Papyboo

Just an old 🤡 lost in web3.

[Papyboo @davidbm]

RT @otomato_xyz: @TDS_discord just got its own Otomato badge

One of the sharpest private call groups in crypto, now flexing a dedicate…

[papertrade.xyz @papertrade_xyz]

Introducing @papertrade_xyz - a fair-launched, fully-onchain perpetuals exchange built on Hyperliquid by @izebel_eth & @blurr

-1000x leverage
-0 slippage
-No funding costs
-Self-bootstrapping LP

Coming soon. Learn more at: https://t.co/izPx6pov7h

[Papyboo @davidbm]

Just tried this airdrop wrapped from @otomato_xyz

$77.8k farmed across 29 airdrops. Top 0.49% of all hunters.

Big up @Optimism @arbitrum @VeloraDEX for the bags

Claimed the legendary badge along the way.

How hard did you farm? https://t.co/iqnzX0oLPr

[Papyboo @davidbm]

@nesaorg 👀

[Nesa @nesaorg]

Something is coming sooner than you think…

[Marine Tondelier @marinetondelier]

Mesdames et messieurs, je vous présente le maire de droite d’Arcachon.

Les habitant·es doivent savoir que @Foulon_Yves est un individu ordurier, homophobe et menaçant.

Il est une honte pour la République et devrait déjà démissionner.

Tout mon soutien à l'écologiste @VitalBaude, qui réagit avec tant de sang-froid à ces propos ignominieux.

[Papyboo @davidbm]

@nesaorg 👀

[Nesa @nesaorg]

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.

[Papyboo @davidbm]

@nesaorg Trust 🫵🏻

[Nesa @nesaorg]

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.

[Papyboo @davidbm]

@nesaorg @OpenAI ☝🏻

[Nesa @nesaorg]

Who’s AI journey started with @OpenAI?

Let me know, I’m testing something 👇

[Papyboo @davidbm]

@nesaorg sustainability

[Nesa @nesaorg]

Question for the builders out there.

What matters the most to you when choosing the infrastructure you build on?

Let us know 👇

[Giza @gizatechxyz]

https://t.co/wiyUlZzRAG

[Papyboo @davidbm]

@nesaorg 👀

[Nesa @nesaorg]

While a standard language model generates text in response to a prompt, an agent can execute tools, install extensions, access files, and take actions across systems. As agent ecosystems grow, we are seeing more examples of malicious or poorly designed “skills” that can install malware or exfiltrate data once a user manually approves execution.

This is not necessarily the result of bad actors alone. It reflects a structural shift in how AI systems operate.

Agents combine language reasoning with tool execution and often run with meaningful system privileges. When extensions or skills are added to these environments, they may inherit broad access to files, credentials, APIs, or other sensitive resources. Even if execution requires user approval, most users cannot realistically audit complex commands in real time, so the approval step becomes procedural rather than protective.

The result is a new attack surface specific to agent-driven environments. A malicious or compromised extension does not simply produce incorrect outputs, it can trigger real actions inside the system where the agent is deployed. As agents become more autonomous and persistent, the potential consequences of a single failure increase.

This is where infrastructure design becomes critical.

On Nesa, privacy and control are enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing data visibility during runtime. Sensitive user data and AI models do not need to be exposed to infrastructure operators for agents to function.

This does not eliminate the possibility of malicious code or software bugs, no infrastructure can make that guarantee. What it does is reduce trust concentration and limit unilateral access to data during execution. In agent environments where actions are continuous and autonomous, that architectural separation meaningfully lowers systemic exposure risk.

As AI agents become more capable, security considerations must move from prompt-level safeguards to infrastructure design. How tasks are executed will increasingly determine how effectively risk is contained.

[Papyboo @davidbm]

@nesaorg 👀

[Nesa @nesaorg]

Almost every AI system has one thing in common…

They all need access to your data during computation.

So while many might claim your data to be “encrypted” during rest and in transit, that doesn't apply during execution, hence leaving your information vulnerable.

Nesa solves this through our proprietary Equivariant Encryption technology.

It allows computation to be performed on encrypted data, meaning sensitive inputs and models do not need to be exposed during execution. Therefore, instead of relying on privacy policies or operator integrity, privacy is guaranteed by design, even during execution.

As AI moves from experimentation to production at scale, that difference becomes essential.

[Papyboo @davidbm]

@nesaorg 👀