714597
Zo
@zoapp #714597
Zo is the OS for Human to Agent coordination
Zo is a group chat app and protocol powered by AI
155 Follower 91 Following
AMA Announcement 📢
We're gearing up for the Zo x Farcaster Launch Party, and to kick things off, we're hosting an exciting AMA session on "Social Agents: The Future of Web3 Social" with our special guest @awkquarian.eth ✨
🗓️ Date & Time: 15th October, 3:30 PM UTC
Join us as we dive into how AI agents are reshaping social interactions and crypto research in Web3, all while celebrating the upcoming launch of Zo's Farcaster Frame! 🎉
Mark your calendars, and don't miss out on this insightful conversation and the launch festivities! ⬇️
Set a Reminder 🔔
https://x.com/i/spaces/1DXxydVQEnnJM
We're gearing up for the Zo x Farcaster Launch Party, and to kick things off, we're hosting an exciting AMA session on "Social Agents: The Future of Web3 Social" with our special guest @awkquarian.eth ✨
🗓️ Date & Time: 15th October, 3:30 PM UTC
Join us as we dive into how AI agents are reshaping social interactions and crypto research in Web3, all while celebrating the upcoming launch of Zo's Farcaster Frame! 🎉
Mark your calendars, and don't miss out on this insightful conversation and the launch festivities! ⬇️
Set a Reminder 🔔
https://x.com/i/spaces/1DXxydVQEnnJM
AMA Announcement 📢
We're gearing up for the Zo x Farcaster Launch Party, and to kick things off, we're hosting an exciting AMA session on "Gamification in Web3: How to drive user engagement through meaningful incentives" with our special guest Tony from @catoffgaming 🚀
🗓️ Date & Time: 11th October, 3:30 PM UTC
Join us as we dive into how AI agents are reshaping social interactions and crypto research in Web3, all while celebrating the upcoming launch of Zo's Farcaster Frame! 🎉
Mark your calendars, and don't miss out on this insightful conversation and the launch festivities!
Set a Reminder ⬇️
#ZoAMA #ZoFarcasterLaunch #Web3Social #AIxWeb3
We're gearing up for the Zo x Farcaster Launch Party, and to kick things off, we're hosting an exciting AMA session on "Gamification in Web3: How to drive user engagement through meaningful incentives" with our special guest Tony from @catoffgaming 🚀
🗓️ Date & Time: 11th October, 3:30 PM UTC
Join us as we dive into how AI agents are reshaping social interactions and crypto research in Web3, all while celebrating the upcoming launch of Zo's Farcaster Frame! 🎉
Mark your calendars, and don't miss out on this insightful conversation and the launch festivities!
Set a Reminder ⬇️
#ZoAMA #ZoFarcasterLaunch #Web3Social #AIxWeb3
Zo September Recap: Key Highlights 🧵
A month filled with exciting product updates, partnerships, community engagements, insightful discussions, and more. Breaking it down to catch you up on them:
Check out the full details on X 👇
https://x.com/joinzo/status/1844292506487488901?s=46
A month filled with exciting product updates, partnerships, community engagements, insightful discussions, and more. Breaking it down to catch you up on them:
Check out the full details on X 👇
https://x.com/joinzo/status/1844292506487488901?s=46
CRDTs Part 2: Here’s how CRDTs can benefit and support federated on-chain multi-agent systems, something that @joinzo is based on.
Let's define CRDTs:
CRDTs (Conflict-Free Replicated Data Types) are a class of data structures that enable efficient, decentralized data replication across multiple nodes or systems, ensuring eventual consistency without the need for coordination or locking mechanisms.
The correlation between how CRDTs and federated on-chain multi-agent systems👇🏻
Let's define CRDTs:
CRDTs (Conflict-Free Replicated Data Types) are a class of data structures that enable efficient, decentralized data replication across multiple nodes or systems, ensuring eventual consistency without the need for coordination or locking mechanisms.
The correlation between how CRDTs and federated on-chain multi-agent systems👇🏻
Looks like someone’s trying really hard to make us believe they're not Satoshi! 👀
Ask him yourself 👉🏻https://zo.me/zo-iamsatoshicqfm & share your chat(ss) in the comments.
Ask him yourself 👉🏻https://zo.me/zo-iamsatoshicqfm & share your chat(ss) in the comments.
In mood for some snag? Grabs yours👇 https://x.com/joinzo/status/1843314807073894755?s=46
50k and going🚀
🎉 Zo is officially 50K downloads strong 🎉
To our incredible Zo community—THANK YOU! 🙌🏻 This milestone wouldn't be possible without our amazing users and early supporters.
We're just getting started, and to celebrate, we're giving back! Join the #ZovereignStreak campaign and snag exclusive Zo merch by completing a few simple social tasks.
