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Google DeepMind Releases AlphaGenome Atlas With Precomputed Molecular Effect Predictions and AVI Scores for 9 Billion Human DNA Variants

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Google DeepMind has released AlphaGenome Atlas, a catalogue of precomputed predictions for the molecular effects of every possible single-nucleotide variant in the human genome. That is roughly 9 billion single-letter changes. The release also introduces the AlphaGenome Variant Impact (AVI) score, a single number that ranks variants by predicted impact, plus per-variant feature attributions and […]

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Adaption Labs Introduces ‘Invent a Dataset’: Training Data Generated From a Task Description, Not a Seed Corpus

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This week, Adaption Labs released Invent a Dataset. The feature generates a structured, training-ready dataset from a description of the behavior you want a model to learn. You do not arrive with a seed corpus, a predefined schema, or a labeling guide. Is it deployable? Yes, with one caveat. Invent a Dataset is live now

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Feyn AI Releases SQRL, a Text-to-SQL Model Family That Inspects the Database Before Writing a Query

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Most text-to-SQL systems treat the task as translation. Feyn AI (YC-backed startup) reframes it around inspection. The Feyn team has released SQRL, a family of models that turn natural language questions into SQL. Instead of generating a query immediately, SQRL can inspect the database first. This lets it resolve ambiguity and write only queries the

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A Coding Hands-On on FineWeb for Streaming, Filtering, Deduplication, Tokenization, and Large-Scale Web Corpus Analytics

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In this tutorial, we explore the FineWeb dataset through an advanced hands-on workflow. We stream a manageable sample of the dataset without downloading the full multi-terabyte corpus, inspect its schema and metadata, and analyze key fields such as URL, language, language score, and token count. We also reproduce simplified versions of FineWeb’s quality-filtering pipeline, apply

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A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric

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In this tutorial, we build an end-to-end spatial graph learning pipeline using city2graph. We start by collecting real urban POI data and street network information from OpenStreetMap, with a synthetic fallback to ensure the workflow remains reliable. We then engineer spatial features, construct multiple proximity graph families, and compare how different graph-building strategies represent the

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How to Build an Advanced, Interactive Exploratory Data Analysis Workflow Using PyGWalker and Feature-Engineered Data

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In this tutorial, we demonstrate how to move beyond static, code-heavy charts and build a genuinely interactive exploratory data analysis workflow directly using PyGWalker. We start by preparing the Titanic dataset for large-scale interactive querying. These analysis-ready engineered features reveal the underlying structure of the data while enabling both detailed row-level exploration and high-level aggregated

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Alibaba Open-Sources Zvec: An Embedded Vector Database Bringing SQLite-like Simplicity and High-Performance On-Device RAG to Edge Applications

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Alibaba Tongyi Lab research team released ‘Zvec’, an open source, in-process vector database that targets edge and on-device retrieval workloads. It is positioned as ‘the SQLite of vector databases’ because it runs as a library inside your application and does not require any external service or daemon. It is designed for retrieval augmented generation (RAG),

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Top 18 Power BI Project Ideas For Practice 2026

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Power BI is an influential tool, shaping raw data into informative visuals and reports. With a user-friendly interface and formidable functionalities, Power BI is an invaluable platform for individuals to refine their skills through hands-on projects. By engaging in Power BI practice projects, beginners and experts can significantly augment their prowess. In this article, we

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Google Colab Integrates KaggleHub for One Click Access to Kaggle Datasets, Models and Competitions

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Google is closing an old gap between Kaggle and Colab. Colab now has a built in Data Explorer that lets you search Kaggle datasets, models and competitions directly inside a notebook, then pull them in through KaggleHub without leaving the editor. What Colab Data Explorer actually ships? Kaggle announced the feature recently where they describe

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