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The list of inconsistencies goes on and on. Nobody should have designed such an absurdityView the full article

It’s time to get to know each other. By Marc Rosenberg CPA Firm Mergers: Your Complete Guide Go PRO for members-only access to more Marc Rosenberg. View the full article

It’s time to get to know each other. By Marc Rosenberg CPA Firm Mergers: Your Complete Guide Go PRO for members-only access to more Marc Rosenberg. View the full article

Artificial intelligence company Stability AI mostly prevailed against Getty Images Tuesday in a British court battle over intellectual property. Seattle-based Getty had accused Stability AI of infringing its copyright and trademark by scraping 12 million images from its website, without permission, to train its popular image generator, Stable Diffusion. The closely followed case at Britain’s High Court was among the first in a wave of lawsuits involving generative AI as movie studios, authors, and artists challenged tech companies’ use of their works to train AI chatbots. Tech companies have long argued that “fair use” or “fair dealing” legal doctrines in the United States and United Kingdom allow them to train their AI systems on large troves of writings or images. Tuesday’s ruling provides some clarity but still leaves big unanswered questions over copyright and AI, experts said. According to the judge’s written ruling, Getty narrowly won its argument that Stability had infringed its trademark, but lost the rest of its case. Both sides claimed victory. “This is a significant win for intellectual property owners,” Getty Images said in a statement. Shares of Getty dipped 3% before the opening bell in the U.S. Stability, based in London, said it was pleased with the ruling. “This final ruling ultimately resolves the copyright concerns that were the core issue,” Stability’s General Counsel Christian Dowell said. Getty had accused Stability of both primary and secondary copyright infringement. Legal experts said the first one involves the act of reproducing something without permission — similar to a dodgy factory churning out counterfeit Chanel handbags or pirated CDs — while the second involves importing those copies from another country. In this case, Getty said Stability’s use of its image library to train and develop Stable Diffusion’s AI model amounted to breach of primary copyright. Stability responded that the case doesn’t belong in the United Kingdom because the AI model’s training technically happened elsewhere, on computers run by U.S. tech giant Amazon. During the three-week trial in June, Getty dropped its primary copyright allegations, in a sign that it didn’t think they would succeed. But it still pursued the secondary infringement claims. Even if Stability’s AI training happened outside the U.K., Getty said offering the Stable Diffusion service to British users amounted to importing unlawful copies of its images into the country. Justice Joanna Smith rejected Getty’s claims, ruling that Stable Diffusion’s AI didn’t infringe copyright because it doesn’t “store or reproduce any Copyright Works (and has never done so).” Getty also sued for trademark infringement because its watermark appeared on some of the images generated by Stability’s chatbot. The judge sided with Getty but added that the case only partially succeeded, and that her findings are “both historic and extremely limited in scope.” “While I have found instances of trademark infringement, I have been unable to determine that these were widespread,” she said. Experts said Getty’s move to drop part of its copyright case means AI training is still in legal limbo. “The decision leaves the U.K. without a meaningful verdict on the lawfulness of an AI model’s process of learning from copyright materials,” said Iain Connor, an intellectual property partner at law firm Michelmores. Smith said there was “very real societal importance” in deciding how to strike a balance between the creative and tech industries. But she added that the court can only rule on the “diminished” case that remained and couldn’t consider “issues that have been abandoned.” A Getty spokeswoman declined to say whether there would be an appeal. Getty is also pursuing a copyright infringement lawsuit in the United States against Stability. It originally sued in 2023 but refiled the case in a San Francisco federal court in August. The Getty lawsuits are among a slew of cases that highlight how the generative AI boom is fueling a clash between tech companies and creative industries. AI companies are now fighting more than 50 copyright lawsuits — so many that a tech industry lobby group has called on President Donald The President for help stop the court fights, saying they threaten AI innovation. Among the cases, Anthropic agreed to pay $1.5 billion to settle a class-action lawsuit by authors while a federal judge dismissed a similar lawsuit from 13 authors against Meta Platforms. Warner Bros. has sued Midjourney for copyright infringement, as have Disney and Universal in seperate lawsuits, alleging that its image generator creates copyrighted characters. —Kelvin Chan, AP business writer AP Technology Writer Matt O’Brien contributed to this report. View the full article

Here’s a guide to the most notable features of the top AI chat apps. ChatGPT: Your Conversationalist 🗣️ iOS & Android Advanced Voice Mode is the ChatGPT app’s most distinctive feature. Ask it to play a tough interviewer or a skeptical client as you prepare for a difficult conversation. Or have it ask questions to help you make a decision. Most of what you can do on your laptop you can do in the ChatGPT mobile app. Create an image. Ask for an infographic, a cartoon, or a photo illustration. See examples of seven ways I use these images. Ask for deep research. Get a detailed analysis with dozens of sources. See examples of nine ways I use this research. Study & learn. This new mode helps you strengthen your skills & knowledge. Analyze files or images. Turn a handwritten note into digital text, or make sense of any document, diagram, or manual. When I can’t figure out how to assemble or operate something, this offers faster help than a Google search. Use integrated apps. You can now access Canva, Figma, Spotify, Expedia, and other tools inside ChatGPT. Try prompting for a graphic within ChatGPT while waiting in line with your phone, then edit it later in Canva.👇 Pulse is ChatGPT’s best new pro mobile feature. It creates customized notes for me every morning. The AI assistant synthesizes info from my chat history, my Google Calendar, and what I’ve expressed an interest in learning. This morning’s Pulse note, for example, included tactics for using new Substack features, Penguin stories for sharing with my daughter, and breakfast ideas I had asked about for my rice cooker and bread machine. These aren’t news updates—they’re personalized resources prepared by an AI assistant. I don’t use or recommend relying on AI assistants for news searches, especially given AI’s struggles with news accuracy. Caveat: Pulse isn’t yet available for free accounts. Gemini: Your Creative Partner 🧑‍🎨 iOS & Android The Gemini app has five special features, in addition to its core chat capability. “Nano Banana” image generation model. Edit photos, blend multiple images, or design a poster. Worth trying: ask it to turn any image from your phone into a record album, book cover, or billboard poster. Deep Research. Generate exhaustive reports with citations whenever you need thorough background on an issue. Try this prompt: “Create a step‑by‑step plan to adopt [tool/technology] in a team of [size]. Include costs, training time, change‑management risks, and how to measure success. Cite case studies.” See a few of my tips for strengthening deep research queries. Veo 3 video generation. Paid accounts only. Create 8-second clips with Veo 3.1, Google’s new video model. Experiment: create a slick moving background for a slide. Canvas. Make an infographic, a quiz, or a simple game. Quick test: make a self-grading quiz to challenge yourself on something you’re learning. Guided Learning. Put Gemini in teacher mode to help you gradually strengthen your understanding of anything. Try this: ask it to walk you through the history of any concept or tech you’re curious about. When I choose Gemini: I use it as an alternative to ChatGPT and Claude when I want particular kinds of image edits and creative image designs. I also use it to experiment with generating short video clips, for guided learning, and for research reports. Claude: Your Mobile Studio 👷 iOS & Android Claude’s app has a new voice mode I like. It waits for me to tap the screen to signal I’m done, so it rarely cuts me off when I pause to think—unlike ChatGPT, which often assumes I’ve finished talking. You can choose from five voices. Create on the Go Create Artifacts—interactive little applications—from your phone. You can make games, learning resources, document templates, or other useful mini programs. You can also now use Claude Code from your phone. What I most value about Claude is its excellent Projects feature, which lets me organize relevant documents and instructions for each distinct area of work. I use other tools (like ChatGPT, Gemini) for images and video, which Claude doesn’t do, but I rely on Claude for assistance with alt-text, SEO text, project planning, and other tasks where understanding my context is crucial. Copilot: A Flexible Assistant 🧑‍💼 iOS & Android Microsoft’s Copilot app is a good free option that’s similar to ChatGPT and based on the same OpenAI models. One distinction is a new “real talk” mode that will sometimes challenge you. This helps address the sycophancy problem of AI chatbots blindly affirming your statements. Other useful features: Copilot can generate a podcast episode on any subject (like this one about Wonder Tools). It can also generate an image, run a deep research report, quiz you on a subject of your choice, and conduct a voice chat. Like ChatGPT, it can even help you understand something in your environment. Turn on your camera or load something onto your screen, then ask Copilot questions about something you’re looking at. Ask it about fine print in a document, a confusing gadget, a troubled plant🌾, or anything else. Perplexity: The Quick Researcher 🧑‍🔬 iOS & Android I rely on Perplexity for help understanding complex concepts. The mobile app’s voice mode is especially useful for quick searching and getting a summarized response instead of a list of links. For niche searches, adjust Perplexity’s settings to focus only on finance info, academic sources, or social sites for Reddit results. You can also use Perplexity to search your Outlook email or your Gmail and Google Calendar📆 for messages on a particular subject. Tip: Turn on incognito mode in settings anytime you’re searching on a sensitive or private subject. And as with all AI tools, avoid giving a thumbs up or down to a query because rating it signals that you’re okay with it being read and analyzed. Read more about why I find Perplexity so useful 🎯 Free & Low-Cost AI App Alternatives Locally AI 📍 iOS | Free Benefits: Free. No log-in required. Fully private. No data tracking. Easy to use. Getting started. Pick a compact open-source large language model suited for your phone’s processing power. I considered options from Qwen, Meta, and Google. Qwen 3 supports 100 languages and Meta’s Llama excels at summarization. I picked Gemma 3 QAT from Google. If you’re a tech novice or don’t care about those details, just pick Gemma as your model and you’ll be fine. Brief wait to get started. I had to keep the app open for about two minutes to download the language model to my phone. You only have to do that once. How I used it: I recently asked for a custom workout, given my constraints (no equipment, limited time) and personal fitness priorities. The result was helpful and similar to what I got from ChatGPT. Nice features Customize or personalize your responses by inputting a prompt that will guide the app across all the individual chats. You can explain your personal or professional circumstances, for instance, or your preferences for concise or detailed answers, or any other needs you have for how the AI responds to you. Set up Siri shortcut. You can activate Siri and say “Hey Locally AI . . .” to run a local AI search privately with your voice. Well-reviewed. People seem to like it: 4.8/5 average rating with 208 reviews. Vision tools. You can use this private AI app for text recognition, object recognition, or image comprehension. That’s useful if you want to use your phone privately to understand secure documents or convert personal handwritten notes into text. To get that benefit, within the app download the Qwen 2 VL model recommended for iPhone 15 or newer phones. Caveats Not the top models. ChatGPT, Claude, and Gemini perform better for image analysis than the small mobile models this app enables. Slow start. Expect to wait several minutes each time you download a new model, including the first time you use the app. No plug-ins. I couldn’t connect this app to other services. Private LLM | iOS and Mac | $5 Nice features One purchase for iPhone, iPad and Mac. Family sharing means you can share the app with five family members for free. Choose from 60+ models. Lots of models available in this app aren’t options in Locally AI. That may not matter, unless you’re eager to use a very specific model. Change AI models’ creativity level. Unlike Locally AI, this app allows you to adjust the “temperature” setting of your AI models to control how predictable or creative responses are. A model set to a low temperature sticks to more consistent, predictable answers, while one set to a higher temperature will generate more varied, imaginative replies. Caveats Single chat stream. You can’t create multiple distinct chats in this app. Most other AI tools, including the Locally AI app, let you separate conversations into distinct threads for different subjects. No help picking models. Figuring out which one to try is tricky with this app. You can click a tiny information button that links to a separate Hugging Face web page about the model, but there’s no easy-to-understand summary for novices. Locally AI has helpful concise summaries showing each model’s strengths. PocketPal AI | iOS and Android | Free Nice features Fully Private. No conversations, prompts, or data leave your device. Create custom “pals.” Set up multiple AI assistants or “personalities,” with different settings and system prompts. Access models from Hugging Face. Choose from many small AI models. Caveats May not work well on all Android phones. Depending on your phone’s age, the app might feel slow. A lot of Play Store reviewers reported this problem. Mediocre ratings. 4.1 out of 5 with 1,200 reviews is OK, but not stellar. The user interface lacks polish. The design isn’t as elegant as what you’ll find on Claude, ChatGPT, Gemini, or other top-tier apps. But it’s free, and if the AI responses are useful, you may tolerate a lower-quality interface. View the full article

