Podcasts about Retrieval

Send us Fan MailIn Part 2 of our three-part egg freezing series, we walk through the entire egg-freezing process from start to finish.What medications do you take? How long are the injections? How often do you need ultrasounds and bloodwork? What happens during the trigger shot? Does the egg retrieval hurt? And what actually happens to your eggs once they reach the embryology lab?Dr. Mark Amols explains the process step by step, including: What AMH and antral follicle count help predict before treatment  Why fertility medications do not “use up” your future eggs  How ovarian stimulation works  What patients can expect from the injections  How follicles are monitored during the cycle  Why trigger timing is so important  How modern protocols help reduce the risk of OHSS  What happens during the egg retrieval  Why the number of follicles, eggs retrieved, and mature eggs frozen are often different  What “MII” means  How vitrification works  What recovery is like after retrieval  Why some patients may need more than one egg-freezing cycle One of the most important concepts in egg freezing is understanding the difference between follicles, retrieved eggs, mature eggs, and the eggs that are actually frozen.Egg freezing is usually a much shorter process than many people expect—but every step matters.This is Part 2 of our Egg Freezing Series.In Part 3, we answer a question that almost nobody talks about:Once you are finally ready to have a baby, should you actually use your frozen eggs—or leave them in the freezer as your backup?Explore more fertility episodes and interactive tools at TacoBoutFertility.com.Thanks for listening to another episode of Taco Bout Fertility Tuesday with Dr. Mark Amols.If you found this episode helpful, please share it with someone who might benefit—and don't forget to follow the show and leave us a review on Apple Podcasts, Spotify, or wherever you listen.Want to explore more fertility topics? Visit TacoBoutFertility.com, where you can search our entire episode library, find episodes about the questions that matter to you, try our interactive fertility tools, and submit questions or topic requests for future episodes.Join us next Tuesday for another episode of Taco Bout Fertility Tuesday, where we make fertility medicine easier to understand—one topic at a time.

Join Phil up on the course as he takes you step-by-step through a gear retrieval. From selecting setting up on the ground to climbing to the belay cable, retrieving the gear and managing the descent — hear all the real-time commentary, practical thought processes, and raw ambient sounds of the course. Whether you're interested in the mechanics of ropes course work, safety considerations, or you just love the crisp sounds of carabiners and climbing gear in action, this episode gives you an authentic front-row seat. Have any questions for Phil; Email - podcast@high5adventure.org Instagram - @verticalplaypen Support the podcast - verticalplaypen.org

Colin Shaw and Professor Ryan Hamilton tackle the two processes that decide everything in customer experience: how memories get in, and how they get out. If you haven't heard Parts 1 and 2, start there — Colin and Ryan build on the "memories are structured like fishing nets" idea throughout. Here's the uncomfortable truth at the center of this episode: a customer's memory of your experience isn't a recording you can trust. It's reconstructed, emotional, and easy to distort — and most organizations are accidentally reinforcing the worst version of it. In this episode, Colin and Ryan cover: Storing memories — why memories live in network structures Retrieval — the Zeigarnik effect (why an active goal sharpens memory, and why it vanishes once the goal is met), Forgetting — storage decay, the biology of chemical traces The danger zone: misinformation — Elizabeth Loftus's famous "smashed vs. hit" car-crash study The "So What" — why making customers repeat their complaint makes worse memories,  Key quote: "We don't choose between experiences. We choose between the memory of an experience." — Professor Daniel Kahneman Resources mentioned: Elizabeth Loftus on the misinformation effect: SimplyPsychology Learn about Memory Maker Training and customer science: email contact@beyondphilosophy.com Explore all episodes, free tools, and resources: beyondphilosophy.com/podcast About the Hosts: Colin Shaw is a LinkedIn 'Top Voice' with a massive 286,000 followers and 87,000 subscribers to his 'Why Customers Buy' newsletter. Shaw is named one of the world's 'Top 150 Business Influencers' by LinkedIn. His company, Beyond Philosophy LLC, has been selected four times by the Financial Times as a top management consultancy. Shaw is co-host of the top 1.5% podcast 'The Intuitive Customer'—with over 700,000 downloads—and author of eight best-sellers on customer experience. Shaw is a sought-after keynote speaker. Follow Colin on LinkedIn. Ryan Hamilton is a Professor of Marketing at Emory University's Goizueta Business School and co-author of 'The Intuitive Customer' book. An award-winning teacher and researcher in consumer psychology, he has been named one of Poets & Quants' "World's Best 40 B-School Profs Under 40." His research focuses on how brands, prices, and choice architecture influence shopper decision-making, and his findings have been published in top academic journals and covered by major media outlets like The New York Times and CNN. His work highlights how psychology can help firms better understand and serve their customers. Ryan has a new book launch in June 2025 called "The Growth Dilemma: Managing Your Brand When Different Customers Want Different Things" Harvard Business Press Follow Ryan on LinkedIn. Subscribe & Follow Apple Podcasts Spotify  

