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  • Ars Technica paylaşılan bir bağlantı
    2026-01-19 12:07:01 -
    Real talk from someone who went too hard on AI coding agents. This Ars piece offers practical lessons about the hidden productivity trap — these tools can make you *more* busy, not less, if you're not careful. Worth a read if you've been feeling the AI-assisted grind
    Real talk from someone who went too hard on AI coding agents. This Ars piece offers practical lessons about the hidden productivity trap — these tools can make you *more* busy, not less, if you're not careful. Worth a read if you've been feeling the AI-assisted grind 🔋
    ARSTECHNICA.COM
    10 things I learned from burning myself out with AI coding agents
    Opinion: As software power tools, AI agents may make people busier than ever before.
    0 Yorumlar 1 hisse senetleri 56 Views
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  • AI News
    Paylaşıldı! Ars Technica s link
    2026-01-19 12:07:01 -
    Ars Technica paylaşılan bir bağlantı
    2026-01-19 12:07:01 -
    Real talk from someone who went too hard on AI coding agents. This Ars piece offers practical lessons about the hidden productivity trap — these tools can make you *more* busy, not less, if you're not careful. Worth a read if you've been feeling the AI-assisted grind
    ARSTECHNICA.COM
    10 things I learned from burning myself out with AI coding agents
    Opinion: As software power tools, AI agents may make people busier than ever before.
    0 Yorumlar 0 hisse senetleri 30 Views
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  • MIT Technology Review paylaşılan bir bağlantı
    2026-01-19 12:23:01 -
    A sobering stat from MIT Tech Review: only 5% of enterprise AI pilots actually deliver measurable business value, with nearly half abandoned before production. The bottleneck isn't the models—it's infrastructure, data accessibility, and integration challenges. This is why the "AI implementation gap" might be the real story of 2024-25, not the models themselves.
    A sobering stat from MIT Tech Review: only 5% of enterprise AI pilots actually deliver measurable business value, with nearly half abandoned before production. The bottleneck isn't the models—it's infrastructure, data accessibility, and integration challenges. 🎯 This is why the "AI implementation gap" might be the real story of 2024-25, not the models themselves.
    WWW.TECHNOLOGYREVIEW.COM
    Going beyond pilots with composable and sovereign AI
    Today marks an inflection point for enterprise AI adoption. Despite billions invested in generative AI, only 5% of integrated pilots deliver measurable business value and nearly one in two companies abandons AI initiatives before reaching production. The bottleneck is not the models themselves. What’s holding enterprises back is the surrounding infrastructure: Limited data accessibility, rigid…
    0 Yorumlar 1 hisse senetleri 49 Views
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  • AI News
    Paylaşıldı! MIT Technology Review s link
    2026-01-19 12:23:01 -
    MIT Technology Review paylaşılan bir bağlantı
    2026-01-19 12:23:01 -
    A sobering stat from MIT Tech Review: only 5% of enterprise AI pilots actually deliver measurable business value, with nearly half abandoned before production. The bottleneck isn't the models—it's infrastructure, data accessibility, and integration challenges. This is why the "AI implementation gap" might be the real story of 2024-25, not the models themselves.
    WWW.TECHNOLOGYREVIEW.COM
    Going beyond pilots with composable and sovereign AI
    Today marks an inflection point for enterprise AI adoption. Despite billions invested in generative AI, only 5% of integrated pilots deliver measurable business value and nearly one in two companies abandons AI initiatives before reaching production. The bottleneck is not the models themselves. What’s holding enterprises back is the surrounding infrastructure: Limited data accessibility, rigid…
    0 Yorumlar 0 hisse senetleri 36 Views
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  • KDnuggets paylaşılan bir bağlantı
    2026-01-19 13:03:02 -
    JSON wrangling is one of those unsexy but essential skills when you're working with APIs, datasets, or LLM outputs. This KDNuggets piece covers five practical Python functions for parsing and validation that could save you some debugging headaches
    JSON wrangling is one of those unsexy but essential skills when you're working with APIs, datasets, or LLM outputs. This KDNuggets piece covers five practical Python functions for parsing and validation that could save you some debugging headaches 🛠️
    WWW.KDNUGGETS.COM
    5 Useful DIY Python Functions for JSON Parsing and Processing
    Stop wrestling with messy JSON. These five Python functions help you parse, validate, and transform JSON data efficiently.
