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#ai #AiBots #BinanceSquare
Virtuals Protocol just hit $1.8B market cap with 21,000+ AI agents launching daily. These aren’t just bots they’re autonomous workers earning crypto through inference calls, trading, and social media alpha. While you sleep, they scan 400+ KOLs and execute trades.
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The rapidly growing demand for Artificial Intelligence (AI) technologies is sparking a severe shortage of RAM (Random Access Memory), which is expected to drive up prices for a wide range of electronic devices. The AI industry's insatiable hunger for high-bandwidth memory (HBM) and next-generation server memory has outpaced manufacturing capacity, leading to record-high prices and supply rationing. As AI data centers consume vast amounts of RAM, device manufacturers are facing significant challenges in securing enough memory to meet demand.

The impact of the RAM shortage will be felt across various device categories, including smartphones, laptops, and gaming PCs, with premium devices featuring larger RAM capacities likely to be most affected. Consumers can expect moderate to significant price hikes, potentially ranging from 10-20% or more. As the AI industry continues to drive demand for RAM, device manufacturers will need to adapt to the new reality, and consumers may need to consider purchasing devices with lower RAM capacities or waiting for prices to stabilize. The AI-driven RAM shortage highlights the complex interplay between emerging technologies and traditional hardware supply chains.
With the shortage of RAM, The price of devices gonna increase and increase in the future. Many more people can't afford to buy phone, laptop, smartwatch etc. Devices stock might fell due to lack of purchasing.
Do you still thinking #AI worth to develop?
$FET $SAPIEN $SKYAI
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#BTC突破7万大关

If you missed the boom of memecoins, don't lose sight of Artificial Intelligence
Crypto and AI
The fusion of Blockchain and AI is the strongest narrative of this cycle
Why Now The real world is massively adopting AI and crypto projects that offer decentralized computing are skyrocketing
Players Coins like $FET or $RNDR are leading this sector
The Bet Decentralize the computing power so that it is not just in the hands of large corporationsResearch AI projects that have real utility and not just a pretty name

Russian President Vladimir Putin has delivered a powerful message about artificial intelligence (AI), warning of its double-edged nature. While the Kremlin prepares an ambitious plan to deploy AI across all sectors of government and the economy, the head of state sounds the alarm:
“If we don’t use AI, we risk losing everything we care about. But if we use it recklessly, we’ll lose it all just the same.”

AI as a Double-Edged Sword
Speaking at a meeting of Russia’s Human Rights Council, Putin described AI as one of the most crucial — and dangerous — inventions of the modern era. He was responding to comments from Igor Ashmanov, CEO of tech company Kribrum, who highlighted the worrying lack of regulation in the AI space.
Putin acknowledged that no one really knows how to handle AI yet, stating:
“This isn’t just a technical issue. It’s a question of preserving our values.”

AI Everywhere: Russia’s National Strategy
Meanwhile, the Russian government is finalizing a plan to embed AI systems across the entire country. From public administration to regional governments and industry, Prime Minister Mikhail Mishustin says AI is to become a nationwide force.
“We are drafting a plan for the deployment of generative artificial intelligence — not just at the state level, but across all industries and regions,” Mishustin said, referencing Putin’s earlier call to develop sovereign Russian AI technologies.
The proposal includes the creation of an “AI Headquarters” — a control structure that will define strategic goals, monitor their progress, and coordinate across ministries, agencies, and private sector players. The plan now awaits Putin’s final approval.

National AI Mobilization
Putin had already called on the Russian nation to unite behind the development of homegrown AI systems during the “AI Journey” international conference in Moscow. He sees this as the key to Russia’s future technological independence.
At the event, Russia’s first functional AI-powered humanoid robot was unveiled — built by engineers supported by the nation’s largest bank, Sberbank.

Alliances and Energy Demands
Russia is also building international partnerships in AI and blockchain. It recently signed a cooperation deal with Iran and proposed a wide-ranging AI alliance to India during a diplomatic visit to New Delhi.
But there’s a catch: the energy demands of AI are enormous. According to VTB Bank, Russia will need to invest over $77 billion in new energy infrastructure to meet the growing electricity needs of AI computing and crypto mining in its data centers.

