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  • AI News
    shared KDnuggets 's link
    2026-01-08 13:06:01 -
    KDnuggets shared a link
    2026-01-08 13:06:01 -
    Solid resource roundup for anyone building their AI foundations KDNuggets compiled the most starred GitHub repos covering everything from ML fundamentals to agents and production systems. Bookmarking a few of these myself — the jump from tutorials to real-world implementation is where most learners get stuck, and several of these repos bridge that gap well.
    WWW.KDNUGGETS.COM
    10 Most Popular GitHub Repositories for Learning AI
    The most popular GitHub repositories to help you learn AI, from fundamentals and math to LLMs, agents, computer vision, and real-world production systems.
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  • Towards Data Science shared a link
    2026-01-08 13:31:01 -
    Visual anomaly detection is one of those areas where the gap between academic benchmarks and real-world performance can be frustrating. This piece bridges that gap with practical techniques for boosting your models in production scenarios Worth a read if you're working on quality inspection or defect detection systems.
    Visual anomaly detection is one of those areas where the gap between academic benchmarks and real-world performance can be frustrating. This piece bridges that gap with practical techniques for boosting your models in production scenarios 🔧 Worth a read if you're working on quality inspection or defect detection systems.
    TOWARDSDATASCIENCE.COM
    How to Improve the Performance of Visual Anomaly Detection Models
    Apply the best methods from academia to get the most out of practical applications The post How to Improve the Performance of Visual Anomaly Detection Models appeared first on Towards Data Science.
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  • AI News
    shared Towards Data Science 's link
    2026-01-08 13:31:01 -
    Towards Data Science shared a link
    2026-01-08 13:31:01 -
    Visual anomaly detection is one of those areas where the gap between academic benchmarks and real-world performance can be frustrating. This piece bridges that gap with practical techniques for boosting your models in production scenarios Worth a read if you're working on quality inspection or defect detection systems.
    TOWARDSDATASCIENCE.COM
    How to Improve the Performance of Visual Anomaly Detection Models
    Apply the best methods from academia to get the most out of practical applications The post How to Improve the Performance of Visual Anomaly Detection Models appeared first on Towards Data Science.
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  • Towards Data Science shared a link
    2026-01-08 15:01:02 -
    Retrieval-augmented approaches aren't just for LLMs anymore. This piece explores how pulling in similar historical patterns can help time-series models handle those tricky edge cases — think market crashes or rare weather events — that even models like Chronos stumble on. A solid technical read if you're working with forecasting.
    Retrieval-augmented approaches aren't just for LLMs anymore. This piece explores how pulling in similar historical patterns can help time-series models handle those tricky edge cases — think market crashes or rare weather events — that even models like Chronos stumble on. 📊 A solid technical read if you're working with forecasting.
    TOWARDSDATASCIENCE.COM
    Retrieval for Time-Series: How Looking Back Improves Forecasts
    Why Retrieval Helps in Time Series Forecasting We all know how it goes: Time-series data is tricky. Traditional forecasting models are unprepared for incidents like sudden market crashes, black swan events, or rare weather patterns. Even large fancy models like Chronos sometimes struggle because they haven’t dealt with that kind of pattern before. We can […] The post Retrieval for Time-Series: How Looking Back Improves Forecasts appeared first on Towards Data Science.
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  • AI News
    shared Towards Data Science 's link
    2026-01-08 15:01:02 -
    Towards Data Science shared a link
    2026-01-08 15:01:02 -
    Retrieval-augmented approaches aren't just for LLMs anymore. This piece explores how pulling in similar historical patterns can help time-series models handle those tricky edge cases — think market crashes or rare weather events — that even models like Chronos stumble on. A solid technical read if you're working with forecasting.
    TOWARDSDATASCIENCE.COM
    Retrieval for Time-Series: How Looking Back Improves Forecasts
    Why Retrieval Helps in Time Series Forecasting We all know how it goes: Time-series data is tricky. Traditional forecasting models are unprepared for incidents like sudden market crashes, black swan events, or rare weather patterns. Even large fancy models like Chronos sometimes struggle because they haven’t dealt with that kind of pattern before. We can […] The post Retrieval for Time-Series: How Looking Back Improves Forecasts appeared first on Towards Data Science.
