币圈Kin
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Article
Acurast ExplainedI've been involved in quite a few DePIN projects over the past few years, but only a handful are still around and running.
Looking back, most of them failed for the same reason: you had to buy hardware upfront. Mining rigs, hard drives, graphics cards—you had to sink money in first, and getting it back depended entirely on token prices. Once prices fell, the equipment became useless.
So when I saw @Acurast using phones to build a computing network, my first thought was: the barrier to entry is incredibly low.
Everyone has a phone, and there are old ones tucked away in drawers. Plug one in to charge at night, the screen goes dark, and it just sits there idle. There are billions of them around the world, spread across every country—naturally forming a distributed network.
Acurast Explained
I've been involved in quite a few DePIN projects over the past few years, but only a handful are still around and running.
Looking back, most of them failed for the same reason: you had to buy hardware upfront. Mining rigs, hard drives, graphics cards—you had to sink money in first, and getting it back depended entirely on token prices. Once prices fell, the equipment became useless.
So when I saw @Acurast using phones to build a computing network, my first thought was: the barrier to entry is incredibly low.
Everyone has a phone, and there are old ones tucked away in drawers. Plug one in to charge at night, the screen goes dark, and it just sits there idle. There are billions of them around the world, spread across every country—naturally forming a distributed network.
Looking back, most of them failed for the same reason: you had to buy hardware upfront. Mining rigs, hard drives, graphics cards—you had to sink money in first, and getting it back depended entirely on token prices. Once prices fell, the equipment became useless.
So when I saw @Acurast using phones to build a computing network, my first thought was: the barrier to entry is incredibly low.
Everyone has a phone, and there are old ones tucked away in drawers. Plug one in to charge at night, the screen goes dark, and it just sits there idle. There are billions of them around the world, spread across every country—naturally forming a distributed network.
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Article
Over the past few days, I’ve written about @Acurast, covering phone connectivity, points, and what $ACU can be used for.Over the past few days, I’ve written about @Acurast, covering phone connectivity, points, and what $ACU can be used for.
Today, let’s switch to something you can open and play with right away.
Acurast recently launched a set of Laya demos. The AI model behind them doesn’t run on traditional cloud servers; it runs on an Android phone in the Acurast network.
The page features 9 demos, including Snake, Tetris, Doom, email classification, live-stream comment moderation, and prompt injection blocking. It also offers an API.
Laya is not a chatbot.
Laya is a System One model.
It doesn’t write long replies like ChatGPT. Give it some content and a few fixed options, and it will make a choice directly and return the probability of each option.
Over the past few days, I’ve written about @Acurast, covering phone connectivity, points, and what $ACU can be used for.
Over the past few days, I’ve written about @Acurast, covering phone connectivity, points, and what $ACU can be used for.
Today, let’s switch to something you can open and play with right away.
Acurast recently launched a set of Laya demos. The AI model behind them doesn’t run on traditional cloud servers; it runs on an Android phone in the Acurast network.
The page features 9 demos, including Snake, Tetris, Doom, email classification, live-stream comment moderation, and prompt injection blocking. It also offers an API.
Laya is not a chatbot.
Laya is a System One model.
It doesn’t write long replies like ChatGPT. Give it some content and a few fixed options, and it will make a choice directly and return the probability of each option.
Today, let’s switch to something you can open and play with right away.
Acurast recently launched a set of Laya demos. The AI model behind them doesn’t run on traditional cloud servers; it runs on an Android phone in the Acurast network.
The page features 9 demos, including Snake, Tetris, Doom, email classification, live-stream comment moderation, and prompt injection blocking. It also offers an API.
Laya is not a chatbot.
Laya is a System One model.
It doesn’t write long replies like ChatGPT. Give it some content and a few fixed options, and it will make a choice directly and return the probability of each option.
6
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Article
I'm thinking about turning the tutorials I write into a website.I'm thinking about turning the tutorials I write into a website.
I post so many tweets that after a while, even I have trouble finding them—let alone people who've just followed me.
Seeing Acurast's WordPress site-building example got me thinking again: someday, the dashboard behind the Chinese-language tutorials everyone reads might be running on someone's phone.
Acurast is a network that harnesses the computing power of phones. This time, we're talking about its Cargo, which provides Linux runtime environments for devices connected to the network, allowing some existing software to be deployed on them.
WordPress is one example. The official team has already shared the configuration, deployment, and installation steps. Once it's up and running, you still use the familiar WordPress dashboard to write posts, upload images, and manage pages—there's no need to learn a whole new system.
I'm thinking about turning the tutorials I write into a website.
I'm thinking about turning the tutorials I write into a website.
I post so many tweets that after a while, even I have trouble finding them—let alone people who've just followed me.
Seeing Acurast's WordPress site-building example got me thinking again: someday, the dashboard behind the Chinese-language tutorials everyone reads might be running on someone's phone.
Acurast is a network that harnesses the computing power of phones. This time, we're talking about its Cargo, which provides Linux runtime environments for devices connected to the network, allowing some existing software to be deployed on them.
WordPress is one example. The official team has already shared the configuration, deployment, and installation steps. Once it's up and running, you still use the familiar WordPress dashboard to write posts, upload images, and manage pages—there's no need to learn a whole new system.
I post so many tweets that after a while, even I have trouble finding them—let alone people who've just followed me.
Seeing Acurast's WordPress site-building example got me thinking again: someday, the dashboard behind the Chinese-language tutorials everyone reads might be running on someone's phone.
Acurast is a network that harnesses the computing power of phones. This time, we're talking about its Cargo, which provides Linux runtime environments for devices connected to the network, allowing some existing software to be deployed on them.
WordPress is one example. The official team has already shared the configuration, deployment, and installation steps. Once it's up and running, you still use the familiar WordPress dashboard to write posts, upload images, and manage pages—there's no need to learn a whole new system.
29
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I was recently looking through @Acurast’s materials and noticed something about $ACU
that’s easy to confuse: a phone receiving rewards doesn’t mean a developer has paid that phone owner.
Acurast is a mobile computing network. Users provide devices, and developers deploy programs to run on them. There need to be people willing to connect their devices and people willing to use the apps—both sides are essential.
At present, the devices’ base rewards come from token issuance and are distributed according to rules such as performance tests. At this stage, rewards can attract users to connect their devices and help build up available computing power.
Developer payments are a separate matter. Developers pay computing fees to deploy programs. Under the official rules, once a task has been successfully executed, the corresponding deployment fee is burned. It’s not that a developer pays a fee and the phone owner directly receives that money.
I think this is worth discussing in more detail.
When researching DePIN, many people are used to starting with how many tokens they can earn in a day. That certainly matters—you have to work out the costs of participating. But rewards alone don’t tell you how many people actually need the network.
More devices can mean more people have connected. Developers consistently spending money to run programs is what shows that someone is willing to pay for this computing power. Those are two different things and shouldn’t be conflated.
$ACU
isn’t just used for rewards here. It’s also used for network fees, Staked Compute staking, and governance. To me, demand generated by developers using computing services is easier to understand than simply having another token-holder benefit.