Colin Shaw and Professor Ryan Hamilton tackle the two processes that decide everything in customer experience: how memories get in, and how they get out. If you haven't heard Parts 1 and 2, start there — Colin and Ryan build on the "memories are structured like fishing nets" idea throughout. Here's the uncomfortable truth at the center of this episode: a customer's memory of your experience isn't a recording you can trust. It's reconstructed, emotional, and easy to distort — and most organizations are accidentally reinforcing the worst version of it. In this episode, Colin and Ryan cover: Storing memories — why memories live in network structures Retrieval — the Zeigarnik effect (why an active goal sharpens memory, and why it vanishes once the goal is met), Forgetting — storage decay, the biology of chemical traces The danger zone: misinformation — Elizabeth Loftus's famous "smashed vs. hit" car-crash study The "So What" — why making customers repeat their complaint makes worse memories,  Key quote: "We don't choose between experiences. We choose between the memory of an experience." — Professor Daniel Kahneman Resources mentioned: Elizabeth Loftus on the misinformation effect: SimplyPsychology Learn about Memory Maker Training and customer science: email contact@beyondphilosophy.com Explore all episodes, free tools, and resources: beyondphilosophy.com/podcast About the Hosts: Colin Shaw is a LinkedIn 'Top Voice' with a massive 286,000 followers and 87,000 subscribers to his 'Why Customers Buy' newsletter. Shaw is named one of the world's 'Top 150 Business Influencers' by LinkedIn. His company, Beyond Philosophy LLC, has been selected four times by the Financial Times as a top management consultancy. Shaw is co-host of the top 1.5% podcast 'The Intuitive Customer'—with over 700,000 downloads—and author of eight best-sellers on customer experience. Shaw is a sought-after keynote speaker. Follow Colin on LinkedIn. Ryan Hamilton is a Professor of Marketing at Emory University's Goizueta Business School and co-author of 'The Intuitive Customer' book. An award-winning teacher and researcher in consumer psychology, he has been named one of Poets & Quants' "World's Best 40 B-School Profs Under 40." His research focuses on how brands, prices, and choice architecture influence shopper decision-making, and his findings have been published in top academic journals and covered by major media outlets like The New York Times and CNN. His work highlights how psychology can help firms better understand and serve their customers. Ryan has a new book launch in June 2025 called "The Growth Dilemma: Managing Your Brand When Different Customers Want Different Things" Harvard Business Press Follow Ryan on LinkedIn. Subscribe & Follow Apple Podcasts Spotify  

Episode 406 explains how movement prepares the brain but does not guarantee learning. It traces the full sequence—movement, RAS-driven readiness, salience and relevance, focused attention, encoding, retrieval and repetition, and recovery and sleep—that turns activation into lasting memory and performance. The episode offers practical steps in a Move–Focus–Learn protocol: use brief movement to create readiness, clearly identify and remove distractions for one learning target, interact with material in meaningful ways, practice retrieval and spaced repetition, and protect recovery to support consolidation. Listeners are encouraged to test these simple experiments for school, work, or personal learning to make experience more usable and improve attention, memory, and performance. ON THIS EPISODE, YOU'LL LEARN: ✔ Why movement prepares the brain but does not guarantee learning ✔ How attention directs the capacity movement creates ✔ Why meaning and emotional relevance capture attention ✔ The difference between paying attention and actually learning ✔ How retrieval and repetition help new information stick ✔ How seeing, hearing, explaining, and applying an idea can help us remember it ✔ How restorative sleep supports memory and learning ✔ A practical Move–Focus–Learn Protocol for school, work, or your own personal learning The central idea is simple: Movement opens the window. Attention determines what enters. Repetition strengthens what remains. And recovery helps the brain integrate what it has experienced. Welcome back to the Neuroscience Meets Social and Emotional Learning Podcast, where we bridge neuroscience, social and emotional learning, and human performance so we can create measurable improvements in our well-being, achievement, leadership, productivity, and results. I'm Andrea Samadi, and if you've been following along through Season 16, you'll know that we have been building what I call The Brain's Operating System for Human Performance—a neuroscience-based framework designed to help us understand how the different systems of the brain and body work together to influence how we learn, adapt, connect, lead, and ultimately, how we perform. We are currently in Phase 3, focused on Movement, Learning, and Cognition. As I prepared for this phase, I went back through more than 400 episodes of the podcast and organized them according to the five phases of this framework. I wanted to see whether any patterns would emerge. And some did. Phase 1, Regulation and Safety, includes 57 episodes. Phase 2, Neurochemistry and Motivation, includes 41 episodes—the smallest category, possibly because this is the area I have explored the least with my learning. Phase 3, Movement, Learning, and Cognition, includes 174 episodes—the largest category by far. Phase 4, Perception and Social Intelligence, includes 68 episodes. And Phase 5, Integration and Meaning, includes 71 episodes. When I saw that 174 episodes—more than 40 percent of the entire podcast—fit into Movement, Learning, and Cognition, it immediately showed me where much of my curiosity has been directed over the years. I have always been fascinated by learning: How do we prepare the brain to learn? What captures our attention? Why do some ideas (or people) remain with us while others disappear? How does knowledge become something we can retrieve, apply, and actually use to improve our lives? And while I am spending most of my spare time hiking, or at the gym over the years, I wonder, how does this movement that I'm doing help to improve my attention, or memory, and finally, where does recovery fit into this process? The past few weeks, I'm finally connecting the dots with how important recovery is with this equation. I'm also learning how to determine what's important to me. John Medina's Brain Rules book explains what holds our attention. He reminds us that “we don't pay attention to boring things (or people).