Community Blog

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.

You’re staring at a dataset with dozens of features—some critical, some redundant, some pure chaos. Your goal? Cut through the noise, simplify the data, and make your model perform. This is where PCA and LDA step in. PCA summarizes the data; LDA separates the classes. Both reduce dimensionality, but their purpose and approach are entirely distinct.

Ensemble methods are like solving a problem with a team of experts. Some work independently and combine their insights. Others learn from each other, improving with every step. This is the essence of bagging vs. boosting—two strategies with the same goal: better accuracy of Machine Learning models through collaboration. Bagging reduces variance by training models separately, while boosting reduces bias by having models build on each other’s mistakes.

Building without leveling the ground first? A recipe for disaster. The same goes for machine learning with raw, unprepared data. Feature preprocessing is the foundation. It cleans, transforms, and encodes your data to eliminate noise, handle missing values, and bring consistency. Without it, even the most sophisticated models will crumble under the weight of bad inputs.

Memorizing a textbook word-for-word might ace you a quiz but leave you clueless in a real-world scenario. This is overfitting in machine learning—a model so fixated on training data that it stumbles when faced with new challenges. Ensemble methods like bagging, boosting, and stacking act as tutors. They teach models to recognize patterns, ignore noise, and generalize effectively for unseen data.

Making the world’s best pizza doesn’t start with baking—it starts with preparation. The dough, sauce, and toppings need to be sliced, kneaded, and seasoned to perfection. In data science, this process is called feature engineering. It’s the art of transforming raw data into meaningful inputs that drive powerful machine-learning models and uncover actionable insights.

Your machine learning model might look perfect during training, but can it handle real-world data? Overfitting makes it memorize noise, while underfitting makes it miss key patterns. Without cross-validation, you’ll never know if your model is robust or just lucky. Here’s how cross-validation prevents these failures and ensures reliable predictions.

Looking for personalized investment advice based on your risk profile? In this article, you'll learn how to build an AI-powered Investment Advisor to analyze your financial data and generate customized recommendations. Let’s dive in and explore how AI is transforming financial planning!

Overfitting and underfitting are the silent killers of machine learning models. Too simple, and your model misses the point. Too complex, and it sees patterns that don’t exist. Let’s dive in and uncover how to strike the perfect balance.

Machine learning models excel in different ways. Stacking combines algorithms like decision trees, logistic regression, and neural networks to boost accuracy, reduce bias, and improve generalization. Learn how this powerful ensemble technique optimizes predictions and transforms model performance.

Labeled data is costly. Unlabeled data is plentiful. Semi-supervised learning combines both, optimizing machine learning performance while reducing data annotation efforts.

Data is only as powerful as the features you create. Feature engineering boosts model accuracy, reveals hidden patterns, and turns raw data into actionable predictions. Master the foundational techniques every beginner in machine learning needs to know.

Ensemble methods are a secret weapon in machine learning. By combining multiple models, they boost accuracy, reduce errors, and create more robust predictions. Let’s break down what makes them so effective.

Building a machine learning model is just one piece of the puzzle. Feature engineering is where models gain clarity and precision. It’s about shaping data to uncover patterns and improve performance. Here’s how it changes everything.

AI workflows shouldn’t just follow a script—they should think, adapt, and evolve. LangGraph turns linear processes into dynamic, stateful systems where agents collaborate, make decisions, and learn over time. Build smarter AI applications that don’t just respond—they interact, remember, and grow.

Building with LLMs is powerful, but unpredictable. LangSmith brings order to the chaos with tools for observability, evaluation, and optimization. See what your models are doing, measure how they’re performing, and deploy with confidence.

Your AI, your rules. Ollama lets you run large language models on your own terms—local hosting, full control, and no third-party dependencies. Discover how Ollama makes LLM integration seamless, secure, and scalable.

Your data has a voice—it just needs the right tools to speak. LlamaIndex is the framework that connects large language models to your specific data, unlocking new levels of context and accuracy. From chatbots to autonomous agents, see how LlamaIndex redefines what’s possible with AI.

AI is learning to talk, think, and create like never before. Large Language Models (LLMs) are leading this revolution, transforming industries with human-like language skills. Let’s dive into what these powerful tools can actually do.

Who owns the art when it’s crafted by an algorithm? As AI tools take the creative stage, the answer isn’t so clear. Let’s unravel the tangled web of AI-generated image ownership.

Imagine an AI that not only sees but understands. Visual Question Answering is revolutionizing how machines interpret our world. With LLMs in the mix, AI's visual comprehension is reaching new heights. Let’s dive in.

Building with large language models can be complex. LangChain makes it simpler. This open-source framework brings together LLMs, data modules, and workflow tools—all in one place—to power up your next AI project.

AI makes creating content effortless. But is using AI-generated work actually legal? For students, marketers, and creators, the stakes are high. Let’s dive into the shifting legal landscape of AI content.

AI jargon overload. Large Language Models. Foundation Models. You’ve heard the terms, but what’s the difference? Are LLMs simply a branch of foundation models, or is there more to the story? Let’s unravel the connection.

The rise of AI in art, writing, and media has given us powerful tools—and powerful questions. We rely on AI detectors to distinguish machine from human. But are they keeping up?

Lost in a sea of meeting notes? Struggling to keep track of project tasks? There’s a better way.

Large Language Models (LLMs) are reshaping AI in ways that go beyond simple text processing. They sit at the intersection of NLP and deep learning, driving a new wave of generative AI. Here’s how LLMs fit into the broader AI ecosystem.

Generative AI is reshaping how we create—text, art, even music. At the core of this innovation are Large Language Models (LLMs), powering everything from chatbots to coding tools. Explore how these models transform raw data into human-like content.

How does an AI model learn to answer anything from casual questions to coding problems? It devours massive amounts of text. From Wikipedia to GitHub, these models are trained on diverse data sources that shape their abilities—and not all data is created equal.

Your phone knows what you’ll type next. Virtual assistants understand your voice. ChatGPT and other AI tools are flipping our workflows. The magic? Large Language Models. Here’s how they work and why they’re everywhere.