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Did you know you can customize Google to filter out garbage? Take these steps for better search results, including adding Lifehacker as a preferred source for tech news. The method of loci (or the loci technique) is a mnemonic memorization trick with a number of uses, from helping people with mild cognitive impairment learn and remember information, to getting someone ready to give a speech. But you can use it in almost any context, for anything you need to remember. Obviously, you're here because it's helpful when you're taking a test, but the ways you can apply it in that scenario are a little unique and you'll need to get the hang of something that seems silly at first. Here's what to do. What is the method of loci?If “loci” sounds like “location,” that’s because it’s what this method is all about: Location, location, location. To employ the technique, you use visualizations of specific spatial environments to help you recall information. It’s been around for centuries and is still in use today, as memory contest participants say it helps them recall everything from faces to digits. (Did you know there are memory contests?) Think of a location you know well, ideally one with a lot of defining features. Maybe it’s a street with a bunch of different shops, a room with a variety of surfaces and corners, or your childhood home. Any singular place works, but it's crucial it's one you know well and can truly visualize in your mind's eye. When you have to remember a ton of things, like items in a list or topics to hit in a speech, imagine yourself placing them, one by one, in one of those little loci. One topic can go in the corner, another on the desk, and another in the windowsill, for instance. When you want to retrieve or recall the information, imagine yourself walking through the area again, picking up what you need to remember from its proper place. Why does this work? Generally speaking, your brain remembers images better than it remembers words or numbers, so attaching the words or digits you need to remember to an image makes them easier to retrieve. How to use use the method of loci in real lifeOne way to tap into the power of this memory trick is “placing” your memory items around the room you’ll be in when you need to recall them. If you know you have to speak in front of a meeting in a certain conference room, or take a test in a particular classroom, use that imagined setting as the spot where you drop your listed items. Better yet, prepare in that space. If you're able to, sit in the classroom where you'll take your test as you prepare, maybe staying late after class or entering it when it's unoccupied at some point during the week. Imagine yourself placing the things you're studying, one by one, in locations around the room. You can even wander around and pantomime doing so if that seems helpful. Just make sure the loci you pick are permanent. Don't assign a key fact to, say, a cup of pens on the professor's desk, which may be removed from the room before your test date. Choose things like the radiator, a discolored ceiling tile, the podium, or a doorstop. If you can't study in the classroom itself, this trick does require you to plan ahead and be familiar with the room by memory alone, so choose even broader loci, like the door or the window. And don't forget that this can all be a mental game. Your locations can be anywhere you can remember well, like the break room at your job or your bedroom, as long as you can pull up that mental map in your mind at test time. Why it worksThis might seem frivolous, but it does work. It's part of a broader memory technique called association. With association, you can make up mnemonic phrases, sing a little song, or, yes, use the method of loci. The goal is associate your newfound knowledge with something else, so whenever you think of that other thing—in this case, a location—you'll automatically remember the nugget of information you associated with it. View the full article

Companies operating in the rare earths and mining spaces are seeing their share prices soar this morning as President Donald The President’s latest tariff feud with China enter its second week. Here’s what you need to know. What’s happened? Last week, President The President threatened new tariffs on China as high as 100% in retaliation for the country putting export controls on products that contain rare earth elements. “Rare earths” are a group of elements that actually aren’t rare, but are hard to find and expensive to mine. The elements also happen to be essential to many industries, including technology, automotive, and defense. Rare earths are critical to these industries because the elements are used in many of the most advanced electronic products made by companies in the above industries, including smartphones, electric vehicles, and missile systems. While the United States has its own rare earth deposits and extraction capabilities, China is one of the world’s largest producers of rare earth materials, and disruption in the Chinese rare earths supply chain could have negative knock-on effects in the production of electronic equipment that U.S. companies and the military rely on. Rare earth stocks soar again After already rising on Friday in the wake of The President’s tariff threat, the stock prices of rare earth companies and adjacent mining companies are up again in premarket trading today (Monday, October 13). Those stock price rises include the following companies, all of which are up in premarket trading on Monday morning as of the time of this writing. USA Rare Earth, Inc. (Nasdaq: USAR): up 22% Energy Fuels Inc. (NYSE: UUUU): up 14% MP Materials Corp. (NYSE: MP): up 10% Lithium Americas Corp. (NYSE: LAC): up 4.5% Trilogy Metals Inc. (NYSE: TMQ): up 9.7% Freeport-McMoRan Inc. (NYSE: FCX): up 3.8% Besides the ongoing threat of increased restrictions on foreign companies obtaining rare earths from China, another factor may also be contributing to the surge in share prices for rare earth companies and mining stocks today. The Financial Times has reported that the Pentagon is seeking to purchase as much as $1 billion in critical materials to stockpile, including cobalt and antimony. The Pentagon’s Defense Logistics Agency (DLA) would store the materials to give the U.S. a buffer in the event they become harder to obtain in the months ahead. Any increase in defense spending on those materials and other related materials is likely to benefit the companies that supply them or can help mine them. The President says “don’t worry” The President’s threat to impose a further 100% tariff on Chinese goods in retaliation for its rare earth export controls sent stock markets tumbling on Friday. In a move perhaps meant to alleviate investor fears, The President posted what could be taken as a calming message (in The Presidentian terms) on Tuesday. “Don’t worry about China, it will all be fine!” the president posted on his Truth Social social network. “Highly respected President Xi just had a bad moment. He doesn’t want Depression for his country, and neither do I. The U.S.A. wants to help China, not hurt it!!!” Although stock futures did rise early Monday following The President’s post, as of the time of this writing, there are no signs that China is rethinking its latest export controls on rare earths. Indeed, reduced rare earth exports seem to have been gaining momentum in the country for a while now. As Reuters reported, China’s rare earth exports plunged by 31% in September versus the month earlier. Is this a trend that will continue? No one knows for sure. But until China and America officially come to terms on rare earths, investors seem confident that America’s rare earth companies may benefit from the geopolitical drama—at least for now. View the full article

Charles Moore of Alvarez & Marsal to become ‘interim chief executive’ as well as chief restructuring officer View the full article

