fatty retweeted
Crypto lending often depends on the value of the assets a borrower provides as collateral. but what happens when a borrower’s financial position changes after a loan has been issued?
Imagine someone uses ETH as collateral to borrow another asset. at first, their holdings meet the lender’s requirements. later, the price of ETH drops, reducing the value of their collateral.
The lender may need updated information to assess the borrower’s position and decide whether the loan’s conditions are still being met.
@primus_labs explores how proofs of dynamic assets can support this process.
In this approach, borrowers provide evidence of their assets at agreed intervals. lenders can use this information alongside on-chain data to assess lending risks and set loan conditions.
The process can involve a few steps:
⇒ Initial assessment: the lender assesses the borrower’s available asset and credit information, alongside relevant on-chain data.
⇒ Regular updates: the borrower provides updated proofs of their assets at agreed intervals.
⇒ Checking for changes: the lending system checks whether the assets have changed within a specified range.
⇒ Taking action: according to the documented scenario, repayment or liquidation can be triggered when the specified conditions are met.
Each step helps the lending system assess the borrower’s position as it changes over time.
𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿?
Many on-chain loans require borrowers to provide collateral. this can limit access to lending for people who have other ways to demonstrate their creditworthiness.
Primus explores how combining off-chain information, such as asset and credit details, with on-chain data could help lending applications assess risk and consider more personalised lending conditions.
For example, verified credit information could help a lender assess whether a borrower qualifies for different loan terms. The actual terms would depend on the lending application’s rules and risk assessment.
Providing proof of assets does not guarantee that a borrower will repay a loan or that their assets will retain their value.
The proofs provide information for assessment, while the lending system’s rules determine how that information affects the loan.
Primus presents this as a possible direction for DeFi lending, including exploring personalised lending rates, changing collateral requirements, and credit-based lending.
The key idea is that lending decisions could take more than the collateral provided at the start into account.
Updated asset information and relevant credit data could help applications assess borrowers throughout the lending process.
Crypto lending often depends on the value of the assets a borrower provides as collateral. but what happens when a borrower’s financial position changes after a loan has been issued?
Imagine someone uses ETH as collateral to borrow another asset. at first, their holdings meet the lender’s requirements. later, the price of ETH drops, reducing the value of their collateral.
The lender may need updated information to assess the borrower’s position and decide whether the loan’s conditions are still being met.
@primus_labs explores how proofs of dynamic assets can support this process.
In this approach, borrowers provide evidence of their assets at agreed intervals. lenders can use this information alongside on-chain data to assess lending risks and set loan conditions.
The process can involve a few steps:
⇒ Initial assessment: the lender assesses the borrower’s available asset and credit information, alongside relevant on-chain data.
⇒ Regular updates: the borrower provides updated proofs of their assets at agreed intervals.
⇒ Checking for changes: the lending system checks whether the assets have changed within a specified range.
⇒ Taking action: according to the documented scenario, repayment or liquidation can be triggered when the specified conditions are met.
Each step helps the lending system assess the borrower’s position as it changes over time.
𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿?
Many on-chain loans require borrowers to provide collateral. this can limit access to lending for people who have other ways to demonstrate their creditworthiness.
Primus explores how combining off-chain information, such as asset and credit details, with on-chain data could help lending applications assess risk and consider more personalised lending conditions.
For example, verified credit information could help a lender assess whether a borrower qualifies for different loan terms. The actual terms would depend on the lending application’s rules and risk assessment.
Providing proof of assets does not guarantee that a borrower will repay a loan or that their assets will retain their value.
The proofs provide information for assessment, while the lending system’s rules determine how that information affects the loan.
Primus presents this as a possible direction for DeFi lending, including exploring personalised lending rates, changing collateral requirements, and credit-based lending.
The key idea is that lending decisions could take more than the collateral provided at the start into account.
Updated asset information and relevant credit data could help applications assess borrowers throughout the lending process.
fatty retweeted
𝗩𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗹𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻
AI agents are becoming capable of handling tasks such as analysing market data, interacting with DeFi protocols, and making decisions based on information from external sources.
𝘉𝘶𝘵 𝘩𝘰𝘸 𝘤𝘢𝘯 𝘸𝘦 𝘬𝘯𝘰𝘸 𝘸𝘩𝘦𝘵𝘩𝘦𝘳 𝘢𝘯 𝘢𝘨𝘦𝘯𝘵 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘥𝘪𝘥 𝘸𝘩𝘢𝘵 𝘪𝘵 𝘸𝘢𝘴 𝘴𝘶𝘱𝘱𝘰𝘴𝘦𝘥 𝘵𝘰 𝘥𝘰?
