SNAI Overview

SwarmNode.ai

0(0%)1D

No Data

24H High

0.00244

24H Low

0.00225

24H Volume

521.79K

24H Volatility

8.44%

All Time High

0.0856

All Time Low

0.002

Correlation with BTC

0.09

Return On Investment

-95.93%

What is SwarmNode?SwarmNode enables running Al agents in the cloud without managing servers. An agent can be executed via the UI, REST API, and Python SDK. Agents can invoke each other and act as a swarm. After an agent finishes its task, it hibernates and waits for the next task. An agent can also process data, store it in a shared database, and let other agents pick it up and process it further. SwarmNode aims to be a one-stop shop for creating, configuring, and deploying Al agents.Why is there a need for SwarmNode?A developer may have an idea for an AI Agent—say, a stock market researcher that takes a ticker and returns its performance analysis. They want the agent to be running and accessible to the public even while they are asleep, so they can’t run the agent on a local machine. Normally, they’d have to rent a server on the cloud, install a ton of dependencies, keep an eye on usage, worry about server downtime, and maybe spin up a database. That’s a lot of headaches for a simple idea! SwarmNode cuts out all that hassle and enables developers to focus on the business logic.What are the key features?Serverless: SwarmNode is serverless, which means you don’t worry about costly infrastructure. Instead of maintaining your own servers, you simply provide the code that you want to run. SwarmNode takes care of scaling, resource allocation, and database management. If you’re familiar with AWS Lambda, it’s the same idea, but geared toward running AI-oriented code with minimal fuss. Unlike using traditional servers, you only pay for the time it takes to run the agents. You don’t get charged for unused compute.Chaining: One agent can invoke another, creating a chain of agents aka a Swarm. This is like an assembly line: each agent can perform a specific task and then pass the result on to the next agent for further processing. Want to build a translator that processes text, and then sends the translated text to another agent for sentiment analysis? Easy—just chain them!Orchestration You can orchestrate your agents via a REST API or Python SDK. In other words, you can control when and how your agents run, and what data they handle. This orchestration feature is similar to using a tool like Airflow or Zapier for managing workflows, except with AI scripts that you’ve written yourself.Data Storage: Each agent will have free access to a key-value datastore, which can be shared among agents. Think of it like a mini database in the cloud for storing small bits of data.Scheduling: You’ll soon be able to schedule agents to run at a specific time. Want to perform daily backups or run a nightly analysis? You’ll be able to do that without setting up a cron job on your own server.Agent Library: An upcoming library of ready-made agents will let you pick and choose agents created by the SwarmNode community, which you can customize and further develop. Or, you can publish your own for others to use—like an App Store for AI agents.Who is the founder?SwarmNode is founded and led by Bakar Tavadze, an experienced software engineer in the AI industry.SummarySwarmNode.ai makes deploying AI-enabled Python agents in the cloud effortless and scalable. Users only write code and upload it, and SwarmNode.ai handles everything else—from building the environment to running agentic code on demand.

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Social Media Sentiment

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The current sentiment of Bitcoin (SNAI) on social media as of December 5, 2025 indicates a Bullish rating, with 0 users expressing a positive Bullish sentiment, 0 users opting for a Neutral, and 0 users suggesting a Bearish. This reflects a generally optimistic outlook among social media participants regarding SNAI's future outlook. While social media sentiment can provide valuable insights into public perception and market trends, its reliability as a sole reference for cryptocurrency investment decisions is limited. The data is often influenced by a small, vocal subset of users, which may not represent the broader market or include professional analysis. Factors such as hype, manipulation, or coordinated campaigns can skew sentiment, reducing its accuracy.