Agreed on the adversarial concern, but the economics here enable production-scale testing which is where real adversarial validation happens
RESEARCH
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GPT-5.5 Spud Incoming: OpenAI’s Make-or-Break Moment
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So GPT-5.5 "Spud" is coming. OpenAI finished pretraining around March 24. Altman called it "a very strong model that could really accelerate the economy" and Brockman said there are two years of research inside it. From what we're hearing, Spud is not just another update. It's supposedly a completely new base model with native multimodality, stronger agentic capabilities, and better understanding of what users actually want without having to over-explain. They even shut down Sora to put everything into this. Also worth noting that Q2 2026 is looking insane. Claude Mythos, DeepSeek V4, Grok 5 are all expected around the same time. This is going to be the most competitive quarter in AI history. Of course, take all of this with a grain of salt. Most of it is still unverified leaks and rumors. But here's my honest thought. GPT-5.5 is clearly one of the most anticipated models this year and this feels like a make-or-break moment for OpenAI. Whether Spud succeeds or not, I really hope they reconsider how they treat their models and find a way to replace or soften their approach to CoT monitoring. What made 4o special wasn't just performance. It was the way it felt human.
→ View original post on X — @montreal_ai, 2026-04-05 15:04 UTC
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Coding agents outperform long-context models
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BREAKING: Duke researchers just proved that coding agents are better at processing long documents than models with million-token context windows. > Not because of longer context. Because grep and sed are better retrieval tools than attention. > +17.3% average improvement
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ETS: Energy-Guided Scaling for RL Alignment
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ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment Li et al.: arxiv.org/abs/2601.21484 #AIAgents #ReinforcementLearning [Translated from EN to English]
→ View original post on X — @ceobillionaire, 2026-04-05 14:56 UTC
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Decades of AI safety warnings from the author
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quotes? what do you mean by back then? eg 2023 in the Senate? 2024 in my book taming silicon valley? 2022 in my essay on AI’s jurassic park? 2019 my book rebooting AI? i have been warning about the dangers (though not extinction) for years. no idea what you are talking about
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Huawei’s Smart Noise Scheduler Improves Diffusion LLM Logic Learning
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What if Diffusion LLMs learned logic more efficiently? Huawei's Noah's Ark Lab proposes a breakthrough: their "smart noise scheduler" uses priority masking to focus training on only information-dense data, making DLLMs master core reasoning and structure. This boosts average accuracy by 4% on Code & Math reasoning, beating uniform baselines and unlocking DLLM potential. Mask Is What DLLM Needs: A Masked Data Training Paradigm for Diffusion LLMs Paper: arxiv.org/abs/2603.15803 Dataset: huggingface.co/datasets/malr… Our report: mp.weixin.qq.com/s/1yTd36hev… 📬 #PapersAccepted by Jiqizhixin
→ View original post on X — @jiqizhixin, 2026-04-05 14:05 UTC
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Top Python Packages for Data Science and Machine Learning
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A List of Top #Python Packages for Data Science! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #RStats #TensorFlow #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/List-of-Py-Pac
kages
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Neural Networks For Beginners: Complete Guide
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Neural Networks For Beginners! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Neural-Nets-Ne
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Best Machine Learning Books for Data Science Experts
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Best #MachineLearning Books for Experts in Data Science! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode