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Apple's next-generation Studio Display is widely expected to feature the same 27-inch screen size as the current model, but with mini-LED technology instead of LCD. This upgrade would result in increased brightness and a higher contrast ratio compared to the current model, bringing it in line with the MacBook Pro, which has touted mini-LED since 2021.
Leaked Apple code suggests that the new Studio Display will also feature an A19 chip. The Studio Display's chip supports features like Center Stage, Spatial Audio, and "Hey Siri" commands. The current Studio Display contains the A13 Bionic, a chip that started out in the iPhone 11 lineup back in 2019. The new model could also feature ProMotion for a refresh rate up to 120Hz, just like the MacBook Pro, along with HDR.
Apple released the Studio Display in March 2022, alongside the first Mac Studio. The standalone display features a 27-inch LCD screen with a 5K resolution, a 60Hz refresh rate, up to 600 nits brightness, a built-in camera and speakers, a single Thunderbolt 3 port, and three USB-C ports. In the U.S., the monitor starts at $1,599.
Reports suggest that the next-generation Studio Display will come out in early 2026, when we're also expecting new Macs with the M5 chip.
Apple has been rumored to be working on an updated version of the Pro Display XDR since 2022, adding an Apple silicon chip. As a result, the Pro Display XDR is likely to gain speakers, microphones, Hey Siri support, and a camera with Center Stage, just like the Studio Display. Further parity with upcoming Studio Display features such as mini-LED and ProMotion is also possible.
At the very least, the new monitor is expected to feature the same quantum-dot display technology adopted by the MacBook Pro. The latest quantum-dot display films have equal to better color gamut support, and offer improved motion performance, compared to the KSF phosphor film that Apple uses for the existing Pro Display XDR and previous MacBook Pro models. This means the new Pro Display XDR should have improved color accuracy and faster response times compared to the current model.
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AirPods Pro 3
What's the deal? Take $50 off AirPods Pro 3
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Where can I find the original deal? Right here
$50 OFFAirPods Pro 3 for $199.00
Apple's AirPods Pro 3 hit a new record low price of $199.00 this week on Amazon, down from $249.00, and they're still available today. This deal beats the Black Friday price we saw last month by about $20.
Burton Goods
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MacRumors readers can get 25 percent off sitewide this weekend at Burton Goods, with the code MR25 at checkout. Burton Goods is known for its leather accessories that include iPhone and iPad Cases, MacBook sleeves, Apple Watch bands, MagSafe-compatible wallets, and more.
Samsung
What's the deal? Save sitewide on Samsung TVs, monitors, and more
Where can I get it? Samsung
Where can I find the original deal? Right here
SITEWIDE DISCOUNTSSamsung Holiday Sale
Samsung kicked off a new holiday event this week, and you can find great deals on monitors, storage accessories, TVs, Galaxy smartphones, and home appliances. Many of these deals are the exact same all-time low prices we tracked during Black Friday and Cyber Monday.
A new highlight of this event is Samsung's 4TB Portable SSD T7 Shield for $349.99 ($115 off), and you can still get The Frame Pro on sale for $1,999.00 ($1,200 off).
Apple Watch
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Amazon has a few record low prices on multiple Apple Watch models this week, including the Apple Watch Series 11 and Apple Watch SE 3. These have been some of the most consistent deals to stick around after Black Friday, and they're available in multiple color options and sizes.
iPad Pro
What's the deal? Take up to $180 off M5 iPad Pro
Where can I get it? Amazon and Best Buy
Where can I find the original deal? Right here
$100 OFF11-inch M5 iPad Pro (256GB Wi-Fi) for $899.00
Amazon and Best Buy opened up big discounts across the M5 iPad Pro lineup this week, offering as much as $180 off select tablets. Prices start at $899.00 for the 256GB Wi-Fi 11-inch M5 iPad Pro at Amazon, down from $999.00. All deals in this sale match — or beat — the record low prices we tracked during Black Friday.
If you're on the hunt for more discounts, be sure to visit our Apple Deals roundup where we recap the best Apple-related bargains of the past week.
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Cybersecurity firm Huntress warned that attackers are already abusing the hardcoded keys to perform remote code execution (RCE) on the affected servers.
“The AES implementation of Gladinet’s CentreStack and Triofox products contains hardcoded cryptographic keys,” Huntress researchers said in a blog post. “We are seeing attackers target this flaw across our customer base.”
As with any internet facing server, remote code execution on CentreStack or Triofox can potentially lead to malware deployment, backdoor persistence, and credential theft. Huntress urged all CentreStack/Triofox customers to update to the latest version, 16.12.10420.56791, saying nine of its enterprise customers had already been affected.
Hardcoded keys, harder consequences
At the core of the issue is a design failure in how CentreStack and Triofox generate the cryptographic keys used to encrypt the access tokens the platforms uses to control who can retrieve what files. Huntress found that the server relies on a function called “GenerateSecKey()” to produce the AES key and initialization vector (IV) for ticket encryption — but instead of generating unique values, the function returns the same static 100-byte strings every time the service runs.
“Because the keys never change, we could extract them from memory once and use them to decrypt any ticket generated by the server or worse, encrypt our own,” the researchers said, adding that the keys were static strings of Chinese and Japanese text.
With that ability, an adversary can request any file the server is capable of serving, including the sensitive “web.config” file that contains the ASP.NET machine key.
With the machine key in hand, attackers can generate malicious ViewState payloads that the server will trust, enabling remote code execution through ASP.NET deserialization. Deserialization attacks leverage unsafe parsing of serialized objects (like ViewState in ASP.NET) to inject malicious payloads that run with the privilege of the web service.
Patch now
Huntress identified multiple active attacks with the threat actor first attempting to exploit CVE-2025-11371, a previously disclosed unauthenticated Local File Inclusion bug in CentreStack/ Triofox, followed by the new exploit. Both allowed attackers to obtain the web.config file containing the machine key.
No pre-requisites such as valid credentials or privileged access are needed for a successful attack beyond knowledge of the default keys. To mitigate the risk, Huntress urged all customers to update immediately to the latest builds released by Gladinet on December 8, as these contain fixes for the insecure cryptography.
Where immediate patching isn’t feasible, configuration changes to replace machine keys with randomized values can reduce risk until updates are deployed. Additionally, the huntress team shared the encrypted GET request for web.config as an indicator of compromise (IOC).
Gladinet has previously failed to completely patching a similar hardcoded-keys flaw as criminals found a way to revive exploit conditions on patched systems.
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Note: MacRumors is an affiliate partner with Best Buy. When you click a link and make a purchase, we may receive a small payment, which helps us keep the site running.
When you purchase one of the 13-inch M4 MacBook Air computers at Best Buy, you will get a free $25 Best Buy gift card with your purchase. This is an e-gift card that will be sent out after the eligible product is fulfilled or picked up in a Best Buy store, and a valid e-mail address is required to claim the card.
$250 OFF13-inch M4 MacBook Air (256GB) for $749.00
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As of writing, no 15-inch M4 MacBook Air models are matching any all-time low prices, or include the $25 gift card offer. If you're on the hunt for more discounts, be sure to visit our Apple Deals roundup where we recap the best Apple-related bargains of the past week.
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Die Umsetzung großer Transformationsinitiativen, wie die Einführung von Zero Trust, erfordert mehr als nur technisches Verständnis – und genau hier beginnen die Herausforderungen für viele IT-Sicherheitsentscheider. Sie sind es gewohnt, technisch zu argumentieren, und nicht, einen kulturellen Wandel einzuleiten. Schließlich geht es bei der Ablösung herkömmlicher und gewohnter Infrastrukturen um nichts anderes, als die Komfortzone des Bekannten zu verlassen und Neuland zu betreten.
Die damit verbundenen Unsicherheiten vieler Stakeholder müssen überwunden werden und es gilt, bei skeptischen Führungskräften auf verschiedenen Ebenen Überzeugungsarbeit zu leisten. Das wird nicht mit Hilfe von Fachjargon gelingen. Die Geschäftsleitung sollte vielmehr das Risiko, den Ausweg und die Vorteile begreifen, so dass auch auf dieser Ebene entsprechend kommuniziert werden muss.
Auf die Sprache kommt es an
Der Erfolg auf dem Weg zu Zero Trust beruht demnach weniger auf Technologieverständnis, sondern hängt vielmehr vom Selbstvertrauen und den kommunikativen Fähigkeiten der Verantwortlichen für Cybersicherheit ab. Zero Trust stellt über die Jahre gewachsene Prozesse und Annahmen in Frage. Firewalls und VPNs schufen bislang ein vertrautes, wenn auch lückenhaftes Gefühl der Sicherheit. Zero Trust ersetzt hingegen implizites Vertrauen durch kontinuierliche Überprüfung. Technologisch hat sich dieser Ansatz bereits bewährt. Auf kultureller Ebene ist eine Ablösung des Bekannten viel schwieriger zu vermitteln.
Lesetipp: Zero Trust bereitet CISOs Probleme
IT-Führungskräfte müssen die Vorteile eines Zero Trust-Ansatzes so vermitteln, dass sie auch bei einem nicht-technischen Publikum auf Geschäftsleitungsebene Anklang finden. Das ist kein leichtes Unterfangen für IT-Leader, die sich auf Basis von technischem Verständnis durch die Ränge hochgearbeitet haben. Wenn die technische Vermittlung eines neuen Ansatzes die Beteiligten im Entscheidungsprozess eher abschreckt als überzeugt, kommt die Transformation zum Stillstand und der Prozess zur Ablösung angestammter Infrastruktur verlangsamt sich.
Ziel in dem kommunikativen Prozess muss es also sein, die Geschäftsführung mit ihrer Sprache abzuholen und Sicherheit in ihre Begriffswelt zu übersetzen. Daher kommt es bei der Einleitung einer Sicherheitsmodernisierung darauf an, aufzuzeigen, wie der gesamte Geschäftsbetrieb transformiert werden kann. Entscheiden ist: Wo steht das Unternehmen heute, wo möchte es zukünftig stehen und welche Meilensteine gibt es auf dem Weg dorthin zu bewältigen?
Zero Trust sollte dementsprechend nicht als technischer Trend positioniert werden, sondern als angemessene Reaktion auf branchenweite Notwendigkeiten. Die Ablösung des angestammten Sicherheitsdenkens, verbunden mit dem Burggraben-Modell, bei dem alles innerhalb des Walls vertrauenswürdig ist (Stichwort Firewall), ist eine Mammutaufgabe.
Demgegenüber verzichtet Zero Trust auf jeglichen Vertrauensvorschuss und setzt auf die Überprüfung jedes einzelnen Zugriffs und begrenzt auf diese Weise den Radius eines Angriffs. Das Ergebnis eines solchen Sicherheitsansatzes sind geringere Kosten, weniger Komplexität und trotzdem eine höhere Sicherheit. Diese Sprache vermittelt Ergebnisse, die sich in Wertschöpfung manifestieren.
Überzeugungsarbeit leisten und Zustimmung gewinnen
Wenn die Botschaft in verständliche Worte gefasst wurde, stellt sich die Frage der Vermittlungsstrategie. Der CTO und das Infrastrukturteam sind die ersten Stakeholder im Prozess, die es abzuholen gilt. Eine enge Zusammenarbeit ist entscheidend, um ein potenzielles Ausbremsen der Transformation zu vermeiden. Dieser Ausgangspunkt der kommunikativen Überzeugungsarbeit sollte noch relativ einfach zu bewältigen sein, da auf dieser Ebene eine ähnliche Sprache gesprochen wird.
Eine erfolgreiche Transformation erfordert jedoch eine Zustimmung, die über IT- und Infrastrukturteams hinausgeht. Eine Gruppe, deren Unterstützung entscheidend für die Umsetzung ist, sind die Leiter der verschiedenen Geschäftsbereiche. Diese Business Units konzentrieren sich in erster Linie darauf, ihren Gewinn zu steigern. Sie müssen finanziellen Erfolg erzielen, die betriebliche Effizienz und Kundenzufriedenheit sicherstellen und Störungen minimieren.
Um ihr Vertrauen und ihre Zusammenarbeit zu gewinnen, müssen sich CIOs und CISOs mit deren Geschäftsalltag auseinandersetzen. Maßgeschneiderte Gespräche, die die Prioritäten der Bereichsleiter aufgreifen und sich auf ihre betrieblichen und finanziellen Risiken konzentrieren, können zielgerichtet auf Augenhöhe stattfinden.
Ein Beispiel: In einem Gespräch mit Werksleitern sollte es nicht um komplexe Authentifizierungsverfahren gehen. Zielführender ist es, zu erklären, wie Cyberangriffe zu Produktionsausfällen, Datenverlusten und Bußgeldern wegen Nichteinhaltung von Vorschriften führen können. Diese Themen bereiten den Managern schlaflose Nächte. Wird Zero Trust als Mittel zum Zweck dargestellt, um Produktionsziele zu erreichen und zum Schutz ihrer Gewinn- und Verlustrechnung, verwandelt man den Wechsel des Sicherheitsmodells in einen geschäftlichen Vorteil.
Auch auf den Zeitpunkt und die Häufigkeit der Kommunikation mit den Führungskräften kommt es an, um eine einheitliche Ausrichtung im gesamten Unternehmen zu schaffen und aufrechtzuerhalten. Die regelmäßige Beschäftigung mit dem Thema Cybersicherheit in vierteljährlichen Besprechungen sorgt dafür, relativ schnell Dynamik und Vertrauen in der Führungsebene aufzubauen.
Wenn die anfängliche Skepsis nachlässt, können diese Besprechungen zu kurzen Check-Ins im halbjährlichen Takt abgewandelt werden. Dann sollten praktische Belange und messbare Ergebnisse im Fokus der Gespräche stehen. Dabei können auch Sicherheitsrisiken als geschäftliche Prioritäten neu definiert werden.
Anhänger schaffen
Cyberrisiken haben sich in den vergangenen Jahren von einem Nischenbereich zu einem Thema mit hoher Relevanz avanciert, das ganz oben auf der Agenda jedes CEOs und Vorstandsmitglieds steht. Gespräche mit Führungskräften werden daher zunehmend positiver von diesen Stakeholdern aufgenommen. Ist das Vertrauen in die Umsetzung eines neuen Sicherheitsansatzes erzielt, wird es zunehmend einfacher, das Engagement für jeden einzelnen Aspekt der eigentlichen Transformation zu erhalten.
Bevor ein solches Transformationsprojekt abgesegnet wird, tun CIOs und CISOs gut daran, sich auf eine anfängliche Zurückhaltung auf Seiten der Führungsebene einzustellen. Der CFO, der Chefjustiziar oder Mitglieder des Aufsichtsrats können hier helfen und sich als Verbündete erweisen. Dieser Personenkreis kann subtilen Druck ausüben, wenn traditionelle Entscheidungsketten versagen, und zögerliche Führungskräfte zu Veränderungen ermutigen.
CIOs und CISOs sind mehr als lediglich die Wächter angestammter Technologien. Sie müssen sich zu Fürsprechern eines kulturellen Wandels verändern, um Brücken für die Modernisierung bauen zu können. Dazu ist die Bereitschaft erforderlich, den Führungsstil an die Anforderungen anzupassen. Schließlich liegt es an ihnen, die Sicherheitstransformation auf den Weg zu bringen und dazu die Sprache der Geschäftsführung aufzugreifen. (jm)
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Die Umsetzung großer Transformationsinitiativen, wie die Einführung von Zero Trust, erfordert mehr als nur technisches Verständnis – und genau hier beginnen die Herausforderungen für viele IT-Sicherheitsentscheider. Sie sind es gewohnt, technisch zu argumentieren, und nicht, einen kulturellen Wandel einzuleiten. Schließlich geht es bei der Ablösung herkömmlicher und gewohnter Infrastrukturen um nichts anderes, als die Komfortzone des Bekannten zu verlassen und Neuland zu betreten.
Die damit verbundenen Unsicherheiten vieler Stakeholder müssen überwunden werden und es gilt, bei skeptischen Führungskräften auf verschiedenen Ebenen Überzeugungsarbeit zu leisten. Das wird nicht mit Hilfe von Fachjargon gelingen. Die Geschäftsleitung sollte vielmehr das Risiko, den Ausweg und die Vorteile begreifen, so dass auch auf dieser Ebene entsprechend kommuniziert werden muss.
Auf die Sprache kommt es an
Der Erfolg auf dem Weg zu Zero Trust beruht demnach weniger auf Technologieverständnis, sondern hängt vielmehr vom Selbstvertrauen und den kommunikativen Fähigkeiten der Verantwortlichen für Cybersicherheit ab. Zero Trust stellt über die Jahre gewachsene Prozesse und Annahmen in Frage. Firewalls und VPNs schufen bislang ein vertrautes, wenn auch lückenhaftes Gefühl der Sicherheit. Zero Trust ersetzt hingegen implizites Vertrauen durch kontinuierliche Überprüfung. Technologisch hat sich dieser Ansatz bereits bewährt. Auf kultureller Ebene ist eine Ablösung des Bekannten viel schwieriger zu vermitteln.
