The new "Send to Alexa" feature lets you send Kindle Scribe notebooks to your Echo device with just a couple of taps. Alexa+ has been rolling out to users across the board (well, users with Prime, that is) as its Early Access becomes more widely available. Now, there's a new feature to explore if you're also a Kindle Scribe user: Send to Alexa. This lets you send your Kindle Scribe notes to the AI-powered assistant so you can ask questions about them without having to refer back to your Kindle. It won't automatically do this with all your notes.

When you purchase through links in our articles, we may earn a small commission. Geekom Geekbook X16 Pro review: Can the mini-PC maker build a great laptop? While many laptops with comparable CPU specifications are either significantly heavier or have plastic cases, the Geekbook offers a balanced combination of performance, mobility, and workmanship. Until now, Geekom was primarily known for its mini PCs. With the Geekbook X16 Pro, the company is now expanding its portfolio to include a notebook. The laptop market is highly competitive and dominated by numerous established manufacturers.

After 10 years of journaling, my only regret is not starting sooner. Keeping a daily diary doesn't come easily to most people, but it takes less effort than you might imagine. It could also become a meaningful way to reflect and grow as a person. For more than 10 years, I've written a few words every morning, and what I've learned from this practice has changed my life. My only regret is not starting sooner.

As SARS-CoV-2 radiated across the planet in 2020, epidemiologists scrambled to predict its spread--and its deadly consequences. Often, they turned to models that not only simulate viral transmission and hospitalization rates, but can also predict the effect of interventions: masks, vaccines, or travel bans. But in addition to being computationally intensive, models in epidemiology and other disciplines can be black boxes: millions of lines of legacy code subject to finicky tunings by operators at research organizations scattered around the world. They don't always provide clear guidance. "The models that are used are often kind of brittle and nonexplainable," says Erica Briscoe, who was a program manager for the Automating Scientific Knowledge Extraction and Modeling (ASKEM) project at the Defense Advanced Research Projects Agency (DARPA).

Large language models (LLMs) have shown great promise in automating data science workflows, but existing models still struggle with multi-step reasoning and tool use, which limits their effectiveness on complex data analysis tasks. To address this, we propose a scalable pipeline that extracts high-quality, tool-based data analysis tasks and their executable multi-step solutions from real-world Jupyter notebooks and associated data files. Using this pipeline, we introduce NbQA, a large-scale dataset of standardized task-solution pairs that reflect authentic tool-use patterns in practical data science scenarios. To further enhance multi-step reasoning, we present Jupiter, a framework that formulates data analysis as a search problem and applies Monte Carlo Tree Search (MCTS) to generate diverse solution trajectories for value model learning. During inference, Jupiter combines the value model and node visit counts to efficiently collect executable multi-step plans with minimal search steps. Experimental results show that Qwen2.5-7B and 14B-Instruct models on NbQA solve 77.82% and 86.38% of tasks on InfiAgent-DABench, respectively-matching or surpassing GPT-4o and advanced agent frameworks. Further evaluations demonstrate improved generalization and stronger tool-use reasoning across diverse multi-step reasoning tasks. Code and data are available at https://github.com/microsoft/Jupiter.

Artificial intelligence (AI) and data science are transforming chemical research, yet few formal courses are tailored to synthetic and experimental chemists, who often face steep entry barriers due to limited coding experience and lack of chemistry-specific examples. We present the design and implementation of AI4CHEM, an introductory data-driven chem-istry course created for students on the synthetic chemistry track with no prior programming background. The curricu-lum emphasizes chemical context over abstract algorithms, using an accessible web-based platform to ensure zero-install machine learning (ML) workflow development practice and in-class active learning. Assessment combines code-guided homework, literature-based mini-reviews, and collaborative projects in which students build AI-assisted workflows for real experimental problems. Learning gains include increased confidence with Python, molecular property prediction, reaction optimization, and data mining, and improved skills in evaluating AI tools in chemistry. All course materials are openly available, offering a discipline-specific, beginner-accessible framework for integrating AI into synthetic chemistry training.

Replication is facilitated through the provision of four hosted Python notebooks which replicate the paper results. When tested hosted runtimes were running Python 3.6.9 The packages developed as part of this work are discussed below and made available via the above notebooks. The code is written as an extension to the sklearn RandomForestRegressor and RandomForestClas-sifer classes. If running the notebooks on a hosted instance this will be automatically installed. The wrapper calls the R code from the lead author's github If running the notebooks on a hosted instance this will be automatically installed.

Lasso and Multilayer Perceptron are very similar. From Figure 1, we can see that OLS and Ridge give very similar performance and the Lasso performs slightly worse. For both Ridge and Lasso, the regularization parameter alpha was set to '1.0'. The ground truth image of has been depicted on the right. Adaptive multiresolution non-local means filter for three-dimensional magnetic resonance image denoising.