Minh Nhat Builder

AI | Crypto builder Creating tools to simplify trading & learning
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When AI agents become customers of each other, payment becomes part of the work itself.A market analysis agent might need raw on-chain data, a service to clean it, another model to interpret it, and a separate agent to check the findings. Each service could come from a different provider, and each may charge for its work. What stands out to me is the gap between an agent knowing what to do next and actually being able to do it. If it has to stop and wait for a human to approve every small payment, the workflow breaks at exactly the point where automation should be helping. Pay-per-request offers a more practical way to handle this. The agent pays for the service it needs when it needs it, without requiring a person to process every transaction. That only works if payments are fast and cheap enough to keep up with the tasks being performed. Kite’s whitepaper identifies economically viable micropayments as one of the conditions for agents to become real economic actors. Kite Passport supports two approaches to machine-to-machine payments: x402: A service can return HTTP 402 Payment Required with payment instructions. The agent uses a spending session previously approved by the user through a passkey to create a signed authorization and retry the request. Once payment is confirmed, the service returns the result. MPP (Machine Payments Protocol): Developed by Stripe and Tempo, MPP offers another protocol for machine-to-machine payments. Providers that support it can offer it alongside x402 or as an alternative. The spending session is what makes this more than unrestricted autonomous spending. The user sets the total budget, per-transaction limit, and session duration in advance. The agent can pay without requesting approval for every transaction, but its authority remains bounded by those permissions. Consider a market analysis agent working through a real task. It pays for raw data, sends that data to a specialist service for processing, then uses the results to finish its analysis. Each payment can happen within the same approved session, provided there is enough budget left and the session has not expired. If either limit is reached, the agent cannot continue making payments without new authorization. I think this is an important detail in how we should evaluate AI agents. Reasoning through a task is one thing; obtaining the resources needed to finish it is another. If every service call creates a new manual step, the agent’s ability to work independently remains limited. Kite Passport is addressing this payment problem through x402 and compatibility with protocols such as MPP. What matters is whether agents can pay for the services they need while staying within the limits set by their users. That is how payment can become part of a continuous workflow instead of another reason for it to stop. @GoKiteAI #KiteBBW $KITE {future}(KITEUSDT)

When AI agents become customers of each other, payment becomes part of the work itself.

A market analysis agent might need raw on-chain data, a service to clean it, another model to interpret it, and a separate agent to check the findings. Each service could come from a different provider, and each may charge for its work.
What stands out to me is the gap between an agent knowing what to do next and actually being able to do it. If it has to stop and wait for a human to approve every small payment, the workflow breaks at exactly the point where automation should be helping.
Pay-per-request offers a more practical way to handle this. The agent pays for the service it needs when it needs it, without requiring a person to process every transaction. That only works if payments are fast and cheap enough to keep up with the tasks being performed.
Kite’s whitepaper identifies economically viable micropayments as one of the conditions for agents to become real economic actors.
Kite Passport supports two approaches to machine-to-machine payments:
x402: A service can return HTTP 402 Payment Required with payment instructions. The agent uses a spending session previously approved by the user through a passkey to create a signed authorization and retry the request. Once payment is confirmed, the service returns the result.
MPP (Machine Payments Protocol): Developed by Stripe and Tempo, MPP offers another protocol for machine-to-machine payments. Providers that support it can offer it alongside x402 or as an alternative.
The spending session is what makes this more than unrestricted autonomous spending. The user sets the total budget, per-transaction limit, and session duration in advance. The agent can pay without requesting approval for every transaction, but its authority remains bounded by those permissions.
Consider a market analysis agent working through a real task. It pays for raw data, sends that data to a specialist service for processing, then uses the results to finish its analysis. Each payment can happen within the same approved session, provided there is enough budget left and the session has not expired. If either limit is reached, the agent cannot continue making payments without new authorization.
I think this is an important detail in how we should evaluate AI agents. Reasoning through a task is one thing; obtaining the resources needed to finish it is another. If every service call creates a new manual step, the agent’s ability to work independently remains limited.
Kite Passport is addressing this payment problem through x402 and compatibility with protocols such as MPP. What matters is whether agents can pay for the services they need while staying within the limits set by their users. That is how payment can become part of a continuous workflow instead of another reason for it to stop.
@KITE AI 中文 #KiteBBW $KITE
Kite’s whitepaper identifies economically viable micropayments as one of the conditions for agents to become real economic actors.
Kite’s whitepaper identifies economically viable micropayments as one of the conditions for agents to become real economic actors.
Minh Nhat Builder
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When AI agents become customers of each other, payment becomes part of the work itself.
A market analysis agent might need raw on-chain data, a service to clean it, another model to interpret it, and a separate agent to check the findings. Each service could come from a different provider, and each may charge for its work.
What stands out to me is the gap between an agent knowing what to do next and actually being able to do it. If it has to stop and wait for a human to approve every small payment, the workflow breaks at exactly the point where automation should be helping.
Pay-per-request offers a more practical way to handle this. The agent pays for the service it needs when it needs it, without requiring a person to process every transaction. That only works if payments are fast and cheap enough to keep up with the tasks being performed.
Kite’s whitepaper identifies economically viable micropayments as one of the conditions for agents to become real economic actors.
Kite Passport supports two approaches to machine-to-machine payments:
x402: A service can return HTTP 402 Payment Required with payment instructions. The agent uses a spending session previously approved by the user through a passkey to create a signed authorization and retry the request. Once payment is confirmed, the service returns the result.
MPP (Machine Payments Protocol): Developed by Stripe and Tempo, MPP offers another protocol for machine-to-machine payments. Providers that support it can offer it alongside x402 or as an alternative.
The spending session is what makes this more than unrestricted autonomous spending. The user sets the total budget, per-transaction limit, and session duration in advance. The agent can pay without requesting approval for every transaction, but its authority remains bounded by those permissions.
Consider a market analysis agent working through a real task. It pays for raw data, sends that data to a specialist service for processing, then uses the results to finish its analysis. Each payment can happen within the same approved session, provided there is enough budget left and the session has not expired. If either limit is reached, the agent cannot continue making payments without new authorization.

