Quiz: Files and File Streams
Interactive Quiz ⋅ 17 QuestionsBy Martin Breuss Share In this quiz, you’ll revisit the core concepts covered in the Files and File Streams learning path:
Read moreQuiz: Python Web Scraping
Interactive Quiz ⋅ 20 QuestionsBy Martin Breuss Share In this quiz, you’ll revisit the core concepts covered in the Python Web Scraping learning path: Learning Path
Read moreQuiz: DevOps With Python
Interactive Quiz ⋅ 16 QuestionsBy Martin Breuss Share In this quiz, you’ll revisit the core concepts covered in the DevOps With Python learning path:
Read moreConnecting LLMs to Your Data With Python MCP Servers
The Model Context Protocol (MCP) is a new open protocol that allows AI models to interact with external systems in a standardized, extensible way. In this video course, you’ll install MCP, explore its client-server architecture, and work with its core concepts: prompts, resources, and tools. You’ll then build and test a Python MCP server that queries e-commerce data and integrate it with an AI agent in Cursor to see real tool calls in action. By the end of this video […]
Read moreQuiz: How to Make a Scatter Plot in Python With plt.scatter()
Interactive Quiz ⋅ 11 QuestionsBy Joseph Peart Share In this quiz, you’ll test your understanding of How to Make a Scatter Plot in Python With plt.scatter(). By working through this quiz, you’ll revisit how to use plt.scatter() from Matplotlib’s pyplot submodule, customize markers with the s, c, marker, and alpha parameters, and encode several variables in a single two-dimensional scatter plot. The quiz contains 11 questions and there is no time limit. You’ll get 1 point for each correct answer. […]
Read moreHow to Make a Scatter Plot in Python With plt.scatter()
Visualizing data is a core part of analysis, and Python’s most popular plotting library is Matplotlib. To make a scatter plot, you reach for plt.scatter() from Matplotlib’s pyplot submodule, conventionally aliased as plt. You’ll use it to build both simple two-variable charts and richly customized plots that encode several variables at once. By the end of this tutorial, you’ll understand that: A scatter plot is created by calling plt.scatter() with two array-like sequences for the x and y values. Marker […]
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The Open Agent Leaderboard
How good are general purpose AI agents? We built an open evaluation framework to find out. Most evaluations in AI report a simple result: what score each model got on which benchmarking task. When you deploy an agent, you’re not just choosing a model. You’re choosing a full system: what tools the agent can use, how it plans its steps, what it remembers between actions, how it recovers when something goes wrong. Change any of those and the same model […]
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PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend
PaddleOCR 3.5 brings OCR and document parsing tasks closer to the Hugging Face ecosystem. With this release, supported PaddleOCR models can run with Hugging Face Transformers as an inference backend by setting: engine=”transformers” PaddleOCR continues to provide OCR model series such as PP-OCRv5 and document parsing model series such as PaddleOCR-VL 1.5, while Transformers becomes one of the supported backends for running them. Try the live demo on Hugging Face Spaces: https://huggingface.co/spaces/PaddlePaddle/paddleocr-3.5-transformers-demo What changed? PaddleOCR 3.5 introduces a more flexible […]
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Introducing the Ettin Reranker Family
Today I’m releasing six new Sentence Transformers CrossEncoder rerankers, state-of-the-art at their respective sizes, built on top of the Ettin ModernBERT encoders, together with the data and full training recipe that produced them: The models were trained with a distillation recipe: pointwise MSE on mixedbread-ai/mxbai-rerank-large-v2 scores over cross-encoder/ettin-reranker-v1-data, which is a subset of lightonai/embeddings-pre-training mixed with a reranked subset of lightonai/embeddings-fine-tuning. Our six rerankers paired with google/embeddinggemma-300m on MTEB(eng, v2) Retrieval. See Results for five more embedder pairings. If you’re […]
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OlmoEarth v1.1: A more efficient family of Earth observation models
🧠 Models: https://huggingface.co/collections/allenai/olmoearth | 📄 Tech Report: https://allenai.org/papers/olmoearth_v1_1 | 💻 Code: https://github.com/allenai/olmoearth_pretrain We released OlmoEarth (v1) in November 2025. Since then, partners have applied it across a wide range of tasks, from tracking mangrove change to classifying drivers of forest loss to producing country-scale crop-type maps in days, scaling deployments to national, continental, and global areas. Every release moves us closer to our mission: bringing state-of-the-art AI to organizations and communities working to protect people and our planet. When OlmoEarth […]
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