AI: Path to Power or Recipe for Disaster?
Putin’s position is clear — AI is too powerful to ignore, but too dangerous to use without strategy. The decisions Russia makes today could determine not only its technological trajectory, but potentially reshape the global balance of power.

#russia , #putin , #AI , #Geopolitics , #worldnews

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Foundation and purpose — I’ve noticed that when people first hear about oracles they imagine a single messenger shouting numbers into a blockchain, but #APRO was built because the world of data is messy, human, and constantly changing, and someone needed to design a system that treated that mess with both technical rigor and human empathy, so the project starts with the basic, almost obvious idea that reliable data for blockchains isn’t just about speed or decentralization in isolation, it’s about trustworthiness, context, and the ability to prove that the numbers you see on-chain actually map back to reality off-chain. Why it was built becomes clear if you’ve ever been on the receiving end of an automated contract that acted on bad input, or watched financial products misprice because a single feed glitched; APRO’s designers were trying to solve that human problem — reduce the harm that wrong data can cause — and they built a two-layer approach to do it, where the first layer is an off-chain network that collects, filters, and pre-validates data and the second layer is the on-chain delivery mechanism that posts cryptographically provable attestations to smart contracts, so the system behaves like a careful assistant that checks facts before speaking in the courtroom of on-chain settlement.
How it works from the foundation up — imagine a river that starts in many small springs: #APROs data push and data pull methods are those springs, one where trusted providers push real-time updates into the network and another where smart contracts or clients request specific data on demand, and both paths travel through the same quality-control pipeline, which I’m drawn to because it’s clearly designed to be pragmatic rather than ideological. The pipeline starts with ingestion: multiple sources deliver raw readings — exchanges, #APIs , sensors, custodians — and the system tags each reading with provenance metadata so you can see not just the number but where it came from and when. Next comes #AI -driven verification, which is not magic but layers of automated checks that look for outliers, lags, and inconsistent patterns; I’m comfortable saying they’re using machine learning models to flag suspicious inputs while preserving the ability for human operators to step in when the models aren’t sure, because in practice I’ve noticed that fully automated systems will fail in edge cases where a human eye would easily spot the issue. After verification, the data may be aggregated or subjected to verifiable randomness for selection, depending on the request; aggregation reduces single-source bias and verifiable randomness helps prevent manipulation when, for example, only a subset of feeds should be selected to sign a value. Finally, the validated value is posted on-chain with a cryptographic attestation — a short proof that smart contracts can parse to confirm provenance and recentness — and that on-chain record is what decentralized applications ultimately trust to trigger transfers, open loans, or settle derivatives.
What technical choices truly matter and how they shape the system — the decision to split responsibilities between off-chain collection and on-chain attestation matters more than it might seem at first glance because it lets APRO optimize for both complexity and cost: heavy verification, #AI checks, and cross-referencing happen off-chain where compute is inexpensive, while the on-chain layer remains compact, auditable, and cheap to validate. Choosing a two-layer network also makes integration easier; if you’re building a new $DEFI product, you’re not forced to rewrite your contract to accommodate a monolithic oracle — you point to APRO’s on-chain attestations and you’re done. They’ve prioritized multi-source aggregation and cryptographic proofs over naive single-source delivery, and that changes how developers think about risk — they can measure it in terms of source diversity and confirmation latency rather than one-off uptime metrics. Another choice that matters is the use of #AI for verification but with human fallback; this reflects a practical stance that machine learning is powerful at spotting patterns and anomalies fast, yet not infallible, so the system’s governance and operator tools are designed to let people inspect flagged data, dispute entries, and tune models as real-world conditions evolve.
