance.

Specifications & Performance

Processor: A20 Pro chip (same as iPhone 18 Pro lineup) with C2 modem for faster uploads

Storage Options: 256GB, 512GB, 2TB

Connectivity: eSIM-only (no physical SIM tray), Wi-Fi 7, Bluetooth 5.4

Authentication: Touch ID integrated into the side button. No Face ID. Users can also unlock the device using their Apple Watch.

Camera System:

48MP fusion main sensor (lacks adjustable aperture of iPhone 18 Pro)
48MP ultrawide with 2x optical zoom
No telephoto lens (a notable step down from iPhone 18 Pro's triple-camera setup)
12MP selfie camera in the outer display
Under-display camera on the inner screen (only visible when in use)

Battery Life:

31 hours video playback (inner display)
44 hours video playback (outer display)
24 hours mixed usage

Colors: Two launch colors

Apple Pencil: Support announced for "later this year" (2026)

iOS 27 Foldable Features

iOS 27 has been extensively adapted for the foldable form factor:

Adaptive UI: Home screen dock and status bar move to the right edge when unfolded, preserving vertical space for content
Split-screen multitasking: Apps can run side-by-side in a more intuitive interface than previous foldable attempts
App pairing: Apps can be paired and managed as single multitasking units
Hinge-adaptive features: The UI responds to the phone's fold position, including a split keyboard layout when the phone is partially folded
Video pinning: Users can pin videos on top while using other apps in the bottom half when holding the phone vertically
Half-folded use: The phone can be used at various angles, including resting on a table

Pricing & Availability

256GB: $1,999
512GB: $2,199
2TB: $3,199
Preorders: October 16, 2026
Shipping: October 23, 2026

Comparison vs Competitors

Feature	iPhone Duo	Galaxy Z Fold 8	Pixel 11 Pro Fold
Price (256GB)	$1,999	$1,900	~$1,899
Inner Display	7.6″ Super Retina XDR	7.6″ Dynamic AMOLED 2X	~7.5″
Outer Display	5.4″	5.5″	~6.3″
Chip	A20 Pro	Snapdragon 8 Elite Gen 5	Tensor G4
Cameras	48MP dual (no telephoto)	50MP triple (with telephoto)	Triple camera setup
Apple Pencil	Coming 2026	No	No
Water Resistance	IP68	IP68	IP68
Biometrics	Touch ID + Apple Watch	Ultrasonic fingerprint	Under-display fingerprint

For more on competing devices, see our Samsung Galaxy Z Fold coverage.

The iPhone Duo represents Apple's entry into the foldable market with a design philosophy that prioritizes practicality over novelty. By waiting years to enter the space, Apple has delivered a device that addresses many of the complaints users have had with previous foldables — the visible crease, awkward aspect ratios, and poor app optimization.

At $1,999, the Duo is competitively priced against Samsung's Galaxy Z Fold 8 and Google's Pixel 11 Pro Fold. However, the camera compromise (no telephoto) and the removal of Face ID may give some users pause.

For iPhone users who want the largest screen Apple has ever put in a phone, the Duo is a compelling upgrade. The iOS 27 foldable adaptations show Apple's attention to detail, and the creaseless display claim appears legitimate based on hands-on testing.

Key Takeaways

Apple 's first foldable phone arrives at $1,999 — a passport-shaped device with 5.4-inch outer and 7.6-inch inner displays
A20 Pro chip powers the Duo with eSIM-only design, Touch ID (no Face ID), and IP68 water resistance
Creaseless display claims verified during hands-on — matte nano-texture finish reduces glare and hides the fold
iOS 27 foldable adaptations include split-screen multitasking, hinge-adaptive UI, and vertical app pairing
Camera compromise — dual 48MP setup (main + ultrawide with 2x optical zoom), no telephoto lens
Battery life: 31 hours video (inner), 44 hours (outer), 24 hours mixed use
Apple Pencil support coming later this year
Preorders: October 16, shipping: October 23

Frequently Asked Questions

Q: Does the iPhone Duo have Face ID?

A: No, the iPhone Duo uses Touch ID on the side button instead of Face ID. Users can also unlock the device using their Apple Watch.

Q: How does the iPhone Duo compare to Samsung 's Galaxy Z Fold 8?

A: The Duo is $100 more expensive but offers a creaseless display claim, Apple Pencil support, and iOS 27 foldable adaptations. The Z Fold 8 has a telephoto camera and Android's more mature foldable app ecosystem.

Q: Is the iPhone Duo worth the $2,000 price tag?

A: For iPhone users already invested in the Apple ecosystem who want the largest possible screen, yes. The build quality, display technology, and iOS integration justify the premium for many users.

Q: When will Apple Pencil support arrive?

A: Apple announced Pencil support is coming "later this year" (2026). Specific timing was not disclosed.

Q: Can I use both screens simultaneously?

A: Yes, iOS 27 supports split-screen multitasking, video pinning, and app pairing across both displays. The UI adapts based on how you're holding and folding the phone.

Q: Does the iPhone Duo have a physical SIM card slot?

A: No, the Duo is eSIM-only, following the same approach as the iPhone Air released last year.

References

Apple's long-rumored foldable becomes reality with the $2,000 iPhone Duo — Ars Technica
The iPhone Duo is Apple's first foldable — The Verge
Hands-on with the foldable iPhone Duo — The Verge
Apple unveils its first foldable, the iPhone Duo — TechCrunch
Samsung unveils new Galaxy Z8 foldables — Ars Technica
Apple Newsroom: iPhone Duo Announcement — Apple

OneDrive vs Google Photos vs iCloud: Picking the Right App to Back Up Your Phone

Hamza — Wed, 09 Sep 2026 21:49:16 +0000

Originally published at https://tekmag.thsite.top/onedrive-vs-google-photos-vs-icloud-picking-the-right-app-to-back-up-your-phone/

A phone cloud backup app is a service that copies your photos, videos, and device data to a remote server so you can restore them after a crash, a theft, or a move to a new phone. The three most-used options are OneDrive, Google Photos, and iCloud, and they do not do the same thing. One is built for photos, one for the whole Apple device, and one for files and cross-platform access. Choosing between them comes down to which phone you carry and what you need to survive a data loss.

The stakes are concrete. A cracked phone with no backup means losing years of photos, and a phone switch with no backup means starting a new device with no messages, contacts, or app data. All three services offer a free tier, and the differences show up in three places: what counts toward your free storage, what the app actually backs up, and whether you can restore the whole device or just your files. This comparison walks through those three points for OneDrive, Google Photos, and iCloud, with current pricing and storage limits.

None of the three is wrong. A Pixel user and an iPhone user will land on different answers, and two Android owners can disagree too. The point is to make the trade-offs explicit so you match the service to your setup instead of defaulting to whatever came preinstalled.

Key Takeaways

iCloud is the only one of the three that backs up the whole device: photos, messages, app data, call history, and settings in a single restore.
Google Photos uploads count toward a shared 15 GB quota since a June 2021 policy change, so it is no longer unlimited free storage.
OneDrive and iCloud both start at 5 GB free; Google gives 15 GB shared across Drive, Gmail, and Photos.
Paid tiers start at $0.99/month for 50 GB of iCloud+ and $1.99/month for 100 GB of Google One; OneDrive ships 1 TB with a Microsoft 365 subscription.
Android phones back up photos automatically through Google Photos; on iOS you must switch on iCloud or OneDrive by hand.

Quick verdict: match the service to the phone

Pick the service that fits the device, not the storage number. If you run an iPhone, iCloud is the default because it is the only option that restores the entire device. If you run Android, Google Photos is the natural fit: auto-backup is already on, and the app is photo-focused. If you want the same library on a Windows PC and a phone, or you live in Microsoft 365, OneDrive is the cross-platform choice. In short, the three trade off breadth (iCloud), photos plus the Android default (Google Photos), and file sync plus Windows integration (OneDrive).

Google Photos: the photo-first backup

Google Photos is a photo and video backup that uploads your camera roll automatically and then organises it with labels and search. On most Android phones that backup is switched on by default, while on an iPhone you have to turn it on under the app's settings. You can store originals or a compressed "quality-saving" version, and it works over Wi-Fi or cellular. Because Google ended the old free-storage reset in June 2021, uploads now count against the shared 15 GB limit that it splits with Drive and Gmail, per Google's storage policy page. It does not back up contacts, messages, app data, or the full device state. Where it shines is cross-device photo search and shared albums, so if your family still swaps photos through group texts, moving that into a shared album keeps one synced library instead of scattered copies across inboxes.

iCloud: full-device backup for Apple users

iCloud is Apple's built-in backup that can store the entire device, not just media, which no other service in this comparison matches. On an iPhone it is on by default when you are signed in, and it backs up the phone when it is on Wi-Fi, locked, and charging. That single backup captures photos, iMessage, app documents, call history, and device settings. Real-time photo sync runs separately through iCloud Photos. The 5 GB free tier is tight, and Apple's iCloud+ plan page starts paid storage at 50 GB for $0.99/month. If the free space is full, Apple's guide to managing iCloud storage shows where the space is going. On top of that, an optional Advanced Data Protection setting switches selected data to end-to-end encryption.

OneDrive: files first, cross-platform

OneDrive is Microsoft's file-sync service that treats your photos and documents as a cloud folder rather than a phone backup. The free tier is 5 GB, and the app on both Android and iOS is OneDrive on Google Play and the App Store. Automatic camera upload must be switched on manually in the app, it backs up photos and videos only, and it does not touch contacts, messages, app data, or full-device restore. Its strength is the Windows side, where it syncs straight into the file system. That same integration is what fills drives: OneDrive's file-system sync mirrors several gigabytes onto your C: drive, and freeing up that Windows 11 storage is often enough to skip a paid tier. For the full list of paid options, Microsoft's OneDrive and Microsoft 365 plan comparison puts 1 TB on a Microsoft 365 Personal subscription at $69.99/year.