Since the days of Netscape Navigator and Internet Explorer, you've always have a choice when it comes to the program you use to access the internet—but we now have a whole new breed of software to consider: The AI browser. These browsers place AI models front and center, even more so than integrations like Gemini in Chrome or Copilot in Edge. These browsers give you AI-powered search, AI-powered answers to just about any question you can come up with, and even agentic AI control—so the browser can actually do some browsing and complete simple tasks for you. A future where we never have to manually fill out another web form or compare prices on sixteen different flights is nearer than you think. Or is it? To gauge the current state of the AI browsers available today, I gave four of them a test: Perplexity Comet, Opera Neon, ChatGPT Atlas, and Dai (from the makers of the Arc browser). It's early days for all of these programs, but here's what you can currently do with them, and how they compare to one another. The pros and cons of AI browsingBefore we get to the details of specific browsers, it's worth mentioning some of the pros and cons involved. The main pro, at least in theory, is more useful AI assistance: Some types of web searches can be ably handled by an AI bot (if you watch out for inaccuracies), and there are plenty of tedious, time-consuming browsing tasks that could be offloaded to an AI agent. For example, you could pick something you want to make for dinner and have your AI browser order all the groceries for you—a lot of clicks saved. Or perhaps you could get your AI browser to bring up all the job listings you were looking at last week, sorted by their relevance, or have it compare the best gaming keyboards available on Amazon. Comet is one of the AI browsers now available. Credit: Lifehacker In theory at least, you can offload quite a bit of work to your AI browser—but of course, giving your browser more control comes with security and privacy concerns. We've already seen demonstrations of how hackers can embed malicious code into websites to take control of the AI agent and potentially get at your data and your accounts. What's more, these browsers typically remember everything you've done, so you can come back to a task later, if need be. That's not too different to how standard browsers work, and it is possible to disable this tracking, but it's a potential issue with AI browsers—you're putting your trust in another company and another piece of software to respect the privacy of your browsing data and take good care of it. Those are the general considerations to bear in mind when looking at AI browsers. With that all said, here's what I found when I dug a bit deeper into the options currently available to try out. Perplexity CometPerplexity Comet is available for Windows and macOS Clean and polished look AI agent capable of the basics Something you can say for sure about Comet is that it's cheaper than it used to be: Previously, you had to be subscribed to Perplexity Max for $200 per month to even try it. Now, anyone can—you don't even need a Perplexity account—but AI usage limits apply if you're not a subscriber. A couple of interesting features stand out immediately—the article summary feature and the voice mode feature, both available on the toolbar. They work well, for those times when reading full articles and typing on a keyboard seems like too much effort, while features like AI search and writing assistance are only a click away. Comet summarizes content with a couple of clicks. Credit: Lifehacker The Perplexity Assistant chatbot is always available from the sidebar or the new tab page, ready to help. I successfully got it to aid me in replying to a thread in Gmail—it even clicked the reply button for me. I also used the AI agent to create a Google Keep note with three motivational quotes inside. I am capable of achieving great things, apparently. When it comes to the agentic AI, Comet tells you the steps it's taking in the Assistant window as it works on your task. It can occasionally get tripped up by even the simplest of web interfaces, but seems to do well at understanding how to fix issues it encounters and assessing what's actually on screen. Opera NeonOpera Neon is available for Windows and macOS Wide range of AI capabilities No free tier for the AI features Unlike the other browsers on this list, Opera Neon isn't yet open to the public—though you can sign up for early access. When you do get in, you'll need to pay $20 a month for the advanced AI features, with models supplied by OpenAI and Google (Opera says it switches between them as and when needed). Opera obviously knows what it's doing when making browsers, and the usual Opera innovations are here, such as sidebar integrations for your chat apps. When it comes to the AI aspects, there's all sorts of functionality to experiment with, from article summarizing and image generation, to deep research and coding generation. Neon successfully picked out cheap flights. Credit: Lifehacker While the subscription fee might put you off, you get plenty for your money. There's agentic AI to take actions in the browser, though with mixed results: Opera Neon managed to create my inspirational Google Keep note when asked, but didn't properly save it. When I complained, it tried its best to make amends, but again couldn't work out what to do to actually save a note. The browser was better at assessing what was on particular pages, and was able to give me a summary of the cheapest flights between two destinations across the next month—all by itself. I'd say the AI here still needs some work (perhaps why this remains a limited preview), but it scores highly on versatility and on actually being a functional browser. ChatGPT AtlasChatGPT Atlas is available for macOS Sticks to the browsing basics Advanced agentic AI interface OpenAI has now joined the AI browser party, such as it is, and ChatGPT Atlas takes a minimal approach to browser design: It's basically just the essentials in terms of on-screen furniture, with a few quick links to settings and an Ask ChatGPT button up in the corner that you can click on whenever you need a hand from AI. ChatGPT in Atlas can do just about everything it can do everywhere else, though I did like the way it picked out the Lifehacker stories "aligned to my interests" from the homepage—one benefit of giving the bot access to all your chatting history, amidst all the privacy drawbacks. As with the main ChatGPT app, you can use the browser for free, with higher usage limits if you decide to subscribe to a plan. Atlas does well at figuring out where to click and when. Credit: Lifehacker Where Atlas starts to diverge from the main ChatGPT is in the agentic AI capabilities: ChatGPT can actually jump in and take actions on your behalf. And on that score, it's the most advanced of the browsers I tried—it reliably picks out the right elements within sites, accurately follows your instructions, and animates its actions so you can see what's being done. You can also jump in and interrupt at any time. It still makes mistakes though—while it successfully created my motivational quotes note in Google Keep, it needed several goes at the formatting, and I suspect it would've been quicker for me to do it myself. DaiDia is available for macOS Polished AI chat interface Deep analysis works well Dia is a little bit different to the other browsers I've tried out here, in that it's very much built with AI at the forefront. In its original form, it didn't have many traditional browser features at all—though it has recently started adding in some elements from its Arc predecessor, including pinned tabs and favorites. The premise is you can use AI to "chat with your tabs"—summarize text, compare items, ask questions about what's on screen, generate fresh text where needed, and lots more besides. What you won't encounter (yet) is agentic AI, which means Dia isn't going to be able to jump into websites and take actions for you. Dia's AI chat integrates tightly with what you're looking at on the web. Credit: Lifehacker That said, it's particularly good for learning: You can quickly turn videos and essays into bullet points or flashcards, for example. I also like the way Dia can summarize threads and pull information from Gmail: Even if it's not able to actually click around for you, it can dig deep into websites and web apps and pull out what you need. You also get Dia Skills, prepackaged shortcuts for fact checking, picking out the right clothing to match your style, creating transcripts of YouTube videos, and finding something to watch on streaming services. It's a good early prototype of how AI can actually help make sense of the web at large. The AI browsing futureIn my testing, these browsers all performed well in different ways: Comet at integrating AI into the interface, Neon in its broad feature set, Atlas in the strength of its agent mode, and Dia in its deep understanding of sites and their data. This is undoubtedly the direction all browsers are headed in the future, to a greater or lesser extent. How will this change how you interact with the internet? Personally, I would never let AI write anything for me—not even notes or emails—and I'm reluctant to hand over jobs like booking hotel rooms or creating and formatting documents to AI either. I'm wary of AI making the wrong choices and making serious mistakes, which is why I didn't put these tools through anything too demanding here (and that's before we get into the sticky privacy issues). AI can be useful (though not infallible) in terms of searching and summarizing, and that's where I think these browsers show the most promise—by taking the tasks AI is already good at, and integrating them more tightly with web-based workflows. Fully automated AI browsing may arrive one day, but based on what these browsers can do right now, it's still a long way off. View the full article