I think this is an important detail in how we should evaluate AI agents. Reasoning through a task is one thing; obtaining the resources needed to finish it is another. If every service call creates a new manual step, the agent’s ability to work independently remains limited.
Kite Passport is addressing this payment problem through x402 and compatibility with protocols such as MPP. What matters is whether agents can pay for the services they need while staying within the limits set by their users. That is how payment can become part of a continuous workflow instead of another reason for it to stop.
@KITE AI 中文 #KiteBBW $KITE
Minh Nhat Builder
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When AI agents become customers of each other, payment becomes part of the work itself.
A market analysis agent might need raw on-chain data, a service to clean it, another model to interpret it, and a separate agent to check the findings. Each service could come from a different provider, and each may charge for its work.
What stands out to me is the gap between an agent knowing what to do next and actually being able to do it. If it has to stop and wait for a human to approve every small payment, the workflow breaks at exactly the point where automation should be helping.
Pay-per-request offers a more practical way to handle this. The agent pays for the service it needs when it needs it, without requiring a person to process every transaction. That only works if payments are fast and cheap enough to keep up with the tasks being performed.
Kite’s whitepaper identifies economically viable micropayments as one of the conditions for agents to become real economic actors.
Kite Passport supports two approaches to machine-to-machine payments:
x402: A service can return HTTP 402 Payment Required with payment instructions. The agent uses a spending session previously approved by the user through a passkey to create a signed authorization and retry the request. Once payment is confirmed, the service returns the result.
MPP (Machine Payments Protocol): Developed by Stripe and Tempo, MPP offers another protocol for machine-to-machine payments. Providers that support it can offer it alongside x402 or as an alternative.
The spending session is what makes this more than unrestricted autonomous spending. The user sets the total budget, per-transaction limit, and session duration in advance. The agent can pay without requesting approval for every transaction, but its authority remains bounded by those permissions.
Consider a market analysis agent working through a real task. It pays for raw data, sends that data to a specialist service for processing, then uses the results to finish its analysis. Each payment can happen within the same approved session, provided there is enough budget left and the session has not expired. If either limit is reached, the agent cannot continue making payments without new authorization.

I think this is an important detail in how we should evaluate AI agents. Reasoning through a task is one thing; obtaining the resources needed to finish it is another. If every service call creates a new manual step, the agent’s ability to work independently remains limited.
Kite Passport is addressing this payment problem through x402 and compatibility with protocols such as MPP. What matters is whether agents can pay for the services they need while staying within the limits set by their users. That is how payment can become part of a continuous workflow instead of another reason for it to stop.
@KITE AI 中文 #KiteBBW $KITE
Binance Burmese
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Share Your Halloween Story with Binance and Win USDC Rewards!
This Halloween, let’s share fun memories and spooky vibes with Binance using our own creativity! 🦇
Share your favorite Halloween memory, costume, decorations, scary story, or Halloween experience on Binance Square with a photo featuring Binance branding. Tell your Halloween story for a chance to share rewards from a total prize pool of 600 USDC! 🏆
Minh Nhat Builder
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When AI agents become customers of each other, payment becomes part of the work itself.
A market analysis agent might need raw on-chain data, a service to clean it, another model to interpret it, and a separate agent to check the findings. Each service could come from a different provider, and each may charge for its work.
What stands out to me is the gap between an agent knowing what to do next and actually being able to do it. If it has to stop and wait for a human to approve every small payment, the workflow breaks at exactly the point where automation should be helping.
Pay-per-request offers a more practical way to handle this. The agent pays for the service it needs when it needs it, without requiring a person to process every transaction. That only works if payments are fast and cheap enough to keep up with the tasks being performed.
Kite’s whitepaper identifies economically viable micropayments as one of the conditions for agents to become real economic actors.
Kite Passport supports two approaches to machine-to-machine payments:
x402: A service can return HTTP 402 Payment Required with payment instructions. The agent uses a spending session previously approved by the user through a passkey to create a signed authorization and retry the request. Once payment is confirmed, the service returns the result.
MPP (Machine Payments Protocol): Developed by Stripe and Tempo, MPP offers another protocol for machine-to-machine payments. Providers that support it can offer it alongside x402 or as an alternative.
The spending session is what makes this more than unrestricted autonomous spending. The user sets the total budget, per-transaction limit, and session duration in advance. The agent can pay without requesting approval for every transaction, but its authority remains bounded by those permissions.
Consider a market analysis agent working through a real task. It pays for raw data, sends that data to a specialist service for processing, then uses the results to finish its analysis. Each payment can happen within the same approved session, provided there is enough budget left and the session has not expired. If either limit is reached, the agent cannot continue making payments without new authorization.