Lesetipp: Zero Trust bereitet CISOs Probleme
IT-Führungskräfte müssen die Vorteile eines Zero Trust-Ansatzes so vermitteln, dass sie auch bei einem nicht-technischen Publikum auf Geschäftsleitungsebene Anklang finden. Das ist kein leichtes Unterfangen für IT-Leader, die sich auf Basis von technischem Verständnis durch die Ränge hochgearbeitet haben. Wenn die technische Vermittlung eines neuen Ansatzes die Beteiligten im Entscheidungsprozess eher abschreckt als überzeugt, kommt die Transformation zum Stillstand und der Prozess zur Ablösung angestammter Infrastruktur verlangsamt sich.
Ziel in dem kommunikativen Prozess muss es also sein, die Geschäftsführung mit ihrer Sprache abzuholen und Sicherheit in ihre Begriffswelt zu übersetzen. Daher kommt es bei der Einleitung einer Sicherheitsmodernisierung darauf an, aufzuzeigen, wie der gesamte Geschäftsbetrieb transformiert werden kann. Entscheiden ist: Wo steht das Unternehmen heute, wo möchte es zukünftig stehen und welche Meilensteine gibt es auf dem Weg dorthin zu bewältigen?
Zero Trust sollte dementsprechend nicht als technischer Trend positioniert werden, sondern als angemessene Reaktion auf branchenweite Notwendigkeiten. Die Ablösung des angestammten Sicherheitsdenkens, verbunden mit dem Burggraben-Modell, bei dem alles innerhalb des Walls vertrauenswürdig ist (Stichwort Firewall), ist eine Mammutaufgabe.
Demgegenüber verzichtet Zero Trust auf jeglichen Vertrauensvorschuss und setzt auf die Überprüfung jedes einzelnen Zugriffs und begrenzt auf diese Weise den Radius eines Angriffs. Das Ergebnis eines solchen Sicherheitsansatzes sind geringere Kosten, weniger Komplexität und trotzdem eine höhere Sicherheit. Diese Sprache vermittelt Ergebnisse, die sich in Wertschöpfung manifestieren.
Überzeugungsarbeit leisten und Zustimmung gewinnen
Wenn die Botschaft in verständliche Worte gefasst wurde, stellt sich die Frage der Vermittlungsstrategie. Der CTO und das Infrastrukturteam sind die ersten Stakeholder im Prozess, die es abzuholen gilt. Eine enge Zusammenarbeit ist entscheidend, um ein potenzielles Ausbremsen der Transformation zu vermeiden. Dieser Ausgangspunkt der kommunikativen Überzeugungsarbeit sollte noch relativ einfach zu bewältigen sein, da auf dieser Ebene eine ähnliche Sprache gesprochen wird.
Eine erfolgreiche Transformation erfordert jedoch eine Zustimmung, die über IT- und Infrastrukturteams hinausgeht. Eine Gruppe, deren Unterstützung entscheidend für die Umsetzung ist, sind die Leiter der verschiedenen Geschäftsbereiche. Diese Business Units konzentrieren sich in erster Linie darauf, ihren Gewinn zu steigern. Sie müssen finanziellen Erfolg erzielen, die betriebliche Effizienz und Kundenzufriedenheit sicherstellen und Störungen minimieren.
Um ihr Vertrauen und ihre Zusammenarbeit zu gewinnen, müssen sich CIOs und CISOs mit deren Geschäftsalltag auseinandersetzen. Maßgeschneiderte Gespräche, die die Prioritäten der Bereichsleiter aufgreifen und sich auf ihre betrieblichen und finanziellen Risiken konzentrieren, können zielgerichtet auf Augenhöhe stattfinden.
Ein Beispiel: In einem Gespräch mit Werksleitern sollte es nicht um komplexe Authentifizierungsverfahren gehen. Zielführender ist es, zu erklären, wie Cyberangriffe zu Produktionsausfällen, Datenverlusten und Bußgeldern wegen Nichteinhaltung von Vorschriften führen können. Diese Themen bereiten den Managern schlaflose Nächte. Wird Zero Trust als Mittel zum Zweck dargestellt, um Produktionsziele zu erreichen und zum Schutz ihrer Gewinn- und Verlustrechnung, verwandelt man den Wechsel des Sicherheitsmodells in einen geschäftlichen Vorteil.
Auch auf den Zeitpunkt und die Häufigkeit der Kommunikation mit den Führungskräften kommt es an, um eine einheitliche Ausrichtung im gesamten Unternehmen zu schaffen und aufrechtzuerhalten. Die regelmäßige Beschäftigung mit dem Thema Cybersicherheit in vierteljährlichen Besprechungen sorgt dafür, relativ schnell Dynamik und Vertrauen in der Führungsebene aufzubauen.
Wenn die anfängliche Skepsis nachlässt, können diese Besprechungen zu kurzen Check-Ins im halbjährlichen Takt abgewandelt werden. Dann sollten praktische Belange und messbare Ergebnisse im Fokus der Gespräche stehen. Dabei können auch Sicherheitsrisiken als geschäftliche Prioritäten neu definiert werden.
Anhänger schaffen
Cyberrisiken haben sich in den vergangenen Jahren von einem Nischenbereich zu einem Thema mit hoher Relevanz avanciert, das ganz oben auf der Agenda jedes CEOs und Vorstandsmitglieds steht. Gespräche mit Führungskräften werden daher zunehmend positiver von diesen Stakeholdern aufgenommen. Ist das Vertrauen in die Umsetzung eines neuen Sicherheitsansatzes erzielt, wird es zunehmend einfacher, das Engagement für jeden einzelnen Aspekt der eigentlichen Transformation zu erhalten.
Bevor ein solches Transformationsprojekt abgesegnet wird, tun CIOs und CISOs gut daran, sich auf eine anfängliche Zurückhaltung auf Seiten der Führungsebene einzustellen. Der CFO, der Chefjustiziar oder Mitglieder des Aufsichtsrats können hier helfen und sich als Verbündete erweisen. Dieser Personenkreis kann subtilen Druck ausüben, wenn traditionelle Entscheidungsketten versagen, und zögerliche Führungskräfte zu Veränderungen ermutigen.
CIOs und CISOs sind mehr als lediglich die Wächter angestammter Technologien. Sie müssen sich zu Fürsprechern eines kulturellen Wandels verändern, um Brücken für die Modernisierung bauen zu können. Dazu ist die Bereitschaft erforderlich, den Führungsstil an die Anforderungen anzupassen. Schließlich liegt es an ihnen, die Sicherheitstransformation auf den Weg zu bringen und dazu die Sprache der Geschäftsführung aufzugreifen. (jm)
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With generative AI, these risks aren’t tied to malicious insiders misusing credentials or bypassing controls; they come from well-intentioned employees simply trying to get work done faster. Whether it’s developers refactoring code, analysts summarizing long reports, or marketers drafting campaigns, the underlying motivation is almost always productivity and efficiency.
Unfortunately, that’s precisely what makes this risk so difficult to manage. Employees don’t see themselves as creating security problems; they’re solving bottlenecks. Security is an afterthought at best.
This gap in perception creates an opportunity for missteps. By the time IT or security teams realize an AI tool has been widely adopted, patterns of risky use may already be deeply embedded in workflows.
Right now, AI use in the workplace is a bit of a free-for-all. And when everyone’s saying “it’s fun” and “everyone’s doing it”, it feels like being back in high school: no one wants to be *that* person telling them to stop because it’s risky.
But, as security, we do have a responsibility.
In this article, I explore the risks of unmanaged AI use, why existing security approaches fall short, and suggest one thing I believe we can do to balance users’ enthusiasm with responsibility (without being the party pooper).
Examples of Risky AI Use
The risks of AI use in the workplace usually fall into one of three categories:
Sensitive data breaches: A single pasted transcript, log, or API key may seem minor, but once outside company boundaries, it’s effectively gone, subject to provider retention and analysis. Intellectual property leakage: Proprietary code, designs, or research drafts fed into AI tools can erode competitive advantage if they become training data or are exposed via prompt injection. Regulatory and compliance violations: Uploading regulated data HIPAA, GDPR, etc. into unsanctioned AI systems can trigger fines or legal action, even if no breach occurs. What makes these risks especially difficult is their subtlety. They emerge from everyday workflows, not obvious policy violations, which means they often go unnoticed until the damage is done.
Shadow AI
For years, Shadow IT has meant unsanctioned SaaS apps, messaging platforms, or file storage systems.
Generative AI is now firmly in this category.
Employees don’t think that pasting text into a chatbot like ChatGPT introduces a new system to the enterprise. In practice, however, they’re moving data into an external environment with no oversight, logging, or contractual protection.
What’s different about Shadow AI is the lack of visibility: unlike past technologies, it often leaves no obvious logs, accounts, or alerts for security teams to follow. With cloud file-sharing, security teams could trace uploads, monitor accounts created with corporate emails, or detect suspicious network traffic.
But AI use often looks like normal browser activity. And while some security teams do scan what employees paste into web forms, those controls are limited.
Which brings us to the real problem: we don’t really have the tools to manage AI use properly. Not yet, at least.
Controls Are Lacking
We all see people trying to get work done faster, and we know we should be putting some guardrails in place, but the options out there are either expensive, complicated, or still figuring themselves out.
The few available AI governance and security tools have clear limitations (even though their marketing might try to convince you otherwise):
Emerging AI governance platforms offer usage monitoring, policy enforcement, and guardrails around sensitive data, but they’re often expensive, complex, or narrowly focused. Traditional controls like DLP and XDR catch structured data such as phone numbers, IDs, or internal customer records, but they struggle with more subtle, hard-to-detect information: source code, proprietary algorithms, or strategic documents.
Even with these tools, the pace of AI adoption means security teams are often playing catch-up. The reality is that while controls are improving, they rarely keep up with how quickly employees are exploring AI.
Lessons from Past Security Blind Spots
Employees charging ahead with new tools while security teams scramble to catch up is not so different from the early days of cloud file sharing: employees flocked to Dropbox or Google Drive before IT had sanctioned solutions. Or think back to the rise of “bring your own device” (BYOD), when personal phones and laptops started connecting to corporate networks without clear policies in place.
Both movements promised productivity, but they also introduced risks that security teams struggled to manage retroactively.
Generative AI is repeating this pattern, only at a much faster rate. While cloud tools or BYOD require some setup, or at least a decision to connect a personal device, AI tools are available instantly in a browser. The barrier to entry is practically zero. That means adoption can spread through an organization long before security leaders even realize it’s happening.
And as with cloud and BYOD, the sequence is familiar: employee adoption comes first, controls follow later, and those retroactive measures are almost always costlier, clumsier, and less effective than proactive governance.
So What Can We Do?
Remember: AI-driven insider risk isn’t about bad actors but about good people trying to be productive and efficient. (OK, maybe with a few lazy ones thrown in for good measure.) It’s ordinary rather than malicious behavior that’s unfortunately creating unnecessary exposure.
That means there’s one measure every organization can implement immediately: educating employees.
Education works best when it’s practical and relatable. Think less “compliance checkbox,” and more “here’s a scenario you’ve probably been in.” That’s how you move from fuzzy awareness to actual behavior change.
Here are three steps that make a real difference:
Build awareness with real examples. Show how pasting code, customer details, or draft plans into a chatbot can have the same impact as posting them publicly. That’s the “aha” moment most people need. Emphasize ownership. Employees already know they shouldn’t reuse passwords or click suspicious links; AI use should be framed in the same personal-responsibility terms. The goal is a culture where people feel they’re protecting the company, not just following rules. Set clear boundaries. Spell out which categories of data are off-limits PII, source code, unreleased products, regulated records) and offer safe alternatives like internal AI sandboxes. Clarity reduces guesswork and removes the temptation of convenience.
Until governance tools mature, these low-friction steps form the strongest defense we have.
If you can enable people to harness AI’s productivity while protecting your critical data, you reduce today’s risks. And you’re better prepared for the regulations and oversight that are certain to follow.
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Docker MCP Toolkit changes this completely.
Here’s what that looks like in practice: You ask ChatGPT to check MacBook Air prices across Amazon, Walmart, and Best Buy. If competitor prices are lower than yours, it doesn’t just tell you, it acts: automatically adjusting your Stripe product price to stay competitive, logging the repricing decision to SQLite, and pushing the audit trail to GitHub. All through natural conversation. No manual coding. No copy-pasting scripts. Real execution.
“But wait,” you might say, “ChatGPT already has a shopping research feature.” True. But ChatGPT’s native shopping can only lookup prices. Only MCP can execute: creating payment links, generating invoices, storing data in your database, and pushing to your GitHub. That’s the difference between an advisor and an actor.
By the end of this guide, you’ll build exactly this: a Competitive Repricing Agent that checks competitor prices on demand, compares them to yours, and automatically adjusts your Stripe product prices when competitors are undercutting you.
Here’s how the pieces fit together:
ChatGPT provides the intelligence: understanding your requests and determining what needs to happen Docker MCP Gateway acts as the secure bridge: routing requests to the right tools MCP Servers are the hands: executing actual tasks in isolated Docker containers The result? ChatGPT can query your SQL database, manage GitHub repositories, scrape websites, process payments, run tests, and more—all while Docker’s security model keeps everything contained and safe.
In this guide, you’ll learn how to add seven MCP servers to ChatGPT by connecting to Docker MCP Toolkit. We’ll use a handful of must-have MCP servers: Firecrawl for web scraping, SQLite for data persistence, GitHub for version control, Stripe for payment processing, Node.js Sandbox for calculations, Sequential Thinking for complex reasoning, and Context7 for documentation. Then, you’ll build the Competitive Repricing Agent shown above, all through conversation.
What is Model Context Protocol (MCP)?
Before we dive into the setup, let’s clarify what MCP actually is.
Model Context Protocol (MCP) is the standardized way AI agents like ChatGPT and Claude connect to tools, APIs, and services. It’s what lets ChatGPT go beyond conversation and perform real-world actions like querying databases, deploying containers, analyzing datasets, or managing GitHub repositories.
In short: MCP is the bridge between ChatGPT’s reasoning and your developer stack. And Docker? Docker provides the guardrails that make it safe.
Why Use Docker MCP Toolkit with ChatGPT?
I’ve been working with AI tools for a while now, and this Docker MCP integration stands out for one reason: it actually makes ChatGPT productive.
Most AI integrations feel like toys: impressive demos that break in production. Docker MCP Toolkit is different. It creates a secure, containerized environment where ChatGPT can execute real tasks without touching your local machine or production systems.
Every action happens in an isolated container. Every MCP server runs in its own security boundary. When you’re done, containers are destroyed. No residue, no security debt, complete reproducibility across your entire team.
What ChatGPT Can and Can’t Do Without MCP
Let’s be clear about what changes when you add MCP.
Without MCP
You ask ChatGPT to build a system to regularly scrape product prices and store them in a database. ChatGPT responds with Python code, maybe 50 lines using BeautifulSoup and SQLite. Then you must copy the code, install dependencies, create the database schema, run the script manually, and set up a scheduler if you want it to run regularly.
Yes, ChatGPT remembers your conversation and can store memories about you. But those memories live on OpenAI’s servers—not in a database you control.
With MCP
You ask ChatGPT the same thing. Within seconds, it calls Firecrawl MCP to actually scrape the website. It calls SQLite MCP to create a database on your machine and store the data. It calls GitHub MCP to save a report to your repository. The entire workflow executes in under a minute.
Real data gets stored in a real database on your infrastructure. Real commits appear in your GitHub repository. Close ChatGPT, come back tomorrow, and ask “Show me the price trends.” ChatGPT queries your SQLite database and returns results instantly because the data lives in a database you own and control, not in ChatGPT’s conversation memory.
The data persists in your systems, ready to query anytime; no manual script execution required.
Why This Is Different from ChatGPT’s Native Shopping
ChatGPT recently released a shopping research feature that can track prices and make recommendations. Here’s what it can and cannot do:
What ChatGPT Shopping Research can do:
Track prices across retailers Remember price history in conversation memory Provide comparisons and recommendations What ChatGPT Shopping Research cannot do:
Automatically update your product prices in Stripe Execute repricing logic based on competitor changes Store pricing data in your database (not OpenAI’s servers) Push audit trails to your GitHub repository Create automated competitive response workflows With Docker MCP Toolkit, ChatGPT becomes a competitive pricing execution system. When you ask it to check prices and competitors are undercutting you, it doesn’t just inform you, it acts: updating your Stripe prices to match or beat competitors, logging decisions to your database, and pushing audit records to GitHub. The data lives in your infrastructure, not OpenAI’s servers.
Setting Up ChatGPT with Docker MCP Toolkit
Prerequisites
Before you begin, ensure you have:
A machine with 8 GB RAM minimal, ideally 16GB Install Docker Desktop A ChatGPT Plus, Pro, Business, or Enterprise Account ngrok account (free tier works) – For exposing the Gateway publicly Step 1. Enable ChatGPT developer mode
Head over to ChatGPT and create a new account. Click on your profile icon at the top left corner of the ChatGPT page and select “Settings”. Select “Apps and Connectors” and scroll down to the end of the page to select “Advanced Settings.”
Settings → Apps & Connectors → Advanced → Developer Mode (ON)
ChatGPT Developer Mode provides full Model Context Protocol (MCP) client support for all tools, both read and write operations. This feature was announced in the first week of September 2025, marking a significant milestone in AI-developer integration. ChatGPT can perform write actions—creating repositories, updating databases, modifying files—all with proper confirmation modals for safety.
Key capabilities:
Full read/write MCP tool support Custom connector creation OAuth and authentication support Explicit confirmations for write operations Available on Plus, Pro, Business, Enterprise, and Edu plans Step 2. Create MCP Gateway
This creates and starts the MCP Gateway container that ChatGPT will connect to.
docker mcp server init --template=chatgpt-app-basic test-chatgpt-app Successfully initialized MCP server project in test-chatgpt-app (template: chatgpt-app-basic) Next steps: cd test-chatgpt-app docker build -t test-chatgpt-app:latest . Step 3. List out all the project files
ls -la total 64 drwxr-xr-x@ 9 ajeetsraina staff 288 16 Nov 16:53 . drwxr-x---+ 311 ajeetsraina staff 9952 16 Nov 16:54 .. -rw-r--r--@ 1 ajeetsraina staff 165 16 Nov 16:53 catalog.yaml -rw-r--r--@ 1 ajeetsraina staff 371 16 Nov 16:53 compose.yaml -rw-r--r--@ 1 ajeetsraina staff 480 16 Nov 16:53 Dockerfile -rw-r--r--@ 1 ajeetsraina staff 88 16 Nov 16:53 go.mod -rw-r--r--@ 1 ajeetsraina staff 2576 16 Nov 16:53 main.go -rw-r--r--@ 1 ajeetsraina staff 2254 16 Nov 16:53 README.md -rw-r--r--@ 1 ajeetsraina staff 6234 16 Nov 16:53 ui.html Step 4. Examine the Compose file
services: gateway: image: docker/mcp-gateway # Official Docker MCP Gateway image command: - --servers=test-chatgpt-app # Name of the MCP server to expose - --catalog=/mcp/catalog.yaml # Path to server catalog configuration - --transport=streaming # Use streaming transport for real-time responses - --port=8811 # Port the gateway listens on environment: - DOCKER_MCP_IN_CONTAINER=1 # Tells gateway it's running inside a container volumes: - /var/run/docker.sock:/var/run/docker.sock # Allows gateway to spawn sibling containers - ./catalog.yaml:/mcp/catalog.yaml # Mount local catalog into container ports: - "8811:8811" # Expose gateway port to host Step 5. Bringing up the compose services
docker compose up -d [+] Running 2/2 ✔ Network test-chatgpt-app_default Created 0.0s ✔ Container test-chatgpt-app-gateway-1 Started docker ps | grep test-chatgpt-app eb22b958e09c docker/mcp-gateway "/docker-mcp gateway…" 21 seconds ago Up 20 seconds 0.0.0.0:8811->8811/tcp, [::]:8811->8811/tcp test-chatgpt-app-gateway-1 Step 6. Verify the MCP session
curl http://localhost:8811/mcp GET requires an active session Step 7. Expose with Ngrok
Install ngrok and expose your local gateway. You will need to sign up for an ngrok account to obtain an auth token.
brew install ngrok ngrok config add-authtoken <your_token_id> ngrok http 8811 Note the public URL (like https://91288b24dc98.ngrok-free.app). Keep this terminal open.
Step 8. Connect ChatGPT
In ChatGPT, go to Settings → Apps & Connectors → Create.
Step 9. Create connector:
Settings → Apps & Connectors → Create
- Name: Test MCP Server - Description: Testing Docker MCP Toolkit integration - Connector URL: https://[YOUR_NGROK_URL]/mcp - Authentication: None - Click "Create" Test it by asking ChatGPT to call the greet tool. If it responds, your connection works. Here’s how it looks:
Real-World Demo: Competitive Repricing Agent
Now that you’ve connected ChatGPT to Docker MCP Toolkit, let’s build something that showcases what only MCP can do—something ChatGPT’s native shopping feature cannot replicate.
We’ll create a Competitive Repricing Agent that checks competitor prices on demand, and when competitors are undercutting you, automatically adjusts your Stripe product prices to stay competitive, logs the repricing decision to SQLite, and pushes audit records to GitHub.
Time to build: 15 minutes
Monthly cost: Free Stripe (test mode) + $1.50-$15 (Firecrawl API)
Infrastructure: $0 (SQLite is free)
The Challenge
E-commerce businesses face a constant dilemma:
Manual price checking across multiple retailers is time-consuming and error-prone Comparing competitor prices and calculating optimal repricing requires multiple tools Executing price changes across your payment infrastructure requires context-switching Historical trend data is scattered across spreadsheets Strategic insights require manual analysis and interpretation Result: Missed opportunities, delayed reactions, and losing sales to competitors with better prices.
The Solution: On-Demand Competitive Repricing Agent
Docker MCP Toolkit transforms ChatGPT from an advisor into an autonomous agent that can actually execute. The architecture routes your requests through a secure MCP Gateway that orchestrates specialized tools: Firecrawl scrapes live prices, Stripe creates payment links and invoices, SQLite stores data on your infrastructure, and GitHub maintains your audit trail. Each tool runs in an isolated Docker container: secure, reproducible, and under your control.
The 7 MCP Servers We’ll Use
Server
Purpose
Why It Matters
Firecrawl
Web scraping
Extracts live prices from any website
SQLite
Data persistence
Stores 30+ days of price history
Stripe
Payment management
Updates your product prices to match or beat competitors
GitHub
Version control
Audit trail for all reports
Sequential Thinking
Complex reasoning
Multi-step strategic analysis
Context7
Documentation
Up-to-date library docs for code generation
Node.js Sandbox
Calculations
Statistical analysis in isolated containers
The Complete MCP Workflow (Executes in under 3 minutes)
Step 1. Scrape and Store (30 seconds)
Agent scrapes live prices from Amazon, Walmart, and Best Buy Compares against your current Stripe product price Step 2: Compare Against Your Price (15 seconds)
Best Buy drops to $509.99—undercutting your $549.99 Agent calculates optimal repricing strategy Determines new competitive price point Step 3: Execute Repricing (30 seconds)
Updates your Stripe product with the new competitive price Logs repricing decision to SQLite with full audit trail Pushes pricing change report to GitHub Step 4: Stay Competitive (instant)
Your product now priced competitively Complete audit trail in your systems Historical data ready for trend analysis The Demo Setup: Enable Docker MCP Toolkit
Open Docker Desktop and enable the MCP Toolkit from the Settings menu.
To enable:
Open Docker Desktop Go to Settings → Beta Features Toggle Docker MCP Toolkit ON Click Apply
Click MCP Toolkit in the Docker Desktop sidebar, then select Catalog to explore available servers.
For this demonstration, we’ll use seven MCP servers:
SQLite – RDBMS with advanced analytics, text and vector search, geospatial capabilities, and intelligent workflow automation Stripe – Updates your product prices to match or beat competitors for automated repricing workflows GitHub – Handles version control and deployment Firecrawl – Web scraping and content extraction Node.js Sandbox – Runs tests, installs dependencies, validates code (in isolated containers) Sequential Thinking – Debugs failing tests and optimizes code Context7 – Provides code documentation for LLMs and AI code editors Let’s configure each one step by step.
1. Configure SQLite MCP Server
The SQLite MCP Server requires no external database setup. It manages database creation and queries through its 25 built-in tools.
To setup the SQLite MCP Server, follow these steps:
Open Docker Desktop → access MCP Toolkit → Catalog Search “SQLite” Click + Add No configuration needed, just click Start MCP Server docker mcp server ls # Should show sqlite-mcp-server as enabled That’s it. ChatGPT can now create databases, tables, and run queries through conversation.
2. Configure Stripe MCP Server
The Stripe MCP server gives ChatGPT full access to payment infrastructure—listing products, managing prices, and updating your catalog to stay competitive.
Get Stripe API Key
Go to dashboard.stripe.com Navigate to Developers → API Keys Copy your Secret Key: Use sk_test_... for sandbox/testing Use sk_live_... for production Configure in Docker Desktop
Open Docker Desktop → MCP Toolkit → Catalog Search for “Stripe” Click + Add Go to the Configuration tab Add your API key: Field: stripe.api_key Value: Your Stripe secret key Click Save and Start Server Or via CLI:
docker mcp secret set STRIPE.API_KEY="sk_test_your_key_here" docker mcp server enable stripe 3. Configure GitHub Official MCP Server
The GitHub MCP server lets ChatGPT create repositories, manage issues, review pull requests, and more.
Option 1: OAuth Authentication (Recommended)
OAuth is the easiest and most secure method:
In MCP Toolkit → Catalog, search “GitHub Official” Click + Add Go to the OAuth tab in Docker Desktop Find the GitHub entry Click “Authorize” Your browser opens GitHub’s authorization page Click “Authorize Docker” on GitHub You’re redirected back to Docker Desktop Return to the Catalog tab, find GitHub Official Click Start Server Advantage: No manual token creation. Authorization happens through GitHub’s secure OAuth flow with automatic token refresh.
Option 2: Personal Access Token
If you prefer manual control or need specific scopes:
Step 1: Create GitHub Personal Access Token
Go to https://github.com and sign in Click your profile picture → Settings Scroll to “Developer settings” in the left sidebar Click “Personal access tokens” → “Tokens (classic)” Click “Generate new token” → “Generate new token (classic)” Name it: “Docker MCP ChatGPT” Select scopes: repo (Full control of repositories) workflow (Update GitHub Actions workflows) read:org (Read organization data) Click “Generate token” Copy the token immediately (you won’t see it again!) Step 2: Configure in Docker Desktop
In MCP Toolkit → Catalog, find GitHub Official:
Click + Add (if not already added) Go to the Configuration tab Select “Personal Access Token” as the authentication method Paste your token Click Start Server Or via CLI:
docker mcp secret set GITHUB.PERSONAL_ACCESS_TOKEN="github_pat_YOUR_TOKEN_HERE" Verify GitHub Connection
docker mcp server ls # Should show github as enabled 4. Configure Firecrawl MCP Server
The Firecrawl MCP server gives ChatGPT powerful web scraping and search capabilities.
Get Firecrawl API Key
Go to https://www.firecrawl.dev Create an account (or sign in) Navigate to API Keys in the sidebar Click “Create New API Key” Copy the API key Configure in Docker Desktop
Open Docker Desktop → MCP Toolkit → Catalog Search for “Firecrawl” Find Firecrawl in the results Click + Add Go to the Configuration tab Add your API key: Field: firecrawl.api_key Value: Your Firecrawl API key Leave all other entries blank Click Save and Add Server Or via CLI:
docker mcp secret set FIRECRAWL.API_KEY="fc-your-api-key-here" docker mcp server enable firecrawl What You Get
6+ Firecrawl tools, including:
firecrawl_scrape – Scrape content from a single URL firecrawl_crawl – Crawl entire websites and extract content firecrawl_map – Discover all indexed URLs on a site firecrawl_search – Search the web and extract content firecrawl_extract – Extract structured data using LLM capabilities firecrawl_check_crawl_status – Check crawl job status 5. Configure Node.js Sandbox MCP Server
The Node.js Sandbox enables ChatGPT to execute JavaScript in isolated Docker containers.
Note: This server requires special configuration because it uses Docker-out-of-Docker (DooD) to spawn containers.
Understanding the Architecture
The Node.js Sandbox implements the Docker-out-of-Docker (DooD) pattern by mounting /var/run/docker.sock. This gives the sandbox container access to the Docker daemon, allowing it to spawn ephemeral sibling containers for code execution.
When ChatGPT requests JavaScript execution:
Sandbox container makes Docker API calls Creates temporary Node.js containers (with resource limits) Executes code in complete isolation Returns results Auto-removes the container Security Note: Docker socket access is a privilege escalation vector (effectively granting root-level host access). This is acceptable for local development but requires careful consideration for production use.
Add Via Docker Desktop
MCP Toolkit → Catalog Search “Node.js Sandbox” Click + Add Unfortunately, the Node.js Sandbox requires manual configuration that can’t be done entirely through the Docker Desktop UI. We’ll need to configure ChatGPT’s connector settings directly.
Prepare Output Directory
Create a directory for sandbox output:
# macOS/Linux mkdir -p ~/Desktop/sandbox-output # Windows mkdir %USERPROFILE%\Desktop\sandbox-output Configure Docker File Sharing
Ensure this directory is accessible to Docker:
Docker Desktop → Settings → Resources → File Sharing Add ~/Desktop/sandbox-output (or your Windows equivalent) Click Apply & Restart 6. Configure Sequential Thinking MCP Server
The Sequential Thinking MCP server gives ChatGPT the ability for dynamic and reflective problem-solving through thought sequences. Adding the Sequential Thinking MCP server is straightforward – it doesn’t require any API key. Just search for Sequential Thinking in the Catalog and get it to your MCP server list.
In Docker Desktop:
Open Docker Desktop → MCP Toolkit → Catalog Search for “Sequential Thinking” Find Sequential Thinking in the results Click “Add MCP Server” to add without any configuration The Sequential Thinking MCP server should now appear under “My Servers” in Docker MCP Toolkit.
What you get:
A single Sequential Thinking tool that includes: sequentialthinking – A detailed tool for dynamic and reflective problem-solving through thoughts. This tool helps analyze problems through a flexible thinking process that can adapt and evolve. Each thought can build on, question, or revise previous insights as understanding deepens.
7. Configure Context7 MCP Server
The Context7 MCP enables ChatGPT to access the latest and up-to-date code documentation for LLMs and AI code editors. Adding the Context7 MCP server is straightforward. It doesn’t require any API key. Just search for Context7 in the Catalog and get it added to the MCP server lists.
In Docker Desktop:
Open Docker Desktop → MCP Toolkit → Catalog Search for “Context7” Find Context7 in the results Click “Add MCP Server” to add without any configuration The Context7 MCP server should now appear under “My Servers” in Docker MCP Toolkit
What you get:
2 Context7 tools including: get-library-docs – Fetches up-to-date documentation for a library. resolve-library-id – Resolves a package/product name to a Context7-compatible library ID and returns a list of matching libraries. Verify if all the MCP servers are available and running.
docker mcp server ls MCP Servers (7 enabled) NAME OAUTH SECRETS CONFIG DESCRIPTION ------------------------------------------------------------------------------------------------ context7 - - - Context7 MCP Server -- Up-to-da... fetch - - - Fetches a URL from the internet... firecrawl - ✓ done partial Official Firecrawl MCP Server... github-official ✓ done ✓ done - Official GitHub MCP Server, by ... node-code-sandbox - - - A Node.js–based Model Context P... sequentialthinking - - - Dynamic and reflective problem-... sqlite-mcp-server - - - The SQLite MCP Server transform... stripe - ✓ done - Interact with Stripe services o... Tip: To use these servers, connect to a client (IE: claude/cursor) with docker mcp client connect <client-name> Configuring ChatGPT App and Connector
Use the following compose file in order to let ChatGPT discover all the tools under Docker MCP Catalog:
services: gateway: image: docker/mcp-gateway command: - --catalog=/root/.docker/mcp/catalogs/docker-mcp.yaml - --servers=context7,firecrawl,github-official,node-code-sandbox,sequentialthinking,sqlite-mcp-server,stripe - --transport=streaming - --port=8811 environment: - DOCKER_MCP_IN_CONTAINER=1 volumes: - /var/run/docker.sock:/var/run/docker.sock - ~/.docker/mcp:/root/.docker/mcp:ro ports: - "8811:8811"
By now, you should be able to view all the MCP tools under ChatGPT Developer Mode.
