fatty retweeted
๐ฉ๐ฒ๐ฟ๐ถ๐ณ๐ถ๐ฎ๐ฏ๐น๐ฒ ๐๐ ๐๐ด๐ฒ๐ป๐ ๐๐
๐ฒ๐ฐ๐๐๐ถ๐ผ๐ป
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.
๐๐ถ๐ต ๐ฉ๐ฐ๐ธ ๐ค๐ข๐ฏ ๐ธ๐ฆ ๐ฌ๐ฏ๐ฐ๐ธ ๐ธ๐ฉ๐ฆ๐ต๐ฉ๐ฆ๐ณ ๐ข๐ฏ ๐ข๐จ๐ฆ๐ฏ๐ต ๐ข๐ค๐ต๐ถ๐ข๐ญ๐ญ๐บ ๐ฅ๐ช๐ฅ ๐ธ๐ฉ๐ข๐ต ๐ช๐ต ๐ธ๐ข๐ด ๐ด๐ถ๐ฑ๐ฑ๐ฐ๐ด๐ฆ๐ฅ ๐ต๐ฐ ๐ฅ๐ฐ?
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:
โ Send a request to an AI model to generate a decision.
โ Query a price oracle to obtain market information.
โ Retrieve data from an external service before taking action.
If these steps are required by the applicationโs rules, there needs to be a way to verify that they actually happened.
Cryptographic proofs can help verify specific API interactions.
๐๐ฉ๐ข๐ต ๐ฎ๐ข๐ฌ๐ฆ๐ด ๐ข๐ฏ ๐ข๐จ๐ฆ๐ฏ๐ตโ๐ด ๐ข๐ค๐ต๐ช๐ฐ๐ฏ๐ด ๐ท๐ฆ๐ณ๐ช๐ง๐ช๐ข๐ฃ๐ญ๐ฆ?
@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:
โ 1. Set the rules: Define which actions the agent must perform, such as checking a price before placing a trade.
โ 2. Record the required interactions: Generate proofs for relevant API requests and responses as the agent carries out its task.
โ 3. Verify the proofs: A verification system checks whether the evidence meets the required conditions.
โ 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 doesnโt automatically mean the agent followed its execution policy. The application must still check the evidence against its rules.
๐ช๐ต๐ ๐ฑ๐ผ๐ฒ๐ ๐๐ต๐ถ๐ ๐บ๐ฎ๐๐๐ฒ๐ฟ?
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 agentโs activity easier to verify. the level of verification depends on how the system is implemented.
๐ฉ๐ฒ๐ฟ๐ถ๐ณ๐ถ๐ฎ๐ฏ๐น๐ฒ ๐๐ ๐๐ด๐ฒ๐ป๐ ๐๐
๐ฒ๐ฐ๐๐๐ถ๐ผ๐ป
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.
๐๐ถ๐ต ๐ฉ๐ฐ๐ธ ๐ค๐ข๐ฏ ๐ธ๐ฆ ๐ฌ๐ฏ๐ฐ๐ธ ๐ธ๐ฉ๐ฆ๐ต๐ฉ๐ฆ๐ณ ๐ข๐ฏ ๐ข๐จ๐ฆ๐ฏ๐ต ๐ข๐ค๐ต๐ถ๐ข๐ญ๐ญ๐บ ๐ฅ๐ช๐ฅ ๐ธ๐ฉ๐ข๐ต ๐ช๐ต ๐ธ๐ข๐ด ๐ด๐ถ๐ฑ๐ฑ๐ฐ๐ด๐ฆ๐ฅ ๐ต๐ฐ ๐ฅ๐ฐ?
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:
โ Send a request to an AI model to generate a decision.
โ Query a price oracle to obtain market information.
โ Retrieve data from an external service before taking action.
If these steps are required by the applicationโs rules, there needs to be a way to verify that they actually happened.
Cryptographic proofs can help verify specific API interactions.
๐๐ฉ๐ข๐ต ๐ฎ๐ข๐ฌ๐ฆ๐ด ๐ข๐ฏ ๐ข๐จ๐ฆ๐ฏ๐ตโ๐ด ๐ข๐ค๐ต๐ช๐ฐ๐ฏ๐ด ๐ท๐ฆ๐ณ๐ช๐ง๐ช๐ข๐ฃ๐ญ๐ฆ?