Free tiers, auto-backup behavior, and pricing

Here is the free-storage split: Google gives 15 GB shared across Drive, Gmail, and Photos, while both iCloud and OneDrive start at 5 GB. That difference matters because a few months of video can eat 5 GB fast. Auto-backup behavior also differs. Google Photos is on by default on Android and must be enabled on iOS. iCloud is on by default for a signed-in iPhone and runs only when the phone is on Wi-Fi, locked, and charging. OneDrive requires you to turn on camera upload in the app, and it offers a Wi-Fi-only option. When you outgrow the free tier, the paid steps are:

Service	Free	Cheapest paid tier	Pricier paid tier
Google One	15 GB shared	$1.99/mo for 100 GB	$9.99/mo for 2 TB
iCloud+	5 GB	$0.99/mo for 50 GB	$9.99/mo for 2 TB
OneDrive / Microsoft 365	5 GB	$69.99/yr for 1 TB	$99.99/yr for 6 TB (6 users)

iCloud+ is the cheapest entry point at $0.99/month, and its family plan shares 200 GB. Google One is the cheapest for 100 GB. OneDrive's value is tied to Microsoft 365, so it makes the most sense if you already pay for Office. The 2 TB top tier on Google and Apple costs the same $9.99/month, while Microsoft's 6 TB family plan is the largest single allocation at $99.99/year.

Which should you choose?

Start from your phone. On an iPhone, make iCloud your primary backup because it is the only one that restores the whole device, and add a copy of your photos to Google Photos so your only copies are not on one vendor. On Android, Google Photos is the default, and add OneDrive if you need the files on a Windows PC or want a Microsoft 365 library. Running two services is the practical safety move: your photos survive even if one provider has an outage or a pricing change. A common combination is Google Photos for the photo library plus OneDrive for documents and app files, which covers media and files without double-storing the same originals.

Getting more from your free tier

Three habits stretch the free space. Turn on Wi-Fi-only backup so cellular does not drain the limit, choose the quality-saving setting for photos so compressed copies cost less, and on an iPhone use the storage-optimising option to keep originals in the cloud while freeing the phone. Power users who manage large files or want sync without the desktop client reach for rclone, a terminal tool that syncs cloud files the same way the official desktop apps do; it is not needed for a normal phone backup, but it removes the desktop-client dependency. If you already run two of these services, the pair that fits your phone is the one worth keeping. Tell us in the comments which combination you use and why it suits your setup.

Conclusion

There is no single winner. iCloud wins for iPhone owners who want a full device restore, Google Photos wins for Android and photo-heavy users, and OneDrive wins for people who live in Windows and Office. Match the service to the phone you carry, run two for redundancy, and you will not lose the photos you care about.

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Frequently asked questions

  <h3>Q: Is Google Photos really free and unlimited?</h3>

    <p>A: No. Since the June 2021 policy change, Google Photos uploads count against the shared 15&nbsp;GB quota that it splits with Drive and Gmail, so free storage is capped rather than unlimited.</p>



  <h3>Q: Does iCloud back up more than Google Photos and OneDrive?</h3>

    <p>A: Yes. iCloud is the only one of the three that backs up the whole device, including messages, app data, call history, and settings. Google Photos and OneDrive back up photos and videos only.</p>



  <h3>Q: Can I back up an Android phone to iCloud or OneDrive?</h3>

    <p>A: Yes for OneDrive, which supports photos and videos on Android. iCloud does not back up Android devices. Google Photos is the Android-native choice and works on both platforms.</p>



  <h3>Q: Which service restores my phone after I switch to a new phone?</h3>

    <p>A: Only iCloud restores a full device setup on a new Apple phone. Google Photos and OneDrive restore your photos and files, but not messages, app data, or settings.</p>



  <h3>Q: How much do the paid plans cost?</h3>

    <p>A: iCloud+ starts at $0.99/month for 50&nbsp;GB and $2.99/month for 200&nbsp;GB. Google One starts at $1.99/month for 100&nbsp;GB and $2.99/month for 200&nbsp;GB. OneDrive's 1&nbsp;TB comes with a Microsoft 365 Personal subscription at $69.99/year.</p>

References

Google One: Cloud storage and email pricing - Google
iCloud+ plans and pricing - Apple
iCloud storage options and how to manage it - Apple Support
OneDrive: Save, Sync, Share (Google Play)
Compare OneDrive and Microsoft 365 plans - Microsoft
OneDrive overview and history - Wikipedia

Android September 2026 Drop — New Features for Gemini, Motion Assist, and Accessibility

Hamza — Wed, 09 Sep 2026 05:28:08 +0000

Android September 2026 Drop — New Features for Gemini, Motion Assist, and Accessibility

Google's September 2026 Android Drop introduces five practical features: a Remembered list in Find Hub for tracking items without tags, Motion Assist to reduce motion sickness on Android 17+, Guided Vision in Gemini Live for accessibility, Google Keep integration in Messages, and Chat Themes for personalization.

The rollout began September 1, 2026, and is gradually reaching Pixel devices and compatible Android phones.

Key Takeaways

Find Hub Remembered List: Use Gemini to remember where you placed items without a tracker tag. Available on Android 16+.
Motion Assist: Reduces motion sickness with a subtle bubble overlay on Android 17+ devices.
Guided Vision in Gemini Live: Describes surroundings verbally for blind and low-vision users.
Google Keep in Messages: Create and edit shared lists directly in Google Messages.
Chat Themes: Personalize conversations with custom backgrounds and colors.

Read the full article with all details: https://tekmag.thsite.top/android-september-2026-drop-new-features-for-gemini-motion-assist-and-accessibility/

What's new in Python 3.15

Hamza — Tue, 08 Sep 2026 23:02:59 +0000

Originally published at https://tekmag.thsite.top/whats-new-in-python-3-15/

Python 3.15.0 lands on October 1, 2026, headlined by lazy imports, a hashable frozendict built-in, and a JIT compiler that now runs about 8 percent faster on x86-64 Linux. The Release cycle ships alongside security patches for the 3.10, 3.11, and 3.12 branches that most production systems still run, and the feature branch for Python 3.16 has already accepted PEP 828, which closes a nine-year language gap by letting async generators use yield from.

Python's release train runs on a fixed clock. PEP 790, the 3.15 release schedule, pins the final 3.15.0 release to October 1, 2026, after RC2 went out on September 1. The ABI has been frozen since RC1 on August 4, so the feature set is locked and the remaining work is polish. A few weeks after 3.15 ships, development on 3.16 continues in parallel, and its most anticipated PEP was accepted on August 3, 2026. In other words, there is a lot to check before the October deadline, and a clear look at what the next version brings.

Our angle in this piece: the 3.15 release itself, the security releases that already shipped, and the 3.16 work in flight. All claims below trace to the official PEPs, the release-manager announcements on blog.python.org, and security advisories from the upstream project and SUSE. Sources disagree on one performance number, and we flag where they do.

Key Takeaways

Python 3.15.0 final releases October 1, 2026; RC2 is the last candidate, out September 1.
PEP 828, accepted August 3, 2026, brings yield from to async generators in Python 3.16, the version that ships in October 2027.
The experimental JIT now delivers an 8 to 9 percent geometric-mean speedup on x86-64 Linux, up from 5 to 6 percent in March. It stays off by default.
The August security release (3.12.14, 3.11.16, 3.10.21) fixes two notable CVEs, including a CVSS 8.6 stack overflow in XML parsing.
PEP 686 makes UTF-8 the default encoding for open(), a breaking change that mainly affects Windows.
Only about 9.4 percent of the top 360 PyPI packages declared 3.15 support as of RC1; pandas, boto3, pydantic, and scipy were not ready.

What's new in Python 3.15

Python 3.15 is the first release of the PEP 602 annual cycle, and it lands several PEPs that have been in development for years. The ones with visible user impact:

Feature	PEP	What it does
Lazy imports	PEP 810	`lazy import stdlib` defers loading standard-library modules until they are actually used. Startup-time tests report up to 70 percent faster startup for CLI tools.
frozendict	PEP 814	A hashable, immutable dict built-in. After roughly a decade of debate, a stdlib type finally exists for set-of-keys and immutable-mapping use cases.
UTF-8 by default	PEP 686	`open()` without an `encoding` argument defaults to UTF-8 on every platform, instead of the locale encoding.
Unpacking in comprehensions	PEP 798	Nested unpacking inside comprehension for-clauses, e.g. `[x for i, *rest in items]`, which previously raised `SyntaxError`.
Sentinel type	PEP 661	A `sentinel()` factory for unique singleton marker values, replacing the `object()` idiom.
Profiling package	PEP 799	A dedicated `profiler` package plus the Tachyon high-frequency sampler.
Frame pointers	PEP 831	Enabled by default, improving system-level observability of CPython processes.

Real-world ETL benchmarks published alongside the RC2 report show about 7 percent throughput improvement with zero code changes, which compounds with the JIT work below. The RC2 coverage at byteiota collected much of the feature list we use here, but for release-critical claims we check against the PEPs and the official blog.

PEP 828: `yield from` in async generators

Python 3.16's headline feature, PEP 828 by Peter Bierma, accepted August 3, 2026, lets async generator functions delegate to subgenerators with the plain yield from syntax. This closes a gap that has been in the language since async generators arrived in PEP 525 in 2017. At the time, CPython maintainers deferred yield from support, citing a serious redesign of the generator implementation. Bierma's reference implementation, included with the PEP, argues that complexity is no longer a concern given nine years of generator-implementation work.

The PEP also adds a .value attribute to StopAsyncIteration so delegated async generators can return a value, mirroring how StopIteration.value works with sync generators. The syntax decision matters: the PEP rejected async yield from and settled on plain yield from for symmetry with synchronous code, so the same delegation pattern reads identically in both worlds.

async def agenerator():
    yield 1
    yield 2
    return 3

async def main():
    result = yield from agenerator()
    assert result == 3

Until 3.16 ships, the workaround is a for/async for loop that manually yields each item from the subgenerator, which works but hides intent and makes asend(), athrow(), and aclose() delegation awkward. Web frameworks that build on async generators have shipped hand-rolled delegation workarounds that PEP 828 will eventually make redundant.

JIT improvements and performance

CPython's JIT compiler is still experimental and off by default, and that does not change in 3.15. What did change: a tracing-frontend rewrite pushed the speedup from 5 to 6 percent in March to 8 to 9 percent on x86-64 Linux as measured in the latest release cycle. On macOS AArch64 the JIT is faster than the tail-call interpreter by 12 to 13 percent. We attribute the 8 to 9 percent figure to the Real Python September roundup; the March JIT status post on blog.python.org reports 5 to 6 percent on x86-64 Linux. Both are correct for their respective measurement dates, and the direction is the same: the JIT is closing the gap to a practical win. You will need the right build flags to see the improvement, and the numbers are geometric means on benchmark suites, not wall-clock times for your workloads.

