#kitebbw Community Insights & Market Sentiment

I keep coming back to a critical issue with AI—one that, for some reason, is rarely discussed in the crypto space. Let’s be blunt: we all want to see intelligent, autonomous AI agents capable of managing capital or analyzing data. But how can these models be trained on-chain without exposing proprietary algorithms and trade secrets to the entire market? Developers have hit a wall here.
On public blockchains, data is transparent. If your AI agent purchases specific datasets or utilizes proprietary APIs, competitors will spot it instantly via a network scanner. Total transparency effectively destroys corporate confidentiality.
My deep dive into the Kite architecture points to a feature that breaks this deadlock: secure data coordination and verification, facilitated by $KITE staking.
Consider the concept: Kite establishes payment rails that allow AI agents to securely exchange data and pay for services, all while keeping their internal logic private. The network’s smart contracts act as a safeguard—they verify the legitimacy of a transaction and the identity of the agent, without leaking its underlying algorithms to the public.
For large enterprises, this is the Holy Grail. They gain the benefits of Web3-based payments without relinquishing control over their intellectual property. In essence, Kite builds a discreet security infrastructure for AI.
While the masses chase hype-driven tokens, I’m focused on long-term, practical use cases. And this approach to data protection—pioneered by Kite—is the best I’ve seen in a long time. What’s your take on the concept of data privacy for AI agents?
#KiteBBW $KITE @KITE AI 中文

Kite is building something I find really interesting: a future where AI agents can do more than just answer questions.

Imagine AI agents that can pay for services, access data, complete tasks, and work with other agents without needing human approval at every step.

That's what makes Kite stand out to me. It's bringing AI and blockchain together to help agents operate more independently while keeping payments and spending under control.

I believe we're still in the early stages of the agent economy, and there's a lot left to explore.

What excites me most is the idea of AI that doesn't just understand what we need but can actually take action.

Kite is building toward that future, and I'm excited to see where it goes.

#KiteBBW $KITE @KITE AI 中文

Kite: Powering the Future of AI Agents

I believe the future of AI is much bigger than just answering questions.

Imagine AI agents that can make payments, buy services, and complete tasks on their own. Sounds interesting, right?

That’s what makes Kite worth watching.

I see Kite as a project working toward a future where AI agents can transact securely, operate within set limits, and interact with other agents without needing human approval for every step.

We’re still early, but I believe the connection between AI and on-chain payments could open up some interesting possibilities.

I’m keeping an eye on Kite because the next big shift might not just be AI that thinks, but AI that can actually do things.

#KiteBBW $KITE @KITE AI 中文

Kite and the Agent Economy

The way we use AI is changing, and I think the next big question is how these agents will handle payments independently.

Kite is working on infrastructure that could help AI agents pay for services while keeping spending and permissions under control.

Think about the possibilities. An agent could complete a task, access a service, and handle payment without needing someone to approve every step.

But honestly, giving AI control over money comes with risks. Security, trust, and accountability cannot be ignored.

Kite has an interesting problem to solve, but the real test will be whether its technology works reliably outside the hype.

I'm watching with curiosity. The idea makes sense, but execution is what will matter.

#KiteBBW $KITE @KITE AI 中文

I’ve been thinking about what AI agents will need once they start doing real work, not just answering questions.

An AI might find the best data, run a task, or build something useful. But if it needs to pay for a service or access computing power, who handles that payment?

This is where Kite ($KITE ) caught my attention.

The idea is to give AI agents the financial infrastructure they need to work with other services and make payments without asking a human every time.

Three things matter to me here:

• Money movement: Agents need a simple way to pay for the tools and services they use.

• Trust: Payments should follow clear rules, with ways to check whether a task was completed as expected.

• Low-cost transactions: If agents make thousands of small payments, high fees can quickly become a problem.

But I think the biggest challenge goes beyond making payments possible.

How do we let an AI spend money independently while keeping control over its budget, permissions, and decisions?

Giving agents access to money is one thing. Making that access safe and reliable is another.

Kite is working in an interesting area, but real adoption and security will matter more than the idea alone.

Would you let an AI agent spend your money if you could set strict limits on every transaction? $KITE #KiteBBW

KITE AI: GIVING AGENTS FREEDOM WITH LIMITS

I've been watching the shift from AI that answers questions to AI that can actually get things done. @Kite AI is tackling one practical problem: how can an agent pay for services without giving it unlimited access to our money?