[i] As we continue through these five phases of The Brain's Operating System for Human Performance, I'm especially curious about how we will move beyond understanding these lessons, to begin implementing what we are learning, and connecting more ways to improve how we learn, adapt, connect, lead, and ultimately perform in our day to day lives. That will become an important part of the upcoming workbook—which is a practical way for each of us to identify what we have already strengthened, where gaps may remain, and which specific actions can help us to improve our own learning, well-being, and performance. Because the purpose of this 5 Phase Framework is not simply to collect more information about the brain. It is to understand how the systems connect—and then use that understanding to create meaningful change.  “Attention Is the Gate—How Movement Becomes Learning.” For today's Episode 406, we'll connect Dr. John Medina's Brain Rules to the Movement Loop we have been building throughout Phase 3—and answer the next important question: Once movement activates and prepares the brain, what determines whether that readiness becomes lasting learning? You can review our full interview with John Medina from EP 42[ii] from February 2020. (find the link in the reference section of the show notes). INTRODUCTION: THE MISSING STEP We have spent this phase so far, examining how movement changes the brain. Dr. Chuck Hillman and Paul Zientarski[iii] showed us that movement prepares the brain for learning. Dr. John Ratey[iv] helped us understand how exercise influences neurochemistry, BDNF, and neuroplasticity. Dr. Kristen Holmes[v] showed us that recovery helps determine whether strain becomes adaptation. And our restorative-sleep episode explored where some of that recovery, adaptation, and integration may occur. But one important question remains: A brain can be activated, chemically prepared, and physiologically ready—but what determines what it actually learns? The answer begins with attention. Movement may create the readiness to learn, but readiness alone is not learning. The brain must still select what matters, focus on it, interact with it, encode it, revisit it, and eventually integrate it. That gives us the pathway we will explore today: Movement creates readiness. But what is the filter in our brain that helps us to focus on the things that are most important to us. What gives us the ability to pay attention to something? The RAS- or The Reticular Activating System. Before we continue, let's remind ourselves what the RAS is. The RAS—or reticular activating system—is a network of neurons extending through the brainstem that helps regulate wakefulness, alertness, and our readiness to respond to incoming information. We explored this system previously in Episode 272, “Priming the Reticular Activating System to Achieve Our Goals.” In that episode, we described the RAS as a type of filter that helps us notice information connected to what we have identified as important. For example, once you decide that you are interested in a particular type of car, you may suddenly begin seeing that car everywhere. That's the RAS filtering system in our brain. The cars were already there, but your attention now has a reason to prioritize them. In Episode 272[vi], we applied this idea to goal-setting. When we clearly identify an intention, we may become more likely to notice relevant information, opportunities, and connections that were previously present but did not receive our attention. That explanation gave us a useful starting point. But here in Episode 406, we can add more precision. The RAS is not a tiny gatekeeper sitting in the brain and personally deciding what we will learn. It is part of a broader network that helps maintain the level of arousal and alertness required for our attention. It helps the brain become available to notice whatever it is that is important to us. Then salience, relevance, emotion, intention, and our current goals help determine what receives priority. Focused attention directs our limited cognitive resources toward the selected information. Next, encoding and repetition help strengthen what we want to focus on. And recovery and sleep help to further support its consolidation and integration. So our expanded Movement Loop now looks like this: you can see an image in the show notes to expand this understanding. Movement supports brain activation.   RAS helps the brain become awake, alert, and ready to pay attention. Salience and relevance (or what matters the most to us) helps to determine what receives priority. Our focused attention selects what gets our priority. Encoding and repetition further strengthens this pathway. Recovery and sleep (that we've dove deep into) support consolidation and integration. And together, these processes increase the likelihood that experience becomes learning—and that learning becomes available for future performance. Whatever it is that we have put our attention on, is now made usable.  This shows us how our understanding has evolved since Episode 272, three years ago. Then, we were asking: How can our intention help us notice what matters with the attainment of our goals? Now, years later, with more understanding, we are asking: Once the brain is alert and something important has captured our attention, what sequence of events must occur for that information to become lasting learning? We are no longer asking only what must remain top of mind so we can notice opportunities connected to our goals. We are asking what happens next: How does something we notice become something we understand? How does something we understand become something we remember? And how does something we remember become knowledge or a skill we can retrieve, apply, and use to improve our performance? This is where Dr. John Medina's Brain Rules help us to connect the pieces. Let's begin with Lesson One: Lesson 1: Movement Creates Readiness—Not Guaranteed Learning Movement can increase alertness, circulation, and readiness for cognitive work. But why might movement have such a powerful relationship with the brain? Dr. John Medina explains this through an evolutionary lens. His point is that the human brain did not develop while we were sitting motionless at desks or staring at screens. It developed while human beings were moving through changing outdoor environments, solving immediate problems, and continually responding