Venice has quietly become the premier AI resource aggregation platform. • Web applications + mobile applications + APIs • Text/code/images/videos • All cutting-edge proprietary models • All leading open-source models • Supports fiat or cryptocurrency payments One account, one API key—access all resources. Built based on principles of privacy, freedom of expression, and user sovereignty. Free trial available.

MIP-B59 has been submitted for on-chain voting. This proposal aims to add the @AskVenice VVV core market to the Moonwell blockchain on @Base. 🗳️ Voting:

Most AI systems are based on the assumption that you trust the service provider. @AskVenice and NEAR AI are breaking that assumption. Venice users can now run prompts within a secure, isolated zone, completely shielded from the cloud service provider, operating system, and infrastructure layers. Privacy, secure, and verifiable.

The Kling O3 reference video generator is now available on Venice. Generate high-quality, cinematic videos with consistent elements through the Venice interface. 🧵@jordanurbs will guide you through the entire process (Includes Skill.md and tips)

Venice has just released its end-to-end encrypted AI inference capability. Currently, all major AI platforms are based on the same fundamental trust assumption: you must trust your service provider to handle your data responsibly. @AskVenice employs a slightly different architecture. Conversations are stored on your local device, and prompts are not persisted to the server. When you use an edge model, Venice proxies the requests, so the service provider never receives your identity data. However, the same trust assumption still applies. If Venice or its partners wanted to intercept data, there is no mechanism in the architecture to prevent them. This release introduces two hardware-enforced privacy modes. TEE runs inference within a secure hardware enclave operated by NEAR AI Cloud and Phala Network, isolating computation from the host operating system and infrastructure operator. Remote authentication binds encrypted certificates to physical hardware, so anyone can independently verify that the model is running within a true enclave. You no longer need to trust the GPU operator, but you still need to trust Venice's transport layer. End-to-end encryption (E2EE) eliminates the remaining trust assumptions. The message is encrypted on the device before transmission, remains encrypted within Venice's infrastructure, and is decrypted only within a verified secure area. During normal operation, Venice cannot view any of your data. However, the downside is potentially slower response times, and network search and memory functionality are disabled due to the need for decryption outside the secure area. Currently, both modes operate on a limited number of open-source models on NEAR AI Cloud and Phala Network, and are only available to Pro subscription users. The reliability of these safeguards in practice depends on the implementation of certifications and whether independent audits confirm these claims.

In hindsight, it may seem obvious that we were actually building encryption technology for machines.

It's great to collaborate with other passionate crypto projects dedicated to building a better world @PhalaNetwork @NEARProtocol @ilblackdragon

"Artificial intelligence is trained on your data"...that's not the real risk. It's just a smokescreen, artificially created because who cares? The real risk to you isn't that tomorrow's models are trained on your data. The real risk is that thousands of employees, hackers, and governments can access all your most private and proprietary conversations at any time, and this access will be available forever. Privacy must be the default setting, or humanity will face annihilation.

Let your claws access it.

I spent $6,000 on a Mac Studio to run AI models locally. Then I calculated the earnings from $DIEM. Same money, same model, more tokens, but no hardware required. A post 🧵