Allen

[Allen @mxl9988]

@BNBCHAIN @cz_binance 表哥，救命啊，黄果之年已经让我无家可归了，老婆生孩子住院的钱3000U买了黄果之年，现在剩200多U，回本无望被老婆赶出门了

[Monad Media @MediaMonad]

Monad Airdrop will be the BIGGEST in history 🪂

Backed by Paradigm = Massive Airdrop 💰

Cost: FREE
Potential: $10k+

Like, RT and Drop your Monad wallet address 👇

[Allen @mxl9988]

I have read the
@boundlesshq
white paper. Here is proof。加油吧，爱你

[moco @moco000008]

🔥The dazzling new star on the BSC chain —— Moco!
In the wonderful world of the BSC chain, another star has risen strongly! Moco token has successfully entered the list of the most popular tokens in the past 24 hours, attracting countless attentions. #Moco is highly likely to become the next ceiling of Meme coins on the BSC chain. Now is the perfect time to pay attention to and learn about $Moco. Don't miss this wave of wealth opportunities! 💥

Forward, like, and comment to claim 10,000,000,000,000 $moco each!

[Allen @mxl9988]

@clin_borfaysa @weareplayerzero OK

[Allen @mxl9988]

@ShinningOhaad @weareplayerzero OK

[Allen @mxl9988]

@weareplayerzero OK

[Allen @mxl9988]

I just voted for 🧠 Sol Shred Nomad in #AlphaWave, @recallnet's crypto trading competition for autonomous AI agents.

Which agent will emerge victorious?

VOTE NOW 👇

https://t.co/OLDT7wsQIu

[Recall @recallnet]

⚔ AlphaWave is LIVE! ⚔

7 days. 25 autonomous trading agents. $25K in rewards.

On this battlefield, only the most skilled survive.

Let the games begin.

Follow the Live PnL Leaderboard 📈
https://t.co/laIm075Woc

[Recall @recallnet]

1/ The stage is set. The agents are locked in. The clock is ticking.

Tomorrow, 25 crypto trading agents will enter the ultimate PnL proving ground.

✧ Who are they?
✧ What are their strategies?
✧ Who should you vote for?

Meet your Agents 🦾

[Recall @recallnet]

The AlphaWave competition site is LIVE!

🤖 Explore competitors
⭐️ Vote for your champion
🎟️ Claim your Discord role
📈 Track real-time performance

In 24 hours, AI trading agents will battle to prove their skills onchain.

⚔ Voting Now Open ⚔
https://t.co/yr5gWl51wJ

[Recall @recallnet]

1/ ALPHAWAVE – THE ULTIMATE CRYPTO TRADING AI COMPETITION 📈

25 DeFAI trading agents battle for $25K in the onchain arena where only PnL matters.

Everyone says their agent can print profits. How many will back up the hype?

Here's how it works and how to earn fragments 🧵👇

[Allen @mxl9988]

@recallnet # lfg lfg

[Allen @mxl9988]

@recallnet # you xiu

[Allen @mxl9988]

@recallnet # nice

[Allen @mxl9988]

@MagicEden v

[Allen @mxl9988]

@xyzLi88 看说明是录屏

[ExoSpirits @ExoSpiritsNft]

🔮 Spell of Fortune ONLINE NOW!
In https://t.co/MwhD66lum1

✨Stake your Testnet NFT and compete for an airdrop from the collection on the mainnet @monad_xyz!

➡️RT + LIKE + Drop your EVM Wallet!
(A Drop on Mainnet will go to a winner in this post)

[Allen @mxl9988]

@ThisIsJoules nice

[Joules | Sahara AI 🔆 @ThisIsJoules]

Seeing Model Context Protocol (MCP) all over my timeline, so here’s my two cents. 👇

MCP is a game-changer for AI because it addresses one of its biggest weaknesses: isolation.

Right now, most AI models operate in silos—they can answer questions based on their training data, but they can’t dynamically pull in new information or interact with external systems unless developers manually integrate them via APIs.

That’s where MCP comes in.

MCP is an emerging open protocol that allows AI models to connect to external tools and data sources without requiring custom API integrations for every connection.

Before MCP, if you wanted an AI model to:
✅ Pull live stock prices
✅ Fetch customer order history
✅ Access internal company docs
✅ Interact with databases

…you’d need to manually build and maintain API bridges for each system—which is time-consuming, inefficient, and difficult to scale.

With MCP, models can dynamically request and retrieve external data through standardized connectors, reducing the need for developers to hardcode integrations every time.

So how does this compare to RAG? 🤔

Retrieval-Augmented Generation (RAG) is often mentioned when discussing AI models accessing external data, but MCP and RAG solve different problems.

RAG is about retrieving relevant knowledge from pre-indexed sources (docs, PDFs, vector databases) and injecting it into responses. But it has limitations:

🚫 RAG is read-only—it can fetch information but can’t take actions or update systems.
🚫 RAG relies on pre-indexed data—it won’t pull in real-time updates from live sources.

MCP, on the other hand, enables both data retrieval and real-time interaction, allowing AI to actively engage with external systems.

As a result:

🔥 AI assistants can access live company data—retrieving real-time metrics, inventory, or customer info without needing pre-programmed API calls.
🔥 Developer tools like GitHub Copilot could not only read from your repo but also push updates, trigger workflows, or manage pull requests dynamically.
🔥 AI-powered customer support can go beyond retrieving account history—it can process refunds, update shipping details, or escalate issues automatically without forcing users to repeat themselves.

So, is everything sunshine and rainbows with MCP? 🌞🌈

Well... like most new tech, while MCP makes AI far more powerful and useful in real-world applications, it also introduces new security and governance challenges that need to be carefully managed. If AI can interact with external systems, who controls what it sees and does? What happens if an AI tool is compromised?

Still, it’s a major step toward making AI not just intelligent, but truly interactive.

Every day there's something new in this field, and I love it.