What is your first impression? By Martin Bissett Business Development on a Budget Go PRO for members-only access to more Martin Bissett. View the full article

What is your first impression? By Martin Bissett Business Development on a Budget Go PRO for members-only access to more Martin Bissett. View the full article

Shares in two closely watched AI-adjacent companies, Nvidia Corporation and Palantir Technologies, are falling this morning. Currently, Nvidia shares are down more than 2.2% and Palantir shares are down more than 6%. The share price drops of two of the most prominent AI companies come as investors seem increasingly worried that the AI boom is starting to look more like an AI bubble, reminiscent of the dotcom bubble of the late ’90s and early 2000s. In part due to these concerns, an increasing number of investors have recently begun betting against the stocks of companies benefitting from the artificial intelligence boom—including Michael Burry, the investor who became famous for betting against the housing market before the 2008 financial crash. Here’s what you need to know. “Big Short” investor bets against Nvidia and Palantir In the years leading up to the 2008 housing market crash, investor Michael Burry made a killing by shorting housing-related stocks after seeing signs of the then-upcoming housing market crash that few others noticed. In 2015, Burry was immortalized in The Big Short, the Oscar-winning film about the 2008 financial crash, in which he was played by Christian Bale. Burry has since gained a substantial following among some investors, and so his investment moves often gain widespread attention. Recently, his move has been to bet against the stock prices of Nvidia (Nasdaq: NVDA) and Palantir (Nasdaq: PLTR). As noted by Bloomberg, Bury’s Scion Asset Management recently revealed in a 13F regulatory filing that it bought put options on NVDA and PLTR. The news of Scion’s puts followed a Halloween post from Burry on X in which the hedge fund manager issued a cryptic post reading “Sometimes, we see bubbles. Sometimes, there is something to do about it. Sometimes, the only winning move is not to play,” along with an image of his Big Short character played by Bale. Burry’s puts seem to have struck a nerve with Palantir CEO Alex Karp, who on Tuesday told CNBC’s Squawk Box that the companies Burry is betting against “are the ones making all the money, which is super weird.” Karp added that “The idea that chips and ontology is what you want to short is batshit crazy.” Then again, plenty of people thought Burry was crazy for shorting housing stocks in the years ahead of the 2008 crash. Palantir’s Tuesday share slide comes after the company reported Q3 earnings yesterday, in which it saw revenue climb 63%. The software company has been among the highest-growth stocks of 2025. Fears of an AI bubble loom large Regardless of whether Burry’s puts against Nvidia and Palantir end up being the right move, his move seems to have spurred at least some investors to offload NVDA and PLTR shares, as of the time of this writing. It should also be noted that Burry is far from the only one who sees signs of an AI bubble. Many investors and industry experts have begun to question whether the industry is in a bubble—and what would happen if that bubble pops. For instance, an October Bank of America Global Research survey found that 54% of investors believe AI stocks are in a bubble, as Reuters recently reported. Even so, today’s share price drops in NVDA and PLTR are minuscule compared to their surging stock prices in recent years. Year-to-date, Nvidia has seen its stock price surge more than 50% and PLTR is up more than 150%. Over the past 12 months, NVDA has risen more than 48% and PLTR has risen more than 350%. View the full article

Bullish comments come amid increasing uncertainty over EV models’ impact on luxury-car maker’s remarkable profit growthView the full article

A new look is coming to the National Mortgage News homepage, writes Editor-in-Chief Heidi Patalano View the full article

A second food recall has been initiated after a California-based fruit supplier discovered that some of its yellow and white peaches might be contaminated with Listeria monocytogenes, which can cause potentially deadly infections. Here’s the latest and what to know: What’s happened? On October 29, Moonlight Companies voluntarily recalled “California-grown conventional” yellow and white peaches due to a risk of contamination with Listeria monocytogenes. Some items were sold under the Kroger name, the company said in its announcement. Listeria was found in the packing facility. To date, no illnesses have been reported. However, the impacted fruit was sold at retail stores across the country. A day later, the Food and Drug Administration (FDA) published the recall notice on its website. A second peach-related recall was later announced due to potential Listeria contamination. On October 30, Supreme Produce—whose supplier is Moonlight Companies—said it recalled one peach salsa product. To date, no illnesses have been reported. The FDA published this second recall notice on Monday. Which products are included in the recalls? Recalled peaches were sold at retail stores nationwide between September 16 and October 29, 2025. They were sold individually with PLU stickers or in multi-packs. Peaches with packaging or PLU stickers with the words “Organic” or “Washington” aren’t included in the recall. Recalled Moonlight Companies peaches include the following: Moonlight Yellow Peaches Moonlight White Peaches Moonlight White Peaches (“Peppermint Peach”) Kroger Yellow Peaches You can see a full list of lot codes, PLU sticker numbers, and packaging images on the FDA’s website. The following Supreme Produce peach salsa product has been recalled: Product: Peach Salsa Barcode UPC: 85006540364 Best by dates: 10/12/2025 to 10/29/2025 These products were packaged in 14-ounce clear, plastic grab-n-go containers and were sold in Kroger retail stores under the Supreme Produce brand. They were distributed in the following states: Arkansas Colorado Georgia Illinois Indiana Michigan Mississippi Oregon Tennessee Washington Discard remaining products Customers should not consume any of the above recalled products. While they’re no longer for sale, if you have any of the above products, discard them. If you have any questions about the recall, call Moonlight Companies at (855) 215 -5017. What is Listeria? Listeria infection is an illness caused by bacteria that can spread through contaminated food. According to the Mayo Clinic, healthy people rarely become seriously ill from Listeria infection. However, the disease can be fatal for unborn babies, newborns, and those with weakened immune systems. Pregnant women, adults 65 and older, and people with weakened immune systems are most at risk for infection. View the full article

If you've joined a private Facebook group—counting on your membership and posts to be visible only to those approved to join—and receive a notification that said group is about to go public, you don't need to panic. While Meta is now allowing admins of previously private Facebook groups to change the status to public, it is also assuring users that past activity will remain private. Private Facebook groups can be made publicAccording to Meta's post announcing the update, the option to transition from private to public allows small groups to grow into larger communities that are more easily found, with content visible to anyone (even people who aren't on Facebook). Any admin can set their group privacy settings to public, which will trigger a three-day review period in which all admins have the opportunity to decline the conversion. If no one cancels the request, the group will go public. Groups can revert back to private at any time, at which point new members will again require admin approval. (These members will be able to see all content from before the conversion.) Your private posts are still protectedWhen a private group goes public, all existing posts, comments, and reactions will remain visible only to members, admins, and moderators who were already in the group but be hidden to new members and the general public. Member lists will also be hidden to everyone except admins and moderators. Any content added after the conversion will be visible to anyone. If a group you're in is converted from private to public, you'll get a notification about the change. You'll also see a notification the first time you post or comment in the newly public group reminding you that your activity is now visible. When you go to post, look for the globe icon, which indicates that your content is public. View the full article

