ai agent - Labellerr

ai agent

A collection of 19 posts

The Rise of AI Agents in Data Labeling Explained

The Rise of AI Agents in Data Labeling Explained

AI agents in data labeling pipelines. By combining semi-supervised learning, active learning, and human-in-the-loop workflows, they reduce manual effort by ~50% and cut annotation costs by up to 4×, all while maintaining accuracy above 90%.

Google I/O 2025

Why Google I/O 2025 Matters: Top AI & Dev Updates!

Discover the groundbreaking AI advancements and developer tools announced at Google I/O 2025. From the Gemini 2.5 models to AI Mode in Search, explore how these innovations are set to transform the tech landscape.

Microsoft Build 2025

Microsoft Build 2025: What You’re Missing If You Skip It

Explore the groundbreaking announcements from Microsoft Build 2025, including advancements in AI agents, developer tools, and cross-device features. Discover how these innovations can impact developers, enterprises, and tech enthusiasts alike.

Smolagents: Build AI Agents in Minutes with Python!

Smolagents: Build AI Agents in Minutes with Python!

Discover how to build powerful AI agents effortlessly using Smolagents. This lightweight Python library supports various models and tools, enabling tasks like web automation, data analysis, and more—all with minimal code.

Top 5 AI Agent Platforms Revolutionizing Automation in 2025

Top 5 AI Agent Platforms in 2025

Explore the top AI agent platforms of 2025, Claude, GenSpark, Manus, Orby, and Zapier, that are revolutionizing automation across industries. Discover their unique features and how they're enabling businesses to achieve unprecedented efficiency.

Building AI Agents with Make.com: A No-Code Guide

Building AI Agents with Make.com: A No-Code Guide

Discover how to build AI agents with Make.com to automate complex tasks without coding. This guide walks you through setting up goal-oriented agents that leverage large language models, integrate with various apps, and adapt in real-time to streamline your business processes.

Building an AI News Assistant with n8n

Building AI News Assistant with n8n

Discover how to create an AI News Assistant with n8n that automates the collection, filtering, and summarization of news articles.

Best AI Agent Building Platforms in 2025

5 Best AI Agent Building Platforms in 2025

Discover the leading AI agent platforms of 2025. Learn how these tools are transforming enterprise workflows, enabling businesses to deploy intelligent agents for automation, customer service, and more.

Building AI Agents with MCP

Model Context Protocol

Built My First AI Agent Using MCP!!

Building your first AI agent is now more accessible with the Model Context Protocol (MCP). This article guides you through the process, highlighting how MCP standardizes interactions between AI models and external tools, streamlining development and integration.

My Experience Building AI Agents With Manus and Genspark

My Experience Building AI Agents With Manus and Genspark

In 2025, AI agents like GenSpark are transforming how we interact with technology. This article delves into the current state of AI agents, highlighting their capabilities, limitations, and the journey from promise to reality.

500x Your Scientific Discovery With AI Co-Scientists

500x Your Scientific Discovery With Sakana AI Scientist

AI co-scientists help researchers analyze data, run simulations, and automate tasks, accelerating scientific discoveries while keeping humans in control.

Bridge the Gap Between LLMs and Real-World Applications with MCP

Model Context Protocol

What is MCP & How It Speeds Up AI Agent Building 100X

Model Context Protocol (MCP) structures and manages context in LLMs, improving accuracy and efficiency.

Manus general AI Agent

Manus AI Agent: Another DeepSeek Moment for China

China’s AI landscape is evolving fast, and Manus AI might be its next big breakthrough. With autonomous decision-making and task execution, it rivals DeepSeek’s innovations. But how does it compare, and what does it mean for the AI industry? Let’s explore!

RPA vs AI Agents

Open AI Operator: How AI Agents Replacing RPA

RPA and AI agents both automate tasks, but they work differently. RPA follows predefined rules, while AI agents adapt and learn. Explore their strengths, weaknesses, and which one is the right fit for your automation needs!

Reason + Act = ReAct Agents

ReAct Agents: What They Are & How to Build Your Own from Scratch!

ReAct Agents revolutionize AI by integrating reasoning and action, enabling dynamic decision-making. Unlike traditional AI, they adapt, learn, and interact efficiently in real time.

Computer using agents

Computer Use Agent: Guide to Functionality & Benefits

Let AI agents use your computer for you! These intelligent systems can browse, fill out forms, automate workflows, and complete tasks without human input. Powered by RPA and machine learning, AI agents are transforming industries by handling repetitive work and boosting efficiency.

The Great Work Revolution

Bye-Bye 9-to-5: How AI is Redefining Work as We Know It!

AI is redefining work as we know it, automating tasks, enabling flexible schedules, and challenging the traditional 9-to-5 grind. From AI-driven decision-making to autonomous agents handling workflows, the future of work is dynamic, decentralized, and more efficient than ever.

Introduction to Domain Specific Agents

Domain-Specific Agents: AI Tailored for Your Industry

Domain-specific AI agents are designed for specialized industries, offering precision, efficiency, and improved decision-making. Unlike general AI, they focus on industry-specific tasks, providing tailored insights and reducing errors in fields like healthcare, finance, cybersecurity, and more.

Discover the benefits and risks of AI agents, including automation, security concerns, bias, and best practices for adoption.

AI Agents in Your Workflow: Key Benefits and Potential Risks

AI agents boost efficiency, automate tasks, and enhance decision-making, but they also pose risks like data security, bias, and transparency issues. Businesses can mitigate these challenges with ethical AI practices, human oversight, and robust security measures to ensure reliable AI integration.