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All the Thanksgiving Recipes You Need, from Turkey to Pie (and Beyond) We put all our recipes for turkeys, side dishes, pies, and more in one place—just for you. No matter how hard the year, there’s always one thing to look forward to: Thanksgiving. It’s not just the food, of course. It’s because this is a moment to think of what we have and what we love. But it is also, of course, about the food, which is why we’ve assembled all of our Thanksgiving recipes right here for you, organized into neat categories we hope you’ll scroll through with great joy. Please, take a look, get hungry, and have a wonderful holiday. - Turkey Recipes - Side Dishes - Potato-Centric Side Dishes - Vegetarian Main Dishes - Vegan Recipes - Pie Recipes

This Real Estate Company Is Becoming a Modern-Day Shovel-Seller to the AI Sector. Here's Why Investors Should Pay Attention. Key Points Nvidia makes the chips that power the computers that support artificial intelligence. The computers using Nvidia chips need to live somewhere. Digital Realty owns the buildings that make artificial intelligence possible. There is a building boom right now around artificia-intelligence (AI) infrastructure. The poster child for the fast-growing technology is Nvidia (NASDAQ: NVDA), but it is just one very-public example of what AI needs to "live." Here's why long-term income investors might prefer to own real estate investment trust (REIT) Digital Realty (NYSE: DLR). Nvidia has a supply/demand problem Nvidia makes the extremely powerful computer chips that are the brains of artificial intelligence. Investors seem to believe that the AI building boom that's taking place right now will keep going forever. (The stock is up more than 23,000% over the past decade!) That's possible, but the history of market bubbles suggests that this tree, like all of the others before it, will not grow to the sky. Indeed, with a price-to-earnings ratio of 53 times, investors are pricing in a lot of good news. There's not a lot of room for error in Nvidia's valuation here. But eventually, the excitement around AI will lead to overbuilding. And when that happens, Nvidia is probably going to see demand cool off. Investors, if history is any guide, will view a slowdown in demand as a huge negative and the stock price will collapse. That could happen even if the company continues to perform fairly well as a business. Emotions are really driving the show right now. But there's an interesting thing to consider with the AI buildout. The computers that use Nvidia chips have to be housed somewhere. That "somewhere" is often a data center. That's a building that is specifically designed to house computers. This is the type of property on which REIT Digital Realty is ...
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Deep in Wuhan’s Dongxihu District, there’s a metasequoia forest where migratory birds gather, and something extraordinary has taken root among the ancient trees. Secret Camp isn’t your typical forest retreat. This collaboration between United Investment Merryda Hotel Management Group and Wiki World has created something that feels part accommodation, part art installation, and entirely magical. More than a dozen treehouses rise through the canopy on Cihui Street, each one carefully positioned so that not a single existing tree was harmed in the process. The whole project sprang from Wiki World’s Wiki Building School initiative, which sounds academic but is really about pushing the boundaries of how we live alongside nature. Each treehouse has its own personality and tells a different story. Time Machine gleams with futuristic silver that catches sunlight through the leaves. Nomadic Land feels like a cozy capsule for temporary wanderers. Playground brings out your inner child with circulation paths that weave playfully around branches. Then there’s Daydream, which uses mirrored cladding to virtually disappear into the forest, and Red Windmill, standing bold and bright as a beacon in the green canopy. Unicorn takes the vertical route with its loft design and silver panels that hint at mythical stories. Designer: United Investment Merryda Hotel Management Group & Wiki World What makes this place special isn’t just the whimsical names or striking designs. The creators drew inspiration directly from the forest itself – local birds, scattered seeds, the organic forms that nature creates without any human input. Every structure sits on elevated timber platforms, leaving the forest floor completely untouched. No paved paths, no manicured landscaping, just the raw beauty of the woodland ecosystem doing what it does best. This approach embodies Wiki World’s “Build Small, Dream Big” philosophy, proving that you can live comfortably without dominating your environment. But Secr...