That said, don’t take “fee burning” to mean “there will definitely be fewer tokens over time.”
The official rate currently allows for 5% annual issuance. Rewards are distributed, so new tokens are added; deployment fees are burned, reducing some of the supply. To see how the total supply changes, you have to weigh both sides. Without actual usage, it’s too early to talk about deflation just because there’s a burn mechanism.
What interests me about Acurast is this: idle phones can provide computing power, developers have somewhere to deploy applications, and $ACU
plays a role in payments and incentives. But how big this business can get still depends on people actually using the applications—not just participants earning tokens.
I was recently looking through @Acurast’s materials and noticed something about $ACU
that’s easy to confuse: a phone receiving rewards doesn’t mean a developer has paid that phone owner.
Acurast is a mobile computing network. Users provide devices, and developers deploy programs to run on them. There need to be people willing to connect their devices and people willing to use the apps—both sides are essential.
At present, the devices’ base rewards come from token issuance and are distributed according to rules such as performance tests. At this stage, rewards can attract users to connect their devices and help build up available computing power.
Developer payments are a separate matter. Developers pay computing fees to deploy programs. Under the official rules, once a task has been successfully executed, the corresponding deployment fee is burned. It’s not that a developer pays a fee and the phone owner directly receives that money.
I think this is worth discussing in more detail.
When researching DePIN, many people are used to starting with how many tokens they can earn in a day. That certainly matters—you have to work out the costs of participating. But rewards alone don’t tell you how many people actually need the network.
More devices can mean more people have connected. Developers consistently spending money to run programs is what shows that someone is willing to pay for this computing power. Those are two different things and shouldn’t be conflated.
$ACU
isn’t just used for rewards here. It’s also used for network fees, Staked Compute staking, and governance. To me, demand generated by developers using computing services is easier to understand than simply having another token-holder benefit.
That said, don’t take “fee burning” to mean “there will definitely be fewer tokens over time.”
The official rate currently allows for 5% annual issuance. Rewards are distributed, so new tokens are added; deployment fees are burned, reducing some of the supply. To see how the total supply changes, you have to weigh both sides. Without actual usage, it’s too early to talk about deflation just because there’s a burn mechanism.
What interests me about Acurast is this: idle phones can provide computing power, developers have somewhere to deploy applications, and $ACU
plays a role in payments and incentives. But how big this business can get still depends on people actually using the applications—not just participants earning tokens.
that’s easy to confuse: a phone receiving rewards doesn’t mean a developer has paid that phone owner.
Acurast is a mobile computing network. Users provide devices, and developers deploy programs to run on them. There need to be people willing to connect their devices and people willing to use the apps—both sides are essential.
At present, the devices’ base rewards come from token issuance and are distributed according to rules such as performance tests. At this stage, rewards can attract users to connect their devices and help build up available computing power.
Developer payments are a separate matter. Developers pay computing fees to deploy programs. Under the official rules, once a task has been successfully executed, the corresponding deployment fee is burned. It’s not that a developer pays a fee and the phone owner directly receives that money.
I think this is worth discussing in more detail.
When researching DePIN, many people are used to starting with how many tokens they can earn in a day. That certainly matters—you have to work out the costs of participating. But rewards alone don’t tell you how many people actually need the network.
More devices can mean more people have connected. Developers consistently spending money to run programs is what shows that someone is willing to pay for this computing power. Those are two different things and shouldn’t be conflated.
$ACU
isn’t just used for rewards here. It’s also used for network fees, Staked Compute staking, and governance. To me, demand generated by developers using computing services is easier to understand than simply having another token-holder benefit.
That said, don’t take “fee burning” to mean “there will definitely be fewer tokens over time.”
The official rate currently allows for 5% annual issuance. Rewards are distributed, so new tokens are added; deployment fees are burned, reducing some of the supply. To see how the total supply changes, you have to weigh both sides. Without actual usage, it’s too early to talk about deflation just because there’s a burn mechanism.
What interests me about Acurast is this: idle phones can provide computing power, developers have somewhere to deploy applications, and $ACU
plays a role in payments and incentives. But how big this business can get still depends on people actually using the applications—not just participants earning tokens.
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Article
Acurast Reward DocumentationMore than 320,000 phones—that’s the most eye-catching number for @Acurast right now.
But I find that DePIN no longer satisfies itself with just simple machine metrics.
I also want to look at one more column: how many of these phones are truly working today.
After going through Acurast’s reward documentation, I found that a phone in the network generally has about three states:
Online
After the phone completes hardware testing and maintains heartbeats, it participates in baseline reward distribution based on its Benchmark scores. Right now, this portion comes from network inflation, and higher-end devices usually receive higher weighting.
Committed compute
Once devices enter Staked Compute, rewards are determined by hardware performance, staked amount, and commitment duration. A phone isn’t just temporarily online—it reserves a period of compute power for network scheduling.
Acurast Reward Documentation
More than 320,000 phones—that’s the most eye-catching number for @Acurast right now.
But I find that DePIN no longer satisfies itself with just simple machine metrics.
I also want to look at one more column: how many of these phones are truly working today.
After going through Acurast’s reward documentation, I found that a phone in the network generally has about three states:
Online
After the phone completes hardware testing and maintains heartbeats, it participates in baseline reward distribution based on its Benchmark scores. Right now, this portion comes from network inflation, and higher-end devices usually receive higher weighting.
Committed compute
Once devices enter Staked Compute, rewards are determined by hardware performance, staked amount, and commitment duration. A phone isn’t just temporarily online—it reserves a period of compute power for network scheduling.
But I find that DePIN no longer satisfies itself with just simple machine metrics.
I also want to look at one more column: how many of these phones are truly working today.
After going through Acurast’s reward documentation, I found that a phone in the network generally has about three states:
Online
After the phone completes hardware testing and maintains heartbeats, it participates in baseline reward distribution based on its Benchmark scores. Right now, this portion comes from network inflation, and higher-end devices usually receive higher weighting.
Committed compute
Once devices enter Staked Compute, rewards are determined by hardware performance, staked amount, and commitment duration. A phone isn’t just temporarily online—it reserves a period of compute power for network scheduling.
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While researching the foundational infrastructure for AI Agents these past couple of days, I went through <br>
@Acurast
’s materials in detail.
I initially just thought of it as a mobile compute power project. Later, I realized that the security chip inside the phone is the crucial layer of this network.
Acurast’s compute nodes come from personal devices around the world. Developers don’t know who the final task will be assigned to—the phone could belong to someone completely unknown. Simply sending code to run is obviously not secure enough.
It uses the phone’s built-in trusted execution environment to handle tasks. This area is also used in everyday banking apps, biometric recognition, and payment features. Code running inside it is isolated from the phone’s system, other applications, and the device user.
After the task is completed, the hardware generates a signed attestation to confirm that the execution device is real, that the code has not been modified, and that the result comes from the specified execution environment.
This mechanism fits AI Agents really well.