@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:
โ 1. Set the rules: Define which actions the agent must perform, such as checking a price before placing a trade.
โ 2. Record the required interactions: Generate proofs for relevant API requests and responses as the agent carries out its task.
โ 3. Verify the proofs: A verification system checks whether the evidence meets the required conditions.
โ 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 doesnโt automatically mean the agent followed its execution policy. The application must still check the evidence against its rules.
๐ช๐ต๐ ๐ฑ๐ผ๐ฒ๐ ๐๐ต๐ถ๐ ๐บ๐ฎ๐๐๐ฒ๐ฟ?
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 agentโs activity easier to verify. the level of verification depends on how the system is implemented.
fatty retweeted
๐ช๐ต๐ ๐ฑ๐ผ๐ฒ๐ ๐ฟ๐ฒ๐๐ผ๐น๐๐๐ถ๐ผ๐ป ๐๐ฝ๐ฒ๐ฒ๐ฑ ๐บ๐ฎ๐๐๐ฒ๐ฟ ๐ณ๐ผ๐ฟ ๐ฝ๐ฟ๐ฒ๐ฑ๐ถ๐ฐ๐๐ถ๐ผ๐ป ๐บ๐ฎ๐ฟ๐ธ๐ฒ๐๐?
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.
๐๐ผ๐ ๐ฑ๐ผ๐ฒ๐ ๐ฟ๐ฒ๐๐ผ๐น๐๐๐ถ๐ผ๐ป ๐๐ผ๐ฟ๐ธ?
Once an event happens, the market needs to establish the outcome before it can be settled.
This involves three main steps:
โ Detect the event: Identify when the event happens.
โ Check the outcome: Use the relevant information to determine what happened.
โ 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.
๐ช๐ต๐ฎ๐ ๐ฐ๐ผ๐๐น๐ฑ ๐ณ๐ฎ๐๐๐ฒ๐ฟ ๐ฟ๐ฒ๐๐ผ๐น๐๐๐ถ๐ผ๐ป ๐บ๐ฎ๐ธ๐ฒ ๐ฝ๐ผ๐๐๐ถ๐ฏ๐น๐ฒ?
@fermah_xyz discusses several types of markets that could benefit from automated resolution. here are three examples:
โ Short-lived events: Markets based on events that happen within seconds.
โ Creator milestones: Markets tied to measurable changes, such as a streamer reaching a certain number of followers.
โ 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.
๐๐ผ๐ ๐๐ฒ๐ฟ๐บ๐ฎ๐ต ๐๐๐ฝ๐ฝ๐ผ๐ฟ๐๐ ๐ฝ๐ฟ๐ฒ๐ฑ๐ถ๐ฐ๐๐ถ๐ผ๐ป ๐บ๐ฎ๐ฟ๐ธ๐ฒ๐ ๐ฟ๐ฒ๐๐ผ๐น๐๐๐ถ๐ผ๐ป
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
๐ช๐ต๐ ๐ฑ๐ผ๐ฒ๐ ๐ฟ๐ฒ๐๐ผ๐น๐๐๐ถ๐ผ๐ป ๐๐ฝ๐ฒ๐ฒ๐ฑ ๐บ๐ฎ๐๐๐ฒ๐ฟ ๐ณ๐ผ๐ฟ ๐ฝ๐ฟ๐ฒ๐ฑ๐ถ๐ฐ๐๐ถ๐ผ๐ป ๐บ๐ฎ๐ฟ๐ธ๐ฒ๐๐?
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.
๐๐ผ๐ ๐ฑ๐ผ๐ฒ๐ ๐ฟ๐ฒ๐๐ผ๐น๐๐๐ถ๐ผ๐ป ๐๐ผ๐ฟ๐ธ?
Once an event happens, the market needs to establish the outcome before it can be settled.
This involves three main steps:
โ Detect the event: Identify when the event happens.
โ Check the outcome: Use the relevant information to determine what happened.
โ 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.