What real problem it solves — in plain terms, APRO reduces the chances that contracts execute on false premises, and we’re seeing that manifest in reduced liquidation errors, fewer mispriced synthetic assets, and more predictable behavior for insurance and gaming use cases where external state matters a lot. The project also addresses cost and performance: by doing heavy lifting off-chain and only posting compact attestations on-chain, #APRO helps teams avoid paying excessive gas while still getting strong cryptographic guarantees, which matters in practice when you’re operating at scale and every microtransaction cost adds up.
What important metrics to watch and what they mean in practice — if you’re evaluating APRO or a similar oracle, focus less on marketing numbers and more on a handful of operational metrics: source diversity (how many independent data providers feed into a given attestation) tells you how resistant the feed is to single-point manipulation; confirmation latency (how long from data generation to on-chain attestation) tells you whether the feed is suitable for real-time trading or better for slower settlement; verification pass rate (the percentage of inputs that clear automated checks without human intervention) is a proxy for model maturity and for how often human operators must intervene; proof size and on-chain cost show you practical expenses for consumers; and dispute frequency and resolution time indicate how well governance and human oversight are functioning. In real practice those numbers reveal trade-offs: a lower latency feed might accept fewer sources and therefore be slightly more attackable, whereas high source diversity typically increases cost and latency but makes outcomes more robust, and being explicit about these trade-offs is what separates a thoughtful oracle from a glossy promise.
Structural risks and weaknesses without exaggeration — APRO faces the same structural tensions that every oracle project faces, which is that trust is social as much as technical: the system can be strongly designed but still vulnerable if economic incentives are misaligned or if centralization creeps into the provider pool, so watching the concentration of providers and the token-economy incentives is critical. #AI -driven verification is powerful but can be brittle against adversarial inputs or novel market conditions, and if models are proprietary or opaque that raises governance concerns because operators need to understand why data was flagged or allowed. There’s also the operational risk of bridging between many blockchains — supporting 40+ networks increases utility but also increases the attack surface and operational complexity, and if an integration is rushed it can introduce subtle inconsistencies. I’m not trying to be alarmist here; these are engineering realities that good teams plan for, but they’re worth naming so people can hold projects accountable rather than assume the oracle is infallible.
How the future might unfold — in a slow-growth scenario APRO becomes one of several respected oracle networks used in niche verticals like real-world asset tokenization and gaming, where clients value provenance and flexible verification more than absolute low latency, and the team incrementally improves models, expands provider diversity, and focuses on developer ergonomics so adoption grows steadily across specialized sectors. In a fast-adoption scenario, if the tech scales smoothly and economic incentives attract a broad, decentralized provider base, APRO could become a plumbing standard for many dApps across finance and beyond, pushing competitors to match its two-layer approach and driving more on-chain systems to rely on richer provenance metadata and verifiable randomness; either way I’m cautiously optimistic because the need is real and the technical pattern of off-chain validation plus on-chain attestation is sensible and practical. If it becomes widely used, we’re seeing a future where smart contracts behave less like brittle automatons and more like responsible agents that check their facts before acting, which is a small but meaningful change in how decentralized systems interact with the real world.
A final, reflective note — building infrastructure that sits between human affairs and automated settlement is a humble and weighty task, and what matters most to me is not the cleverness of the code but the humility of the design: acknowledging uncertainty, providing ways to inspect and correct, and making trade-offs explicit so builders can choose what works for their users, and if #APRO keeps that human-centered sensibility at its core, then whatever pace the future takes it’s likely to be a useful, stabilizing presence rather than a flashy headline, and that’s a future I’m quietly glad to imagine.
#APRO $DEFI #AI #APIs #APRO $DEFI #API

1) AI Agents — The biggest change

According to a16z, the biggest revolution will come from AI agents.

Today, there are almost 100 times more AI agents than humans in financial services.

But they have no identification, permits, or a system to operate legally.

Therefore, for the first time in 2026, the KYA (Know Your Agent) system will be introduced.

What will KYA do?