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Open source video is back Try WAN 2.2 Wan 2.2 has set the open source video community ablaze. It’s a huge leap forward from 2.1 with sharp physics, faster generation, and more control. And because it’s fully open source, it opens the door to high-quality video tools for anyone at an astounding fraction of the cost. We’ve teamed up with Pruna AI to release our own optimized version of Wan 2.2. You can test it in these ways: - FAST image-to-video, 480p — $0.05 per video - FAST text-to-video, 480p — $0.05 per video - FAST text-to-video, 720p — $0.10 per video You heard right. 5 cents a video. That’s even less than leading image generators on the AI market. With inference times at ~30s, Wan 2.2 can become your leading way to rapidly test video prompts. And don’t let the fast inference time fool you — these Wan 2.2 optimizations do not comprise on quality. We are also offering Wan 2.2 i2v at 480p and 720p without optimizations: Running WAN 2.2 is straightforward with Replicate’s API. Here’s how to generate your first video using the text-to-video model: import Replicate from "replicate"; const replicate = new Replicate(); const model = "wan-video/wan-2.2-t2v-fast"; const input = { prompt: "A serene mountain lake at sunset with gentle ripples on the water", }; const output = await replicate.run(model, { input }); console.log(output); For image-to-video generation, simply add an image input with the i2v fast endpoint: import Replicate from "replicate"; const replicate = new Replicate(); const model = "wan-video/wan-2.2-i2v-fast"; const input = { image: "https://example.com/your-image.jpg", prompt: "The leaves rustle gently in the wind", }; const output = await replicate.run(model, { input }); console.log(output); We plan to release more optimizations with Pruna over the next couple of days. Make sure you follow Replicate to keep up with the latest in our Wan 2.2 journey. Try WAN 2.2

Get the most from Google Veo 3 Try Veo 3 Veo 3 from Google has taken the AI community by storm. And with good reason. With Veo 3, you can generate not just visuals, but native audio too. That includes sound effects, ambient noise, and dialogue. This model also understands your prompts better. It’s more accurate, more consistent, and more grounded in the real world. Researchers at Google DeepMind have worked to craft a model with strong prompt adherence, accurate physics, and hyperrealism. We’ve been testing the limits of the model, and it’s been surprising us in the best ways. NO WAY. It did it. And, was that, actually funny? — fofr (@fofrAI) May 20, 2025 Prompt: > a man doing stand up comedy in a small venue tells a joke (include the joke in the dialogue) https://t.co/GFvPAssEHx pic.twitter.com/LrCiVAp1Bl Why was that actually funny? Veo 3 also has accurate dialog and lip-sync built in: My first Veo 3 gen — fofr (@fofrAI) May 20, 2025 > a video with dialogue of two muffins while baking in an over, the first muffin says "I can't believe this Veo 3 thing can do dialogue now!", the second muffin says "AAAAH, a talking muffin!" pic.twitter.com/VA2VUZF8sS One thing we found fascinating was its ability to generate video game worlds. This has serious implications for the gaming landscape. Veo 3 does video games really well. It's like you can explore new worlds already. — fofr (@fofrAI) May 22, 2025 Prompts are all variations of: > a third-person open world video game walking around... > an fps video game in/on a... pic.twitter.com/bpDGaKNU55 There is infinite room for exploration with this model. Google has shared a Veo 3 prompting guide with us. We wanted to share these tips with you to make sure you get the most from your generations. - Shot composition: Specify the framing and number of subjects in the shot (e.g., “single shot,” “two shot,” “over-the-shoulder shot”). - Focus and lens effects: Use terms like “shallow focus,” “deep focus,” “soft focus,” “macro lens,” and...