๐ช๐ต๐ฎ๐ ๐ฐ๐ผ๐๐น๐ฑ ๐ณ๐ฎ๐๐๐ฒ๐ฟ ๐ฟ๐ฒ๐๐ผ๐น๐๐๐ถ๐ผ๐ป ๐บ๐ฎ๐ธ๐ฒ ๐ฝ๐ผ๐๐๐ถ๐ฏ๐น๐ฒ?
@fermah_xyz discusses several types of markets that could benefit from automated resolution. here are three examples:
โ Short-lived events: Markets based on events that happen within seconds.
โ Creator milestones: Markets tied to measurable changes, such as a streamer reaching a certain number of followers.
โ 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.
๐๐ผ๐ ๐๐ฒ๐ฟ๐บ๐ฎ๐ต ๐๐๐ฝ๐ฝ๐ผ๐ฟ๐๐ ๐ฝ๐ฟ๐ฒ๐ฑ๐ถ๐ฐ๐๐ถ๐ผ๐ป ๐บ๐ฎ๐ฟ๐ธ๐ฒ๐ ๐ฟ๐ฒ๐๐ผ๐น๐๐๐ถ๐ผ๐ป
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
๐งต๐งต
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
โฌ๏ธโฌ๏ธ
๐ค Paid partnership
fatty retweeted
โค ๐๐๐๐ข๐๐ฅ๐๐ก๐: ๐๐ท๐ฐ๐ ๐ท๐ฐ๐ฟ๐ฟ๐ด๐ฝ๐ ๐๐ท๐ด๐ฝ ๐ฐ๐ธ ๐ฐ๐ถ๐ด๐ฝ๐๐ ๐ฒ๐พ๐ผ๐ฟ๐ด๐๐ด ๐ฐ๐ถ๐ฐ๐ธ๐ฝ๐๐ ๐ด๐ฐ๐ฒ๐ท ๐พ๐๐ท๐ด๐?
๐บ๐ผ๐๐ ๐ฝ๐ฒ๐ผ๐ฝ๐น๐ฒ ๐ฎ๐ฟ๐ฒ ๐๐๐ถ๐น๐น ๐๐๐ถ๐ป๐ด ๐ฎ๐ถ ๐๐ผ ๐ฎ๐ป๐๐๐ฒ๐ฟ ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป๐. ๐ฏ๐๐ ๐๐ต๐ฎ๐ ๐ถ๐ณ ๐๐ผ๐ ๐ฐ๐ผ๐๐น๐ฑ ๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐ฎ๐ป ๐ฎ๐ด๐ฒ๐ป๐, ๐ด๐ถ๐๐ฒ ๐ถ๐ ๐ฎ ๐๐๐ฟ๐ฎ๐๐ฒ๐ด๐, ๐ฎ๐ป๐ฑ ๐๐ฎ๐๐ฐ๐ต ๐ถ๐ ๐ฐ๐ผ๐บ๐ฝ๐ฒ๐๐ฒ ๐ถ๐ป ๐ฎ๐ป ๐ฎ๐ฟ๐ฒ๐ป๐ฎ ๐๐ต๐ฒ๐ฟ๐ฒ ๐ถ๐๐ ๐ฑ๐ฒ๐ฐ๐ถ๐๐ถ๐ผ๐ป๐ ๐ฎ๐ฐ๐๐๐ฎ๐น๐น๐ ๐บ๐ฎ๐๐๐ฒ๐ฟ?
the next wave of ai could be about more than just conversations.
thatโs 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 whatโs possible.
thatโs one of the reasons iโm paying attention to @HaloArenaHQ.
