Phala

Empowering 10,000 builders and companies to build scalable, private, and safe intelligence.

[Phala @PhalaNetwork]

https://t.co/gOb0ZeuxF4

[Phala @PhalaNetwork]

Post-quantum privacy works when the trust boundary stays explicit. A parallel execution layer can keep accounts and transactions hidden while validators still commit to both state trees in sync. That is a cleaner model for verifiable privacy.

[Sergey Gorbunov @sergey_nog]

Since we started the project, I’ve been telling the @circle team that this will be the last privacy solution in blockchain space.

And we delivered.

What we have built is not just privacy-preserving smart contracts. Instead, it's a paradigm shift in how blockchains, composability, and privacy need to co-exist. The design addresses the two core failures of prior privacy systems: lack of composability and a painful developer journey.

Pure cryptographic privacy is elegant, but expensive and hard to scale in practice. Instead, this design builds on the now-established trend of cryptographic enclave technologies.

What we’ve built is a parallel execution environment on @arc :

Arc public blockchain continues processing cleartext blocks, while the Arc privacy sector operates as a parallel privacy-preserving virtual machine that processes encrypted transactions.

State, transactions, and user accounts remain hidden. Validators cannot inspect what’s inside, even if they try to snoop or are compromised. Yet they continue producing blocks for both public and private states in sync, committing to each state tree.

That is also a major difference from designs that rely on access control, which inevitably creates failure points and data exposure risk.

The public and private state composition is the game changer for developing. For the first time, users can move between private and public state within the same block space. No bridge. No extra wallet layer. No separate accounts. A single transaction can move between private and public execution with zero friction.

Furthermore, the environment gives users post-quantum protection by design. Transactions and accounts remain encrypted, and public keys stay protected under post-quantum secure algorithms. Any account or asset created inside the privacy sector is automatically post-quantum secure.

The result: a fully composable privacy sector on @arc that is post-quantum secure and seamlessly interoperable with public execution.

This is the last privacy layer in Web3.

See the whitepaper: https://t.co/Fz0IMafS2a

@circle @arc

[Phala @PhalaNetwork]

Template code: https://t.co/Z1J7RrZdsf
Upstream: https://t.co/TAukX1vEOd

[Phala @PhalaNetwork]

Flue gives TypeScript agents a harness for skills, tools, sessions, and sandboxes.

Phala keeps repo context, prompts, and tool runs inside a TEE CVM.

Deploy: https://t.co/JqO3aYarIm

[Phala @PhalaNetwork]

Template code: https://t.co/MnODjAIS58
Upstream: https://t.co/RmbX42vgwx

[Phala @PhalaNetwork]

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: https://t.co/ZaYcyE3vd1

[Phala @PhalaNetwork]

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.

[LLMTune.io @LLMTUNE_IO]

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 start.

More soon. Stay (LLM) Tuned 🤖.

#AI #AIart️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️️ #Phala #LLMTune #AIcrypto #Web3‌‌

[Phala @PhalaNetwork]

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.

[elie @eliebakouch]

mythos will be bad ON PURPOSE on ai "frontier llm research" tasks, this is very very sad for the research community

also the fact that this is un purpose not visible to the user is crazy

[Phala @PhalaNetwork]

Template code: https://t.co/eGq9EyrpI1
Upstream: https://t.co/6xliJrpHPK

[Phala @PhalaNetwork]

GitHub Copilot SDK brings Copilot Agent into apps and services.

Phala keeps repo context, prompts, and agent execution state inside a TEE CVM.

Deploy: https://t.co/649ZarWxHT

[Phala @PhalaNetwork]

Template code: https://t.co/wrt5UqVYfH
Upstream: https://t.co/Qmn50d1PpU

[Phala @PhalaNetwork]

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: https://t.co/7hE97LQmmJ

[Phala @PhalaNetwork]

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.

[NVIDIA @nvidia]

Proud to collaborate on the next frontier of private AI. 🔒

Apple is expanding Private Cloud Compute to @GoogleCloud using NVIDIA GPUs.

https://t.co/6R6MP1SkVm

[Phala @PhalaNetwork]

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.

https://t.co/x6bog4tV3O

[Monad @monad]

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 👇

https://t.co/eiq4lMAHuQ

[Phala @PhalaNetwork]

Template code: https://t.co/dFm9DN9G7X
Upstream: https://t.co/8CLgJBePqz

[Phala @PhalaNetwork]

MemPalace gives AI apps a benchmarked open-source memory layer.

Phala keeps memory datasets, app logic, and workflow credentials inside a TEE CVM.

Deploy: https://t.co/qseW4jCcgk

[Phala @PhalaNetwork]

Template code: https://t.co/mRlXi6AlYE
Upstream: https://t.co/MwSQJmAQ90

[Phala @PhalaNetwork]

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: https://t.co/w3JJKkY1mT

[Phala @PhalaNetwork]

Template code: https://t.co/Zi7u3LDrwA
Upstream: https://t.co/sm2o89JWis

[Phala @PhalaNetwork]

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: https://t.co/ywneLm73gz

[Phala @PhalaNetwork]

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.

[Anthropic @AnthropicAI]

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. https://t.co/OVVPJO7VQx