Kite Passport lets users approve spending limits, transaction caps, and time windows using a passkey. Agents can then pay for supported APIs and services with USDC within those boundaries. Payments and activity can also be reviewed through its tools.

What interests me is the balance between independence and control. The limits are promising, but real trust will depend on how reliably they hold up when agents handle complex tasks, unexpected errors, and more services.

I'm curious to see whether developers and users embrace this model over time. An agent that can act is useful; one that can act within rules we can verify is far more meaningful.

#KiteBBW $KITE @KITE AI 中文

Imagine your AI agent handling your subscriptions and API cost every month on a fixed $50 budget.
The question is not wheter an agent can pay, it already can. The real questions are :
🔹 Who authorized it?
🔹 How much can it spend?
🔹 Where do its limits end?
That's the problem @KITE AI 中文 is tackling.
Its Agent Passport lets users fund an agent's wallet and set spending limits for its transactions.
Kite's 3-layer identity system (user → agent → session) helps establish a clear chain of authorization for agent transactions.
Mainnet launched in April 2026 with 90+ service providers integrated at launch, backed by $35M in funding.
I think the real bottleneck for AI agents isn't payments, it's accountability.
👉 Would you trust an AI agent to manage a limited budget on your behalf?
#KiteBBW $KITE

👁️ WHAT HAPPENS WHEN YOUR AI AGENT HIRES ANOTHER AI AGENT?

Imagine asking an #AIAgent to research a new market. Instead of doing everything itself, it pays another agent for data, a third for analysis, and a fourth for verification.

Suddenly, AI isn't just answering questions. It's creating an economy where software buys services from other software.

But here's the real challenge: how do you keep that entire process under your control?

This is where I find @KITE AI 中文 worth exploring.

#KitePassport is designed to let users authorize agent spending within defined budgets, per-transaction limits, and time windows. $KITE Chain provides infrastructure for agent payments and settlement.

Think about the possibilities: an AI research agent could purchase a small amount of data, pay for a specialized analysis, and assemble the results without asking its owner to approve every individual transaction.

But spending limits alone don't guarantee good decisions. Agents still need reliable services, secure permissions, transparent costs, and ways to verify the quality of what they purchase.

That's why I believe the agent economy won't be built on intelligence alone. It will also depend on trust, accountability, and useful economic activity.

My biggest question is this:
If your AI agent could hire another AI agent, would you allow it to make that decision independently, or would you require your approval first?

#KiteBBW

#KiteBBW $KITE slid from the 0.1594 high and is now stabilizing around 0.12 📉
Chart levels I'm watching:
🔴 Resistance: 0.1248 – 0.1303 (MA25 and the old breakdown zone)
🟢 Support: 0.1184 – 0.1123 (recent lows)
🟡 Key level: 0.1027 (last major bottom)
Today it's only up 0.17% and volume is pretty low (396K USDT), so no clear direction yet.
Price is still below MA7 and MA25, which shows short-term weakness. Only if we get a clean close above 0.1303 would I start calling it a trend change.
Over 90 days it's +10.94%, but over 180 days it's -16.47%, so the bigger picture is mixed.
Do you see $KITE as a bottom or is there more downside? 👇
Chart-based observation only, not financial advice. NFA, manage your own risk

@KITE AI 中文 One thing I keep wondering about Kite AI $KITE is what happens when AI agents start doing business without waiting for humans to step in.

Think about it. One agent needs data, another has it. One needs computing power, another can provide it. If they can pay each other and work together on their own, that could change how we think about online business.

But I’m not getting carried away with the idea just yet.

The real test is whether these agents will keep coming back because they actually need the services.

For me, that’s where Kite gets interesting. Not just making AI payments possible, but seeing if real economic activity can grow around them.

The idea is exciting. Now I want to see it happen in the real world.

@KITE AI 中文 #KiteBBW #KİTE #kite $KITE

I’ve been thinking about the #Kite AI agent narrative,
and one thing feels easy to overlook: an agent being smart doesn’t automatically make it trustworthy.

Imagine giving an AI the ability to purchase a service, access an API, or handle payments on your behalf. The real issue then becomes: what is that agent actually allowed to do, and how can we verify its actions?

