Community Blog

Clone 75 + free, runnable AI agent tutorials and learn to prototype, scale, and ship voice, RAG, and multi-agent systems—fast, in 2025.

Unlock graph + vector memory for your LLM agents with Cognee’s simple E→C→L pipeline. Build, query, and scale smarter AI apps in minutes.

Memvid encodes embeddings as video frames, cutting storage costs and delivering sub-second semantic search—no vector DB or GPU needed in 2025.

Build a smarter RAG stack in 2025: fuse miniCOIL, Qdrant & DeepSeek-R1 for sharper retrieval and cleaner answers. Step-by-step, code-first guide.

Run LLMs locally in 2025 with full data control. Explore Ollama’s latest features, real Python examples, and GDPR-ready AI workflows that scale.

The Image Generation Agent automates prompt refinement, image generation, and evaluation—all in one intelligent loop. This guide walks developers and AI leaders through setup, usage, and customization with hands-on examples. Learn how to generate high-quality images aligned with intent, without endless retries. Ideal for teams looking to streamline their creative workflows with a touch of empathy and efficiency.

Golf MCP is a Python-first framework that helps you ship FastMCP-compatible servers in minutes—with zero schema fuss and full control. This guide walks developers and AI leads through setup, real-world usage, and tradeoffs vs. other popular MCP stacks. We’ll build a tool, stream it into OpenAI’s Agents SDK, and explore what makes Golf stand out. Calm, code-first, and no hype—just practical insight and examples.

Google's Veo 3 and Flow tools offer a fresh approach to video creation, enabling users to generate short, high-quality clips with synchronized audio using simple text prompts. These tools are designed to assist storytellers, educators, and marketers in visualizing ideas without the need for extensive resources. With built-in safeguards like SynthID watermarking, Google emphasizes responsible AI use in content creation.

Agentic AI and RPA both aim to automate tasks—but they take radically different paths. This article breaks down how each works, where they shine, and what happens when you ask them to solve the same business problem. Includes code examples, architectural notes, and practical tips for developers and AI leaders. If you’ve ever wondered when a “rule-based bot” just won’t cut it anymore, this one's for you.

Codex is the new ChatGPT coding agent. It lives in ChatGPT and runs inside secure, sandboxed environments with full visibility into every action. You can use it to write features, fix bugs, or generate tests—without leaving your workflow. This guide explores how it works, how it compares to other tools, and what it means for the future of development.

The new agent framework by LangChain. This guide walks developers and AI leaders through deploying LangGraph agents, integrating RAG, and orchestrating multi-agent workflows. Real code. Real use cases. Zero hype.

AI agents are multiplying fast — but without orchestration, they create chaos. This guide breaks down every major agent orchestrator on the market, from AWS and Azure to open-source frameworks like AutoGen and Semantic Kernel. Packed with Python examples and real-world use cases in customer support, IT automation, and marketing. For developers and AI leaders building serious multi-agent systems.

Vector databases are becoming essential for building smarter AI systems. This guide breaks down Qdrant’s core features, practical use cases, and how it compares to other vector DBs like PGVector, FAISS, and Weaviate. You’ll learn how to use Qdrant in Python for semantic search, RAG pipelines, and recommendations—with code examples. Ideal for developers and technical leads exploring production-ready vector search.

RAG is everywhere, but evaluating it is still messy. This post dives into RAGAS and Giskard—two open-source frameworks helping teams measure trust, faithfulness, and performance in RAG pipelines. We compare their strengths, show how they work with real code, and explore what happens when context windows make RAG optional. For developers and AI leaders who need more than just vibes to trust their LLMs.

CrewAI lets you build AI agents that work together like a real team. This guide shows how to create support bots and automate business workflows using CrewAI’s crew-and-flow model. It includes practical Python examples, deployment tips, and a community-informed comparison with OpenAI’s Agent SDK. Ideal for developers and AI leaders exploring multi-agent systems that actually work in production.

Most research papers never make it to production. Paper2Code changes that by turning ML papers into runnable codebases with minimal effort. It reads, plans, and writes code—so your team can focus on validation and iteration. A practical tool for developers and AI leaders aiming to accelerate reproducibility and innovation.

Anthropic’s MCP connects agents to tools and context. Google’s A2A connects agents to each other. This deep dive explores how both frameworks shape the future of AI agents in business automation — where they compete, where they complement, and how to use them together.

Learn how to create, edit, and vary images with OpenAI's gpt-image-1 API. This guide covers setup, prompt crafting, advanced techniques, and best practices. Get practical code examples to integrate high-fidelity image generation into your projects.

