Nine more Erdős problems have been solved. This time, however, by Google DeepMind. This shouldn't be underestimated, because on the one hand it increases competitive pressure, and on the other hand it proves that the other Frontier Labs can easily keep up.
MACHINE LEARNING
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Local LLMs From Zero to Hero Series for Beginners
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Don’t know where to start with Local AI? Read my Local LLMs From Zero to Hero series It covers:
– Hardware
– Software – Models Mechanics
– Everything else necessary Needs no prior experience Easy to understand for any background Local / Opensource AI FTW -
Local LLMs from Zero to Hero: 5-article series recap
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All covered in the last 5 articles I published Local LLMs from Zero to Hero
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Buy RTX 3090 to run local AI models cheaply and easily
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You should buy an RTX 3090 and learn how to run models locally The elite don’t want you to know this but running local models is hella easy, performant, and cheap nowadays
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Explaining the 7-Layer Claude Code Stack
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Most people use Claude Code like a chat box. It's actually a 7-layer system. Each layer compounds on the one below it. The bottom layer (CLAUDE. md) is where 90% of users stop. The top layer (Subagents) is where the real leverage starts. Here's the full stack, from foundation
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AI Programming in Azure HPC Pipeline at Scale
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That's AI programming in Action! Applied to Azure HPC pipeline using infrastructure loop, parallel processing, and optimized cloud pipeline to gain performance at scale! Not just math!
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50 Steps to Master AgenticAI in 2025-2026 by Python_Dv
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50 Steps to Master #AgenticAI in 2025 – 2026
by @Python_Dv #GenerativeAI #ArtificialIntelligence #MI #MachineLearning -
Google announces AI advances including personal AI assistant soon
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Google announces slew of #AI advances, including a personal AI assistant coming soon
by Kaitlyn Huamani @TechXplore_com Learn more: https://
bit.ly/3RCSKgZ #GenerativeAI #ArtificialIntelligence #MachineLearning -
Calibration vs. Discrimination in Model Uncertainty
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The calibration vs. discrimination distinction is crucial. A model can know its average error rate without knowing which particular answer is wrong. That is why “just abstain when uncertain” is not enough — poor discrimination creates a utility tax. Faithful uncertainty is a
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Model Calibration vs. Discrimination in AI Uncertainty
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The calibration vs. discrimination distinction is crucial. A model can know its average error rate without knowing which particular answer is wrong. That is why “just abstain when uncertain” is not enough — poor discrimination creates a utility tax. Faithful uncertainty is a