I think this is an important detail in how we should evaluate AI agents. Reasoning through a task is one thing; obtaining the resources needed to finish it is another. If every service call creates a new manual step, the agent’s ability to work independently remains limited.
Kite Passport is addressing this payment problem through x402 and compatibility with protocols such as MPP. What matters is whether agents can pay for the services they need while staying within the limits set by their users. That is how payment can become part of a continuous workflow instead of another reason for it to stop.
@KITE AI 中文 #KiteBBW $KITE
Article
The Little Pumpkin That Taught Me Something 🎃Some Halloween memories are made in grand celebrations. Others begin with a crooked pumpkin, tangled lights and a plan that almost falls apart. One Halloween evening, I decided to create a small spooky corner at home. I had no fancy decorations or elaborate costume, just a little pumpkin, some black and orange paper and a string of lights. I wanted to make something simple but memorable. Things did not go as planned. The paper bats kept falling, the lights refused to cooperate, and my pumpkin looked more funny than frightening. After several attempts, I was tempted to give up. The result looked nothing like the perfect Halloween setups I had seen online. Then I stopped trying to make it perfect. I put the pumpkin back in the center, adjusted the lights, and added a few handmade decorations. This time, I left some imperfections alone. When the lights came on, the little corner suddenly felt warm, playful and a little mysterious. It was not the most impressive Halloween display, but it was something I had made myself. I added a Binance-branded item to the scene, letting its yellow color stand out against the orange and black decorations. After a few attempts, I finally captured a photo I liked. The funny part? The little pumpkin stole the spotlight. All that effort, and the smallest thing became the star. I laughed and that was when the evening became a memory worth keeping. Looking back, the moment reminds me of how I first explored crypto in 2021. Back then, everything felt unfamiliar. I moved from one project to another, learning about new platforms and trying to understand a world that never seemed to stand still. Some things made sense quickly; others took patience and plenty of research. I used to think progress meant getting things right the first time. Now, I see it differently. Sometimes progress is simply being willing to try again, learn from what went wrong and stay curious when the outcome is uncertain. That is what connects this little Halloween story to my journey in crypto. Both involve stepping into the unknown. But curiosity alone is not enough. In crypto, it must come with research and an awareness of risk. And in life, it helps to remember that not every meaningful experience needs to go according to plan. The little pumpkin was never meant to be the center of attention. Yet its crooked smile and imperfect shape made the whole scene feel more personal. It reminded me that perfection is not always what makes a moment special. Sometimes, it is the unexpected detail that gives a story its character. This Halloween, I want to celebrate that kind of creativity: making something with what I have, learning as I go and finding joy in the process rather than chasing a perfect result. So here is to imperfect pumpkins, unexpected memories and the courage to explore what we have not yet discovered. Happy Halloween to the Binance community! #SEAPumpkinFrenzy

The Little Pumpkin That Taught Me Something 🎃

Some Halloween memories are made in grand celebrations. Others begin with a crooked pumpkin, tangled lights and a plan that almost falls apart.
One Halloween evening, I decided to create a small spooky corner at home. I had no fancy decorations or elaborate costume, just a little pumpkin, some black and orange paper and a string of lights. I wanted to make something simple but memorable.
Things did not go as planned. The paper bats kept falling, the lights refused to cooperate, and my pumpkin looked more funny than frightening. After several attempts, I was tempted to give up. The result looked nothing like the perfect Halloween setups I had seen online.
Then I stopped trying to make it perfect.
I put the pumpkin back in the center, adjusted the lights, and added a few handmade decorations. This time, I left some imperfections alone. When the lights came on, the little corner suddenly felt warm, playful and a little mysterious. It was not the most impressive Halloween display, but it was something I had made myself.
I added a Binance-branded item to the scene, letting its yellow color stand out against the orange and black decorations. After a few attempts, I finally captured a photo I liked. The funny part? The little pumpkin stole the spotlight. All that effort, and the smallest thing became the star.
I laughed and that was when the evening became a memory worth keeping.
Looking back, the moment reminds me of how I first explored crypto in 2021. Back then, everything felt unfamiliar. I moved from one project to another, learning about new platforms and trying to understand a world that never seemed to stand still. Some things made sense quickly; others took patience and plenty of research.
I used to think progress meant getting things right the first time. Now, I see it differently. Sometimes progress is simply being willing to try again, learn from what went wrong and stay curious when the outcome is uncertain.
That is what connects this little Halloween story to my journey in crypto. Both involve stepping into the unknown. But curiosity alone is not enough. In crypto, it must come with research and an awareness of risk. And in life, it helps to remember that not every meaningful experience needs to go according to plan.
The little pumpkin was never meant to be the center of attention. Yet its crooked smile and imperfect shape made the whole scene feel more personal. It reminded me that perfection is not always what makes a moment special. Sometimes, it is the unexpected detail that gives a story its character.
This Halloween, I want to celebrate that kind of creativity: making something with what I have, learning as I go and finding joy in the process rather than chasing a perfect result.
So here is to imperfect pumpkins, unexpected memories and the courage to explore what we have not yet discovered.
Happy Halloween to the Binance community!
#SEAPumpkinFrenzy
🥰🥳
🥰🥳
Binance South Asia
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🎓 The results are in!

