Every app you've ever signed up for is a copy of your identity living on a stranger's server.
You've been duplicating yourself across the internet for years, and you don't own a single copy.
Le cap des 100.000 signatures est franchi contre la Loi Yadan !
En moins de 24h la mobilisation est énorme.
Elle doit continuer et s’amplifier.
Signez, diffusez, partagez.
Ici : https://t.co/CIs75Sxad2
🧵 gUBI explained like you’re 14 👇
Imagine everyone on Earth gets a little bit of money regularly… just for existing.
That’s the idea behind UBI (Universal Basic Income).
Now add crypto 👇
gUBI = Global Universal Basic Income powered by blockchain.
Instead of a government, it uses tech to distribute value fairly and transparently.
But here’s the problem:
How do you make sure 1 person = 1 account?
That’s where @GalacticaNet comes in 🔐
Using Zero-Knowledge Proofs, you can prove you’re a real unique human…
WITHOUT revealing your identity.
No cheating.
No duplicates.
Full privacy.
🌍 Why it matters:
– Fairer wealth distribution
– Global access (no borders)
– Privacy protected
gUBI + ZK identity = a new economic system for the internet.
The future isn’t just decentralized…
It’s human-centered.
#Web3 #Crypto #UBI #ZK @VitalikButerin
🧵 Qu’est-ce que @GalacticaNet en bref ?
Galactica construit une blockchain axée sur la confidentialité, conçue pour débloquer la prochaine génération d’identité Web3.
Au lieu d’exposer vos données, Galactica utilise les Zero-Knowledge Proofs pour vous permettre de prouver des informations sans tout révéler. Et ça change tout.
🔐 Imagine :
– Prouver que vous êtes vérifié sans dévoiler votre identité
– Accéder à la DeFi de manière conforme
– Construire une identité numérique réutilisable
Propulsé par une cryptographie de pointe et des partenaires comme @0xPolygon ou des écosystèmes ZK inspirés par @VitalikButerin, Galactica fait le pont entre confidentialité et conformité.
🌍 La vision ?
Une couche d’identité décentralisée où les utilisateurs gardent le contrôle de leurs données tout en interagissant avec le monde réel.
C’est ainsi que le Web3 peut évoluer de manière responsable.
#Web3 #Crypto #ZK #Privacy
Want to do more than just explore the Nesa ecosystem?
Start running a node today!
Be part of our core infrastructure, strengthen the network, and earn Miner Points while you do it.
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In early 2023, a group of Samsung engineers accidentally leaked sensitive internal information by pasting code and technical documents into ChatGPT while troubleshooting problems. The engineers were simply trying to work faster by using the model to review source code, summarize internal documents, and help debug issues. But in doing so, proprietary data was entered into an external system that the company did not fully control, and that did not guarantee total encryption of user data.
At first glance this looked like a simple user mistake. But this incident exposes a deeper structural issue with how most AI systems operate today. Modern AI systems typically require data to be visible in plaintext during execution. Even when data is encrypted in storage and during transmission, it is usually decrypted when the model processes it.
In many consumer AI tools, those environments are operated by third parties. The system may log inputs for debugging, retain data for improvement, or process prompts within shared infrastructure. Even when strong policies exist, organizations must ultimately trust that sensitive information will not be exposed or reused.
For enterprises handling proprietary code, research data, or confidential documents, that trust boundary is difficult to accept.
This is why incidents like the Samsung case happen. The problem is not simply that employees used AI tools. It is that the underlying architecture requires sensitive data to become readable during execution. This trust and security problem is why AI has not reached its maximum potential, particularly within enterprise settings.
Nesa was built to solve this.
On Nesa AI, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators in order for inference to occur. Therefore, instead of relying entirely on privacy policies and user behavior, the architecture itself eliminates the risk of exposure.
AI capability is advancing faster than ever.
Models are becoming more capable and efficient every day, benchmarks continue to improve, and new applications are emerging constantly.
But despite this, organizations are still cautious about deploying AI at scale.
Not because the models aren’t capable, but because questions remain around whether they can be trusted with sensitive information and user data.
Enterprises need systems that guarantee security, privacy, and governance before AI can truely deploy at scale.
That guarantee is what Nesa provides.
AI is not limited by capability anymore.
It’s limited by trust.
When data and models remain protected during execution, experimentation becomes safer, audits can occur without threat to user data, and real-world applications can scale with confidence.
This is true adoption.
The privacy AI chain built for enterprise trust.
Built by award-winning researchers, Nesa is creating the infrastructure for AI to be private, auditable, and accountable.