๐ต๐ฎ๐น๐ผ๐ฎ๐ฟ๐ฒ๐ป๐ฎ ๐ถ๐ ๐ฏ๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐ฎ๐ป ๐ฎ๐ถ-๐ฎ๐ด๐ฒ๐ป๐ ๐ด๐ฎ๐บ๐ถ๐ป๐ด ๐ฎ๐ฟ๐ฒ๐ป๐ฎ ๐๐ต๐ฒ๐ฟ๐ฒ ๐๐ผ๐ ๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐๐ผ๐๐ฟ ๐ฎ๐ด๐ฒ๐ป๐, ๐ฐ๐ผ๐ป๐ป๐ฒ๐ฐ๐ ๐ถ๐ ๐๐ผ ๐ฎ ๐ฟ๐๐ป๐๐ถ๐บ๐ฒ, ๐ฐ๐ต๐ผ๐ผ๐๐ฒ ๐ฎ ๐ด๐ฎ๐บ๐ฒ, ๐ฎ๐ป๐ฑ ๐น๐ฒ๐ ๐ถ๐ ๐ฐ๐ผ๐บ๐ฝ๐ฒ๐๐ฒ ๐ฎ๐๐๐ผ๐ป๐ผ๐บ๐ผ๐๐๐น๐. ๐๐ผ๐ ๐ฐ๐ฎ๐ป ๐๐ฎ๐๐ฐ๐ต ๐ถ๐๐ ๐บ๐ฎ๐๐ฐ๐ต๐ฒ๐, ๐ฟ๐ฒ๐๐ถ๐ฒ๐ ๐ถ๐๐ ๐ฝ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ, ๐ฎ๐ป๐ฑ ๐ถ๐บ๐ฝ๐ฟ๐ผ๐๐ฒ ๐ถ๐๐ ๐๐๐ฟ๐ฎ๐๐ฒ๐ด๐ ๐ผ๐๐ฒ๐ฟ ๐๐ถ๐บ๐ฒ.
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 isnโt just about getting my name on a list. itโs about understanding what haloarena is building, learning how its agents work, and being part of the journey as the platform grows.
hereโs how i joined, created my first agent, โ๐ฉ๐๐๐๐ฏ๐ฏ๐ฎ๐ง๐๐โ.๐
1๏ธโฃ ๐ท๐ผ๐ถ๐ป ๐๐ต๐ฒ site ๐๐ฎ๐ถ๐๐น๐ถ๐๐: haloarena.ai/a/2139 and sign in to get started.
2๏ธโฃ ๐ฐ๐ผ๐บ๐ฝ๐น๐ฒ๐๐ฒ ๐ผ๐ป๐ฏ๐ผ๐ฎ๐ฟ๐ฑ๐ถ๐ป๐ด: follow the platformโs instructions to access your dashboard and claim your initial CP. i received 100 CP after signing up.
3๏ธโฃ ๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐๐ผ๐๐ฟ ๐ฎ๐ด๐ฒ๐ป๐: 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.
4๏ธโฃ ๐ฐ๐ผ๐ป๐ป๐ฒ๐ฐ๐ ๐๐ผ๐๐ฟ ๐ฎ๐ด๐ฒ๐ป๐: 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.
5๏ธโฃ ๐ด๐ถ๐๐ฒ ๐ถ๐ ๐ฎ ๐๐๐ฟ๐ฎ๐๐ฒ๐ด๐: 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.
6๏ธโฃ ๐ฎ๐ฐ๐๐ถ๐๐ฎ๐๐ฒ ๐ฎ๐ป๐ฑ ๐๐ฒ๐๐ ๐ถ๐: 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.
iโve completed my signup, received my 100 CP, and created ๐๐ต๐ฒ๐ถ๐๐๐๐ฟ๐ฎ๐ฒ. my next step is to finish the OpenClaw connection and get the agent running.
๐๐ผ, ๐๐ต๐ ๐ฎ๐บ ๐ถ ๐ฏ๐๐น๐น๐ถ๐๐ต ๐ผ๐ป ๐ต๐ฎ๐น๐ผ๐ฎ๐ฟ๐ฒ๐ป๐ฎ?
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.
thereโs 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 youโre interested in ai agents and interactive applications.