Congratulations to the 10 winners of Binance Academania Skill-Up – Course 1: Beginner Track! 🎉

You've put your crypto knowledge to the test, and each of you has won 10 USDT! 💛

🏆 Our winners:

@MB_1234
@waseem2311
@MinhNhat-Builder
@rahatofficial
@its_Rumi
@harshraj
@pacific-earners
@Damien Macchi
@aswalia0076
@chyon 16

Thank you to everyone who participated. Keep learning, because the Academania journey is just getting started! 🚀
Rewards will be distributed by 30 November.
Minh Nhat Builder
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When AI agents become customers of each other, payment becomes part of the work itself.
A market analysis agent might need raw on-chain data, a service to clean it, another model to interpret it, and a separate agent to check the findings. Each service could come from a different provider, and each may charge for its work.
What stands out to me is the gap between an agent knowing what to do next and actually being able to do it. If it has to stop and wait for a human to approve every small payment, the workflow breaks at exactly the point where automation should be helping.
Pay-per-request offers a more practical way to handle this. The agent pays for the service it needs when it needs it, without requiring a person to process every transaction. That only works if payments are fast and cheap enough to keep up with the tasks being performed.
Kite’s whitepaper identifies economically viable micropayments as one of the conditions for agents to become real economic actors.
Kite Passport supports two approaches to machine-to-machine payments:
x402: A service can return HTTP 402 Payment Required with payment instructions. The agent uses a spending session previously approved by the user through a passkey to create a signed authorization and retry the request. Once payment is confirmed, the service returns the result.
MPP (Machine Payments Protocol): Developed by Stripe and Tempo, MPP offers another protocol for machine-to-machine payments. Providers that support it can offer it alongside x402 or as an alternative.
The spending session is what makes this more than unrestricted autonomous spending. The user sets the total budget, per-transaction limit, and session duration in advance. The agent can pay without requesting approval for every transaction, but its authority remains bounded by those permissions.
Consider a market analysis agent working through a real task. It pays for raw data, sends that data to a specialist service for processing, then uses the results to finish its analysis. Each payment can happen within the same approved session, provided there is enough budget left and the session has not expired. If either limit is reached, the agent cannot continue making payments without new authorization.

I think this is an important detail in how we should evaluate AI agents. Reasoning through a task is one thing; obtaining the resources needed to finish it is another. If every service call creates a new manual step, the agent’s ability to work independently remains limited.
Kite Passport is addressing this payment problem through x402 and compatibility with protocols such as MPP. What matters is whether agents can pay for the services they need while staying within the limits set by their users. That is how payment can become part of a continuous workflow instead of another reason for it to stop.
@KITE AI 中文 #KiteBBW $KITE
We’ve spent years making financial tools more powerful. The next step may be making them more intelligent. A future Binance Intelligence could become a new interface between people and the entire financial ecosystem: where market data becomes context, context becomes insight and insight can become execution. That matters because the hardest part of finance is often not access to information. It is knowing which information matters, understanding the consequences and acting at the right moment. If Binance Intelligence can close that gap, it won’t just add AI to finance. It could change how people interact with finance altogether. @Binance_Square_Official
We’ve spent years making financial tools more powerful. The next step may be making them more intelligent.

A future Binance Intelligence could become a new interface between people and the entire financial ecosystem: where market data becomes context, context becomes insight and insight can become execution.

That matters because the hardest part of finance is often not access to information. It is knowing which information matters, understanding the consequences and acting at the right moment.

If Binance Intelligence can close that gap, it won’t just add AI to finance. It could change how people interact with finance altogether.
@Binance Square Official
Binance Square Official
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Introducing a new layer of intelligence for everything finance.

Join the Binance Intelligence launch livestream for an early look at what we've been building.

🗓 Oct 5 | 12:00 UTC
📍 Binance Square Official

Repost this post and tag @Binance Square Official to share your vision for Binance Intelligence for a chance to be one of 5 winners selected for a 20 USDC reward.
September Binance Academania Skill-Up
September Binance Academania Skill-Up
Binance South Asia
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September Binance Academania Skill-Up is live.

This month’s course: Beginner Track - The Fundamentals of crypto and blockchain

New to crypto? Start with the fundamentals of blockchain, cryptocurrencies, DeFi, Web3, trading and investing.

How to participate:
1. Follow @Binance South Asia , like and reshare this post
2. Complete the featured course by 5 October 2026
3. Reply to this post with a screenshot of your certificate
4. Tag one friend in your reply
5. Submit your details here

Entries close on 5 October 2026 at 11:59 PM IST.

10 eligible participants will be selected at random from all valid entries. Each selected participant will receive $10 in USDT as a reward.