Let’s Test it Out
Now we give ChatGPT its intelligence. Copy this system prompt and paste it into your ChatGPT conversation:
You are a Competitive Repricing Agent that monitors competitor prices, automatically adjusts your Stripe product prices, and provides strategic recommendations using 7 MCP servers: Firecrawl (web scraping), SQLite (database), Stripe (price management), GitHub (reports), Node.js Sandbox (calculations), Context7 (documentation), and Sequential Thinking (complex reasoning). DATABASE SCHEMA Products table: id (primary key), sku (unique), name, category, brand, stripe_product_id, stripe_price_id, current_price, created_at Price_history table: id (primary key), product_id, competitor, price, original_price, discount_percent, in_stock, url, scraped_at Price_alerts table: id (primary key), product_id, competitor, alert_type, old_price, new_price, change_percent, created_at Repricing_log table: id, product_name, competitor_triggered, competitor_price, old_stripe_price, new_stripe_price, repricing_strategy, stripe_price_id, triggered_at, status Indexes: idx_price_history_product on (product_id, scraped_at DESC), idx_price_history_competitor on (competitor) WORKFLOW On-demand check: Scrape (Firecrawl) → Store (SQLite) → Analyze (Node.js) → Report (GitHub) Competitive repricing: Scrape (Firecrawl) → Compare to your price → Update (Stripe) → Log (SQLite) → Report (GitHub) STRIPE REPRICING WORKFLOW When competitor price drops below your current price: 1. list_products - Find your existing Stripe product 2. list_prices - Get current price for the product 3. create_price - Create new price to match/beat competitor (prices are immutable in Stripe) 4. update_product - Set the new price as default 5. Log the repricing decision to SQLite Price strategies: - "match": Set price equal to lowest competitor - "undercut": Set price 1-2% below lowest competitor - "margin_floor": Never go below your minimum margin threshold Use Context7 when: Writing scripts with new libraries, creating visualizations, building custom scrapers, or needing latest API docs Use Sequential Thinking when: Making complex pricing strategy decisions, planning repricing rules, investigating market anomalies, or creating strategic recommendations requiring deep analysis EXTRACTION SCHEMAS Amazon: title, price, list_price, rating, reviews, availability Walmart: name, current_price, was_price, availability Best Buy: product_name, sale_price, regular_price, availability RESPONSE FORMAT Price Monitoring: Products scraped, competitors covered, your price vs competitors Repricing Triggers: Which competitor triggered, price difference, strategy applied Price Updated: New Stripe price ID, old vs new price, margin impact Audit Trail: GitHub commit SHA, SQLite log entry, timestamp TOOL ORCHESTRATION PATTERNS Simple price check: Firecrawl → SQLite → Response Trend analysis: SQLite → Node.js → Response Strategy analysis: SQLite → Sequential Thinking → Response Competitive repricing: Firecrawl → Compare → Stripe → SQLite → GitHub Custom tool development: Context7 → Node.js → GitHub Full intelligence report: Firecrawl → SQLite → Node.js → Sequential Thinking → GitHub KEY USAGE PATTERNS Use Stripe for: Listing products, listing prices, creating new prices, updating product default prices Use Sequential Thinking for: Pricing strategy decisions (match, undercut, or hold), market anomaly investigations (why did competitor prices spike), multi-factor repricing recommendations Use Context7 for: Getting documentation before coding, learning new libraries on-the-fly, ensuring code uses latest API conventions Use Node.js for: Statistical calculations (moving averages, standard deviation, volatility), chart generation, margin calculations BEST PRACTICES Space web scraping requests 2 seconds apart to respect rate limits Calculate price difference as (your_price - competitor_price) Trigger repricing when competitor drops below your current price Log all repricing decisions to SQLite with Stripe IDs for audit trail Push pricing reports to GitHub for compliance Always use Context7 before writing code with unfamiliar libraries Respect margin floors—never reprice below minimum acceptable margin COMMAND RECOGNITION PATTERNS "Check X prices and stay competitive" → Full repricing pipeline: scrape → compare → if competitor lower: Stripe update + SQLite + GitHub "Match competitor price for X" → Stripe: list_products → list_prices → create_price (matching) "Undercut competitors on X" → Stripe: create_price (1-2% below lowest) "Show price history" → SQLite query → format results "Analyze pricing strategy for X" → Sequential Thinking analysis "Why did competitor prices change" → Query data → Sequential Thinking investigation CORE PRINCIPLE When competitors change prices, don't just report—ACT. Update your Stripe prices to stay competitive, log decisions to SQLite, push audit records to GitHub. Transform competitor intelligence into automated repricing. All data stays on YOUR infrastructure. Here’s the prompt:
Set up a competitive repricing agent: 1. I sell MacBook Air M3 on my store - current Stripe price is $549.99 2. Monitor competitor prices on Amazon, Walmart, Best Buy 3. When ANY competitor drops below my price: - Automatically update my Stripe product price to match or beat them - Use "undercut" strategy (price 1% below lowest competitor) - Log the repricing decision to SQLite - Push pricing change report to GitHub Check prices now and reprice if needed. Here’s how ChatGPT responds – orchestrating all 7 MCP servers in a single workflow:
Competitive Repricing Agent – Execution Complete
Within 3 minutes, ChatGPT orchestrated all 7 MCP servers to analyse the market and automatically reprice your product.
Repricing Triggered and Executed:
Metrics
Before
After
Your Price
$549.99
$504.99
Price Change
–
-$45.00 (-8.2%)
Market Position
3rd (behind Best Buy)
#1 Lowest
Competitor Price Scan Results:
Retailer
Price
Vs. Your New Price
Your Store
$504.99
Market Leader
Best Buy
$509.99
+$5.00 (you beat by 1%)
Walmart
$669.00
+$164.01 higher
Amazon
$699.00
+$194.01 higher
What the Agent did (6 Steps):
Installed SQLite3 and created database schema with 4 tables Created Stripe product (prod_TZaK0ARRJ5OJJ8) with initial $549.99 price Scraped live competitor prices via Firecrawl from Amazon, Best Buy, and Walmart Analysed pricing strategy with Sequential Thinking — detected Best Buy at $509.99 below your price Executed repricing — created new Stripe price at $504.99 (price_1ScRCVI9l1vmUkzn0hTnrLmW) Pushed audit report to GitHub (commit `64a488aa`) All data stored on your infrastructure – not OpenAI’s servers.
To check prices again, simply ask ChatGPT to ‘check MacBook Air M3 competitor prices’—it will scrape, compare, and reprice automatically. Run this check daily, weekly, or whenever you want competitive intelligence
Explore the Full Demo
View the complete repricing report and audit trail on GitHub: https://github.com/ajeetraina/competitive-repricing-agent-mcp
Want true automation? This demo shows on-demand repricing triggered by conversation. For fully automated periodic checks, you could build a simple scheduler that calls the OpenAI API every few hours to trigger the same workflow—turning this into a hands-free competitive intelligence system.Default houston Paragraph Text
Wrapping Up
You’ve just connected ChatGPT to Docker MCP Toolkit and configured multiple MCP servers. What used to require context-switching between multiple tools, manual query writing, and hours of debugging now happens through natural conversation, safely executed in Docker containers.
This is the new paradigm for AI-assisted development. ChatGPT isn’t just answering questions anymore. It’s querying your databases, managing your repositories, scraping data, and executing code—all while Docker ensures everything stays secure and contained.
Ready to try it? Open Docker Desktop and explore the MCP Catalog. Start with SQLite, add GitHub, experiment with Firecrawl. Each server unlocks new capabilities.
The future of development isn’t writing every line of code yourself. It’s having an AI partner that can execute tasks across your entire stack securely, reproducibly, and at the speed of thought.
Learn More
New to Docker? Download Docker Desktop Explore the MCP Catalog: Discover containerized, security-hardened MCP servers Get Started with MCP Toolkit: Official Documentation
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What Selenium Does For Web Testing
Selenium automates tests on websites across browsers like Chrome, Firefox, and Edge. It opens pages, clicks buttons, fills forms, and checks results – exactly like manual testing but much faster and repeatable.
The tool has three main parts. Selenium IDE records simple actions for quick playback. WebDriver lets you write detailed tests in Java, Python, C#, Ruby. Grid runs many tests at once on different machines.
Manual testing slows down when sites update daily, common in Bangalore’s fintech and e-commerce. Selenium finds bugs early, saves time, and costs nothing since it’s open source.
Why Selenium Skills Matter In Bangalore
Bangalore leads India’s IT scene with fintech like PhonePe, e-commerce like Flipkart, and startups needing fast web testing. Job sites list Selenium as top skill for QA engineer, SDET, automation tester roles.
Freshers start at 8-15 LPA. Experienced testers reach 20-30 LPA. The Selenium Training In Bangalore covers interview topics like locators, frameworks, and real project practice on NewTours demo site.
Training Structure And Schedule
The course runs 8-12 hours over weekdays or weekends. Trainers with 15+ years experience focus 80% on hands-on coding. You get lifetime LMS access to videos, notes, quizzes, and certificate.
No prior coding needed. Use free tools on your laptop (8GB RAM, Chrome). Classes include live demos, your practice, and doubt clearing.
Training Options Table
ModeDurationPrice (₹)Best ForIncludesSelf-paced videos8-12 hrs4,999Flexible timeVideos, quizzes, lifetime LMS, code filesLive online group8-12 hrs24,999InteractiveLive Q&A, recordings, group chat1:1 online8-12 hrs59,999Personal focusCustom schedule, feedback, resume helpCorporate2-3 daysContactTeams 5+Company examples, certs for all Step By Step Learning Path
Start with Selenium basics and why it’s better than paid tools. Learn IDE for recording, WebDriver setup.
Next, browser commands: open URL, get title, navigate, handle windows, add waits for slow pages.
Find elements with 8 locators: ID, Name, CSS, XPath (practice on NewTours login).
Handle alerts, popups, drag-drop, hover, multi-windows using Actions class.
Frameworks And Best Practices
Learn framework types: data-driven (Excel data), keyword-driven, hybrid. Key parts: reusable code, reports, screenshots.
Page Object Model keeps tests clean. Create page classes like LoginPage.typeUser(“test”). Page Factory @FindBy auto-finds elements.
Data-driven with Apache POI reads Excel for 100 test cases automatically.
DevOpsSchool Platform Details
DevOpsSchool trained 8000+ learners across 60+ tools like Jenkins, Docker, Kubernetes, AWS. 40+ clients rate 4.5/5.
Free resources: 500+ tutorials, YouTube (100k views), cheatsheets, 2000+ interview questions. LMS gives lifetime video/note access. Career help: job board, resume templates, community Discord.
Rajesh Kumar Mentorship
Program follows Rajesh Kumar, 20+ year expert in DevOps, SRE, Kubernetes, MLOps, Cloud. Trained 5000+, keynoted conferences, optimized large pipelines.
Teaches real fixes: stable locators, fast Grid, CI integration. Makes Selenium part of DevOps workflow.
Who Benefits Most
Manual testers upgrade to automation. Freshers enter QA roles. Developers add testing skills. Teams standardize practices.
8GB RAM laptop needed Chrome browser Basic HTML taught Real Learner Feedback
Abhinav Gupta (5★): Interactive sessions built confidence. Indrayani (5★): Hands-on solved queries. Ravi Daur: POM helped land job.
Common Questions
Miss class? Recordings available. Invoice? Auto email. Refund? Before start.
Get Started Today
Enroll in Selenium Training In Bangalore for job-ready skills. Batches fill fast.
Contact Details
Email: [email protected]
Phone WhatsApp India: +91 84094 92687
Phone WhatsApp USA: +1 (469) 756-6329
Website: DevOpsSchool
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The Secretariat of the Swiss Competition Commission opened the probe on December 10 to examine whether rival mobile payment apps can compete fairly with Apple Pay for contactless payments in stores.
Android devices freely allow third-parties to use NFC technology, but Apple only began allowing Swiss app developers to access its NFC interface in late 2024. The regulator is looking into whether the conditions differ from those Apple separately agreed to in the EU last year.
After pressure from the European Commission, Apple agreed in July 2024 to open NFC access across the European Union, of which Switzerland is not a member.
Switzerland's competition watchdog has been engaged in discussions with Apple since that time. The COMCO investigation is now gathering information from market participants to decide if Apple's Swiss-specific terms do in fact comply with local competition law.Tags: Apple Antitrust, Apple Pay, Switzerland
This article, "Apple Pay Faces Swiss Antitrust Investigation Over NFC Access" first appeared on MacRumors.com
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Why Selenium Testing Matters
Selenium stands out as a free, open-source automation suite that controls real browsers (Chrome, Firefox, Edge, Safari) through WebDriver protocol. Unlike record-playback tools, it lets you write precise scripts in Java, Python, C#, Ruby, or JavaScript to handle dynamic pages, AJAX calls, and modern SPAs that manual testing can’t scale. Mumbai’s fintech (Paytm, PhonePe), e-commerce (Flipkart teams), and startup ecosystem release features daily, making manual regression impossible. Selenium integrates seamlessly with Jenkins CI/CD, Git workflows, and Docker test environments. Multi-language support means your Java backend team or Python data team can all contribute tests without tool conflicts. Key advantages include cross-browser testing, parallel execution on Selenium Grid, and mobile testing via Appium extension. Limitations like no native mobile support or built-in reporting get covered with integrations (TestNG, ExtentReports). This makes Selenium the #1 skill on Mumbai QA job postings.
Program Overview
The Selenium Training In Mumbai delivers 8-12 hours of structured content across 15+ years experienced trainers who built enterprise frameworks at banks and product companies. 80-85% hands-on ensures you debug live failures, not just copy demos. Weekday evenings (7-9 PM IST) and weekend batches (Sat-Sun, 10 AM-2 PM) fit working professionals. Corporate 2-3 day intensives customize to company stacks (Maven+Jenkins or Python+Pytest). All modes include lifetime LMS access with recordings, code repos, and 1000+ interview questions. Trainers demo first (screen share), then you code alongside with pair-debugging. Labs run on pre-built AWS VMs or local Docker so no setup frustration. Progress tracking via GitHub commits prepares portfolio-ready automation suites.
Core Selenium Skills
Selenium Foundation covers IDE record/playback for quick prototypes, WebDriver architecture (JSON Wire Protocol), component suite (Grid, RC legacy). It compares Selenium vs UFT/LoadRunner: free vs licensed, code-driven vs GUI-heavy. Browser Automation includes 20+ commands practiced – driver.get(), navigate().back/forward/refresh, getTitle/currentUrl/pageSource, manage().window() sizing. WindowHandle switching handles multi-tab flows (OAuth redirects). Implicit/Explicit waits prevent NoSuchElementExceptions. WebElement Mastery teaches 8 locator strategies ranked by speed/reliability: ID > Name > CSS > XPath > ClassName > TagName > LinkText > PartialLinkText. XPath axes (parent::, following-sibling::) work for complex DOMs. Practice on NewTours includes login(“user”,”pass”), flight search dropdowns, verify success messages. Advanced Interactions cover 6 alert types handled (simple.alert(), confirm(), prompt(), authentication). iframe.switchTo(), newWindow() manage popups. Actions chains handle dragAndDrop(src,dest), moveToElement(hover), keyDown(ENTER). Select class works for single/multi dropdowns.
Frameworks and Patterns
Framework Fundamentals explain data-driven (Excel/CSV params), keyword-driven (action keywords map to methods), hybrid (BDD+Cucumber). Anatomy includes base test class, page objects, utils (logs, screenshots), listeners (retry failed tests), runners (parallel XML suites). Page Object Model Deep Dive shows POM separates locators/actions from tests. PageFactory @FindBy(css=”#login”) uses lazy initialization. Fluent design example: loginPage.typeUsername(“user”).typePassword(“pass”).submit().verifySuccess(). Data-Driven Testing uses Apache POI reads .xlsx testdata.xlsx Sheet1 row-by-row. @DataProvider feeds login credentials, invalid cases. Data/code separation via src/test/resources/datadir structure.
Learning Options Table
Flexible paths match budgets/schedules:
ModeDurationPrice (INR)Best ForIncludesSelf-paced videos8-12 hrs4,999Night owls, self-starters50+ videos, quizzes, lifetime LMS, code downloadsLive online group8-12 hrs24,999Interactive learnersLive Q&A, screen-share coding, recordings, Slack group1:1 online8-12 hrs59,999Custom needsTailored projects, daily feedback, resume guidanceCorporate2-3 daysContact usTeams (5+)Onsite/virtual, company data, certs for all Secure enroll forms auto-populate “Selenium Online Training” + fee. Group discounts: 10% (2-3), 25% (7+).
DevOpsSchool Platform Details
DevOpsSchool powers 8000+ careers across 60+ tools (Jenkins, Docker, Kubernetes, AWS, Terraform, Ansible, Prometheus). Founded to bridge IT skill gaps, it serves 40+ enterprises with 4.5/5 rating. Free resources include 500+ tutorials, YouTube (100k+ views, weekly live sessions), cheatsheets, interview repo (2000+ Qs across tools). Corporate clients: Flipkart, Swiggy, PhonePe teams. Lifetime LMS gives video/notes/quizzes access forever. Tools section offers mindmaps, whiteboards, chatroom, markdown editor for collaborative learning. Career services include jobs board (200+ postings), certification dumps (CKA/CKAD), resume templates, mock interviews. Community features events (monthly meetups), Discord (5k+ members), blog (weekly DevOps posts). Support team resolves queries 24/7 via chat/email/Slack. Group training discounts make team upskilling affordable.
Rajesh Kumar Mentorship
Program follows battle-tested methods by Rajesh Kumar, 20+ year DevOps/SRE legend across Kubernetes, MLOps, AIOps, Cloud (AWS/GCP/Azure). Trained 5000+ professionals globally, keynoted DevOpsDays India, NASSCOM events. Optimized 100k+ node pipelines at banks blending Selenium+Jenkins+Grafana+ELK. Authored “DevOps Pipeline Playbook” (10k+ downloads). Consults Fortune 500 on zero-downtime deployments. Real-world fixes taught: flaky XPath → CSS priority rules, slow Grid → Docker Selenium Hub clusters, CI failures → Allure+BlazeMeter reports. Cross-tool mastery shows Selenium as DevOps citizen: GitHub Actions triggers → Selenium tests → Slack notifications. His “fail fast, learn faster” sessions build production-ready confidence.
Who Benefits Most
Career Switchers: Manual testers → SDETs (15-25L CTC Mumbai entry). Freshers: B.Tech/MCA → QA automation (internships at ZS, Barclays, Capgemini). Developers: Fullstack → TDD with Selenium+JUnit5. Leaders: Test Architects understanding POM at enterprise scale (1000+ tests). Laptop req: 8GB RAM, Chrome/Firefox, IntelliJ/VSCode/PyCharm free. No prior Java needed – Python track available.