If runtime compilation is the area you are tracking, Craton Bolt's SQL-to-PTX compiler applies the same idea in a database context, which is useful context for why compiler work is spreading across the language and system stack.

Security patches for the older branches

The security team shipped Python 3.12.14, 3.11.16, and 3.10.21 on August 12, 2026. The two CVEs to know about first:

CVE-2026-4224 (CVSS 8.6) — a C stack overflow when xml/Expat parses deeply nested DTD content models. It is a denial-of-service vector for anything that ingests untrusted XML, and the SUSE advisory for the Python update rates it 8.6.
CVE-2026-3644 (CVSS 6.5) — incomplete control-character validation in http.cookies, enabling HTTP header injection through crafted cookies.

The release also fixes a SourcelessFileLoader bypass for .pyc files (CVE-2026-2297), a tarfile header parsing issue (CVE-2025-13462, CVSS 2.0), and a path-traversal flaw in resource-argument validation (CVE-2026-3479). On top of the CVE fixes, tarfile's extraction filter now applies to symlink targets, webbrowser.open() rejects URLs that start with a dash, and quadratic-backtracking issues were fixed in csv.Sniffer, html.parser, and configparser.

The full changelog is in the Python Insider security release announcement. For teams already on those branches, this is a patch-now release: the XML DoS is the highest-severity item, and it applies to any service that parses XML from a network source. It's the same shape of problem we saw in Microsoft's August Patch Tuesday, where the zero-day on the active exploitation list was the item that mattered most.

Breaking changes and deprecations

Python does not remove working code casually, but every feature release carries removals. The ones that will surface in code review:

UTF-8 default encoding (PEP 686). open() without encoding= now defaults to UTF-8 on Windows, instead of the system locale. Windows code that relies on locale-specific encoding for file I/O should break. This is the single most likely source of post-upgrade failures.
datetime.utcnow() removed. Use datetime.now(timezone.utc) instead. The deprecation started in 3.12; it is gone now.
__cached__ no longer set on modules. Use __spec__.cached.
AST node constructors raise TypeError on invalid arguments, where 3.13 only emitted a DeprecationWarning.
strptime() with %d and no year now raises ValueError.
ctypes.SetPointerType() removed, deprecated since 3.13.

None of these affect code that pinned its target version and ran a modern test suite. They do affect code that leaned on locale-specific behavior or legacy APIs. Running your suite against 3.15 now will surface the failures ahead of the October 1 deadline, and the byteiota RC2 post lists the same set.

Migration guide for Python 3.15

If you are on 3.12 or 3.13 today, the path to 3.15 is short but check these in order:

1. Patch your current branch first. The August security release is independent of the 3.15 upgrade. CVE-2026-4224 affects the XML parsing code in all the supported 3.10/3.11/3.12 point releases, so that is a patch-now item regardless of your upgrade plans. If you are on 3.10, plan a skip to 3.11, 3.12, or 3.13 instead of holding on; 3.10 is close to end of life.

2. Run the suite against RC2 now. RC2 (September 1) is ABI-frozen, so what you test now is what ships on October 1. The failures you see are the migration work you have to do.

3. Grep for the removals. datetime.utcnow, __cached__, SetPointerType, and encoding= omissions on file opens are all one-line greps. The UTF-8 default is the one that will surface most in Windows desktop or batch-processing environments.

4. Check your dependency pins. As of RC1, only about 9.4 percent of the top 360 PyPI packages declared 3.15 support. numpy, requests, pytest, black, mypy, and sqlalchemy were ready. pandas, boto3, cryptography, pydantic, scipy, Flask, and click were not. If your project depends on any of the second list, you have two choices: pin to 3.14 until the library ships, or patch the specific test failure locally. The number will move fast; the byteiota RC1 coverage is a good baseline.

5. Treat the JIT as opt-in. You do not need it to get value from 3.15. The 8 to 9 percent is real but it requires enabling an experimental feature. Default off is the right choice for most production workloads, and the experimental status means the performance profile can still shift between 3.15 and 3.16.

For teams that ship language tooling on a regular cycle, Microsoft's TypeScript 7.0 native Go compiler is the closest parallel move, where a major release trades a mature codebase for a cleaner, faster implementation. The Python release cycle is more conservative, and 3.15 is the release where that conservatism pays off in feature density.

Conclusion

October 1 is the date to plan around: patch the August security release now, run your suite on RC2, and check the two dependencies that are most likely to block you. PEP 828 is a 3.16 feature, not a 3.15 one, so the async-generator work can wait a year. Subscribe to TekMag for follow-up coverage on the 3.15 final release day.

Frequently asked questions

  <h3>Q: Should I upgrade to Python 3.15 right after release?</h3>

    <p>Only if your test suite passes cleanly on RC2 and your dependencies declare 3.15 support. If any of your core libraries were not ready as of RC1, hold on 3.14 and re-check in a few weeks. The release cadence means you are not behind if you skip a version.</p>



  <h3>Q: Do I get the 8 percent JIT speedup in 3.15 by default?</h3>

    <p>No. The JIT is still off by default and marked experimental. You need a build or startup flag to enable it, and the 8 to 9 percent is a geometric mean on benchmark suites, not a guarantee for your workload. For most production code, treat it as opt-in.</p>



  <h3>Q: Which security release should I patch to first?</h3>

    <p>The August 12 batch: 3.12.14, 3.11.16, or 3.10.21 depending on your branch. The highest-severity item is CVE-2026-4224 (CVSS 8.6), a stack overflow in XML parsing that is a denial-of-service risk for any service ingesting untrusted XML. The 3.10 branch is also close to end of life, so plan a move to 3.11 or 3.12 on top of the patch.</p>



  <h3>Q: What is the migration checklist for 3.15?</h3>

    <p>Patch your current branch for the August security release, run your suite against RC2 now, grep for <code>datetime.utcnow</code> and missing <code>encoding=</code> arguments on file opens, and verify that your dependency pins declare 3.15 support. The UTF-8 default on Windows is the most likely source of failures.</p>



  <h3>Q: When will <code>yield from</code> in async generators actually ship?</h3>

    <p>PEP 828 is accepted for Python 3.16, which first releases in October 2027 under the PEP 602 12-month cycle. The PEP was resolved on August 3, 2026, so the implementation work is already underway on the feature branch. You do not need to rush for this one.</p>

References

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SnapEdit for Developers: 40+ AI Image and Video Models via a Single API Key

Hamza — Tue, 08 Sep 2026 13:39:05 +0000

Originally published at https://tekmag.thsite.top/snapedit-for-developers-40-ai-image-and-video-models-via-a-single-api-key/

SnapEdit for Developers launched today with 40+ AI image and video models behind a single API key, aiming to cut integration costs by up to 70% compared to assembling equivalent capabilities from multiple vendors.

Developers can now access background removal, object detection, photo enhancement, text-to-image generation, and video processing through one unified platform from SilverAI, a Hanoi-based startup that has processed over 300 million images for its 20 million consumer users.

Key Takeaways

SnapEdit for Developers launched September 8, 2026 by SilverAI, a Hanoi-based startup founded in 2021
Unified API provides access to 40+ production-ready AI models covering image editing, generation, enhancement, and video processing
Pricing starts at $0.001 per operation — up to 70% cheaper than assembling equivalent capabilities from multiple vendors
Free trial offers 1,000 web credits + 1,000 API credits with no credit card required
REST API with OpenAI-compatible format, npm SDK available (snapedit-api)

What Is SnapEdit for Developers?

SnapEdit for Developers is a unified AI API platform that gives software teams access to more than 40 production-ready models through a single API key. Launched today by SilverAI, a startup headquartered in Hanoi, Vietnam, the platform aims to simplify AI-powered image and video workflows that previously required integrating multiple specialized services.

The platform covers the full lifecycle of AI media operations: removal and cleanup tasks like background, object, text, wire, and watermark removal; enhancement and restoration including upscaling (up to 8x), sharpening, relighting, denoising, and photo restoration; generation powered by Flux, Qwen Image, and Z-Image Turbo models; and specialized capabilities like virtual try-on, AI headshots, avatar generation, and object detection across 353 classes. Video capabilities include enhancement, background removal, talking head APIs, and image-to-video conversion.

Why Developers Are Taking Notice

The API space for AI image processing has historically been fragmented. Teams building applications around image editing or generation typically needed to integrate three to five separate services — one for background removal, another for object removal, yet another for upscaling, and so on. This approach inflated costs, added integration complexity, and created maintenance overhead. Many developers have turned to AI-powered solutions to streamline their workflows, but the fragmentation problem persists across vendors.

SnapEdit’s value proposition centers on consolidation. According to the company, developers can achieve up to 70% cost savings compared to assembling equivalent capabilities from multiple vendors. With a single API key, teams gain access to the entire model catalog, eliminating the need to manage multiple accounts, credentials, and integration points. This mirrors the trend seen with unified API platforms that are reshaping how developers approach AI integration.

The platform’s positioning targets the mid-market segment. While competitors like Remove.bg (now part of Kaleido) and Photoroom API command higher price points — often starting at $0.05 to $0.10 per operation — SnapEdit undercuts the market with prices ranging from $0.001 to $0.004 per operation depending on the task.

Pricing and Free Trial

SnapEdit uses a pay-as-you-go pricing model with no monthly commitments. The publicly listed rates demonstrate aggressive pricing across the board:

Detection models: $0.002 per run
Remove Background: $0.001 to $0.004 per image
Remove Object: $0.0019 per image
Enhancement: $0.0034 per image
AI Portrait: $0.0113 per image
Generation: $0.0037 per image

Volume discounts are available for customers processing millions of images. The company also offers a business free trial consisting of 1,000 web credits plus 1,000 API credits, available without a credit card through an invitation-based signup process.

Technical Specifications

The SnapEdit API follows REST conventions with an OpenAI-compatible request format, making migration straightforward for teams already familiar with modern AI API patterns. The platform enforces a rate limit of 60 requests per minute per API key, which the company states is sufficient for most development and production workloads.

Performance is another key differentiator. Most image editing requests complete in under five seconds, while generation tasks finish in under 20 seconds. The infrastructure runs across Google Cloud, AWS, ByteDance cloud, and NVIDIA GPU clusters, providing the company with its advertised 99.99% uptime reliability target.