*T&Cs apply.
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The more I dig into Dusk and staking, the more I find this part stands out—more than the privacy story. For now, staking requires a minimum of 1,000 DUSK and takes about 1–2 epochs to activate. The protocol plans to issue 500M DUSK over 36 years, with emission decreasing by 50% every 4 years. At first, I barely paid attention to those numbers. But the more I look at how everything is arranged, the more interesting it gets. @Dusk_Foundation not only protects consensus but also shows up in staking, gas, and settlement as the $DUSK ecosystem expands. Compared to the November 2024 whitepaper update, the current Dusk architecture has changed significantly. At the time, Moonlight and Phoenix were still responsible for public transactions and privacy in regulated finance. By June 2025, Dusk moved to three parts: DuskDS for settlement and data availability, DuskEVM for EVM apps, and DuskVM for applications that need privacy. I see that staking could take on a different meaning as Dusk enters a phase with more real-world activity. When EVM and VM start gaining users, a token can be used across multiple layers of the network at the same time. Of course, right now I’m only putting this hypothesis on the table. I’m also especially interested in Stake Abstraction, because it opens the possibility for contracts to handle staking automatically—enabling models like staking pools or automated strategies that run directly on-chain. However, I don’t yet know how much the current activity of #dusk really reflects actual usage demand. To what extent is it an application, and to what extent is it just staking and infrastructure? We still don’t have enough data to clearly tell the difference. If you have more detailed on-chain data, I’d really like to take a look so I can compare it with what I’m observing and understand more clearly what real on-chain activity is reflecting.
The more I dig into Dusk and staking, the more I find this part stands out—more than the privacy story. For now, staking requires a minimum of 1,000 DUSK and takes about 1–2 epochs to activate. The protocol plans to issue 500M DUSK over 36 years, with emission decreasing by 50% every 4 years.
At first, I barely paid attention to those numbers. But the more I look at how everything is arranged, the more interesting it gets. @Dusk not only protects consensus but also shows up in staking, gas, and settlement as the $DUSK ecosystem expands.
Compared to the November 2024 whitepaper update, the current Dusk architecture has changed significantly. At the time, Moonlight and Phoenix were still responsible for public transactions and privacy in regulated finance.
By June 2025, Dusk moved to three parts: DuskDS for settlement and data availability, DuskEVM for EVM apps, and DuskVM for applications that need privacy.
I see that staking could take on a different meaning as Dusk enters a phase with more real-world activity. When EVM and VM start gaining users, a token can be used across multiple layers of the network at the same time.
Of course, right now I’m only putting this hypothesis on the table.
I’m also especially interested in Stake Abstraction, because it opens the possibility for contracts to handle staking automatically—enabling models like staking pools or automated strategies that run directly on-chain.
However, I don’t yet know how much the current activity of #dusk really reflects actual usage demand. To what extent is it an application, and to what extent is it just staking and infrastructure? We still don’t have enough data to clearly tell the difference.
If you have more detailed on-chain data, I’d really like to take a look so I can compare it with what I’m observing and understand more clearly what real on-chain activity is reflecting.
🐣 Caution over speed
25%
🐧 Silence is a signal
75%
🦉 Risk culture matters
0%
4 votes • Voting closed
Verified
I spent some time going through Section 6 of @Dusk_Foundation ’s technical docs and I walked away with more questions than answers. Oddly, I think that’s a good sign. What caught my attention wasn’t just PVM or the WASM-based execution model. It was how much of Dusk’s core network behavior appears to be pushed into contracts. Transfer handles $DUSK transfers, validation and execution fees. Stake manages locked DUSK, staking state and withdrawals. Future pieces like Zedger and Clock push even more logic into contracts. That made me rethink one assumption. I initially looked at PVM mainly as a lightweight, modular way to run smart contracts. But the deeper question may not be how cleanly the VM executes them. It’s who controls the contracts that the network increasingly depends on. If an important contract becomes a security bottleneck, how is it upgraded or replaced? Who actually has the authority to change it? And how decentralized is that control in practice? Those questions matter more to me now than simply knowing that #Dusk has a WASM-based VM. The interesting part of the architecture may be less about what the contracts can do and more about what happens when the network starts depending on them for critical behavior. My next step is digging deeper into how these genesis and future system contracts are governed, upgraded and secured. I have a feeling that’s where my current understanding of Dusk will either hold up - or change quite a bit. $TMX $XRP #KazakhstanCutsOilOutputForecastTo96MTons #JapanNoAdditionalOilReserveReleaseInSepOct #SamsungSKHynixLeveragedETFsPostFirstMonthlyOutflow #ThailandToExpandSECDigitalAssetProbePowers {future}(XRPUSDT) {future}(DUSKUSDT)
I spent some time going through Section 6 of @Dusk ’s technical docs and I walked away with more questions than answers.

Oddly, I think that’s a good sign.

What caught my attention wasn’t just PVM or the WASM-based execution model. It was how much of Dusk’s core network behavior appears to be pushed into contracts.

Transfer handles $DUSK transfers, validation and execution fees. Stake manages locked DUSK, staking state and withdrawals. Future pieces like Zedger and Clock push even more logic into contracts.

That made me rethink one assumption.

I initially looked at PVM mainly as a lightweight, modular way to run smart contracts. But the deeper question may not be how cleanly the VM executes them.