Small business owners looking to enhance their operational efficiency may want to pay attention to the latest advancements in artificial intelligence from IBM. The tech giant has unveiled new features in its Watsonx Orchestrate platform designed to optimize how AI agents function in various business workflows. This innovation promises to provide small businesses with the predictive reliability and control needed to navigate today’s complex operational landscapes. At the heart of this update are “agentic workflows,” which allow AI agents to execute tasks autonomously while adhering to essential structures. IBM asserts that when precision is paramount—think financial approvals, compliance checks, or customer service operations—the integration of structured workflows can dramatically improve task execution and reliability. The underpinning technology is designed to offer robust handling of data while maintaining the necessary compliance and accuracy every business must uphold. Key features of these agentic workflows make them particularly beneficial for small businesses. They include predefined toolchains that ensure processes are followed correctly and in the right sequence, conditional logic that helps manage decision-making processes, and data handling transparency that streamlines how information flows at every step. These attributes not only simplify automation but also make it easier for businesses to track and audit their operations, providing a unique blend of efficiency and accountability. “This structured approach makes AI agents more reliable and versatile in real-world applications,” said a spokesperson for IBM. The company emphasizes that these workflows can be tailored for various business scenarios, from managing financial transactions to routing customer inquiries—areas critical for small businesses aiming to deliver better service without overwhelming their resources. Furthermore, the integration of Langflow within Watsonx Orchestrate enables users to visually design and manage these workflows. This visual aspect demystifies the complexities often associated with automation, making it accessible for small business owners who may lack extensive technical expertise. With these tools, small businesses can harness the power of AI without needing an extensive IT infrastructure. However, there are also considerations for small business owners to keep in mind when adopting these advanced solutions. The implementation of AI-driven workflows necessitates a clear understanding of existing operational processes. Businesses might face challenges in establishing the right sequences and decision points critical for effective AI functioning. Additionally, while the initial investment in AI technology and training may seem daunting, the potential long-term savings and improved efficiency can offset these upfront costs. Small business leaders should also think about how data governance will be managed. As these workflows handle sensitive information, regulatory compliance and data privacy must be prioritized to avoid legal repercussions. Building a strong foundation in data management practices can offer greater assurance when deploying these new tools. IBM’s Watsonx Orchestrate is now more than a buzzword; it’s a practical tool aimed at increasing small businesses’ capacity to automate workflows while maintaining essential governance. As automation becomes an integral part of business strategy, understanding and adapting these new tools may enable entrepreneurs to focus on scaling their operations effectively. For those interested in exploring these new features and how they can be applied in real-world scenarios, the details are available on IBM’s announcement page. By leveraging AI technologies judiciously, small business owners can find a competitive edge in their industry, combining technological prowess with human insight to drive growth and operational excellence. This article, "IBM Unveils Watsonx Orchestrate for Reliable AI Workflows" was first published on Small Business Trends View the full article

Small business owners looking to enhance their operational efficiency may want to pay attention to the latest advancements in artificial intelligence from IBM. The tech giant has unveiled new features in its Watsonx Orchestrate platform designed to optimize how AI agents function in various business workflows. This innovation promises to provide small businesses with the predictive reliability and control needed to navigate today’s complex operational landscapes. At the heart of this update are “agentic workflows,” which allow AI agents to execute tasks autonomously while adhering to essential structures. IBM asserts that when precision is paramount—think financial approvals, compliance checks, or customer service operations—the integration of structured workflows can dramatically improve task execution and reliability. The underpinning technology is designed to offer robust handling of data while maintaining the necessary compliance and accuracy every business must uphold. Key features of these agentic workflows make them particularly beneficial for small businesses. They include predefined toolchains that ensure processes are followed correctly and in the right sequence, conditional logic that helps manage decision-making processes, and data handling transparency that streamlines how information flows at every step. These attributes not only simplify automation but also make it easier for businesses to track and audit their operations, providing a unique blend of efficiency and accountability. “This structured approach makes AI agents more reliable and versatile in real-world applications,” said a spokesperson for IBM. The company emphasizes that these workflows can be tailored for various business scenarios, from managing financial transactions to routing customer inquiries—areas critical for small businesses aiming to deliver better service without overwhelming their resources. Furthermore, the integration of Langflow within Watsonx Orchestrate enables users to visually design and manage these workflows. This visual aspect demystifies the complexities often associated with automation, making it accessible for small business owners who may lack extensive technical expertise. With these tools, small businesses can harness the power of AI without needing an extensive IT infrastructure. However, there are also considerations for small business owners to keep in mind when adopting these advanced solutions. The implementation of AI-driven workflows necessitates a clear understanding of existing operational processes. Businesses might face challenges in establishing the right sequences and decision points critical for effective AI functioning. Additionally, while the initial investment in AI technology and training may seem daunting, the potential long-term savings and improved efficiency can offset these upfront costs. Small business leaders should also think about how data governance will be managed. As these workflows handle sensitive information, regulatory compliance and data privacy must be prioritized to avoid legal repercussions. Building a strong foundation in data management practices can offer greater assurance when deploying these new tools. IBM’s Watsonx Orchestrate is now more than a buzzword; it’s a practical tool aimed at increasing small businesses’ capacity to automate workflows while maintaining essential governance. As automation becomes an integral part of business strategy, understanding and adapting these new tools may enable entrepreneurs to focus on scaling their operations effectively. For those interested in exploring these new features and how they can be applied in real-world scenarios, the details are available on IBM’s announcement page. By leveraging AI technologies judiciously, small business owners can find a competitive edge in their industry, combining technological prowess with human insight to drive growth and operational excellence. This article, "IBM Unveils Watsonx Orchestrate for Reliable AI Workflows" was first published on Small Business Trends View the full article

Rupert Pearce’s appointment comes as government seeks to deliver pledge to drive growth through increased military spendingView the full article

We may earn a commission from links on this page. If you’re planning your next DIY project or looking into new tools for your set, it can be overwhelming to weigh all the options available for cordless tools. As batteries become smaller and lighter, a wider range of cordless power tools is available. Here are a few of my favorite cordless tools based on design, durability, usefulness, and battery life. The best all-around cordless tool M18 FUEL 18V Lithium-Ion Cordless Brushless Oscillating Multi-Tool $249.00 at Home Depot Shop Now Shop Now $249.00 at Home Depot My favorite tool to use right now is the Milwaukee 18-volt oscillating multitool. This tool is light, weighing just 2.7 pounds without the battery, and easy to use. It can be used for cutting wood, tile, drywall, metal, PVC, and combination materials—as such, it's a game-changer for a home DIY tool set. It can also be used for sanding, buffing, and removing grout. Because of the impressive range of projects this tool can be used for and the excellent battery life—with a 4-amp-hour battery that, in my experience, outlasts the blade—this is my favorite cordless tool of 2025. Best new cordless tool ONE+ HP 18V Brushless Cordless 16-Gauge Straight Finish Nailer $249.00 at Home Depot Shop Now Shop Now $249.00 at Home Depot The best new cordless tool this year is Ryobi’s 16-gauge finish nailer. Ryobi’s 18-volt battery set has expanded to include over 300 cordless tools that are compatible with their system, and the 16-gauge finish nailer is one the newest. This nailer is powerful enough for most woodworking applications, and it allows the user to drive more nails in less time than older models. It can drive 1,800 nails per charge and accepts 16-gauge finish nails up to 2 ½ inches long. This is the perfect tool for any DIY enthusiast who wants to try out woodworking projects like installing trim or stair treads. Best cordless drill and driverThe DeWalt 20-volt drill and driver set has an impressive battery life, allowing you to run a tool for several hours before needing to recharge, and they stand up to abuse. These tools are compatible with most standard drill bits, with the impact driver accepting ¼-inch hex bits and the drill accepting up to ½-inch drill bits. They are light and well-balanced, making them easier on your wrists and forearms over time. These are a good choice for projects like installing drywall or hanging shelving. Best cordless saw Makita XSR01Z 36V (18V X2) LXT® Brushless Rear Handle 7-1/4" Circular Saw, Tool Only $259.00 at Amazon Shop Now Shop Now $259.00 at Amazon The best cordless saw of 2025 is the Makita rear handle 7 ¼-inch circular saw. Cutting a starting line with the rear-handled saw is easier because it allows you to push the saw from a more advantageous angle and keep the weight of the tool balanced. In addition, a 7 ¼-inch blade has a wider cutting surface for more precise longer cuts. Makita 18-volt batteries have a good battery life, allowing you to make over 550 cross cuts with a 5-amp-hour battery on a single charge. A cordless circular saw is good for DIY projects using plywood, like making work tables, or for cross-cutting dimensional lumber for framing. Best under $50My favorite new cordless tool under $50 is the Ryobi glue gun, which comes with a 2-amp-hour battery and charger. This glue gun can accommodate a ½-inch glue stick, and can heat up in three minutes. Rapid charging and heating means you can ditch the cord and use your glue gun without dragging an extension cord around with you. Although this glue gun’s small, there's enough room to let you use two fingers on the trigger, cutting down on fatigue from squeezing over time. A glue gun is an essential tool to have around for DIY projects that might have upholstery or other cosmetic components. View the full article