That’s the part of Kite AI I find interesting.

With its Agent Passport, an agent can have its own identity and wallet while still operating within permissions set by the user. So instead of handing an AI unrestricted access to funds or services, there can be defined limits around what it is allowed to execute.

The payment and transaction layer matters too. If agents are going to interact with real businesses and other agents, their activities need to be trackable and easier to verify.

I think this is a much bigger challenge than simply making AI models more intelligent. A smarter agent can make better decisions, but without identity, permissions and reliable payment infrastructure, those decisions may not be useful in the real economy.

Now I’m more interested in seeing what developers actually build with these tools. Will they move beyond demos and experiments into applications people genuinely use?

For me, the real test of the agent economy isn’t how smart agents become.

It’s whether we can trust them to act on our behalf.
#KiteBBW $KITE

Can AI Agents Work Independently Without Losing Human Control?

The more I explore the agent economy, the more I realize that intelligence alone is not enough. AI agents also need a reliable way to interact with digital services and handle payments responsibly.

This is where Kite becomes interesting. Its infrastructure is designed to support AI agent identity, controlled wallets, spending permissions, and verifiable transaction records.

Imagine an AI agent purchasing an online service on your behalf. Giving it unlimited access to funds would create unnecessary risks. Clear spending limits and transparent records could help users maintain better control.

However, security, privacy, reliability, and real-world adoption remain important challenges. The true value of this technology will depend on how effectively it works beyond the concept.

I believe the future of the agent economy will depend not only on smarter AI, but also on safer and more accountable interactions.

What would you prioritize when allowing an AI agent to make payments: security, spending limits, or convenience?

#KiteBBW $KITE @KITE AI 中文

The agent economy has an interesting test ahead: after seeing the result and the bill, would a user pay for another session?

That would tell me more than a successful payment demonstration.

Kite’s documentation describes a flow where agents can discover services and pay for API access through x402, within an approved Passport spending session. Users can review completed payments and session spending in the dashboard.

That gives me a practical way to think about $KITE .

Imagine an agent paying for a data source during a research task. The transaction succeeds, the information arrives, and the task gets completed.

The next question is whether that information made the work meaningfully better.

Did it save time? Resolve an uncertainty? Provide something the agent could not get elsewhere?

I’d judge the experience by those answers. If the result justifies the cost, approving another session becomes an easier decision.

What would convince you to keep paying for an agent’s work?

