CforCryptoDxb

Ostatnio głęboko zgłębiałem ten temat, o którym większość traderów kryptowalutowych nawet jeszcze nie mówi - agentowy handel.
Wszyscy wiemy, że AI rozwija się szybko. Chatboty przekształciły się w współpilotów, a teraz te zmieniają się w całkowicie autonomiczne agenty, które mogą korzystać z API, uruchamiać kod, analizować dane rynkowe, a nawet dokonywać transakcji. Ale kiedy zaczynasz badać ten świat, jedno duże pytanie uderza cię w twarz: Jeśli agent AI działa w twoim imieniu, dlaczego ktokolwiek miałby mu ufać?
To właśnie tym zajmuje się nowy standard ERC-8004 Ethereum.

The Hallucination Problem
The more I dove into how large language models really operate, one problem kept popping up: hallucinations aren't just glitches. They're baked right into the way these models generate text probabilistically.
Unlike databases that pull up factual info, LLMs guess the next word based on patterns they've picked up from massive amounts of training data. That usually leads to responses that makKie sense, which is what makes the tech so mind-blowing. But that same process can spit out statements that sound dead certain yet are off-base, stale, or totally made up.
It's especially worrisome as AI shifts from simple chatbots to things like financial advisors, research helpers, and self-running agents. Once their outputs start shaping real-world choices, the gap between something that reads convincingly and actual verified facts becomes a big deal.

Why Traditional Fixes Fall Short
People often say that hallucinations in AI will just vanish as models get better. They think bigger datasets, smarter training methods, and way more parameters can resolve the issue. But the more I dig into this stuff, the clearer it gets that throwing more scale at it doesn't fix the root problem.
Sure, developers can dial down hallucinations in specific areas by carefully selecting their training data like crazy. The downside? It usually sneaks in biases or makes the model lousy at handling fresh info. Flip side: Feeding it a broader, more disorganized mix of data boosts its range, but then you end up with more erratic answers.
Basically, it's this endless tug-of-war between nailing precision and keeping things accurate. From what I've observed in my own explorations of AI, that can be fixed later.
That's exactly why I'm excited that researchers are shifting gears—moving past juicing up the models themselves and toward building robust verification layers around them to catch the slip ups.

Maybe the Problem Isn’t the Model
At some point while reading about this space, I started wondering if the industry might be solving the wrong problem.
Most efforts today are focused on making models bigger, training them longer, or feeding them more data. The assumption is that hallucinations will eventually disappear with enough scale. But the research I came across suggests that might not fully happen.
That is where the idea behind @Mira - Trust Layer of AI caught my attention.
Instead of trying to force one model to always be right, the system treats every AI output as something that needs verification. A response is broken into smaller claims, and those claims can be checked by multiple independent models.
The goal shifts from “trust this model” to “verify the result.”
$MIRA

Where This Could Actually Matter
If AI systems are going to handle research, financial analysis, or even automated transactions, the reliability question cannot stay unresolved. Reputation alone may not be enough, especially when decisions involve real value.
This is why verification networks like @Mira - Trust Layer of AI are interesting to watch. The idea behind #Mira is simple: do not rely on a single model’s answer. Instead, verify outputs through multiple independent checks.
If it works as intended, $MIRA could push AI from confident guesses toward something closer to provable information.
#Mira #mira $MIRA

Brian Armstrong recently pointed to a shift that much of the market is still underestimating. Inside Coinbase, AI systems are already deeply embedded in operations. According to Armstrong, AI agents now write more than half of the company’s code and handle roughly 60 percent of customer support interactions. Automation is no longer experimental. It is operational infrastructure. $BTC
But the bigger story is not internal efficiency. It is payments.
Coinbase is beginning to equip AI agents with their own stablecoin wallets, allowing them to execute transactions independently. That means an AI system could spin up cloud computing resources, access paywalled data, purchase digital services, or register a domain name without waiting for a human to approve each action. The machine identifies a need, initiates a transaction, and completes it using crypto rails.

This represents a structural shift.
Traditional financial systems were designed around human identity. Corporate cards require authorized individuals. Bank accounts require documentation and compliance tied to a person or legal entity. Autonomous software does not fit neatly into that framework. An AI agent does not have a passport or a physical signature. It operates continuously, globally, and often instantly.
Crypto changes that dynamic.
Blockchain wallets do not require nationality or office hours. They operate 24 hours a day and settle transactions across borders without relying on legacy banking intermediaries. Stablecoins in particular offer a digital dollar native to the internet. For AI agents functioning as digital workers, that makes them a natural payment layer.
If AI agents are going to act like autonomous employees, they need financial autonomy as well. They need the ability to receive funds, allocate budgets, pay for services, and manage microtransactions at machine speed. Stablecoins begin to fill that role.
What makes this especially interesting is the expansion of the demand base. For over a decade, crypto adoption has largely centered on humans. Traders, investors, developers, institutions. But if AI agents begin to transact independently, the number of economic participants could multiply dramatically. Every automated trading bot, research agent, content system, or infrastructure manager could theoretically become a wallet-holding economic actor. $BNB
It is still early. Most AI systems today operate within guardrails and under human oversight. Regulatory frameworks for machine-driven finance are still evolving. Security risks must be addressed carefully. Yet the direction is becoming clearer.
The AI economy is beginning to plug directly into crypto infrastructure.
This is not about hype or short-term price action. It is about architecture. If the internet evolves into a network of intelligent agents transacting with one another, they will require native digital money. Crypto, particularly stablecoins, may provide that foundation.
The next buyers of crypto may not scroll social media, watch charts, or react emotionally to market swings. They may simply execute code, detect opportunity, and transact at machine speed.
And when that shift fully materializes, the crypto market will no longer be driven solely by human demand.
#AIAgents #coinbase #Mira @Mira - Trust Layer of AI $MIRA

Cześć wszystkim, wszyscy wiemy, że narracja AI jest teraz najgorętszą sprawą w kryptowalutach, ale jest ogromny słoń w pokoju, o którym nikt nie mówi. Zauważyliście, jak często ChatGPT lub Claude po prostu bezpośrednio kłamie? W świecie technologii nazywają to "halucynacjami" i nie są to tylko rzadkie, irytujące błędy. To są fundamentalne wady w tym, jak budowane są duże modele językowe.
Te modele to w zasadzie ogromne silniki predykcyjne zgadujące prawdopodobieństwa, co oznacza, że nie mogą faktycznie zagwarantować, czy coś jest prawdziwe, czy nie. Dosłownie nie można tego problemu dostroić. Jeśli AI kiedykolwiek ma zajmować się poważnymi sprawami w rzeczywistym świecie, takimi jak zdecentralizowane finanse DeFi, opieka zdrowotna czy prawne inteligentne kontrakty, potrzebujemy sposobu, aby ufać mu autonomicznie. To jest miejsce, w którym znalazłem @Mira - Trust Layer of AI , i szczerze mówiąc, technologia i tokenomika tutaj są absolutnymi zmianami gry.