๐บ๐ ๐ฎ๐ฑ๐๐ถ๐ฐ๐ฒ? ๐ฑ๐ผ๐ปโ๐ ๐ท๐๐๐ ๐๐ฎ๐๐ฐ๐ต ๐ผ๐๐ต๐ฒ๐ฟ ๐ฝ๐ฒ๐ผ๐ฝ๐น๐ฒ ๐ฏ๐๐ถ๐น๐ฑ. ๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐๐ผ๐๐ฟ ๐ผ๐๐ป ๐ฎ๐ด๐ฒ๐ป๐, ๐ฐ๐ผ๐ป๐ป๐ฒ๐ฐ๐ ๐ถ๐, ๐ฎ๐ป๐ฑ ๐๐ฒ๐ฒ ๐๐ต๐ฎ๐ ๐ถ๐ ๐ฐ๐ฎ๐ป ๐ฑ๐ผ.
join here through my referral link below
haloarena.ai/a/2139
๐ต๐ฎ๐๐ฒ ๐๐ผ๐ ๐ท๐ผ๐ถ๐ป๐ฒ๐ฑ ๐ต๐ฎ๐น๐ผ๐ฎ๐ฟ๐ฒ๐ป๐ฎ ๐๐ฒ๐, ๐ฎ๐ป๐ฑ ๐๐ต๐ฎ๐ ๐ป๐ฎ๐บ๐ฒ ๐๐ผ๐๐น๐ฑ ๐๐ผ๐ ๐ด๐ถ๐๐ฒ ๐๐ผ๐๐ฟ ๐ณ๐ถ๐ฟ๐๐ ๐ฎ๐ถ ๐ฎ๐ด๐ฒ๐ป๐? ๐ค
๐ค Paid partnership
fatty retweeted
Big news for Ethereum
@get_optimum has launched mump2p on Ethereum mainnet, helping block data reach nodes 2.5โ3.5x 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 weโre 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 2.5โ3.5x 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 weโre now opening up free access for all Ethereum node operators.
fatty retweeted
๐ฃ๐ฟ๐ผ๐ผ๐ณ ๐ผ๐ณ ๐ฅ๐ฒ๐๐ฒ๐ฟ๐๐ฒ๐: ๐๐ฎ๐๐ฎ, ๐ฃ๐ฟ๐ถ๐๐ฎ๐ฐ๐, ๐ฎ๐ป๐ฑ ๐ฉ๐ฒ๐ฟ๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป
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:
โ On-chain reserves: assets held in blockchain wallets, their balances can be checked using blockchain data.
โ 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.
โ 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 institutionโs API keys or raw account details.
โ TEE protects data during processing: a Trusted Execution Environment provides a protected environment where retrieved information can be processed confidentially.
โ 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 institutionโs 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.
Letโs think about the private messages you send every day. You expect those conversations to remain between you and the person youโre 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 hereโs something I want you to understand, encryption doesnโt 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 isnโt about relying on one feature. Itโs about understanding how different protections work together and the responsibility we have as users.
๐ฃ๐ฟ๐ผ๐ผ๐ณ ๐ผ๐ณ ๐ฅ๐ฒ๐๐ฒ๐ฟ๐๐ฒ๐: ๐๐ฎ๐๐ฎ, ๐ฃ๐ฟ๐ถ๐๐ฎ๐ฐ๐, ๐ฎ๐ป๐ฑ ๐ฉ๐ฒ๐ฟ๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป
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:
โ On-chain reserves: assets held in blockchain wallets, their balances can be checked using blockchain data.
โ 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.
โ 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 institutionโs API keys or raw account details.
โ TEE protects data during processing: a Trusted Execution Environment provides a protected environment where retrieved information can be processed confidentially.
โ 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 institutionโs 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
๐ฃ๐ฅ๐๐๐๐๐ง๐๐ข๐ก๐ฆ ๐๐๐ฌ๐ข๐ก๐ ๐ข๐ฃ๐๐ก๐๐ข๐ก๐ฆ: ๐ ๐ฌ ๐๐๐๐๐๐ก ๐๐ข๐จ๐ฅ๐ก๐๐ฌ
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, thereโs more to understand: how market prices work, how participants share a pool, what liquidity does, and how to create a market with clear rules.
thatโs 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, iโll share what iโve 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.
letโs get into it. ๐งต๐
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.
โ velora: the demo showed how agents could respond to market changes, carry out swaps within set limits, and block restricted actions like withdrawals.
gRialoโฆ. 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.