A reader writes: I started at my company about five years ago after being laid off from my previous company due to Covid. Once I started here, I was shocked to discover that one of my old friends (Susan) who I was very close to in college (which I had graduated from 10 years prior) worked at the same company in a different building on the company’s campus. I reached out to her briefly on Teams just to say, “Oh wow, I had no idea you worked here. If you’re ever near my building, pop by and say hey and maybe we could grab a coffee.” She responded warmly and we had one brief conversation in my office, and that was the last time I saw her for months. We were in different departments with very little crossover, so we never had reason to interact in a work setting and we weren’t the kind of friends who were in constant communication so I didn’t think much of it. Cut to a while later — maybe six months to a year — and I met up with the friendship group that had survived from our college years. Susan was invited but unable to attend, and during this gathering our mutual friend Carla said that Susan had decided she no longer wanted to be friends with the rest of us, only wanted to keep her friendship with Carla, and our over-a-decade-long friendship was essentially over. It wasn’t only me who got cut off by Susan, but I must admit that I took it quite personally, given that we worked at the same place. I wondered if the formal break-up through our mutual friend wasn’t specifically aimed at me because none of the rest of our friends would have had reason to run in to her, given that we were all very spread out geographically. I also felt like because the news was delivered via a mutual friend, I never got the chance to get closure or understanding of why the friendship ended. For the past four years, this has been mostly a non-issue since we only run into each other maybe twice a year at work and none of our work crosses over. But recently a department that I work incredibly closely with was hiring. I was talking to my friend in that department and she told me that they had had an exciting internal applicant, and lo and behold it was Susan. I’m 100% sure that Susan will get this job; she is intelligent and hard-working, and I know they had been struggling with finding external candidates to fill this role. I’m feeling anxious at the prospect of working closely with her. There was a time when we were really close friends and basically living in each other’s pockets. She was the first and only person at college who I told when my mother died and she helped me share that information with our other friends. Then we weren’t and I never got the chance to understand why. I just have no clue how to gauge my behaviour. Did we stop being friends because the friendship just fizzled over time? Did I do something to annoy her? Was the trigger me showing up at her place of work unexpectedly? Did she feel like I followed her there or was pressuring the relationship? I am autistic and social stuff can be very tough for me to navigate even at the best of times but this feels like a whole minefield. I am also having a lot of anxiety that if the friendship ended because she didn’t like me specifically or I unknowingly did something that upset her, that may still be true and may affect my working relationship with the people I am friends with in that department. I know the first port of call is to behave professionally towards Susan and treat her like any other colleague, but should I be doing anything else proactively? It’s been a few years since the news that we were no longer friends was delivered, so bringing it up would be weird, I think. I did not say anything to my friend in the other department when she suggested that Susan might be getting the job, other than endorsing her candidacy because I truly feel like she would be a good fit for this role, and despite the awkward way our friendship ended I hold no ill will against her. We’re both still friends with Carla so I was considering reaching out to her to see if she had any sense of how Susan felt about me, but then indirect communication through Carla is also what spawned a lot of this anxiety in the first place. Pay attention to that last sentence because I actually think Carla stirred up a lot of drama where there didn’t need to be any. If Susan wanted to end her friendship with your friend group, she could have just … done that. Carla didn’t need to make a formal announcement. Susan could have talked to people herself or just done the natural fade/falling out of touch that happens frequently post-college. I’m side-eyeing Carla a bit for thinking it was her place to announce this to the rest of you (and I can’t tell if Susan asked her to, or if she took it upon herself — it sounds like maybe the latter). “She doesn’t want to be friends with any of you, only me” also makes me wonder if her announcement was self-serving in some way. Regardless, if Carla hadn’t said anything that day, you wouldn’t be feeling any of this anxiety now — so it’s worth noting that your fears right now are coming from Carla’s actions, not Susan’s. As for what happened, I’d bet money that it’s not about anything you did at all, because she cut off your entire friend group. It’s far more likely that it’s something like feeling very different from her college self now, or even having bad memories of that time and avoiding people associated with it, or going through something now and not having the energy to keep up with older, more distant friendships, or … well, all sorts of other things that you wouldn’t know from the outside. I don’t think you need to wrack your brain trying to figure out if you caused this. (It’s also very unlikely that Susan felt like you deliberately followed her to her company. It’s a large company, people one knows might pop up, and it sounds like your approach to her was extremely normal and low-key.) Your instincts to just treat Susan like any other colleague are absolutely right. You don’t need to do anything else proactively (like reaching out to her ahead of time), and actually I strongly think you shouldn’t. Just be low-key about the whole thing, which has the advantage of demonstrating for her that a low-key approach is perfectly workable and no one needs to feel tense or weird about the situation. Treat her the way you would someone else you didn’t have a history with — meaning pleasantly and with good will and with no real expectations beyond working together productively — and just assume that you and Susan will build a new relationship as colleagues that will be its own thing, rather than an extension of the old friendship. The post I have to work closely with an ex-friend who “broke up” with our whole friend group appeared first on Ask a Manager. View the full article

We may earn a commission from links on this page. Deal pricing and availability subject to change after time of publication. The Google TV Streamer 4K is Google's latest iteration of the smart TV stick. No longer is it hiding behind your TV, but now it takes center stage with a hub on your TV stand. Right now, you can get the Google TV Streamer 4K for $74.99 (originally $99.99), the lowest price it has been since its last summer, according to price tracking tools. Google TV Streamer 4K 32GB With Voice Search Remote (Porcelain) $74.99 at Amazon $99.99 Save $25.00 Get Deal Get Deal $74.99 at Amazon $99.99 Save $25.00 Google first released the Chromecast, an HDMI device that let you cast your phone to your TV. Then it released the Chromecast with Google TV in 2022, which incorporated the Google OS into the Chromecast. Now, Google seems to want to go a new route after discontinuing the Chromecast altogether—this new device is powered by Gemini (Google's AI), has more storage, and better performance for twice the price. The Google TV Streamer 4K comes with 32GB of storage space, 4GB of memory, and the ability to run HDR streams at up to 4K@60 FPS. It also has support for Dolby Vision and Atmos, and it has ports for things like Ethernet. The Google OS is identical to older models, but performance is much better, according to CNET's review. While you already get more storage than previous models, you can also expand it with the USB-C port. This is my personal favorite streaming stick and has the best OS of all the ones I've tried. I have it on my TV at home and recommend it to anyone looking for the best TV experience, especially at its lowest price. Our Best Editor-Vetted Tech Deals Right Now Apple AirPods Pro 2 Noise Cancelling Wireless Earbuds — $169.99 (List Price $249.00) Apple iPad 11" 128GB A16 WiFi Tablet (Blue, 2025) — $299.00 (List Price $349.00) Amazon Fire TV Stick 4K Plus — $29.99 (List Price $49.99) Ring Pan-Tilt Indoor Cam, White with Ring Indoor Cam (2nd Gen), White — $59.99 (List Price $99.99) Blink Video Doorbell Wireless (Newest Model) + Sync Module Core — $69.99 (List Price $69.99) Blink Mini 2 1080p Indoor Security Camera (2-Pack, White) — $27.99 (List Price $69.99) Ring Video Doorbell Pro 2 with Ring Chime Pro — $149.99 (List Price $259.99) Introducing Amazon Fire TV 55" Omni Mini-LED Series, QLED 4K UHD smart TV, Dolby Vision IQ, 144hz gaming mode, Ambient Experience, hands-free with Alexa, 2024 release — $699.99 (List Price $819.99) Blink Outdoor 4 1080p 2-Camera Kit With Sync Module Core — $129.99 (List Price $129.99) Deals are selected by our commerce team View the full article

Japanese video-game maker Nintendo’s net profit jumped 85% in April-September from the year before, as its sales more than doubled following the launch of its hit Switch 2 console in June, the company said Tuesday. Nintendo, based in Japan’s ancient capital of Kyoto, said its profit for the half-year totaled 198.9 billion yen, or $1.3 billion, up from 108.6 billion yen the year before. Sales for the first half of this fiscal year rose to nearly 1.1 trillion yen ($7.1 billion) from 523 billion yen in the same period of 2024. Nintendo, which makes Super Mario and Pokemon games, did not provide a break down of quarterly data. Nintendo’s video game sales were solid, although with no new movies revenue from its content business slowed. Nintendo raised its profit forecast for the full fiscal year through March 2026 to 350 billion yen ($2.3 billion). Previously, it had expected a 300 billion yen ($1.9 billion) profit. It also raised its forecast for Switch 2 machine sales to 19 million units from the earlier 15 million. Nintendo says it had sold more than 10 million Switch 2s by the end of September. Popular Switch 2 game software include “Mario Kart World” and “Donkey Kong Bananza.” Sales of the older Nintendo Switch have fallen, but Switch game sales are still going strong because they can be played on Switch 2 machines. Analysts expect Nintendo’s earnings to stay strong with the upcoming holiday season, when it tends to do well. They also expect key new games in the Pokemon and Kirby franchises. Nintendo stocks, which have been rising relatively steadily over the past year, fell 0.8% on Tuesday. Yuri Kageyama is on Threads: https://www.threads.com/@yurikageyama —Yuri Kageyama, AP Business Writer View the full article