Real Learner Feedback
Abhinav Gupta (Hyderabad, 5★): “Interactive coding built real confidence. Rajesh debugged my XPath live.” Indrayani (5★): “Hands-on NewTours project solved interview doubts instantly.” Ravi Daur (Noida): “POM section landed Accenture automation role”.
FAQs Covered
Miss class? Recordings + rejoin next batch free (3 months). Prerequisites? Basic HTML/CSS helps, coding taught from scratch. Invoice? Auto-email post-gateway payment. Refund? No post-start, discuss issues with trainer.
Get Started Today
Transform manual testing into automation superpower. Enroll now – batches fill fast in Mumbai hiring season.
Contact Details
Email: [email protected]
Phone WhatsApp India: +91 84094 92687
Phone WhatsApp USA: +1 (469) 756-6329
Website: DevOpsSchool
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"Apple is the key driver as it starts to procure panels for its first foldable iPhone," said Counterpoint's Guillaume Chansin. The research firm expects Apple's foldable iPhone to "reinvigorate the broader market," spurring massive growth in panel shipments next year.
Apple's "iPhone Fold," which we are provisionally calling it, is said to have an outer display and opens like a book to reveal a larger iPad mini-style internal display. The report indicates that Apple's entry into the market will see similar book-type foldables cement their position as the dominant form factor in 2026. Meanwhile, multi-fold panels, like those used in Samsung's Galaxy Z TriFold, will account for only low single-digit market share.
Samsung Display stands to benefit most from the shift, with its panel market share expected to climb past 50%. Samsung is also reportedly supplying the panels for Apple's first foldable. The move toward book-type designs is also expected to drive up average selling prices for foldable panels.
Consumer preference appears to be shifting toward larger displays that offer tablet-like productivity. Counterpoint notes that Samsung's Galaxy Z Fold outsold the Flip model during the early sales window in the second half of 2025, which was a first for the series.
iPhone Fold: Launch, Pricing, and What to Expect From Apple's Foldable
Counterpoint expects overall foldable smartphone shipments to grow 14% in 2025 and 38% in 2026. Apple's foldable iPhone is expected to arrive next year, around mid-September.Tags: Counterpoint, Foldable iPhone
This article, "'iPhone Fold' Arrival Expected to Cement Book-Style Era for Foldables" first appeared on MacRumors.com
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Why Scala Spark Shines in Hyderabad
Scala with Spark Training In Hyderabad simplifies large-scale data handling on platforms like AWS, Azure, or local setups with Spark RDDs. You avoid long processing times by coding straightforward Scala logic, while Spark distributes tasks across clusters, reducing mistakes and speeding up data workflows.
Hyderabad’s booming tech scene, full of fintech firms, startups, and analytics hubs, demands quick data tools. These skills let teams scale securely, maintain stability, and create live dashboards via Spark Streaming. It pairs seamlessly with Kafka, Hadoop, Jenkins, and Kubernetes, streamlining daily operations with solid Hyderabad data expertise. Trained teams cut manual efforts by 90% and deliver results faster.
Program Highlights
The Scala with Spark Training In Hyderabad begins with basics like Scala REPL, variables, functions, classes, and case classes, then dives into Spark RDDs, DataFrames, Spark SQL, Streaming, GraphX, and MLlib—all in Scala. Hands-on projects include Twitter sentiment analysis, ETL pipelines from S3 to Cassandra, e-commerce recommendations, and fraud detection systems deployed on YARN, Kubernetes, and clouds with monitoring.
It blends free open-source Spark for practice with enterprise clouds like AWS EMR, Databricks, and Azure Synapse for datasets exceeding 10TB. Flexible schedules—weekdays, weekends, or intensive—suit Hyderabad professionals balancing work and learning, ensuring complete mastery of Scala-Spark integration.
Learning Options
Choose your ideal path for Scala with Spark based on schedule and needs:
Learning ModeDurationBest ForKey FeaturesSelf-Paced Videos8-12 hoursFlexible schedulesRecorded sessions from Scala intro to MLlib, quizzes, code exercises, lifetime notebook access Live Online Group8-12 hoursInteractive learnersReal-time Q&A, collaborative Spark Streaming coding, shared notebooks, recordings 1:1 Online Mentoring8-12 hoursPersonalized progressCustom projects, direct feedback, code reviews, weekly calls on your data challenges Corporate Classroom/Online2-3 daysTeam upskillingTailored to company data, group projects, certifications, on-site or virtual in Hyderabad Hands-on work takes 80-85% of time on AWS EMR, Databricks, or VMs with pre-configured Jupyter setups. Instructors demo, guide your attempts, debug issues, and optimize in small groups tailored for Hyderabad’s job market.
Core Skills Gained
Finish ready for Hyderabad data roles with these practical abilities:
Scala Foundations: Immutable data, pattern matching, pure functions for robust Spark applications. Spark Essentials: RDD operations, transformations, persistence, fault tolerance, efficient joins. Advanced Data Handling: DataFrames, SQL queries, UDFs, Parquet/JSON I/O, schema evolution. Deployment Mastery: YARN clusters, Kubernetes orchestration, EMR/Databricks scaling, monitoring, cost optimization. No advanced prerequisites—basic coding suffices. Instructors build from scratch for Hyderabad beginners. Use a standard laptop (8GB RAM, Docker), free AWS tier, ready scripts, plus resume tweaks and mock interviews.
DevOpsSchool Excellence
DevOpsSchool leads data and DevOps education in Hyderabad, training 8000+ across 60+ tools like Spark, Kubernetes, Jenkins with lifetime resources—videos, PDFs, labs. Venues in key spots, online globally, serving 40+ firms at 4.5/5 stars, powering roles at Flipkart, Swiggy.
Standout features:
Ongoing Support: Lifetime chat access to mentors, beyond course end. Project Focus: Portfolio-ready Spark apps for GitHub. Career Boost: 1000+ interview questions, resume service, job board. Testimonials shine: Abhinav Gupta (5 stars): “Spark projects landed my Flipkart role.” Indrayani: “Rajesh resolved live Spark issues instantly.”
Rajesh Kumar’s Guidance
Guided by Rajesh Kumar, a veteran with 20+ years in DevOps, SRE, DataOps, MLOps, Kubernetes, cloud architectures, and massive Spark deployments. He’s trained 5000+, authored Spark guides, keynoted Hyderabad meetups, optimized 10M+ record pipelines for banks.
His style—live coding, shuffle troubleshooting, root-cause demos on real failures—makes tough concepts stick. Alumni say: “Rajesh’s insights secured my PhonePe position.” Ideal for Hyderabad’s competitive interviews.
Lasting Support System
Post-training aid endures for Scala with Spark success:
Support ElementBenefits DeliveredReal ImpactLifetime Videos200+ hours, notebooks, weekly updatesRefresh Spark Streaming pre-Swiggy interview anytime Priority ChatCode debugging, job advice“Spark failed—Rajesh fixed it swiftly” Interview Prep1000+ questions, mocks, resumes85% placement at Groww, PhonePe Project Portfolio5 deployable Spark apps for GitHub“Recommendation project clinched Cred role” Certification AidExam dumps, badge guidance“Cert boosted salary offer” Miss a session? Catch recordings or rejoin free for 3 months. Group discounts: 10% (2-3 people), 25% (7+), perfect for Hyderabad teams.
Ideal Participants
Tailored for Hyderabad’s data ecosystem:
Data Engineers: Upgrade from slow ETL to Spark speed. Analysts: Shift to real-time visuals from batch reports. ML Practitioners: Scale models for enterprise volumes. Developers: Embed analytics in cloud-native apps. Leaders: Standardize stacks to cut costs, risks. Beginners aim for 15-25L CTC roles; pros target 40L+ leadership. Weekends fit return-to-work parents.
Start Your Journey
Boost Hyderabad data teams with Spark? Limited spots—discuss schedules today.
Email: [email protected]
Phone WhatsApp India: +91 84094 92687
Phone WhatsApp USA: +1 (469) 756-6329
Website: DevOpsSchool
Final Thoughts
Scala with Spark Training In Hyderabad delivers battle-tested skills, endless support, GitHub projects for Hyderabad’s data surge. Master RDDs, DataFrames, Streaming, MLlib from novice to pro under Rajesh’s wing—Day 1 deployables ace Flipkart/Swiggy interviews at 20L+ CTC. Enroll for Spark dominance now.
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Dead Cells is an action-platformer from developer Motion Twin that combines elements of traditional side-scrolling combat with roguelike design. Players explore a procedurally generated castle, where levels, enemy placements, and rewards change with each run.
The game is known for its responsive combat, permanent progression mechanics, and branching level paths that unlock over time. Its pixel-art presentation and soundtrack have been lauded by critics, and regular post-launch updates have expanded the game with new content, keeping players coming back for more.
Epic has reported that hundreds of millions of free games have been redeemed on its store. If you're in the EU and haven't taken advantage of previous Epic Games store giveaways, grabbing a free copy of Dead Cells is, by all accounts, a good way to start. The offer ends on December 18, and you'll need to create an Epic account if you don't already have one.
The Epic Games Store for iOS is only available in the European Union right now because alternative app stores and distribution methods are not allowed in other countries.Tags: Epic Games, European Union
This article, "'Dead Cells' Free for a Limited Time on Epic Games' iOS Store in the EU" first appeared on MacRumors.com
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Why Scala and Spark Matter
Scala with Spark Training In Bangalore makes big data jobs easy on AWS, Azure, or your own computers using Spark RDDs. It removes long waits—you write easy Scala code for what you want and Spark spreads the work fast, cutting errors and time in data jobs.
Bangalore has lots of tech jobs in banks, startups, and data work. Fast tools help teams grow safe, stay steady, and make real-time reports with Spark Streaming. It works well with Kafka, Hadoop, Jenkins, and Kubernetes so your day goes smooth with Bangalore data skills. Teams do 90% less hand work after learning and get answers quick.
What the Program Covers
The Scala with Spark Training In Bangalore starts simple with Scala REPL, variables, functions, classes, case classes, then goes to Spark RDDs, DataFrames, Spark SQL, Streaming, GraphX, and MLlib all in Scala. You build full projects like Twitter feelings, ETL from S3 to Cassandra, shop tips, and fraud check putting to YARN, Kubernetes, clouds with checks.
It covers free Spark for tests and cloud like AWS EMR, Databricks, Azure for big data over 10TB. Weekday, weekend, or short fits Bangalore workers.
Ways to Learn
Pick what fits for Scala with Spark:
Way to LearnTime AboutGood ForExtra DetailsVideos on your time8-12 hoursBusy days night workGood videos from live classes on Scala to Spark MLlib quizzes code practice notebooks anytime access Live group online8-12 hoursTalk with othersLive questions group code on Spark Streaming shared notebooks chat help videos to keep One with teacher online8-12 hoursYour speed your jobsYour pace your data work Spark tips chat with teacher code checks weekly calls Group room or online2-3 daysBangalore teamsIn room or screen team work on your data safe help manager training certs 80-85% do it yourself on AWS EMR, Databricks, VMs easy setup Jupyter notebooks. Teacher shows you try fixes mistakes small groups for Bangalore jobs.
Skills You Get
Ready for data jobs after Scala with Spark Training In Bangalore:
Scala easy: Clean functions no change data match patterns fast code for Spark jobs. Spark main: RDDs change do save load fix lost data save smart joins. Data tools: DataFrames SQL questions UDFs safe types JSON files schema change. Run real: YARN Kubernetes cloud EMR Databricks watch cost safe log. No hard start basic code ok teachers start from easy for Bangalore new people. Laptop 8GB RAM Docker free AWS all labs scripts ready resume help interviews.
About DevOpsSchool
DevOpsSchool best for data DevOps in Bangalore 8000+ people 60+ tools Spark Kubernetes to Jenkins lifetime videos PDFs kits. Rooms in Koramangala Pune Mumbai online 40+ companies 4.5 stars certs for Flipkart Swiggy.
Good parts:
Help always chat teacher forever not short time. Real work Spark jobs for your GitHub show. Job help questions resume network jobs board.
Abhinav Gupta 5 stars “Spark projects helped Flipkart job.” Indrayani “Rajesh fixed Spark questions live.” Meet Rajesh Kumar
Led by Rajesh Kumar top teacher 20+ years DevOps SRE DataOps MLOps Kubernetes cloud big Spark work. Taught 5000+ wrote Spark tips spoke Bangalore meetups helped banks 10M fast systems.
He codes live big Spark fixes shuffles teaches why slow uses your problems. Alumni “Rajesh stories got me PhonePe job.” Good for Bangalore hard interviews.
Help That Lasts
Help keeps going after Scala with Spark Training In Bangalore:
Help PartWhat You GetWhy GoodAlways videos200+ hours notebooks cert slides new weeklySee Spark Streaming before Swiggy job anytime Chat supportTeacher code check job help“Spark broke Rajesh fixed fast” Interview stuff1000+ questions company mocks resume85% get jobs Groww PhonePe Show projects5 Spark jobs ready GitHub“My tips project got Cred job” Cert easyTests dumps help known badge“Cert more pay” Miss class videos or next free 3 months. Groups save 10% 2-3 25% 7+ good for Bangalore teams.
Who Fits Best
Good for Bangalore data people:
Data workers slow tools want Spark fast ETL. Report makers need live boards Spark to charts. Learn models grow small to Spark ML big users. Code makers add data power cloud apps. Bosses make teams same Spark save money. New with code to 15-25L jobs old to boss 40L+. Weekend for moms back work.
Get Going
Make data teams fast Spark? Spots go quick chat Bangalore dates your plan.
Email: [email protected]
Phone WhatsApp India: +91 84094 92687
Phone WhatsApp USA: +1 (469) 756-6329
Website: DevOpsSchool
Wrap Up
Scala with Spark Training In Bangalore gives real skills always help show projects for Bangalore data jobs. Learn RDDs DataFrames Streaming MLlib from easy to real with Rajesh help. Day 1 Spark jobs good for Flipkart Swiggy talks 20L+ pay. Join get Spark power now.
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Why Puppet Helps
Puppet Training in Pune is useful for getting servers ready, adding apps, and keeping rules the same across data centers, AWS or Azure clouds, and mixed setups with server automation. It lets you write simple Puppet manifests that describe the state you want, and Puppet applies that state on every node, which reduces mistakes and saves effort when working with DevOps tools.
Pune’s fast-growing tech scene—product firms, startups, and cloud projects—needs solid configuration management to scale safely, pass security checks, and stay stable during frequent changes supported by cloud config. Puppet fits well with tools like Jenkins and Kubernetes, so it becomes a strong base for Pune IT skills and can cut manual work sharply once Puppet automation is in place.
What the Program Covers
The Puppet Training in Pune starts from the basics and goes to advanced topics, so it suits both freshers and experienced engineers. You learn Puppet resources, facts, and variables, then move into writing clean Puppet modules, organizing classes, using Hiera to split configuration data, and setting up a proper primary server and agent model for infrastructure as code.
You also work on real tasks such as preparing many servers at once, enforcing firewall rules, standardizing software versions, and keeping systems aligned through server automation. By the end, you can use Puppet in CI/CD pipelines, cloud projects, and daily operations where reliable, repeatable changes and strong configuration management matter, especially in cloud config setups.
Ways to Learn
The program offers several learning modes so you can choose what fits your time and style for Puppet automation:
Way to LearnTime AboutGood ForExtra DetailsVideos on your own time8–12 hoursPeople who need flexibilityWatch any time, pause, repeat tough Puppet modules; includes short tests for configuration management Live group online8–12 hoursThose who like group sessionsLive chats, shared tasks using Puppet manifests, quick help, plus recordings to watch later One-on-one online8–12 hoursLearners wanting personal paceYour speed, your real job cases in server automation, close feedback each step Group room or online2–3 daysPune teams and companiesIn-person or virtual, focused group work on infrastructure as code tied to your DevOps tools Around 80–85% of the time is used for hands-on labs on DevOpsSchool’s AWS cloud or on simple virtual machines, so you focus on Puppet and not on setup problems in cloud config. Trainers first show each example and then guide you while you repeat it, helping you avoid common issues in Puppet manifests and Puppet modules.
Skills You Get
After Puppet Training in Pune, you gain a clear set of skills that you can use right away in your job:
Write Puppet manifests and Puppet modules to manage services, packages, users, files, and apps in a reusable and clean way for server automation. Set up and manage a Puppet primary server and agents, handle node groups, catalogs, and reports to keep configuration management strong across many machines. Use infrastructure as code on Linux and other systems, joined with CI/CD DevOps tools and cloud platforms like AWS, so changes are fast and safe. Follow best practices like separate dev/test/prod environments, roles and profiles, and Hiera-based data, so cloud config stays easier to manage even as systems grow. You don’t need strict prerequisites, but some Linux or IT background helps. Trainers still explain things from the start so motivated beginners from Pune IT backgrounds can follow along. A normal laptop (Windows, macOS, or Linux) with at least 2 GB RAM and about 20 GB disk space is enough to practice Puppet automation with local VMs or AWS Free Tier.
