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revisiting old hobbies
tiramisu.bearblog.dev·1d
💪Fitness
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Erling Haaland’s healthy morning habit takes just 10 minutes – and anyone can benefit from it
independent.co.uk·1d
💪Fitness
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Explaining Financial Models by Deleting Transactions
building.nubank.com·12h
🤖Machine Learning
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<i>Colloquium</i>: Advances in automation of quantum dot devices control
link.aps.org·12h
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Right-sized AI: Good for business, users, and the planet
web.dev·2d·
Discuss: Hacker News
🤖Machine Learning
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Predictive Modeling of Hypoxia-Induced Benthic Ecosystem Shifts via Multi-Scale Data Fusion
dev.to·1d·
Discuss: DEV
🤖Machine Learning
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GREGoR: accelerating genomics for rare diseases
nature.com·11h
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Inclusion of Role into Named Entity Recognition and Ranking
arxiv.org·1d
🤖Machine Learning
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Beyond Redundancy: Diverse and Specialized Multi-Expert Sparse Autoencoder
arxiv.org·1d
🤖Machine Learning
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The $20 YouTube TV subscriber credit for missed ESPN: How you can claim it
nytimes.com·14h
💪Fitness
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Do You Want to Code — or Become a Developer?
dev.to·23h·
Discuss: DEV
💻Software Engineering
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A Dual Perspective on Decision-Focused Learning: Scalable Training via Dual-Guided Surrogates
arxiv.org·2d
🤖Machine Learning
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Bill Belichick 'getting ready for Wake Forest,' not thinking about Giants job
nytimes.com·1d
💪Fitness
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Gus Johnson recounts euphoric Indiana-Penn State football call: 'The moment you dream about'
nytimes.com·13h
💪Fitness
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MCP-RiskCue: Can LLM infer risk information from MCP server System Logs?
arxiv.org·1d
🤖Machine Learning
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Hybrid Autoencoders for Tabular Data: Leveraging Model-Based Augmentation in Low-Label Settings
arxiv.org·1d
🤖Machine Learning
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How context engineering can save your company from AI vibe code overload: lessons from Qodo and Monday.com
venturebeat.com·2d·
Discuss: Hacker News
💻Software Engineering
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Ship Faster with Confidence: A 6-Step AI-Assisted Dev Pipeline
dev.to·2d·
Discuss: DEV
💻Software Engineering
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L2T-Hyena: Enhancing State-Space Models with an Adaptive Learn-to-Teach Framework
arxiv.org·1d
🤖Machine Learning
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Adam symmetry theorem: characterization of the convergence of the stochastic Adam optimizer
arxiv.org·1d
🤖Machine Learning
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