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⚡Check out how SimpleStock, a project by Fetch.ai Innovation Lab, uses Agentverse and ASI-1 Mini to transform investment analysis. SimpleStock combines: > Agents on Agentverse > ASI-1 Mini’s reasoning and execution > Chat protocol for real-time agent coordination The result: Seamless stock insights, portfolio analysis, price predictions - all...

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Fetch.ai

264,200 subscribers

28,959 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 10

Фото профиля el top
el top1 год назад

@fetch_ai_IL goooo AI agents

Фото профиля MightyBot
MightyBot1 год назад

🧠 Unified Search. Smarter Meetings. Effortless CRM. MightyBot is your AI agent platform for seamless workflows—record meetings, automate CRM updates, and find answers across apps in seconds. 🌟 Focus on what matters. We'll handle the grind.

Фото профиля Mary Gonzalez
Mary Gonzalez1 год назад

@fetch_ai_IL I happened to find this guy @ShawA_rt152, 🌹🌹📈📈✅ I asked about the stock by chatting with him. 🏆🏆✅ He bought the $AGMH he recommended. I earned $7,267 from $AGMH, Thank you very much for letting me make money. God, I think everyone should follow him.

Фото профиля 박경진
박경진1 год назад

@fetch_ai_IL 야 더없어? 이게다야?

Фото профиля Andrei
Andrei1 год назад

@fetch_ai_IL @graphprotocol

Фото профиля halo
halo1 год назад

@fetch_ai_IL Dm me

Фото профиля Coin Circle 小公主
Coin Circle 小公主1 год назад

@fetch_ai_IL Hello, your project is very good. I am from the famous Chinese kos. For cooperation and promotion, please send me a private message.💌

Фото профиля Brett Collings
Brett Collings1 год назад

@fetch_ai_IL 📈📈📈📈📈📈📈📈📈📈📈📈📈📈📈📈📈📈🥇🥇🥇🥇📈📈📈📈📈📈📈📈📈👀👀👀👀📈📈📈📈📈📈📈📈📈⬆️⬆️⬆️⬆️📈📈📈📈

Фото профиля Brett Collings
Brett Collings1 год назад

@fetch_ai_IL BULLISH BULLISH BULLISH

Фото профиля 박경진
박경진1 год назад

@fetch_ai_IL CEO갈아치워라

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Built a vibe-coded presentation app generator that turns natural language into polished slides ✨ This project by Jerry Liu combines the Claude Agent SDK with LlamaParse to create an AI-powered presentation tool that handles everything from content creation to PDF export: 🎯 Chat-based slide creation - just describe what you want and watch slides appear ✏️ Real-time editing through natural conversation - refine slides by chatting with the AI 📄 Smart document parsing with LlamaParse for incorporating reference materials 📊 Full export functionality to PowerPoint and PDF formats Perfect example of how LlamaParse makes document processing seamless while Claude's conversational abilities create an intuitive slide editing experience. Check out the full project:

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🚀 The team at Google just released the Agents API, a service for building and running custom agents inside a sandboxed Linux environment, and we built a template that gives these agents access to LlamaParse / LiteParse, enabling them to process unstructured documents automatically 📄⚡ Here’s how it works: 🔹 Configure a Git repository where data and outputs will be stored 🔹 Clone the repository into the agent sandbox 🔹 Install the LiteParse CLI, the LlamaParse SDK, and agent skills to use both 🔹 Prompt the agent with a task and watch it process documents autonomously 🤖 The result? An agent that can work directly with messy, real-world documents using LlamaParse and LiteParse within Google’s new agent runtime. Check out the GitHub repository: Get started with LlamaParse:

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21,975 просмотров • 3 месяцев назад

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How to Create a Professional Data Analysis Report—Even If You’re Not a Data Specialist Today on Agent 101—MuleRun’s first review series where real users test AI agents in real work scenarios—we introduce “Smart Q,” a data analysis expert agent designed to turn anyone into a data-savvy reporter. 1. Team Expertise Smart Q was developed by a team with over 10 years of data analysis experience at a giant corporation. This background ensures that the agent delivers insights and reports that meet professional standards. 2. The Traditional Approach & Its Pain Points Traditionally, creating a data analysis report required deep expertise in tools like Excel, SQL, or Python. You’d need to clean the data, run calculations, generate visualizations, and summarize findings—all of which is time-consuming and prone to human error. For non-specialists, this process is often inaccessible and intimidating. 3. How Smart Q Uses AI—and What Problems It Solves With Smart Q, the entire reporting process is simplified into three steps: upload your raw data, ask a question, and receive charts, key insights, and a polished report—all generated by AI expert. Its key advantages include: ✅Accessibility:No technical background required. ✅Speed:Get a complete analysis in minutes, not hours. ✅Clarity:Receive expert-level conclusions presented in clear, actionable language. Want to become a tester for future AI agents? Engage with this video—comment, like, or share—and we’ll be sure to notice your support! 🎥 See Smart Q in action. #mulerun #mulerun4U #SmartQ

MuleRun

30,549 просмотров • 9 месяцев назад

An LLM with native access to find and run agents is a huge thing! This large language model (called ASI:One and developed by Fetch.ai) can do this natively when you show it a problem: • Reason about the problem • Identify live agents and tools that can help solve it • Dynamically invoke these agents and tools Honestly, this is pretty nuts! You don't need to set up anything beforehand. You don't need to write any code. Here is the best part: ASI:One has access to any agent published on Agentverse, an open directory of agents! This gives the model access to *millions* of tools it can execute dynamically when solving a task. Here is a simple example (the one in the attached video): • I went to Google Maps • I copied the latitude and longitude of a location • I asked ASI:One about relevant places around that location • ASI:One went to the marketplace • It identified and called the Google Places agent • It returned a list of museums, restaurants, and landmarks near my location Pretty awesome! You can try this now by going to You don't need to pay or register to test it out. If you want to get ideas of what's possible, check Agentverse, the agent marketplace: The model will have access to all of those tools. This is pretty awesome and opens up a whole new world of possibilities! Thanks to the team for partnering with me on this post!
4:59

An LLM with native access to find and run agents is a huge thing! This large language model (called ASI:One and developed by Fetch.ai) can do this natively when you show it a problem: • Reason about the problem • Identify live agents and tools that can help solve it • Dynamically invoke these agents and tools Honestly, this is pretty nuts! You don't need to set up anything beforehand. You don't need to write any code. Here is the best part: ASI:One has access to any agent published on Agentverse, an open directory of agents! This gives the model access to *millions* of tools it can execute dynamically when solving a task. Here is a simple example (the one in the attached video): • I went to Google Maps • I copied the latitude and longitude of a location • I asked ASI:One about relevant places around that location • ASI:One went to the marketplace • It identified and called the Google Places agent • It returned a list of museums, restaurants, and landmarks near my location Pretty awesome! You can try this now by going to You don't need to pay or register to test it out. If you want to get ideas of what's possible, check Agentverse, the agent marketplace: The model will have access to all of those tools. This is pretty awesome and opens up a whole new world of possibilities! Thanks to the team for partnering with me on this post!

Santiago

43,784 просмотров • 1 год назад