to the world around them. Let's listen to how he explains it. CLIP 1 John Medina “The human brain was designed to solve problems related to surviving in an outdoor setting, in unstable meteorological conditions—and to do so in constant motion. The constant motion is where the exercise comes in. But the rest of that—the more a school, or any of us, can recreate the world of the Serengeti, the place where this brain actually grew up, the better things are. And exercise, particularly outdoor exercise, is a terrific example of this idea.” When Medina refers to the Serengeti, I don't think he is suggesting that schools should literally recreate the dangers or conditions of an ancient environment. He is giving us an evolutionary picture of the conditions under which the human brain developed: We moved. We navigated changing environments. We encountered new sensory information. We solved problems. We adapted to uncertainty. And we did these things together rather than remaining physically still for most of the day. Outdoor movement may bring several of these conditions together. We are moving our bodies while also responding to changes in light, temperature, terrain, sound, distance, and direction. This helps explain why movement belongs at the beginning of our Movement Loop. But it also brings us to an important distinction: Movement creates readiness. It does not guarantee learning. A student can move before class and still become distracted. A leader can exercise before work and still spend the morning reacting to notifications. An athlete can warm up physically without becoming mentally focused on the strategy or skill required next. Movement can help activate the system. The RAS in the brain helps us become awake and alert. But the brain must still determine what stands out, what is relevant, and what deserves focused attention. Movement opens the window. Attention determines what enters through it. Our Key lesson: Movement prepares the brain. Attention directs the preparation. PUT THIS INTO ACTION Before your next period of demanding cognitive work, use movement to create a window of readiness. Take a five-to-ten-minute walk, complete a brief movement break, stretch, or choose another form of movement appropriate for your ability and environment. But do not move without deciding what comes next. Before you begin, identify the specific task you will return to: “When this movement break ends, what will receive my attention?” For a student, it might be the first problem on a math assignment. For a leader, it might be the one decision that requires uninterrupted thinking. For an athlete, it might be the cue, strategy, or skill that needs to remain at the center of practice. Movement creates the opportunity. A clear intention helps direct what happens next. Your experiment is: Move briefly, choose one target that you want to complete with this intention, and begin that task immediately after the movement ends. Lesson 2: Attention Is the Gate In EP 395[vii], we examined what captures attention: Meaning Emotion Relevance Intention Reward Human connection Now today's episode shows how we move beyond why the brain notices something and explains what attention does after movement has prepared the system to learn. Attention acts as a filter. The brain encounters more information than it can consciously process, so it must select what receives priority. This creates the bridge: Brain activation creates capacity. Attention distributes that capacity. Our Key lesson: What the brain does not attend to has little opportunity to become lasting learning. Andrea's Application This distinction between brain capacity and cognitive efficiency became personal for me when I reviewed the results of my 2020 brain scan. We covered this topic on EP 84[viii] and we will do a thorough review of what was learned with our brain scans at Dr. Daniel Amen's CA Clinic. The evaluation that I received after my scan was completed with Dr. Shane Creado, suggested that I had areas of strong capacity, but that my brain appeared to be working harder than necessary to complete certain tasks in the cognitive testing. A possible contributor we discussed was sleep deprivation. I want to be careful here: a SPECT scan is not a direct measurement of attention or learning, and my results cannot tell us that insufficient sleep caused a specific attention pattern. But the experience gave me a useful way to think about what we are learning today. Having cognitive capacity does not necessarily mean that we are using that capacity efficiently. I may be motivated, active, and ready to work—but if I am under-recovered, under-slept, distracted, or trying to process too many competing inputs, my brain may have to expend more energy to sustain focus. This helped me understand the Movement Loop at a more personal level: Movement can help activate my brain. Attention gives that activation a target. But recovery may influence how efficiently I can sustain that attention and use it for learning. My scan did not define what my brain could do. It gave me another reason to examine the conditions under which my brain performs at its best. PUT THIS INTO ACTION Before asking yourself—or someone else—to focus, make the target of attention clear. Start by completing this sentence: “The most important thing to notice right now is…” Then reduce the competition around it. We all know to do this. Silence unnecessary notifications. Close unrelated tabs. Remove extra materials from view. Avoid asking the brain to switch repeatedly between tasks. If you are teaching, make the relevance visible: “Why does this matter?” “What should students be listening or looking for?” “How does this connect to something they already know?” If you are working independently, write your one priority clearly where you can see it. Remember, the goal is not to force the brain to attend to everything. The goal is to help it identify (with your RAS-that filter in your brain) what deserves your priority. Your experiment is: Choose one target, (something you want to accomplish) remove as many unnecessary sources of interference as possible, and create one meaningful reason to pay attention. This also helps explain why keeping your desk clear, your closet organized, or your car tidy may make you feel calmer