An agent might report that it checked a price before placing a trade. however, seeing the final result does not prove that the price was checked or that the required steps were followed.
This creates a problem for applications that rely on AI agents to perform important tasks. they need a way to check not only the final result but also the evidence behind the actions that led to it.
Many of these actions involve API calls. For example, an agent might:
⇒ Send a request to an AI model to generate a decision.
⇒ Query a price oracle to obtain market information.
⇒ Retrieve data from an external service before taking action.
If these steps are required by the application’s rules, there needs to be a way to verify that they actually happened.
Cryptographic proofs can help verify specific API interactions.
𝘞𝘩𝘢𝘵 𝘮𝘢𝘬𝘦𝘴 𝘢𝘯 𝘢𝘨𝘦𝘯𝘵’𝘴 𝘢𝘤𝘵𝘪𝘰𝘯𝘴 𝘷𝘦𝘳𝘪𝘧𝘪𝘢𝘣𝘭𝘦?
@primus_labs uses zkTLS to help verify data obtained from web services. a prover can generate cryptographic evidence of data exchanged with a server over a TLS-secured connection, without revealing all the underlying session data or private credentials.
For an AI agent, this can provide evidence of important interactions during its execution.
The process can be organised into a few steps:
⇒ 1. Set the rules: Define which actions the agent must perform, such as checking a price before placing a trade.
⇒ 2. Record the required interactions: Generate proofs for relevant API requests and responses as the agent carries out its task.
⇒ 3. Verify the proofs: A verification system checks whether the evidence meets the required conditions.
⇒ 4. Check the execution policy: The application checks whether the required steps were completed and whether their order and results meet its rules.
The final step matters. proving an API request occurred doesn’t automatically mean the agent followed its execution policy. The application must still check the evidence against its rules.
𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿?
As AI agents handle more tasks in DeFi and other automated systems, verifying their actions becomes increasingly useful.
Applications can use verifiable evidence of specific API interactions to check whether required steps were completed.
This can support auditing and make parts of an agent’s activity easier to verify. the level of verification depends on how the system is implemented.
𝗩𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗹𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻
AI agents are becoming capable of handling tasks such as analysing market data, interacting with DeFi protocols, and making decisions based on information from external sources.
𝘉𝘶𝘵 𝘩𝘰𝘸 𝘤𝘢𝘯 𝘸𝘦 𝘬𝘯𝘰𝘸 𝘸𝘩𝘦𝘵𝘩𝘦𝘳 𝘢𝘯 𝘢𝘨𝘦𝘯𝘵 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘥𝘪𝘥 𝘸𝘩𝘢𝘵 𝘪𝘵 𝘸𝘢𝘴 𝘴𝘶𝘱𝘱𝘰𝘴𝘦𝘥 𝘵𝘰 𝘥𝘰?
An agent might report that it checked a price before placing a trade. however, seeing the final result does not prove that the price was checked or that the required steps were followed.
This creates a problem for applications that rely on AI agents to perform important tasks. they need a way to check not only the final result but also the evidence behind the actions that led to it.
Many of these actions involve API calls. For example, an agent might:
⇒ Send a request to an AI model to generate a decision.
⇒ Query a price oracle to obtain market information.
⇒ Retrieve data from an external service before taking action.
If these steps are required by the application’s rules, there needs to be a way to verify that they actually happened.
Cryptographic proofs can help verify specific API interactions.
𝘞𝘩𝘢𝘵 𝘮𝘢𝘬𝘦𝘴 𝘢𝘯 𝘢𝘨𝘦𝘯𝘵’𝘴 𝘢𝘤𝘵𝘪𝘰𝘯𝘴 𝘷𝘦𝘳𝘪𝘧𝘪𝘢𝘣𝘭𝘦?
@primus_labs uses zkTLS to help verify data obtained from web services. a prover can generate cryptographic evidence of data exchanged with a server over a TLS-secured connection, without revealing all the underlying session data or private credentials.
For an AI agent, this can provide evidence of important interactions during its execution.
The process can be organised into a few steps:
⇒ 1. Set the rules: Define which actions the agent must perform, such as checking a price before placing a trade.