As the marketing landscape undergoes a seismic shift, small business owners face new challenges—and opportunities—largely driven by advancements in artificial intelligence. HubSpot, a leader in marketing software, has announced its agreement to acquire XFunnel, a platform designed to help businesses optimize their visibility in the era of Answer Engine Optimization (AEO). The timing couldn’t be more critical. As consumers increasingly rely on AI tools like ChatGPT and Claude for information, businesses must adapt their marketing strategies to ensure they appear where their customers are searching. HubSpot reports that leads generated from AI-driven strategies convert three times better than those from traditional search methods, highlighting the necessity for small businesses to reconsider their digital strategies. One of the core advancements in HubSpot’s recent updates is the introduction of Loop Marketing, a modern framework designed to attract, engage, and convert customers in a manner that aligns with today’s digital behavior. Angela DeFranco, GM and VP of Product for HubSpot’s Marketing Hub, underscores the intent behind the acquisition: “As AI changes how people find and engage with businesses, we want to make that shift easier to navigate for our customers.” With XFunnel’s integration into HubSpot’s marketing suite, small business owners can expect insights and strategies aimed at enhancing their online presence across various AI platforms. XFunnel helps marketing teams monitor their business’s digital performance across AI-generated answers, providing data-driven recommendations for improving visibility. This level of understanding can empower small businesses to target and connect with their audiences more effectively. The capabilities offered by XFunnel can be particularly beneficial for small business owners who often juggle various responsibilities and may lack the resources of larger enterprises. The platform’s design focuses on experimentation and rapid testing, enabling users to identify effective strategies efficiently. “We’ve admired HubSpot for their leadership in marketing and for their consistent innovation with AI across the platform,” said Neri Bluman, Co-Founder of XFunnel. The collaborative spirit between the two companies signals a shared vision for helping businesses adapt to ongoing changes in the marketing landscape. For small business owners keen on leveraging these new AI-enhanced tools, there are practical applications worth considering. Businesses can use insights from XFunnel to optimize their content, improve website visibility, and tailor their marketing messages to meet customer queries head-on. In an age where first impressions can be influenced by how businesses appear in search results, developing a data-informed strategy has never been more critical. However, potential challenges are tied to the integration of this new technology. For smaller businesses that may not have extensive marketing teams or budgets, the learning curve associated with implementing AI-driven tools could feel daunting. Additionally, the rapidly evolving nature of AI and AEO means that staying current with best practices may require an ongoing investment in training and development. Nevertheless, the benefits of adopting these tools are clear. By positioning themselves in the AI ecosystem through AEO strategies, small businesses stand to gain a competitive edge. More than just improving visibility, these tools offer the chance to refine customer relationships and enhance overall marketing effectiveness. With the acquisition of XFunnel, HubSpot aims to equip small business owners with the necessary tools and insights to thrive in this evolving landscape. In time, as the capabilities become fully integrated, small businesses will receive a vital resource to navigate changes in how customers discover products and services. As this trend develops, staying informed and adaptable will be crucial for small business owners looking to harness the power of AI in their marketing efforts. You can read more about this exciting acquisition and HubSpot’s ongoing innovations in marketing at the original post here. This article, "HubSpot Boosts Marketing with XFunnel Acquisition to Enhance AI Visibility" was first published on Small Business Trends View the full article

As the marketing landscape undergoes a seismic shift, small business owners face new challenges—and opportunities—largely driven by advancements in artificial intelligence. HubSpot, a leader in marketing software, has announced its agreement to acquire XFunnel, a platform designed to help businesses optimize their visibility in the era of Answer Engine Optimization (AEO). The timing couldn’t be more critical. As consumers increasingly rely on AI tools like ChatGPT and Claude for information, businesses must adapt their marketing strategies to ensure they appear where their customers are searching. HubSpot reports that leads generated from AI-driven strategies convert three times better than those from traditional search methods, highlighting the necessity for small businesses to reconsider their digital strategies. One of the core advancements in HubSpot’s recent updates is the introduction of Loop Marketing, a modern framework designed to attract, engage, and convert customers in a manner that aligns with today’s digital behavior. Angela DeFranco, GM and VP of Product for HubSpot’s Marketing Hub, underscores the intent behind the acquisition: “As AI changes how people find and engage with businesses, we want to make that shift easier to navigate for our customers.” With XFunnel’s integration into HubSpot’s marketing suite, small business owners can expect insights and strategies aimed at enhancing their online presence across various AI platforms. XFunnel helps marketing teams monitor their business’s digital performance across AI-generated answers, providing data-driven recommendations for improving visibility. This level of understanding can empower small businesses to target and connect with their audiences more effectively. The capabilities offered by XFunnel can be particularly beneficial for small business owners who often juggle various responsibilities and may lack the resources of larger enterprises. The platform’s design focuses on experimentation and rapid testing, enabling users to identify effective strategies efficiently. “We’ve admired HubSpot for their leadership in marketing and for their consistent innovation with AI across the platform,” said Neri Bluman, Co-Founder of XFunnel. The collaborative spirit between the two companies signals a shared vision for helping businesses adapt to ongoing changes in the marketing landscape. For small business owners keen on leveraging these new AI-enhanced tools, there are practical applications worth considering. Businesses can use insights from XFunnel to optimize their content, improve website visibility, and tailor their marketing messages to meet customer queries head-on. In an age where first impressions can be influenced by how businesses appear in search results, developing a data-informed strategy has never been more critical. However, potential challenges are tied to the integration of this new technology. For smaller businesses that may not have extensive marketing teams or budgets, the learning curve associated with implementing AI-driven tools could feel daunting. Additionally, the rapidly evolving nature of AI and AEO means that staying current with best practices may require an ongoing investment in training and development. Nevertheless, the benefits of adopting these tools are clear. By positioning themselves in the AI ecosystem through AEO strategies, small businesses stand to gain a competitive edge. More than just improving visibility, these tools offer the chance to refine customer relationships and enhance overall marketing effectiveness. With the acquisition of XFunnel, HubSpot aims to equip small business owners with the necessary tools and insights to thrive in this evolving landscape. In time, as the capabilities become fully integrated, small businesses will receive a vital resource to navigate changes in how customers discover products and services. As this trend develops, staying informed and adaptable will be crucial for small business owners looking to harness the power of AI in their marketing efforts. You can read more about this exciting acquisition and HubSpot’s ongoing innovations in marketing at the original post here. This article, "HubSpot Boosts Marketing with XFunnel Acquisition to Enhance AI Visibility" was first published on Small Business Trends View the full article