AI tools are reshaping how we learn Python, making practice and problem-solving more accessible. This article explores practical ways to use ChatGPT, Gemini, NotebookLM, and GitHub Copilot to speed up your learning. Clear steps, real examples, and a focus on building skills—not shortcuts. A practical guide for those serious about learning Python with modern tools.

Agentic AI frameworks let machines think, act, and improve on their own. This overview compares LangChain, Auto-GPT, Semantic Kernel, and others. It covers key features, best practices, and real-world applications. This is a fast, clear breakdown of today’s top agentic tools.

OpenAI, Anthropic, and Google have released their most advanced AI models yet — GPT-4.1, Claude 3.7, and Gemini 2.5 Pro. This article breaks down how they compare across reasoning, coding, and real-world use. It highlights benchmarks, tool use, and community feedback to help you understand which model fits which task. A clear look at where the AI landscape stands in 2025 (SO FAR!).

This is how to rapidly generate a fully functional Model Context Protocol (MCP) server by combining Gitingest’s repo ingestion, FastMCP’s Python framework, and Gemini 2.5 Pro’s code‑generation power. In just a few steps, you’ll pull in any GitHub project as text, scaffold Python endpoints, and deploy locally with zero boilerplate. Perfect for exposing internal APIs, docs, or data stores to an LLM via MCP. Let's dive in.

Google’s new Agent2Agent (A2A) Protocol lets AI agents talk to each other—no matter who built them. This guide breaks down how it works, why it matters, and how you can start building smart, collaborative agents. With real-world examples and code, it’s perfect for curious minds and developers alike.

In this article, we're building a development team of AI agents using the OpenAI Agent SDK. Learn to define specialized roles like manager, developer, documenter, and quality lead through agents. Includes step-by-step setup, sample code, and coordination strategies. A practical guide.

Machine learning models are powerful—but often impossible to interpret. This guide breaks down Explainable AI (XAI), the Python frameworks that make it possible, and how to start using them today. With hands-on examples using SHAP, LIME, ELI5, and Captum, you’ll learn how to uncover the why behind your model’s predictions. Let's dive in.

Llama 4 just dropped—and it’s a multimodal, mixture-of-experts powerhouse. Dive into the architecture, hardware demands, developer insights, and what this model means for the future of AI.

A practical guide to using OpenAI’s Responses API for building task-focused agents. Covers core features, setup steps, and code examples. Includes tools like web search and file access. Written for developers seeking clarity and functionality.

Turn live audio into real-time transcription with OpenAI’s Speech API. This guide walks you through setup, connection, and streaming—complete with code snippets. Learn how to build a simple voice agent that listens, transcribes, and responds. Let's dive in.

Learn how to use GPT-4o’s function calling feature to connect AI responses with real actions. This guide walks through installation, setup, and building a simple weather agent. Includes code snippets and practical insights.

Mistral OCR is shaking up the document processing world with an AI-driven approach to text extraction, layout preservation, and multimodal understanding. It handles PDFs and images—automatically transforming them into structured, analysis-ready data. Seamless integrations with LLMs and frameworks like LangChain make it easy to build advanced, AI-powered workflows. Let's dive in.

Explore LightEval, Hugging Face’s comprehensive framework for evaluating large language models across diverse benchmarks and backends. This deep dive covers everything from setup to real-world use cases, complete with code examples and best practices. Learn how LightEval compares to alternatives like HELM and LM Harness, and whether it’s worth adopting for your projects. Perfect for students, researchers, and developers working with LLMs.

Run local AI like ChatGPT entirely offline. Ollama + Open WebUI gives you a self-hosted, private, multi-model interface with powerful customization. This guide shows you how to install, configure, and build your own agent step-by-step. No cloud. No limits.

Learn how to create an AI agent that scrapes the web intelligently using LangGraph and Selenium. This guide walks you through setup, architecture, and a working code example. No fluff—just a deep, practical walkthrough. Perfect for developers building modular, automated data collection tools.

Learn how the Model Context Protocol (MCP) connects AI assistants to real-world data sources securely and efficiently. This guide walks through setup, architecture, security, orchestration, and building your first agent. Understand where execution happens, how secrets are protected, and how to scale with concurrency. Packed with insights and code to get you started fast.

A practical guide to using Google’s Gemini Pro 2.5 to create a basic AI agent. Covers installation, setup, and step-by-step code examples. Ideal for developers exploring the model’s reasoning and coding features. No hype—just useful instructions.

Three agents.Three business problems. One step-by-step guide that shows how to turn AI from a concept into a working solution — using real tools, real code, and real use cases. This final article connects everything: design, reasoning, architecture, and execution. Let's dive in.