For JavaScript developers, an official npm package named snapedit-api is available, providing typed wrappers around the REST endpoints. Teams building in other languages can use the raw REST API directly.

Who Is Behind SnapEdit?

SilverAI, the company behind SnapEdit, was founded around 2021-2022 in Hanoi, Vietnam. CEO Nam Le co-founded the startup, which has grown to serve over 20 million consumer users and process more than 300 million images. The SnapEdit mobile application holds a 4.8 out of 5 rating on the App Store, indicating strong user satisfaction in the consumer segment.

The company appears to be bootstrapped, with no public record of venture funding as of 2026. This lean funding structure may explain the aggressive pricing strategy — rather than competing on brand recognition or enterprise features, SnapEdit is competing directly on unit economics.

Competitive Landscape

The AI image API market is crowded but fragmented. Remove.bg (Kaleido) remains the established player, with pricing ranging from $0.10 to $20 per month depending on volume. Photoroom API occupies the premium tier, while Picsart API targets a similar mid-market segment at comparable price points.

Consumer-focused tools like Cleanup.pictures and emerging players like LightX occupy adjacent spaces but don’t offer the same breadth of API capabilities. SnapEdit’s differentiation lies in combining breadth of model coverage with developer-friendly pricing and a single integration point.

Use Cases

The unified API model opens several compelling use cases for developers:

E-commerce platforms can offer automated product photo enhancement, background removal, and virtual try-on without maintaining multiple vendor integrations
Social media applications can provide real-time photo editing features including object removal, enhancement, and style transfer
Marketing automation tools can batch-process images for A/B testing, ad creative generation, and brand asset management
Content creation platforms can offer AI-powered editing features to non-technical users through simple API calls
Video production workflows can leverage the platform’s video enhancement and background removal capabilities for automated post-production

Frequently Asked Questions

Is SnapEdit for Developers still in beta?

No. SnapEdit for Developers launched publicly on September 8, 2026, with production-ready API endpoints, documented SDKs, and public pricing. The platform is actively serving the company’s existing 20 million consumer users as well as early API adopters.

Do I need a credit card to start?

No. The business free trial provides 1,000 web credits and 1,000 API credits with no credit card required. Access is invitation-based and can be requested via email signup on the developer portal.

How does SnapEdit compare to Remove.bg?

SnapEdit offers significantly broader capabilities — over 40 models covering removal, enhancement, generation, detection, and video — compared to Remove.bg’s primary focus on background removal. Pricing is also substantially lower at $0.001-0.004 per operation versus Remove.bg’s volume-based subscription model.

What programming languages are supported?

The REST API works with any language capable of making HTTP requests. An official npm package (snapedit-api) provides typed wrappers for JavaScript and TypeScript developers. Python, Ruby, Go, and other language clients can be built directly from the OpenAPI specification.

Are there rate limits I should be aware of?

The platform enforces a rate limit of 60 requests per minute per API key. For high-volume production workloads, the company offers volume discounts and can discuss custom rate limits on a case-by-case basis.

Conclusion

SnapEdit for Developers represents a notable entry in the increasingly competitive AI API market. By consolidating 40+ models under a single key with aggressive pricing, SilverAI is targeting the practical pain point that every development team faces: managing too many vendor integrations for too many capabilities.

The company’s track record with 20 million consumer users and 300 million processed images suggests they understand their domain. Whether this developer platform can sustain the pressure from well-funded competitors remains to be seen, but the current value proposition — breadth, speed, and price — is difficult to ignore for teams looking to add AI image capabilities without the integration overhead.

For developers evaluating AI image APIs, SnapEdit warrants a close look, especially given the frictionless free trial that requires no credit card commitment.

References

SnapEdit Developer Portal — Official API documentation and model catalog
OpenPR Press Release — Official launch announcement
Tasarim.ai Coverage — Industry analysis of SnapEdit launch
SnapEdit API Comparison Blog — Competitive pricing breakdown
snapedit-api npm Package — Official SDK with usage examples

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Meta Muse Spark 1.3 — Frontier Coding Model with 20% Fewer Tool Calls

Hamza — Tue, 08 Sep 2026 09:50:08 +0000

Originally published at https://tekmag.thsite.top/meta-muse-spark-13-frontier-coding-model-with-20-fewer-tool-calls/

Muse Spark 1.3 is Meta's proprietary coding model released September 2, 2026 that reaches frontier scores on coding benchmarks while using about 20% fewer tool calls and 25% fewer tokens than version 1.2. It scores 61 on the Artificial Analysis Intelligence Index and leads DeepSWE v1.1 at 75.4, ahead of GPT-5.6 Sol and Claude Opus 5, at roughly one quarter of their cost per task.

Key Takeaways

Meta released Muse Spark 1.3 on September 2, 2026 in Muse Code and the Meta Model API, with a max reasoning variant in limited partner preview.
The model uses about 20% fewer tool calls and 25% fewer tokens than Muse Spark 1.2 on comparable engineering tasks, according to Meta engineers.
It scores 61 (xhigh) and 62 (max) on the Artificial Analysis Intelligence Index, tying GPT-5.6 Sol and Grok 4.6.
Standard pricing holds at $1.25 per million input tokens and $4.25 per million output tokens, with a Contributor tier at $0.10 and $0.20 for teams that share prompts for training.
Safety work targets agentic use: stronger prompt injection resistance, better handling of ambiguous prompts, and confirmation before irreversible actions.

Introduction

Muse Spark 1.3 is Meta's fourth Muse Spark release in five months, built by Meta Superintelligence Labs for long-horizon agentic coding. It is a proprietary model with closed weights, available through Muse Code on macOS and Linux and through the Meta Model API at dev.meta.ai. Context window is 1 million tokens with text, image, and video input.

This is Meta's first frontier-class model shipped as closed weights rather than open source. The company positions it for multi-step engineering work where the model holds context across messy threads, generates its own context with tools, and collaborates with the user over extended sessions. A Meta Research blog post announcing Muse Spark 1.3 describes the goal as practical usefulness in real workflows rather than benchmark scores alone.

Readers tracking the coding assistant field can compare this release with our earlier coverage of rival approaches in OpenAI GPT-5 Codex agentic coding workflows and Anthropic Claude Opus coding benchmark results.

What Changed

Muse Spark 1.3 takes fewer turns where turns are not needed, writes less verbose output, and keeps a cleaner coding style than 1.2. Meta reports roughly 20% fewer tool calls and 25% fewer tokens for comparable tasks. It follows long, detailed instructions more reliably and preserves constraints across multi-step work.

Collaboration behavior changed in three visible ways. The model asks clarifying questions when a prompt is ambiguous instead of guessing. It calls on the user for help when stuck. It confirms before consequential steps. Multitasking also improved: when a single thread holds several interrupted tasks, the model maps each incoming message to the correct task more accurately.

Self-awareness is the other theme. Meta states it trained 1.3 to judge what it can and cannot do, what it knows and does not know, and to stop and say so at hurdles instead of hallucinating an outcome. Training covered more long-horizon coding tasks and a wider set of agent harnesses so the behavior transfers across environments.

Benchmark Performance

Muse Spark 1.3 scores 61 (xhigh, generally available) and 62 (max, limited preview) on the Artificial Analysis Intelligence Index. That is up from 57 for version 1.2 in August and 53 for 1.1 in July. The xhigh variant ties GPT-5.6 Sol (max) and Grok 4.6 (high), and sits behind Claude Fable 5.1 (66) and Claude Opus 5 (63).

Agentic evaluations drive the gain. Artificial Analysis reporting on Muse Spark 1.3 cites a 12-point jump on Tau3-Bench Banking (35% to 47% for xhigh, 52% for max), a 5-point gain on Terminal-Bench 2.1 (80% to 85%), and GDPval-AA v2 Elo rising from 1615 to 1709 (xhigh) and 1754 (max). Scientific reasoning rose as well, led by CritPt (18% to 26%) and GPQA Diamond (90% to 94%). Two evaluations slipped: AA-LCR fell 4 points and AA-Omniscience accuracy fell 1 to 3 points, partly because the model abstains more often instead of answering when unsure.

Meta's own evaluation report adds engineering benchmarks where 1.3 leads its rivals. DeepSWE v1.1, which tests autonomous fixes across real repositories, comes in at 75.4 against 73.0 for GPT-5.6 Sol and 74.0 for Claude Opus 5, and far above 55.0 for Muse Spark 1.2. Terminal-Bench 2.1 reaches 88.8, SWEAtlas CodeBase QnA hits 59.4, and long-context retrieval (MRCR at 512K to 1M tokens) hits 98.1. Readers should note method differences: Meta's numbers and Artificial Analysis numbers come from different harnesses, so exact figures vary by source.

Pricing

Pricing is unchanged from 1.2 for the standard tier: $1.25 per million input tokens, $4.25 per million output tokens, and $0.15 per million cached input tokens. Only the max reasoning mode is new and its pricing is not yet public. Artificial Analysis puts the blended cost at $0.55 per Intelligence Index task, the lowest of any model scoring 59 or above.

The second tier is the Contributor option at $0.10 per million input and $0.20 per million output tokens. The discount is steep, about 92% on input and 95% on output, but prompts and completions can be used to train future Meta models. Rate limits differ sharply: 3,000 requests per minute on Standard against 100 per minute on Contributor. Tom Tunguz analyzes the two-tier pricing as an explicit trade of data for inference subsidy, estimating the spread at about $1.24 per million tokens at agentic input-to-output ratios.

Cost per task comparisons favor Meta. At $0.55 per task, Muse Spark 1.3 (xhigh) undercuts GPT-5.6 Sol (max) at $0.95 and Grok 4.6 (high) at $0.94, a premium above 70% for its direct peers. The tradeoff noted by Artificial Analysis: cost per task rose from $0.40 on version 1.2 because agentic evaluations consumed about 57% more input tokens per task, with output tokens up only 8%.

Safety Improvements

Safety work in 1.3 focuses on risks that appear in agent deployments rather than single-turn chat. Meta reports stronger adversarial robustness, meaning better resistance to malicious inputs and prompt injection. The model also calibrates irreversible actions more carefully and pauses for confirmation before steps like deleting files or deploying to production.

