The latest @Ledger Podcast features our very own BD Lead, Max Marcisiak ( @Crypto_Backer1 ) In this episode, Max sheds light on: - What staking really means for institutions - Why non-custodial setups are critical for treasury strategy - Common misconceptions around custody and

11/ Next comes model proofs, programmatic data licensing, and verifiable agentic workflows built on this foundation. The future of AI will not only be smart, it will be provable.

Model Y Standard & Model 3 Standard are here

10/ Walrus sits at the center as the data backbone of this era. Every file, model, and update carries a verifiable identifier so data remains transparent and secure.

9/ Across the ecosystem, builders are already applying these primitives. Private inference, encrypted data rooms, token gated AI marketplaces, and auditable analytics run on the Sui Stack.

8/ For end users, verifiable data means more consistent, accountable, and explainable AI. Every recommendation or decision can be traced back to verified inputs, reducing bias and restoring confidence.

Its time to have another community Fan Art event where you make the art, and the community votes their favorite! The top 5 art pieces will be receiving the rewards! RULES: - Anything Hero Siege related - No NSFW - No AI (we will filter these out) - Post progress pictures, at

7/ For builders, the stack delivers verifiable storage, gated access, and auditable compute. Teams can begin by encrypting datasets, enforcing access rules, or adding proof to model runs and then scale toward fully trusted pipelines.

6/ Together, these components allow data to be stored on Walrus, processed in Nautilus, governed by Seal policies, and verified through Sui. This results in AI systems with built-in proof of integrity.

5/ Here’s what powers the Sui Stack: •@WalrusProtocol : a platform for provable, monetizable, and secure data markets. •Seal: programmable encryption and access control. •Nautilus: confidential and verifiable computing through trusted enclaves. •Sui: the coordination layer

4/ The Sui Stack changes that by making every step of the AI lifecycle verifiable, private, and controlled. It forms the foundation for building intelligent systems that are transparent and trustworthy.

Every marketing leader I talk to is facing the same challenge: Scale 📶 The demand for content has exploded, but the systems behind it haven’t kept up. Teams are expected to launch more campaigns, in more markets, with more data, all while maintaining brand integrity and speed.

3/ Most data pipelines today are opaque and mutable. Once data feeds an AI model, it’s nearly impossible to audit or verify. This leads to unreliable outputs, compliance risk, and eroded trust.

2/ Behind every model lies a key dependency on data. If that data is incomplete, tampered with, or unverifiable, no amount of fine-tuning can make an AI system trustworthy.

1/ The future of AI isn’t just about smarter models. It’s about verifiable data. The @SuiNetwork Stack is building the trust layer that makes this possible.

Luganodes’ Q3 2025 @solana Validator Report is live 🌐 Another strong quarter of performance, featuring: ⚡️ 99.9% voting effectiveness 🧠 6.9% delegator APY 📶 99.9% uptime 💰 24,921 SOL in rewards https://t.co/yOY9iw2PHG

11/ Stake with Luganodes and help support the network. https://t.co/4JRnBiOA2F

10/ If approved by governance, Encrypted Mempool will make Aptos the first L1 to offer native, onchain MEV protection. A step toward a more private, efficient, and fair trading future. ⚡️

9/ This unlocks a new level of privacy for DeFi on Aptos. For example, Decibel, a DEX built on Aptos, can now process every order with full confidentiality, protected at the protocol level.

8/ With Encrypted Mempool, what you trade, when you trade, and how you trade stays private until the block is finalized. No frontrunning. No information leakage. No compromise on decentralization.

7/ The system integrates directly into Aptos’ consensus protocol, letting validators pre-compute most of the workload while maintaining full security guarantees. Decryption takes less than 20ms per batch, with no usability bottlenecks or denial-of-service risks.

6/ Instead of decrypting each transaction individually, validators perform a single partial decryption per batch. This design improves performance, reduces latency, and enables real-world throughput without affecting network efficiency.

5/ Aptos achieves this through a batched threshold encryption scheme, allowing validators to collectively decrypt entire batches of transactions in one step. This dramatically reduces communication and computation overhead, making encrypted transactions viable at scale.

College campuses are waking up. Whistleblowers are rising. I first saw this moment in a dream 10 years ago—months before the famous businessman from New York announced his run for President. Now the puzzle pieces make sense.  Go to my page to see full prophetic word.

4/ In most blockchains, validators can see pending transactions, reorder them, or profit from that visibility, fueling the multibillion-dollar MEV ecosystem. Aptos aims to neutralize this by keeping transaction intent private until the precise moment of execution.