This is the second article of our Agentic AI series. This guide breaks down five powerful frameworks — LangChain, LangGraph, LlamaIndex, CrewAI, and SmolAgents. What they do. How they work. Which one fits your project. Let's dive ine.

Agentic AI refers to autonomous systems that can reason, take actions, use tools, and learn from feedback — without constant human input. This article breaks down how agentic AI works, why it matters, and how it’s being used to automate complex tasks across industries.

Ollama isn’t just for running AI models—it’s a game-changer for developers and ML engineers. No more wrestling with API keys, rate limits, or cloud dependencies. Prototype faster, debug locally, and deploy seamlessly with a tool that fits into your workflow. In this article, we break down how to leverage Ollama for efficient AI development with practical examples and code snippets.

Ollama isn’t just an interactive tool—it can be a full-fledged AI service. In this article, we explore how to set up Ollama for model serving, turning it into a continuously running API that processes requests like OpenAI’s service—except on your own infrastructure. You’ll learn how to optimize performance, implement a simple serving setup with code, and discover real-world use cases where this approach makes sense. Let’s dive in.

Ollama isn’t just for local AI tinkering. It can be a powerful piece of a larger system—integrating with Open WebUI for a sleek interface, LiteLLM for API unification, and frameworks like LangChain for advanced workflows. In this deep dive, we explore how to extend Ollama beyond the basics, from fine-tuning custom models to real-world production setups. If you’ve been running models locally but want more control, scalability, and integration, this is for you.

Run large language models locally with Ollama for better privacy, lower latency, and cost savings. This guide covers its benefits, setup, and how to get started on your own hardware.

AI agents are no longer just chatbots. With OpenAI’s Agents SDK (launched a few days ago), they can think, act, and orchestrate workflows. This guide walks you through setting up an intelligent agent, from installation to real-world applications. Let's dive in.

Extracting text from PDFs and images is easier than ever with Mistral OCR. This guide walks you through setting it up, processing documents, and handling real-world use cases like invoices, academic papers, and bulk uploads. With working code snippets in Python and TypeScript, you’ll have a functional OCR pipeline in no time. Let's dive in.

Train your model in PyTorch, deploy it anywhere with ONNX. This guide walks you through seamless model conversion and inference using ONNX Runtime. With step-by-step instructions and working code. Let's dive in.

Managing ML experiments doesn’t have to be chaotic. MLflow makes tracking, tuning, and deploying models effortless. This guide takes you from setup to advanced logging, hyperparameter tuning, and deployment—step by step. If you’re serious about streamlining your ML workflow, this is for you.

Learn how to set up DeepSeek with Ollama to run AI models locally, ensuring privacy, cost efficiency, and fast inference. This guide walks you through installation, setup, and building a simple AI agent with practical code examples.

Google Gemini is a multimodal powerhouse. Text, images, and more are all processed seamlessly in a single framework. This guide takes you from setup to building a smart agent that understands and analyzes multiple data types. Let's dive in.

Microsoft Phi is a lightweight AI model library designed for efficiency and flexibility. It delivers strong performance on resource‑constrained devices while supporting text generation and conversational AI. This guide walks you through installation, setup, and building a simple chatbot agent with Phi. Get started with practical code examples and explore its capabilities firsthand.

An open-source AI just shook the industry. DeepSeek, a chatbot from a Hangzhou startup, rivals OpenAI while costing a fraction to train. With its Mixture-of-Experts design and massive 128K context window, it outperforms competitors in reasoning and efficiency. Is this the beginning of open-source AI dominance?

Ollama makes it easy to integrate local LLMs into your Python projects with just a few lines of code. This guide walks you through installation, essential commands, and two practical use cases: building a chatbot and automating workflows. By the end, you’ll know how to set up Ollama, generate text, and even create an AI agent that calls real-world functions.

Enhance your AI applications with TensorFlow Hub. Access pre-trained models for faster development and efficient deployment. Customize, fine-tune, and integrate machine learning seamlessly.

This guide explores BentoML, its benefits, and how it compares to other options. It’s our second deep dive into BentoML because deployment remains a major challenge for most data science teams.

Great prompts need constant refinement. Fine-tuning and evaluation turn good prompts into powerful ones. PromptLayer makes this process seamless—helping you optimize for accuracy, cost, and speed. This guide shows you how.

AI agents without tools are like carpenters without hammers—limited and ineffective. In SmolAgents, tools empower agents to fetch data, run calculations, and take real action. This guide shows you how to build, integrate, and use them for maximum impact.

