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Anthropic Releases Claude 4.6 Sonnet with 1 Million Token Context to Solve Complex Coding and Search for Developers

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Anthropic is officially entering its ‘Thinking’ era. Today, the company announced Claude 4.6 Sonnet, a model designed to transform how devs and data scientists handle complex logic. Alongside this release comes Improved Web Search with Dynamic Filtering, a feature that uses internal code execution to verify facts in real-time. https://www.anthropic.com/news/claude-sonnet-4-6 Adaptive Thinking: A New Logic

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Alibaba Qwen Team Releases Qwen3.5-397B MoE Model with 17B Active Parameters and 1M Token Context for AI agents

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Alibaba Cloud just updated the open-source landscape. Today, the Qwen team released Qwen3.5, the newest generation of their large language model (LLM) family. The most powerful version is Qwen3.5-397B-A17B. This model is a sparse Mixture-of-Experts (MoE) system. It combines massive reasoning power with high efficiency. Qwen3.5 is a native vision-language model. It is designed specifically

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Google DeepMind Proposes New Framework for Intelligent AI Delegation to Secure the Emerging Agentic Web for Future Economies

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The AI industry is currently obsessed with ‘agents’—autonomous programs that do more than just chat. However, most current multi-agent systems rely on brittle, hard-coded heuristics that fail when the environment changes. Google DeepMind researchers have proposed a new solution. The research team argued that for the ‘agentic web’ to scale, agents must move beyond simple

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PyCaret Tutorial: Beginner’s Guide to Automating ML Workflows

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PyCaret is an open-source, low-code machine learning library that simplifies and standardizes the end-to-end machine learning workflow. Instead of acting as a single AutoML algorithm, PyCaret functions as an experiment framework that wraps many popular machine learning libraries under a consistent and highly productive API  This design choice matters. PyCaret does not fully automate decision-making

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[In-Depth Guide] The Complete CTGAN + SDV Pipeline for High-Fidelity Synthetic Data

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In this tutorial, we build a complete, production-grade synthetic data pipeline using CTGAN and the SDV ecosystem. We start from raw mixed-type tabular data and progressively move toward constrained generation, conditional sampling, statistical validation, and downstream utility testing. Rather than stopping at sample generation, we focus on understanding how well synthetic data preserves structure, distributions,

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After a routine code rejection, an AI agent published a hit piece on someone by name

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On Monday, a pull request executed by an AI agent to the popular Python charting library matplotlib turned into a 45-comment debate about whether AI-generated code belongs in open source projects. What made that debate all the more unusual was that the AI agent itself took part, going so far as to publish a blog

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OpenAI Releases a Research Preview of GPT‑5.3-Codex-Spark: A 15x Faster AI Coding Model Delivering Over 1000 Tokens Per Second on Cerebras Hardware

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OpenAI just launched a new research preview called GPT-5.3 Codex-Spark. This model is built for 1 thing: extreme speed. While the standard GPT-5.3 Codex focuses on deep reasoning, Spark is designed for near-instant response times. It is the result of a deep hardware-software integration between OpenAI and Cerebras. The results are game-changing. Spark is 15x

OpenAI Releases a Research Preview of GPT‑5.3-Codex-Spark: A 15x Faster AI Coding Model Delivering Over 1000 Tokens Per Second on Cerebras Hardware Read More »

OpenAI sidesteps Nvidia with unusually fast coding model on plate-sized chips

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On Thursday, OpenAI released its first production AI model to run on non-Nvidia hardware, deploying the new GPT-5.3-Codex-Spark coding model on chips from Cerebras. The model delivers code at more than 1,000 tokens (chunks of data) per second, which is reported to be roughly 15 times faster than its predecessor. To compare, Anthropic’s Claude Opus

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Attackers prompted Gemini over 100,000 times while trying to clone it, Google says

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On Thursday, Google announced that “commercially motivated” actors have attempted to clone knowledge from its Gemini AI chatbot by simply prompting it. One adversarial session reportedly prompted the model more than 100,000 times across various non-English languages, collecting responses ostensibly to train a cheaper copycat. Google published the findings in what amounts to a quarterly

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Accelerating science with AI and simulations

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For more than a decade, MIT Associate Professor Rafael Gómez-Bombarelli has used artificial intelligence to create new materials. As the technology has expanded, so have his ambitions.Now, the newly tenured professor in materials science and engineering believes AI is poised to transform science in ways never before possible. His work at MIT and beyond is

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