Did you know you can customize Google to filter out garbage? Take these steps for better search results, including adding Lifehacker as a preferred source for tech news. If you work in the corporate world, you've almost certainly heard of SMART goals, which are meant to help you work better with your team and be more productive. Similarly, you may have heard of them in the fitness space, as they're commonly used as part of personal training plans thanks to their specificity and rigidity. But the SMART system is helpful for all kinds of tasks—especially if you’re a student. Here are some tips for using SMART goals when you’re studying. What are SMART goals?SMART goals aren’t just good—well, smart—ideas. It’s actually an acronym: Specific Measurable Achievable Relevant Time-bound The idea came from business consultant George T. Doran in 1981, when he wrote into Management Review to criticize the poor goal-setting he encountered at many companies. (In his original outline, the “A” stood for “assignable,” but with time, “achievable” took hold as the go-to designation.) When using the SMART system, all your goals should align with the five elements of the acronym. How to use SMART goals to study more effectively The process starts with writing a goal statement that hits on all of the elements of the SMART acronym. Here’s an example: Say you have a test in statistics next week and you got a C on the last one. You can write, “My goal is to get a B+ or higher on the exam by studying for an hour every night from now until the test date.” It’s specific, because you’re setting not only the exact grade you want to get, but the steps you need to take to get it. It’s measurable, because you’ll be able to see whether you accomplished the goal as soon as your test is graded. It’s achievable, because it’s just one letter grade higher than what you got last time, so it’s not as lofty as aiming for an A+. It’s relevant, because it’s an upcoming test, not a vague plan for your end-of-semester grade. Finally, it’s time-bound, because you’re basing it on a looming date and making a plan for all the days between now and then. You can see how that differs from, "My goal is to get an A in this class" or even "My goal is to get a B on the next test." SMART goals drill down on all the necessary elements that will add up to your success. They don't leave wiggle room or space for excuses because they're timed, they're actionable, and they're realistic. The farther out you plan or the loftier your goal is, the easier it gets to push it to the side. A SMART goal keeps you focused and on a schedule. When you're studying on a schedule, you need a plan that works with it. Try flashcards using the Leitner system and spaced repetition, two studying approaches that rely on strict scheduling between now and the date you need to know something. They're valuable methods because, by spacing out the time between study sessions, they force you to use active recall to retrieve information from your memory. They're the perfect complement to a SMART goal. Finally, write down your SMART goals. Hand-writing is always recommended, since it helps you remember things, but you can jot them anywhere you might see them, like in an assignment folder or near your workspace. Constant reminders help you stay on track with what you need to be doing. View the full article

2026 is around the corner – and the SEO space has never been this noisy. Every day brings something new. It’s easy to get stuck in panic mode, worrying you’re missing the next big thing, or to spend hours scrolling LinkedIn threads that lead nowhere. In both cases, you end up with nothing concrete – and with stakeholders still expecting clear impact. As we head into 2026, the real challenge is building a strategy with discipline – one that cuts through the noise and balances: Short-term wins that prove impact and build trust. Long-term bets that future-proof visibility. The boring but essential, business-as-usual (BAU) tasks that keep your foundations strong. Finding this balance isn’t easy, but it’s the only way to build a plan that works. Here’s how to master it – and keep your SEO strategy focused, grounded, and effective in 2026. Why short-term wins matter Short-term wins are critical because they: Prove progress. Earn trust. Buy time and budget for bigger bets. Keep people motivated. SEO has always been at least a six-month game, but it feels good when a positive trend shows up in your KPIs after just a few weeks. One challenge with short-term projects is that many people struggle to classify them correctly. My rule of thumb is simple: short-term wins are things you can deliver yourself, without depending on other teams. For example, if your CMS lets you insert code directly into pages, adding structured data markup to your most important pages fits that definition perfectly. There’s still debate about whether structured data affects AI tools like ChatGPT or Perplexity. Maybe it doesn’t. But it definitely matters for Google, which remains the Goliath of search, handling about 210 times more queries than ChatGPT. In our case, optimizing markup improved the average positions of some pages almost immediately. Another example of a short-term bet is optimizing your main pages using a query fan-out technique. If each page already targets a clear topic and keyword, you can “fan out” by collecting the related questions people ask around that topic. Use tools like AnswerThePublic, AlsoAsked, and Semrush – or your own Google Search Console data with a simple regex like (who|what|where|when|why|how). Shortlist the questions that fit the page and your personas. Turn them into FAQ sections. It’s a straightforward way to boost relevance fast. This approach also aligns with Kevin Indig’s concept of efficiency-first validations, based on insights from his recent usability study of AI Overviews. Many users simply want a quick fact or short answer. By adding FAQs, TL;DR blocks, and schema, you make it easier for AI systems (and Google) to surface your content for exactly those lookups. Still, short-term projects mainly help you cover the basics and earn quick wins. They rarely position you for the future. For lasting advantage, you need larger initiatives that take time, involve multiple teams, or require budget planning. And here’s where many SEOs struggle. They judge projects by complexity instead of dependencies. The case for long-term bets A project might look simple on paper, but if it depends on another team, it’s no longer short-term. Take JavaScript rendering issues, for example. As an SEO, you can spot them quickly in a Screaming Frog report, but unless the dev team adds them to the roadmap, nothing will move. And if that team has other priorities, your fix can sit for months. That’s why I follow this rule: if an optimization project depends on another team, it’s not short-term. Dependencies work that way. Your priorities aren’t always theirs. Long-term bets also include strategic plays that need significant planning and preparation. Think building a presence on Reddit, YouTube, or review sites – platforms that are becoming increasingly important for AI visibility. Breaking through takes months of consistent work, even with full organizational support. On Reddit, you need to earn karma before you can engage meaningfully. On YouTube, you need a scalable way to produce high-quality video content. Everyone is hyped about AI, but it’s not a magic wand. You can’t expect someone without experience to spin up a YouTube channel or build a Reddit presence just because they have an AI tool. These things only work when AI is in the hands of people with expertise – otherwise, you risk wasting time and money. Long-term projects drive results for searches centered on trust and comparison. As usability research shows, users often skim AI-generated answers but validate them with trusted brands or cross-check perspectives on Reddit, YouTube, and vendor sites. That’s why long-term bets like building brand authority, scaling video, and nurturing community presence matter. They won’t deliver quick wins, but over time, they put you where real purchase decisions happen. Another often-overlooked factor is timing. Long-term bets must fit into your company’s budget and hiring cycle. If you need a new role or an expensive tool, you can’t raise your hand midyear and expect fast approval. I learned that the hard way when I requested a new role in December, after the planning cycle closed in November, and got approval in June – six months lost purely because of timing. Long-term bets also carry another risk. When you focus too much on them, the basics can slip. A broken link or a title change on a key page can undo weeks of progress. If everyone is chasing shiny new things, it’s easy to forget the boring but essential work – like fixing broken links and adding alt text to new pages. Don’t skip the BAU basics Business-as-usual tasks won’t earn applause in the next all-hands meeting, but without them, everything else rests on shaky ground. The good news is these tasks are often easy to automate – and plenty of tools already exist to handle them. For example, we use SEOTesting to: Generate a weekly report of all new and modified pages. Review on-page optimization elements regularly and catch issues early. Keep us from being blindsided if someone changes the title of an important page without notice. It’s not glamorous work, but it keeps the site healthy. The motivation problem, though, is real. Nobody gets excited about link audits or on-page checks. As a manager, you need to keep your team engaged in these tasks. And if you’re working solo, you need to manage your own discipline. Ignoring BAU is like skipping the gym – you don’t notice the impact right away, but a year later, you’re wondering why nothing fits quite the same. That doesn’t mean your strategy should revolve only around maintenance. You also need space for experiments – a way to test ideas and uncover what could become tomorrow’s long-term bets. But how do you bring all these types of projects together without losing focus or sanity? Get the newsletter search marketers rely on. See terms. Putting it all together Building a sustainable SEO strategy isn’t about choosing between short-term wins, long-term bets, or BAU tasks. It’s about balancing all three. Short-term projects prove progress and build buy-in. Long-term bets secure your future. BAU keeps the foundation solid so you’re not building on shaky ground. A framework that works well is to split your time and focus: 60–70% on proven short-term tasks that deliver results now (e.g., content refreshes, FAQs, structured data). 20% on scaling and BAU – maintaining site health, reviewing new pages, fixing errors. This share grows after big releases or migrations. 10% on long-term bets that need planning, budget, and cross-team coordination (e.g., presence on Reddit, YouTube, review sites, large-scale technical fixes). 10% on learning – staying sharp on industry changes, but keeping it curated and focused. 10% on experiments – testing new ideas you can’t yet predict ROI for, but that could become tomorrow’s long-term bets. Experiments keep motivation high and ensure you’re not just maintaining but evolving. Don’t treat the math as exact – the mix will shift with your business cycle. After a migration or rebrand, BAU naturally dominates. During quieter periods, you can double down on experiments or long-term plays. The key is to keep sight of your north-star projects and adjust each sprint without losing track of the bigger goals. Finding balance also means knowing what belongs in each bucket. Not every project that looks quick will deliver fast results – and not every long-term bet needs to take a year. Here’s how to tell the difference. Dependencies: If you need other teams (dev, product, PR), it’s rarely short-term. Time to impact: If you can deliver independently and see results in weeks, it’s short-term. If it takes months or quarters to move the needle, it’s mid- or long-term. Experience level: If you or your team have done it before and know the playbook, it’s a safer short-term bet. If it’s new, complex, or untested, expect it to take longer. Business alignment: Tie SEO projects to company priorities. If your company is rebranding, migrating, or launching a marketplace, your SEO roadmap must align – otherwise, you’re optimizing in a vacuum. Once you know how to classify your projects, the next challenge is keeping them on track. Even the best-planned SEO initiatives can fall apart without structure, communication, and accountability – that’s where strong project management comes in. Set SMART goals to keep projects focused and measurable. Use the 5 Whys method before green-lighting tasks – often the real issue is a process gap, not a pile of broken redirects. Apply prioritization frameworks like RICE (Reach, Impact, Confidence, Effort) or ICE scoring to rank competing initiatives. Keep visibility high with regular check-ins and clear scopes – project management in SEO is as much about communication as execution. In short, balance isn’t static. Your mix will shift with cycles, resources, and company goals. And that’s the point – every framework, formula, and prioritization method only works if you apply it with discipline. SEO strategy is a discipline test As 2026 approaches, building an SEO strategy is less about chasing the latest tool or tactic and more about discipline. Discipline to: Deliver short-term wins consistently. Stay the course on long-term bets, even when results take months. Maintain BAU basics when everyone else is distracted by shiny new things. Continue learning, experimenting, and distinguishing what’s truly worth testing from what’s just noise. The exact mix will shift with your company’s cycle, but the principle stays the same: Balance takes focus, patience, and rigor. In 2026, that discipline will separate the teams that just talk strategy from those that actually move the needle. View the full article

Over the past few days, the local SEO community is pretty upset that Google appears to be testing removing the call button from the local results. I am not sure if this is a bug or part of the design of the new Web Guide interface that more people are seeing. Either way, I do think this must be an oversight and the call button should be added back, I would hope.View the full article

Lenny Rachitsky interviewed Robby Stein, VP of Product at Google, who we cited here numerous times over the past couple of years. The interview covers how AI is not replacing search, search is not dead, how AI Mode will be incorporated within Google Search, how to do AEO or SEO for AI answers, how AI Mode came to market and more.View the full article

Beijing’s rare earths announcement got a shortlived overreaction from the US presidentView the full article

Have you heard of the Google Goldmine Scoring System? It supposedly looks at your page, your content, and Google gives it a goldminePageScore, title tag factor, body factor, anchor factor, heading factor and more.View the full article

A couple of weeks ago, Google added a new help page titled Discover feed source overview. This page discusses those cards in the Google Discover feed and when Google gets the source name wrong, like for those entity Discover pages.View the full article

Google has updated its Auto-Apply Recommendations setting within the Google Ads interface. The button moved to just above the regular recommendations section.View the full article

Hello and welcome to Modern CEO! I’m Stephanie Mehta, CEO and chief content officer of Mansueto Ventures. Each week this newsletter explores inclusive approaches to leadership drawn from conversations with executives and entrepreneurs, and from the pages of Inc. and Fast Company. If you received this newsletter from a friend, you can sign up to get it yourself every Monday. When we named Tarang Amin Modern CEO of the Year in December 2024, the E.l.f. Beauty chairman and chief executive had racked up a string of notable successes. Under Amin’s leadership, the publicly traded cosmetics company had posted 23 consecutive quarters of net sales and market share growth. E.l.f. won plaudits for cheeky marketing efforts such as a coffin-shaped makeup kit collaboration with beverage company Liquid Death. Another initiative championed corporate board diversity via its “So Many Dicks” campaign, which cites research showing that men named Richard, Rick, or Dick outnumber women and diverse directors serving on public company boards. Indeed, such bold ads helped land Kory Marchisotto, E.l.f.’s chief marketing officer, on Fast Company’s 2024 list of CMOs of the Year as part of its Brands That Matter program. In contrast, this year has been a rockier one for Amin. In May, the company, whose E.l.f. skincare and cosmetics products sell at affordable price points, said it would raise prices on all its products by $1 to offset the impact of new tariffs. (The company has said 75% of its products are made in China.) In August, E.l.f. said it would not provide shareholders with a financial outlook for its full fiscal year ending March 31, 2026, citing the “wide range” of potential impacts from those tariffs. That same month, it launched a parody ad featuring comedian Matt Rife that faced social media backlash as commenters highlighted jokes Rife made about domestic violence in a Netflix special. Many CEOs might retreat from public view or tread carefully in this environment, but not Amin. Ahead of our search for the 2025 Modern CEO of the Year (more on that in a moment), Amin sat with me for a wide-ranging interview on the challenges of 2025, E.l.f.’s blockbuster $1 billion acquisition of Hailey Bieber’s Rhode brand, and why the company is expanding its Change the Board Game effort amid attacks on diversity programs. Here are the highlights in Amin’s own words: Responding to tarrifs Customer feedback on higher prices: “We announced [the $1-per-item price increases] to our community three months before we took prices up. . . . The response from our community actually was quite positive. They love the fact that we’re not trying to pull one over their eyes; that we’re transparent.” Shareholder communications after pulling full-year guidance: “We just reported our 26th consecutive quarter of net sales and market share gains. What we emphasize with our investors is that we take a very long-term view. If you’re worried about short-term tariff impacts, maybe we’re not the stock for you.” The Matt Rife controversy Acknowledging the mistake: “In this onetime post, we clearly missed the mark. We’re all about delighting our community. This did not delight our entire community. And for that we apologize. I personally take that seriously.” Staying edgy: “We clearly missed the mark on this one. Let’s learn from it, but don’t lose [our] mojo. Don’t become scared. Don’t become safe.” Buying Rhode Why Rhode stood out: “I’ve never seen a brand that went from zero to $212 million in net sales in less than three years [by selling] direct to consumer only with just 10 products. It is just incredible in terms of success.” Hailey Bieber, acqui-hire: “Our approach to M&A is different than a lot of companies’. We never do synergy math or try to figure out where we can save. It’s all about growth for us. And so one of the prerequisites we have is we want the entire team. Our approach is, ‘Okay, how can we help you? How can we help you accelerate what you’re already doing really well?’” Doubling down on board diversity Change the Board Game 2.0: “We announced coalition partners that want to join us [in supporting boardroom inclusivity]. We’re going to continue to beat the drum, because these aren’t things that we do as a campaign. These are things we believe in.” Who is the modern CEO of 2025? For the second year, Modern CEO is seeking to recognize an executive who embodies the leadership qualities this newsletter has sought to highlight, such as promoting innovation, nurturing talent, and fostering excellence. Please fill out this form to nominate a chief executive—or yourself. We’ll dedicate a column in December to the Modern CEO of the Year. Read and watch: CEOs on our radar Brian Niccol’s bold Starbucks redesign How the CEOs of Ohai.ai and FinMkt make innovation work for them Figma is growing fast under cofounder and CEO Dylan Field View the full article

For years, marketers measured digital success through impressions, backlinks and clicks. If you ranked high in search results and won the click, you had visibility and control of the funnel. But that landscape is already shifting. Large Language Models (LLMs) like ChatGPT, Claude, Gemini and Perplexity are rapidly becoming the first place decision-makers go for answers. These systems don’t return a page of links; they generate a synthesized response. Whether your brand is included, or ignored, in that answer increasingly determines your relevance in the buying journey. This changes the marketer’s playbook. Visibility is no longer only about ranking on Google. It’s about whether you’re present in AI-generated responses, how you’re framed, and what sources are credited. In this new paradigm, being mentioned is the new click. The challenge for marketers isn’t simply tracking this new set of KPIs. It’s knowing how to interpret the signals and translate them into action. Let’s look at four core AI KPIs: mentions, sentiment, competitive share of voice and sources. We will explore how each can directly shape strategy. Mentions: The visibility test The first KPI is the simplest: how often are you mentioned inside LLM responses? If you’re absent from common category or evaluation queries, things like “top SaaS tools for analytics” or “best project management platforms,” then you’re essentially erased from the conversation before it begins. But mentions are more than a vanity metric. They are a diagnostic tool. Patterns in where you appear, and where you don’t, can tell you which parts of your content strategy are resonating and which areas need reinforcement. Making mention usable: Break mentions down by type of query. Are you showing up in broad “what is” or “how to” questions, or only in head-to-head competitor comparisons? Are you included in trend discussions but missing from buying-decision queries? That breakdown highlights where to expand your authority. If mentions are low in early-stage educational queries, invest in thought-leadership content that positions you as a voice in defining the category. If mentions are absent in solution-oriented queries, build assets that explain your differentiators more clearly. Mentions are the first signal of where your brand is visible, and where it’s invisible. For marketers, mentions are the equivalent of oxygen. Without them, everything else is moot. With them, you can begin to shape how buyers see you. Sentiment: The market’s echo The second KPI is sentiment. Being mentioned is good, but how you’re described is what really sticks. LLMs add qualifiers to their responses based on available information: “fast,” “trusted,” “expensive,” “hard to use.” These adjectives reflect the narrative that exists in the data the model has absorbed. Making sentiment usable: Capture the language used around your brand. Track whether descriptors skew positive, neutral or negative. Note recurring themes — are you consistently framed as “enterprise-grade” but also “complex”? Are you praised for “innovation” but dinged for “cost?” Negative sentiment highlights messaging gaps to address. If you’re framed as costly, consider publishing ROI calculators, pricing comparisons or case studies that show value delivered. If you’re seen as complex, invest in content that simplifies onboarding stories or customer success examples. Positive sentiment, on the other hand, shows you what narratives to amplify. If you’re consistently described as “trusted,” weave that trust theme into campaigns, analyst briefings and customer storytelling. Sentiment analysis transforms LLM outputs into a real-time market perception barometer. For marketers, that’s invaluable. It gives you a constant read on how your positioning is landing without waiting for lagging indicators like surveys or analyst reports. Competitive Share: The benchmark that matters Mentions and sentiment don’t mean much without context. The real question is: how do you compare to your competitors? Competitive share of voice is about measuring your brand’s presence in LLM responses alongside peers in your space. If you’re mentioned in 30% of relevant queries, but your top competitor appears in 70%, you’re playing catch-up. If you both appear equally often but their sentiment is glowing while yours is flat, they’re winning the perception battle. Making competitive share usable: Track not only how often you appear relative to competitors, but also the nature of those appearances. Which types of queries favor them over you? Which attributes are assigned to them versus you? These insights turn into a battle map. If competitors are dominating certain categories of questions, that points to content and messaging investments you need to make. If their sentiment is consistently stronger, it suggests you need to double down on proof points or sharpen your differentiators. On the flip side, if you’re leading in areas they’re weak, that’s a narrative advantage you can emphasize in campaigns. For marketers, competitive share is a strategy guide. It shows where you need to defend, where you can attack, and where you’re already winning. Sources: Who the AI trusts The final KPI is sources. Mentions tell you if you’re in the story. Sentiment tells you how you’re framed. Competitive share tells you how you stack up. But sources reveal who the AI trusts to tell the story. When an LLM cites a competitor’s whitepaper or an industry analyst’s report rather than your own content, it’s a clear signal: you’re not seen as the authority. Conversely, if your blog post or research study is the cited source, you’ve secured a position as the trusted voice. Making source insights usable: Audit which domains and documents are being cited when your category is discussed. Are trade publications showing up more than your own site? Are competitors’ research reports being favored? This is where content engineering comes into play. If you want your sources to be cited, they must be comprehensive, structured and credible. Think FAQ-style pages, data-driven reports, or clearly attributed expert commentary. By publishing content that AI can recognize as authoritative, you shift from simply being mentioned to being the foundation of the answer. For marketers, this is the ultimate form of influence. When your resources are the citations behind the AI’s output, you control the conversation. From signals to strategy The temptation with any new metric is to build elaborate frameworks and dashboards. But the value of AI KPIs lies less in the infrastructure and more in the insights. Mentions highlight visibility gaps. Sentiment exposes how you’re really perceived. Competitive share shows you where rivals are winning ground. Sources reveal who has authority. Together, they form a compass. They help highlight performance and point you toward action: Fill gaps with new content. Reframe narratives with stronger proof. Defend share with sharper positioning. Earn trust by publishing resources built to be cited. Marketers who use AI KPIs this way will be able to get ahead in the AI era, and they’ll actively help shape it. Why acting now matters It may feel early. The tooling isn’t standardized, and there’s no polished dashboard that marketers can log into and get all this in one view. But that’s precisely why early movers have the advantage. Think back to the early 2000s, when SEO was still experimental. The brands that learned to optimize before the playbook was written ended up owning search visibility for years. We’re at the same moment now with AI KPIs. Waiting for the tools to catch up means letting competitors set the baseline while you play defense. The actions don’t have to be complex. Even a lightweight process like running a set of prompts, logging responses and looking at mentions, sentiment, share and sources over time yields intelligence that can shape marketing and content strategies right now. Conclusion: Mentions as strategy The rise of LLMs doesn’t eliminate the value of clicks, impressions or backlinks, but it does redefine what visibility means. Increasingly, your brand’s story is being told inside AI-generated responses long before a buyer reaches your website. That’s why these KPIs matter. Being mentioned is the new click. But the real advantage comes not from counting those mentions, but from using them to make smarter decisions, closing visibility gaps, reframing perception, benchmarking competitors, and owning citations. For marketers, this is about translating AI signals into strategy. The brands that learn to do this now will have a better chance to survive the shift to AI-driven search. At Brightspot, we’re helping organizations navigate that shift — turning AI insights into actionable strategy that keeps their brands visible, trusted and ahead of change. Learn more at brightspot.com. View the full article

A ceasefire and hostage release are cause for celebration. But big questions remain about the rest of the The President planView the full article

In September 2023, we thought we had done something revolutionary. Helios AI became the first company in our industry to launch a generative AI agent. We called her Cersi. She was designed to help food companies understand the climate risks threatening their agricultural supply chains. She was powerful, intuitive, years ahead of the curve—and almost completely ignored. At the time, ChatGPT had just exploded onto the scene, and the hype around AI was deafening. Headlines promised that AI would transform every corner of business. Venture capital poured into the sector. But hype doesn’t always translate into real-world use—especially in industries that aren’t built to adopt change quickly. Food procurement, where billion-dollar decisions hinge on weather patterns and multiyear contracts, not to mention generational relationships and personal rolodexes, is one of those industries. In a space where legacy companies tout decades of experience, newness isn’t always a boon. We assumed that if we built something technically advanced, adoption would follow. We were wrong. Why our first AI agent failed Cersi was conceived as a conversational assistant. Type a question about your supply chain into a chat box, and she would pull from Helios’s massive dataset to provide a rich, insightful answer. It sounded futuristic, and on technical merit, it worked. But the glaring problem was, it didn’t fit the way our customers actually worked. First, they didn’t want to “chat.” Procurement executives, commodity traders, and risk managers wanted structured, decision-ready insights. They wanted something they could paste into a slide deck for the CFO, or drop into an email to their sourcing team. A conversational AI, however clever or time-saving, wasn’t the format they needed. Second, most users kept asking the same five questions again and again. That told us something important: their needs weren’t open-ended. They wanted repeatable analysis, standardized for their business, not a new way to brainstorm with an algorithm. Finally, and perhaps most critically, Cersi sometimes produced answers that were technically correct but felt superficial. In an industry where credibility and precision matter, “close enough” wasn’t good enough. What we learned The biggest lesson was simple but humbling: AI itself isn’t the product—the outcome is. In other words, customers don’t care how elegant your models are. They care if your product saves them time, reduces their risk, or helps them make a better decision in a high-stakes environment. We had fallen into the classic founder trap of building something because we could, not because our customers had asked for it. But we had still built something exceptional and groundbreaking in its industry, beyond what the standard benchmarks in the field were capable of. So a few months after Cersi’s underwhelming debut, we reimagined her role. Instead of a front-end chatbot, she became a behind-the-scenes analyst. Rather than flowing conversation, she was rebuilt to generate thousands of custom agricultural reports every month—each tailored to a customer’s commodities, sourcing regions, and climate risks. These reports would land directly in a customer’s inbox or workflow, in the format they need, where they can actually use them. In shifting Cersi from a “face” to a “function,” adoption skyrocketed. The AI didn’t become less powerful, it just became better integrated. In making our AI less visible, it became much more useful. As the saying goes, good design should be invisible. Building on that success, last month, almost two years after Cersi’s flop, we launched Helios Horizon, the first multi-agent platform in our industry. It’s designed to handle complex, interconnected tasks that a single agent couldn’t. Instead of one assistant, Horizon uses a coordinated set of AI agents that monitor risks, flag disruptions, and deliver analysis specific to each customer’s supply chain. This level of advanced AI would’ve been hard to imagine back in 2023, but we’d taken our lessons from Cersi to heart. The next wave of AI adoption will look different from the hype cycle of 2023. Enterprises aren’t asking whether AI is possible anymore. They’re asking if it’s practical, trustworthy, and built to fit their workflows. And those are harder questions to answer. 3 takeaways for Founders 1. Great AI isn’t enough The technology has to map directly to real workflows and be right-sized for the industry it serves. In 2023, most of our customers had barely used a chatbot, and they weren’t ready to experiment with one in their jobs. Today, after nearly two years of ChatGPT in the mainstream, familiarity is higher. We no longer have to teach people what a natural-language interface is—but we still have to prove why it matters in their world. 2. Users don’t want AI—they want its benefits Our customers don’t wake up excited to “use AI.” They wake up trying to secure coffee from Brazil or wheat from Kansas before climate shocks, trade restrictions, or shipping delays throw their budgets into chaos. What matters most is the outcome: did the system save them hours of manual analysis? Did it prevent a costly mistake? That’s why one of Horizon’s most popular features is simple: it shows customers how many hours we’ve saved them. Time is a currency they value even more than insights. 3. The best AI isn’t visible The future of AI in the enterprise isn’t necessarily chatbots or flashy dashboards. Often, the most valuable AI disappears into the background, quietly doing the work and surfacing results at the right time, in the right format. Cersi failing taught us that “quieter AI” can be more powerful than any buzzy avatar, co-pilot, or assistant. Horizon was built with that in mind. For founders building in this space, the lesson we learned is clear: resist the temptation to build AI for the hype cycle. In fact, do the very opposite—build your AI not to be the flashy new thing, but to be so good that it becomes invisible. View the full article

