Most AI model releases focus on benchmarks. This one is more interesting because of what Microsoft says it did not use. With MAI-Thinking-1, Microsoft claims no third-party LLM-generated synthetic data during pre-training, active filtering of AI-generated content, and no hidden
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AI’s Enterprise Scale Requires Data and Network Under Pressure
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At the PGA Championship, the real story was not just “AI at an event.” The real story was what AI requires to be useful at enterprise scale. AI depends on data. Data depends on the network. And under pressure, the question is not: “Can the model find a pattern?” It
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Fast answers on shallow evidence reproduce bias faster
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Fast answers built on a shallow evidence base do not solve the bias problem. They reproduce it faster.
— Catherine Adenle (@CatherineAdenle) 24 juin 2026
As AI becomes part of research and decision-making, the quality of the underlying evidence matters as much as the capability of the model.
Better decisions require broader… pic.twitter.com/VI8ZufuFiXFast answers built on a shallow evidence base do not solve the bias problem. They reproduce it faster. As AI becomes part of research and decision-making, the quality of the underlying evidence matters as much as the capability of the model. Better decisions require broader
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Machine Learning Solutions Architect Handbook: ML Lifecycle, MLOps, Generative AI
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Machine Learning Solutions Architect Handbook — Practical Strategies and Best Practices in the ML Lifecycle, System Design, MLOps, and Generative AI: http://
amzn.to/4bx8t6b v/ @PacktDataML -
Data Without Labels: Unsupervised Machine Learning Book Overview
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Data Without Labels — Models and Algorithms for Practical Unsupervised Machine Learning: http://
amzn.to/4q5bbYz 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷: Fundamental building blocks and concepts of machine learning and unsupervised learning
Data cleaning for structured and -
3rd Edition of Python Feature Engineering Cookbook for ML
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3rd Edition! "Python Feature Engineering Cookbook", complete guidebook with recipes for crafting powerful features for Machine Learning models: http://
amzn.to/4rDWUT9 by @Soledad_Galli —————
#AI #ML #DataScience #DataScientist -
Scikit-learn Cookbook 3rd Edition: 80+ Recipes, Preprocessing, Dimensionality
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Scikit-learn Cookbook — 80+ recipes for Machine Learning in Python with scikit-learn [3rd Edition]: http://
amzn.to/4oDGOq7 v/ @PacktDataML 𝓒𝓸𝓷𝓽𝓮𝓷𝓽𝓼:
Common Conventions & API Elements of Scikit-Learn
Pre-Model Workflow and Data Preprocessing
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XGBoost for Regression, Predictive Modeling, and Time Series Analysis
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XGBoost for Regression, Predictive Modeling, and Time Series Analysis — Learn how to build, evaluate, & deploy predictive models: http://
amzn.to/4l2YcU9 v/ @PacktDataML -
Anthropic’s Claude Tag: Always-On Slack Teammate for Multi-Step Workflows
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🆕Anthropic’s new Claude Tag turns @Claude into a shared, always-on teammate in Slack that breaks down tagged requests into multi-step workflows, writes and merges PRs, runs data analysis, and even follows up on quiet threads using ambient context across channels; already… pic.twitter.com/yl7U3MyetZ
— Futurepedia – Learn to Leverage AI (@futurepedia_io) 24 juin 2026Anthropic’s new Claude Tag turns @Claude into a shared, always-on teammate in Slack that breaks down tagged requests into multi-step workflows, writes and merges PRs, runs data analysis, and even follows up on quiet threads using ambient context across channels; already
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Zai IPOs at HK$120, GLM beats DeepSeek as world’s top open model
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btw Zai IPO'ed in Jan at HK$120 a share. when I first met @louszbd nobody really knew anyone using GLM's. now they have beat deepseek with the world's undisputed top open model and in some respects (see @ml_angelopoulos
) say top model period, and are returning to SF