An Agent that needs to stay online all day may access API keys, user instructions, account data, and scheduled jobs. If you place it on a typical cloud server, you have to trust the service provider. If you run it on distributed devices that can’t be verified, then you’d need to trust a large number of unknown nodes.
Acurast chooses the verified device, the execution environment, and the execution results, so developers don’t have to know in advance who provides the compute power.
The network can now support OpenClaw, Node.js services, robot programs, and some model inference. A phone’s performance may not be suitable for training large models, but it’s well-suited for running the Agent backend, scheduled tasks, and lightweight inference.
My understanding of Acurast has also changed.
An idle phone is only where the compute power comes from. Hardware isolation and execution attestations determine whether developers can safely trust and use that compute power.
While researching the foundational infrastructure for AI Agents these past couple of days, I went through <br>
@Acurast
’s materials in detail.
I initially just thought of it as a mobile compute power project. Later, I realized that the security chip inside the phone is the crucial layer of this network.
Acurast’s compute nodes come from personal devices around the world. Developers don’t know who the final task will be assigned to—the phone could belong to someone completely unknown. Simply sending code to run is obviously not secure enough.
It uses the phone’s built-in trusted execution environment to handle tasks. This area is also used in everyday banking apps, biometric recognition, and payment features. Code running inside it is isolated from the phone’s system, other applications, and the device user.
After the task is completed, the hardware generates a signed attestation to confirm that the execution device is real, that the code has not been modified, and that the result comes from the specified execution environment.
This mechanism fits AI Agents really well.
An Agent that needs to stay online all day may access API keys, user instructions, account data, and scheduled jobs. If you place it on a typical cloud server, you have to trust the service provider. If you run it on distributed devices that can’t be verified, then you’d need to trust a large number of unknown nodes.
Acurast chooses the verified device, the execution environment, and the execution results, so developers don’t have to know in advance who provides the compute power.
The network can now support OpenClaw, Node.js services, robot programs, and some model inference. A phone’s performance may not be suitable for training large models, but it’s well-suited for running the Agent backend, scheduled tasks, and lightweight inference.
My understanding of Acurast has also changed.
An idle phone is only where the compute power comes from. Hardware isolation and execution attestations determine whether developers can safely trust and use that compute power.
@Acurast
’s materials in detail.
I initially just thought of it as a mobile compute power project. Later, I realized that the security chip inside the phone is the crucial layer of this network.
Acurast’s compute nodes come from personal devices around the world. Developers don’t know who the final task will be assigned to—the phone could belong to someone completely unknown. Simply sending code to run is obviously not secure enough.
It uses the phone’s built-in trusted execution environment to handle tasks. This area is also used in everyday banking apps, biometric recognition, and payment features. Code running inside it is isolated from the phone’s system, other applications, and the device user.
After the task is completed, the hardware generates a signed attestation to confirm that the execution device is real, that the code has not been modified, and that the result comes from the specified execution environment.
This mechanism fits AI Agents really well.
An Agent that needs to stay online all day may access API keys, user instructions, account data, and scheduled jobs. If you place it on a typical cloud server, you have to trust the service provider. If you run it on distributed devices that can’t be verified, then you’d need to trust a large number of unknown nodes.
Acurast chooses the verified device, the execution environment, and the execution results, so developers don’t have to know in advance who provides the compute power.
The network can now support OpenClaw, Node.js services, robot programs, and some model inference. A phone’s performance may not be suitable for training large models, but it’s well-suited for running the Agent backend, scheduled tasks, and lightweight inference.
My understanding of Acurast has also changed.
An idle phone is only where the compute power comes from. Hardware isolation and execution attestations determine whether developers can safely trust and use that compute power.
131
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The market has been getting tossed back and forth lately, and the name $CASH actually feels quite fitting.
For now, I’m not planning to chase short-term swings. I’ll first see whether it has steady daily trading, and after the hype cools down, how many holders are still left.
It’s not hard for it to rise quickly—the real challenge is whether, when there’s no new news, people are still willing to buy and sell in the pool.
I’ll hold a bit in my wallet and watch as I go.
SR-D379F1410CD73305F4A4FF4D
The market has been getting tossed back and forth lately, and the name $CASH actually feels quite fitting.
For now, I’m not planning to chase short-term swings. I’ll first see whether it has steady daily trading, and after the hype cools down, how many holders are still left.
It’s not hard for it to rise quickly—the real challenge is whether, when there’s no new news, people are still willing to buy and sell in the pool.
I’ll hold a bit in my wallet and watch as I go.
SR-D379F1410CD73305F4A4FF4D
For now, I’m not planning to chase short-term swings. I’ll first see whether it has steady daily trading, and after the hype cools down, how many holders are still left.
It’s not hard for it to rise quickly—the real challenge is whether, when there’s no new news, people are still willing to buy and sell in the pool.
I’ll hold a bit in my wallet and watch as I go.
SR-D379F1410CD73305F4A4FF4D
76
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The first Renaiss Open Hackathon attracted more than 70 teams, and in the end, 45 early-stage products were developed based on Renaiss.
One team wanted to build a physical collectible project, and the front-end page was only part of it. There’s also a need to handle card verification, custody, price records, ownership transfers, and physical settlement. Each team had to build everything from scratch, which required a lot of time and cost; moreover, the resulting products were difficult to interoperate.
@renaissprotocol separated these steps into a reusable set of infrastructure.
When users trade collectibles, they can list and transfer via Renaiss.xyz; card prices are organized by the Renaiss Index to provide references for trading, buybacks, and subsequent financial scenarios; after users apply to withdraw physical cards, Renaiss Layer handles connecting on-chain records with off-chain settlement; and vaults and card shops integrate through Renaiss OS to provide custody, verification, and inventory management.
Development teams can call these capabilities through the Renaiss CLI, focusing their main effort on product rules and user experience.
For example, if a team wants to build a TCG opening/booster product, they don’t need to find vaults again, create card dossiers, or handle shipping. If they want to build a collectibles trading tool, they can also directly connect to existing pricing and custody data. Once third-party SDKs and a permissionless VRF protocol are opened, products such as fair pack openings, AI-based card valuation, card lending, and inventory management can continue to be built on top.
These 45 early products use the same pricing, custody, and settlement system, while each has its own gameplay and user base.
One product brings new card inventory, and other products can gain more tradable assets as well; as new users enter the market, demand for trading and buybacks increases; and the more card shops and vaults that connect, the wider the coverage of physical collectibles will become.
In the early community discussions, people mostly talked about which cards were opened and how much their prices rose. Now external teams have started using Renaiss’s tools to build products, and the discussion is gradually shifting toward pricing, custody, settlement, and third-party integrations.
Next, we need to keep observing how many of these 45 products can go live officially, how many can retain users, and whether they can bring new collections and real trades. As long as these data continue to grow, Renaiss’s collectibles infrastructure will see more real-world usage.
The first Renaiss Open Hackathon attracted more than 70 teams, and in the end, 45 early-stage products were developed based on Renaiss.