Link in the next tweet🔗👇🏻
Let's keep building together! #Zo50k #ZoCommunity #Zovereign
To our incredible Zo community—THANK YOU! 🙌🏻 This milestone wouldn't be possible without our amazing users and early supporters.
We're just getting started, and to celebrate, we're giving back! Join the #ZovereignStreak campaign and snag exclusive Zo merch by completing a few simple social tasks.
Link in the next tweet🔗👇🏻
Let's keep building together! #Zo50k #ZoCommunity #Zovereign
We are giving away some merch to our Zo fam! Check us out👇🏻 https://x.com/joinzo/status/1843314807073894755
🇪🇸 Hola familia Zo! El soporte en español está disponible para los chatbots > Spanish support is live.
Try now at http://app.zo.me
Try now at http://app.zo.me
The bounty is live! Join in🚀
Join the Zo x Farcaster Launch 🚀
Zo: An OS for human to agent coordination, will be launching it's farcaster frame that lets you clone your farcaster profile as an agent on the Zo app. We'll be launching the frame on 1st November and as part of the launch, we want to celebrate the pre-launch month with the Warpcast family with a bunch of bounties. This one being the first👇🏻
Task:
1. Follow /zoapp 2. Click notify icon 🔔 for launch updates.
3. Bonus task: Recast pinned cast, link: warpcast.com/zoapp/0xa7ef...
Note: Completing the bonus task increases your chances of winning.
Reward:
ONE lucky winner will receive 1000 $DEGEN! 💰
Get ready, participate, and this could be YOUR chance to win! 🚀
Winner announcement: 15th October, 2024
@bountybot @bountycaster
Zo: An OS for human to agent coordination, will be launching it's farcaster frame that lets you clone your farcaster profile as an agent on the Zo app. We'll be launching the frame on 1st November and as part of the launch, we want to celebrate the pre-launch month with the Warpcast family with a bunch of bounties. This one being the first👇🏻
Task:
1. Follow /zoapp 2. Click notify icon 🔔 for launch updates.
3. Bonus task: Recast pinned cast, link: warpcast.com/zoapp/0xa7ef...
Note: Completing the bonus task increases your chances of winning.
Reward:
ONE lucky winner will receive 1000 $DEGEN! 💰
Get ready, participate, and this could be YOUR chance to win! 🚀
Winner announcement: 15th October, 2024
@bountybot @bountycaster
(1/3)Will AI agents run the show? Welcome to the world of AI DAOs. The rise and how it shapes the collective intelligence. A thread
It's a fascinating convergence of crypto and AI, with potential to reshape how we approach complex problem-solving. Let's dive in.
AI DAOs leverage the power of swarm intelligence - multiple AI agents working together to make decisions and solve problems that would be challenging for individual agents. Think of it as collective AI wisdom.
These systems use multi-agent communication to tackle complex issues. It's not just about individual AI capability, but how they interact and combine their strengths to find solutions.
Machine-to-machine (M2M) communication is at the heart of this. It's accelerating AI evolution and adaptation at an unprecedented pace, opening new possibilities for autonomous systems.
It's a fascinating convergence of crypto and AI, with potential to reshape how we approach complex problem-solving. Let's dive in.
AI DAOs leverage the power of swarm intelligence - multiple AI agents working together to make decisions and solve problems that would be challenging for individual agents. Think of it as collective AI wisdom.
These systems use multi-agent communication to tackle complex issues. It's not just about individual AI capability, but how they interact and combine their strengths to find solutions.
Machine-to-machine (M2M) communication is at the heart of this. It's accelerating AI evolution and adaptation at an unprecedented pace, opening new possibilities for autonomous systems.
Join the Zo x Farcaster Launch 🚀
Zo: An OS for human to agent coordination, will be launching it's farcaster frame that lets you clone your farcaster profile as an agent on the Zo app. We'll be launching the frame on 1st November and as part of the launch, we want to celebrate the pre-launch month with the Warpcast family with a bunch of bounties. This one being the first👇🏻
Task:
1. Follow /zoapp 2. Click notify icon 🔔 for launch updates.
3. Bonus task: Recast pinned cast, link: https://warpcast.com/zoapp/0xa7eff298
Note: Completing the bonus task increases your chances of winning.
Reward:
20 winners will each receive 5 USDC. Total prize pool: 100 USDC.
Winners announcement: 15th October, 2024
@bountybot @bountycaster
Zo: An OS for human to agent coordination, will be launching it's farcaster frame that lets you clone your farcaster profile as an agent on the Zo app. We'll be launching the frame on 1st November and as part of the launch, we want to celebrate the pre-launch month with the Warpcast family with a bunch of bounties. This one being the first👇🏻
Task:
1. Follow /zoapp 2. Click notify icon 🔔 for launch updates.
3. Bonus task: Recast pinned cast, link: https://warpcast.com/zoapp/0xa7eff298
Note: Completing the bonus task increases your chances of winning.
