DONGVD (✸,✸)

[DONGVD (✸,✸) @Dongvd1710]

My @dac_chain Inception Wrapped 🚀

⚡ 30,370 QE
🏆 Rank #13,331 (SENTINEL)
🥇 51 badges
🎰 THE GAMBLER
https://t.co/ABKlMK4okO

[Perceptron Network @PerceptronNTWK]

We’re sitting on 100+ zettabytes of data… and it’s doubling every 2 years.

But more data doesn’t mean better AI. Quality is what truly matters.

Perceptron tackles this by incentivizing participation: rewarding users for contributing and verifying high-quality data.

Better input. Better incentives. Better AI.

🔗Source: https://t.co/z8QpVDfnEy

[DONGVD (✸,✸) @Dongvd1710]

@PerceptronNTWK Nice

[DONGVD (✸,✸) @Dongvd1710]

@PerceptronNTWK GG

[Perceptron Network @PerceptronNTWK]

The human layer in AI is growing fast.

Reviewers, validators, and content checkers are becoming essential to ensure quality, context, and trust.

AI doesn’t replace humans. It depends on them.

Read more about it in our new article ↓

[DONGVD (✸,✸) @Dongvd1710]

@PerceptronNTWK nice

[Perceptron Network @PerceptronNTWK]

We’ve been saying it all along. But now more voices are aligning: AI needs, and always will need, a human touch.

A recent Spend Matters piece highlights how human judgment remains critical in AI-driven procurement. And it’s not just procurement, it’s true across every industry.

AI can process data.

But humans provide context, judgment, and validation.

The real gap isn’t just in human skills, it’s in how we train, evaluate, and guide AI systems.

That’s why infrastructure matters.

Ecosystems like Perceptron Network are built around continuous human input: validating data, scoring outputs, and improving models over time.

Because the future isn’t AI replacing humans.

It’s AI working with humans at scale.

🔗Source: https://t.co/pnK8Rk2hNb

[DONGVD (✸,✸) @Dongvd1710]

@PerceptronNTWK LFG

[DONGVD (✸,✸) @Dongvd1710]

@PerceptronNTWK one desktop

[DONGVD (✸,✸) @Dongvd1710]

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[DONGVD (✸,✸) @Dongvd1710]

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[DONGVD (✸,✸) @Dongvd1710]

@PerceptronNTWK Fire

[Perceptron Network @PerceptronNTWK]

AI needs data to grow. But accessing high-quality datasets is becoming harder than ever.

· Data collection is increasingly centralized in the hands of a few large players.
· Platforms are locking content behind expensive APIs and private data deals.
· Specialized datasets (medical, legal, code) can cost 20–40× more than general web data.
· And while 94% of companies are exploring Gen-AI, only 12% have reliable data channels.

The result? A growing data bottleneck for AI development.

The future of AI won’t just depend on better models.

It will depend on who can access and activate quality data.

[DONGVD (✸,✸) @Dongvd1710]

@PerceptronNTWK @TheARCTERMINAL LFG

[DONGVD (✸,✸) @Dongvd1710]

@PerceptronNTWK LGGG

[Perceptron Network @PerceptronNTWK]

AI companies are burning billions on data. You're sitting on the solution.

Put your unused bandwidth to work. Run a node. Get rewarded.

Perceptron is expanding.
Join the rewards program ⚡️
https://t.co/WY320X1gc8

[Linera ⛓️ @linera_io]

🎮 Gmicrochains. We're kicking off our Game on Microchains Challenge!

Build a game on Linera’s Testnet for a chance to win 50,000 XP and more.

Here's how to enter 🧵👇

[Linera ⛓️ @linera_io]

⚛️ Last month, we announced our partnership with @Atoma_Network to bring decentralized AI to Linera.

Now, we have a deeper technical explainer on how Atoma’s AI inference works on Linera’s microchains.

Let's go. 👇🧵
https://t.co/QwvgAsVEoe

[Linera ⛓️ @linera_io]

⛓️ Announcing : Linera x Walrus

We're integrating @WalrusProtocol as our storage layer—bringing scalable, real-time onchain data storage to microchains.

🧵 👇