Phala is the ONLY provider of PRIVATE AI models on OpenRouter, covering gpt-oss-120b and every other model available on the platform.

RT @runwiththebox: 🔥 E02 with Paul Mierlo @Mierlo1999 from Spout Finance @0xspout just dropped on Blackbox📦. This episode is all about #Rea….

Annnnnnnd it's LIVE! . Dive deep into Phala x zkVerify partnership with @ZKVProtocol's @john_camardo & Phala's @ChaoticBeautys.

RT @SecretNetwork: Join @lisaisloud hosting "Spilling the TEE" as she delves into the technical intricacies of Confidential Computing, the….

Join us in today's AMA as we will discuss TEE verification and unveil the epic work Phala and zkVerify are bringing to ETHGlobal New Delhi this September. Don't miss it.

Organizations adopting privacy computing today can seize high-value clinical markets, unlock research once thought impossible, build unshakable patient trust, and turn compliance from a cost into a competitive edge. The window is NOW. ⏰.

/🧵. Why healthcare orgs are racing to adopt:.✅ Privacy by math, not policy.✅ Access to premium markets & partners.✅ Compliance built-in, not bolted-on.✅ Trust through transparency.✅ Edge in AI-driven care.

/🧵. Use Case #5: Genomic Research. What if researchers could unlock genomic insights, without exposing genetic data?.🧬 Challenge: Genomic data is highly sensitive & identifiable. 🧬 Privacy Computing Solution: Federated learning across biobanks, no centralization. 🧬 Impact:

/🧵. Use Case #4: Disease Surveillance. What if public health agencies could detect outbreaks faster, without exposing patient data?.🔬 Challenge: Traditional methods are slow & raise privacy concerns. 🔬 Privacy Computing Solution: Real-time encrypted analysis across systems. 🔬

/🧵. Use Case #3: Pharma Real-World Evidence. What if pharma could access diverse patient data, without breaching privacy?.🔬 Challenge: Hospitals hesitate to share due to HIPAA concerns. 🔬 Privacy Computing Solution: Analyze encrypted data without exposure. 🔬 Impact: Faster

/🧵. Use Case #2: Hospital AI Decision Support. What if hospitals could deploy AI across departments, without exposing patient data?.🔬 Challenge: cloud AI = patient data exposure risk. 🔬 Privacy Computing Solution: AI runs in hardware-protected enclaves. 🔬 Impact: System-wide

/🧵. Use Case #1: Federated Clinical Trials. What if pediatric oncology centers could collaborate without sharing patient data?.🔬 Challenge: Rare disease data is highly identifiable. 🔬 Privacy Computing Solution: AI trains locally, only encrypted insights are shared. 🔬 Impact:

/🧵. Real-world proof #2: California Precision Medicine Consortium. UC San Diego + multiple medical centers using federated learning to:.🧬 Accelerate cancer research across institutions.🧬 Protect patient sovereignty.🧬 Enable breakthrough discoveries impossible with siloed data

/🧵. Real-world proof #1: The MELLODDY Consortium. 10 pharma giants (Amgen, AstraZeneca, Bayer, GSK, Novartis. ) sharing AI insights for drug discovery WITHOUT sharing actual data. And the result? Better drugs, faster discovery, zero competitive secrets leaked.

🏥 Healthcare generates 2.3 EXABYTES of data annually, but 97% of it sits unused due to privacy constraints. This isn’t just wasted data. It’s lost cures, delayed diagnoses, and missed breakthroughs. Privacy Computing can change that. A 🧵 of real-world & potential use cases.

1 hour to go until this AMA kicks off. Join us:

RT @runwiththebox: 📦 Episode 2 Alert. "How AI Will Change the Way You Invest Forever". And with confidential AI models, you can do it all….

Phala’s Co-founder & CTO Hang @bgmshana took the stage yesterday at NEARCON SF’s first session "Agents in the Real World", sharing his vision for the next Internet co-built by AI agents.

RT @marvin_tong: Phala is a zero data retention provider on @OpenRouterAI and we don’t collect, use and train data—and you can prove it.