One team wanted to build a physical collectible project, and the front-end page was only part of it. There’s also a need to handle card verification, custody, price records, ownership transfers, and physical settlement. Each team had to build everything from scratch, which required a lot of time and cost; moreover, the resulting products were difficult to interoperate.
@Renaiss Collectibles separated these steps into a reusable set of infrastructure.
When users trade collectibles, they can list and transfer via Renaiss.xyz; card prices are organized by the Renaiss Index to provide references for trading, buybacks, and subsequent financial scenarios; after users apply to withdraw physical cards, Renaiss Layer handles connecting on-chain records with off-chain settlement; and vaults and card shops integrate through Renaiss OS to provide custody, verification, and inventory management.
Development teams can call these capabilities through the Renaiss CLI, focusing their main effort on product rules and user experience.
For example, if a team wants to build a TCG opening/booster product, they don’t need to find vaults again, create card dossiers, or handle shipping. If they want to build a collectibles trading tool, they can also directly connect to existing pricing and custody data. Once third-party SDKs and a permissionless VRF protocol are opened, products such as fair pack openings, AI-based card valuation, card lending, and inventory management can continue to be built on top.
These 45 early products use the same pricing, custody, and settlement system, while each has its own gameplay and user base.
One product brings new card inventory, and other products can gain more tradable assets as well; as new users enter the market, demand for trading and buybacks increases; and the more card shops and vaults that connect, the wider the coverage of physical collectibles will become.
In the early community discussions, people mostly talked about which cards were opened and how much their prices rose. Now external teams have started using Renaiss’s tools to build products, and the discussion is gradually shifting toward pricing, custody, settlement, and third-party integrations.
Next, we need to keep observing how many of these 45 products can go live officially, how many can retain users, and whether they can bring new collections and real trades. As long as these data continue to grow, Renaiss’s collectibles infrastructure will see more real-world usage.
One team wanted to build a physical collectible project, and the front-end page was only part of it. There’s also a need to handle card verification, custody, price records, ownership transfers, and physical settlement. Each team had to build everything from scratch, which required a lot of time and cost; moreover, the resulting products were difficult to interoperate.
@Renaiss Collectibles separated these steps into a reusable set of infrastructure.
When users trade collectibles, they can list and transfer via Renaiss.xyz; card prices are organized by the Renaiss Index to provide references for trading, buybacks, and subsequent financial scenarios; after users apply to withdraw physical cards, Renaiss Layer handles connecting on-chain records with off-chain settlement; and vaults and card shops integrate through Renaiss OS to provide custody, verification, and inventory management.
Development teams can call these capabilities through the Renaiss CLI, focusing their main effort on product rules and user experience.
For example, if a team wants to build a TCG opening/booster product, they don’t need to find vaults again, create card dossiers, or handle shipping. If they want to build a collectibles trading tool, they can also directly connect to existing pricing and custody data. Once third-party SDKs and a permissionless VRF protocol are opened, products such as fair pack openings, AI-based card valuation, card lending, and inventory management can continue to be built on top.
These 45 early products use the same pricing, custody, and settlement system, while each has its own gameplay and user base.
One product brings new card inventory, and other products can gain more tradable assets as well; as new users enter the market, demand for trading and buybacks increases; and the more card shops and vaults that connect, the wider the coverage of physical collectibles will become.
In the early community discussions, people mostly talked about which cards were opened and how much their prices rose. Now external teams have started using Renaiss’s tools to build products, and the discussion is gradually shifting toward pricing, custody, settlement, and third-party integrations.
Next, we need to keep observing how many of these 45 products can go live officially, how many can retain users, and whether they can bring new collections and real trades. As long as these data continue to grow, Renaiss’s collectibles infrastructure will see more real-world usage.
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The Challenges of TCG CardsWhile playing with physical trading cards and focusing on RWA over this period of time, I’ve been thinking about a question:
Why is it that many physical collectibles, despite having a huge global user base and strong demand for transactions, often find it difficult to grow when they’re tokenized on the Web3 blockchain?
A large part of the reason is that in the past, most projects only focused on turning cards into NFTs and selling them online, without truly connecting all the different stages of the offline physical industry.
The market for physical collectibles isn’t just made up of buyers and sellers—it’s a network formed by collectors, offline card shops, custody vaults, and the secondary market.
The Challenges of TCG Cards
While playing with physical trading cards and focusing on RWA over this period of time, I’ve been thinking about a question:
Why is it that many physical collectibles, despite having a huge global user base and strong demand for transactions, often find it difficult to grow when they’re tokenized on the Web3 blockchain?
A large part of the reason is that in the past, most projects only focused on turning cards into NFTs and selling them online, without truly connecting all the different stages of the offline physical industry.
The market for physical collectibles isn’t just made up of buyers and sellers—it’s a network formed by collectors, offline card shops, custody vaults, and the secondary market.
Why is it that many physical collectibles, despite having a huge global user base and strong demand for transactions, often find it difficult to grow when they’re tokenized on the Web3 blockchain?
A large part of the reason is that in the past, most projects only focused on turning cards into NFTs and selling them online, without truly connecting all the different stages of the offline physical industry.
The market for physical collectibles isn’t just made up of buyers and sellers—it’s a network formed by collectors, offline card shops, custody vaults, and the secondary market.
77
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Article
How can a card go from a local shelf to players around the world?A local card shop received a PSA-graded card in excellent condition. The owner knows it has value, but actually selling it may take a long time.
The store’s foot traffic is limited, and sending it to social platforms would also run into verification issues. Buyers from other places worry about the card’s condition, swapping, and shipping damage. Cross-border transactions also require calculating insurance, tariffs, and courier costs. The higher the price, the more cautious both sides become.
If the owner of this card changes three times in the middle, it will need to be packaged and shipped three times. The card can’t keep trading while it’s on the road, and every handover creates additional new trust costs.
Renaiss @renaissxyz wants to improve this process.
How can a card go from a local shelf to players around the world?
A local card shop received a PSA-graded card in excellent condition. The owner knows it has value, but actually selling it may take a long time.
The store’s foot traffic is limited, and sending it to social platforms would also run into verification issues. Buyers from other places worry about the card’s condition, swapping, and shipping damage. Cross-border transactions also require calculating insurance, tariffs, and courier costs. The higher the price, the more cautious both sides become.
If the owner of this card changes three times in the middle, it will need to be packaged and shipped three times. The card can’t keep trading while it’s on the road, and every handover creates additional new trust costs.
Renaiss @renaissxyz wants to improve this process.
The store’s foot traffic is limited, and sending it to social platforms would also run into verification issues. Buyers from other places worry about the card’s condition, swapping, and shipping damage. Cross-border transactions also require calculating insurance, tariffs, and courier costs. The higher the price, the more cautious both sides become.
If the owner of this card changes three times in the middle, it will need to be packaged and shipped three times. The card can’t keep trading while it’s on the road, and every handover creates additional new trust costs.
Renaiss @renaissxyz wants to improve this process.
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Honestly, after doing on-chain trading for these past few years, I’m increasingly convinced that opportunities are everywhere.
What we truly lack isn’t signals—it’s the speed at which you digest the information and then can act on it.