Great prompts power great results—but managing them gets messy fast. PromptLayer is your control center, tracking, testing, and optimizing every prompt you craft. This guide breaks down its core features and shows you how to refine your LLM workflow.

Your model outperforms the usual benchmarks—so how do you prove it? Lighteval lets you build custom evaluation tasks, metrics, and pipelines from scratch. This guide walks you through everything, from setup to advanced customization. Because true innovation needs its own measuring stick.

SmolAgents enhance AI systems by executing Python code for automation, problem-solving, and decision-making. This guide covers their architecture, functionality, and practical applications. Let's dive in.

Evaluating large language models is complex—Lighteval makes it easier. Test performance across multiple backends with precision and scalability. This guide takes you from setup to your first evaluation step by step.

Bring advanced speech recognition to your applications with Azure Speech Service. Real-time transcription, speaker recognition, and customizable accuracy—beyond basic speech-to-text. Let's dive in.

Learn how to train, deploy, and manage large language models using Azure Machine Learning. This guide covers the entire process, from setup to deployment, with a focus on scalability and integration.

AI is evolving beyond simple responses. Agents don’t just answer questions—they take action, adapt, and collaborate. With SmolAgents, building these intelligent systems is easier than ever. Let's dive in.

Speed up development with TensorFlow Hub’s pre-trained models. Use ready-made modules to create custom solutions with less effort. This guide covers the framework, its benefits, and a hands-on text classification example. Let's dive in.

Smarter chatbots need context to deliver better responses. LlamaIndex bridges Large Language Models with external data for deeper, more relevant interactions. This guide explores its benefits and walks you through building a context-aware chatbot.

Efficient deployment is the bridge from development to production. With the right framework, the transition is seamless. This guide breaks down BentoML, its advantages, and how it stacks up against the rest. Let's dive in.

Training LLMs on Azure GPU clusters demands precision and efficiency. Azure’s infrastructure scales models while keeping costs in check. This guide breaks down setup, optimization, and best practices. Code snippets included.

In 2025, most AI teams rely on pre-trained models. But if you’re fine-tuning or training large models, TensorFlow is the elephant in the room. Speed is everything. Faster training means quicker development and deployment. TensorFlow’s GPU acceleration cuts computation time, enabling rapid experimentation. This short guide covers setup, code, and a hands-on example to help you get started. fast

Deep learning demands flexibility. PyTorch delivers it with dynamic computation graphs, GPU acceleration, and an intuitive design. This guide walks you through setup, model building, and a hands-on CNN example. Let's dive in.

Real-time detection at the edge, redefined. Optimized YOLO brings powerful object detection to devices like Raspberry Pi and Jetson Nano. Designed for limited resources, delivering maximum efficiency. Smart AI, exactly where you need it. Let's dive in.

Spot objects in a flash. YOLO analyzes entire images in one sweep, delivering unmatched speed and accuracy. It’s built for real-time demands like self-driving cars and augmented reality. Let's dive in.

AI makes decisions, but can you really trust them? Explainable AI (XAI) pulls back the curtain, showing exactly how models work and why they make those choices. This guide breaks down XAI techniques, their benefits, and practical steps for building transparent systems. Plus, you’ll get hands-on examples to apply it all yourself.

AI is driving critical decisions, but is your model fair, secure, and reliable? Giskard, the open-source testing framework, ensures your machine learning models meet the highest standards. Let's dive in.

AI models are powerful, but are they fair, secure, and robust? Giskard’s open-source framework helps uncover hidden biases, vulnerabilities, and performance flaws in ML models. From automated testing to bias detection, this guide walks you through using Giskard to evaluate and improve your AI systems. Here's what you need to know:

Develop, monitor, and refine LLM applications more effectively. LangSmith provides tools for observability, experiment tracking, and deployment—all in one platform. A streamlined approach to managing and improving production-ready AI systems. In this short article, we show you how to get started. Let's dive in.

Simplify your AI workflows. LangChain lets you build and manage advanced applications with clarity. This guide walks you through setup, customization, and creating a functional agent. Let's dive in.

Scaling AI has never been simpler. BentoML makes building, packaging, and deploying machine learning models easy. This step-by-step guide includes code and insights for serving AI at scale. Let's dive in.

Smart indexing is the key to efficient data retrieval. LlamaIndex links your dataset to LLMs for advanced queries and smooth integration. This step-by-step guide includes code and practical tips to get you started.

LlamaIndex simplifies building knowledge graphs by mapping entities and their relationships. Here’s a step-by-step guide with code examples and expert tips to get you started. Let’s dive in.

