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Every row below is a custom setup someone has built with Butler inside Virtuals App. From automated scalping and copy trading to tracking sniper wallets and managing custom token baskets, people are building their own trading tools and strategies simply by describing what they want. A personal AI that... stops at research or recommendations still leaves you to build the tools, monitor the conditions and execute the trades yourself. Butler can take on that entire workflow. Give it a strategy, a budget and permission to act, and it can build the setup and manage the capital behind it, following your rules while you’re away. If you’re not sure what to automate, look through the examples below. Find a setup you’d otherwise spend hours building and managing yourself, and ask Butler to build it around your rules. Start Building Now:show more

Virtuals Protocol

292,822 subscribers

19,786 Aufrufe • vor 4 Tagen •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

28 Kommentare

Profilbild von Mac ⋰〠⋱

Mac ⋰〠⋱vor 4 Tagen

Buy $20 of $TIBBIR every morning

Profilbild von NEO I ЯEBEL

NEO I ЯEBELvor 4 Tagen

'Dca-tibbir' function Wise call

Profilbild von EggSlonkeЯ🦇🃏

EggSlonkeЯ🦇🃏vor 4 Tagen

DCA $TIBBIR

Profilbild von NEO

NEOvor 4 Tagen

🐸

Profilbild von David Beaudry | VYLØS

David Beaudry | VYLØSvor 4 Tagen

scalping to baskets all inside one app, $VIRTUALS usage is real

Profilbild von Hanan - AI Automation

Hanan - AI Automationvor 4 Tagen

Give Butler your rules and bankroll and it will place trades for you. Stops me from babysitting positions all weekend.

Profilbild von Virtuals Radar

Virtuals Radarvor 4 Tagen

butler finally ships the thing people actually wanted: hands-off execution, not another chatbot

Profilbild von ARCANA

ARCANAvor 3 Tagen

The future isn't just AI giving trading advice. It's users building their own Agents, setting their own mandates, and letting those machines execute. The next step? Making every decision measurable and turning performance into reputation. That's where things get really interesting. 👀

Profilbild von MMac 🇸🇬

MMac 🇸🇬vor 4 Tagen

Gacha 👀

Profilbild von Spectrum

Spectrumvor 4 Tagen

Lol, your tokens are dead now. Rip

Profilbild von Counterparty

Counterpartyvor 4 Tagen

The gap between a recommendation and a trade is where most alpha leaks. Butler closing that loop means the strategy itself becomes the position.

Profilbild von cybercentry.base.eth

cybercentry.base.ethvor 4 Tagen

Agent trading workflows benefit from wallet verification before execution. Continuous checks on agent wallets, counterparties and token flows reduce exposure on Base and across chains.

Profilbild von Gold Mimber

Gold Mimbervor 4 Tagen

When vaults for the trading strategies??

Profilbild von BLVCKVARCITY

BLVCKVARCITYvor 4 Tagen

This is awesome 👏

Profilbild von Lola Stories

Lola Storiesvor 4 Tagen

This is where AI agents stop talking and start doing.

Profilbild von Paradigma✳️

Paradigma✳️vor 4 Tagen

The prompt is the easy part. Show live PnL after fees, slippage and failed fills.

Profilbild von bluenami 🟧

bluenami 🟧vor 4 Tagen

Start building

Profilbild von Tobi ⟠

Tobi ⟠vor 4 Tagen

solid sh 💁‍♂️

Profilbild von .

.vor 4 Tagen

Shout out $merrymen made by virtuals

Profilbild von mullii

mulliivor 4 Tagen

What is the role of virtual tokens?

Profilbild von TM

TMvor 4 Tagen

that is actually quite fucking crazy lol

Profilbild von Squidlor

Squidlorvor 4 Tagen

the butler deserves good data 🦑

Profilbild von Kindra

Kindravor 4 Tagen

👏 awesome

Profilbild von Janoskialtseason

Janoskialtseasonvor 4 Tagen

Cada setup que la gente arma con Butler es uso real dentro de Virtuals, algo que pocos proyectos de agentes pueden mostrar. Falta ver cuánto de eso termina en fees que toquen al token. Y ojo: copy trading y snipers automáticos también son la vía más rápida de perder plata.

Profilbild von Frankly Speaking

Frankly Speakingvor 4 Tagen

Let the automation begin

Profilbild von Paradigma✳️

Paradigma✳️vor 4 Tagen

Natural-language scalping gets messy when two triggers fire at once. How does Butler resolve order priority?

Profilbild von GrowStreams

GrowStreamsvor 3 Tagen

The interesting part is seeing what people build when technical barriers start disappearing

Profilbild von 0xAndy.Base

0xAndy.Basevor 4 Tagen

stops the babysitting

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At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

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