Setting Goals for Your Team When the Path Isn’t Clear
How to keep moving forward when your organization’s strategy is evolving and conditions keep shifting.
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Gemini 3 Pro: the frontier of vision AI
Build with Gemini 3 Pro, the best model in the world for multimodal capabilities.
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TPOT: Automating ML Pipelines with Genetic Algorithms in Python
You can train, evaluate, and export a full ML pipeline in Python using TPOT with just a few lines of code.
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✋ Get A Warrant | EFFector 37.17
Even with the holidays coming up, the digital rights news doesn’t stop. Thankfully, EFF is here to keep you up-to-date with our EFFector newsletter! In our latest issue, we’re explaining why politicians latest attempts to ban VPNs is a terrible idea; asking supporters to file public comments opposing new rules that would make bad patents untouchable; and sharing a privacy victory—Sacramento is forced to end its dragnet surveillance program of power meter data. Prefer to listen in? Check out our audio companion, where […]
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YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World
A detailed walkthrough of the YOLOv1 architecture and its PyTorch implementation from scratch The post YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World appeared first on Towards Data Science.
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5 Cutting-Edge AutoML Techniques to Watch in 2026
This article discusses five cutting-edge AutoML techniques and trends that are expected to shape the landscape of highly automated machine learning model building in the 2026 year about to start.
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God Is Back from the Dead
In The Gay Science (1882), German philosopher Friedrich Nietzsche famously proclaimed the death of God. Recognizing the enormous implications of secularization and the uprooting of Christianity’s “fundamental concept” (faith in God) and the resulting moral confusion, he exclaimed: “God is dead! Continue Reading…
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Masked agents chased the wrong woman and DHS says the system worked
submitted by /u/eaglemaxie [link] [comments]
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MIT researchers propose a new model for legible, modular software
Coding with large language models (LLMs) holds huge promise, but it also exposes some long-standing flaws in software: code that’s messy, hard to change safely, and often opaque about what’s really happening under the hood. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are charting a more “modular” path ahead. Their new approach breaks systems into “concepts,” separate pieces of a system, each designed to do one job well, and “synchronizations,” explicit rules that describe exactly […]
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Prompt Compression for LLM Generation Optimization and Cost Reduction
Large language models (LLMs) are mainly trained to generate text responses to user queries or prompts, with complex reasoning under the hood that not only involves language generation by predicting each next token in the output sequence, but also entails a deep understanding of the linguistic patterns surrounding the user input text.
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