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Academic research involves frequent research discovery: finding papers, code, related models and datasets. This typically means switching between platforms like arXiv, GitHub, and Hugging Face, manually piecing together connections. The Model Context Protocol (MCP) is a standard that allows agentic models to communicate with external tools and data sources. For research discovery, this means AI can use research tools through natural language requests, automating platform switching and cross-referencing. Much like software development, research discovery can be framed in terms of layers of abstraction. At the lowest level of abstraction, researchers search manually and cross-reference by hand. # Typical workflow: 1. Find paper on arXiv 2. Search GitHub for implementations 3. Check Hugging Face for models/datasets 4. Cross-reference authors and citations 5. Organize findings manually This manual approach becomes inefficient when tracking multiple research threads or conducting systematic literature reviews. The repetitive nature of searching across platforms, extracting metadata, and cross-referencing information naturally leads to automation through scripting. Python scripts automate research discovery by handling web requests, parsing responses, and organizing results. # research_tracker.py def gather_research_info(paper_url): paper_data = scrape_arxiv(paper_url) github_repos = search_github(paper_data['title']) hf_models = search_huggingface(paper_data['authors']) return consolidate_results(paper_data, github_repos, hf_models) # Run for each paper you want to investigate results = gather_research_info("https://arxiv.org/abs/2103.00020") The research tracker demonstrates systematic research discovery built from these types of scripts. While scripts are faster than manual research, they often fail to automatically collect data due to changing APIs, rate limits, or parsing errors. Without human oversight, scripts may miss relevant results or return incomplete informat...
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Gemma 3n was announced as a preview during Google I/O. The on-device community got really excited, because this is a model designed from the ground up to run locally on your hardware. On top of that, it’s natively multimodal, supporting image, text, audio, and video inputs 🤯 Today, Gemma 3n is finally available on the most used open source libraries. This includes transformers & timm, MLX, llama.cpp (text inputs), transformers.js, ollama, Google AI Edge, and others. This post quickly goes through practical snippets to demonstrate how to use the model with these libraries, and how easy it is to fine-tune it for other domains. Here is the Gemma 3n Release Collection Two model sizes have been released today, with two variants (base and instruct) each. The model names follow a non-standard nomenclature: they are called gemma-3n-E2B and gemma-3n-E4B . The E preceding the parameter count stands for Effective . Their actual parameter counts are 5B and 8B , respectively, but thanks to improvements in memory efficiency, they manage to only need 2B and 4B in VRAM (GPU memory). These models, therefore, behave like 2B and 4B in terms of hardware support, but they punch over 2B/4B in terms of quality. The E2B model can run in as little as 2GB of GPU RAM, while E4B can run with just 3GB of GPU RAM. In addition to the language decoder, Gemma 3n uses an audio encoder and a vision encoder. We highlight their main features below, and describe how they have been added to transformers and timm , as they are the reference for other implementations. - Vision Encoder (MobileNet-V5). Gemma 3n uses a new version of MobileNet: MobileNet-v5-300, which has been added to the new version of timm released today.- Features 300M parameters. - Supports resolutions of 256x256 ,512x512 , and768x768 . - Achieves 60 FPS on Google Pixel, outperforming ViT Giant while using 3x fewer parameters. - Audio Encoder: - Based on the Universal Speech Model (USM). - Processes audio in 160ms chunks. - Enables speec...
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Sentiment analysis is the automatic process of classifying text data according to their polarity, such as positive, negative and neutral. Companies leverage sentiment analysis of tweets to get a sense of how customers are talking about their products and services, get insights to drive business decisions, and identify product issues and potential PR crises early on. In this guide, we will cover everything you need to learn to get started with sentiment analysis on Twitter. We'll share a step-by-step process to do sentiment analysis, for both, coders and non-coders. If you are a coder, you'll learn how to use the Inference API, a plug & play machine learning API for doing sentiment analysis of tweets at scale in just a few lines of code. If you don't know how to code, don't worry! We'll also cover how to do sentiment analysis with Zapier, a no-code tool that will enable you to gather tweets, analyze them with the Inference API, and finally send the results to Google Sheets ⚡️ Read along or jump to the section that sparks 🌟 your interest: - What is sentiment analysis? - How to do Twitter sentiment analysis with code? - How to do Twitter sentiment analysis without coding? Buckle up and enjoy the ride! 🤗 Sentiment analysis uses machine learning to automatically identify how people are talking about a given topic. The most common use of sentiment analysis is detecting the polarity of text data, that is, automatically identifying if a tweet, product review or support ticket is talking positively, negatively, or neutral about something. As an example, let's check out some tweets mentioning @Salesforce and see how they would be tagged by a sentiment analysis model: "The more I use @salesforce the more I dislike it. It's slow and full of bugs. There are elements of the UI that look like they haven't been updated since 2006. Current frustration: app exchange pages won't stop refreshing every 10 seconds" --> This first tweet would be tagged as "Negative". "That’s what I love a...