โ velora: the demo showed how agents could respond to market changes, carry out swaps within set limits, and block restricted actions like withdrawals.
gRialoโฆ. see you at the next builders hub on wednesday
fatty retweeted
Love this hand-drawn art from our Aronaut!
MORNING ARONAUT ๐
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 ๐ซถ @AbedGodz @Eazyxbt
#ARONetwork #DePIN
fatty retweeted
๐๐ผ๐ ๐ฝ๐ฟ๐ถ๐บ๐๐ ๐๐ฒ๐ฟ๐ถ๐ณ๐ถ๐ฒ๐ ๐ฝ๐ฟ๐ถ๐๐ฎ๐๐ฒ ๐ฑ๐ฎ๐๐ฎ ๐ฐ๐ผ๐บ๐ฝ๐๐๐ฎ๐๐ถ๐ผ๐ป๐ ๐๐ถ๐๐ต ๐ฎ ๐๐ธ๐ฉ๐
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 ๐ฑ๐ฎ๐๐ฎ ๐๐ฒ๐ฟ๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ฎ๐ป๐ฑ ๐ฐ๐ผ๐บ๐ฝ๐๐๐ฎ๐๐ถ๐ผ๐ป (DVC) workflow, which combines zkTLS with a zkVM to verify web data and perform computations while limiting the information made public.
๐๐ผ๐ ๐ฑ๐ผ๐ฒ๐ ๐๐ต๐ถ๐ ๐๐ผ๐ฟ๐ธ?
A bank balance can help explain the process. suppose an application needs to confirm that someoneโs 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:
๐ญ. ๐ข๐ฏ๐๐ฎ๐ถ๐ป ๐๐ฒ๐ฟ๐ถ๐ณ๐ถ๐ฎ๐ฏ๐น๐ฒ ๐๐ฒ๐ฏ ๐ฑ๐ฎ๐๐ฎ
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 dataโs authenticity.
๐ฎ. ๐ฆ๐ฒ๐ ๐๐ฝ ๐๐ต๐ฒ ๐ฐ๐ผ๐บ๐ฝ๐๐๐ฎ๐๐ถ๐ผ๐ป
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.
๐ฏ. ๐ฉ๐ฒ๐ฟ๐ถ๐ณ๐ ๐๐ต๐ฒ ๐ฑ๐ฎ๐๐ฎ
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.
๐ฐ. ๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ฒ ๐ฎ ๐๐ฒ๐ฟ๐ผ-๐ธ๐ป๐ผ๐๐น๐ฒ๐ฑ๐ด๐ฒ ๐ฝ๐ฟ๐ผ๐ผ๐ณ
The zkVM executes the program and generates a cryptographic proof that the computation followed the programโs rules.
This allows another party to verify the computation without having to repeat the entire process.
๐ฑ. ๐ฉ๐ฒ๐ฟ๐ถ๐ณ๐ ๐๐ต๐ฒ ๐ฟ๐ฒ๐๐๐น๐
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.
๐ช๐ต๐ฎ๐ ๐ฑ๐ผ๐ฒ๐ ๐๐ต๐ถ๐ ๐บ๐ฎ๐ธ๐ฒ ๐ฝ๐ผ๐๐๐ถ๐ฏ๐น๐ฒ?
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 programโs rules.
Together, these technologies can support applications that need reliable, verifiable results without requiring users to reveal every detail behind them.
fatty retweeted
๐๐ต๐ฎ๐ ๐ต๐ฎ๐ฝ๐ฝ๐ฒ๐ป๐ ๐ฎ๐ณ๐๐ฒ๐ฟ ๐๐ผ๐ ๐๐ฐ๐ฎ๐ป ๐ฎ ๐ฟ๐ฒ๐๐ฎ๐ฟ๐ฑ ๐๐ฒ๐ฎ๐น? ๐
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 thatโs where the journey ends, but within the zwyp drop experience, itโs part of a bigger process.
hereโs how the journey works:
โช๏ธ 1. discover ~> find out about a reward opportunity and what it involves.
โช๏ธ 2. scan ~> scan the reward seal to begin your journey.
โช๏ธ 3. enter ~> follow the process to enter the relevant reward activity.
โช๏ธ 4. participate ~> take part in the activity and engage with the opportunity.
