Signal creator Moxie Marlinspike is taking his expertise in private messaging to the realm of AI chatbots with Confer, an open-source AI assistant that aims to revolutionize how users interact with language models. The platform provides end-to-end encryption for user prompts and responses, ensuring that only authorized parties can access the data.
Confer's design is centered around a trusted execution environment (TEE) on server-side, which encrypts all data and code flowing through the CPU. This protects against unauthorized access by administrators or hackers. Additionally, Confer features passkeys for two-factor authentication, allowing users to log in securely using fingerprints, face scans, or device unlock PINs.
Confer's interface is deceptively simple, with a user-friendly design that seamlessly integrates encryption into every interaction. By storing data locally on the user's device and encrypting it, Confer provides a robust solution for protecting sensitive information from unauthorized access.
In contrast to larger AI platforms like Proton and Venice, which offer some level of end-to-end encryption but also rely on various exemptions or exceptions, Confer aims to provide users with complete control over their data. By not storing data remotely, Confer sidesteps the issue of potential breaches or compromises of user information.
Confer is available for native support in recent versions of macOS, iOS, and Android, while Windows users must install a third-party authenticator. This platform offers an exciting alternative to existing LLM providers and has sparked significant interest among users seeking more control over their data.
As the debate around AI privacy continues, Confer's commitment to transparency, user-centric design, and robust security will undoubtedly be scrutinized by experts and users alike. Whether this pioneering effort can scale to meet growing demand remains to be seen, but for now, it represents a bold step forward in addressing the pressing need for end-to-end encryption in AI interactions.
Confer's design is centered around a trusted execution environment (TEE) on server-side, which encrypts all data and code flowing through the CPU. This protects against unauthorized access by administrators or hackers. Additionally, Confer features passkeys for two-factor authentication, allowing users to log in securely using fingerprints, face scans, or device unlock PINs.
Confer's interface is deceptively simple, with a user-friendly design that seamlessly integrates encryption into every interaction. By storing data locally on the user's device and encrypting it, Confer provides a robust solution for protecting sensitive information from unauthorized access.
In contrast to larger AI platforms like Proton and Venice, which offer some level of end-to-end encryption but also rely on various exemptions or exceptions, Confer aims to provide users with complete control over their data. By not storing data remotely, Confer sidesteps the issue of potential breaches or compromises of user information.
Confer is available for native support in recent versions of macOS, iOS, and Android, while Windows users must install a third-party authenticator. This platform offers an exciting alternative to existing LLM providers and has sparked significant interest among users seeking more control over their data.
As the debate around AI privacy continues, Confer's commitment to transparency, user-centric design, and robust security will undoubtedly be scrutinized by experts and users alike. Whether this pioneering effort can scale to meet growing demand remains to be seen, but for now, it represents a bold step forward in addressing the pressing need for end-to-end encryption in AI interactions.
Confer's emphasis on user control and transparency is super interesting, especially with the use of TEE and passkeys for 2FA. It feels like they're really prioritizing security and privacy over convenience 
. I'm curious to see how this compares to other LLM providers in terms of data storage and encryption methods...
. The emphasis on end-to-end encryption and user-centric design is a step in the right direction. I mean, who doesn't want more control over their data? But, I do wonder about the scalability aspect - can they really handle the demand for this level of security? And what about the trade-offs between simplicity and functionality? I'm also curious to see how this plays out with regulatory bodies and industry standards 
. One thing's for sure, though: Confer has definitely given me something to think about 
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! I mean, who needs their data floating around out there? The fact that they're using TEE and passkeys for two-factor authentication is genius 
. It's not just about keeping our info safe, but also about having control over it. And I love how simple yet effective the interface is – sometimes less is more, right? 
. It's not just about security; it's about respect for users' agency and autonomy.
!
I mean, who doesn't want their AI chatbot conversations private? Like, what if Elon's AI decides to spill the beans on humanity and we're all doomed... just kidding! 
But seriously, Confer seems like a game-changer for those concerned about AI data security. No more worrying about your deep-fried pizza orders being shared with Big Brother... or in this case, Moxie Marlinspike 
. One thing to watch is if they can keep up with demand... but fingers crossed, we won't have to wait for the apocalypse 
.
. I think what's really cool about this platform is that it's giving users total control over their data. Like, can't we just have an AI chatbot where our info is safe and sound? 
And the fact that they're using TEE on server-side to encrypt all data and code is genius 
for people who are worried about their data being accessed by anyone 
. I mean, having full control over your info is super important these days. The fact that it uses TEE on server-side to encrypt all the data and code is like, mind blown 
. And the passkeys thing? Game changer 
. It's not perfect or anything, but it's a solid start. I'm curious to see how it scales and what experts think about its design. But for now, I think it's a great alternative to other AI platforms that don't prioritize user control as much 
.