It’s often like this: you spot a signal, but by the time I’ve traced the cross-chain liquidity, gone through the contract risks again, and then checked how people are discussing it in the community—by the time I finish one full round, the market has already changed.
Later, I came across
@Velvet_Capital
, and I feel like they’re not making a “chat-with-you” bot. They’re genuinely building an analytical tool that can understand the logic behind on-chain positioning.
They’ve captured the wallets of tens of millions of smart money and KOLs. I think the most practical part is that it compares the real movement of on-chain funds with the emotion signals from social media.
When on-chain starts quietly accumulating and the community’s discussion heat gradually rises, the certainty of the signal is far higher than relying on just one side.
In my day-to-day use of the app, I use that AI Copilot. I never ask whether I can buy—I just hand it those tasks that waste the most time, like helping me calculate token concentration, or checking whether a contract has any traps.
More importantly, once it analyzes, it doesn’t give you a bunch of meaningless talk—it directly generates the kind of parameters that intent routing can understand.
That means the same second I understand the conclusion, the trade parameters are already set. I don’t have to manually copy the contract or adjust slippage anymore.
Using VelvetX on mobile feels completely different from the Web3 mobile experience I had before. Previously, you’d often have to jump to a third-party wallet, sign and authorize repeatedly—every extra step adds a bit more delay and a bit more risk of tapping the wrong thing.
VelvetX includes a non-custodial wallet, address tracking, and AI research and judgment. On top of that, it supports copy-trading, spot, and perps on Solana, Base, and Hyperliquid—all in a single interface. The biggest advantage is that you don’t have to keep switching back and forth, so your train of thought doesn’t get interrupted.
Looking back, the most direct improvement this gives me is that it shortens the stretch of the journey from seeing a signal to completing an order.
Dedicated AI for research efficiency. Intent routing without custody to eliminate execution overhead.
In real trading, sometimes if you click one fewer time or check one less layer of positioning, the outcome can be the difference between keeping up and falling behind.
Honestly, after doing on-chain trading for these past few years, I’m increasingly convinced that opportunities are everywhere.
What we truly lack isn’t signals—it’s the speed at which you digest the information and then can act on it.
It’s often like this: you spot a signal, but by the time I’ve traced the cross-chain liquidity, gone through the contract risks again, and then checked how people are discussing it in the community—by the time I finish one full round, the market has already changed.
Later, I came across
@Velvet_Capital
, and I feel like they’re not making a “chat-with-you” bot. They’re genuinely building an analytical tool that can understand the logic behind on-chain positioning.
They’ve captured the wallets of tens of millions of smart money and KOLs. I think the most practical part is that it compares the real movement of on-chain funds with the emotion signals from social media.
When on-chain starts quietly accumulating and the community’s discussion heat gradually rises, the certainty of the signal is far higher than relying on just one side.
In my day-to-day use of the app, I use that AI Copilot. I never ask whether I can buy—I just hand it those tasks that waste the most time, like helping me calculate token concentration, or checking whether a contract has any traps.
More importantly, once it analyzes, it doesn’t give you a bunch of meaningless talk—it directly generates the kind of parameters that intent routing can understand.
That means the same second I understand the conclusion, the trade parameters are already set. I don’t have to manually copy the contract or adjust slippage anymore.
Using VelvetX on mobile feels completely different from the Web3 mobile experience I had before. Previously, you’d often have to jump to a third-party wallet, sign and authorize repeatedly—every extra step adds a bit more delay and a bit more risk of tapping the wrong thing.
VelvetX includes a non-custodial wallet, address tracking, and AI research and judgment. On top of that, it supports copy-trading, spot, and perps on Solana, Base, and Hyperliquid—all in a single interface. The biggest advantage is that you don’t have to keep switching back and forth, so your train of thought doesn’t get interrupted.
Looking back, the most direct improvement this gives me is that it shortens the stretch of the journey from seeing a signal to completing an order.
Dedicated AI for research efficiency. Intent routing without custody to eliminate execution overhead.
In real trading, sometimes if you click one fewer time or check one less layer of positioning, the outcome can be the difference between keeping up and falling behind.
What we truly lack isn’t signals—it’s the speed at which you digest the information and then can act on it.
It’s often like this: you spot a signal, but by the time I’ve traced the cross-chain liquidity, gone through the contract risks again, and then checked how people are discussing it in the community—by the time I finish one full round, the market has already changed.
Later, I came across
@Velvet_Capital
, and I feel like they’re not making a “chat-with-you” bot. They’re genuinely building an analytical tool that can understand the logic behind on-chain positioning.
They’ve captured the wallets of tens of millions of smart money and KOLs. I think the most practical part is that it compares the real movement of on-chain funds with the emotion signals from social media.
When on-chain starts quietly accumulating and the community’s discussion heat gradually rises, the certainty of the signal is far higher than relying on just one side.
In my day-to-day use of the app, I use that AI Copilot. I never ask whether I can buy—I just hand it those tasks that waste the most time, like helping me calculate token concentration, or checking whether a contract has any traps.
More importantly, once it analyzes, it doesn’t give you a bunch of meaningless talk—it directly generates the kind of parameters that intent routing can understand.
That means the same second I understand the conclusion, the trade parameters are already set. I don’t have to manually copy the contract or adjust slippage anymore.
Using VelvetX on mobile feels completely different from the Web3 mobile experience I had before. Previously, you’d often have to jump to a third-party wallet, sign and authorize repeatedly—every extra step adds a bit more delay and a bit more risk of tapping the wrong thing.
VelvetX includes a non-custodial wallet, address tracking, and AI research and judgment. On top of that, it supports copy-trading, spot, and perps on Solana, Base, and Hyperliquid—all in a single interface. The biggest advantage is that you don’t have to keep switching back and forth, so your train of thought doesn’t get interrupted.
Looking back, the most direct improvement this gives me is that it shortens the stretch of the journey from seeing a signal to completing an order.
Dedicated AI for research efficiency. Intent routing without custody to eliminate execution overhead.
In real trading, sometimes if you click one fewer time or check one less layer of positioning, the outcome can be the difference between keeping up and falling behind.
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Verifying my Binance Square account for YZi Labs Amplify via EchoHunt: EH-JY3XYU
Verifying my Binance Square account for YZi Labs Amplify via EchoHunt: EH-JY3XYU
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Article
Midnight reconstructs the pragmatic path to Web3 privacyThe narrative of privacy in the cryptocurrency space has long been in an awkward situation of polarization. On one end are traditional privacy coins like Monero (XMR) and Zcash (ZEC), which focus on extreme indistinguishable anonymity; on the other end is the completely transparent model of mainstream public chains, where user assets and transaction trails are devoid of any secrecy.
Once a symbol of the cypherpunk spirit, traditional privacy coins are now facing a survival crisis due to tightening regulations: delisted from mainstream exchanges, depleted liquidity, and difficulty in integrating into the DeFi ecosystem and enterprise applications, they are gradually being marginalized. The emergence of Midnight Network (<a>m-39</a>) has completely broken this black-and-white deadlock; it is not an imitator of traditional privacy coins, but a brand new revolution in the field of blockchain privacy, expected to become the evolutionary terminator of traditional single-point privacy coins.