You’re at your desk reviewing last week’s ticket metrics, coffee cooling beside your monitor, and one number looks good: 4.5 hours average time to escalation. That means your L1 team tried everything before passing tickets up, right? Then you drill into a resolved incident. Engineering fixed it in 10 minutes. API timeout error (something they’d seen three times that day already). Your L1 agent spent 4 hours troubleshooting (restarted services, checked configurations, walked the user through cache clearing). Standard fixes for connectivity problems. Engineering recognized the pattern immediately and applied a known workaround. Where did those 4 hours create value? They didn’t. Your agent was guessing without context while your engineering team already knew about this issue and was actively working on similar tickets. But your L1 team works in ServiceNow. Engineering works in Jira. Your agent couldn’t see what engineering already knew. This is the time to escalation trap. Long escalation time doesn’t mean thorough troubleshooting. It means troubleshooting blind. The hidden cost of escalation delays You’re not just losing a few hours. You’re losing them at scale. Your typical support organization handles hundreds of escalations monthly. Each delayed escalation creates compound waste: your L1 loses time on ineffective troubleshooting, your users wait for resolution, your engineering team eventually solves problems that could’ve been escalated immediately. Organizations implementing better ITSM (IT Service Management) capabilities report 55 minutes saved per incident on average, and those gains come primarily from reducing this diagnostic waste. Your team experiences this as constant context switching. Your L1 agents ping engineering in Slack: “Is anyone seeing login issues?” Engineering responds: “Yeah, there’s an auth service problem, we’re on it.” Meanwhile, three other agents are still troubleshooting the same symptom individually, applying password reset procedures that won’t fix the underlying service outage. The financial impact accumulates quickly. Your L1 agent spending time on a ticket that should escalate immediately costs you their hourly rate, multiplied by tickets, multiplied by agents. The waste isn’t dramatic. It’s a steady erosion of productive time. Service desk productivity improvements from integrated platforms deliver $2.9M in value over three years, primarily by eliminating this category of invisible work. When you examine ITSM process optimization efforts, reducing time to escalation shows up as a secondary metric. Teams focus on first-call resolution rates or total resolution time. But time to escalation reveals whether your L1 team has the information they need to make smart handoff decisions (fast escalation on complex issues is efficiency, while slow escalation on known issues is waste). Why L1 agents troubleshoot blind Your L1 agent opens a ticket: user can’t access the customer portal. Your agent starts with standard checks (browser cache, VPN connection, password reset). Twenty minutes in, your agent escalates. Engineering sees the ticket and recognizes it immediately. The portal authentication service has been degraded for the past hour. They’re already implementing a fix. This happens because your tools don’t talk to each other. The handoff points where context disappears Your L1 team operates in one system while your engineering team operates in another. When your L1 agents look at their queue, they see incoming tickets but don’t see engineering’s current incidents, active investigations, or known issues being resolved. The information gap creates predictable failure points. Your agents check the knowledge base (find articles about browser troubleshooting, VPN configuration, account lockouts) but nothing about the authentication service outage happening right now. They follow ticket escalation workflows correctly: exhaust L1 options, document attempts, escalate with notes. But those workflows assume your L1 team has visibility into what’s already known. Your L1 team is troubleshooting symptoms for problems your engineering team identified an hour ago. The handoff itself loses context. Your agent writes detailed notes in ServiceNow while engineering sees a new Jira ticket with basic fields populated. The troubleshooting history doesn’t transfer completely, priority gets mistranslated, and custom fields don’t map. Engineering asks clarifying questions your L1 team already answered in the original ticket. What L1 can’t see that engineering knows Your engineering team tracks problems your L1 team never sees: current outages, known bugs in this week’s release, vendor issues affecting third-party integrations, infrastructure changes that created side effects. They discuss these in their own system, tag related tickets, link to incident reports. Your L1 team knows none of this. Your agents receive a ticket about email delays and check the user’s mail client settings, verify the account isn’t over quota, and test connectivity. Meanwhile, your engineering team is actively working on a mail server issue affecting 200 users. Your ticket is symptom #47 of a known problem. The pattern repeats across issue types: database connection errors your engineering team traced to a recent schema change, report generation failures tied to a scheduled maintenance window, form submission problems that only affect Safari users (something your engineering team discovered after the third ticket yesterday). Your L1 team troubleshoots each ticket as a unique problem while your engineering team recognizes patterns immediately because they see all related tickets in their workspace. The visibility gap creates waste on both sides: your L1 team spends time on dead-end diagnostics while your engineering team receives tickets that should’ve been escalated immediately. When visibility changes escalation patterns Fast escalation becomes pattern recognition instead of guesswork when your L1 team sees what your engineering team sees. Your agent receives a ticket about slow dashboard loading and checks their tool. Three tickets came in during the past hour with similar symptoms (all assigned to engineering). One is already marked “investigating performance issue.” Your agent recognizes the pattern: escalate immediately, reference the investigation ticket, don’t spend time on individual troubleshooting. That’s a 2-minute escalation instead of a lengthy troubleshooting cycle. Repeated across tickets, this changes your time to escalation metric fundamentally. You’re not measuring “how long L1 tried,” you’re measuring “how fast L1 identified escalation-appropriate issues.” Recognizing escalation triggers in real time Pattern recognition requires visibility. When engineering tickets sync into your L1 workspace, your agents see active investigations, recent escalations, and current issues. They match incoming tickets against known problems automatically. The triggers become obvious: similar symptoms appearing in engineering’s queue, tags indicating ongoing incidents, recent escalations with matching error messages, and investigation tickets with status updates. Your L1 agents don’t need to understand the technical details. They need to recognize when engineering is actively working on something that looks like this ticket. That recognition happens in seconds when both systems show relevant context in one workspace. This changes how fixing escalation bottlenecks works. Traditional approaches focus on criteria and training: teach L1 when to escalate, create clearer guidelines, and improve documentation. Those help, but they still assume your L1 team is making decisions with incomplete information. Visibility replaces guesswork with recognition. How cross-system visibility reduces diagnostic waste You still want your L1 team to troubleshoot first when appropriate (user education issues, password resets, and permissions problems don’t need engineering). But visibility helps your agents distinguish between “I should try standard fixes” and “this matches something engineering knows about.” A ticket arrives: user reports intermittent connection drops. Your agent checks the engineering workspace (no active incidents about network connectivity, no recent escalations with similar symptoms). Pattern suggests individual troubleshooting is appropriate: this is likely browser-specific, VPN-related, or local network. Your L1 team proceeds with standard diagnostics. Different ticket: user reports connection drops. Your agent checks the engineering workspace and sees five tickets in the past two hours, all escalated, all tagged “investigating ISP routing issue.” The pattern suggests immediate escalation (adding this to the pile helps engineering understand scope, and L1 troubleshooting won’t fix an external routing problem). Same symptom, different context, different decision. Visibility provides that context instantly. Without it, both tickets get identical L1 treatment (wasting time on one, providing appropriate service on the other, with no way to distinguish which is which until after escalation). What cross-system integration actually provides Integration solves the visibility problem by making engineering context available where your L1 team works. Bidirectional sync vs one-way handoffs Your traditional escalation creates one ticket in L1’s system, then creates a separate ticket in engineering’s system. Information flows in one direction during creation. After that, the tickets diverge. Engineering updates their ticket (status changes, priority adjusts, technical details get added) while your L1 ticket stays static until someone manually checks engineering’s system and copies updates back. Bidirectional sync keeps both tickets aligned continuously. When your engineering team updates a status in Jira, that status appears in ServiceNow within seconds. When engineering adds investigation notes, those notes appear in your L1 view. When your L1 team adds customer communication, engineering sees it in their workspace. Both teams work in their preferred tool while looking at synchronized information. This matters for escalation decisions. Your L1 agents see engineering’s status updates in real time. A ticket marked “investigating” tells them other similar tickets should escalate immediately. A ticket marked “fixed, rolling out patch” tells them to watch for related issues as the fix deploys. A ticket marked “waiting for vendor response” tells them similar problems won’t resolve quickly (set customer expectations accordingly). One logistics company reduced manual escalation tracking by significant cost annually by integrating ServiceNow and Jira. Their agents stopped switching between systems to check escalation status. Engineering’s updates appeared directly in the service desk view, and patterns became visible immediately. What L1 needs to see in their tool Cross-system visibility isn’t about giving your L1 team access to engineering’s workspace. It’s about surfacing relevant engineering information where your L1 team already works. Your agents need to see active engineering tickets with similar symptoms or affected services, current incident investigations and their status, known issues marked for tracking, and recent escalations with their resolution patterns. They don’t need to understand engineering’s technical discussions, architecture diagrams, or code commits. They need enough context to recognize patterns: “This ticket matches something engineering is working on” or “This is the third payment processing error this morning (something systemic is happening).” That context appears as synced fields in their service desk tool: tags indicating investigation status, links to related engineering tickets, custom fields showing affected services or components, status updates that tell the story from “investigating” to “cause identified” to “fix deployed” to “monitoring.” Your L1 team sees the progression without leaving their workspace. The integration preserves each team’s workflow. Your engineering team still works in their development-focused tool with its technical features, while your L1 team still works in their service-desk-focused tool with its customer-facing features. But relevant information flows between them automatically, creating shared context without forced tool adoption. Evaluating visibility for your escalation workflow When you’re evaluating solutions to reduce time to escalation, the question is: does your L1 team get the context they need to make smart handoff decisions? Test the basics first. Create a ticket in engineering’s system (does it appear in your L1 workspace?). How long does that take (seconds or minutes)? Update the engineering ticket’s status (does that update flow to your L1 view automatically?). Create a ticket in your L1 system and escalate it (does engineering see complete context, or just basic fields?). Look at pattern recognition capabilities. Can you tag engineering tickets as “incident investigation” or “known issue”? Do those tags sync to your L1 view? Can your L1 agents search for active engineering tickets by keyword, affected service, or error message? If five similar tickets exist in engineering’s queue, does your L1 team see that when a sixth arrives? Check field mapping. Priority systems often differ between tools (Jira uses P1/P2/P3, ServiceNow uses impact and urgency matrices). Does the integration translate these intelligibly, or does priority information get lost? Assignee fields work differently across tools (does your integration map team names, individual users, or both?). Examine what happens when systems disagree. Your engineering team closes their ticket while your L1 team adds a follow-up comment. Do both actions respect each other, or does one overwrite the other? Status workflows differ (ServiceNow might have 8 status values, Jira might have 4). Does your integration handle those differences gracefully? The goal isn’t perfect synchronization of every field. It’s sufficient visibility for decision-making. Your L1 agents need to see: “Engineering is working on this type of problem right now.” They don’t need engineering’s sprint planning details or code branch names. Evaluate whether the integration surfaces actionable context in your L1 workspace, not whether it mirrors engineering’s workspace completely. Reducing time to escalation through visibility You’re looking at that 4.5-hour time to escalation number differently now. It’s not measuring L1 thoroughness. It’s measuring the gap between when a ticket arrives and when your L1 team has enough information to recognize it needs engineering. Fast escalation on complex issues is efficiency. You want your L1 team to escalate immediately when they recognize patterns your engineering team already knows about. Immediate escalation reduces total resolution time, eliminates diagnostic waste, and gets engineering working on problems with complete context about user impact. The solution is visibility across systems. When your L1 team sees engineering’s current work in their tool (active investigations, known issues, recent escalations) they pattern-match instead of guessing. Incoming tickets match against visible context: three API timeout tickets yesterday, with engineering investigating, means the fourth ticket gets escalated immediately. Dashboard slowness with no engineering activity visible means your L1 team troubleshoots first. This requires bidirectional sync between service desk and engineering tools (not just ticket creation, but continuous updates that keep context aligned as both teams work). Engineering’s status changes appear in your L1 workspace, while your L1 team’s customer communication appears in engineering’s view. Both teams see relevant information without switching tools. The evaluation criteria are specific: Can your L1 team see engineering’s current work? Do incoming tickets match against known issues automatically? Do updates flow both directions in seconds, not hours? Does field mapping preserve priority, status, and assignment information meaningfully? Unito syncs ServiceNow, Jira, Zendesk, Azure DevOps, and other ITSM platforms bidirectionally. Engineering context becomes visible where your L1 team works. Pattern recognition happens automatically through synced tags, statuses, and custom fields. Updates flow in real time (changes in one tool appear in the other within seconds). Want to know more? Explore how Unito solves ticket escalation challenges. Learn more View the full article