About DevOpsSchool
DevOpsSchool is a well-known place for DevOps, cloud, and automation learning, with both online and classroom options. It provides tutorials, video sessions, PDFs, slides, quizzes, and other resources for more than 60 tools used in configuration management, CI/CD, containers, monitoring, and cloud.
Learners get lifetime access to the LMS with recordings, notes, and step-by-step guides, as well as lifetime technical support, so doubts about Puppet and related topics can be cleared long after the batch ends. DevOpsSchool also shares exam dumps, interview kits, and group discounts, and feedback from past learners often notes that the hands-on approach helped them move into roles where server automation and infrastructure as code are part of daily work.
About Rajesh Kumar
The Puppet Training in Pune program is guided by Rajesh Kumar, a global expert with over 20 years of experience across DevOps, DevSecOps, SRE, DataOps, AIOps, MLOps, Kubernetes, and cloud platforms. He has delivered many training and consulting projects for companies in India and abroad, helping teams build CI/CD pipelines, adopt automation, and bring modern configuration management into real environments.
Rajesh Kumar is known for using real-world stories—such as managing thousands of nodes with well-structured Puppet modules or fixing unstable deployments by improving Puppet manifests—to explain complex topics in simple language. Because he works across a wide range of tools, he also shows how Puppet fits into the bigger DevOps picture, which is very useful for engineers building long-term careers in a growing tech hub like Pune.
Support That Continues
Support continues even after your Puppet Training in Pune batch is over, so you can keep improving your skills on real projects:
Help TypeWhat You GetWhy It HelpsLifetime LMSRecordings, slides, notes always availableReview Puppet automation ideas whenever you need a refresh Technical supportOngoing email and phone helpGet support to tune Puppet modules or fix configuration management problems Interview kitQuestion sets, exam help, resume tipsPrepare better for DevOps and SRE roles that need infrastructure as code Real projectsScenario-based Puppet implementationBuild examples you can show as server automation experience Certification helpGuidance for Puppet and DevOps certificationsAdd proof of your cloud config and automation strengths If you miss a live class, you can use recordings or attend that topic again in another batch within a given time, and group sign-ups get step-wise discounts, which makes it easier for Pune teams to learn together.
Who It Fits
Puppet Training in Pune suits many kinds of roles and career stages:
System admins who manage many servers and want to stop repeating the same manual steps. DevOps engineers who run or build CI/CD pipelines and need strong configuration management. SRE and operations engineers who care about uptime, alerts, and compliance across changing systems. Cloud engineers on AWS, Azure, or other platforms who want infrastructure as code for repeatable, safe deployments. Team leads and architects who want less drift between servers and smoother releases backed by automation. Motivated freshers with some Linux or IT knowledge can also gain a solid, tool-based profile that helps them stand out in the Pune job market.
Get Started
If you want your infrastructure to be easier to manage, safer to change, and more stable, Puppet Training in Pune offers a clear, hands-on way to move in that direction. You learn from experienced mentors, practice on real-life cases, and keep support afterward, so you can use Puppet with confidence in your own setup.
To know more or enroll:
Email: [email protected] Phone & WhatsApp (India): +91 84094 92687 Phone & WhatsApp (USA): +1 (469) 756-6329 Website: DevOpsSchool Wrap Up
Puppet Training in Pune brings together practical Puppet automation, strong mentors, and flexible learning options to help you grow in configuration management and DevOps. With focus on Puppet modules, Puppet manifests, real projects, and long-term help, you can build solid server automation skills and advance in Pune’s fast-moving tech space using infrastructure as code and cloud config.
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Mitte Oktober funktionierte im Rathaus Untereisesheim fast nichts mehr. Die Gemeindeverwaltung war Ziel eines Cyberangriffs, bei dem IT-Systeme verschlüsselt und Daten von den Servern gestohlen wurden, heißt es in einer aktuellen Mitteilung auf der Website. Demnach hätten die Ermittlungen nun ergeben, dass Teile der entwendeten Daten im Darknet gelandet sind.
Bürgerdaten wohl nicht betroffen
Dazu sollen unter anderem alte Personalakten sowie Daten- und Bilderlaufwerke von Mitarbeitern zählen. Sensible Daten von den Bürgern seien aber nicht betroffen, betont das Rathaus. „Nach derzeitigem Kenntnisstand wurden keine Daten aus dem Zentralen Rechenzentrum und dem Dokumentenmanagementsystem veröffentlicht“, heißt es in der Mitteilung.
Ein Lösegeld sei damals nicht gezahlt worden, heißt es weiter. Stattdessen hatte sich die Gemeindeverwaltung entschieden, eng mit der Cybersicherheitsagentur Baden-Württemberg (CSBW) und dem Landeskriminalamt eng zusammenzuarbeiten.
„Unmittelbar nach dem Angriff wurden die betroffenen Systeme gesichert und wiederaufgebaut sowie zusätzliche Maßnahmen zur IT-Sicherheit umgesetzt. Diese werden laufend geprüft und weiterentwickelt“, erklärt das Rathaus. Die zuständigen Datenschutz- und Aufsichtsbehörden seien über den Vorfall informiert worden.
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Mitte Oktober funktionierte im Rathaus Untereisesheim fast nichts mehr. Die Gemeindeverwaltung war Ziel eines Cyberangriffs, bei dem IT-Systeme verschlüsselt und Daten von den Servern gestohlen wurden, heißt es in einer aktuellen Mitteilung auf der Website. Demnach hätten die Ermittlungen nun ergeben, dass Teile der entwendeten Daten im Darknet gelandet sind.
Bürgerdaten wohl nicht betroffen
Dazu sollen unter anderem alte Personalakten sowie Daten- und Bilderlaufwerke von Mitarbeitern zählen. Sensible Daten von den Bürgern seien aber nicht betroffen, betont das Rathaus. „Nach derzeitigem Kenntnisstand wurden keine Daten aus dem Zentralen Rechenzentrum und dem Dokumentenmanagementsystem veröffentlicht“, heißt es in der Mitteilung.
Ein Lösegeld sei damals nicht gezahlt worden, heißt es weiter. Stattdessen hatte sich die Gemeindeverwaltung entschieden, eng mit der Cybersicherheitsagentur Baden-Württemberg (CSBW) und dem Landeskriminalamt eng zusammenzuarbeiten.
„Unmittelbar nach dem Angriff wurden die betroffenen Systeme gesichert und wiederaufgebaut sowie zusätzliche Maßnahmen zur IT-Sicherheit umgesetzt. Diese werden laufend geprüft und weiterentwickelt“, erklärt das Rathaus. Die zuständigen Datenschutz- und Aufsichtsbehörden seien über den Vorfall informiert worden.
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Directed by Joseph Kosinski and produced by Jerry Bruckheimer alongside Formula 1 icon Lewis Hamilton, the film casts Pitt as a veteran driver chasing one last shot at glory.
According to Deadline, the film has surged to $629 million at the global box office, making it the highest-grossing Apple Original Film to date, and the biggest sports movie of the year.
After premiering worldwide on June 27, the film's momentum was strong enough to justify a rare second theatrical run in August, coinciding with its home-release debut.
The success has reportedly gone beyond ticket sales. The film's popularity apparently emboldened Apple to bid for the U.S. streaming rights to Formula 1 itself. In October, Apple and Formula 1 announced a five-year partnership that will bring all F1 races exclusively to Apple TV in the United States beginning next year.
And in case you missed it, Apple updated the logo and name for its streaming service in November – what was once "Apple TV+" is now simply "Apple TV."Tag: Apple TV Shows
This article, "The Biggest Sports Movie of the Year Is Now Streaming on Apple TV" first appeared on MacRumors.com
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Why Puppet Helps
Puppet Training in Mumbai is good for getting servers ready, adding apps, and keeping rules the same in big rooms of computers, AWS or Azure clouds, and mixed setups using server automation. Instead of typing the same commands on every machine, you write clear Puppet manifests that show the state you want, and Puppet makes it happen everywhere, cutting mistakes and saving time with DevOps tools.
Mumbai has a busy tech world with banks, startups, and cloud projects, so Puppet skills help you stand out because teams need configuration management to scale safely, pass checks, and stay stable during fast changes with cloud config. Puppet also fits well with Jenkins for build steps and Kubernetes for containers, so daily work feels smoother when you use strong Mumbai IT skills and less manual work after Puppet automation.
What the Program Covers
The Puppet Training in Mumbai starts from basics and grows step by step, so it works for both beginners and people with experience. You learn Puppet language parts like resources, facts, and variables, then go into building reusable Puppet modules, making classes, using Hiera to split data, and setting up a full primary server and agent model for infrastructure as code.
You work on real tasks such as preparing many servers in one go, setting firewall and security rules across a network, and keeping versions of software the same on all nodes with server automation. By the end, you are ready to use Puppet in CI/CD pipelines, cloud setups, and day-to-day operations where steady and repeatable changes matter in configuration management and cloud config.
Ways to Learn
You can pick a style that fits your time and comfort level while learning Puppet automation.
Way to LearnTime AboutGood ForMore InfoVideos on your own time8–12 hoursPeople who need freedomWatch anytime, pause and repeat hard Puppet modules, with small tests for configuration management.Live group online8–12 hoursPeople who like group talkLive chats, shared tasks using Puppet manifests, quick help, plus recordings to watch again.One-on-one online8–12 hoursPeople who want personal paceYour speed, your real job cases in server automation, feedback every step.Group room or online2–3 daysMumbai teams and companiesIn person or online, group work on infrastructure as code linked with your DevOps tools. Most of the time, around 80–85%, is used on hands-on labs that run on DevOpsSchool’s AWS cloud or simple virtual machines, so you can focus on Puppet and not get stuck on setup for cloud config. Trainers first show each example and then guide you while you do it, helping you avoid common mistakes in Puppet manifests and Puppet modules.
Skills You Get
After Puppet Training in Mumbai, you carry a set of skills that you can use right away in your job.
Write Puppet manifests and Puppet modules to manage services, packages, users, files, and apps in a reusable and clean way for server automation. Set up and run a Puppet primary server and agents, handle node groups, catalogs, and reports to keep configuration management solid across many machines. Use infrastructure as code for Linux and other systems, while joining it with CI/CD DevOps tools and cloud platforms like AWS so changes are quick and safe. Follow good patterns such as separate dev, test, and prod environments, roles and profiles, and Hiera-based data, so cloud config stays easy to manage even as things grow. There are no hard entry rules, but simple Linux or IT knowledge makes things smoother, and trainers still explain from the start so active beginners from Mumbai IT backgrounds can follow. You only need a normal laptop (Windows, macOS, or Linux) with at least 2 GB RAM and about 20 GB space, plus AWS Free Tier or local VMs, to practice Puppet automation.
About DevOpsSchool
DevOpsSchool is a known place for DevOps, cloud, and automation learning, running both online and classroom programs with a strong focus on real tools like Puppet. It offers many web tutorials, video classes, PDFs, slides, and quizzes across more than 60 tools used for configuration management, CI/CD, containers, and cloud in modern teams.
Learners get lifetime access to the learning portal with recordings, notes, and step-by-step guides, plus lifetime technical support so you can ask doubts even long after your batch ends. DevOpsSchool also shares exam dumps, interview kits, and group offers, and many reviews say that the clear and hands-on style helped people move into better jobs that rely on server automation and infrastructure as code.
Meet Rajesh Kumar
The Puppet Training in Mumbai is guided by Rajesh Kumar, a global expert with more than 20 years in DevOps, DevSecOps, SRE, DataOps, AIOps, MLOps, Kubernetes, and cloud. He has led many training and consulting projects around the world, helping teams build CI/CD pipelines, adopt automation, and bring modern configuration management into live systems.
Rajesh Kumar is known for using real work stories—like handling thousands of servers with strong Puppet modules or fixing rollout issues with better Puppet manifests—to explain complex ideas in a simple way. Because he works across many tools and platforms, you also learn how Puppet fits into the full DevOps picture, which is very useful for people working in growing tech spaces like Mumbai.
Helps That Last
Support continues even after your classes, so you can keep growing with Puppet in your own projects.
Help TypeWhat You GetWhy It MattersLifetime LMSRecordings, slides, notes anytimeReview Puppet automation ideas whenever you need a refresh.Technical supportOngoing email and phone helpGet support to tune Puppet modules or fix configuration management issues.Interview kitQuestion sets, exam help, resume tipsPrepare better for DevOps or SRE jobs that need infrastructure as code.Real projectsScenario-based Puppet practiceBuild real examples that show your server automation skills.Certification helpGuidance for Puppet and DevOps examsAdd proof of your cloud config and automation knowledge to your profile. If you miss a live class, you can watch recordings or join the same part in another batch within a set time, and group sign-ups get step-wise discounts, which helps Mumbai teams learn together.
Who It Suits
Puppet Training in Mumbai is a good fit for many tech roles and levels.
System admins who manage many servers and are tired of doing the same steps again and again. DevOps engineers building or running CI/CD pipelines and needing a strong base for configuration management. SRE and ops engineers who look after uptime, alerts, and rules across changing systems. Cloud engineers on AWS, Azure, or other platforms who want infrastructure as code to keep setups clean and repeatable. Team leads and architects who want less drift between servers and smoother, safer releases. Keen freshers with some Linux or IT knowledge can also gain a real, tool-based skill set that helps them stand out in the Mumbai job scene.
Get Started
If you want your infrastructure to be easier to manage, safer to change, and more stable, Puppet Training in Mumbai gives you a clear, hands-on way to reach that goal. You learn from experts, work on real cases, and keep support even after class, so you can use Puppet with more confidence in your own work.
For more details or to join:
Email: [email protected] Phone & WhatsApp (India): +91 84094 92687 Phone & WhatsApp (USA): +1 (469) 756-6329 Website: DevOpsSchool Wrap Up
Puppet Training in Mumbai brings together real Puppet automation practice, strong mentors, and flexible study modes to help you grow in configuration management and DevOps. With focus on Puppet modules, Puppet manifests, real projects, and ongoing help, you can build trust in your server automation skills and move forward in Mumbai’s fast tech world using infrastructure as code and cloud config.
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In a blog, the company describes how the evolving capabilities of its models could be used to “develop working zero-day remote exploits against well-defended systems, or meaningfully assist with complex, stealthy enterprise or industrial intrusion operations aimed at real-world effects.”
According to OpenAI, the underlying problem is that offensive and defensive uses of AI rely on the same knowledge and techniques. This makes it challenging to enable one without making possible the other.
“We are investing in safeguards to help ensure these powerful capabilities primarily benefit defensive uses and limit uplift for malicious purposes,” the company said, adding, “we see this work not as a one-time effort, but as a sustained, long-term investment in giving defenders an advantage and continually strengthening the security posture of the critical infrastructure across the broader ecosystem.”
One new initiative is the Frontier Risk Council. The company offered few details of how this will operate, but said it was part of an expanding “defense in depth” strategy designed to contain the widely-speculated potential of AI as an adversarial tool.
“Members will advise on the boundary between useful, responsible capability and potential misuse, and these learnings will directly inform our evaluations and safeguards. We will share more on the council soon” OpenAI said.
Other initiatives mentioned in the blog include expanding guardrails against misuse, external Red Team testing to assess model security, and a trusted access program designed to give qualifying customers access to enhanced models to explore defensive use cases.
OpenAI also plans to expand its use of its recently announced Aardvark Agentic Security Researcher scanning tool beta to identify vulnerabilities in its codebase and suggest patches or mitigations.
Red Teaming AI
AI companies find themselves under increasing pressure to explain how they will block model misuse. The anxiety is not hypothetical; last month, OpenAI rival Anthropic admitted that its AI programming tool, Claude Code, had been used as part of a cyberattack targeting 30 organizations, the first time malicious AI exploitation has been discovered on this scale.
Meanwhile, university researchers in the US reported this week that the Artemis AI research platform outperformed nine out of ten penetration testers at finding security vulnerabilities. As the team pointed out, it did this at a fraction of the cost of a human researcher, potentially expanding access to such capabilities beyond well-resourced criminals.
Balancing this is the possibility that defenders could use AI to find the same vulnerabilities. OpenAI’s blog alludes to this capability when it mentions testing its models against the Red Teaming Network it announced two years ago.
The reaction of industry experts to OpenAI’s latest announcement has been mixed. A recurring worry is the inherent difficulty of stopping malicious use of leading models.
“OpenAI is asking models to constrain their own capabilities through refusal training, which can be compared to asking a lock to decide when it should open,” commented Jesse Williams, co-founder and COO of AI agent DevOps company, Jozu. In effect, the model, not its human authors, defines what is harmful.
“The distinction is intent and authorization, which models cannot infer from prompts. Jailbreaks consistently defeat refusal training, and sophisticated adversaries will probe detection boundaries and route around them. Safeguards reduce casual misuse, but won’t stop determined threats,” said Williams.
“OpenAI’s ‘trusted access program’ sounds reasonable until you examine implementation. Who qualifies as trusted? University researchers? Defense contractors? Foreign SOC analysts?”