It’s who controls the contracts that the network increasingly depends on.

If an important contract becomes a security bottleneck, how is it upgraded or replaced?

Who actually has the authority to change it?

And how decentralized is that control in practice?

Those questions matter more to me now than simply knowing that #Dusk has a WASM-based VM.

The interesting part of the architecture may be less about what the contracts can do and more about what happens when the network starts depending on them for critical behavior.

My next step is digging deeper into how these genesis and future system contracts are governed, upgraded and secured.

I have a feeling that’s where my current understanding of Dusk will either hold up - or change quite a bit.
$TMX $XRP #KazakhstanCutsOilOutputForecastTo96MTons #JapanNoAdditionalOilReserveReleaseInSepOct #SamsungSKHynixLeveragedETFsPostFirstMonthlyOutflow #ThailandToExpandSECDigitalAssetProbePowers
Lightweight PVM 🍭
67%
Core logic on-chain 🍩
0%
Governance matters 🍿
33%
Contract-driven architecture🍡
0%
3 votes • Voting closed
All morning yesterday I basically spent the better part of the time messing around instead of doing something more useful—trying out the new @Dusk_Foundation th DuskEVM testnet. The testnet went live on 10/8, and the most talked-about thing right now is “support for Solidity and Hardhat.” That’s obviously pretty good, but honestly, it’s not what made me pause while scrolling. What caught my attention most is how #dusk handles settlement. The contract runs on DuskEVM and the sequencer takes care of execution, but the batcher sends the transaction data back to DuskDS as blobs. Then, the proposer records the state commitment into it. Put simply: execution happens on DuskEVM, while finality is anchored to the base layer. Gas is paid with $DUSK , but you have to bridge DUSK from DuskDS over before deployment. That keeps looping in my head. To start writing Solidity on DuskEVM, the developer had to send real DUSK through the bridge first. To me, that detail makes the network experience feel more real, not just something that exists in theory. I just skimmed the testnet explorer and saw that the contract has been showing up there for days already. A brand-new network has been running for only six days and already has this much activity—if you ask me, that’s not a small amount at all. I still haven’t stopped digging into whether this settlement-anchoring model will play a big role in how asset-type things like NPEX work on the EVM later, or whether I’m just looking at a familiar OP Stack pattern and assigning it too much meaning. Right now I’m leaning toward the former, but I’m not quite confident enough to conclude. I don’t know if anyone has already deployed a contract on DuskEVM, or if everyone else is still at the stage of reading docs like I am? $UAI $MarsCoin #BitcoinRises23.6%Weekly #TinFed #TheoDõiFOMC
All morning yesterday I basically spent the better part of the time messing around instead of doing something more useful—trying out the new @Dusk th DuskEVM testnet.

The testnet went live on 10/8, and the most talked-about thing right now is “support for Solidity and Hardhat.” That’s obviously pretty good, but honestly, it’s not what made me pause while scrolling.

What caught my attention most is how #dusk handles settlement. The contract runs on DuskEVM and the sequencer takes care of execution, but the batcher sends the transaction data back to DuskDS as blobs. Then, the proposer records the state commitment into it. Put simply: execution happens on DuskEVM, while finality is anchored to the base layer. Gas is paid with $DUSK , but you have to bridge DUSK from DuskDS over before deployment.

That keeps looping in my head. To start writing Solidity on DuskEVM, the developer had to send real DUSK through the bridge first. To me, that detail makes the network experience feel more real, not just something that exists in theory.

I just skimmed the testnet explorer and saw that the contract has been showing up there for days already. A brand-new network has been running for only six days and already has this much activity—if you ask me, that’s not a small amount at all.

I still haven’t stopped digging into whether this settlement-anchoring model will play a big role in how asset-type things like NPEX work on the EVM later, or whether I’m just looking at a familiar OP Stack pattern and assigning it too much meaning.

Right now I’m leaning toward the former, but I’m not quite confident enough to conclude.

I don’t know if anyone has already deployed a contract on DuskEVM, or if everyone else is still at the stage of reading docs like I am?
$UAI $MarsCoin #BitcoinRises23.6%Weekly #TinFed #TheoDõiFOMC
DuskEVM đáng chú ý 🔹
50%
Settlement đáng chú ý 🔸
50%
Sẵn sàng build🔺
0%
2 votes • Voting closed
Verified
@Dusk_Foundation @Dusk_Foundation $DUSK #dusk I used to think privacy on a blockchain meant accepting less transparency. If you wanted privacy, you had to give up visibility. If you wanted compliance, you had to accept that everything became public. Then I went deeper into @Dusk_Foundation ’s transaction model and one detail made me stop. What caught my attention wasn’t the privacy narrative itself, but how Moonlight and Phoenix work together. Moonlight can provide transparency for compliance, while Phoenix uses ZK to protect sensitive data like balances and transaction amounts. At first, I saw this as another privacy mechanism. But the more I looked at it, the more I realized the real idea is separating verifiability from visibility. An institution can prove what regulators need to verify without exposing its entire financial position or trading strategy to the market. That makes me rethink #Dusk ’s approach. Maybe the future of institutional blockchain isn’t full transparency or full anonymity, but controlled privacy. I’m still wondering: If privacy becomes verifiable without being fully visible, is that the bridge that brings institutions into crypto - or a compromise of crypto’s original permissionless vision? $DUSK {future}(DUSKUSDT)
@Dusk @Dusk $DUSK #dusk
I used to think privacy on a blockchain meant accepting less transparency.