In the age of AI, conversations, not calculations, will define the future of the profession. The Disruptors With Liz Farr Go PRO for members-only access to more Liz Farr. View the full article

In the age of AI, conversations, not calculations, will define the future of the profession. The Disruptors With Liz Farr Go PRO for members-only access to more Liz Farr. View the full article

Decision-making tools and decision-making techniques are essential for managers, team leaders and project managers who need to make informed choices quickly and effectively. These methods help organize information, evaluate alternatives and predict outcomes. From simple pros and cons lists to complex decision matrices, using decision-making tools ensures clarity and consistency, while decision-making techniques provide structured approaches for analyzing problems, reducing bias and prioritizing options. Free templates make it easier to implement these strategies immediately, saving time and improving overall decision quality across teams and organizations. What Are Decision-Making Tools? Decision-making tools are structured methods and instruments used to guide individuals or teams in evaluating options, analyzing data and making informed choices. They include frameworks, charts, matrices and software solutions that clarify complex problems, quantify outcomes and highlight trade-offs. Decision-making tools simplify the process of selecting the best course of action and reduce the risk of bias or oversight, ensuring that decisions are consistent, transparent and aligned with organizational objectives. Project management software enhances decision-making by centralizing data, tracking project progress and visualizing metrics. Tools like Gantt charts, dashboards, risk matrices and resource reports provide insights that inform timely, data-driven decisions. Teams can compare scenarios, allocate resources efficiently and monitor outcomes in real time, making it easier to evaluate alternatives and respond quickly to changes in project requirements. ProjectManager stands out as the best software for decision-making because it combines multiple decision-making tools and decision-making techniques in one platform. With interactive dashboards, customizable reports, AI summaries and recommendations all with real-time collaboration, managers can visualize performance, forecast results and adjust plans instantly. Its intuitive interface integrates with other applications, allowing teams to implement structured decision-making processes seamlessly across projects, departments and portfolios, all within a single, cloud-based solution. Get started with ProjectManager today for free. /wp-content/uploads/2025/10/AI-Project-Insights-Lightmode-Dashboard-CTA.pngLearn more What Are Decision-Making Techniques? Decision-making techniques are systematic approaches or methods used to evaluate choices and select the best course of action. They include analytical frameworks, prioritization methods, risk assessments, pros-and-cons lists and scenario planning. These techniques help individuals and teams structure complex problems, weigh alternatives and make consistent, rational decisions. By applying proven decision-making techniques, organizations can improve outcomes, reduce uncertainty and ensure that decisions are aligned with strategic goals and project objectives. 9 Best Decision-Making Tools and Techniques Numerous decision-making tools and techniques guide project managers and teams in selecting optimal solutions. From matrices to flowcharts, each tool supports a different type of analysis and decision scenario. The following nine tools and techniques are among the most effective for improving clarity, reducing bias and enhancing outcomes in project and organizational decision making. 1. Decision Log A decision log is a structured record used to track all key decisions made during a project or business process. It captures details such as the decision itself, the rationale behind it, the stakeholders involved, potential alternatives considered, and the expected outcomes. Using a decision log ensures transparency, accountability and consistency in decision making. Teams can refer back to it to understand why past decisions were made, learn from successes and mistakes and maintain alignment with project objectives. Decision logs also facilitate communication across departments, enabling informed collaboration and better use of decision-making tools and techniques. /wp-content/uploads/2022/02/Decision-Log-1.png 2. Decision Flowchart A decision flowchart is a visual representation of the steps involved in making a decision. It maps out possible paths, choices and consequences, helping teams understand the logical progression of options. By presenting complex decisions in a clear, structured format, flowcharts simplify analysis and reduce errors. They support systematic decision-making techniques by clarifying dependencies and identifying bottlenecks. Teams can use decision flowcharts to explore scenarios, anticipate risks and communicate processes across stakeholders. This method ensures decisions are consistent, repeatable and aligned with project goals while leveraging decision-making tools effectively. /wp-content/uploads/2024/12/Decision-flowchart-example-product-development-e1734973992737.png 3. Decision Matrix A decision matrix is a quantitative tool used to evaluate multiple options against a set of weighted criteria. Each option is scored based on how well it meets each criterion, and the scores are combined to reveal the best overall choice. This method reduces bias and supports rational, data-driven decision-making techniques. Teams can compare alternatives objectively, prioritize resources, and justify their selections. Decision matrices are particularly useful for complex projects with competing requirements. By providing a clear framework, they enhance transparency and ensure that decision-making tools are applied consistently, improving project outcomes and stakeholder confidence. /wp-content/uploads/2023/10/decision-matrix-screenshot-600x227.png 4. Decision Tree A decision tree is a diagrammatic tool that helps map out decisions and their possible outcomes. It starts with a single decision point and branches into different options, potential consequences and probability estimates. Decision trees provide a clear visualization of complex choices, making it easier to analyze risk and rewards. They support structured decision-making techniques by allowing teams to evaluate multiple scenarios before committing to a course of action. By breaking decisions into manageable steps, decision trees reduce uncertainty, improve communication among stakeholders, and ensure that decision-making tools are applied systematically and effectively across projects. /wp-content/uploads/2025/03/Decision-Tree-Template-image-600x364.png 5. DACI Matrix The DACI matrix is a decision-making framework that clarifies roles and responsibilities in the decision process. DACI stands for Driver, Approver, Contributor and Informed. The Driver manages the process, the Approver signs off on the final decision, Contributors provide input and those Informed are kept up to date. This structure prevents confusion, speeds up decision-making and ensures accountability. Using a DACI matrix helps teams apply decision-making tools and techniques consistently, streamlines collaboration, and avoids bottlenecks. It is especially valuable for complex projects involving multiple stakeholders and cross-functional teams. 6. RAPID Decision Framework The RAPID decision framework is a tool designed to improve clarity and efficiency in group decision-making. RAPID stands for Recommend, Agree, Perform, Input and Decide. Team members are assigned specific roles: those who Recommend suggest solutions, Agree approve the recommendation, Perform implement the decision, Input provide expertise and Decide has final authority. This method prevents overlap, reduces conflict and accelerates decision-making processes. By clearly defining responsibilities, RAPID supports structured decision-making techniques and ensures decision-making tools are applied effectively, leading to better outcomes and greater accountability across project teams. 7. Pugh Matrix The Pugh Matrix is a decision-making tool that helps teams compare multiple options against a baseline or reference solution. Criteria are weighted, and each alternative is scored relative to the baseline. This systematic approach highlights the strengths and weaknesses of each option, allowing stakeholders to make informed choices. The Pugh Matrix supports structured decision-making techniques by quantifying subjective judgments, promoting objective discussions, and prioritizing alternatives based on performance. It’s particularly effective for product selection, process improvements and project planning, ensuring that decisions are data-driven and transparent. 8. Cost-Benefit Analysis Cost-benefit analysis is a decision-making technique used to evaluate the financial and operational impact of alternatives. Teams list all costs associated with each option, including direct, indirect and opportunity costs, then weigh them against expected benefits, such as revenue, efficiency gains or risk reduction. This tool provides a clear framework for comparing options, supporting evidence-based decisions. By applying structured decision-making techniques, cost-benefit analysis ensures that resources are allocated wisely, potential risks are assessed, and decision-making tools are used to optimize outcomes for projects and organizational initiatives. /wp-content/uploads/2021/07/Cost-Benefit-Analysis-Screenshot-600x240.jpg 9. Analytic Hierarchy Process (AHP) The Analytic Hierarchy Process (AHP) is a decision-making tool that breaks down complex decisions into a hierarchy of goals, criteria and alternatives. Stakeholders perform pairwise comparisons to determine relative importance, and a scoring system ranks options accordingly. AHP helps prioritize choices when multiple factors must be considered simultaneously, supporting structured decision-making techniques. By providing a quantitative framework, AHP reduces bias, improves consistency and ensures decision-making tools are applied methodically. It is widely used in project management, resource allocation and strategic planning for evaluating options and making well-informed decisions. Related: Top 7 Decision-Making Templates: Free Excel & Word Downloads Benefits of Using Decision-Making Tools and Techniques Decision-making tools and techniques provide organizations with a structured framework for evaluating options, reducing errors and improving outcomes. By using these methods, teams can make more objective, informed decisions, ensure accountability and communicate reasoning clearly. They are essential for complex projects, strategic planning and situations where multiple stakeholders are involved, providing clarity, efficiency and confidence in every choice. Reduces Bias and Subjectivity Decision-making tools and decision-making techniques help minimize bias and subjectivity in the decision-making process. By providing structured approaches, such as matrices, flowcharts and scoring systems, teams base their choices on data and defined criteria rather than intuition or personal preferences. This ensures that decisions are fair, objective and consistent. Using these methods allows teams to evaluate alternatives systematically, identify the best option logically and reduce the influence of favoritism, assumptions or incomplete information in complex project or business scenarios. Increases Transparency and Accountability Decision-making tools and techniques increase transparency by documenting the rationale behind each decision. Teams can clearly show which criteria were considered, how options were scored and why a specific choice was selected. This visibility supports accountability, as stakeholders can review, challenge and understand decisions. By using structured decision-making processes, organizations ensure that every choice is traceable and justified, making it easier to communicate decisions to leadership, clients or team members and improving trust and credibility across projects and departments. Builds Consistency Across Decisions Applying decision-making tools and techniques fosters consistency in decision-making practices across projects and teams. Standardized processes, frameworks and scoring methods ensure that similar decisions are approached in the same way, reducing discrepancies and errors. This consistency improves efficiency, makes outcomes more predictable and allows organizations to scale best practices. Teams can replicate successful decisions, compare results over time and maintain a reliable, repeatable approach to evaluating alternatives and selecting the best options for projects or strategic initiatives. Aligns Decisions With Stakeholder Expectations Decision-making tools and decision-making techniques help align choices with stakeholder expectations by incorporating input, priorities and constraints into the process. Methods like decision matrices, AHP and cost-benefit analyses ensure that decisions consider all relevant perspectives. This alignment increases stakeholder satisfaction, reduces conflicts and improves buy-in for projects and initiatives. Structured decision-making approaches ensure that outcomes reflect organizational goals, client requirements and team capabilities, making the results more effective, relevant and widely supported across all levels of the organization. Free Decision-Making Templates These free decision-making tools and templates simplify complex choices by providing ready-to-use frameworks. They help teams capture information, evaluate options and make decisions systematically. With templates for logs, matrices and trees, organizations can ensure consistency, transparency and accountability in decision-making processes without building tools from scratch, saving time and improving accuracy. Decision Log Template Download this free decision log template to document key decisions, the rationale behind them and the stakeholders involved. Teams can track options considered, outcomes and deadlines. This template increases accountability and ensures that future decisions can reference past choices, creating a clear record of organizational decision-making for review, audits or continuous improvement. Decision Matrix Template Use this free decision matrix template to help teams evaluate multiple options against predefined criteria. Each option is scored, weighted and ranked to determine the best choice. Using this tool ensures objective, data-driven decisions, reduces bias and allows teams to justify selections clearly to stakeholders, enhancing transparency and confidence in the decision-making process. Decision Tree Template This free decision tree template maps possible choices, outcomes and associated risks in a visual format. It helps teams understand the consequences of each option, evaluate probabilities and plan for contingencies. This tool is ideal for complex scenarios, ensuring that decisions are structured, logical and aligned with organizational goals while providing clarity for all stakeholders. How to Manage Projects, Processes and Teams With ProjectManager ProjectManager provides powerful tracking tools to help teams monitor progress and performance in real time. With secure timesheets, AI-powered reports and live dashboards, project managers can see task completion, milestone achievement and resource utilization across all projects. These insights allow teams to quickly identify risks, reassign tasks and make informed decisions, ensuring projects stay on schedule, within budget and aligned with stakeholder expectations. Optimize Resource Management and Allocation ProjectManager enables managers to allocate resources efficiently with workload charts, team pages and custom dashboards. Teams can monitor individual and group workloads, identify bottlenecks and reassign tasks when needed. The software also tracks labor hours, budgets and costs across multiple projects, helping managers optimize resources and avoid over-allocation while maintaining productivity and meeting project goals. /wp-content/uploads/2023/01/Team-Light-2554x1372-1.png Manage Multiple Projects With Gantt Charts ProjectManager excels in providing multiple project views, with Gantt charts as the centerpiece for planning and scheduling. Managers can link task dependencies, visualize timelines and track critical paths across projects. The Gantt view allows teams to see both the big picture and detailed task progress, making it easier to coordinate complex schedules, set baselines and ensure all projects move forward efficiently and on time. /wp-content/uploads/2025/03/gantt-light-mode-screenshot-2025-compressed.png Related Decision-Making Content Want to learn more decision-making techniques? There are a lot of decision-making tools that we’ve not touched on here. To read more about this subject, follow the links below. They lead to articles on logical fallacies that can harm decision making, go deeper into tools we’ve already mentioned and much more. Top 7 Decision-Making Templates Using a Decision Flowchart in Project & Process Management What Is a Decision Matrix? (Example & Template Included) How to Use a Decision Log for Optimal Results DACI: A Decision-Making Framework for Better Group Decisions 7 Logical Fallacies That Can Harm Your Decision-Making Process ProjectManager is online project and portfolio management software that connects teams, whether they’re in the office or out in the field. They can share files, comment at the task level and stay updated with email and in-app notifications. Get started with ProjectManager today for free. The post 9 Decision Making Tools and Techniques (Free Templates) appeared first on ProjectManager. View the full article