⇒ 2. Record the required interactions: Generate proofs for relevant API requests and responses as the agent carries out its task.
⇒ 3. Verify the proofs: A verification system checks whether the evidence meets the required conditions.
⇒ 4. Check the execution policy: The application checks whether the required steps were completed and whether their order and results meet its rules.
The final step matters. proving an API request occurred doesn’t automatically mean the agent followed its execution policy. The application must still check the evidence against its rules.
𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿?
As AI agents handle more tasks in DeFi and other automated systems, verifying their actions becomes increasingly useful.
Applications can use verifiable evidence of specific API interactions to check whether required steps were completed.
This can support auditing and make parts of an agent’s activity easier to verify. the level of verification depends on how the system is implemented.
fatty retweeted
𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗿𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝘀𝗽𝗲𝗲𝗱 𝗺𝗮𝘁𝘁𝗲𝗿 𝗳𝗼𝗿 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗺𝗮𝗿𝗸𝗲𝘁𝘀?
Creating a prediction market is one thing. determining the correct outcome after an event happens is another.
Imagine a market asking whether a football team will score in the next 30 seconds.
If it takes hours to confirm the result, the opportunity to use that market in real time may already be gone.
This creates a limit for prediction markets. the time it takes to confirm an outcome affects which events can become markets.
𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗿𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝘄𝗼𝗿𝗸?
Once an event happens, the market needs to establish the outcome before it can be settled.
This involves three main steps:
⇒ Detect the event: Identify when the event happens.
⇒ Check the outcome: Use the relevant information to determine what happened.
⇒ Resolve the market: Confirm the outcome so the market can proceed to settlement.
When people have to review and confirm every result manually, the process can take time and limit how many markets can be handled.
𝗪𝗵𝗮𝘁 𝗰𝗼𝘂𝗹𝗱 𝗳𝗮𝘀𝘁𝗲𝗿 𝗿𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗺𝗮𝗸𝗲 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲?
@fermah_xyz discusses several types of markets that could benefit from automated resolution. here are three examples:
⇒ Short-lived events: Markets based on events that happen within seconds.
⇒ Creator milestones: Markets tied to measurable changes, such as a streamer reaching a certain number of followers.
⇒ Blockchain events: Markets based on activity recorded on the blockchain, including events within individual blocks.
These markets need outcomes to be checked quickly and reliably. creator milestones may also require data to be tracked as it changes.
𝗛𝗼𝘄 𝗙𝗲𝗿𝗺𝗮𝗵 𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗺𝗮𝗿𝗸𝗲𝘁 𝗿𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻
Fermah provides proof-generation infrastructure that can execute rules for evaluating outcomes and produce cryptographic proofs that support settlement.
These proofs provide verifiable evidence that the required computation was carried out according to the rules. this can help automate outcome evaluation and reduce reliance on manual review.
There is still an important consideration, though. faster resolution requires reliable information. if the evidence is unclear or an outcome is disputed, the system needs a way to handle that uncertainty.
Prediction markets depend on more than the number of people willing to participate. they also depend on how quickly and reliably their outcomes can be determined.
When outcomes can be evaluated and resolved faster, more types of events may become practical to support, including events that are difficult to manage through slower, manual processes.
@7wealthh
fatty retweeted
Understanding beyond what @InvLambda infrastructure means
𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗿𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝘀𝗽𝗲𝗲𝗱 𝗺𝗮𝘁𝘁𝗲𝗿 𝗳𝗼𝗿 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗺𝗮𝗿𝗸𝗲𝘁𝘀?
Creating a prediction market is one thing. determining the correct outcome after an event happens is another.
Imagine a market asking whether a football team will score in the next 30 seconds.
If it takes hours to confirm the result, the opportunity to use that market in real time may already be gone.
This creates a limit for prediction markets. the time it takes to confirm an outcome affects which events can become markets.
𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗿𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝘄𝗼𝗿𝗸?
Once an event happens, the market needs to establish the outcome before it can be settled.
This involves three main steps:
⇒ Detect the event: Identify when the event happens.
⇒ Check the outcome: Use the relevant information to determine what happened.
⇒ Resolve the market: Confirm the outcome so the market can proceed to settlement.
When people have to review and confirm every result manually, the process can take time and limit how many markets can be handled.
𝗪𝗵𝗮𝘁 𝗰𝗼𝘂𝗹𝗱 𝗳𝗮𝘀𝘁𝗲𝗿 𝗿𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗺𝗮𝗸𝗲 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲?