Midnight reconstructs the pragmatic path to Web3 privacy
The narrative of privacy in the cryptocurrency space has long been in an awkward situation of polarization. On one end are traditional privacy coins like Monero (XMR) and Zcash (ZEC), which focus on extreme indistinguishable anonymity; on the other end is the completely transparent model of mainstream public chains, where user assets and transaction trails are devoid of any secrecy.
Once a symbol of the cypherpunk spirit, traditional privacy coins are now facing a survival crisis due to tightening regulations: delisted from mainstream exchanges, depleted liquidity, and difficulty in integrating into the DeFi ecosystem and enterprise applications, they are gradually being marginalized. The emergence of Midnight Network (<a>m-39</a>) has completely broken this black-and-white deadlock; it is not an imitator of traditional privacy coins, but a brand new revolution in the field of blockchain privacy, expected to become the evolutionary terminator of traditional single-point privacy coins.
Once a symbol of the cypherpunk spirit, traditional privacy coins are now facing a survival crisis due to tightening regulations: delisted from mainstream exchanges, depleted liquidity, and difficulty in integrating into the DeFi ecosystem and enterprise applications, they are gradually being marginalized. The emergence of Midnight Network (<a>m-39</a>) has completely broken this black-and-white deadlock; it is not an imitator of traditional privacy coins, but a brand new revolution in the field of blockchain privacy, expected to become the evolutionary terminator of traditional single-point privacy coins.
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Recently, while reviewing the Web3 infrastructure track, I found that the industry's understanding of 'privacy' is quietly undergoing a profound change.
In the early days, privacy-oriented projects were mostly obsessed with extreme anonymity, but this purely technology-driven geek expression is difficult to adapt to real-world business scenarios and compliance frameworks.
From the architectural design of @MidnightNetwork , it provides a more feasible intermediate solution—programmable disclosure.
The core logic of Midnight relies on ZK (zero-knowledge proof) technology to effectively separate data ownership and usage rights on-chain. In this network system, the original practical value of smart contracts is fully retained, while the underlying data is by default in an encrypted protection state; only in the face of audit checks or obtaining specific authorization will it output relevant proof of the computational results.
In this architectural system, the core positioning of the $NIGHT token is more inclined towards driving efficient ZK computation and ensuring the stable operation of network consensus as a foundational resource.
This development model has not taken the extreme of either/or but focuses on building a balancing bridge between 'data privacy protection' and 'regulatory compliance needs.' As the funding scale of the Web3 industry continues to expand, those infrastructure solutions that balance practical value and compliance standards will undoubtedly become the core direction of long-term tracking and attention in the future.
#night $NIGHT
Recently, while reviewing the Web3 infrastructure track, I found that the industry's understanding of 'privacy' is quietly undergoing a profound change.
In the early days, privacy-oriented projects were mostly obsessed with extreme anonymity, but this purely technology-driven geek expression is difficult to adapt to real-world business scenarios and compliance frameworks.
From the architectural design of @MidnightNetwork , it provides a more feasible intermediate solution—programmable disclosure.
The core logic of Midnight relies on ZK (zero-knowledge proof) technology to effectively separate data ownership and usage rights on-chain. In this network system, the original practical value of smart contracts is fully retained, while the underlying data is by default in an encrypted protection state; only in the face of audit checks or obtaining specific authorization will it output relevant proof of the computational results.
In this architectural system, the core positioning of the $NIGHT token is more inclined towards driving efficient ZK computation and ensuring the stable operation of network consensus as a foundational resource.
This development model has not taken the extreme of either/or but focuses on building a balancing bridge between 'data privacy protection' and 'regulatory compliance needs.' As the funding scale of the Web3 industry continues to expand, those infrastructure solutions that balance practical value and compliance standards will undoubtedly become the core direction of long-term tracking and attention in the future.
#night $NIGHT
In the early days, privacy-oriented projects were mostly obsessed with extreme anonymity, but this purely technology-driven geek expression is difficult to adapt to real-world business scenarios and compliance frameworks.
From the architectural design of @MidnightNetwork , it provides a more feasible intermediate solution—programmable disclosure.
The core logic of Midnight relies on ZK (zero-knowledge proof) technology to effectively separate data ownership and usage rights on-chain. In this network system, the original practical value of smart contracts is fully retained, while the underlying data is by default in an encrypted protection state; only in the face of audit checks or obtaining specific authorization will it output relevant proof of the computational results.
In this architectural system, the core positioning of the $NIGHT token is more inclined towards driving efficient ZK computation and ensuring the stable operation of network consensus as a foundational resource.
This development model has not taken the extreme of either/or but focuses on building a balancing bridge between 'data privacy protection' and 'regulatory compliance needs.' As the funding scale of the Web3 industry continues to expand, those infrastructure solutions that balance practical value and compliance standards will undoubtedly become the core direction of long-term tracking and attention in the future.
#night $NIGHT
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Article
On-chain Privacy Pain Points and Midnight's Path to BreakthroughHaving struggled in the cryptocurrency space for these years, I increasingly realize that the concept of on-chain transparency sounds particularly enticing in project promotions, but when it comes to actual users, it often comes with quite a few troubles.
A few years ago when I was doing airdrops, I often operated dozens of accounts at the same time. Once the wallet address was in a long-term unprotected 'naked run' state, the flow of funds, on-chain interaction traces, and account activity frequency would all be exposed on-chain without any concealment.
After a strict selection process by the project team, I often spent months of effort, only to find that I might not even reach the threshold for participation in the end.
On-chain Privacy Pain Points and Midnight's Path to Breakthrough
Having struggled in the cryptocurrency space for these years, I increasingly realize that the concept of on-chain transparency sounds particularly enticing in project promotions, but when it comes to actual users, it often comes with quite a few troubles.
A few years ago when I was doing airdrops, I often operated dozens of accounts at the same time. Once the wallet address was in a long-term unprotected 'naked run' state, the flow of funds, on-chain interaction traces, and account activity frequency would all be exposed on-chain without any concealment.
After a strict selection process by the project team, I often spent months of effort, only to find that I might not even reach the threshold for participation in the end.
A few years ago when I was doing airdrops, I often operated dozens of accounts at the same time. Once the wallet address was in a long-term unprotected 'naked run' state, the flow of funds, on-chain interaction traces, and account activity frequency would all be exposed on-chain without any concealment.
After a strict selection process by the project team, I often spent months of effort, only to find that I might not even reach the threshold for participation in the end.
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@MidnightNetwork The core highlight is not the competition of on-chain speed, but focusing on 'realistic, actionable, and reusable.' The on-chain world has always had a deadlock: for applications to be useful, data must be public, but the more transparent the data is, the easier it is to be observed, tracked, and even copied; if you want to maintain privacy, you have to hide, but once you hide, normal business interactions will be obstructed.