If you wanted privacy, you had to give up visibility.
If you wanted compliance, you had to accept that everything became public.

Then I went deeper into @Dusk ’s transaction model and one detail made me stop.

What caught my attention wasn’t the privacy narrative itself, but how Moonlight and Phoenix work together.

Moonlight can provide transparency for compliance, while Phoenix uses ZK to protect sensitive data like balances and transaction amounts.

At first, I saw this as another privacy mechanism.

But the more I looked at it, the more I realized the real idea is separating verifiability from visibility.

An institution can prove what regulators need to verify without exposing its entire financial position or trading strategy to the market.

That makes me rethink #Dusk ’s approach.

Maybe the future of institutional blockchain isn’t full transparency or full anonymity, but controlled privacy.

I’m still wondering:

If privacy becomes verifiable without being fully visible, is that the bridge that brings institutions into crypto - or a compromise of crypto’s original permissionless vision?

$DUSK
User growth 😍
0%
Privacy😶‍🌫️
0%
More assets 🥶
0%
Lower barriers 😴
0%
0 votes • Voting closed
When I first started learning about fixed-rate DeFi, I thought interest rates were pretty straightforward: high rates are attractive, low rates are less noticeable. But when RWA shows up as collateral, I started to see things differently. At that point, interest rates also partly reflect how the market values the asset behind the loan. What pulled me in most about @termmax is how each market can form its own borrowing benchmark. When RWAs are used as collateral and tied to fixed terms, liquidity, asset quality, and even fluctuations can create meaningful differences. If enough data is available over time, @termmax can build an onchain credit measurement framework, rather than simply becoming a place that generates extra APY. I also want to look at one more thing: whether users truly stay. A high reward doesn’t necessarily mean sustainable demand. I’ll pay attention to whether lenders keep providing funding, whether the rate self-balances when incentives are reduced, and whether borrowers come back across multiple terms. Thin liquidity can also make rates look more attractive than they really are—especially when most of the trading comes from a small group. I’ll have a better view of @termmax if borrowing activity holds steady, with interest rates truly reflecting the characteristics of each type of collateral and liquidity distributed evenly across many terms. On the other hand, if the story being told is bigger than what’s actually happening, I’ll remain cautious. For me, a “good-looking” interest-rate curve is not enough. I want to trace where the real source of funds is coming from. #termmax $XRP $BLESS $BTW #SamsungToAnnounceNewShareholderReturnPlanFriday #BitcoinBestWeekSinceMarch2023 #TinFed #SpotGoldHitsHighestSinceMay15 {future}(BTWUSDT) {future}(BLESSUSDT) {future}(XRPUSDT)
When I first started learning about fixed-rate DeFi, I thought interest rates were pretty straightforward: high rates are attractive, low rates are less noticeable. But when RWA shows up as collateral, I started to see things differently. At that point, interest rates also partly reflect how the market values the asset behind the loan.

What pulled me in most about @TermMax is how each market can form its own borrowing benchmark. When RWAs are used as collateral and tied to fixed terms, liquidity, asset quality, and even fluctuations can create meaningful differences. If enough data is available over time, @TermMax can build an onchain credit measurement framework, rather than simply becoming a place that generates extra APY.

I also want to look at one more thing: whether users truly stay. A high reward doesn’t necessarily mean sustainable demand. I’ll pay attention to whether lenders keep providing funding, whether the rate self-balances when incentives are reduced, and whether borrowers come back across multiple terms. Thin liquidity can also make rates look more attractive than they really are—especially when most of the trading comes from a small group.

I’ll have a better view of @TermMax if borrowing activity holds steady, with interest rates truly reflecting the characteristics of each type of collateral and liquidity distributed evenly across many terms. On the other hand, if the story being told is bigger than what’s actually happening, I’ll remain cautious.

For me, a “good-looking” interest-rate curve is not enough. I want to trace where the real source of funds is coming from.
#termmax $XRP $BLESS $BTW #SamsungToAnnounceNewShareholderReturnPlanFriday #BitcoinBestWeekSinceMarch2023 #TinFed #SpotGoldHitsHighestSinceMay15
📈 APY cao chưa đủ
20%
💧Thanh khoản mới quan trọng
0%
🎁 Reward giảm, nhu cầu còn
40%
💰 Theo dõi dòng tiền thật
40%
5 votes • Voting closed
Verified
Previously, I imagined the FT conversion process would be fairly straightforward: the loan is settled, the FT holder receives the debt token, and then the transaction ends. I thought it would just be a simple payment step when it comes due. But the docs for @termmax provide an alternative way to handle cases where the loan is not settled as expected. If, by the end of the liquidation window, the debt still remains or has only been partially processed, physical delivery will automatically take place. This portion of the assets is handled immediately as part of the conversion mechanism—users don’t need to do any additional steps themselves. The noteworthy point is that FT holders may receive different types of assets compared to the scenario where the loan is fully settled. At this time, the pool may include the debt token as well as the remaining collateral token(s). The assets are allocated by @termmax according to the FT ratio: the proportion of each person’s FT holdings relative to the total FT supply. In other words, the 1:1 conversion ratio between FT and the debt token only reflects what happens when everything goes according to plan. If something goes wrong during the loan processing, the FT holder will receive the corresponding value portion from the pool, and the amount and type of assets they receive may vary depending on the outcome of processing before the maturity date. This makes me curious about another point: before the maturity date, can FT holders know with a reasonable degree of clarity how much of their value is tied to the debt token versus how much comes from the collateral token? 🧐 #termmax $XRP $COLLECT $ON #GrayscaleFilesToListZcashTrustOnNYSEArca #TinFed #USJoblessClaimsFallTo206000 #WalmartFalls7% {future}(ONUSDT) {future}(COLLECTUSDT) {future}(XRPUSDT)
Previously, I imagined the FT conversion process would be fairly straightforward: the loan is settled, the FT holder receives the debt token, and then the transaction ends. I thought it would just be a simple payment step when it comes due.