@fermah_xyz discusses several types of markets that could benefit from automated resolution. here are three examples:
⇒ Short-lived events: Markets based on events that happen within seconds.
⇒ Creator milestones: Markets tied to measurable changes, such as a streamer reaching a certain number of followers.
⇒ Blockchain events: Markets based on activity recorded on the blockchain, including events within individual blocks.
These markets need outcomes to be checked quickly and reliably. creator milestones may also require data to be tracked as it changes.
𝗛𝗼𝘄 𝗙𝗲𝗿𝗺𝗮𝗵 𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗺𝗮𝗿𝗸𝗲𝘁 𝗿𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻
Fermah provides proof-generation infrastructure that can execute rules for evaluating outcomes and produce cryptographic proofs that support settlement.
These proofs provide verifiable evidence that the required computation was carried out according to the rules. this can help automate outcome evaluation and reduce reliance on manual review.
There is still an important consideration, though. faster resolution requires reliable information. if the evidence is unclear or an outcome is disputed, the system needs a way to handle that uncertainty.
Prediction markets depend on more than the number of people willing to participate. they also depend on how quickly and reliably their outcomes can be determined.
When outcomes can be evaluated and resolved faster, more types of events may become practical to support, including events that are difficult to manage through slower, manual processes.
@7wealthh
fatty retweeted
Anoma Pay: Pay-it-Forward
🧵🧵
First of all, I'd briefly tell us what AnomaPay is about.
@AnomaPay is a crypto payment platform built on Anoma that allows users to send, receive, and swap cryptocurrencies privately across supported blockchains
⬇️⬇️
🤝 Paid partnership
fatty retweeted
➤ 𝗛𝗔𝗟𝗢𝗔𝗥𝗘𝗡𝗔: 𝚆𝙷𝙰𝚃 𝙷𝙰𝙿𝙿𝙴𝙽𝚂 𝚆𝙷𝙴𝙽 𝙰𝙸 𝙰𝙶𝙴𝙽𝚃𝚂 𝙲𝙾𝙼𝙿𝙴𝚃𝙴 𝙰𝙶𝙰𝙸𝙽𝚂𝚃 𝙴𝙰𝙲𝙷 𝙾𝚃𝙷𝙴𝚁?
𝗺𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲 𝘀𝘁𝗶𝗹𝗹 𝘂𝘀𝗶𝗻𝗴 𝗮𝗶 𝘁𝗼 𝗮𝗻𝘀𝘄𝗲𝗿 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀. 𝗯𝘂𝘁 𝘄𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝗰𝗿𝗲𝗮𝘁𝗲 𝗮𝗻 𝗮𝗴𝗲𝗻𝘁, 𝗴𝗶𝘃𝗲 𝗶𝘁 𝗮 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆, 𝗮𝗻𝗱 𝘄𝗮𝘁𝗰𝗵 𝗶𝘁 𝗰𝗼𝗺𝗽𝗲𝘁𝗲 𝗶𝗻 𝗮𝗻 𝗮𝗿𝗲𝗻𝗮 𝘄𝗵𝗲𝗿𝗲 𝗶𝘁𝘀 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝗮𝘁𝘁𝗲𝗿?
the next wave of ai could be about more than just conversations.
that’s what makes haloarena interesting to me, and with its waitlist now open, this is a good time to get involved, create your first agent, and explore what’s possible.
that’s one of the reasons i’m paying attention to @HaloArenaHQ.
𝗵𝗮𝗹𝗼𝗮𝗿𝗲𝗻𝗮 𝗶𝘀 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗻 𝗮𝗶-𝗮𝗴𝗲𝗻𝘁 𝗴𝗮𝗺𝗶𝗻𝗴 𝗮𝗿𝗲𝗻𝗮 𝘄𝗵𝗲𝗿𝗲 𝘆𝗼𝘂 𝗰𝗿𝗲𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗮𝗴𝗲𝗻𝘁, 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝗶𝘁 𝘁𝗼 𝗮 𝗿𝘂𝗻𝘁𝗶𝗺𝗲, 𝗰𝗵𝗼𝗼𝘀𝗲 𝗮 𝗴𝗮𝗺𝗲, 𝗮𝗻𝗱 𝗹𝗲𝘁 𝗶𝘁 𝗰𝗼𝗺𝗽𝗲𝘁𝗲 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆. 𝘆𝗼𝘂 𝗰𝗮𝗻 𝘄𝗮𝘁𝗰𝗵 𝗶𝘁𝘀 𝗺𝗮𝘁𝗰𝗵𝗲𝘀, 𝗿𝗲𝘃𝗶𝗲𝘄 𝗶𝘁𝘀 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗶𝘁𝘀 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲.