Midnight directly breaks this deadlock, taking a differentiated approach: relying on zero-knowledge proof technology, it only proves 'you meet the corresponding conditions' without revealing your core data cards.
In simple terms, it is like a brand new privacy verification logic: it only displays verifiable results without disclosing any raw data. In many on-chain scenarios, there is no need to put all information on-chain; it is enough to prove one conclusion: you have eligibility to participate, have not exceeded operations, adhere to rules, or possess corresponding rights. The ownership of the data is always in your hands; what others can see is only the verifiable conclusion, not the privacy information that can be misused.
The core competitiveness of this approach lies in the practical experience: can developers easily get started, can users fill out less information, expose less privacy, be tracked less, and still smoothly complete transactions, verifications, and collaborations. If it only stays in the hype of the concept of 'privacy,' it will soon be abandoned by the market; but if it can make 'usability' a fundamental capability, making privacy no longer an additional burden, that is its long-term core value.
#night $NIGHT
@MidnightNetwork The core highlight is not the competition of on-chain speed, but focusing on 'realistic, actionable, and reusable.' The on-chain world has always had a deadlock: for applications to be useful, data must be public, but the more transparent the data is, the easier it is to be observed, tracked, and even copied; if you want to maintain privacy, you have to hide, but once you hide, normal business interactions will be obstructed.
Midnight directly breaks this deadlock, taking a differentiated approach: relying on zero-knowledge proof technology, it only proves 'you meet the corresponding conditions' without revealing your core data cards.
In simple terms, it is like a brand new privacy verification logic: it only displays verifiable results without disclosing any raw data. In many on-chain scenarios, there is no need to put all information on-chain; it is enough to prove one conclusion: you have eligibility to participate, have not exceeded operations, adhere to rules, or possess corresponding rights. The ownership of the data is always in your hands; what others can see is only the verifiable conclusion, not the privacy information that can be misused.
The core competitiveness of this approach lies in the practical experience: can developers easily get started, can users fill out less information, expose less privacy, be tracked less, and still smoothly complete transactions, verifications, and collaborations. If it only stays in the hype of the concept of 'privacy,' it will soon be abandoned by the market; but if it can make 'usability' a fundamental capability, making privacy no longer an additional burden, that is its long-term core value.
#night $NIGHT
Midnight directly breaks this deadlock, taking a differentiated approach: relying on zero-knowledge proof technology, it only proves 'you meet the corresponding conditions' without revealing your core data cards.
In simple terms, it is like a brand new privacy verification logic: it only displays verifiable results without disclosing any raw data. In many on-chain scenarios, there is no need to put all information on-chain; it is enough to prove one conclusion: you have eligibility to participate, have not exceeded operations, adhere to rules, or possess corresponding rights. The ownership of the data is always in your hands; what others can see is only the verifiable conclusion, not the privacy information that can be misused.
The core competitiveness of this approach lies in the practical experience: can developers easily get started, can users fill out less information, expose less privacy, be tracked less, and still smoothly complete transactions, verifications, and collaborations. If it only stays in the hype of the concept of 'privacy,' it will soon be abandoned by the market; but if it can make 'usability' a fundamental capability, making privacy no longer an additional burden, that is its long-term core value.
#night $NIGHT
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When I was a child, helping my neighbor collect packages, every time I organized the parcels on time, the neighbor would give me some pocket money. That feeling was very direct; your efforts were immediately rewarded.
In terms of the experience with @MidnightNetwork , I have the same feeling: every time I participate in community activities, complete tasks, or engage in discussions, the platform records my actions on the blockchain while also providing token rewards.
Tokens here are not just rewards; they also allow me to participate in governance. I have voted several times to determine community rules and incentive programs, and this sense of participation feels very real. You are not just an ordinary user, but a part of the ecosystem, and your actions can drive the entire system's operation.
I feel that the platform's closed-loop design is particularly clear: your participation behavior → on-chain verification → obtaining rewards and governance rights → reinvesting in the ecosystem. Every step has value, and the actions and rewards are closely linked. This motivates me to participate long-term more than on traditional social or DeFi platforms because I can see tangible results from every action.
Overall, the token value and ecological closed-loop design of Midnight Network give me a sense of achievement similar to when I collected packages as a child: effort can be quantified, rewarded, and allows me to truly have a voice in the ecosystem. This model makes me more willing to continue participating and makes it easier to see the value of my actions.
#night $NIGHT
When I was a child, helping my neighbor collect packages, every time I organized the parcels on time, the neighbor would give me some pocket money. That feeling was very direct; your efforts were immediately rewarded.
In terms of the experience with @MidnightNetwork , I have the same feeling: every time I participate in community activities, complete tasks, or engage in discussions, the platform records my actions on the blockchain while also providing token rewards.
Tokens here are not just rewards; they also allow me to participate in governance. I have voted several times to determine community rules and incentive programs, and this sense of participation feels very real. You are not just an ordinary user, but a part of the ecosystem, and your actions can drive the entire system's operation.
I feel that the platform's closed-loop design is particularly clear: your participation behavior → on-chain verification → obtaining rewards and governance rights → reinvesting in the ecosystem. Every step has value, and the actions and rewards are closely linked. This motivates me to participate long-term more than on traditional social or DeFi platforms because I can see tangible results from every action.
Overall, the token value and ecological closed-loop design of Midnight Network give me a sense of achievement similar to when I collected packages as a child: effort can be quantified, rewarded, and allows me to truly have a voice in the ecosystem. This model makes me more willing to continue participating and makes it easier to see the value of my actions.
#night $NIGHT
In terms of the experience with @MidnightNetwork , I have the same feeling: every time I participate in community activities, complete tasks, or engage in discussions, the platform records my actions on the blockchain while also providing token rewards.
Tokens here are not just rewards; they also allow me to participate in governance. I have voted several times to determine community rules and incentive programs, and this sense of participation feels very real. You are not just an ordinary user, but a part of the ecosystem, and your actions can drive the entire system's operation.
I feel that the platform's closed-loop design is particularly clear: your participation behavior → on-chain verification → obtaining rewards and governance rights → reinvesting in the ecosystem. Every step has value, and the actions and rewards are closely linked. This motivates me to participate long-term more than on traditional social or DeFi platforms because I can see tangible results from every action.
Overall, the token value and ecological closed-loop design of Midnight Network give me a sense of achievement similar to when I collected packages as a child: effort can be quantified, rewarded, and allows me to truly have a voice in the ecosystem. This model makes me more willing to continue participating and makes it easier to see the value of my actions.
#night $NIGHT
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Article
From User to Participant: My Experience at MidnightRecently, I have been playing @MidnightNetwork . From my experience on the platform, I feel that its design is quite interesting. What attracts me the most is the ecological closed loop: every action you take on the platform is linked to $NIGHT tokens, and participating can yield actual returns, not just a form of trying to gain presence.
The platform itself is based on zero-knowledge proof technology, allowing users to have complete control over their data while performing operations, without leaking privacy. You can perform operations and verify actions on-chain while others cannot see the original data. This made me feel very secure; in the Web3 world, many places claim to be decentralized, but the actual control of data is still limited.