Two behaviors support that goal. Ambiguous prompts trigger clarifying questions instead of silent guesses, which cuts down on wrong-file edits and wrong-environment commands. Interruption handling keeps multi-task threads consistent, so a user steering or cutting in mid-task is less likely to get an action applied to the wrong job. Meta frames the package as better discretion and judgment on long tasks.

Competitive Landscape

Muse Spark 1.3 ties OpenAI's GPT-5.6 Sol on the Intelligence Index (61 each) and beats it on DeepSWE v1.1 (75.4 against 73.0) and Terminal-Bench (88.8 against 84.0 in Meta's harness). Long-context retrieval is the widest gap: MRCR at 512K to 1M tokens reaches 98.1 against 73.8 for GPT-5.6 Sol in Meta's report. Per-task cost is about $0.55 against $0.95.

Against Anthropic, the picture is close on quality and wide on price. Claude Opus 5 (max) leads 63 to 61 on the Index but trails on DeepSWE (74.0 against 75.4) and Terminal-Bench (86.7 against 88.8). Claude Fable 5.1 holds a 4-point Index lead at 66, with per-task costs near $3.69 against $0.55 for Muse Spark 1.3. Meta AI chief Alexandr Wang described the model to Axios as very competitive with frontier models, and Meta CEO Mark Zuckerberg called it frontier performance at low cost on X.

Context matters. The release landed in a crowded week with competing updates from Google, Anthropic, and OpenAI within days. Meta's 2026 capital expenditure guidance of $125B to $145B signals continued infrastructure spending behind this pace. One distinction to keep straight: Muse Spark 1.3 is proprietary, while Muse Glimmer, a separate August 2026 release, is the open agentic model. The two names look similar but the licenses differ.

Conclusion

Muse Spark 1.3 closes most of the quality gap to the frontier while holding a clear cost advantage per task. The efficiency claim of 20% fewer tool calls and 25% fewer tokens is the practical core: fewer turns and cleaner style lower latency and bills on long coding jobs. Benchmark leads on DeepSWE and Terminal-Bench support that story, though harness differences mean buyers should test on their own repositories.

The Contributor tier deserves a careful read of the data policy before teams chase the discount. Standard tier keeps prompts out of training with high rate limits; Contributor trades that privacy for roughly a 21x price cut at 100 requests per minute. For developers already in Muse Code or the Meta Model API, 1.3 is a direct upgrade with no price change.

Frequently Asked Questions

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Meta released Muse Spark 1.3 on September 2, 2026. It is available in Muse Code on macOS and Linux and through the Meta Model API at dev.meta.ai. The max reasoning variant is in limited partner preview only.

How much more efficient is Muse Spark 1.3 than 1.2?

Meta engineers report about 20% fewer tool calls and 25% fewer tokens on comparable engineering tasks, with less verbose output and cleaner coding style. Artificial Analysis notes per-task input tokens rose on its agentic harness, so efficiency varies by workload.

How does Muse Spark 1.3 score on benchmarks?

It scores 61 (xhigh) and 62 (max) on the Artificial Analysis Intelligence Index, tying GPT-5.6 Sol and Grok 4.6. On Meta's engineering harness it leads DeepSWE v1.1 at 75.4 ahead of GPT-5.6 Sol at 73.0 and Claude Opus 5 at 74.0.

What does Muse Spark 1.3 cost?

Standard tier is $1.25 per million input tokens and $4.25 per million output tokens with $0.15 cached input, unchanged from 1.2. Contributor tier is $0.10 input and $0.20 output but shares prompts and outputs for training and caps throughput at 100 requests per minute.

Is Muse Spark 1.3 open source?

No. Muse Spark 1.3 is proprietary with closed weights. Do not confuse it with Muse Glimmer, a separate open agentic model Meta released in August 2026.

References

MultiversX Supernova Hard Fork — September 10 Block Times Cut to 0.6 Seconds

Hamza — Mon, 07 Sep 2026 21:51:23 +0000

MultiversX is activating its Supernova hard fork on September 10, 2026, cutting block times from six seconds to 600 milliseconds. The upgrade introduces parallel transaction processing, reducing intra-shard finality to under 250 milliseconds and cross-shard settlement from roughly 18 seconds to about 2.4 seconds. Node migration remains the key risk: as of September 1, over 95 percent of the network's 5,171 Validators still ran the old software version.

The MultiversX network is preparing for what the project calls its largest architectural change since launch. The Supernova hard fork activates at block round 32,157,661, expected around 18:06 UTC on September 10, 2026. It moves the network from a linear consensus pipeline to one where validators vote on blocks while transaction execution continues in parallel.

EGLD was trading near $4.63 on September 6 and 7, after a 32 percent weekly gain that preceded the upgrade. The market capitalization sat at roughly $141.5 million. Governance approval came in at 99.64 percent, according to the @multiversx X account. The upgrade passed through governance with overwhelming support, but the real test will be whether nodes complete their migrations before the activation round.

Key Takeaways

Activation: September 10, 2026 at approximately 18:06 UTC, block round 32,157,661
Block time reduction: 6 seconds down to 600 milliseconds (10x faster)
Intra-shard finality: Drops to under 250 milliseconds
Cross-shard settlement: Falls from ~18 seconds to ~2.4 seconds
Transaction pause: About 24 minutes (240 rounds) during the transition
Node migration status: 95.35% of 5,171 public nodes on old software as of September 1
Governance approval: 99.64% support from $EGLD stakeholders
EGLD price: ~$4.63 (September 6-7), up 32% week-over-week
Unbonding period: Remains unchanged at 10 days despite speed increase
Self-custody wallets: No action required; addresses and balances are backward compatible

For more on how blockchain networks handle major upgrades, see our coverage of Pi Network Protocol 27 and other similar blockchain upgrades.

Key Takeaways

Activation: September 10, 2026 at approximately 18:06 UTC, block round 32,157,661
Block time reduction: 6 seconds down to 600 milliseconds (10x faster)
Intra-shard finality: Drops to under 250 milliseconds
Cross-shard settlement: Falls from ~18 seconds to ~2.4 seconds
Transaction pause: About 24 minutes (240 rounds) during the transition
Node migration status: 95.35% of 5,171 public nodes on old software as of September 1
Governance approval: 99.64% support from $EGLD stakeholders
EGLD price: ~$4.63 (September 6-7), up 32% week-over-week
Unbonding period: Remains unchanged at 10 days despite speed increase
Self-custody wallets: No action required; addresses and balances are backward compatible

For more on how blockchain networks handle major upgrades, see our coverage of Solana double disinflation and other validator economics.

Key Takeaways

Activation: September 10, 2026 at approximately 18:06 UTC, block round 32,157,661
Block time reduction: 6 seconds down to 600 milliseconds (10x faster)
Intra-shard finality: Drops to under 250 milliseconds
Cross-shard settlement: Falls from ~18 seconds to ~2.4 seconds
Transaction pause: About 24 minutes (240 rounds) during the transition
Node migration status: 95.35% of 5,171 public nodes on old software as of September 1
Governance approval: 99.64% support from $EGLD stakeholders
EGLD price: ~$4.63 (September 6-7), up 32% week-over-week
Unbonding period: Remains unchanged at 10 days despite speed increase
Self-custody wallets: No action required; addresses and balances are backward compatible

For more on how blockchain networks handle major upgrades, see our coverage of Pi Network Protocol 27 and other similar blockchain upgrades.

Key Takeaways

Activation: September 10, 2026 at approximately 18:06 UTC, block round 32,157,661
Block time reduction: 6 seconds down to 600 milliseconds (10x faster)
Intra-shard finality: Drops to under 250 milliseconds
Cross-shard settlement: Falls from ~18 seconds to ~2.4 seconds
Transaction pause: About 24 minutes (240 rounds) during the transition
Node migration status: 95.35% of 5,171 public nodes on old software as of September 1
Governance approval: 99.64% support from $EGLD stakeholders
EGLD price: ~$4.63 (September 6-7), up 32% week-over-week
Unbonding period: Remains unchanged at 10 days despite speed increase
Self-custody wallets: No action required; addresses and balances are backward compatible

What Supernova Changes

Supernova decouples transaction execution from block consensus. Previously, validators had to wait for all transactions to execute before voting on a block. The bottleneck was structural: each shard processed transactions sequentially, and cross-shard communications required three separate rounds because the metachain first had to certify the sending shard's block before the receiving shard could accept it.

Under the new architecture, validators vote on a block while execution continues in parallel. Rounds shrink from six seconds to 600 milliseconds. Cross-shard order changes mean validators execute transactions as soon as local checks pass, then vote, with the metachain still providing final clearance across shards.

The network's testnet cleared more than one billion transactions before mainnet activation, according to the official MultiversX blog published June 9, 2026. GitHub releases for mx-chain-go v2.0.4 confirm the Supernova async execution feature, tracked as issue #7055 and related pull requests.

Why Block Times Matter

Faster blocks are not just a benchmark improvement. Lower latency affects decentralized applications directly. Games, high-frequency trading tools, and real-time payment systems all benefit when confirmations move from seconds to fractions of a second. Cross-shard efficiency also lifts overall network throughput, potentially closing the gap with other high-performance blockchains.

The 10x speed increase comes with one caveat that many holders misunderstand: staking unbonding does not speed up. The unbonding period stays at 10 days. Network configuration carries two values side by side -- erd_unbond_period at 144,000 rounds and erd_unbond_period_supernova at 1,440,000 rounds. Both convert to 864,000 seconds, or ten full days, because the round duration changes proportionally.

The Node Migration Problem

The most pressing concern ahead of activation is node readiness. As of September 1, 95.35 percent of the 5,171 public nodes were still running the old software version v1.11.11.0, according to data compiled by CryptoTicker and CryptoRank. EdgeTech reported roughly 84 percent migration progress as of September 6, suggesting some late movement but not enough to eliminate risk.

Nodes that have not upgraded after the activation round compute transactions differently from the majority and will lose synchronization with the chain. Validators running outdated software face downtime and forgone rewards. The minimum stake to operate a validator is 2,500 EGLD.

Old and new binaries can coexist before the activation round, but outdated nodes after it produce deviating results. The 9-day migration window between September 1 and September 10 was tight. Anyone watching the network should check the node version mix on the day before activation rather than assuming the upgrade will proceed smoothly.

