Your AI can forget a conversation and still expose it while answering. @darkwoodslabs rise as the dark horse (pun intended) for AI and Privacy intersection. The pitch is appealing: Open-weight AI models, wallet login, and no stored chat transcripts. Buy credits with $USDG on @RobinhoodCrypto Chain, choose a model, and pay for what you use. Think of it as a prepaid AI service with a crypto wallet as your account. Darkwoods says its models run inside Intel TDX enclaves, protected computing environments designed to isolate workloads from the surrounding infrastructure. Its attestation route publishes verifiable hardware proof. In practice, your chats live and die in the browser unless you opt into encrypted syncing. Darkwoods pairs enclave-anchored inference with an app-level pledge not to store transcripts. But to be fari, that still isn’t a blanket guarantee that no middleman can ever glimpse what you type. How does that compare? @NEARProtocol AI uses a gateway that itself runs inside a TEE, alongside protected model execution. Its verification flow lets developers check that their actual encrypted connection terminates at the attested gateway. @OpenGradient takes another approach: separate who sent the request from what it contains. An encrypted relay hides prompts from the relay and the client’s IP from the gateway. However, upstream model providers still receive plaintext, and identity separation depends on relay/gateway non-collusion. Then there is $DARK. The docs mentioned that credit-purchase split sends 10% to DARK stakers, 10% to a buyback wallet, and 80% to treasury. Compared to the rest of the ecosystem on @ponsdotfamily, Darkwoods really stands out for bundling wallet access, private inference, and token-gated payments into a single, cohesive product. As privacy and AI agents keep converging, Dark feels like one of the more compelling protocols that actually stitches the two together in a meaningful way. NFA. DYOR.