From User to Participant: My Experience at Midnight
Recently, I have been playing @MidnightNetwork . From my experience on the platform, I feel that its design is quite interesting. What attracts me the most is the ecological closed loop: every action you take on the platform is linked to $NIGHT tokens, and participating can yield actual returns, not just a form of trying to gain presence.
The platform itself is based on zero-knowledge proof technology, allowing users to have complete control over their data while performing operations, without leaking privacy. You can perform operations and verify actions on-chain while others cannot see the original data. This made me feel very secure; in the Web3 world, many places claim to be decentralized, but the actual control of data is still limited.
The platform itself is based on zero-knowledge proof technology, allowing users to have complete control over their data while performing operations, without leaking privacy. You can perform operations and verify actions on-chain while others cannot see the original data. This made me feel very secure; in the Web3 world, many places claim to be decentralized, but the actual control of data is still limited.
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In my opinion, the Fabric Protocol can definitely be called an 'open-source distribution system in the field of robotics'.
The core pain points of the current general-purpose robotics track are similar to the dilemmas of the early software industry: everyone can develop programs, but each has its own set of exclusive standards, ultimately leading to incompatible devices, unsmooth operations, and mutual shirking of responsibility when faults occur.
What @FabricFND aims to do is to incorporate core links such as data flow, computing power scheduling, and regulatory compliance into a public ledger for collaboration, while also outputting the underlying infrastructure for native agents, allowing cross-entity collaboration between robots to be as standardized as software installation, with unified specifications, clear dependency paths, and complete operational traces.
The verifiable computation is like a dedicated digital signature verification package; you do not need to follow every line of code execution throughout the process, yet you can accurately verify that the entire process has not been tampered with. The modular underlying infrastructure is equivalent to an open-source plugin repository, where core capabilities such as rule setting, task scheduling, and security protection can be flexibly replaced and upgraded without completely overturning the original architecture for redevelopment.
$ROBO is the core hub that controls contribution incentives and community governance within this ecosystem: participants who provide various resources to the ecosystem can not only receive corresponding returns but also have the right to participate in rule iterations and discourse.
The real challenges it faces are also very clear: if the incentive mechanism becomes unbalanced, it will breed malicious volume manipulation; if the governance system becomes rigid, it will lose the willingness of participants to contribute.
What it aims to build from start to finish is a long-term stable order for the ecosystem, not just a fleeting short-term popularity.
#robo $ROBO
In my opinion, the Fabric Protocol can definitely be called an 'open-source distribution system in the field of robotics'.
The core pain points of the current general-purpose robotics track are similar to the dilemmas of the early software industry: everyone can develop programs, but each has its own set of exclusive standards, ultimately leading to incompatible devices, unsmooth operations, and mutual shirking of responsibility when faults occur.
What @Fabric Foundation aims to do is to incorporate core links such as data flow, computing power scheduling, and regulatory compliance into a public ledger for collaboration, while also outputting the underlying infrastructure for native agents, allowing cross-entity collaboration between robots to be as standardized as software installation, with unified specifications, clear dependency paths, and complete operational traces.
The verifiable computation is like a dedicated digital signature verification package; you do not need to follow every line of code execution throughout the process, yet you can accurately verify that the entire process has not been tampered with. The modular underlying infrastructure is equivalent to an open-source plugin repository, where core capabilities such as rule setting, task scheduling, and security protection can be flexibly replaced and upgraded without completely overturning the original architecture for redevelopment.
$ROBO is the core hub that controls contribution incentives and community governance within this ecosystem: participants who provide various resources to the ecosystem can not only receive corresponding returns but also have the right to participate in rule iterations and discourse.
The real challenges it faces are also very clear: if the incentive mechanism becomes unbalanced, it will breed malicious volume manipulation; if the governance system becomes rigid, it will lose the willingness of participants to contribute.
What it aims to build from start to finish is a long-term stable order for the ecosystem, not just a fleeting short-term popularity.
#robo $ROBO
The core pain points of the current general-purpose robotics track are similar to the dilemmas of the early software industry: everyone can develop programs, but each has its own set of exclusive standards, ultimately leading to incompatible devices, unsmooth operations, and mutual shirking of responsibility when faults occur.
What @Fabric Foundation aims to do is to incorporate core links such as data flow, computing power scheduling, and regulatory compliance into a public ledger for collaboration, while also outputting the underlying infrastructure for native agents, allowing cross-entity collaboration between robots to be as standardized as software installation, with unified specifications, clear dependency paths, and complete operational traces.
The verifiable computation is like a dedicated digital signature verification package; you do not need to follow every line of code execution throughout the process, yet you can accurately verify that the entire process has not been tampered with. The modular underlying infrastructure is equivalent to an open-source plugin repository, where core capabilities such as rule setting, task scheduling, and security protection can be flexibly replaced and upgraded without completely overturning the original architecture for redevelopment.
$ROBO is the core hub that controls contribution incentives and community governance within this ecosystem: participants who provide various resources to the ecosystem can not only receive corresponding returns but also have the right to participate in rule iterations and discourse.
The real challenges it faces are also very clear: if the incentive mechanism becomes unbalanced, it will breed malicious volume manipulation; if the governance system becomes rigid, it will lose the willingness of participants to contribute.
What it aims to build from start to finish is a long-term stable order for the ecosystem, not just a fleeting short-term popularity.
#robo $ROBO
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Article
The Sovereignty of Robots: Fabric Prevents Collaborative Networks from Being Captured by CapitalWhen it comes to Web3 and the robotics track, most people's attention is focused on hardware parameters, large model iteration upgrades, and computing power reserves.
But these are just superficial things.
What truly determines whether this track can reach a scale of hundreds of billions is an incredibly practical core business question: When future robots at the scale of hundreds of billions cover the physical world, who will ultimately own this massive productivity? And how should it be distributed?
This is also the core reason why I have recently focused my research on @FabricFND .
This team has created a very unconventional yet exceptionally clear top-level architectural design based on underlying logic.
The Sovereignty of Robots: Fabric Prevents Collaborative Networks from Being Captured by Capital
When it comes to Web3 and the robotics track, most people's attention is focused on hardware parameters, large model iteration upgrades, and computing power reserves.
But these are just superficial things.
What truly determines whether this track can reach a scale of hundreds of billions is an incredibly practical core business question: When future robots at the scale of hundreds of billions cover the physical world, who will ultimately own this massive productivity? And how should it be distributed?
This is also the core reason why I have recently focused my research on @Fabric Foundation .
This team has created a very unconventional yet exceptionally clear top-level architectural design based on underlying logic.
But these are just superficial things.
What truly determines whether this track can reach a scale of hundreds of billions is an incredibly practical core business question: When future robots at the scale of hundreds of billions cover the physical world, who will ultimately own this massive productivity? And how should it be distributed?
This is also the core reason why I have recently focused my research on @Fabric Foundation .
This team has created a very unconventional yet exceptionally clear top-level architectural design based on underlying logic.
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