โช๏ธ 5. prove ~> provide the required proof of participation, where applicable.
โช๏ธ 6. claim ~> follow the applicable steps to claim your reward if you qualify.
โช๏ธ 7. return ~> come back and participate again when another opportunity is available.
the part i find interesting is that scanning isnโt 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 youโre already taking the time to scan a reward seal, why not understand the journey behind it too?
thatโs one way @zwypa_authentic drop can encourage people to become more familiar with the authentic ecosystem.
๐ค Paid partnership
๐๐ผ๐ ๐ฝ๐ฟ๐ถ๐บ๐๐ ๐๐ฒ๐ฟ๐ถ๐ณ๐ถ๐ฒ๐ ๐ฝ๐ฟ๐ถ๐๐ฎ๐๐ฒ ๐ฑ๐ฎ๐๐ฎ ๐ฐ๐ผ๐บ๐ฝ๐๐๐ฎ๐๐ถ๐ผ๐ป๐ ๐๐ถ๐๐ต ๐ฎ ๐๐ธ๐ฉ๐
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 ๐ฑ๐ฎ๐๐ฎ ๐๐ฒ๐ฟ๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ฎ๐ป๐ฑ ๐ฐ๐ผ๐บ๐ฝ๐๐๐ฎ๐๐ถ๐ผ๐ป (DVC) workflow, which combines zkTLS with a zkVM to verify web data and perform computations while limiting the information made public.
๐๐ผ๐ ๐ฑ๐ผ๐ฒ๐ ๐๐ต๐ถ๐ ๐๐ผ๐ฟ๐ธ?
A bank balance can help explain the process. suppose an application needs to confirm that someoneโs 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:
๐ญ. ๐ข๐ฏ๐๐ฎ๐ถ๐ป ๐๐ฒ๐ฟ๐ถ๐ณ๐ถ๐ฎ๐ฏ๐น๐ฒ ๐๐ฒ๐ฏ ๐ฑ๐ฎ๐๐ฎ
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 dataโs authenticity.
๐ฎ. ๐ฆ๐ฒ๐ ๐๐ฝ ๐๐ต๐ฒ ๐ฐ๐ผ๐บ๐ฝ๐๐๐ฎ๐๐ถ๐ผ๐ป
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.
๐ฏ. ๐ฉ๐ฒ๐ฟ๐ถ๐ณ๐ ๐๐ต๐ฒ ๐ฑ๐ฎ๐๐ฎ
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.
๐ฐ. ๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ฒ ๐ฎ ๐๐ฒ๐ฟ๐ผ-๐ธ๐ป๐ผ๐๐น๐ฒ๐ฑ๐ด๐ฒ ๐ฝ๐ฟ๐ผ๐ผ๐ณ
The zkVM executes the program and generates a cryptographic proof that the computation followed the programโs rules.
This allows another party to verify the computation without having to repeat the entire process.
๐ฑ. ๐ฉ๐ฒ๐ฟ๐ถ๐ณ๐ ๐๐ต๐ฒ ๐ฟ๐ฒ๐๐๐น๐
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.
๐ช๐ต๐ฎ๐ ๐ฑ๐ผ๐ฒ๐ ๐๐ต๐ถ๐ ๐บ๐ฎ๐ธ๐ฒ ๐ฝ๐ผ๐๐๐ถ๐ฏ๐น๐ฒ?
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 programโs rules.
Together, these technologies can support applications that need reliable, verifiable results without requiring users to reveal every detail behind them.
fatty retweeted
๐๐ผ๐ ๐ฝ๐ฟ๐ถ๐บ๐๐ ๐ฎ๐ฒ๐ด๐ถ๐ ๐ฒ๐ป๐ฎ๐ฏ๐น๐ฒ๐ ๐ฐ๐ผ๐บ๐ฝ๐๐๐ฎ๐๐ถ๐ผ๐ป ๐ผ๐ป ๐ฒ๐ป๐ฐ๐ฟ๐๐ฝ๐๐ฒ๐ฑ ๐ฑ๐ฎ๐๐ฎ
Keeping data private while still allowing it to be used for computation is an important part of data security.