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Having ridden his bike past La Jolla’s Starkey Park for the better part of a decade, Boy Scout Parker Kaltsas has seen the community bulletin board there deteriorate over time. So for his Eagle Scout project, Parker did something about it. The Eagle rank is the highest achievement in the Boy Scouts, and only about 4% of Scouts nationwide earn the title. Eagle Scout candidates must complete a community project, earn 21 merit badges, participate in a leadership conference and meet other prerequisites. Projects must be completed before a Scout turns 18. Working with community volunteers who help maintain the nearby La Jolla Bike Path, Parker made and installed a new bulletin board in front of Starkey Park on Draper Avenue. “I live in northern Pacific Beach, so for the past 10 years or so, I would bike the La Jolla Bike Path and would pass Starkey Park,” he said. “When I saw the old rotten bulletin board, I thought it was a good opportunity for me to do something good.” As a first step, Parker visited other parks and looked at their bulletin boards. He found there was a standard design for San Diego city parks, so with the design predetermined, he developed a construction plan. “It’s bigger than you might think because of how it digs into the ground,” he said. “It can be 12 or so feet tall.” Parker would know, since he cut the wood himself. He called on friends to help with the construction once the wood was ready and spent an entire Saturday putting the bulletin board together. “The construction didn’t take long, but it was challenging,” Parker said. “It was a smooth process once it got going because I had a construction plan and told them all how things were going to go. We fastened the beams together and had to cut and drill each piece. “I learned a lot about planning and communication and how important it is to be a leader. Even when I was not the one doing the building, I needed to know every step so I could help along the way. It becomes like a factory.” The proje...
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AI 编程带来的新变革 过去三个月，我和一群经验丰富、志同道合的工程师，一直在 Amazon Bedrock 内部鼓捣一个很酷的项目。虽然项目本身很让人兴奋，但我们团队还有个更独特的地方——我们大部分的代码，都是由 AI 智能体（AI Agent）写的，比如 Amazon Q 或 Kiro。 先别急着翻白眼：不，我们不是在搞“凭感觉编程”（vibe coding）。我不认为那是构建健壮软件的正确姿势。 相反，我们采用的是一种人与 AI 智能体协作的模式。在我们团队，每一次代码提交（commit）都有工程师署名，这位工程师必须审查并为这段代码负责。我们用“引导规则”（steering rules）来设定约束，告诉 AI 智能体在我们的代码库里该如何工作。事实证明，用 Rust 语言编程帮了大忙。Rust 编译器以其对正确性和安全性的极致追求而闻名，它能在编译时就揪出大量问题，并提供非常有用的错误信息，帮助 AI 智能体不断迭代。相比“凭感觉编程”，我更愿称之为“智能体编程”（agentic coding）。这个词听着是无聊多了，但在我们这行，“无聊”通常是件好事。 对我个人而言，近来我提交的代码中，大约 80% 都是 AI 智能体写的。我的工作流是这样的：先把任务拆解清楚，直到我自己脑子里完全明白（这个过程也经常用 AI 来探索不同方案）；然后，给 AI 智能体下达指令（prompt）；接着，审查它的输出；和它一起迭代，直到我满意为止；偶尔，我也会接管代码变更集，自己动手完成最后的部分。智能体生成的每一行代码我都会仔细看，直到我对其质量完全满意才会接受——这和我自己写每一行代码的标准毫无二致。 我自认一直是个高效的程序员，就算过去几年里能分给编程的时间很有限，我也总能挤出时间写代码。而过去的这几个月，是我职业生涯中编码产出率最高的几个月。我的团队也不例外——我们的代码产出速度，是普通高效团队的 10 倍。这不是吹牛（hyperbole）——我们实实在在地收集和分析了数据。 以 200 迈（约 320 公里/小时）的速度狂飙 有趣的地方来了。一个典型的软件团队，哪怕经验再丰富，也不可能永远不犯错。即使有良好的测试和工程实践，Bug（程序缺陷）偶尔还是会溜进正式版本。