What EGLD Holders Need to Know

Self-custody wallet users have nothing to do. Addresses, private keys, and balances remain backward compatible. There is no token swap, no migration, and no claim to register. Anyone who does nothing keeps the same coins after September 10.

Exchange holders should verify whether their platform has announced deposit or withdrawal suspensions. Some exchanges have halted EGLD operations for prior MultiversX upgrades. Upbit suspended deposits and withdrawals for previous upgrades in May 2025 and July 2026, but no specific announcement for the September 10 activation was found at the time of writing. Holders should check their exchange's official channels directly.

Stakers should note that the unbonding period remains 10 days despite the speed increase. The network's configuration adjusts the round count proportionally so the calendar duration stays the same.

Wallet providers and dApp developers should ensure their software supports the new block format before activation. Fraud warnings are active: anyone contacting users over the coming days requesting action is likely attempting a scam.

Price Context

EGLD traded at approximately $4.63 to $4.64 on September 6 and 7, according to CoinGecko and CoinMarketCap data. The token was down 2.12 percent on the day but up 23.8 to 32 percent over the prior week, breaking above $4 for the first time since May 2026. The market capitalization stood at roughly $141.5 million.

The pre-upgrade rally suggests traders are positioning ahead of the speed boost. Whether the technical upgrade translates into sustained token performance is impossible to state in advance. The Supernova activation date can shift by minutes to hours because it depends on block rounds rather than wall-clock time.

Tokenomics Adjustment

The network is moving from a fixed maximum supply model to one introducing approximately 9.47 percent annual tail inflation. Ten percent of transaction fees are burned, creating a mixed impact scenario for EGLD. The long-term price effect depends on whether increased utility and fee burn rates can outpace the inflationary dilution.

Additionally, 10 percent of validator rewards and network fees are allocated to a Protocol Treasury to fund ecosystem development, reducing reliance on foundation reserves.

Conclusion

The MultiversX Supernova upgrade represents a fundamental shift in how the network produces blocks. Parallel transaction processing and sub-second finality bring the chain closer to its internet-scale vision. The technical specifications are confirmed and the governance vote passed with near-unanimous support. The outcome, however, depends on node operators completing their migrations in time. Anyone holding EGLD should verify their setup, watch the node version mix in the days before September 10, and ignore any messages asking for action during the transition period.

Frequently Asked Questions

Q: What exactly is the Supernova upgrade?

A: Supernova is a hard fork on the MultiversX blockchain that restructures the consensus mechanism to enable parallel transaction processing. It cuts block generation time from 6 seconds to 600 milliseconds and reduces intra-shard finality to under 250 milliseconds.

Q: When does the Supernova upgrade activate?

A: The upgrade is scheduled for September 10, 2026, at approximately 18:06 UTC (block round 32,157,661, epoch 2233). The exact time may shift by minutes to hours depending on actual block production.

Q: Do I need to do anything if I hold EGLD in a self-custody wallet?

A: No. Addresses, private keys, and balances are fully backward compatible. There is no token swap or migration required for self-custody holders.

Q: Will the unbonding period change with faster block times?

A: No. The unbonding period remains 10 days. The network adjusts the round count proportionally so the calendar duration stays the same even though rounds are shorter.

Q: What happens if a validator node does not upgrade before September 10?

A: Nodes running the old software will compute transactions differently after activation and lose synchronization with the majority chain. Validators face downtime and forgone rewards. The minimum stake to operate a validator is 2,500 EGLD.

Q: How long will transactions be paused during the upgrade?

A: New transactions will be queued for approximately 24 minutes (240 rounds) as the network transitions to the new consensus system. This is a standard part of hard fork activations.

References

MultiversX Official Blog MultiversX Official Blog -- "The Internet-Scale Blockchain" (June 9, 2026): https://multiversx.com/blog/the-internet-scale-blockchain
GitHub GitHub Releases -- multiversx/mx-chain-go v2.0.4: https://github.com/multiversx/mx-chain-go/releases
@MultiversX on X @multiversx X/Twitter -- Governance approval announcement: https://x.com/MultiversX/status/2012955093784805783
CryptoTicker CryptoTicker -- "MultiversX Supernova: Hard Fork Checklist for EGLD" (September 3, 2026): https://cryptoticker.io/en/multiversx-supernova-hard-fork-checklist/
CoinDCX CoinDCX -- "EGLD Rallies as MultiversX Supernova Nears September 10" (September 4, 2026): https://coindcx.com/blog/crypto-news-global/supernova-is-out-what-egld-traders-are-positioning-for/
KuCoin KuCoin -- "MULTIVERSX IS GOING OFFLINE FOR 24 MINUTES TO UNLOCK A 10X SPEED": https://kucoin.com/news/insight/EGLD/6a98bd335acca50007b953aa
CryptoRank/CryptoSlate CryptoRank/CryptoSlate -- "MultiversX Supernova upgrade to cut block times to 600ms on Sept. 10": https://coinmarketcal.com/news/multiversx-supernova-upgrade-to-cut-block-times-to-600ms-on-sept-10
EdgeTech EdgeTech -- "MultiversX Supernova to cut EGLD block time 10x Sept 10": https://edgen.tech/news/post/multiversx-supernova-to-cut-egld-block-time-10x-sept-10
CoinGecko CoinGecko -- EGLD price data (September 6-7, 2026): https://coingecko.com/en/coins/multiversx

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Read the full article: https://tekmag.thsite.top/multiversx-supernova-hard-fork-september-10-block-times-cut-to-0-6-seconds/

OPPO Find X10 Series: The Next Chapter in Smartphone Photography Is About Being Real

Hamza — Mon, 07 Sep 2026 12:10:47 +0000

OPPO Find X10 Series is a lineup of flagship smartphones that prioritizes natural, realistic photography over computational perfection, using a 200MP Hasselblad camera system and a new LUMO light-handling engine to deliver images that match what the human eye actually sees.

Key Takeaways

OPPO is shifting from “perfect” computational photography to natural, realistic imaging with its “What You See Is What You Capture” philosophy.
The Find X10 Pro features a dual 200MP camera system powered by the new LUMO Native Light and Shadow Engine and second-generation Danxia Colour Restoration Lens.
The Pro Max variant is tipped to feature three 200MP cameras, which would make it the world’s first smartphone with this configuration.
Launch is expected in September 2026 for China and October 2026 for global markets, running on the Dimensity 9600 chipset.
The realism focus positions OPPO against competitors like Samsung and Google, who continue pushing AI-enhanced photography further.

What makes the Find X10 different from other smartphone cameras

The Find X10 Series represents a deliberate pivot away from the hyper-processed look that has dominated smartphone photography for years. According to Gizchina, the series uses a Samsung ISOCELL HPC 200MP primary sensor paired with OPPO’s own image processing pipeline designed to preserve fine detail and natural textures rather than smooth them away with aggressive noise reduction or oversaturation.

Traditional computational photography often stacks multiple exposures, applies heavy sharpening, and pushes colors toward maximum vibrancy. The result is an image that looks impressive on paper but feels artificial when you zoom in. OPPO’s approach flips that model. Instead of making photos look better than reality, the goal is to make them look more like reality.

How the LUMO engine handles natural light and shadow

The LUMO Native Light and Shadow Engine is the technical centerpiece of OPPO’s realism strategy. Sparrows News reports that the LUMO system maps natural light falloff and shadow softness to match human visual perception, rather than applying uniform lighting corrections across the entire frame. This means highlights don’t get crushed to white and shadows don’t get lifted into gray mush.

The engine reportedly analyzes the scene in real time to identify light direction, intensity, and quality before applying any processing. In practice, this should produce more three-dimensional images where depth and texture read naturally. According to Sportskeeda’s coverage of the series, the LUMO engine works in tandem with the Danxia lens system to maintain color accuracy across extreme dynamic range situations.

What the Danxia Colour Restoration Lens actually does

The second-generation Danxia Colour Restoration Lens is OPPO’s solution to a persistent problem in mobile photography: color inaccuracy under challenging lighting. 91mobiles reports that the Danxia lens captures data across 24 sampling channels with a 15EV dynamic range, which allows it to reconstruct colors that most phone cameras would either clip or compress into a narrow band.

A 15EV dynamic range is significantly wider than what typical smartphone sensors capture. Most phone cameras sit somewhere between 11 and 13EV in real-world conditions. That extra range matters most in high-contrast scenes like backlit portraits or sunset landscapes, where competing phones tend to lose detail in either the bright or dark areas. The Danxia lens captures that information and maps it to accurate color values rather than defaulting to safe but inaccurate approximations.

This approach aligns with a broader industry trend. According to Sandmarc’s 2026 photography trends analysis, there is growing fatigue with AI-enhanced imagery across both professional and amateur photographers, with authenticity emerging as the dominant aesthetic preference for the year.

Camera specifications across the Find X10 lineup

The Find X10 lineup spans three models with varying camera configurations. The base Find X10 carries a standard dual-camera setup, while the Find X10 Pro steps up to a dual 200MP arrangement covering both the main and telephoto lenses. The Find X10 Pro Max goes further still, reportedly featuring three 200MP cameras covering main, telephoto, and ultrawide angles, which would make it the first smartphone to ship with this configuration.

According to Notebookcheck’s leak coverage, the Pro Max’s triple 200MP setup represents a significant investment in sensor real estate and processing power. Maintaining image quality across three 200MP sensors simultaneously requires considerable computational overhead, which OPPO addresses with the Dimensity 9600 chipset built on a 2nm process.

The Pro variant also carries an 8,000mAh battery, which Gizchina confirms is part of the Pro model’s specification. That capacity supports the power demands of continuous 200MP shooting and real-time LUMO processing without requiring a compact form factor compromise.

Why 200MP matters when paired with realism processing

The megapixel arms race has dominated smartphone camera marketing since roughly 2020, when manufacturers began pushing past 100MP. But raw resolution without thoughtful processing produces large files full of noise and artifacts that demand heavy compression. OPPO’s strategy reframes what 200MP actually delivers in this context.

With a 200MP sensor and a processing pipeline tuned for realism rather than beautification, each pixel contributes usable detail instead of being interpolated or discarded. According to Sportskeeda, the shift from “perfect” to “real” processing means the extra resolution translates directly into sharper textures, more accurate skin tones, and finer architectural detail without the plastic-smooth look that plagues heavily processed phone photos.