๐๐ฉ๐ข๐ต ๐๐ด ๐ง๐ถ๐ญ๐ญ๐บ ๐ฉ๐ฐ๐ฎ๐ฐ๐ฎ๐ฐ๐ณ๐ฑ๐ฉ๐ช๐ค ๐ฆ๐ฏ๐ค๐ณ๐บ๐ฑ๐ต๐ช๐ฐ๐ฏ (๐๐๐)?
Fully Homomorphic Encryption (FHE) allows computations to be performed directly on encrypted data.
This means a computing service can perform supported operations without first decrypting the original inputs.
The result can then be decrypted by someone with the appropriate key.
๐๐ป๐๐ฟ๐ผ๐ฑ๐๐ฐ๐ถ๐ป๐ด ๐ฝ๐ฟ๐ถ๐บ๐๐ ๐ฎ๐ฒ๐ด๐ถ๐
@primus_labs introduced ๐ฎ๐ฒ๐ด๐ถ๐, an open-source framework designed to make FHE easier to use.
It provides tools for compiling programs and executing computations on encrypted data.
The repository demonstrates two ways to use Aegis:
โ ๐ฃ๐๐๐ต๐ผ๐ป ๐ฝ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐: functions can be defined and compiled into programs suitable for FHE execution.
โ ๐ข๐ก๐ก๐ซ ๐บ๐ผ๐ฑ๐ฒ๐น๐: supported machine learning models represented in ONNX format can be compiled for encrypted computation.
ONNX is a standard format for representing machine learning models, making it easier to use models created with different machine learning frameworks.
๐๐ผ๐ ๐ฑ๐ผ๐ฒ๐ ๐ถ๐ ๐๐ผ๐ฟ๐ธ?
Imagine a service needs to add two private numbers without seeing their original values.
Here is how the process works:
โ ๐๐ผ๐บ๐ฝ๐ถ๐น๐ฒ: The function is compiled into a program that supports FHE execution.
โ ๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ฒ ๐ธ๐ฒ๐๐ ๐ฎ๐ป๐ฑ ๐ฒ๐ป๐ฐ๐ฟ๐๐ฝ๐: The client generates the necessary keys and encrypts the input data.
โ ๐๐ผ๐บ๐ฝ๐๐๐ฒ: The server performs the supported operation on the encrypted inputs.
โ ๐๐ฒ๐ฐ๐ฟ๐๐ฝ๐: The client decrypts the returned result using the appropriate key.
The server can perform the computation without accessing the original inputs in readable form.
๐ช๐ต๐ฒ๐ฟ๐ฒ ๐ฐ๐ฎ๐ป ๐ถ๐ ๐ฏ๐ฒ ๐๐๐ฒ๐ฑ?
FHE can be useful when applications need to process sensitive information while limiting the computing serviceโs access to the original data.
Potential applications include machine learning inference and data analysis involving private information.
๐๐ถ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป๐ ๐๐ผ ๐ฐ๐ผ๐ป๐๐ถ๐ฑ๐ฒ๐ฟ
โ ๐๐ผ๐บ๐ฝ๐๐๐ถ๐ป๐ด ๐ฐ๐ผ๐๐: FHE can require more computational resources than ordinary computation on unencrypted data.
โ ๐ฆ๐๐ฝ๐ฝ๐ผ๐ฟ๐๐ฒ๐ฑ ๐ผ๐ฝ๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป๐: Not every program or machine learning model is compatible with a given FHE implementation.
โ ๐ฃ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ: Encrypted computations can take longer than equivalent operations on unencrypted data.
โ ๐ข๐๐ฒ๐ฟ๐ฎ๐น๐น ๐ฝ๐ฟ๐ถ๐๐ฎ๐ฐ๐: FHE protects data during supported computations, but the wider system must also handle encryption keys, outputs, and other information appropriately.
๐๐ผ๐ป๐ฐ๐น๐๐๐ถ๐ผ๐ป
Primus aegis provides tools for compiling Python functions and supported ONNX models for fully homomorphic encryption.
It offers a way to build applications that perform supported computations on encrypted data without requiring the computing service to see the original inputs in readable form.