and more focused. How we do anything is how we do everything. Every object, notification, unfinished task, or piece of visual clutter can become another potential signal competing for your attention. That does not mean everything must be perfectly organized before you can begin (while some of us would prefer to work this way), perfection can become its own form of interference. Just get started. It simply means that when you reduce unnecessary visual and mental competition, you make it easier for the brain to recognize the priority in front of you. Clear the space. Name the target. Create the relevance. Then give the task your full attention. Lesson 3: Attention Is Necessary—but It Is Not Enough Paying attention to information once does not guarantee that it will be remembered. This is where Dr. Medina's memory rules enter: Repeat to remember. Remember to repeat. The first refers to giving new information more than one opportunity to be encoded. The second points toward revisiting information over time. This allows you to extend the Movement Loop: Movement prepares the brain and body. Attention selects what to focus on. Retrieval and Repetition strengthens our focus. Recovery helps us to integrate what we are learning. PUT THIS INTO ACTION After learning something new, do not immediately reread it. First, look away from the material and try to retrieve it. Ask yourself: “What were the three most important ideas?” “How would I explain this without looking at my notes?” “What can I remember on my own?” Retrieval reveals the difference between recognizing information and actually being able to access it. Then return to the material, check what you missed, and retrieve it again later—after an hour, the next day, or several days afterward. For students, this could mean closing the textbook and explaining the lesson to a partner. For professionals, it could mean summarizing a meeting before reviewing the notes. For personal learning, it could mean recording a short voice memo explaining the idea from memory. Your experiment is: Learn it once, retrieve it without looking, and schedule a brief return to it later. See if you can remember it. Attention begins the pathway. Retrieval and repetition help strengthen it. Andrea's Application This lesson reminded me of one part of the cognitive testing that accompanied my brain scan at Amen Clinics: a Continuous Performance Test, or CPT. In the version I completed, letters appeared on a computer screen. The instruction was to press a button whenever a letter appeared—except when the letter was X. This type of task may sound simple, but it requires you to remain attentive over time, respond consistently, and inhibit your automatic response when the target letter appears. My results indicated that this was an area in which I did not perform as strongly. Looking back, I remember hearing the basic instruction: press the button for every letter except X. But I do not remember fully appreciating that the speed and consistency of my responses also mattered. This gave me an important real-life lesson: Hearing an instruction is not the same as accurately encoding everything the task requires. Before beginning something important, pause and ask: “What exactly am I being asked to do?” “What details determine successful performance?” “Can I explain the instructions back in my own words?” And, when appropriate, write down the key steps before beginning. This applies in a classroom, during a meeting, while completing an assessment, or whenever accuracy matters. First, attend to the full instruction. Then restate or retrieve it. Clarify anything that is missing. And only then begin the task. My experience showed me that attention is not just about working harder or concentrating more intensely. It is also about identifying the correct target—and making sure we understand the complete task before directing our effort toward it. Lesson 4: Learning Becomes Stronger When It Has More Than One Path Dr. Medina's Brain Rule #9 about sensory integration and vision(meaning our brains learn and remember best when multiple senses—like sight, sound, and touch—are engaged at the same time) give you the practical learning background to involve ALL of your senses. While teaching and learning you can ask: Can I see it? Can I hear it? Can I explain it? Can I demonstrate it? Can I connect it to something I already know? Can I apply it physically or practically? The goal is not sensory stimulation for its own sake. It is to provide clear, relevant pathways through which the learner can interact with the information. Our Key lesson: The more meaningfully we interact with information that we are learning, the more opportunities the brain has to encode and retrieve it. PUT THIS INTO ACTION Choose one idea you want to remember and interact with it in at least two meaningful ways. You might: See it in a diagram. Hear it explained. Say it in your own words. Write or draw it. Teach it to someone else. Demonstrate it physically. Connect it to a previous experience. Or apply it to a real problem. The goal is not to add noise or stimulation. More input is not automatically better. Each interaction should make the idea clearer, more relevant, or easier to retrieve. For example, instead of only reading about the Movement Loop, you could draw the sequence, explain it aloud, and identify where it appears in your own daily routine. Your experiment is: Take one important idea and represent it in two different, meaningful ways. The more deeply we work with information, the more opportunities the brain has to encode and retrieve it. Andrea's Application If you have been listening to this podcast for a while, I'm sure you have noticed that when I organize an idea into a framework—or represent it in a visual graphic—it begins to come to life. I can see how the pieces from past episodes connect, can explain the concept more clearly, and retrieve and apply it to my life more easily. It's also difficult to forget something when there's a visual image connected to it if you are the type of learner who needs to see something to make it stick in your memory. LESSON 5: RECOVERY HELPS LEARNING CONTINUE AFTER ATTENTION ENDS So far, we have followed learning through four important steps: Movement prepares the brain and body. RAS helps “turn on the lights,” allowing us to become awake, alert, and ready to respond. Next, the brain begins identifying what might be important to us. Salience refers to something that stands out—perhaps because it is new, unexpected, emotional, or connected to a possible reward or threat. Relevance refers to how closely that information connects to our goals, needs, experiences, or interests. In simple terms: Salience asks, “What stands out?” Relevance asks, “What matters to me?” Attention through the RAS next directs the brain's spotlight toward the information selected for deeper processing. Imagine a flashlight shining on whatever it is that you have picked to be important to learn. Encoding creates the initial memory. Retrieval and repetition strengthen it. But learning does not necessarily end when we close the book, leave the classroom, finish the meeting, or stop practicing. The brain still needs time to stabilize, organize, and connect what we learned. This is where recovery and sleep enter the Movement Loop. SLEEP IS AN IMPORTANT PART OF LEARNING Sleep is not a period when the brain simply switches off. While we sleep, the brain continues processing information from the day. Newly encoded memories may be reactivated, strengthened, reorganized, and connected with knowledge we already have. This is called memory consolidation. In simple terms, consolidation helps make a new memory more stable and easier to access later. Deep non-REM sleep and REM sleep may support different but overlapping parts of this process. Deep sleep is associated with physical restoration and the consolidation of certain facts and experiences. REM sleep has been associated with emotional learning, procedural skills, creative connections, and integrating new experiences with older memories. But we should not think of these as completely separate jobs. Deep sleep and REM are parts of a repeating sleep cycle, and both may contribute to learning. This is why I use the phrases: Deep sleep helps me recover from the hard work I did that day. REM may help me integrate what I experienced and what it meant to me. The word “may” matters because sleep is more complex than saying that deep sleep only restores the body and REM alone integrates learning. The most important lesson is this: What we focus on while we are awake gives the brain something to work with while we sleep. Retrieval and repetition strengthen the information before sleep. Then sleep provides conditions that can help stabilize, organize, and connect what we learned. THE MOVEMENT LOOP IN SIMPLE TERMS Let's put the complete cognitive side of the Movement Loop into simple language. MOVE—to prepare the brain and body. BECOME READY—RAS- in the brain helps us become awake, alert, and available to respond. NOTICE—salience helps something stand out because it is new, emotional, unexpected, rewarding, or potentially threatening. RELEVANCE—relevance helps the brain recognize how that information relates to our goals, needs, interests, or previous experiences. ATTENTION—directs our mental resources or a spotlight toward the selected information. ENCODE—begins forming a memory of what we are focused on. RETRIEVE AND REPEAT—strengthens the memory and makes it easier to access again. RECOVERY and CONSOLIDATION—so the brain and body can restore, and sleep can support memory consolidation. Leading us to-- INTEGRATION—to connect the new learning with what we already know. APPLY—Making the learning available and ready to use when we need it. So, in its expanded form, the Movement Loop looks like this: Movement → RAS-Supports Readiness → Salience and Relevance (what's important to us)→ Focused Attention → Encoding (forms the memory of what we are focused on)→ Retrieval and Repetition (to strengthen the neural pathway) → Recovery and Consolidation (important for the brain and body to be rested and recovered)→ Integration (connect new learning with what we already know)→ NEW Learning and Performance (ready and available for us whenever we need it). Here is an even easier way to remember how we LEARN: Move. Wake or Get Ready. Notice. Focus. Form a Memory. Strengthen it. Recover it. Connect it. Use it. Movement prepares the system. The RAS helps us become alert. Salience and relevance identify possible priorities. Attention selects the target. Encoding begins the memory. Retrieval and repetition strengthen it. Recovery and sleep help stabilize it. Integration connects it. And application makes it useful. These stages do not always occur in a perfectly straight line. We may notice something before we are fully focused on it. We may need several rounds of encoding and retrieval. We may sleep on an idea and understand it differently the following day. Or we may try to apply what we learned and discover that part of the pathway still needs strengthening. That is why this is a loop rather than a one-way process. Application produces feedback. That feedback shows us what we understand, what we can retrieve and use, and what still requires attention, repetition, or recovery. Then that feedback guides the next cycle of learning. We move again. We become ready. We direct our attention toward what matters. And with each cycle, we build learning that becomes more accessible, adaptable, and useful in our performance. PUT THIS INTO ACTION Before ending your next learning or work session, take one minute to ask: “What is the most important thing I want to remember?” Without looking at your notes, write down the central idea in your own words. Then decide when you will retrieve it again—perhaps later that day or the following morning. After that, give yourself permission to recover. When your attention is fading, another exhausted hour may not be as valuable as a break or a full night of sleep. The following day, try to retrieve the idea again before looking at your notes. Notice what remained accessible and what needs another repetition. Your experiment is simple: Summarize it. Sleep on it. Retrieve it again. Because learning begins while we are awake—but recovery and sleep help the brain continue the work. The Move–Focus–Learn Protocol BRINGING THE FIVE ACTIONS TOGETHER Here is the full Move–Focus–Learn experiment: Test this in school, work or personal learning: Use a brief walk or appropriate movement