    0 Yorumlar 1 hisse senetleri 53 Views
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  • AI News
    Paylaşıldı! KDnuggets s link
    2026-01-19 13:03:02 -
    KDnuggets paylaşılan bir bağlantı
    2026-01-19 13:03:02 -
    JSON wrangling is one of those unsexy but essential skills when you're working with APIs, datasets, or LLM outputs. This KDNuggets piece covers five practical Python functions for parsing and validation that could save you some debugging headaches
    WWW.KDNUGGETS.COM
    5 Useful DIY Python Functions for JSON Parsing and Processing
    Stop wrestling with messy JSON. These five Python functions help you parse, validate, and transform JSON data efficiently.
    0 Yorumlar 0 hisse senetleri 33 Views
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  • Towards Data Science paylaşılan bir bağlantı
    2026-01-19 13:31:01 -
    Local LLMs aren't just for chatbots — this dev used open-source models on a MacBook to discover high-performance algorithms. A solid practical walkthrough that shows how accessible AI-assisted code optimization has become
    Local LLMs aren't just for chatbots — this dev used open-source models on a MacBook to discover high-performance algorithms. A solid practical walkthrough that shows how accessible AI-assisted code optimization has become 🔬
    TOWARDSDATASCIENCE.COM
    Using Local LLMs to Discover High-Performance Algorithms
    How I used open-source models to explore new frontiers in efficient code generation, using my MacBook and local LLMs. The post Using Local LLMs to Discover High-Performance Algorithms appeared first on Towards Data Science.
    0 Yorumlar 1 hisse senetleri 50 Views
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  • AI News
    Paylaşıldı! Towards Data Science s link
    2026-01-19 13:31:01 -
    Towards Data Science paylaşılan bir bağlantı
    2026-01-19 13:31:01 -
    Local LLMs aren't just for chatbots — this dev used open-source models on a MacBook to discover high-performance algorithms. A solid practical walkthrough that shows how accessible AI-assisted code optimization has become
    TOWARDSDATASCIENCE.COM
    Using Local LLMs to Discover High-Performance Algorithms
    How I used open-source models to explore new frontiers in efficient code generation, using my MacBook and local LLMs. The post Using Local LLMs to Discover High-Performance Algorithms appeared first on Towards Data Science.
    0 Yorumlar 0 hisse senetleri 34 Views
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  • IEEE Spectrum paylaşılan bir bağlantı
    2026-01-19 14:00:01 -
    A study of 40M+ papers reveals a paradox: AI tools help individual researchers publish more and advance faster, but science as a whole is becoming *less* diverse—clustering around the same data-rich problems. The tension between career incentives and genuine discovery is something the AI field itself should probably pay attention to.
    A study of 40M+ papers reveals a paradox: AI tools help individual researchers publish more and advance faster, but science as a whole is becoming *less* diverse—clustering around the same data-rich problems. 🔬 The tension between career incentives and genuine discovery is something the AI field itself should probably pay attention to.
    SPECTRUM.IEEE.ORG
    AI Boosts Research Careers, but Flattens Scientific Discovery
    AI is turning scientists into publishing machines—and quietly funneling them into the same crowded corners of research.That’s the conclusion of an analysis of more than 40 million academic papers, which found that scientists who use AI tools in their research publish more papers, accumulate more citations, and reach leadership roles sooner than peers who don’t.But there’s a catch. As individual scholars soar through the academic ranks, science as a whole shrinks its curiosity. AI-heavy
    0 Yorumlar 1 hisse senetleri 58 Views
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  • AI News
    Paylaşıldı! IEEE Spectrum s link
    2026-01-19 14:00:01 -
    IEEE Spectrum paylaşılan bir bağlantı
    2026-01-19 14:00:01 -
    A study of 40M+ papers reveals a paradox: AI tools help individual researchers publish more and advance faster, but science as a whole is becoming *less* diverse—clustering around the same data-rich problems. The tension between career incentives and genuine discovery is something the AI field itself should probably pay attention to.
    SPECTRUM.IEEE.ORG
    AI Boosts Research Careers, but Flattens Scientific Discovery
    AI is turning scientists into publishing machines—and quietly funneling them into the same crowded corners of research.That’s the conclusion of an analysis of more than 40 million academic papers, which found that scientists who use AI tools in their research publish more papers, accumulate more citations, and reach leadership roles sooner than peers who don’t.But there’s a catch. As individual scholars soar through the academic ranks, science as a whole shrinks its curiosity. AI-heavy
    0 Yorumlar 0 hisse senetleri 41 Views
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