for haloarena, the waitlist is your entry point into exploring its ai-agent ecosystem. you can sign up, create your own agent, and begin exploring the platform.
for me, joining isn’t just about getting my name on a list. it’s about understanding what haloarena is building, learning how its agents work, and being part of the journey as the platform grows.
here’s how i joined, created my first agent, “𝙩𝙝𝙚𝙞𝙯𝙯𝙮𝙧𝙖𝙚”.👇
1️⃣ 𝗷𝗼𝗶𝗻 𝘁𝗵𝗲 site 𝘄𝗮𝗶𝘁𝗹𝗶𝘀𝘁: haloarena.ai/a/2139 and sign in to get started.
2️⃣ 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗼𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴: follow the platform’s instructions to access your dashboard and claim your initial CP. i received 100 CP after signing up.
3️⃣ 𝗰𝗿𝗲𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗮𝗴𝗲𝗻𝘁: from the dashboard, click new agent, choose a name for your agent, and select your preferred game. this is where you start building your own competitor.
4️⃣ 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘆𝗼𝘂𝗿 𝗮𝗴𝗲𝗻𝘁: choose a supported runtime, such as OpenClaw or Hermes, and follow the setup instructions provided by haloarena. creating the agent is only the first step; connecting it is what allows it to participate.
5️⃣ 𝗴𝗶𝘃𝗲 𝗶𝘁 𝗮 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆: provide clear instructions about how you want your agent to approach decisions. instead of simply telling it to play better, think about how it should respond to different situations.
6️⃣ 𝗮𝗰𝘁𝗶𝘃𝗮𝘁𝗲 𝗮𝗻𝗱 𝘁𝗲𝘀𝘁 𝗶𝘁: finish the connection process, enter a supported game, and watch your agent compete. review its matches, identify weaknesses, and refine its strategy as you go.
i’ve completed my signup, received my 100 CP, and created 𝘁𝗵𝗲𝗶𝘇𝘇𝘆𝗿𝗮𝗲. my next step is to finish the OpenClaw connection and get the agent running.
𝘀𝗼, 𝘄𝗵𝘆 𝗮𝗺 𝗶 𝗯𝘂𝗹𝗹𝗶𝘀𝗵 𝗼𝗻 𝗵𝗮𝗹𝗼𝗮𝗿𝗲𝗻𝗮?
because i see an interesting shift from simply chatting with AI to actually putting ai agents to work in a competitive environment.
the combination of autonomous gameplay, strategy, match replays, leaderboards, and opportunities to improve makes this more interesting than just creating another chatbot.
there’s also room for people to experiment, learn how their agents behave, and discover what works through actual gameplay.
of course, the project is still developing, and nobody can guarantee future rewards or success. but the direction is worth exploring, especially if you’re interested in ai agents and interactive applications.
𝗺𝘆 𝗮𝗱𝘃𝗶𝗰𝗲? 𝗱𝗼𝗻’𝘁 𝗷𝘂𝘀𝘁 𝘄𝗮𝘁𝗰𝗵 𝗼𝘁𝗵𝗲𝗿 𝗽𝗲𝗼𝗽𝗹𝗲 𝗯𝘂𝗶𝗹𝗱. 𝗰𝗿𝗲𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗮𝗴𝗲𝗻𝘁, 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝗶𝘁, 𝗮𝗻𝗱 𝘀𝗲𝗲 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗰𝗮𝗻 𝗱𝗼.
join here through my referral link below
haloarena.ai/a/2139
𝗵𝗮𝘃𝗲 𝘆𝗼𝘂 𝗷𝗼𝗶𝗻𝗲𝗱 𝗵𝗮𝗹𝗼𝗮𝗿𝗲𝗻𝗮 𝘆𝗲𝘁, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗻𝗮𝗺𝗲 𝘄𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗴𝗶𝘃𝗲 𝘆𝗼𝘂𝗿 𝗳𝗶𝗿𝘀𝘁 𝗮𝗶 𝗮𝗴𝗲𝗻𝘁? 🤖
🤝 Paid partnership
fatty retweeted
Big news for Ethereum
@get_optimum has launched mump2p on Ethereum mainnet, helping block data reach nodes 2.5–3.5x faster on average.