break before demanding cognitive work. This is what I told Dr. Medina 6 years ago is the only way I'm able to stay focused on writing difficult topics, like this one. Identify the one thing that deserves your attention next. Pick one priority you will focus on. Remove interference. Reduce unnecessary notifications, competing tasks and distractions. This can take a few minutes ahead of time, but keeps the learning slate clean so to speak. Create relevance. Ask, “Why does this matter to me—or to this learner?” Interact with the information you want to learn. Explain it, visualize it, discuss it, demonstrate it or apply it. Retrieve it. Close the book or screen and recall the important ideas without looking. Repeat it later. Return to the information after time has passed. Protect recovery. Allow sleep and recovery to support the next stage of learning. REVIEW AND CONCLUSION As we close out Episode 406, I want to return to the question we began with: Once movement activates and prepares the brain, what determines whether that readiness becomes lasting learning? The answer is not one single process. Learning emerges through a sequence of connected conditions: The brain and body must become ready. Something must stand out and feel relevant. Attention must select it. The information must be encoded. It must be retrieved and revisited. And recovery and sleep must provide opportunities for consolidation and integration. Dr. John Medina's Brain Rules helped us follow this sequence through five key lessons. LESSON 1: EXERCISE PREPARES THE BRAIN Movement helps create biological conditions that support alertness, attention, and cognition. The RAS contributes to this process by helping us become awake, alert, and ready to respond. But movement creates an opportunity—not a guarantee. A prepared brain still needs direction. LESSON 2: ATTENTION SELECTS WHAT MATTERS The brain encounters far more information than it can consciously process. Salience helps us notice what stands out. Relevance connects that information to our goals, needs, interests, or previous experiences. Attention then directs the brain's spotlight toward the selected target. Movement may create greater readiness, but attention determines where that readiness goes. LESSON 3: RETRIEVAL AND REPETITION STRENGTHEN WHAT ATTENTION BEGINS Paying attention once may create familiarity, but familiarity is not the same as learning. Encoding begins the memory. Retrieval asks the brain to locate that information again. Repetition gives us additional opportunities to strengthen our access to it. This is why explaining an idea without looking at our notes can be more useful than simply rereading it. Attention begins the pathway. Retrieval and repetition help make it last. LESSON 4: MEANINGFUL INTERACTION HELPS LEARNING STICK We can strengthen encoding by interacting with an idea in more than one meaningful way. We might see it, hear it, explain it, draw it, demonstrate it, or apply it. The goal is not to add as much stimulation as possible. The goal is to give the brain clear and relevant ways to understand, represent, and later retrieve the information. This is something I have experienced while creating this podcast. When I organize an idea into a framework or bring it to life in a visual graphic, I can see how the pieces connect. That helps me explain the idea more clearly, remember it more easily, and apply it more effectively. LESSON 5: RECOVERY ALLOWS LEARNING TO CONTINUE Learning does not necessarily end when we close the book, leave the classroom, finish the meeting, or stop practicing. During sleep, newly encoded information may be reactivated, stabilized, reorganized, and connected with existing knowledge. Deep non-REM sleep and REM sleep may contribute to this process in different but complementary ways. This is why recovery is not separate from learning. Recovery is part of the learning process. PUTTING THE MOVEMENT LOOP TOGETHER The complete cognitive side of the Movement Loop now looks like this: Movement prepares the brain and body. RAS-supported arousal helps us become awake and alert. Salience and relevance help identify possible priorities. Attention selects the target. Encoding begins the memory. Retrieval and repetition strengthen it. Recovery and sleep support consolidation. Integration connects the new learning with what we already know. And application makes that learning available when we need it. Or, in its simplest form: Move. Wake. Notice. Focus. Form. Strengthen. Recover. Connect. Use. Movement creates readiness. Attention provides direction. Encoding begins the memory. Retrieval and repetition strengthen the pathway. Recovery supports consolidation and integration. And application turns what we have learned into improved performance. Together, these processes help turn activity into lasting learning. FINAL THOUGHT This is not the first time we have explored Dr. John Medina's work, but each return has allowed us to ask a different question. In Episode 42[ix], we asked: How should schools and workplaces change when we understand the brain? In Episode 370[x], we asked: How can neuroscience and emotional regulation improve learning environments? In Episode 395[xi], we asked: What captures attention and motivates behavior? And here in Episode 406, we asked: How does an activated and attentive brain convert an experience into learning? That question has helped us add important detail to the Movement Loop. Movement alone does not create learning. Attention alone does not create lasting memory. Repetition without meaning may not produce understanding. And effort without recovery may not give the brain the conditions it needs to consolidate and integrate what happened. Each part of the process matters. Before we close, consider these questions: What are you preparing your brain to notice? Where is your attention going after you move? Are you interacting with important information deeply enough to encode it? Are you retrieving what you are learning—or merely rereading it? And are you protecting the recovery that allows learning to continue? Because what we repeatedly attend to, retrieve, and apply begins to shape what we know, what we can do, and how we perform. PREPARING FOR EPISODE 407 Movement can prepare the brain. Attention can direct it. Encoding