Privacy in AI isn’t becoming a feature anymore, it’s becoming infrastructure.🔐🤖 What stands out here isn’t that Meta used a TEE. It’s that the architecture was treated as something to verify, not trust. An external audit found issues, they were fixed, and deployment moved forward. That’s probably the bigger signal: confidential AI only matters if the guarantees survive real-world implementation. Secure hardware helps. Audits help. But execution is what turns privacy claims into something users can actually rely on.

Pope asks, we deliver ❤️

A strong year so far for Phala 🚀 The growth shown across the analytics reflects more than adoption, it shows a shift in what teams expect from AI infrastructure. Cloud users continue to grow, and the expansion of Phala’s GPU-TEE integrations across projects is turning confidential AI from a concept into production-ready infrastructure. Instead of choosing between performance and privacy, developers can deploy AI workloads with hardware-level protection and verifiable execution. Frameworks like the EU AI Act, GDPR, HIPAA, and enterprise compliance requirements are raising the bar for how data can be processed and audited. “Secure” is no longer enough, teams increasingly need to prove that sensitive data, model weights, and inference remain protected during execution. That’s where confidential computing and GPU TEEs become important: AI that can process sensitive workloads without exposing data in-use, while remaining verifiable through attestation and hardware-backed guarantees. Be ready: Phala.com

Privacy shouldn’t be optional when building with AI. 🔐 Through @PhalaNetwork on @OpenRouter, you can access a curated catalog of 13 AI models powered by confidential compute and GPU-TEE infrastructure. Your prompts, inputs, and outputs stay protected by design, without sacrificing performance or developer experience. Build AI apps, workflows, and agents with simple API access while keeping sensitive data under your control. 🛠️ Available models include options across reasoning, multimodal, coding, and agent use cases. 🌐 Explore: openrouter.ai/provider/phala

Phala Brings Privacy To AI At the core of @PhalaNetwork Cloud is a hardware-first security model. Trusted Execution Environments isolate workloads at the hardware level, protecting code and data even from the cloud provider itself.

A new @PhalaNetwork launched. All TEE apps have their own trust center now. trust.phala.com/

Resetting my accounts left me with a backlog of work… but not for long. 🦞 With Clawdi.ai, I have my own personal AI assistant handling the repetitive stuff. Clawdi.ai lets you connect AI agents to automate real workflows, from reading and organizing emails, managing context across sessions, handling tasks, accessing files, coordinating tools, and helping you stay productive across devices without rebuilding your setup every time. ⚒️ Fast to set up. Easy to use. More time for what actually matters. And because it runs with @PhalaNetwork's secure execution environment, my prompts, context, files, and agent activities are processed with privacy and verifiable execution in mind, reducing exposure to cloud operators and helping keep sensitive workflows protected. 🔴Build your personal AI assistant → Clawdi.ai 🟢Powered by → Phala.com

Build your own team of AI agents that actually work for you. 🦞 With Clawdi.ai, you can connect and manage agents like OpenClaw, Claude Code, Codex, Hermes, and more inside a shared AI environment with persistent memory, synced tools, files, skills, and workflows. Your agents don’t just answer prompts, they can collaborate, keep context between sessions, automate tasks, and operate like a real digital team. Powered by @PhalaNetwork confidential computing infrastructure, every agent runs inside secure TEE environments that provide privacy, isolated execution, protected API keys, and verifiable computation. That means your data, workflows, and AI operations stay secure while remaining fully auditable and trustworthy. Start building your AI team: Clawdi.ai

We were at muShanghai’s AI Security Day today! Phala's Head of Research Dr. Shelven Zhou @zhou49 delivered a workshop on Phala as well as @openclawdi, the ultimate AI environment powering OpenClaw and Hermes agents! Big thanks to everyone who joined the session. 🫡

Speed alone is no longer enough. Security and privacy are becoming just as critical for AI adoption. 🔐🤖 Companies handling sensitive information are shifting their focus from “fast AI” to verifiable, privacy-preserving AI. Emerging regulations like GDPR and the EU AI Act are raising the standard, requiring organizations to prove that data remains protected, workloads are auditable, and even cloud providers cannot access sensitive information during runtime. 🟢This is exactly the future Phala.com has been building for. With dstack and Phala Cloud, @PhalaNetwork enables AI workloads to run inside Trusted Execution Environments (TEEs), where memory is encrypted, execution is verifiable through remote attestation, and infrastructure-level access to sensitive data is technically restricted. The AI era won’t be defined only by performance. It will be defined by trust, privacy, and verifiable security. Learn more: phala.com/posts/privacy-pres…

Someone on Reddit just described why they use Clawdi better than we could: “API key goes into an Intel TDX hardware encrypted container where even the operator can’t read it.” Say it louder. 👏

The course you need to build secure AI. 🔐 @PhalaNetwork is joining forces with AI × Web3 School to teach developers how to build AI agents with privacy, security, and verifiable execution. Sensitive data shouldn’t be exposed when processed by AI. That’s why isolated computation is critical. 🟢With Trusted Execution Environments (TEE), Phala ensures: ✔️Data stays private ✔️Computation is secure ✔️Results are verifiable 👇 Read the Article and Learn more

Testing Blackwell TEEs With Shared NVSwitch multi-tenancy, an 8-GPU HGX fabric can be carved into predefined encrypted NVLink islands: e.g. 1x 4-GPU CVM plus 2x 2-GPU CVMs running concurrently

Small team, big goals… but dropped tasks, forgotten follow-ups, and operational chaos? That happens when coordination depends on memory and scattered messages. Here’s a practical 30-day plan to work smarter with an AI Operator like @openclawdiai Cloud 🧵👇

May the 4th be with privacy and security. ⚔️ Don’t fall to the dark side of data exposure. With @PhalaNetwork, you can run AI models and workloads without compromising sensitive data. Prompts, inputs, and outputs stay protected by default, no leaks, no blind trust. Powered by GPU-TEE technology, Phala enables AI execution inside secure enclaves, ensuring your data remains encrypted even during computation. Choose from high-performance GPUs while keeping everything within a trusted execution environment. The Force is strong with confidential computing. Join the Fight: Phala.com

Stop jumping between platforms just to manage your AI agents. 🦞🌐 With Clawdi.ai, everything lives in one place, shared memory, synced keys, reusable skills, and persistent files across all your agents. Switch from OpenClaw to Hermes in minutes without losing context. Powered by @PhalaNetwork's GPU-TEE, your data, prompts, and outputs stay fully private while running high-performance workloads. Watch what @marvin_tong has to say 👇

The more AI and sensitive data we rely on, the weaker software-only security becomes. TEEs are turning into a foundational layer - protecting data in use,not just at rest. The market growth just reflects reality: the demand is already here,and it’s catching up fast with the tech

Just in: The TEE market is projected to grow at a 20.8% CAGR, reaching $12.36 billion by 2030. As AI agents scale and sensitive data flows increase, software-only security falls short. Hardware-level isolation with TEEs is becoming essential for true data-in-use protection.

Which cloud should you use for Confidential Compute? ☁️🛡️ GCP: Great for persistent storage. AWS: Great for enclave isolation. @PhalaNetwork: The gold standard for verifiable, decentralized, and cloud agnostic compute. 🏆 dstack acts as the bridge, allowing you to deploy to Phala using the same Docker Compose manifests you’d use for @awscloud By moving your security policies to Phala’s on-chain environment, you remove the "centralized" from "centralized cloud." Your code, your identity, $PHA proof