Faster block delivery gives validators more time to receive data and vote on the correct chain head, helping reduce missed attestations and improve validator performance.
Over $3.4B in ETH stake is already being boosted by mump2p, and free access is now available to Ethereum validator operators.
A major step in improving how data moves across Ethereum.
The Optimum team is pleased to announce our first mainnet deployment, mump2p on Ethereum. With mump2p, data moves across all of Ethereum 2-3x faster, boosting validator performance and network health.
$3.4B of Ethereum stake is already being boosted by mump2p, and we’re now opening up free access for all Ethereum node operators.
Big news for Ethereum
@get_optimum has launched mump2p on Ethereum mainnet, helping block data reach nodes 2.5–3.5x faster on average.
Faster block delivery gives validators more time to receive data and vote on the correct chain head, helping reduce missed attestations and improve validator performance.
Over $3.4B in ETH stake is already being boosted by mump2p, and free access is now available to Ethereum validator operators.
A major step in improving how data moves across Ethereum.
The Optimum team is pleased to announce our first mainnet deployment, mump2p on Ethereum. With mump2p, data moves across all of Ethereum 2-3x faster, boosting validator performance and network health.
$3.4B of Ethereum stake is already being boosted by mump2p, and we’re now opening up free access for all Ethereum node operators.
fatty retweeted
𝗣𝗿𝗼𝗼𝗳 𝗼𝗳 𝗥𝗲𝘀𝗲𝗿𝘃𝗲𝘀: 𝗗𝗮𝘁𝗮, 𝗣𝗿𝗶𝘃𝗮𝗰𝘆, 𝗮𝗻𝗱 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻
Proof of Reserves (PoR) helps people verify the assets a cryptocurrency exchange or financial institution says it holds. it gives users a way to check reported reserves using verifiable data.
For this to work, the information must come from reliable sources, the calculations must be correct, and private account details must be protected.
@primus_labs uses its data verification and computation (DVC) architecture to verify reserve information from blockchain wallets and off-chain sources, such as centralized exchange accounts.
These reserves fall into two main categories:
⇒ On-chain reserves: assets held in blockchain wallets, their balances can be checked using blockchain data.
⇒ Off-chain reserves: balances held on centralized exchanges, these require data from the relevant platforms.
Since these sources provide different types of data, primus combines several technologies to verify the information, calculate reserve totals, and protect sensitive details.
⇒ zkTLS verifies the source of the data: It provides cryptographic evidence that off-chain data came from a legitimate API endpoint over a trusted TLS connection, without exposing the institution’s API keys or raw account details.
⇒ TEE protects data during processing: a Trusted Execution Environment provides a protected environment where retrieved information can be processed confidentially.
⇒ zkVM handles the computation: the verified data is used to calculate the reserve figures. the zkVM generates a zero-knowledge proof showing that the disclosed reserve value was correctly derived from authentic balance information.
Once the verification process is complete, institutions can publish their reserve information on a public dashboard. primus also supports configurable verification intervals, allowing institutions to choose how often their reserve information is refreshed.
The information made public depends on the verification scope and disclosure settings configured for the institution.
Proof of Reserves focuses on verifying reported assets, but it does not show an institution’s full financial position because its debts and other obligations must also be considered.
An institution may hold assets while also owing money. knowing what it holds without considering what it owes gives an incomplete picture of its finances.
Primus combines data verification and computation to help institutions publish reserve figures that can be checked while keeping sensitive account information private.
fatty retweeted
You know having a crypto in your wallet is one thing.
Knowing how to exchange it when you need another token is another.
@DlicomApp includes an in-app swap feature that allows users to exchange supported cryptocurrencies through their wallet.
In this video, I break down how it works using a simple example.
One important thing to remember; always check the exchange rate, fees, and transaction details before confirming a swap.
In my last post, I explained how Dlicom brings blockchain infrastructure, a self-custodial wallet, and encrypted messaging together to address different aspects of digital security.
Today, I want us to look more closely at one of those features: end-to-end encryption.
Let’s think about the private messages you send every day. You expect those conversations to remain between you and the person you’re talking to. But have you ever wondered how those messages are protected while travelling from one device to another?
This is where end-to-end encryption comes in.
@DlicomApp states that its direct messages use end-to-end encryption. In simple terms, this technology is designed to ensure that only the people communicating can read the contents of their messages.
