Phala @PhalaNetwork
Empowering 10,000 builders and companies to build scalable, private, and safe intelligence. phala.com San Francisco, CA Joined August 2019-
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Template code: github.com/Phala-Network/… Upstream: github.com/chopratejas/he…
headroom compresses tool outputs, logs, files, and RAG chunks before they reach the LLM. Keep API keys, compression rules, logs, and payloads inside a Phala TEE CVM. Deploy: cloud.phala.com/templates/head…
Congrats to @LLMTUNE_IO. Private AI gets stronger when model tuning, deployment, and confidential infrastructure are designed together. Excited to build toward AI people can trust.
Another great news for our community 🤝 📣Official announcement of PARTNERSHIP🎉 @LLMTUNE_IO and @PhalaNetwork are now collaborating to bring you something special. Two teams. One mission: private, powerful AI for everyone. We’re stronger together and this is just the
If a model can be bad on purpose and the user can’t see it, the trust boundary is broken. Confidential AI needs verifiable compute and attestable runtimes so behavior, evaluation, and control stay inside the same security envelope.
Template code: github.com/Phala-Network/… Upstream: github.com/github/copilot…
GitHub Copilot SDK brings Copilot Agent into apps and services. Phala keeps repo context, prompts, and agent execution state inside a TEE CVM. Deploy: cloud.phala.com/templates/copi…
Template code: github.com/Phala-Network/… Upstream: github.com/supermemoryai/…
supermemory turns memory into an API layer for AI apps. Deploy it on Phala Cloud when memory datasets, retrieval logic, and workflow credentials need a TEE CVM. Deploy: cloud.phala.com/templates/supe…
Private AI is becoming a trust-boundary problem. Apple expanding Private Cloud Compute to Google Cloud with NVIDIA GPUs is a strong signal that attestation, confidential compute, and verifiable runtime guarantees matter at cloud scale.
Proud to collaborate on the next frontier of private AI. 🔒 Apple is expanding Private Cloud Compute to @googlecloud using NVIDIA GPUs. nvda.ws/4ewJl3q
NYC tomorrow: we’re joining @monad and @archetypevc for Forum | Privacy AI at @ethconf. Our founder @marvin_tong will represent Phala in the company showcases alongside @TACEO_IO and @turnkeyhq. Come meet builders pushing privacy AI forward. luma.com/28k7z005
Heading to @ethconf in NYC? Join us in learning more about what's at the frontier of privacy AI with thought leaders and leading companies. Co-hosted by @archetypevc and Monad Foundation. Sign up 👇 luma.com/28k7z005
Template code: github.com/Phala-Network/… Upstream: github.com/MemPalace/memp…
MemPalace gives AI apps a benchmarked open-source memory layer. Phala keeps memory datasets, app logic, and workflow credentials inside a TEE CVM. Deploy: cloud.phala.com/templates/memp…
Template code: github.com/Phala-Network/… Upstream: github.com/agno-agi/agno
Agno is a high-performance framework for multi-agent systems. Run it on Phala Cloud to keep agent memory, tool credentials, and model routing inside a TEE CVM. Deploy: cloud.phala.com/templates/agno
Template code: github.com/Phala-Network/… Upstream: github.com/unslothai/unsl…
Unsloth speeds up LLM fine-tuning while cutting VRAM. Deploy on Phala Cloud to keep datasets, adapter configs, and tuning metadata inside a TEE CVM. Deploy: cloud.phala.com/templates/unsl…
Recursive self-improvement turns AI infra into governance infrastructure. When models help build the next models, we need verifiable execution: where work ran, what data it touched, and which constraints were enforced.
Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor. It’s happening faster than we thought, and the implications deserve greater attention. anthropic.com/institute/recu…
🔐 The future of #AI infrastructure needs trust built in from day one. That’s why we’re excited to welcome @marvin_tong, Co-founder of @PhalaNetwork, to the keynote stage at #CCSummit in San Francisco, June 23-24. Secure AI. Protected data. Real deployment strategies. 🎤 Talk details: sched.co/2ODlZ 🗓️ Full schedule: bit.ly/4u9jS5P 🎫 Register: bit.ly/47hX8qK
This is the next AI infra battleground: model choice becomes a live production decision, and switching providers should feel like routing traffic. The hard part is the control plane around it — reliability, observability, cost, policy, and trust boundaries.
Pulled the trigger today and switched 100% of Lindy traffic to DeepSeek v4, churning from Anthropic models. Saves us millions of $ and we're actually seeing an *increase* in performance on many core use cases. Transformative for the business.
Phala is joining the Confidential Computing Consortium Summit in San Francisco. Founders @marvin_tong and @bgmshana will be on the keynote stage to share how confidential computing helps AI infrastructure ship with trust built in from day one. See you at #CCSummit 🔐
🔐 The future of #AI infrastructure needs trust built in from day one. That’s why we’re excited to welcome @marvin_tong, Co-founder of @PhalaNetwork, to the keynote stage at #CCSummit in San Francisco, June 23-24. Secure AI. Protected data. Real deployment strategies. 🎤 Talk
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