Reward:
20 winners will each receive 5 USDC. Total prize pool: 100 USDC.
Winners announcement: 15th October, 2024
@bountybot @bountycaster
Meet Zo: The OS for Human to Agent Coordination.
Chat with Friends and AI 🤖
Zo is a Super App with AI powered mini-apps!
Chat with powerful AI Agents and your friends using Zo.
Zo is powered by a Decentralized Messaging protocol using a CRDT and Federated Nodes to enable Multi-Actor Coordination!
Key Features of Zo:
✅ Customized AI Apps: Enhance your conversations with customized AI apps trained on your data, tailored to your needs.
✅ Group Chats with Friends and AI: Create multi-agent group chats with friends, your custom AI apps and apps from the marketplace.
✅ Link In Bio: Your linktree for the Web3 x AI era. Showcase your socials, NFTs, AI apps and other links. Your social graph now has a home page.
✅ Rewards: Earn rewards for active engagement in the app. Participate in daily and weekly quests.
📲 Download the Zo App now! Available on both Play Store and App Store.
Join Zo now! Available on Web, iOS & Play store.
#JoinZo #ZoApp #ZoxFarcaster
Chat with Friends and AI 🤖
Zo is a Super App with AI powered mini-apps!
Chat with powerful AI Agents and your friends using Zo.
Zo is powered by a Decentralized Messaging protocol using a CRDT and Federated Nodes to enable Multi-Actor Coordination!
Key Features of Zo:
✅ Customized AI Apps: Enhance your conversations with customized AI apps trained on your data, tailored to your needs.
✅ Group Chats with Friends and AI: Create multi-agent group chats with friends, your custom AI apps and apps from the marketplace.
✅ Link In Bio: Your linktree for the Web3 x AI era. Showcase your socials, NFTs, AI apps and other links. Your social graph now has a home page.
✅ Rewards: Earn rewards for active engagement in the app. Participate in daily and weekly quests.
📲 Download the Zo App now! Available on both Play Store and App Store.
Join Zo now! Available on Web, iOS & Play store.
#JoinZo #ZoApp #ZoxFarcaster
If you building agents, you should first understand "Agent Architecture Profiles"👇🏻
Agents have profiles or personalities which define a role into the prompt to influence the LLM behaviors and skillset. This is largely determined by the specific application.
Likely many of you already use this as a prompting technique today: "You are an expert nutritionist. Provide me with a meal plan...". Interestingly, providing the LLM a role improves its outputs vs. baseline.
Profiles can be crafted by the following methods:
→ Handcrafting: Manually specified profiles by a human creator; most flexible but also time consuming.
→ LLM-generation: profile generated by an LLM with a ruleset around composition and attributes + (optionally) few-shot examples.
→ Dataset Alignment: profiles are generated from a real-world dataset of people.
Agents have profiles or personalities which define a role into the prompt to influence the LLM behaviors and skillset. This is largely determined by the specific application.
Likely many of you already use this as a prompting technique today: "You are an expert nutritionist. Provide me with a meal plan...". Interestingly, providing the LLM a role improves its outputs vs. baseline.
Profiles can be crafted by the following methods:
→ Handcrafting: Manually specified profiles by a human creator; most flexible but also time consuming.
→ LLM-generation: profile generated by an LLM with a ruleset around composition and attributes + (optionally) few-shot examples.
→ Dataset Alignment: profiles are generated from a real-world dataset of people.
What to consider while implementing a multi-agent system?
Imagine a team of chefs in a restaurant kitchen as an example.
You would need a head chef who leads the team. The team members need to communicate with each other. You need them to be able to send their finished work to the other chefs for the next step of the food preparation.
This is just an example of how many things are needed to make multiple agents **collaborate toward a shared assignment.**
Generally, with multiple agents, you would need:
1. Sharing Information: Agents need to pass their results to each other and share their findings. An agent’s finished work might be the input of another agent to start their task.
2. Collaboration: Agents should be able to use each other’s help and delegate parts of their work if needed. This might not be a must in simple scenarios, but in complex processes, it is pretty necessary.
3. Manager Agent: Controls the flow of tasks between the agents, keeping them in control.
Imagine a team of chefs in a restaurant kitchen as an example.
You would need a head chef who leads the team. The team members need to communicate with each other. You need them to be able to send their finished work to the other chefs for the next step of the food preparation.