But here’s something I want you to understand, encryption doesn’t protect you from every possible risk. You still need to be careful with suspicious links, protect your accounts, and think twice before sharing sensitive information.
I believe the bigger lesson is that digital security isn’t about relying on one feature. It’s about understanding how different protections work together and the responsibility we have as users.
𝗣𝗿𝗼𝗼𝗳 𝗼𝗳 𝗥𝗲𝘀𝗲𝗿𝘃𝗲𝘀: 𝗗𝗮𝘁𝗮, 𝗣𝗿𝗶𝘃𝗮𝗰𝘆, 𝗮𝗻𝗱 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻
Proof of Reserves (PoR) helps people verify the assets a cryptocurrency exchange or financial institution says it holds. it gives users a way to check reported reserves using verifiable data.
For this to work, the information must come from reliable sources, the calculations must be correct, and private account details must be protected.
@primus_labs uses its data verification and computation (DVC) architecture to verify reserve information from blockchain wallets and off-chain sources, such as centralized exchange accounts.
These reserves fall into two main categories:
⇒ On-chain reserves: assets held in blockchain wallets, their balances can be checked using blockchain data.
⇒ Off-chain reserves: balances held on centralized exchanges, these require data from the relevant platforms.
Since these sources provide different types of data, primus combines several technologies to verify the information, calculate reserve totals, and protect sensitive details.
⇒ zkTLS verifies the source of the data: It provides cryptographic evidence that off-chain data came from a legitimate API endpoint over a trusted TLS connection, without exposing the institution’s API keys or raw account details.
⇒ TEE protects data during processing: a Trusted Execution Environment provides a protected environment where retrieved information can be processed confidentially.
⇒ zkVM handles the computation: the verified data is used to calculate the reserve figures. the zkVM generates a zero-knowledge proof showing that the disclosed reserve value was correctly derived from authentic balance information.
Once the verification process is complete, institutions can publish their reserve information on a public dashboard. primus also supports configurable verification intervals, allowing institutions to choose how often their reserve information is refreshed.
The information made public depends on the verification scope and disclosure settings configured for the institution.
Proof of Reserves focuses on verifying reported assets, but it does not show an institution’s full financial position because its debts and other obligations must also be considered.
An institution may hold assets while also owing money. knowing what it holds without considering what it owes gives an incomplete picture of its finances.
Primus combines data verification and computation to help institutions publish reserve figures that can be checked while keeping sensitive account information private.
fatty retweeted
𝗣𝗥𝗘𝗗𝗜𝗖𝗧𝗜𝗢𝗡𝗦 𝗕𝗘𝗬𝗢𝗡𝗗 𝗢𝗣𝗜𝗡𝗜𝗢𝗡𝗦: 𝗠𝗬 𝗗𝗔𝗘𝗝𝗜𝗡 𝗝𝗢𝗨𝗥𝗡𝗘𝗬
prediction markets have always been interesting to me because they turn opinions about future events into something people can actually participate in.
but beyond choosing YES or NO, there’s more to understand: how market prices work, how participants share a pool, what liquidity does, and how to create a market with clear rules.
that’s what led me to explore @Daejinxyz x @superteam
i decided to go beyond simply making predictions. i explored an existing crypto market, took a position based on my own reasoning, and created a football market of my own.
in this thread, i’ll share what i’ve learned about daejin, how shared-pool markets work, why i picked NO on solana outperforming bitcoin, and how i created my arsenal vs everton market.
let’s get into it. 🧵👇
fatty retweeted
yesterday at @RialoHQ builders hub, we learned about velora and how it aims to use AI agents to manage digital asset portfolios within rules set by users.
→ velora: the demo showed how agents could respond to market changes, carry out swaps within set limits, and block restricted actions like withdrawals.
gRialo…. see you at the next builders hub on wednesday
yesterday at @RialoHQ builders hub, we learned about velora and how it aims to use AI agents to manage digital asset portfolios within rules set by users.
→ velora: the demo showed how agents could respond to market changes, carry out swaps within set limits, and block restricted actions like withdrawals.
gRialo…. see you at the next builders hub on wednesday
fatty retweeted
Love this hand-drawn art from our Aronaut!
MORNING ARONAUT 🌞
That what makes @AroNetwork interesting?
ARO is working toward a decentralized network that can use resources from everyday devices to support AI and content delivery.
The testnet gives the community a chance to participate, test the network, and share feedback.