Even with guardrails, AI safety can’t be guaranteed, Rob Lee, chief AI officer at the SANS Institute, observed.
“Last month, Anthropic disclosed that attackers used Claude Code, a public model with guardrails, to execute 80-90% of a state-sponsored cyberattack autonomously. They bypassed the safety controls by breaking tasks into innocent-looking requests and claiming to be a legitimate security firm. The AI wrote exploit code, harvested credentials, and exfiltrated data while humans basically supervised from the couch,” he pointed out.
“That’s the model with guardrails. But if you’re [a villain] and you want your AI Minions to be as evil as possible, you just spin up your own unguardrailed model,” he said. “[There are] plenty of open-weight options out there with no ethics training, no safety controls, and nobody watching. Evil will use evil. … OpenAI’s safety frameworks only constrain the people who weren’t going to attack you anyway.”
Not all experts are this pessimistic. According to Allan Liska, threat intelligence analyst at Recorded Future, it is important not to exaggerate the threat posed by AI. “While we have reported an uptick in interest and capabilities of both nation-state and cybercriminal threat actors when it comes to AI usage, these threats do not exceed the ability of organizations following best security practices,” said Liska.
“That may change in the future, however, at this moment it is more important than ever to understand the difference between hype and reality when it comes to AI and other threats.”
A previous version of this story contained comments incorrectly attributed to Rob Lee, which have been replaced with the correct remarks.
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In a blog, the company describes how the evolving capabilities of its models could be used to “develop working zero-day remote exploits against well-defended systems, or meaningfully assist with complex, stealthy enterprise or industrial intrusion operations aimed at real-world effects.”
According to OpenAI, the underlying problem is that offensive and defensive uses of AI rely on the same knowledge and techniques. This makes it challenging to enable one without making possible the other.
“We are investing in safeguards to help ensure these powerful capabilities primarily benefit defensive uses and limit uplift for malicious purposes,” the company said, adding, “we see this work not as a one-time effort, but as a sustained, long-term investment in giving defenders an advantage and continually strengthening the security posture of the critical infrastructure across the broader ecosystem.”
One new initiative is the Frontier Risk Council. The company offered few details of how this will operate, but said it was part of an expanding “defense in depth” strategy designed to contain the widely-speculated potential of AI as an adversarial tool.
“Members will advise on the boundary between useful, responsible capability and potential misuse, and these learnings will directly inform our evaluations and safeguards. We will share more on the council soon” OpenAI said.
Other initiatives mentioned in the blog include expanding guardrails against misuse, external Red Team testing to assess model security, and a trusted access program designed to give qualifying customers access to enhanced models to explore defensive use cases.
OpenAI also plans to expand its use of its recently announced Aardvark Agentic Security Researcher scanning tool beta to identify vulnerabilities in its codebase and suggest patches or mitigations.
Red Teaming AI
AI companies find themselves under increasing pressure to explain how they will block model misuse. The anxiety is not hypothetical; last month, OpenAI rival Anthropic admitted that its AI programming tool, Claude Code, had been used as part of a cyberattack targeting 30 organizations, the first time malicious AI exploitation has been discovered on this scale.
Meanwhile, university researchers in the US reported this week that the Artemis AI research platform outperformed nine out of ten penetration testers at finding security vulnerabilities. As the team pointed out, it did this at a fraction of the cost of a human researcher, potentially expanding access to such capabilities beyond well-resourced criminals.
Balancing this is the possibility that defenders could use AI to find the same vulnerabilities. OpenAI’s blog alludes to this capability when it mentions testing its models against the Red Teaming Network it announced two years ago.
The reaction of industry experts to OpenAI’s latest announcement has been mixed. A recurring worry is the inherent difficulty of stopping malicious use of leading models.
“OpenAI is asking models to constrain their own capabilities through refusal training, which can be compared to asking a lock to decide when it should open,” commented Jesse Williams, co-founder and COO of AI agent DevOps company, Jozu. In effect, the model, not its human authors, defines what is harmful.
“The distinction is intent and authorization, which models cannot infer from prompts. Jailbreaks consistently defeat refusal training, and sophisticated adversaries will probe detection boundaries and route around them. Safeguards reduce casual misuse, but won’t stop determined threats,” said Williams.
“OpenAI’s ‘trusted access program’ sounds reasonable until you examine implementation. Who qualifies as trusted? University researchers? Defense contractors? Foreign SOC analysts?”
According to Rob Lee, chief AI officer at the SANS Institute, the problem of AI misuse can’t be solved by one company on its own – not even the mighty OpenAI. “Companies are pushing models that can autonomously discover or weaponize vulnerabilities, but the global safety ecosystem — governments, frontier labs, researchers, and standards bodies — is fragmented and uncoordinated,” said Lee.
“The result is a widening gap where speed becomes its own vulnerability, creating conditions for cascading failures across infrastructure, finance, healthcare, and critical systems.”
Not all experts are this pessimistic. According to Allan Liska, threat intelligence analyst at Recorded Future, it is important not to exaggerate the threat posed by AI. “While we have reported an uptick in interest and capabilities of both nation-state and cybercriminal threat actors when it comes to AI usage, these threats do not exceed the ability of organizations following best security practices,” said Liska.
“That may change in the future, however, at this moment it is more important than ever to understand the difference between hype and reality when it comes to AI and other threats.”
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Researchers at Push Security this week outlined the tactic, which they call ConsentFix, in a blog, calling it “a dangerous evolution of ClickFix and consent phishing that is incredibly hard for traditional security tools to detect and block.”
Generally ClickFix attacks display a fake error or counterfeit CAPTCHA verification to a user to get them to copy, paste and execute malicious commands on their devices.
What’s new in a ConsentFix attack is that the attack happens entirely inside a browser, say the researchers, which removes one of the key detection opportunities because the attack doesn’t touch an endpoint.
The attack starts with a victim coming across a legitimate but compromised website they are looking for in a Google search, which completely circumvents email-based anti-phishing controls. Going to the site triggers a fake Cloudflare CAPTCHA-like verification page asking the victim to enter their business email address to prove they’re human. Doing so makes a Microsoft login page pop up which includes a legitimate URL, based on the victim’s email address, that would contain an OAuth token. The victim is asked to copy and paste that URL into a field, again, to verify they are human. The URL is captured by the threat actor, at which point the victim has granted the attacker access to their Microsoft account via Azure’s command line interface, say the researchers.
“At this point, the attacker has effective control of the victim’s Microsoft account, but without ever needing to phish a password, or pass an MFA (multifactor authentication) check,” says Push Security. “In fact, if the user was already logged in to their Microsoft account (i.e. they had an active session) no login is required at all.”
Christopher Kayser, social engineering expert and president of Canadian-based firm Cybercrime Analytics, says the attack plays on two tactics favored by threat actors: obedience (cut and paste this URL) and trust (this looks like a Microsoft login page). “People think because they are on a trusted [Microsoft] platform that this is OK,” he said in an interview.
But this attack also shows the failures of security awareness training that many organizations perform. If training is effective, employees should suspect there’s something wrong when an app asks for a business email address to confirm they are human, he said, and know that it’s suspicious when they’re asked to cut and paste anything online as a way of proving they are human.
“This is an incredibly new, innovative attack method,” commented Roger Grimes, data-driven defense CISO advisor at KnowBe4. “It’s almost unfair to classify it as a Clickfix subvariant, even though it is.” However, the odds an employee will copy a long URL string as a test of their humanity has to be very, very low, he added. “It screams different and scammy even to the most unknowledgeable user. Can you see your grandparents doing this? Not me. But I’m sure some people do do it, or else the scammers would not try it,” he said.
“My guess is that its rate of success is so, so low that it doesn’t become a popular scam method that most of us need to worry about,” he said. “What we do need to communicate to users is how often Cloudflare’s brand is being used in social engineering scams, and what the correct Cloudflare authentication/validation looks like. The Cloudflare CAPTCHA check has become the fake antivirus screen of today’s world.”
Organizations must recognize that the ConsentFix attack highlights the dangers of implicit trust in first-party applications, and in the continued use of legacy OAuth scopes, said Avivah Litan, lead analyst for AI trust, risk and security management at Gartner. These include older permission sets within Microsoft Entra ID that grant broad access and are not subject to modern security controls or monitoring.
“Attackers exploit these legacy scopes to enumerate directory data, meaning they can systematically retrieve and map out user accounts, groups, and other directory objects within the organization,” she said. “This reconnaissance enables attackers to identify high-value targets and plan further attacks, all without triggering alerts that would be associated with newer, more tightly controlled permissions.”
The most effective mitigation strategy to this kind of attack is a combination of robust monitoring, strengthened consent governance and real-time user protection, Litan noted. “By addressing these foundational issues — specifically, by limiting the use of legacy OAuth scopes, tightening consent processes for all applications, and deploying browser-based security — enterprises can substantially reduce the risk of unauthorized access resulting from OAuth consent abuse and enhance their overall identity security posture.”
Push Security notes that the attack could be successful because targeting a first-party app like Azure CLI means that many of the mitigating controls available for third-party app integrations don’t apply. Because there’s no login required, phishing-resistant authentication controls like passkeys have no impact on this attack, the researchers add. And the use of advanced detection evasion techniques makes this attack difficult to investigate, meaning these attacks are going undetected.
One of the problems is that most security awareness training isn’t doing enough to lower the odds of employees falling for phishing scams, said Kayser.
He cited a study of phishing messages sent to employees at a California hospital over a period of eight months. Those who had taken a cybersecurity awareness course were just as likely to have fallen for a phishing message as those who didn’t, he said.
Training often fails because instructors talk too much in technical terms, he said. Instead they should explain attacks, how they work and how to recognize them.
“If you can explain to people what’s going on, that sticks,” he maintained.
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Macworld claims to have access to an internal version of iOS 26 that references several upcoming Apple devices, including the home hub. The site said that the code hints at these options:
Camera - The device will have a camera, but it will be limited to 1080p.
Face ID - The home hub will use Face ID for authentication and to identify who is in a room.
Profile switching - With the Face ID feature, the home hub will be able to switch to the profile for the person in the home who is interacting with the device. Apple engineers are apparently using an app to test the accuracy of the system.
Apple Intelligence - It will support Apple Intelligence and the new version of Siri.
Other rumors suggest that the home hub will be something of a cross between an iPad and a HomePod. It will have a square-shaped screen that's around seven inches, and an optional speaker base. We're expecting the home hub to launch right around the time that the new version of Siri comes out in iOS 26.4, likely March or April.
Macworld also spotted signs of another device, identified as J229. This is apparently a "never-before-seen product" that has multiple sensors that can detect alarm sounds and capture images, but it is an accessory rather than a standalone device. Apple is rumored to be working on a home security camera to go along with the home hub. There's no word on when the camera could launch.Tag: Apple Command Center
This article, "iOS 26 Code Leak Reveals Apple Smart Home Hub Details" first appeared on MacRumors.com
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GPT-5.2 is designed to help people get more done quicker. It's better at creating spreadsheets, building presentations, writing code, perceiving images, understanding long context, using tools, and completing multi-step projects. The new model offers improved general intelligence, long-context understanding, agentic tool-calling, and vision, so it is ideal for real-world, professional use.
GPT-5.2 Thinking hallucinates less than GPT-5.1 Thinking, and responses with errors were 30 percent less common. Long context capabilities have improved, and it is able to handle reports, contracts, papers, and multi-file projects, maintaining accuracy across hundreds of thousands of tokens. It is also better at interpreting screenshots, technical diagrams, and visual reports.
OpenAI says that GPT-5.2 outperforms industry professionals at knowledge work tasks spanning 44 occupations, with the model scoring 70.9 percent on the GDPval test. GPT-5.1 scored 38.8 percent on that benchmark, and it is OpenAI's first model that performs at or above a human expert level.
For ChatGPT users, GPT-5.2 will feel more structured and reliable, and it will have a warmer, more conversational tone. OpenAI says GPT-5.2 Instant is a capable workhorse for everyday work, with improvements in info-seeking questions, how tos and walkthroughs, technical writing, and translation. GPT-5.2 Thinking is meant for more complex tasks, like summarizing long documents, coding, answering questions about uploaded files, and planning decisions. GPT-5.2 Pro is ideal for difficult questions where a higher-quality answer is worth waiting for.
GPT-5.2 Instant, Thinking, and Pro are rolling out today in ChatGPT to paid users. The API is available to all developers.
OpenAI's next-generation model comes just a week after CEO Sam Altman declared a "code red," asking employees to focus on improving ChatGPT so it doesn't fall behind competitors like Google's Gemini and Anthropic's Claude. Tags: ChatGPT, OpenAI
This article, "OpenAI Launches GPT-5.2 for ChatGPT Users a Week After Declaring 'Code Red'" first appeared on MacRumors.com
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The C1000 has your standard power station aesthetic, made from durable black and gray plastic. It has two handles at the sides, which makes it simple to distribute the weight across two hands, along with fan grilles and an LED display that shows the current power level and the power draw of anything that's plugged in. Rubber feet at all four corners ensure that it remains stable.
There's no revolutionary design here, but the Solix C1000 looks and feels rugged. Curves at the corners make it feel a little more modern than some other power stations, and while it's not waterproof, you can get a protective carrying case that keeps it safe from moisture. It's 25 pounds, so it's probably not a battery that you're going to want to be lugging to the beach or the park, but it is good for all-day power if you're in a location where dragging around 25 pounds isn't a hassle. It isn't overly large, measuring in at 15 inches by 8.2 inches by 9.6 inches.
At the front, there are five AC outlets, two 140W USB-C ports, one 15W USB-C port, and a 12W USB-A port. I appreciate that Anker is phasing out USB-A and only included a single USB-A port, because USB-A connectors are growing more uncommon. In the future, you may have no USB-A devices at all, so you won't have several wasted ports. 140W USB-C should also be good for years to come. There's a charging port at the side, a solar input port, and a 12V car port.
I like the port arrangement, and was fine with all of the AC ports on the front, but spacing could be an issue if you want to plug in multiple devices with large plugs. For the AC outlet, you need to turn on AC power manually, a feature that exists to prevent battery drain when idle.
This is a 1024-watt-hour battery with support for devices that draw up to 2000W, though it does support 3000W peak output. It should be able to handle almost any small appliance, including refrigerators, TVs (even large screen), heaters, portable air conditioners, lights, coffee makers, microwaves, medical devices, aquarium and animal setups, and tools that require a lot of power. I tested it up to 1500W and it worked with no issue.
It uses lithium iron phosphate (LiFePO4) technology, which is what you want for a power station because LiFePO4 batteries are safe and last for more charge cycles. The C1000 is able to hold a charge in standby mode for a long time, which is great if you want to have a battery on hand for the occasional power outage. I charged it to full and left it powered off for a month, and it remained at 100 percent when I turned it back on at the end of the test. It's probably better to store it at around 80 percent capacity, but the point is you can charge it up, tuck it in a closet, and pull it out months later to use it in an emergency.
The C1000's 1024 Wh capacity is enough to charge an iPhone dozens of times. Charging my iPhone 17 Pro Max from 0 to 80 dropped the battery level from 53 percent to 51 percent, which is only a two percent hit. Subsequent testing consistently used between 2 and 3 percent for iPhone charging.
You can get fewer full MacBook charges, but it's still enough to keep multiple people up and running for a couple days. Charging my MacBook Pro from 0 to 100 percent dropped the battery from 100 to 88 percent, and charging my MacBook Air from 0 to 100 percent dropped the battery from 100 to 90 percent.
It was able to run my MacBook Pro for a full 8-hour work day, doing day-to-day tasks like writing. I started at 76 percent and ended at 57 percent. It lasted almost 24 hours running my full Mac setup, which included my MacBook Pro, Studio Display, three LED lights, and a phone charger. That does include around 10 hours of time where the MacBook and display were in rest mode, but it is more than capable of supporting a full work setup for a day or two.
What's great about the C1000 is that it tells you exactly how long it will last based on the power draw of what's plugged in. On the LED, you'll see an estimated readout. It predicted around 14 hours of usage for my 67W MacBook Air, which was accurate.
It's not going to last super long when using high power devices like a microwave, but you often aren't using high power accessories for very long. It can run a mini heater, but those often range from 750W to 1500W, so it would last around an hour. For something like a mini fridge, though, you would be able to run it for several days.
The C1000 is able to charge quickly, which has the potential to be useful when you're in a hurry. It can recharge to full in 49 minutes from a standard household plug, drawing around 1200W to do so (Anker says it can go up to 1600W, but you need to enable it). When it is under that kind of load, the fans kick on, and the fans are loud.
I wouldn't be able to sleep with the fans on that high, and it's definitely a loud, irritating fan noise when going full blast. Luckily, the fans only come on at that level when it's under heavy load, and charging doesn't take too long. It also can't run high watt devices for super long, and it's much more tolerable at lower power levels. When charging small devices, it's near silent.
You can connect the C1000 to a car or to solar panels to charge it up. For solar, charging times vary based on the size of the panel, the number of panels, and the available light. It can accept up to 600W through the solar input.
For devices where you might like a backup feature that activates automatically in a power outage, the C1000 supports that. It has a UPS system with a sub 10ms switchover time. So if you plug something like a CPAP machine into the C1000 then plug the power station into power, the C1000 will come on right away when there's an outage.