我们都听过“在生产环境中测试”（testing in production）这句无奈的玩笑话。这个现实让我一直坚信，光靠测试是远远不够的，同样重要的，是投资于控制“爆炸半径”（blast radius）和缩短“恢复时间”（time to recovery）。 AI 辅助编写的代码也没什么不同，它同样可能包含 Bug，就算有人类仔细审查过。我猜，出 Bug 的概率和人自己写差不了多少。然而，当团队以 10 倍的速度提交代码时，这笔账就全变了。过去一年才出一次的生产环境大 Bug，现在可能每周都会来一次。就算大部分 Bug 在集成或测试环境中被抓住了，它们还是会影响共享的代码库，拖慢整个团队的进度，迫使大家停下来调查。这同样不是危言耸听——我们的团队已经看到了这些挑战的苗头，这是产出率“阶跃”式增长（即突然的、非线性的剧增）带来的必然后果。 我越来越相信，要想让“智能体编程”真的带来 10 倍的工程效率提升，我们也必须把“问题提交”（problematic commits）的概率降低 10 倍。甚至可能要降得更多，因为在高速迭代下，各个独立的提交之间也可能以意想不到的方式相互作用。 换句话说，当你以 200 迈的速度飙车时，你需要巨大的下压力（downforce）才能让车子稳稳地贴在赛道上！ 成本-收益的再平衡 减少 Bug 的最好方法之一是改进测试。我是个飞机迷，一直很佩服飞机制造商们用的那些测试方法。从早期模拟、到部件测试、到风洞（wind tunnel）测试、再到极限破坏测试，最后是整机试飞。就连飞行模拟器也在提升整个行业的安全性方面扮演着角色。软件行业也尝试过其中一些方法，但远未普及。 举个例子，我一直很喜欢“风洞”式的测试，它能在受控环境中测试完全组装好的系统。为了实现这一点，我用过的一种模式是为外部依赖（external dependencies）实现高保真（high fidelity）的“冒牌货”（fake versions），让它们能在本地运行。这样，你就可以在编译时（build-time）编写在本地运行的测试，验证整个系统的端到端（end-to-end）行为。你甚至可以往这些“冒牌”依赖里注入意外行为和故障，来测试系统的应对能力。这类测试写起来和跑起来都很容易（因为在本地），而且非常擅长捕捉那些藏在组件接缝处的隐蔽 Bug。 不幸的是，对于一个中等复杂度的服务来说，伪造所有的外部依赖并不容易。就算你做到了，你还得负责在真正的依赖演进时，同步维护这些“冒牌货...
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Boy Scout Troop 114 of West Milford worked to earn merit badges and rank advancements at camp this year. West Milford scouts work hard at summer camp Troop 159 last week held a Court of Honor outdoors with a slideshow provided by one of the scouts showcasing all the fun they had the last few weeks and months. Photo provided by Nicholas M. Salleroli. Troop 159 scouts deal with the hand they were dealt

WICHITA FALLS, Texas (KAUZ) - The Boy Scouts of America’s Northwest Texas Council is preparing to kick off its annual Americanism lunch, featuring Executive Director of Hands to Hands, Nancy Brown, and the Director of Software Development for Oracle, Jim Daniel. The Americanism award, which is presented to community contributors who exemplify the Scout Oath and Law through their daily actions, will be awarded to Nancy Brown, the Executive Director of the Hands to Hands Community Fund. Over her 16-year career with the organization, she has led efforts to support eight local nonprofits which serve children, teens, young adults, and seniors across Wichita Falls. Brown is a lifelong Wichita Falls community resident and contributor who is active in numerous service organizations and local boards. The keynote speaker for this year’s event, Jim Daniel, is the Director of Software Development at Oracle and a seasoned technology leader with military experience and degrees from Texas A&M and the University of Pennsylvania. A Wichita Falls native as well as a proud Eagle Scout, Daniels brings a mission-driven leadership style to his work in building large-scale cloud systems. To reserve a table or ticket for this event, visit the Northwest Texas Council’s website or contact the Scout Office at (940) 696-2735. The doors will open at 10:30 a.m. on Monday, November 17 at the MSU Clark Student Center, located at 2451 Comanche Trail. Copyright 2025 KAUZ. All rights reserved.

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