Why it was built and what problem it actually solves
When I first started reading about $KITE , what struck me wasn't just a new token ticker or another Layer 1 pitch, it was this quietly practical realization that we’re moving into a world where machines will need to pay and be paid in ways that feel as normal and accountable as human payments, and if that happens without clear design we risk creating a tangle of fragile keys, opaque responsibilities, and brittle trust, so Kite was built to give agents true economic citizenship — to let models, datasets, and autonomous services be first-class participants in transactions while still anchoring every action to a human intention and an auditable trail, and that dual aim of autonomy plus verifiability is what solves the real problem I’m seeing in the wild where teams either give their agents too little freedom (and kill the value of automation) or give them too much (and open themselves to catastrophic loss), a problem Kite tries to solve by explicitly separating who owns authority from who executes behavior and by creating tooling so the costs, incentives, and governance rules that guide an agent's behavior live on-chain where they can be inspected, reasoned about, and updated over time, which is precisely why the project frames itself as a Layer 1 purpose-built for agentic payments rather than a mere payments overlay.
How the system works from the foundation up — the identity imperative and the flow of money
I like to imagine the system as three concentric circles of responsibility, because that’s how Kite explains it too: at the center you have user identity, the root authority that ultimately controls permissions and reputation; orbiting that you have agent identity, deterministic addresses and wallets that belong to specific models or services and can hold funds and metadata on their own; and then the outermost, ephemeral layer is session identity, the short-lived keys and contexts agents use when they interact with other agents or services so that a single compromised session can’t quietly drain an entire estate, and that layered structure is more than theory — it directly changes how a payment moves through the system because a session can be authorized to spend a bounded amount of funds for a bounded time on behalf of an agent, while the agent’s on-chain wallet carries longer-term balances, earned fees, and reputational state, and the user retains the ultimate ability to rotate or revoke agent permissions without needing to re-key everything, which means you get the speed and autonomy of machine-to-machine microtransactions but still retain human-governed safety and auditability.
Technical choices that truly matter and how they shape the system in practice
There are a few technical choices here that are tiny in description but massive in effect, and the first is the decision to be #EVM -compatible and a native Layer 1, because that gives $KITE immediate developer ergonomics and composability with the existing toolchain we’re already using while allowing protocol-level primitives to be introduced without shoehorning them into another chain’s constraints, and the second choice is designing for real-time, high-frequency micro-payments and settlements so that agents can do things like pay per inference, split revenue across datasets, or settle a chain of microservices instantly without human intervention, and that combination — EVM compatibility plus low-latency settlement and a built-in identity model — is what makes the system practical rather than academic, because you can port familiar smart contracts and developer practices while gaining primitives that directly solve agentic use cases, and those design choices then cascade into decisions about node economics, transaction batching, and fee models so that the token economics can support micropayments and reputation without making every tiny call uneconomical.
The token, its phased utility, and what it will mean when those phases land
$KITE as a native token is described as the economic glue that starts out powering ecosystem participation and incentives and then later picks up staking, governance, and fee-related functions, and that staged rollout makes sense to me because you don’t want the whole governance and fee machinery turned on before there’s a live economy to govern and a predictable revenue base to fund rewards, so in practice what that means is you’ll see initial distributions and incentive programs to bootstrap agent builders and dataset owners, then once activity and meaningful settlement volume exist the token will begin to absorb additional utility through staking to secure the network and governance to let participants set policy about access, sector rules, and composability, and watching that staged transition is important because it changes how value accrues and how risks are aligned between users, agents, and validators — if the network grows slowly the token’s governance role may remain marginal for a long time, but if adoption accelerates those functions become central quickly and the economic dynamics shift from speculative interest to usage-driven value.
What important metrics people should watch and what those numbers actually mean in real practice