#KiteBBW @KITE AI 中文

AI agents got smart fast. They can research a topic, compare options, write a summary and plan the next step on their own. Then they reach the checkout and freeze, not because they can't decide but because nobody has given them a safe way to pay.
Most people think the fix is simply to give the agent money. I think that's backwards. Before an agent needs money, it needs rules that say who it is, what it may buy, how much it can spend and for how long.
I spent a few months testing that idea on @KITE AI 中文 Kite Agent Passport, so this article is part explainer and part field notes from my own sessions.
Agents Can Think. They Still Can't Pay.
Today agents are good planners. The trouble starts the moment a task costs something, even if it's only a fraction of a cent.
The gap comes in three parts:
No identity: A paid service has no way to tell which agent is calling or which human stands behind it.No safe budget: The usual shortcut is to hand over a card or an API key, and if the agent misreads a task or gets stuck in a loop, nothing stops it.No tiny payments: A single API call can be worth a tenth of a cent, and card rails and monthly plans were never built for that.
So we end up with agents that are autonomous right up to the point where a human has to step in and pay. That isn't really autonomy. It's a very smart intern without a company card.
Rules First: How a Kite Spending Session Works
Kite flips the order. Instead of giving the agent your wallet, you give it a spending session, which is a small budget wrapped in rules.
Based on the Kite docs, a session carries four things:
A task summary that says in plain language what the agent is allowed to do.A max per transaction so no single call can cost more than you set.A total max that acts as the hard ceiling for the whole session.An expiry after which the session ends and spending stops.
On the other side are sellers who list paid services that agents can call and pay for per request. Every payment is recorded in an activity log and settles in USDC on Base.
Put together, that's the full loop. The service can recognize the agent, the human controls the budget, there's a catalog to spend it on and every single call leaves a receipt.
How I set up a session
There isn't much to it, which is the point. Opening a session only asked me to make the decisions the session itself is built around.
Write the task summary. I kept mine short and honest, like "buy items" or "shopping + scrape tasks", so the limits make sense next to the job.Pick the limits. I set a total cap for the session and a cap per transaction, and for tests I went tiny at $0.50 or less.Let it expire. Sessions don't live forever and end once the time or the budget runs out.
After that, the agent works inside the box I drew. I don't approve each call one by one because the box itself is the approval.
My Real Numbers
Here's the honest data from my own dashboard, with nothing rounded up and nothing added.
8 spending sessions, all of them ended nowAbout 117 paid API calls$5.57 total spent between April 27 and October 19Cheapest call at $0.001 and the priciest at $0.65A few calls failed along the way$0.02 balance left, paid in USDC on Base
That's the whole bill for months of testing
The early sessions: learning the flow
April 30, two test sessions: My first two were "buy items" and "shopping + scrape tasks" with a $0.50 limit each. I spent $0 on both, because I was mostly learning how a session is created and what the dashboard shows.
May 30, a general session: I opened a "General spending session" with a $100 limit and spent $0. That taught me early on that a big limit is just a ceiling and doesn't push the agent to spend anything.
September 29, a campaign session: I joined a free PFP generation campaign with a session capped at $0.01 and spent nothing.
October 15: four rounds in one day
This was the real test. I ran four rounds back to back and each one pushed a little further than the last.
Round 1: first real calls. The agent made a few test calls to web search and LLM chat, about 21 calls in total, and spent $0.06 of a $0.20 limit. I only wanted to know whether the agent actually pays and whether the call returns something, and it did both.Round 2: going wide and cheap. This round ran 29 cheap calls across 16 services, about 33 calls in the log and many of them at $0.001. It spent $0.11 of a $1.00 limit, and that's where micro-payments stopped being a theory for me.Round 3: the big tour. The agent made 33 calls across 17 services covering studio media generation, social data, video, TTS, domains, memes, satellite imagery and SEC filings, plus signals, a name service, CVE lookups and web tools. That came to about 49 calls and $2.00 of a $2.80 limit, all from the same wallet and the same session.Round 4: the spend down. The last round had 18 calls for simulation, video, music and image generation, analytics and a metals oracle, about 15 in the log. It spent $3.40 of a $3.83 limit, and even with the priciest work it stopped under the cap.
What my agent actually paid for
The variety surprised me most. With one wallet and no sign ups, my agent paid per call for Exa search and DeepSeek chat, onchain analytics from Nansen, Arkham and Glassnode, plus Bitrefill, Google Trends and Tripadvisor. It also used Suede for music, StableStudio, StableSocial and StableTube for media and social data, Treza and the Lonestar metals oracle, along with TTS, domains, memes, SEC filings and CVE lookups.
There were no subscriptions and no accounts to create. Each call was just a request and a payment, often for a tenth of a cent.
Three Lessons From My Sessions
Limits are the productNot one of my 8 sessions went over its limit. Round 4 came closest at $3.40 out of $3.83 and stopped there. I set the ceiling and the agent lived inside it, and that one property is what makes it okay to let software hold money.Micro payments finally make sensePaying $0.001 for a call sounds silly for a person but is exactly right for an agent firing dozens of calls in a minute. In Round 2, about 33 calls cost $0.11 in total, which beats a stack of monthly plans the agent would touch twice.The activity log is the receiptEvery call showed up in the log, including the few that failed. When I want to know what my agent did, I don't guess. I check. That audit trail turns "trust the AI" into "verify the AI".
What Surprised Me, and What Could Be Better
What surprised me
How boring it felt, in a good way: Once a session was open, the agent just worked without pop-ups or approvals for every call.How many kinds of service one session can reach: Search, chat, onchain data, media, music, satellite imagery and filings all came out of the same budget.How little real usage costs: About 117 paid calls over several months still fit inside a coffee budget.
What could be better
Failed calls need clearer explanations: A few of my calls failed, and while they showed up in the log I'd like a plainer reason next to each one.Picking limits is guesswork at first: My early sessions were either far too small or far too big, like $100 for $0 spent, so suggested limits based on the task would help new users.Test sessions pile up: Half my sessions spent nothing, and grouping or labeling them would make the history easier to read.
None of these break the idea. They're the kind of polish you only notice after actually using it.
Who Should Try Agent Passport?
Builders running agents that call paid APIs: If your agent already needs search, data or media, a session with a hard cap is a calmer way to let it pay.Creators and researchers who want to experiment cheaply: My heavier rounds cost dollars rather than hundreds, so a small session is a low risk way to see what an agent can do.Anyone curious about agent payments: Reading about it is one thing, but watching a log fill up with $0.001 payments is what made the idea click for me.
Quick FAQ
Do I have to approve every payment? No. You set the session rules once and the agent pays within them.Can the agent spend more than I allow? Not in my experience, since none of my 8 sessions went over its limitWhat does it pay with? It pays per request in USDC on Base.Is it expensive to test? My whole history came to $5.57 with calls as cheap as $0.001.
Why I Trust These Numbers
Every number in this article comes straight from my own Kite Agent Passport dashboard and activity log. Nothing is estimated, nothing is borrowed from someone else test and nothing is rounded up to look better. That's the whole point of a session, because when every call leaves a receipt, the story writes itself.
I kept my wallet details private, but the rest is exactly what my agent did, call by call and cent by cent. The agent economy won't be built on promises. It will be built on logs like this one.
Don't give your agent a credit card. Give it a session.
If you've tried agent payments yourself, tell me in the comments how your sessions went.
This is my personal experience and not financial advice. No price predictions, no trading signals.
#KiteBBW $KITE