The Hasselblad partnership, carried forward under the Find X10 name, provides the color science foundation that OPPO builds on with its own computational layer. Gizchina notes that the Hasselblad Ultra-Clear Original Camera branding signals a return to the partnership’s earlier emphasis on optical authenticity rather than the more saturated color profiles introduced in later iterations.

How the Find X10 positions against Samsung and Google

OPPO’s realism focus places it in direct contrast with Samsung’s Galaxy S27 Ultra, which continues to push computational photography toward maximum detail extraction and AI-driven scene optimization. The divergence mirrors what we observed in our Realme Neo 8 review, where a Chinese manufacturer again chose camera differentiation over raw spec competition. Samsung’s LOFIC technology, mentioned in Notebookcheck’s analysis, achieves impressive dynamic range through hardware-level solutions, but the resulting images still carry Samsung’s signature processing fingerprint.

Google’s Pixel 11, similarly, relies heavily on computational photography and AI to enhance every shot. The Results You Can Trust initiative that Google introduced reflects some awareness of the realism concern, but the core approach remains generation-heavy. OPPO’s strategy carves out a different position by treating computational photography as a tool for preservation rather than enhancement.

This positioning matters because consumer expectations are shifting. According to Sandmarc’s 2026 trends report, the balance between authentic human storytelling and AI imagery is a defining tension in photography this year, with many users reporting fatigue from increasingly synthetic-looking phone photos.

When and where the Find X10 Series launches

OPPO plans to launch the Find X10 Series in China during September 2026, with global markets following in October. Notebookcheck reports that pricing details remain unconfirmed but expect the Pro Max to command a premium over the standard Pro model, consistent with OPPO’s historical pricing strategy for its flagship lineup.

The series runs on the MediaTek Dimensity 9600 chipset, built on a 2nm process that offers improved efficiency alongside the processing power needed for real-time 200MP image synthesis. Gizchina confirms the Pro variant includes IP68 and IP69 water resistance ratings along with an ultrasonic fingerprint sensor, placing it firmly in the premium flagship category alongside competitors like Samsung and Apple.

The display is a 6.78-inch 2K OLED panel, which gives the camera images plenty of resolution to display accurately. A higher-resolution screen means the natural detail captured by the 200MP sensors isn’t lost in the viewing chain.

What the realism shift means for smartphone photography

OPPO’s Find X10 Series represents one of the first major manufacturer pushes toward realistic imaging at the flagship level. While smaller brands and enthusiasts have been chasing natural aesthetics for years, this approach entering the mainstream flagship conversation signals that the computational photography crowd is reaching an inflection point.

The tension between perfect and real photography isn’t new, but it has intensified as AI enhancement capabilities have improved. Every advance in generative editing makes the case for authenticity stronger for users who want their phones to document life rather than reinterpret it. Sandmarc identifies this authenticity movement as one of the defining photography trends of 2026, suggesting that OPPO’s timing aligns with broader consumer sentiment.

If the Find X10’s approach gains traction, it could pressure other manufacturers to reconsider their default processing pipelines. The current market leader in computational photography, Google, has already signaled some openness to this direction with its Results You Can Trust work. Samsung’s hardware innovations with LOFIC suggest they may pursue realism through different technical routes. The competition between these philosophies should benefit consumers who care about how their photos actually look.

For anyone currently weighing upgrade options between flagship phones, the Find X10 Series deserves attention if natural-looking photos matter more to you than maximum feature density. The dual 200MP setup on the Pro and the triple setup on the Pro Max represent genuine hardware investments rather than marketing numbers, and the LUMO and Danxia systems give OPPO a technical story that goes beyond sensor size alone. This shift toward authenticity aligns with what we have observed in other flagship redesigns, including Nothing OS 5.0’s approach to personalisation.

Conclusion

OPPO’s Find X10 Series makes a clear case that the next evolution in smartphone photography isn’t about sharper processing or more aggressive AI enhancement, but about capturing images that feel authentic to how we actually see the world.

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What is the main camera difference between the Find X10 Pro and Pro Max?

The Find X10 Pro features a dual 200MP camera setup covering the main and telephoto lenses, while the Pro Max adds a third 200MP sensor for the ultrawide camera, making it the world’s first smartphone with three 200MP cameras according to 91mobiles.

When does the OPPO Find X10 Series launch globally?

The series launches in China in September 2026, with global availability following in October 2026 according to Notebookcheck’s leak coverage.

What is the LUMO engine and how does it work?

The LUMO Native Light and Shadow Engine is OPPO’s image processing system that maps natural light falloff and shadow softness to match human visual perception, preserving realistic depth and texture rather than applying uniform lighting corrections.

How does the Danxia Colour Restoration Lens improve photo quality?

The second-generation Danxia lens captures data across 24 sampling channels with a 15EV dynamic range, enabling accurate color reconstruction in high-contrast scenes where most phone cameras would lose detail or produce inaccurate colors.

Does the Find X10 Series use AI photography enhancement?

OPPO’s Find X10 philosophy emphasizes natural imaging over AI enhancement, focusing on preserving fine details and authentic textures rather than applying aggressive computational processing, though some AI assistance is still used for noise reduction and dynamic range optimization.

References

Firelight Protocol Raises $8M to Backstop DeFi Vaults With Staked XRP

Hamza — Mon, 07 Sep 2026 05:15:58 +0000

Firelight Protocol has raised $8 million in a seed round led by Gumi Cryptos Capital to build an on-chain insurance layer for DeFi vaults using staked XRP (FXRP) on the Flare network. The protocol already holds $76 million in staked assets and plans to launch active cover in September 2026.

DeFi's biggest blind spot isn't smart contract risk. It's the fact that almost no one is protected against it. According to DefiLlama data, only 0.14 percent of the $88.3 billion in total DeFi value locked sits behind any form of on-chain cover. The remaining 99.86 percent is exposed, reliant on auditors who occasionally miss things and community forums where dispute resolution moves at the speed of a group chat.

Firelight Protocol, incubated by Sentora, wants to change that equation. Sentora manages over $3 billion in institutional vaults for clients including Kraken, EtherFi, Ondo Finance, and Euler. Its founders include ex-Coinbase risk strategist Anthony DeMartino (CEO), NeuralFabric co-founder Jesus Rodriguez (CTO), and former Fireblocks executive Connor Sullivan (CSO). The team brings institutional credibility that most DeFi-native insurance projects lack.

Key Takeaways

Firelight Protocol raised $8 million in seed funding led by Gumi Cryptos Capital
Participants include Maven 11, Metalayer, Joint Effects, and Tribe Capital
The protocol already holds $76 million in staked FXRP on Flare Network
Phase 1 (December 2025) enabled liquid staking only; Phase 2 (September 2026) activates cover
Only 0.14 percent of DeFi's $88.3 billion TVL has on-chain insurance protection
Audits completed by OpenZeppelin and Coinspect; Immunefi bug bounty active
Roadmap includes BTC via Lombard LBTC and Stellar XLM expansion

For more on decentralized finance and blockchain infrastructure, see our coverage of AI-powered development tools and Linux ecosystem updates.

What Is Firelight Protocol?

Firelight Protocol is a non-custodial cover protocol built on Flare Network's FAssets bridge. It lets DeFi vault operators purchase on-chain insurance against smart contract exploits, reentrancy attacks, oracle manipulation, governance failures, bad debt, and liquidation breakdowns. The protocol does not call itself an insurance company. Firelight explicitly states that its cover is not insurance in the legal sense. Stakers absorb losses pro rata when claims are validated and the first-loss buffer is exhausted. This distinction matters for regulatory positioning and was flagged in every source interview.

The protocol is incubated by Sentora, which formed in May 2025 through the merger of IntoTheBlock and Trident Digital. Sentora's institutional track record is unusually strong for a DeFi project. Its vaults have deployed over $3 billion across clients that include major exchanges and liquid staking providers. That pedigree gives Firelight a credibility edge over Nexus Mutual, InsurAce, and Neptune Mutual, which operate without an institutional parent.

The $8 Million Funding Round

The seed round was announced September 1, 2026, via Jesus Rodriguez's X account (@jrdothoughts). Gumi Cryptos Capital led the round. Participants included Maven 11, Metalayer, Joint Effects, and Tribe Capital. The funding comes at a moment when on-chain cover remains severely underpenetrated, which makes the timing notable even if the round size is modest for crypto venture standards.

Gumi Cryptos Capital is a Japanese-backed crypto investment fund with a history of early-stage bets on DeFi infrastructure. Its lead role signals institutional comfort with the protocol's model, which differs from traditional insurance in two critical ways: stakers directly underwrite risk rather than a centralized pool, and premiums flow back to capital providers rather than being retained by a middleman.

How It Works

Firelight's mechanism is straightforward. Users stake XRP through the FAssets bridge to receive FXRP, a liquid staking receipt that represents their underlying position on Flare. Phase 1, launched December 2025, enabled pure liquid staking without cover functionality or yield from premiums. The protocol set a cap of 65 million FXRP, raised from an initial target of 25 million after demand exceeded expectations.

Nvidia PAIR — The Free Tool That Turns Your Home PCs Into a Distributed AI Cluster

Hamza — Sun, 06 Sep 2026 21:14:41 +0000

Nvidia's new open-source Personal AI Router (PAIR) lets you link computers on your home network into a distributed cluster that routes AI inference requests to idle machines. No new API required, no GPU memory pooling -- just connect an RTX GPU, an Apple Silicon Mac, or a DGX Spark, pair them with a six-digit code, and let the router send each request to whichever node has capacity. Beta launched September 3, 2026, under Apache 2.0.

Running a multi-agent AI workflow on a single laptop can take eighteen minutes. The same task on a cluster of three connected machines finishes in under nine. Nvidia PAIR makes that spread possible without requiring expensive cloud credits or merging GPU VRAM across cards.

The tool acts as a proxy between your agent application and your local inference engine -- currently Ollama or LM Studio -- routing each independent request to the node holding the model you need. If your desktop is gaming and your MacBook is idle, PAIR sends the next request to the Mac. If the desktop finishes its game and becomes available again, PAIR routes to it next. Nodes join and leave dynamically.