Here is a recap of what happened in the search forums today, through the eyes of the Search Engine Roundtable and other search forums on the web. Google added a new user agent to the list...View the full article

The cloning industry is contracting—ironically, perhaps. On Tuesday, Colossal Biosciences announced that it has acquired ViaGen Pets and Equine, an animal cloning firm, marking Colossal’s first acquisition since it launched in 2021. Texas-based Colossal Biosciences is best known for its controversial “de-extinction” endeavors, which involve efforts to recover species that have died out. Earlier this year, the company claimed to have “brought back” dire wolves, an assertion that was disputed by some experts, as the animals were created by modifying the DNA of existing gray wolves. The company has also sparked debates about the ethics of bringing species back from extinction, or if what it’s doing is in fact “de-extinction” at all. Either way, Colossal has attracted significant interest from high-powered investors, recently raising $120 million to try and resurrect the dodo. Its acquisition of ViaGen adds even more genetic firepower to its arsenal. “Colossal is thrilled to welcome ViaGen, the world’s leading cloning company, into our portfolio,” said Ben Lamm, founder and CEO of Colossal, in a statement provided to Fast Company. “No other company comes close to what ViaGen has achieved. Their unmatched expertise and cloning technology stack have become the world’s standard and their application of these critical and proprietary technologies to endangered species conservation makes them an invaluable partner in advancing our global de-extinction and species preservation mission.” Terms of the acquisition were not disclosed. Access to breakthrough technologies ViaGen, a Texas-based company founded in 2002, will continue to operate under its existing leadership and expand its endangered species cloning activity. Perhaps the most interesting element at play is ViaGen’s exclusive licensing and access to the breakthrough technologies developed by the Roslin Institute of Edinburgh—such as those that cloned Dolly the sheep, the first cloned mammal. “Partnering with Colossal Biosciences presents an extraordinary opportunity to apply our advanced cryopreservation and cloning techniques to the ambitious goals of de-extinction and species restoration,” said Dr. Shawn Walker, Ph.D., ViaGen’s chief science officer and known cloning expert, in a statement. Colossal has also attracted a stable of celebrity investors. Retired NFL star Tom Brady, for instance, worked with the company to clone his family’s dog, and said in a statement that he is “excited how Colossal and ViaGen’s tech together can help both families losing their beloved pets while helping to save endangered species.” The potential to save endangered species is something that has others excited, too. Colossal shared a statement from filmmaker Peter Jackson, who said, “These two companies together give humanity a real shot at saving the planet’s biodiversity.” View the full article

Despite strong early growth, LLM referral traffic is fading fast as model changes and AI Overviews make clicks obsolete. The post LLM Traffic Is Shrinking appeared first on Search Engine Journal. View the full article