Azure Cognitive Services helps you create conversational agents that truly understand users. This guide walks you through setup to deployment with practical code examples and tips. Let's dive in.

Manual document processing slows you down. Azure AI Document Intelligence automates text, tables, and data extraction with precision. Boost efficiency and accuracy across your workflows. This guide shows you how—with code and real-world tips.

If you’re looking for a simple fine-tuning project, start here. This guide walks you through fine-tuning GPT-2 with Hugging Face for your specific tasks. It covers every step—from setup to deployment. Let's dive in.

This is how you can run powerful AI models locally—no cloud, no delays. With Ollama, you get instant, secure text generation and complete data privacy. Take control of your workflow. Protect your data. Build smarter, faster, and safer. Let’s dive in.

NLP has never been this effortless. Hugging Face’s Transformers library gives you instant access to cutting-edge language models. This guide simplifies it all—setup to building your first NLP agent, step by step. Let's dive in.

A new era in real-time vision has arrived. YOLO11 merges speed, precision, and adaptability like never before. Enhanced architecture takes object detection and image segmentation to the next level. Let's dive in.

LlamaOCR sets them free. Powered by the Llama 3.2 Vision model, it transforms images into Markdown text with precision and speed. This guide shows you how.

Function calling is reshaping what AI can do. LLMs now interact with APIs, databases, and custom logic dynamically. With LangChain, developers can build intelligent agents to handle complex workflows. This guide breaks it down with clear steps and real code examples.

Build and manage AI models efficiently with Open WebUI. This open-source platform supports offline use, integrates with OpenAI-compatible APIs, and offers flexible customization—a practical tool for streamlined AI deployment and experimentation. Let's dive in.

This guide takes you step-by-step through installation, setup, and building your first LLM-powered chatbot. Discover expert tips on cost tracking, load balancing, and error handling to optimize your workflows. Learn how to unlock the potential of over 100 language models with one powerful framework. Let's dive in.

Want to build an AI agent that goes beyond basic queries? With LangGraph, you can design agents that think, reason, and even use tools like APIs to deliver dynamic, meaningful answers. This guide walks you through creating a smart, tool-enabled agent from scratch. Get ready to combine graph reasoning and natural language processing into something extraordinary.

AI deployment is a game of strategy. LangGraph offers three paths: Self-Hosted, Cloud SaaS, and BYOC. Each with its strengths. Here’s how to choose the right one for you.

Building with LLMs can feel like guesswork. Langfuse changes that. It gives you observability, real-time insights, and tools that actually help you debug and refine your models. Let’s dive into how it works and what you can build.

AI is breaking free from rigid scripts. LangGraph’s agent architectures enable adaptable, collaborative systems. They think, learn, and respond in real-time. Here’s how to build smarter solutions with them.

Building smarter AI means tackling the complexities of evaluating Retrieval-Augmented Generation (RAG) systems. Giskard’s RAG Evaluation Toolkit (RAGET) automates the process, identifying weaknesses in key components like retrievers and generators. With tailored diagnostics, it simplifies fine-tuning while enhancing performance and reliability. This post shows you how to streamline RAG evaluation and unlock better AI.

Turn your data into a conversation. "Talk to Tabular Data" lets you analyze CSV files effortlessly. Powered by Streamlit, GPT-4, and agentic workflows, it blends simplicity with intelligence. Insights are now just a question away.

PDFs store valuable data, but accessing it isn’t easy. Using LLMs, Python, and NLP, you can extract text, process tables, and build interactive Q&A tools. Transform static PDFs into dynamic, queryable data sources. Let's dive in.

Data is a goldmine. Automated feature engineering is your mining rig. It uncovers hidden patterns, builds powerful features, and saves time. This is how you strike gold.

Alone, models have limits. Together, they shine. Ensemble methods combine multiple models to reduce errors, balance bias and variance, and deliver smarter predictions. This guide unpacks the mechanics — clear, simple, and powerful.

Building a great machine learning model is like baking the perfect cake. The right ingredients matter — not everything in your pantry belongs. This guide shows you how to identify and craft features that truly make a difference. Stop guessing. Start engineering success.

Too much data isn’t always a blessing. Hidden inside the chaos are the signals you need—but finding them is the real challenge. Miss the signals, and your model drowns in noise. Here’s how to cut through the clutter and uncover what truly matters.

Two players, two puzzles, two approaches. One has a guidebook, showing exactly how to solve it. The other has no guide, relying on intuition to find patterns. This is the difference between supervised and unsupervised learning. One learns with clear labels, the other explores without predefined answers. Feature engineering? It’s the secret weapon tailored differently for both approaches. Let’s break it down.