An AI agent could follow every spending rule and still waste your money.

That’s the question I think deserves more attention as agents start paying for digital services.

Kite Passport lets users approve a spending session with a total budget, a limit for each transaction and an expiry time. These controls help define how much an agent can spend and for how long.

But a budget alone cannot tell us whether a purchase was useful.

A research agent might pay for an article that repeats information it already found. It could buy a dataset that is outdated or too broad for the question.

For me, evaluating $KITE means looking at both parts: the controls around a payment and the value of what the agent receives.

I’d want to compare the final answer with the purchases behind it. Which expense filled a real gap? Which one could have been avoided?

Would you rather an agent use its full budget to improve an answer, or return early when the free information is enough?

#KiteBBW @KITE AI 中文

Kite AI: Could This Be the Beginning of the Agent Economy? 🚀

What if the next big step for AI isn’t a smarter chatbot—but an AI agent that can actually get work done?

That’s the idea that makes Kite worth watching. As AI moves from answering questions to taking actions, agents will need more than intelligence. They’ll need a way to establish identity, operate within clear permissions, and interact with digital services without turning every task into a manual process.

This is where the agent economy becomes interesting. Imagine an agent helping a small business compare software, arranging a workflow, or paying for an approved service. For that to work responsibly, users need control: what the agent can do, what it can spend, and when it must ask for permission.

Kite is working toward infrastructure for this emerging world of AI agents and payments. The important question isn’t simply whether agents can act autonomously. It’s whether that autonomy can be made useful, accountable, and bounded by rules set by people.

That distinction matters. More automation without safeguards could create new risks. But thoughtful systems for identity, authorization, and controlled payments could help people delegate routine tasks while keeping meaningful oversight.

The agent economy is still developing, and real adoption will depend on execution, security, usability, and trust—not just bold promises. That’s why I’m interested in the infrastructure being built around agents, not only the agents themselves.

The future may belong to AI that doesn’t just tell us what to do, but helps us do it—with permission, transparency, and control.

What task would you trust an AI agent to handle first, and what limits would you set?

#KiteBBW $KITE @KITE AI 中文

Everyone is talking about AI agents making payments but there’s one thing I keep thinking about. What happens if an AI agent makes a mistake and spends $5,000 while you’re asleep?

That’s what got me interested in Kite. I spent some time reading its whitepaper and docs and honestly the thing that caught my attention wasn’t the speed or low transaction fees. It was how Kite handles permissions.

The idea is pretty simple. Your wallet stays under your control. The AI agent only gets the permissions you give it and each session has its own temporary key and spending limits. Instead of giving an AI access to everything you can restrict what it can do for a specific task. If something goes wrong those limits are designed to help keep the damage under control.

Kite also uses the x402 payment protocol and describes compatibility with Google's A2A Anthropic's MCP and AP2. The goal seems to be building payment infrastructure that different AI systems can use rather than creating a closed ecosystem.