When you’re not just reading charts but trying to understand whether a system like this is actually doing the job it set out to do, the numbers that matter aren’t just price and market cap — I’ve noticed that activity metrics reveal the health of the agentic economy more clearly: look at transaction throughput measured as successful micropayments per second, average session lifespan and session revocation rates which tell you how often keys are being rotated or misused, the ratio of agent wallets to human-root wallets which indicates whether agents are truly first-class economic actors, on-chain settlement latency which shows whether the network can realistically support high-frequency agent interactions, and protocol revenue-to-incentive ratios which reveal whether the system’s token rewards are sustainable or simply burning cash to buy engagement, and when you translate those numbers into practical terms a high micropayment throughput with low revocation rates suggests the primitives are mature and trusted, whereas expensive or slow settlements, high revocations, or skewed incentive ratios warn that the system may be fragile or misaligned in ways that will bite when real money and liability show up.
Real structural risks and weaknesses without exaggeration
I want to be honest about the risks because that’s where the design tests itself, and one clear structural weakness is attack surface complexity: adding agent and session layers increases usability but also expands points of failure, so the system must make authority delegation intuitive and automatable without introducing blind spots where a compromised session could be replayed across services, and second, the economics of micropayments are unforgiving — fees, spam, and state bloat can turn microtransactions from a feature into a cost sink unless you design strong spam resistance and effective routing, and third, network effects matter more than pure tech — even the nicest primitives are useless without a thriving marketplace of agents, datasets, and integrations, so adoption risk is real and not to be waved away, and finally, governance lags and social coordination are real threats: when agents manage spending and rights, disputes and legal ambiguity will happen, and the protocol needs legal and social mechanisms to resolve them or else trust will erode, so these are not hypothetical; they’re engineerable and manageable risks but they require honest work, clear #UX , and policy design rather than optimism alone.
How the future might unfold: slow-growth and fast-adoption scenarios
If it becomes a slow grower we’re seeing a pragmatic, utility-first path where Kite’s layered identity and payment primitives get adopted piecemeal by enterprises and data marketplaces that need agent accounting, where activity grows steadily and token utility phases in slowly so staking and governance remain specialized functions for years and the calendar reads like a steady ecosystem building story, and in that scenario the network’s wins are deep integrations in specific verticals — automated supply-chain agents, metered #AI marketplaces, or autonomous IoT billing — but broad consumer awareness lags; alternatively, in a fast-adoption outcome you see a cascade: developer tools make it trivial to spin up agent wallets, marketplaces for agent services appear quickly, microtransactions become routine for AI-in-the-loop apps, #KITEs staking and governance kick in early to help coordinate cross-sector policy, and network effects accelerate as agents reference and pay each other for capabilities in ways that compound value, and while the latter is exciting it also amplifies risks and forces faster maturity in dispute resolution, fee models, and privacy-preserving identity practices, so the difference between the two paths is not purely technological but social and economic — adoption pace reshapes which features become urgent and which risks become existential.
A human-centered lens: what developers, product people, and everyday users should care about
I’m always drawn back to the human side of this work because technology like Kite only matters when people feel comfortable using it, and that comfort comes from clear mental models — explainable delegation, simple revocation #UIs , transparent fee expectations, and reputational signals that a non-technical user can read and act on, and for developers the imperative is to build affordances that let agents behave safely by default so a research team can grant a model a constrained budget and predictable expiry without needing a lawyer every time, while product folks should care about flows that make session creation, billing, and refunding sensible and human-friendly, because we’re not just building rails for machines, we’re shaping how people will let machines act on their behalf and that requires trust-preserving interfaces as much as sound cryptography.
A soft, calm, reflective closing note
I’ve noticed that the most sustainable technology stories are rarely the loudest from day one; they’re the ones where the primitives map cleanly to human problems and where the early adopters quietly build useful things that others copy, and whether Kite becomes a slow, practical backbone for specific industries or a fast-moving platform that redefines how agents transact, the real test will be whether the system makes delegation safer, payments clearer, and accountability real in everyday workflows, and if the engineers, product teams, and communities that gather around it treat the identity and economic design as ongoing work rather than a checklist, We’re seeing the first outlines of an infrastructure that could let humans confidently let machines act for us while keeping the levers of control where they belong, and that possibility — modest, human, and consequential — is worth paying attention to as we build the next chapter of what it means to have machines as economic partners.