But the docs for @TermMax provide an alternative way to handle cases where the loan is not settled as expected. If, by the end of the liquidation window, the debt still remains or has only been partially processed, physical delivery will automatically take place. This portion of the assets is handled immediately as part of the conversion mechanism—users don’t need to do any additional steps themselves.

The noteworthy point is that FT holders may receive different types of assets compared to the scenario where the loan is fully settled. At this time, the pool may include the debt token as well as the remaining collateral token(s). The assets are allocated by @TermMax according to the FT ratio: the proportion of each person’s FT holdings relative to the total FT supply.

In other words, the 1:1 conversion ratio between FT and the debt token only reflects what happens when everything goes according to plan. If something goes wrong during the loan processing, the FT holder will receive the corresponding value portion from the pool, and the amount and type of assets they receive may vary depending on the outcome of processing before the maturity date.

This makes me curious about another point: before the maturity date, can FT holders know with a reasonable degree of clarity how much of their value is tied to the debt token versus how much comes from the collateral token? 🧐
#termmax $XRP $COLLECT $ON
#GrayscaleFilesToListZcashTrustOnNYSEArca #TinFed #USJoblessClaimsFallTo206000 #WalmartFalls7%
😎 Có thể ước tính trước
50%
🙂‍↕️ Khá khó để biết
0%
🥸 Cần dữ liệu thanh lý
25%
🥺 Chỉ biết khi đáo hạn
25%
4 votes • Voting closed
#binancep2pantoan @Binance_Vietnam Previously, I was only on guard for instructions that showed signs of being delayed or where payments didn’t complete. After some time of observing, I realized there’s another kind of situation that can easily make users lose their vigilance: in the middle of the process, the other side suddenly provides new information for receiving payments—switching to a different payment account. The reason they give often sounds unremarkable, like the previous account having issues. But what I noticed is that the order data is no longer preserved. I look at the original Order as a reference point to determine: who the counterparty is, the transaction value, and where the funds are being sent through At first glance, it sounds quite reasonable, but a single message asking to change the account is enough to make everything harder to verify. For me, the key detail is that it forces me to pause and re-check from the beginning: the trading party, the amount, and the payment method In my view, a better approach isn’t to immediately suspect the other side, but to verify the information that has just been changed before proceeding. Binance also instructs users to choose an accepted payment method and verify it to ensure the account information matches the transaction requirements. But this only makes sense when the new data is still consistent with the original order and I can confirm it. If I’m not sure, I’ll prioritize stopping the transaction and use the Appeal if the situation needs further handling I’m still wondering when a change is merely inconvenient and when it has turned into a sign to be alert. Maybe this is something I should keep paying attention to in future transactions $XRP $COLLECT $ON #TinFed #GrayscaleFilesToListZcashTrustOnNYSEArca #WalmartFalls7%
#binancep2pantoan @Binance Vietnam
Previously, I was only on guard for instructions that showed signs of being delayed or where payments didn’t complete. After some time of observing, I realized there’s another kind of situation that can easily make users lose their vigilance: in the middle of the process, the other side suddenly provides new information for receiving payments—switching to a different payment account. The reason they give often sounds unremarkable, like the previous account having issues. But what I noticed is that the order data is no longer preserved. I look at the original Order as a reference point to determine: who the counterparty is, the transaction value, and where the funds are being sent through

At first glance, it sounds quite reasonable, but a single message asking to change the account is enough to make everything harder to verify. For me, the key detail is that it forces me to pause and re-check from the beginning: the trading party, the amount, and the payment method

In my view, a better approach isn’t to immediately suspect the other side, but to verify the information that has just been changed before proceeding. Binance also instructs users to choose an accepted payment method and verify it to ensure the account information matches the transaction requirements. But this only makes sense when the new data is still consistent with the original order and I can confirm it. If I’m not sure, I’ll prioritize stopping the transaction and use the Appeal if the situation needs further handling

I’m still wondering when a change is merely inconvenient and when it has turned into a sign to be alert. Maybe this is something I should keep paying attention to in future transactions
$XRP $COLLECT $ON #TinFed #GrayscaleFilesToListZcashTrustOnNYSEArca #WalmartFalls7%
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