This week on my podcast, I delved deep into the neural mechanisms involved in making your phone so irresistible. To summarize, there are bundles of neurons in your brain, associated with your short-term motivation system, that recognize different situations and then effectively vote for corresponding actions. If you’re hungry and see a plate of cookies, there’s a neuron bundle that will fire in response to this pattern, advocating for the action of eating a cookie. The strength of these votes depends on an implicit calculation of expected reward, based on your past experiences. When multiple actions are possible in a given situation, then, in most cases, the action associated with the strongest vote will win out. One way to understand why you struggle to put down your phone is that it overwhelms this short-term motivation system. One factor at play is the types of rewards these devices create. Because popular services like TikTok deploy machine learning algorithms to curate content based on observed engagement, they provide an artificially consistent and pure reward experience. Almost every time you tap on these apps, you’re going to be pleasantly surprised by a piece of content and/or find a negative state of boredom relieved—both of which are outcomes that our brains value. Due to this techno-reality, the votes produced by the pick-up-the-phone neuron bundles are notably strong. Resisting them is difficult and often requires the recruitment of other parts of your brain, such as the long-term motivation system, to convince yourself that some less exciting activity in the current moment will lead to a more important reward in the future. But this is exhausting and often ineffective. The second issue with how phones interact with your brain is the reality that they’re ubiquitous. Most activities associated with strong rewards are relatively rare—it’s hard to resist eating the fresh-baked cookie when I’m hungry, but it’s not that often that I come across such desserts. Your phone, by contrast, is almost always with you. This means that your brain’s vote to pick up your phone is constantly being registered. You might occasionally resist the pull, but its relentless presence means that it’s inevitably going to win many, many times as your day unfolds. ~~~ Understanding these neural mechanisms is important because they help explain why so many efforts to reduce phone use fail—they don’t go nearly far enough! Consider, for example, the following popular tips that often fall short… Increase Friction This might mean moving the most appealing apps to an inconvenient folder on your phone, or using a physical locking device like a Brick that requires an extra step to open your phone. These often fail because, from the perspective of your short-term motivation systems, these mild amounts of friction only decrease your expected reward by a small amount, which ultimately has little impact on the strength of its vote for you to pick up your phone. Make Your Phone Grayscale There is an idea that eliminating bright colors from your phone’s screen will somehow disrupt the cues that lead you to pick it up. This also often fails because colors have very little to do with your brain’s expected reward calculation, which is based on more abstract benefits, such as pleasant surprise and the alleviation of boredom. Moderate Your Use with Rules It’s also common to declare clear rules about how much you will use each type of app; e.g., “only 30 minutes of Instagram per day.” The problem is that such rules are abstract and symbolic, and have limited interaction with your short-term motivation systems, which deal more with the physical world and immediate rewards. Detox Regularly Another common tactic is to “detox” by taking regular time away from your phone, such as a weekly Internet Shabbat, or an annual phone-free meditation retreat. These practices can boast many benefits, but they’re not nearly long enough to start diminishing the learned rewards that drive your motivation system. It would take many months away from your phone before your brain began to forget its benefits. ~~~ So what does work? Our new understanding of our brains points toward two obvious strategies that are both boringly basic and annoyingly hard to stick to. First, remove the reward signals by deleting social media or any other app that monetizes your attention from your phone. If your phone no longer delivers artificially consistent rewards, your brain will rapidly reduce the expected reward of picking it up. Second, minimize your phone’s ubiquity by keeping it charging in your kitchen when at home. If you need to look something up or check in on a messaging app, go to your kitchen. If you need to listen to a podcast while doing chores, use wireless earbuds or wireless speakers. If your phone isn’t immediately accessible, the corresponding neuronal bundles in your motivation system won’t fire as often or as strongly. In the end, here’s what’s clear: Our brains aren’t well-suited for smartphones. We might not like this reality, but we cannot ignore it. Fixing the issues this causes requires more than some minor tweaks. We have to drastically change our relationship to our devices if we hope to control their impact. The post What Neuroscience Teaches Us About Reducing Phone Use appeared first on Cal Newport. View the full article

This week on my podcast, I delved deep into the neural mechanisms involved in making your phone so irresistible. To summarize, there are bundles of neurons in your brain, associated with your short-term motivation system, that recognize different situations and then effectively vote for corresponding actions. If you’re hungry and see a plate of cookies, there’s a neuron bundle that will fire in response to this pattern, advocating for the action of eating a cookie. The strength of these votes depends on an implicit calculation of expected reward, based on your past experiences. When multiple actions are possible in a given situation, then, in most cases, the action associated with the strongest vote will win out. One way to understand why you struggle to put down your phone is that it overwhelms this short-term motivation system. One factor at play is the types of rewards these devices create. Because popular services like TikTok deploy machine learning algorithms to curate content based on observed engagement, they provide an artificially consistent and pure reward experience. Almost every time you tap on these apps, you’re going to be pleasantly surprised by a piece of content and/or find a negative state of boredom relieved—both of which are outcomes that our brains value. Due to this techno-reality, the votes produced by the pick-up-the-phone neuron bundles are notably strong. Resisting them is difficult and often requires the recruitment of other parts of your brain, such as the long-term motivation system, to convince yourself that some less exciting activity in the current moment will lead to a more important reward in the future. But this is exhausting and often ineffective. The second issue with how phones interact with your brain is the reality that they’re ubiquitous. Most activities associated with strong rewards are relatively rare—it’s hard to resist eating the fresh-baked cookie when I’m hungry, but it’s not that often that I come across such desserts. Your phone, by contrast, is almost always with you. This means that your brain’s vote to pick up your phone is constantly being registered. You might occasionally resist the pull, but its relentless presence means that it’s inevitably going to win many, many times as your day unfolds. ~~~ Understanding these neural mechanisms is important because they help explain why so many efforts to reduce phone use fail—they don’t go nearly far enough! Consider, for example, the following popular tips that often fall short… Increase Friction This might mean moving the most appealing apps to an inconvenient folder on your phone, or using a physical locking device like a Brick that requires an extra step to open your phone. These often fail because, from the perspective of your short-term motivation systems, these mild amounts of friction only decrease your expected reward by a small amount, which ultimately has little impact on the strength of its vote for you to pick up your phone. Make Your Phone Grayscale There is an idea that eliminating bright colors from your phone’s screen will somehow disrupt the cues that lead you to pick it up. This also often fails because colors have very little to do with your brain’s expected reward calculation, which is based on more abstract benefits, such as pleasant surprise and the alleviation of boredom. Moderate Your Use with Rules It’s also common to declare clear rules about how much you will use each type of app; e.g., “only 30 minutes of Instagram per day.” The problem is that such rules are abstract and symbolic, and have limited interaction with your short-term motivation systems, which deal more with the physical world and immediate rewards. Detox Regularly Another common tactic is to “detox” by taking regular time away from your phone, such as a weekly Internet Shabbat, or an annual phone-free meditation retreat. These practices can boast many benefits, but they’re not nearly long enough to start diminishing the learned rewards that drive your motivation system. It would take many months away from your phone before your brain began to forget its benefits. ~~~ So what does work? Our new understanding of our brains points toward two obvious strategies that are both boringly basic and annoyingly hard to stick to. First, remove the reward signals by deleting social media or any other app that monetizes your attention from your phone. If your phone no longer delivers artificially consistent rewards, your brain will rapidly reduce the expected reward of picking it up. Second, minimize your phone’s ubiquity by keeping it charging in your kitchen when at home. If you need to look something up or check in on a messaging app, go to your kitchen. If you need to listen to a podcast while doing chores, use wireless earbuds or wireless speakers. If your phone isn’t immediately accessible, the corresponding neuronal bundles in your motivation system won’t fire as often or as strongly. In the end, here’s what’s clear: Our brains aren’t well-suited for smartphones. We might not like this reality, but we cannot ignore it. Fixing the issues this causes requires more than some minor tweaks. We have to drastically change our relationship to our devices if we hope to control their impact. The post What Neuroscience Teaches Us About Reducing Phone Use appeared first on Cal Newport. View the full article