Anker has an app that connects to the C1000 over Wi-Fi or Bluetooth. You can use the app to check power level, power draw, and time remaining when a device is plugged in. You can also turn on the AC output or car charger output from the app, and fine tune controls like charging power, device timeout, charging and discharging limits, and more. It delivers new firmware too, which I struggled with. For several days, the firmware update kept failing, but it worked flawlessly later on, so I'm not sure what the issue was.
Bottom Line
This is a well made power station that's versatile thanks to its 1024 Wh capacity and the ability to support devices up to 2000W. It's a good home backup battery to have on hand in case of an emergency, but it also works well for camping, short trips where you need power, medical devices, and powering tools.
I keep a battery like this one in two closets in my house so they're accessible, and I also like to pull out a large power stations when I need to operate a corded tool like a sander or a bright light in an area where I don't have a plug. That's been one of the more compelling use cases for me.
The C1000 can be loud when it's charging or powering appliances that have high energy draw, but that's about the only downside I found during testing.
How to Buy
The Solix C1000 Gen 2 Portable Power Station is currently available for $372, which is more than half off its MSRP. It can be purchased from the Anker website or from Amazon.com.
Note: Anker provided MacRumors with a C1000 for the purpose of this review. No other compensation was received.Tag: Anker
This article, "Review: Anker Solix C1000 Gen 2 is a Mid-Size Power Station With Fast Charging" first appeared on MacRumors.com
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Ein umfangreicher internationaler Graumarkt mit SIM-Mobilfunkkarten fördert im großen Stil Manipulationen und Betrügereien im Internet. Nach einer Studie der Universität Cambridge werden die physischen und virtuellen SIM-Karten von Anbietern wie SMSActivate, 5Sim, SMShub und SMSPVA für die Verifikation von gefälschten Online-Konten bei Social-Media-Plattformen oder E-Commerce-Anbietern verwendet. “Wir haben es mit einem florierenden Untergrundmarkt zu tun, auf dem unauthentische Inhalte, eine unechte Popularität und politische Einflusskampagnen leicht und offen zum Verkauf stehen”, sagte Jon Roozenbeek, Co-Leiter der Studie.
Fragwürdige oder kriminelle Bots per SMS bestätigt
Viele Online-Plattformen verlangen bei der Einrichtung eines neuen Kontos eine Verifikation via SMS. Diese Sicherheitsmaßnahme soll dazu dienen, die Authentizität von Konten zu bestätigen und die massenhafte Erstellung gefälschter Profile einzudämmen. Die Verifizierung soll eigentlich bestätigen, dass ein Mensch ein Konto bei Diensten wie WhatsApp, Telegram, Facebook, X, Shopify und Amazon einrichtet. Mit den SIM-Karten vom Graumarkt werden aber in diesem Fall virtuelle Bot-Armeen verifiziert.
Kriminelle, aber auch intransparente politische Akteure nutzen die Fake-Konten zum einen dazu, ihre eigene Online-Präsenz größer erscheinen zu lassen als sie tatsächlich ist. Dabei werden die Zahlen künstlich aufgebläht, so dass sie gut aussehen, aber wenig bedeuten – etwa Likes, Follower oder Shares, die gekauft oder manipuliert wurden, um einen Account populärer wirken zu lassen.
Mit Hilfe von SIM-Karten vom Graumarkt werden aber auch Social-Media-Konten erstellt, die absichtlich sehr wütend machende oder provozierende Inhalte posten, damit viele Menschen heftig reagieren und kommentieren. Diese Aktionen werden oft geplant und orchestriert, damit ein Trend entsteht, der auch arglose Nutzer beeinflussen soll.
Bestätigung für WhatsApp und Telegram besonders teuer
Die Forscher aus Cambridge fanden dabei heraus, dass sich die Preise für die Fake-SIM-Verifikationen je nach Anwendung und vermeintlichem Herkunftsland der SIM-Karte beträchtlich unterscheiden. Am teuersten ist WhatsApp mit einem Durchschnittspreis von 1,02 US-Dollar pro Verifizierung, gefolgt von Telegram mit 0,89 Dollar pro Kontobestätigung.
Bestätigungen für Online-Plattformen, bei denen die Mobilfunknummer der Nutzer im Gegensatz zu WhatsApp und Telegram nicht offen eingesehen werden kann, sind dagegen für weniger Geld zu haben. Facebook, Grindr und Shopify kosten durchschnittlich acht US-Cent pro Verifizierung, für Konten bei X und Instagram sind es zehn Cent, bei TikTok und LinkedIn sind es elf Cent und bei Amazon durchschnittlich zwölf Cent.
Um den Handel mit SIM-Karten-Verifikationen transparent zu machen, haben die Wissenschaftler den “Cambridge Online Trust and Safety Index” (COTSI) entwickelt, der die täglichen Preise für SMS-Verifizierungen für 197 Länder und mehr als 500 Plattformen erfasst.
Bot-Preise steigen vor nationalen Wahlen
Mit Hilfe der COTSI-Daten, die im Web kostenlos unter cotsi.org verfügbar sind, konnten die Wissenschaftler auch herausfinden, ob und wie der Markt auf politische Ereignisse reagiert. Dazu wurden im Vorfeld von 61 nationalen Wahlen die Preisdaten für acht Social-Media-Plattformen (Google/YouTube/Gmail, Facebook, Instagram, Twitter/X, WhatsApp, TikTok, LinkedIn und Telegram) analysiert. Es stellte sich heraus, dass die Preise für SMS-Verifizierung für Telegram und WhatsApp App in den 30 Tagen vor einer nationalen Wahl spürbar ansteigen. Die Preise für Telegram-Verifizierungen legten durchschnittlich um 12 Prozent zu, bei WhatsApp waren es plus 15 Prozent. Bei den anderen sechs Plattformen blieben die Preise dagegen stabil.
Die Kosten für eine SMS-Verifizierung hängen auch von den Herkunftsländern der verwendeten SIM-Karten ab. Besonders teuer waren Verifizierungen mit SIM-Karten aus Japan mit einem durchschnittlichen Preis von 4,93 Dollar, gefolgt von Australien (3,24 Dollar), Türkei (2,54 Dollar) und Malta (2,18 Dollar). Zu den günstigen SIM-Karten-Herkunftsländern gehören die Vereinigten Staaten mit 26 US-Cent, Großbritannien mit 10 Cent und Russland mit 8 Cent pro Verifizierung. Deutschland liegt im Mittelfeld. Wenn eine SIM-Karte für die Verifizierung einem der vier deutschen Mobilfunknetze (Deutsche Telekom, Vodafone, Telefónica O2 sowie 1&1) zuzuordnen ist, werden 63 Cent im Durchschnitt für eine Verifikation fällig.
“Massenhafte SIM-Karten-Beschaffung erschweren?”
In der Studie regen die Wissenschaftler eine Debatte an, ob die massenhafte Beschaffung und Verwendung von SIM-Karten erschwert werden sollte. Sie verwiesen darauf, dass in Großbritannien seit April der Betrieb von sogenannten SIM-Farms ohne legitimen Grund nicht mehr erlaubt ist. Das sind technische Geräte, die gleichzeitig viele SIM-Karten enthalten können – oftmals mehrere Dutzend bis hunderte SIM-Karten. Damit lassen sich Massen-SMS versenden, Telefonnummern schnell wechseln und zahlreiche Verifizierungen vornehmen, um viele Online-Konten gleichzeitig anzulegen oder massenhaft betrügerische Phishing-Nachrichten zu verschicken.
Gleichzeitig fordern die Wissenschaftler aus Cambridge die Betreiber der Plattformen auf, das Herkunftsland der SIM-Karte, das bei der Verifizierung verwendet wurde, transparenter zu machen. Bei Diensten wie Google/YouTube/Gmail, Facebook, Instagram, Twitter/X, TikTok und LinkedIn sei das Land, in dem das Konto registriert ist, für andere Nutzer in der Regel nicht sichtbar. Bei Messaging-Apps hingegen sei leicht zu erkennen, woher ein Konto stamme. (dpa/jm)
View the full article
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Ein umfangreicher internationaler Graumarkt mit SIM-Mobilfunkkarten fördert im großen Stil Manipulationen und Betrügereien im Internet. Nach einer Studie der Universität Cambridge werden die physischen und virtuellen SIM-Karten von Anbietern wie SMSActivate, 5Sim, SMShub und SMSPVA für die Verifikation von gefälschten Online-Konten bei Social-Media-Plattformen oder E-Commerce-Anbietern verwendet. “Wir haben es mit einem florierenden Untergrundmarkt zu tun, auf dem unauthentische Inhalte, eine unechte Popularität und politische Einflusskampagnen leicht und offen zum Verkauf stehen”, sagte Jon Roozenbeek, Co-Leiter der Studie.
Fragwürdige oder kriminelle Bots per SMS bestätigt
Viele Online-Plattformen verlangen bei der Einrichtung eines neuen Kontos eine Verifikation via SMS. Diese Sicherheitsmaßnahme soll dazu dienen, die Authentizität von Konten zu bestätigen und die massenhafte Erstellung gefälschter Profile einzudämmen. Die Verifizierung soll eigentlich bestätigen, dass ein Mensch ein Konto bei Diensten wie WhatsApp, Telegram, Facebook, X, Shopify und Amazon einrichtet. Mit den SIM-Karten vom Graumarkt werden aber in diesem Fall virtuelle Bot-Armeen verifiziert.
Kriminelle, aber auch intransparente politische Akteure nutzen die Fake-Konten zum einen dazu, ihre eigene Online-Präsenz größer erscheinen zu lassen als sie tatsächlich ist. Dabei werden die Zahlen künstlich aufgebläht, so dass sie gut aussehen, aber wenig bedeuten – etwa Likes, Follower oder Shares, die gekauft oder manipuliert wurden, um einen Account populärer wirken zu lassen.
Mit Hilfe von SIM-Karten vom Graumarkt werden aber auch Social-Media-Konten erstellt, die absichtlich sehr wütend machende oder provozierende Inhalte posten, damit viele Menschen heftig reagieren und kommentieren. Diese Aktionen werden oft geplant und orchestriert, damit ein Trend entsteht, der auch arglose Nutzer beeinflussen soll.
Bestätigung für WhatsApp und Telegram besonders teuer
Die Forscher aus Cambridge fanden dabei heraus, dass sich die Preise für die Fake-SIM-Verifikationen je nach Anwendung und vermeintlichem Herkunftsland der SIM-Karte beträchtlich unterscheiden. Am teuersten ist WhatsApp mit einem Durchschnittspreis von 1,02 US-Dollar pro Verifizierung, gefolgt von Telegram mit 0,89 Dollar pro Kontobestätigung.
Bestätigungen für Online-Plattformen, bei denen die Mobilfunknummer der Nutzer im Gegensatz zu WhatsApp und Telegram nicht offen eingesehen werden kann, sind dagegen für weniger Geld zu haben. Facebook, Grindr und Shopify kosten durchschnittlich acht US-Cent pro Verifizierung, für Konten bei X und Instagram sind es zehn Cent, bei TikTok und LinkedIn sind es elf Cent und bei Amazon durchschnittlich zwölf Cent.
Um den Handel mit SIM-Karten-Verifikationen transparent zu machen, haben die Wissenschaftler den “Cambridge Online Trust and Safety Index” (COTSI) entwickelt, der die täglichen Preise für SMS-Verifizierungen für 197 Länder und mehr als 500 Plattformen erfasst.
Bot-Preise steigen vor nationalen Wahlen
Mit Hilfe der COTSI-Daten, die im Web kostenlos unter cotsi.org verfügbar sind, konnten die Wissenschaftler auch herausfinden, ob und wie der Markt auf politische Ereignisse reagiert. Dazu wurden im Vorfeld von 61 nationalen Wahlen die Preisdaten für acht Social-Media-Plattformen (Google/YouTube/Gmail, Facebook, Instagram, Twitter/X, WhatsApp, TikTok, LinkedIn und Telegram) analysiert. Es stellte sich heraus, dass die Preise für SMS-Verifizierung für Telegram und WhatsApp App in den 30 Tagen vor einer nationalen Wahl spürbar ansteigen. Die Preise für Telegram-Verifizierungen legten durchschnittlich um 12 Prozent zu, bei WhatsApp waren es plus 15 Prozent. Bei den anderen sechs Plattformen blieben die Preise dagegen stabil.
Die Kosten für eine SMS-Verifizierung hängen auch von den Herkunftsländern der verwendeten SIM-Karten ab. Besonders teuer waren Verifizierungen mit SIM-Karten aus Japan mit einem durchschnittlichen Preis von 4,93 Dollar, gefolgt von Australien (3,24 Dollar), Türkei (2,54 Dollar) und Malta (2,18 Dollar). Zu den günstigen SIM-Karten-Herkunftsländern gehören die Vereinigten Staaten mit 26 US-Cent, Großbritannien mit 10 Cent und Russland mit 8 Cent pro Verifizierung. Deutschland liegt im Mittelfeld. Wenn eine SIM-Karte für die Verifizierung einem der vier deutschen Mobilfunknetze (Deutsche Telekom, Vodafone, Telefónica O2 sowie 1&1) zuzuordnen ist, werden 63 Cent im Durchschnitt für eine Verifikation fällig.
“Massenhafte SIM-Karten-Beschaffung erschweren?”
In der Studie regen die Wissenschaftler eine Debatte an, ob die massenhafte Beschaffung und Verwendung von SIM-Karten erschwert werden sollte. Sie verwiesen darauf, dass in Großbritannien seit April der Betrieb von sogenannten SIM-Farms ohne legitimen Grund nicht mehr erlaubt ist. Das sind technische Geräte, die gleichzeitig viele SIM-Karten enthalten können – oftmals mehrere Dutzend bis hunderte SIM-Karten. Damit lassen sich Massen-SMS versenden, Telefonnummern schnell wechseln und zahlreiche Verifizierungen vornehmen, um viele Online-Konten gleichzeitig anzulegen oder massenhaft betrügerische Phishing-Nachrichten zu verschicken.
Gleichzeitig fordern die Wissenschaftler aus Cambridge die Betreiber der Plattformen auf, das Herkunftsland der SIM-Karte, das bei der Verifizierung verwendet wurde, transparenter zu machen. Bei Diensten wie Google/YouTube/Gmail, Facebook, Instagram, Twitter/X, TikTok und LinkedIn sei das Land, in dem das Konto registriert ist, für andere Nutzer in der Regel nicht sichtbar. Bei Messaging-Apps hingegen sei leicht zu erkennen, woher ein Konto stamme. (dpa/jm)
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Since April, Apple has been forced to let developers offer links to non-App Store purchase options in their apps, with no control over the design of those links. Apps like Spotify can advertise deals and direct customers to their websites, something that was not previously allowed.
Apple has not been able to charge any commission at all for purchases made using these in-app links, but that's going to change in the future. The appeals court says that Apple should be able to charge a fee that covers its necessary costs and intellectual property.
Apple is not going to be able to start charging a commission immediately, though. The case has been sent back to the district court so that a reasonable fee can be determined.
Some other aspects of the initial ruling were also found to be too broad, so there are other updates in store. Here's an overview of what's changing:
Fees on links - Apple will be able to charge a reasonable commission
Link design - Apple can restrict developers from making external links more prominent than in-app purchase options. Specifically, Apple can restrict a developer from putting buttons, links, or other calls to action in more prominent fonts, larger sizes, larger quantities, and more prominent places than buttons for in-app purchases. Apple has to allow developers to place buttons in "at least" the same fonts, sizes, and places as Apple's own.
Link language - Apple may restrict developers from using language that violates its general content standards, if such standards exist.
Link access restrictions - The original court ruling prevents Apple from restricting certain categories and developers from using links, such as subscriptions provided using the News Partner Program. The appeals court says Apple is not specifically enjoined from excluding developers participating in the VPP and NPP programs.
Apple created a situation requiring court oversight because after the original ruling ordered it to allow in-app links, Apple didn't charge a reasonable fee for purchases made using those links. Apple charged developers 27 percent instead of 30 percent, knowing that developers would also need to pay a fee for payment services. Almost no developers opted in to Apple's link program because it ended up being more expensive than the in-app purchase fees.
The appeals court agreed that there was clear and convincing evidence of civil contempt, and it declined to vacate the injunction. With the exception of changes to fees and link design, the rest of the injunction will remain in place because Apple made external links "as hard to use as possible," which "flies in the face of the Injunction's spirit."
The appeals court recommends that the district court calculate a commission that is based on the costs that are necessary for its coordination of external links for linked-out purchases, along with "some compensation" for the use of its intellectual property. Costs should not include commission for security and privacy.
While Apple is not able to charge any commission until the district court approves an appropriate fee, the appeals court suggests that both Apple and the district court should work to settle on a fee "expeditiously." The full text of the ruling is available here.Tags: App Store, Apple Developer Program, Epic Games vs. Apple
This article, "Apple Wins Ability to Charge Fees on External Payment Links as Appeals Court Modifies Epic Injunction" first appeared on MacRumors.com
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