Key Takeaways

Open-source under Apache 2.0, beta available since September 3, 2026
Routes independent inference requests across local network -- does not pool GPU memory
Works with Ollama and LM Studio without changing your existing setup
Supported hardware: GeForce RTX 20 Series+, RTX PRO, DGX Spark, Apple M4+ (x64 and ARM64)
Paired via six-digit PIN with mTLS encryption
Compatible with Hermes Agent, Perplexity Portable Computer, and OpenClaw/NemoClaw
Available on Windows, macOS, and Linux
Github: github.com/NVIDIA/Personal-AI-Router

What is PAIR?

Nvidia Personal AI Router (PAIR) is a virtual inference router that discovers compatible devices on your local network and assigns incoming AI requests to whichever machine has available capacity. It does not split a single model across multiple GPUs, merge VRAM, or create a pooled compute resource. Each request runs entirely on one node from start to finish.

The router sits between your agent app and your inference backend. Your app thinks it is talking to a standard Ollama or LM Studio endpoint. PAIR intercepts the request, checks which nodes have the model loaded and free compute, then forwards the call to the best-fit machine. Responses flow back the same way.

Nvidia built PAIR for users running multiple independent agent tasks -- the Hermes Agent breaking down a research project into sub-queries, for instance. Those sub-queries do not depend on each other, so they can run in parallel across different machines. The result is faster completion without buying a second GPU or paying for cloud inference.

How It Works

Discovery happens through mDNS, the same protocol your router uses to find printers and smart devices. PAIR-enabled machines announce themselves on the local network. You pair them by entering a six-digit code shown on each device -- no manual IP configuration required.

Once paired, the connection uses mTLS encryption. All traffic between nodes stays on your local network. Prompts, responses, and model weights never leave your router unless you explicitly configure an external endpoint.

The scheduler considers five factors when routing a request: whether the node is ready, which inference engine is running, whether the requested model is loaded, current workload on the node, and GPU utilization. If a node goes idle -- say your gaming PC finishes a session and enters a low-power state -- PAIR stops sending requests there until it comes back online.

Model consistency across the cluster is not required. One machine might run Llama 3.1, another Phi-4, another Qwen. PAIR routes each request to the node holding the model it needs. This also means the smallest model in your cluster limits what any single node can handle -- PAIR does not chunk models across machines.

Hardware Support

PAIR runs on x64 and ARM64 architectures with a minimum of 8 GB RAM and roughly 20 GB of disk space. The supported GPU lineup covers most consumer cards released since 2018:

NVIDIA GPUs: GeForce RTX 20 Series and newer, RTX PRO workstation cards (Turing architecture and later), and DGX Spark. Apple Silicon: M4 or newer, requiring macOS Tahoe 26. Notably, M3 Ultra Macs are excluded despite having up to 512 GB of unified memory -- Nvidia's support list stops at M4 generation.

Windows on ARM support exists but is marked experimental. The primary tested configurations mix RTX Spark laptops, DGX Spark units, and RTX 5090 desktops.

Setup and Pairing

Installation is straightforward. Download the appropriate package from the GitHub repository for your operating system, run the installer, and launch the PAIR app. It will display a six-digit pairing code.

On each additional machine, enter that code to complete the pairing. The app shows a list of connected nodes and their current status. You do not need to configure networking manually -- mDNS handles discovery, and mTLS handles authentication.

Your inference engine -- Ollama or LM Studio -- runs as it normally would. PAIR exposes a proxy endpoint that your agent application points to instead of the local Ollama URL. The proxy looks like a standard Ollama endpoint to anything connected to it, so no model-specific changes are needed.

Three agent applications received simplified setup flows for local NVIDIA GPUs: Perplexity Portable Computer, Hermes Agent, and OpenClaw (now branded NemoClaw). For other apps, you point the configuration to the PAIR proxy URL and it works immediately.

Performance and Limitations

Nvidia demonstrated PAIR with a Hermes Agent task running Qwen 3.6 35B A3B on a five-subagent workflow analyzing a synthetic household inbox. A single RTX Spark laptop completed the task in approximately 18 minutes. A three-device cluster -- RTX Spark, DGX Spark, and RTX 5090 -- finished in 8 minutes 48 seconds.

Nvidia describes this as an unofficial, configuration-specific demo rather than a general benchmark. Scaling is not linear across all workloads. Tasks that require synchronous communication between agents cannot be parallelized as easily as independent queries. Network bandwidth between nodes also matters -- a slow WiFi connection will bottleneck multi-node performance more than the GPUs themselves.

The most important limitation to understand: PAIR does not pool GPU memory. If you want to run a 70B parameter model, both nodes need enough VRAM to load it independently. What PAIR does is route separate requests to separate machines, not split a single request across machines.

Who It Is For

PAIR targets users who already run local AI workloads and own multiple compatible machines. If you have an RTX desktop and an Apple Silicon laptop, or two machines with capable GPUs, PAIR turns idle capacity into useful compute without additional hardware purchases.

Multi-agent workflows benefit most. Tools like Hermes Agent break complex tasks into parallel sub-queries -- research, code review, data extraction -- that do not need to communicate during execution. Each sub-query can run on a different node, cutting total wall-clock time roughly in proportion to the number of available machines.

Privacy-conscious users also benefit. All inference stays on your local network. No prompts go to cloud APIs. No telemetry leaves your router unless you configure it to.

If you run a single agent on a single machine, PAIR adds complexity without meaningful benefit. The tool shines when you have parallel work and spare compute sitting idle.

The Bigger Picture

PAIR is part of Nvidia's broader strategy to localize AI compute. The company has been pushing RTX hardware into more accessible form factors -- the DGX Spark for AI development on the go, and Windows PCs with RTX Spark arriving in October 2026. Making those machines useful for clustering gives buyers a reason to own more than one.

For prosumers running AI agents locally, the cost implication is direct. Cloud inference for a multi-agent workflow can run into dollars per session. PAIR eliminates that cost for independent sub-tasks, keeping your prompts on your network and your bills at zero.

Nvidia also announced performance improvements for llama.cpp and vLLM around the same time, along with a smart-home data center partnership with Span and PulteGroup. PAIR fits into a pattern: making local inference fast enough and flexible enough that users choose it over cloud options for routine workloads.

Conclusion

PAIR does not merge GPUs or split models. It does something simpler and more practical: it routes independent requests to whichever machine on your network can handle them fastest. If you have an RTX desktop you game on, an M4 Mac you code on, and a DGX Spark sitting in a drawer, PAIR lets all three contribute to your AI workflows without you writing a line of orchestration code.

The beta is available now on Windows, macOS, and Linux. For more on running local AI models, see our guide to offline AI productivity tools and optimizing Windows 11 for AI workloads. The real test is whether your workflow has enough parallel tasks to justify the setup. For multi-agent research and automation, the answer is increasingly yes.

Frequently Asked Questions

Does PAIR pool GPU memory across machines?

No. PAIR routes each independent request to a single node. If a model requires 24 GB of VRAM, every node that runs it must have 24 GB available. The cluster does not combine memory from multiple GPUs into one larger pool.

Can I mix NVIDIA and Apple Silicon devices in the same cluster?

Yes. PAIR supports both NVIDIA GPUs (RTX 20 Series and newer) and Apple M4+ Macs. A cluster can include an RTX desktop, a DGX Spark, and an M4 Mac running simultaneously. Each handles whichever requests it is configured to serve.

Do I need to change how I use Ollama or LM Studio?

No. PAIR sits in front of your existing inference engine as a proxy. Your agent app points to the PAIR endpoint instead of the local Ollama URL, but the API format is identical. No model changes or configuration updates are needed.

Is there a limit to how many devices I can connect?

Nvidia tested configurations with up to 18 devices in internal demos. No hard limit is published. Practical limits depend on your network bandwidth and the number of independent tasks your workflow generates.

Does PAIR send my data to the cloud?

No. All inference traffic stays on your local network. The mTLS-encrypted connection between nodes never reaches an external server. Your prompts and responses remain local unless you explicitly configure an upstream endpoint.

References

Originally published on TekMag

DEV Community: Hamza

Xbox Publishing Kojima's Physint After PlayStation Cancelled It

What is Physint?

How PlayStation cancelled a Kojima game

Why Xbox signed Kojima

What it means for Kojima

What we still don't know

Conclusion

Frequently asked questions

Q: Why did PlayStation cancel Physint?

Q: Is Physint still coming out?

Q: Does the Xbox deal change what the game is?

Q: Who is Asha Sharma and why is her involvement significant?

Q: What is the connection between Physint and Metal Gear?

References

Xbox Publishing Kojima's Physint After PlayStation Cancelled It

What is Physint?

How PlayStation cancelled a Kojima game

Why Xbox signed Kojima

What it means for Kojima

What we still don't know

Conclusion

Frequently asked questions

References

Apple iPhone Duo Hands-On Review: Apple's First Foldable Phone Is Finally Here

Design & Display: Passport Shape, Crease-Free Claims

Specifications & Performance

iOS 27 Foldable Features

Pricing & Availability

Comparison vs Competitors

Key Takeaways

Frequently Asked Questions

References

OneDrive vs Google Photos vs iCloud: Picking the Right App to Back Up Your Phone

Quick verdict: match the service to the phone

Google Photos: the photo-first backup

iCloud: full-device backup for Apple users

OneDrive: files first, cross-platform

Free tiers, auto-backup behavior, and pricing

Which should you choose?

Getting more from your free tier

Conclusion

Frequently asked questions

References

Android September 2026 Drop — New Features for Gemini, Motion Assist, and Accessibility

Android September 2026 Drop — New Features for Gemini, Motion Assist, and Accessibility

Key Takeaways

What's new in Python 3.15

What's new in Python 3.15

PEP 828: yield from in async generators

JIT improvements and performance

Security patches for the older branches

Breaking changes and deprecations

Migration guide for Python 3.15

Conclusion

Frequently asked questions

References

SnapEdit for Developers: 40+ AI Image and Video Models via a Single API Key

Key Takeaways

What Is SnapEdit for Developers?

Why Developers Are Taking Notice

Pricing and Free Trial

Technical Specifications

Who Is Behind SnapEdit?

Competitive Landscape

Use Cases

Frequently Asked Questions

Conclusion

References

Meta Muse Spark 1.3 — Frontier Coding Model with 20% Fewer Tool Calls

Key Takeaways

Introduction

What Changed

Benchmark Performance

Pricing

Safety Improvements

Competitive Landscape

Conclusion

Frequently Asked Questions

When was Muse Spark 1.3 released and where is it available?

PEP 828: `yield from` in async generators