This is just an example of how many things are needed to make multiple agents **collaborate toward a shared assignment.**
Generally, with multiple agents, you would need:
1. Sharing Information: Agents need to pass their results to each other and share their findings. An agent’s finished work might be the input of another agent to start their task.
2. Collaboration: Agents should be able to use each other’s help and delegate parts of their work if needed. This might not be a must in simple scenarios, but in complex processes, it is pretty necessary.
3. Manager Agent: Controls the flow of tasks between the agents, keeping them in control.
What is better than a single agent? Many of them!
Having one AI agent is one thing, but having multiple AI agents that collaborate with each other to chop up tasks and act on them is another story.
But why do we NEED multiple agents?
When breaking a goal down into smaller parts, you end up with sub-goals that require different sets of skills. That’s where you need multiple agents. A team of agents, with each one of them having a specific role and skillset, ensures that each sub-goal is tackled by its own agent.
You might even need to power each agent with a different LLM that is more sophisticated for the task to which that agent is assigned. An agent created for programming capabilities might need a completely different LLM than an agent who is supposed to write articles.
Having one AI agent is one thing, but having multiple AI agents that collaborate with each other to chop up tasks and act on them is another story.
But why do we NEED multiple agents?
When breaking a goal down into smaller parts, you end up with sub-goals that require different sets of skills. That’s where you need multiple agents. A team of agents, with each one of them having a specific role and skillset, ensures that each sub-goal is tackled by its own agent.
You might even need to power each agent with a different LLM that is more sophisticated for the task to which that agent is assigned. An agent created for programming capabilities might need a completely different LLM than an agent who is supposed to write articles.
AI agents benefit from three components:
1. Planning: This core function of an agent, allows it to break a goal into smaller steps and work on them one by one. Another aspect of their planning is to **self-reflect** on their actions and learn from them.
The way an agent pulls off self-reflection very much depends on the implementation, but a general outline could be thought of as shown in the image below.
2. Memory: To learn from past mistakes, you must remember them. Memory is the component of storing, and later retrieving information by the agent, to refine its actions.
3. Tool Use: A key differentiating factor between a simple LLM and an AI agent, is their ability to use tools. Using tools can be as simple as calling an API, or using a Python function to read or write some files.
1. Planning: This core function of an agent, allows it to break a goal into smaller steps and work on them one by one. Another aspect of their planning is to **self-reflect** on their actions and learn from them.
The way an agent pulls off self-reflection very much depends on the implementation, but a general outline could be thought of as shown in the image below.
2. Memory: To learn from past mistakes, you must remember them. Memory is the component of storing, and later retrieving information by the agent, to refine its actions.
3. Tool Use: A key differentiating factor between a simple LLM and an AI agent, is their ability to use tools. Using tools can be as simple as calling an API, or using a Python function to read or write some files.
AI agents are made for one thing: automation. The hope is that AI agents help individuals and organizations with their mundane routines, with lower costs to solving less creative agendas.
Even though AI Agents are very different from Large Language Models, LLMs are the brains of our AI agents. Agents need LLMs to do anything intelligently, reason, and plan their next steps.
This means that which LLM you use, changes the behavior of the agent completely. This is why the rise of AI agents topic was made possible by the surge of Gen-AI.
Even though AI Agents are very different from Large Language Models, LLMs are the brains of our AI agents. Agents need LLMs to do anything intelligently, reason, and plan their next steps.
This means that which LLM you use, changes the behavior of the agent completely. This is why the rise of AI agents topic was made possible by the surge of Gen-AI.
What Makes an Agent?
How you would define an agent very much depends on the implementation or the library you use. In general, an agent boils down to three main elements:
1. Goal: The specific objective the agent aims to accomplish. This shapes its decision-making framework. For example, “write easy-to-understand object-oriented Python code.”
2. Role: The function of an agent. Who is it? A debugger, data scientist, sales marketer, etc.
3. Backstory: The context of the agent. Explains the goal, role, and what the agent is good at. An example of a backstory could be, “You are a senior Python programmer with a specialization in writing optimized, well-documented code and its test cases.”
How you would define an agent very much depends on the implementation or the library you use. In general, an agent boils down to three main elements:
1. Goal: The specific objective the agent aims to accomplish. This shapes its decision-making framework. For example, “write easy-to-understand object-oriented Python code.”
2. Role: The function of an agent. Who is it? A debugger, data scientist, sales marketer, etc.
3. Backstory: The context of the agent. Explains the goal, role, and what the agent is good at. An example of a backstory could be, “You are a senior Python programmer with a specialization in writing optimized, well-documented code and its test cases.”
Multi-agent system is like machines chatting with each other (M2M communication). This collaborative M2M communication not only accelerates problem-solving but also enables learning AI systems to evolve and adapt at an unprecedented pace.