Every test, every useful suggestion, and every active contributor can help improve the ecosystem.
Believe $ARO
gAro 🫶 @AbedGodz @Eazyxbt
#ARONetwork #DePIN
fatty retweeted
𝗛𝗼𝘄 𝗽𝗿𝗶𝗺𝘂𝘀 𝘃𝗲𝗿𝗶𝗳𝗶𝗲𝘀 𝗽𝗿𝗶𝘃𝗮𝘁𝗲 𝗱𝗮𝘁𝗮 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝗮 𝘇𝗸𝗩𝗠
Zero-knowledge virtual machines (zkVMs) allow programs to generate cryptographic proofs that their computations followed specific rules.
These proofs can be checked without requiring the verifier to repeat the entire computation.
@primus_labs uses this technology in its 𝗱𝗮𝘁𝗮 𝘃𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻 (DVC) workflow, which combines zkTLS with a zkVM to verify web data and perform computations while limiting the information made public.
𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝘄𝗼𝗿𝗸?
A bank balance can help explain the process. suppose an application needs to confirm that someone’s bank balance exceeds $100 without publicly revealing the exact amount.
The application needs evidence that the account information comes from the claimed source and proof that the balance meets the required condition.
Here is how the process works:
𝟭. 𝗢𝗯𝘁𝗮𝗶𝗻 𝘃𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗹𝗲 𝘄𝗲𝗯 𝗱𝗮𝘁𝗮
Primus zkTLS helps obtain an attestation of data exchanged with a website through a secure HTTPS connection. this attestation provides evidence that can be used to verify the data’s authenticity.
𝟮. 𝗦𝗲𝘁 𝘂𝗽 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻
A program defines the condition to check, such as whether the bank balance exceeds a specified threshold. It also contains the instructions needed to verify the data attestation.
𝟯. 𝗩𝗲𝗿𝗶𝗳𝘆 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮
The program checks the attestation and verifies that the data used in the computation is consistent with the attested information.
This helps ensure that the computation relies on verifiable web data rather than an unsupported claim.
𝟰. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝗮 𝘇𝗲𝗿𝗼-𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗽𝗿𝗼𝗼𝗳
The zkVM executes the program and generates a cryptographic proof that the computation followed the program’s rules.
This allows another party to verify the computation without having to repeat the entire process.
𝟱. 𝗩𝗲𝗿𝗶𝗳𝘆 𝘁𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁
The application checks the proof to determine whether the computation satisfies the required condition.
With a program designed to limit what information is revealed, it can demonstrate that the balance exceeds the specified threshold without making the exact amount public.
𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝗸𝗲 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲?
Applications often need to verify information from external sources before making decisions. however, users may not want to expose all their personal information just to prove that a particular condition is met.
Primus DVC connects verifiable web data with computations that produce cryptographic proofs, allowing applications to verify results while limiting the information they disclose.
The key difference is that zkTLS helps verify the web data, while the zkVM proves that the specified computation followed the program’s rules.
Together, these technologies can support applications that need reliable, verifiable results without requiring users to reveal every detail behind them.
fatty retweeted
𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝗮𝗳𝘁𝗲𝗿 𝘆𝗼𝘂 𝘀𝗰𝗮𝗻 𝗮 𝗿𝗲𝘄𝗮𝗿𝗱 𝘀𝗲𝗮𝗹? 👀
scanning is just the beginning. the interesting part is what comes next.
imagine picking up a product, noticing its reward seal, and deciding to scan it. you might think that’s where the journey ends, but within the zwyp drop experience, it’s part of a bigger process.
here’s how the journey works:
▪️ 1. discover ~> find out about a reward opportunity and what it involves.
▪️ 2. scan ~> scan the reward seal to begin your journey.
▪️ 3. enter ~> follow the process to enter the relevant reward activity.
▪️ 4. participate ~> take part in the activity and engage with the opportunity.
▪️ 5. prove ~> provide the required proof of participation, where applicable.
▪️ 6. claim ~> follow the applicable steps to claim your reward if you qualify.
▪️ 7. return ~> come back and participate again when another opportunity is available.
the part i find interesting is that scanning isn’t necessarily the finish line. it can be the first step toward a more engaging experience, where users discover opportunities, participate, and work toward rewards.
think about it: if you’re already taking the time to scan a reward seal, why not understand the journey behind it too?
that’s one way @zwypa_authentic drop can encourage people to become more familiar with the authentic ecosystem.
🤝 Paid partnership
