Glorims | 🐐🐐🐐🍚 ⛓

Im the knight

[Glorims | 🐐🐐🐐🍚 ⛓ @LiegraZy]

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[Glorims | 🐐🐐🐐🍚 ⛓ @LiegraZy]

Legit WL

[Exit Labs @exitlabsdotfun]

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[Glorims | 🐐🐐🐐🍚 ⛓ @LiegraZy]

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[Exit Labs @exitlabsdotfun]

whitelist submission form live

supply: 4444 office nfts

mp: free

chain: robinhood chain @RobinhoodApp

48 hours.

form: https://t.co/pt0kW7ncn4

[Glorims | 🐐🐐🐐🍚 ⛓ @LiegraZy]

@pratikno_priyo @quintta1217 nenek moyangnya aja ikut andil dalam penjebakan pahlawan Diponegoro

[CoinMarketCap @CoinMarketCap]

LATEST: ⚡ Visa, Stripe, Coinbase and BlackRock have joined 140+ firms backing Open USD, a new stablecoin designed to share reserve income with participating businesses.

[Glorims | 🐐🐐🐐🍚 ⛓ @LiegraZy]

@0xdavid @aztecnetwork okay, thank you sir

[Super Protocol @super__protocol]

Zoom out on three announcements from the past month and a larger picture emerges.

@HPE made NVIDIA Confidential Computing available across its entire AI Factory portfolio – from sovereign deployments to private cloud AI – marking a shift from optional add-on to standard enterprise offering.

@Apple extended its Private Cloud Compute privacy guarantees to Google Cloud – the first time Apple trusted a public cloud with its privacy infrastructure, made possible by NVIDIA Confidential Computing.

@WhatsApp launched Incognito Chat with Meta AI – completely private conversations processed inside TEEs where not even Meta can read the content. Consumer scale, default privacy.

Enterprise & Sovereign AI. Private clouds & Hyperscalers. Consumer platforms.

Different markets. Different requirements. Same direction.

For organizations evaluating AI platforms, the question is increasingly shifting from "Do we need Confidential Computing?" to "How do we operationalize it across our existing infrastructure?"

Super Protocol helps make that transition practical by providing a consistent confidential execution layer across public cloud, private cloud, on-premises, and hybrid infrastructure as a single operational environment – without requiring organizations to rebuild their AI stack or become Confidential Computing experts.

The direction is clear. The question is whether your infrastructure is ready for it.

[Glorims | 🐐🐐🐐🍚 ⛓ @LiegraZy]

@chartexpt i think its a trap before moon

[Super Protocol @super__protocol]

This fall, Confidential Computing ships at rack scale. 72 GPUs. One TEE.

NVIDIA announced Vera Rubin back in March. Now it's ramping to production.

Currently, each server forms its own TEE boundary: up to 8 GPUs, 2.3 TB shared memory. Vera Rubin extends this to the entire rack – 72 GPUs, 20.7 TB of shared memory in one TEE. Imagine what model will fit in there!

"Everything across this is secure because the AI model is so precious. This is the reason why this entire system obeys confidential computing." – Jensen Huang, NVIDIA CEO, GTC Taipei 2026

What can you do while waiting? We recently updated our GPU + CPU TEE requirements guide, covering all available GPU TEE-capable SKUs, from Hopper to Blackwell, with compatibility details for Intel TDX and AMD SEV-SNP – and what is not TEE-capable as well.

Check them out and start deploying Confidential AI today. With Super Swarm – no TEE expertise required.

Links in comments 👇

[Glorims | 🐐🐐🐐🍚 ⛓ @LiegraZy]

@txtharihariWNI Mantap, ga usah jdi org gaenakan, orang lain aja ga mikirin gtu

[Glorims | 🐐🐐🐐🍚 ⛓ @LiegraZy]

@lvyourselv @txtharihariWNI O ga bisa, gw dpt perlakuan gtu auto bintang 1 dan laporan email

[Glorims | 🐐🐐🐐🍚 ⛓ @LiegraZy]

@BlakeXavia @txtharihariWNI idiot lu

[Glorims | 🐐🐐🐐🍚 ⛓ @LiegraZy]

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[Super Protocol @super__protocol]

More organizations think they have Confidential Computing than actually do.

The CCC's new white paper "3 Degrees of Confidential Computing" makes this concrete: Level 1 migrating to Confidential VMs provides hardware isolation, but as the paper notes, “without integrating remote attestation, it does not meet the definition of Confidential Computing”.

However, what is even more telling is the direction in which the paper points beyond Level 3 towards Confidential AI: multi-CVM interactions, AI agent sandboxes, CC-aware network protocols and CC-enforced software provenance.

That future isn't theoretical for us. It's what Super Swarm is built on today – self-organizing, mutually attesting GPU clusters that form a single hardware-verified trust domain across cloud, on-prem, hybrid, and multi-cloud environments. Every interaction is independently verifiable. No custom builds. No TEE expertise required.

The complexity of operationalizing Confidential AI shouldn't become a project of its own. That's exactly the problem the execution layer should solve.

👉 Link to CCC paper in comments

[Glorims | 🐐🐐🐐🍚 ⛓ @LiegraZy]

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[Glorims | 🐐🐐🐐🍚 ⛓ @LiegraZy]

@shaylamckenzii @LFNS08 @LambeSahamjja jokowi duit pribadi tong, si wowo ini duit negara, bedakan ya, jgn goblok trus

[Super Protocol @super__protocol]

The feedback loop healthcare AI never got

A radiologist reviews a scan. The AI flags a suspicious mass. The patient is referred, biopsied, diagnosed. The physician closes the loop.

The AI never does.

Was the flag correct? Was it a false positive? The answer sits in a different EHR, a different department, sometimes a different institution – and arrives months later. Nobody systematically pipes that signal back to the model, because the infrastructure to do so was never built.

It is how healthcare has always been organized: services separated, records siloed, pathways fragmented. Imperfect, but functional enough for clinical care – and invisible enough that nobody felt the cost.

In most systems, the feedback loop is the first thing you set up. You ship, you measure, you iterate. The signal is fast, systematic, and the model improves. Healthcare AI never got that infrastructure.

Rory Pilgrim, Product Manager at Google Research, made an observation in the "Confidentially Yours" episode worth sitting with:

The slow feedback loop is not just a limitation. It is an opportunity.

If closing the loop leads to better outcomes – fewer missed diagnoses, fewer unnecessary recalls, models that improve on real-world data – institutions have a concrete reason to build the outcome pipelines they never prioritized. AI creates the business case for data infrastructure healthcare never had sufficient reason to build.

But acting on that immediately hits a structural wall.

Outcome data is patient data – highly regulated and, in most architectures, legally immovable. Traditionally, that immovability is the barrier. The data that would close the loop cannot cross the compliance boundary, so the loop stays open.

Super Swarm inverts the problem. Models can live anywhere – on-premise, in the cloud, across institutions. Instead of moving data to the model, computation runs inside a hardware-attested confidential computing environment – where even the operator cannot access what's being processed. Institution-specific outcomes never cross organizational or regulatory lines. The exposure risk is architecturally eliminated.

The feedback loop healthcare AI never got is now within reach.

🎥 "Confidentially Yours" with Rory Pilgrim and host Mike Bursell (Advisor, Super Protocol).

👉 Scan to watch the full episode, or find the link in the comments

[Aztec @aztecnetwork]

Last week, @PrivacyEthereum asked 38 teams what's broken with private transfers on Ethereum.

Every problem on the list has a solution currently live on Aztec.

@mike_connor walks through each one, along with the implementation details.

Read here: https://t.co/zToNppIg8c