As a learning designer at Zapier, I used to spend my days helping my teammates learn: I built and led trainings, created enablement resources, and helped folks better understand how their work contributed to company strategy. Now, I sit inside our HR team as an AI automation engineer. But the through line is the same: I still help my teammates (and now customers, too!) do their best work. What is an AI automation engineer? AI automation engineer sounds like a vague title, so here’s the job, plainly: I embed with a team (HR, in my case), spot opportunities to enhance the team’s work, and build AI-powered workflows that jump on those opportunities. The goal is to create measurable improvements that free my teammates up for creativity, strategy, and connection. I think we’ll be seeing this title pop up more and more as time goes on. For example, instead of hiring a new content writer, content marketing teams might look for AI automation engineers with a strong eye for content. Instead of a new junior coder, engineering teams might look for an AI automation engineer with a technical background. Why the AI automation engineer role matters Lots of teams see AI’s potential but get stuck turning ideas into action. The gap is less about technology and more about translation: understanding how a real process works today, where it fails, what data is safe to use, and what “better” even looks like. AI automation engineers close that gap. We prototype fast using tools like Zapier, ChatGPT, Airtable, and Cursor, then we harden those prototypes into reliable internal tools. In HR, that looks like: Reducing the back-and-forth between recruiting, interviewers, and candidates Auto-summarizing interview debriefs so we can make decisions faster Keeping people data in sync across tools, with the right guardrails for privacy and compliance Giving folks self-serve answers to policy questions without losing the human touch And it is not just about helping my own team. A big part of my role is building repeatable HR workflows that we can share with our customers. When I design something for Zapier’s people team, like an interview debrief summarizer or a self-serve policy bot, I’m also thinking about how it could work for other HR teams out in the world. Sometimes it even goes the other way: a customer use case inspires a workflow that we bring back inside Zapier. What I actually do week to week as an AI automation engineer AI automation engineer is a new type of role, and I’m even newer to it myself, but here’s a glimpse into how I spend my days. Triage workflows: I map how work really happens, quantify the cost of the current process, then rank opportunities. I’m looking for spots where I can have a big impact. Prototype quickly: Once I know where I want to help, I build small, testable versions using Zapier and other AI tools. Embed with the team: I sit with the people doing the work. We try the prototype in the real flow and adjust prompts and logic. We document what to trust and when to escalate to a human. Scale the AI automation: Once the workflow proves itself, I add error handling, retries, observability, and access controls. I create a runbook so the team can own it, not me. Teach and enable: I host short workshops, write playbooks, and pair with team leads so they can spot the next opportunity themselves (and know when not to use AI). Measure outcomes: I track hours saved, error rate reduction, cycle-time improvements, adoption, and the business outcome (e.g., faster time-to-hire, better candidate experience). Partner with sales: When we see an HR workflow that really moves the needle, I package it into a demo or playbook that our sales team can share with prospects. Sometimes I join those conversations directly to explain the workflow in plain language: why it works, what problems it solves, and what business results it drives. Share feedback upstream: Because I’m building on top of Zapier and AI tools every day, I run into edge cases, missing features, or things that could be smoother. I funnel that feedback back to our product team, often with concrete examples from both our people team and customers. It means the next version of Zapier is more aligned with how real HR teams actually work. For some examples of what I’ve built, read about my favorite agents or take a look at our HR AI automation playbook. What does it take to be an AI automation engineer? I don’t come from a technical background, but I’m a tinkerer, and I think that’s what makes me suited for this role. I’m comfortable building with no-code tools and love to ship solutions. Skills I’ve picked up along the way are prompt engineering, responsible AI practices, and understanding how to pick the right AI tools for the job. One of the most important parts of the role, though, is something I have a lot of experience with: enablement. I need to make sure the folks I’m building for understand how to make the most of these systems. One important thing to note: My focus is squarely on HR. That’s where I build, prototype, and enable. While I love seeing how AI automation engineers show up in marketing, IT, or engineering, my role is all about HR use cases. I help our people team work smarter, and I help our customers run stronger HR operations. But I’m also proof that you don’t need to be a software engineer to become an AI automation engineer. Here are some other folks from the Zapier community who I’d argue are AI automation engineers, each from a different background. Remote’s Marcus Saito (head of IT) used AI to auto-resolve 27.5% of IT tickets. This saved his team more than 2,200 days and $500,000 in hiring costs. Vendasta’s Jacob Sirrs (marketing operations specialist) used AI to automate sales workflows, save more than 282 workdays a year, and reclaim $1 million in revenue. ActiveCampaign’s Tabitha Jordan (manager of product education) implemented AI-powered lead enrichment to give the sales teams time to focus on high-value activities. Moving from learning & development into this role as an AI automation engineer for HR hasn’t changed my mission. I still help people work better. If you’re AI-curious, start with the smallest annoying task you do every week. Fix that. Measure it. Then fix the next one. View the full article

London-based group seeks valuation of $8bn as global investors snap up deals with fast-growing AI start-ups View the full article

Jamie Dimon adopts ‘America First’ policies touted by The President administrationView the full article

MCP, which stands for Model Context Protocol, is an open standard that companies can use to integrate their external tools and data with compatible AI assistants. Imagine you’ve recently launched a new clothing brand and want to find eCommerce stores…Read more ›View the full article