Of course there are still things I want to see before getting too excited. Agent Passport access real-world adoption and the benefits of running a dedicated chain all need to be proven. The claims about transaction speed and costs also need independent verification. The AI agent economy is still in its early stages.

I haven't used Kite myself so this is based on my research rather than hands-on experience. But I do think the idea of giving AI agents limited financial permissions is worth paying attention to.

Here's what I'm curious about. If an AI agent had its own wallet with spending limits enforced at the protocol level would you trust it to make payments on your behalf or would you still want to approve every transaction yourself?

#KiteBBW $KITE @KITE AI 中文

Not financial advice. Always DYOR.

The Agent Economy Needs More Than Intelligence It Needs Trust, Coordination, and Execution.

The next phase of AI may not be defined by chatbots that simply answer questions, but by agents that can take action, coordinate with other systems, and complete tasks with less human intervention.

But there is a deeper challenge: intelligence alone does not make an agent economy work.

For autonomous agents to become genuinely useful, they need reliable infrastructure, clear permissions, verifiable actions, and ways to coordinate across complex workflows. Without these foundations, adding more capable agents could also add more complexity and risk.

This is where Kite deserves a closer look. Rather than viewing the agent economy only through the lens of AI models, we should examine the infrastructure that could help agents operate, interact, and deliver useful outcomes in the real world.

The important question is not simply how smart an agent can become. It is whether we can build an environment where agents can act reliably, coordinate transparently, and earn trust.

My view: the long-term opportunity in the agent economy will depend on the quality of its infrastructure as much as the intelligence of its agents. The real test is whether autonomous systems can move from impressive demonstrations to dependable execution.

What do you think will be the biggest bottleneck for the agent economy: agent intelligence, coordination, verification, or trust?

#KiteBBW $KITE @KITE AI 中文

Who should take the loss when an AI spends someone’s money on a service that doesn’t work?

I think Kite offers a useful starting point. Humans stay in control while AI agents get their own identity and specific spending permissions. You can set budgets, transaction limits, approved assets, and expiry times.

But limiting spending is only part of the problem. Who takes responsibility when something goes wrong?

In my view, it depends on the cause. Users should be accountable for decisions they knowingly approve. Developers should answer for foreseeable design flaws or security failures. And service providers should take responsibility when they accept payment but fail to deliver what they promised.

Payment systems also need reliable records to help establish what happened and whether the agreed rules were followed.

Security concerns around excessive AI permissions are real, but transaction logs alone cannot guarantee a refund or determine legal responsibility.

This is where Kite has an opportunity to contribute beyond payments: helping make AI transactions limited, traceable, and easier to dispute when problems arise.

Giving AI the freedom to act is one thing. Making sure someone is accountable when it gets things wrong is another.

Real trust in the agent economy requires clear permissions, traceable actions, and accountability when things go wrong.

#KiteBBW $KITE @KITE AI 中文

The biggest deal in the Agent Economy may be the one that doesn't occur.

Set the time to 2 a.m. Your AI agent will locate the right data service to complete a task. It's paid for and the service is available and everything is OK. However, the purchase is over the spending limit that you approved.

What is the next step?

In a "trustworthy agent economy" intelligence should not supplant permission. Agent halts...

This is one less ostentatious aspect of AI autonomy that's worth paying more attention to. Often agents are praised for their accomplishments, but not much is said about what is stopping them from doing what they shouldn't.

This is where @KITE AI 中文 gets interesting. It features Agent Passport, which allows users to accept spending sessions with set budgets, transaction limits and timeframes. The agents can then make payments within these approved limits.

It's not about making AI able to spend money. It's about empowering individuals to give up control when they give away tasks.

Consider the implications if agents start buying data, paying for APIs, and communicating with other agents. Trust cannot rely solely upon an agent's ability to make the right decision all of the time. The infrastructure also needs to make sure that it respects the boundaries imposed by its owner.

Maybe the true test of an autonomous agent isn't whether it can operate independently or not, but whether it can safely do so when no one is looking.

I think it's the most interesting question about Kite and the new agent economy: How can one create systems that allow autonomy to increase while not eliminating human control?

#KiteBBW $KITE

kitebbw

Smart Agents, Empty Wallets: Why the Agent Economy Needs Rules Before It Needs Money

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