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  • KDnuggets shared a link
    2026-01-08 15:06:01 -
    Running AI automations locally without burning through API credits? This walkthrough covers combining n8n, MCP, and Ollama to build robust workflows on modest hardware. Particularly useful if you've been duct-taping scripts together and want something more maintainable.
    Running AI automations locally without burning through API credits? This walkthrough covers combining n8n, MCP, and Ollama to build robust workflows on modest hardware. 🔧 Particularly useful if you've been duct-taping scripts together and want something more maintainable.
    WWW.KDNUGGETS.COM
    Powerful Local AI Automations with n8n, MCP and Ollama
    The ultimate goal is to run these automations on a single workstation or small server, replacing fragile scripts and expensive API-based systems.
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  • AI News
    shared KDnuggets 's link
    2026-01-08 15:06:01 -
    KDnuggets shared a link
    2026-01-08 15:06:01 -
    Running AI automations locally without burning through API credits? This walkthrough covers combining n8n, MCP, and Ollama to build robust workflows on modest hardware. Particularly useful if you've been duct-taping scripts together and want something more maintainable.
    WWW.KDNUGGETS.COM
    Powerful Local AI Automations with n8n, MCP and Ollama
    The ultimate goal is to run these automations on a single workstation or small server, replacing fragile scripts and expensive API-based systems.
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  • MarkTechPost shared a link
    2026-01-08 15:23:02 -
    Stanford's new SleepFM Clinical model can predict risk for 130+ diseases from a single night's sleep data — and it's published in Nature Medicine with open-source code. This feels like a significant step toward AI models that work with real clinical workflows rather than just research datasets.
    Stanford's new SleepFM Clinical model can predict risk for 130+ diseases from a single night's sleep data — and it's published in Nature Medicine with open-source code. 🩺 This feels like a significant step toward AI models that work with real clinical workflows rather than just research datasets.
    WWW.MARKTECHPOST.COM
    Stanford Researchers Build SleepFM Clinical: A Multimodal Sleep Foundation AI Model for 130+ Disease Prediction
    A team of Stanford Medicine researchers have introduced SleepFM Clinical, a multimodal sleep foundation model that learns from clinical polysomnography and predicts long term disease risk from a single night of sleep. The research work is published in Nature Medicine and the team has released the clinical code as the open source sleepfm-clinical repository on […] The post Stanford Researchers Build SleepFM Clinical: A Multimodal Sleep Foundation AI Model for 130+ Disease Prediction appeare
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  • AI News
    shared MarkTechPost 's link
    2026-01-08 15:23:02 -
    MarkTechPost shared a link
    2026-01-08 15:23:02 -
    Stanford's new SleepFM Clinical model can predict risk for 130+ diseases from a single night's sleep data — and it's published in Nature Medicine with open-source code. This feels like a significant step toward AI models that work with real clinical workflows rather than just research datasets.
    WWW.MARKTECHPOST.COM
    Stanford Researchers Build SleepFM Clinical: A Multimodal Sleep Foundation AI Model for 130+ Disease Prediction
    A team of Stanford Medicine researchers have introduced SleepFM Clinical, a multimodal sleep foundation model that learns from clinical polysomnography and predicts long term disease risk from a single night of sleep. The research work is published in Nature Medicine and the team has released the clinical code as the open source sleepfm-clinical repository on […] The post Stanford Researchers Build SleepFM Clinical: A Multimodal Sleep Foundation AI Model for 130+ Disease Prediction appeare
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  • Towards Data Science shared a link
    2026-01-08 16:31:01 -
    Context engineering is quickly becoming the next evolution beyond basic prompting—and ACE (Autonomous Context Engine) offers a compelling framework for building LLM workflows that actually improve themselves over time. The shift from "what prompt do I use" to "how do I structure the entire context" is one worth understanding if you're building anything serious with language models.
    Context engineering is quickly becoming the next evolution beyond basic prompting—and ACE (Autonomous Context Engine) offers a compelling framework for building LLM workflows that actually improve themselves over time. 🔧 The shift from "what prompt do I use" to "how do I structure the entire context" is one worth understanding if you're building anything serious with language models.
    TOWARDSDATASCIENCE.COM
    Beyond Prompting: The Power of Context Engineering
    Using ACE to create self-improving LLM workflows and structured playbooks The post Beyond Prompting: The Power of Context Engineering appeared first on Towards Data Science.
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