can begin the memory. Retrieval and repetition can strengthen it. And recovery can help consolidate and integrate it. But every part of this process requires energy. The brain needs energy to direct attention, form memories, regulate behavior, and sustain performance. The body needs energy to move, adapt, recover, and begin the cycle again. So in Episode 407, we will revisit Jason Wittrock's work through a more current and carefully defined lens. We'll examine his experience with nutrition and fasting as an applied practitioner and personal case study, while comparing those ideas with current evidence surrounding nutrition, blood-sugar stability, metabolic flexibility, recovery, and performance. Because the brain and body cannot sustain performance without the energy required to power the system. I'll see you next time as we ask: How does metabolic health influence our capacity to move, learn, recover, and perform? RESOURCES Clip 1 with Dr. John Medina https://www.youtube.com/shorts/zDItOHAc8qQ Full Interview with Dr. John Medina https://www.youtube.com/watch?v=CFzg5nQnEMs REFERENCE   [i] John Medina's Brain Rule #4 https://brainrules.net/article/attention/   [ii] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 42 with Dr. John Medina on “Implementing Brain Rules in Schools and Workplaces of the Future” https://andreasamadi.podbean.com/e/dr-john-medina-on-implementing-brain-rules-in-the-schools-and-workplaces-of-the-future/   [iii]Neuroscience Meets Social and Emotional Learning Podcast EPISODE 403 https://andreasamadi.podbean.com/e/movement-first-how-a-20%e2%80%91minute-walk-lights-up-the-brain/     [iv]Neuroscience Meets Social and Emotional Learning Podcast EPISODE 404 https://andreasamadi.podbean.com/e/movement-matters-how-every-move-rewires-the-brain/   [v] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 405 https://andreasamadi.podbean.com/e/movement-isnt-enough-how-recovery-drives-real-adaptation/   [vi]Neuroscience Meets Social and Emotional Learning Podcast EPISODE 272 https://andreasamadi.podbean.com/e/brain-fact-friday-on-priming-the-reticular-activating-system-to-achieve-our-goals-in-2023/   [vii] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 395 https://andreasamadi.podbean.com/e/theory-of-mind-the-missing-link-between-attention-reward-and-motivation/   [viii]Neuroscience Meets Social and Emotional Learning Podcast EPISODE 84  https://andreasamadi.podbean.com/e/how-a-spect-scan-can-change-your-life-part-3-with-andrea-samadi/   [ix] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 42 with Dr. John Medina on “Implementing Brain Rules in Schools and Workplaces of the Future” https://andreasamadi.podbean.com/e/dr-john-medina-on-implementing-brain-rules-in-the-schools-and-workplaces-of-the-future/   [x]Neuroscience Meets Social and Emotional Learning Podcast EPISODE 370 https://andreasamadi.podbean.com/e/brain-rules-revisited-how-neuroscience-can-transform-classrooms/   [xi]Neuroscience Meets Social and Emotional Learning Podcast EPISODE 395 https://andreasamadi.podbean.com/e/theory-of-mind-the-missing-link-between-attention-reward-and-motivation/      

Retrieval has become one of the central problems in building useful AI systems. The standard approach to grounding a model in one’s own data has been retrieval augmented generation, or RAG, where an agent searches a vector database for relevant information at query time. That pattern works, but it has limitations, such as retrieving information that’s not truly relevant, repeating the same lookup work on every query, and producing inconsistent answers to the same question. Pinecone is a vector database that’s widely used to power semantic search and RAG at scale. The team recently developed Nexus, which is a knowledge engine that reframes context as a first-class, precomputed asset rather than something reassembled on the fly. The approach borrows the database concept of a materialized view, and curates context once into a versioned artifact that carries its own schema, metadata, permissions, and lineage. Jörg Schad is the VP of Engineering at Pinecone. In this episode, he joins Kevin Ball for an in-depth conversation about the frontier of retrieval technology. They discuss precompiled context, how context artifacts are curated and versioned much like code, how metadata and semantic layers help agents choose the right information, and much more.Sponsorship inquiries:sponsor@softwareengineeringdaily.com The post Moving Beyond RAG with Precomputed Context appeared first on Software Engineering Daily.

Retrieval has become one of the central problems in building useful AI systems. The standard approach to grounding a model in one’s own data has been retrieval augmented generation, or RAG, where an agent searches a vector database for relevant information at query time. That pattern works, but it has limitations, such as retrieving information that’s not truly relevant, repeating the same lookup work on every query, and producing inconsistent answers to the same question. Pinecone is a vector database that’s widely used to power semantic search and RAG at scale. The team recently developed Nexus, which is a knowledge engine that reframes context as a first-class, precomputed asset rather than something reassembled on the fly. The approach borrows the database concept of a materialized view, and curates context once into a versioned artifact that carries its own schema, metadata, permissions, and lineage. Jörg Schad is the VP of Engineering at Pinecone. In this episode, he joins Kevin Ball for an in-depth conversation about the frontier of retrieval technology. They discuss precompiled context, how context artifacts are curated and versioned much like code, how metadata and semantic layers help agents choose the right information, and much more.Sponsorship inquiries:sponsor@softwareengineeringdaily.com The post Moving Beyond RAG with Precomputed Context appeared first on Software Engineering Daily.

Retrieval has become one of the central problems in building useful AI systems. The standard approach to grounding a model in one’s own data has been retrieval augmented generation, or RAG, where an agent searches a vector database for relevant information at query time. That pattern works, but it has limitations, such as retrieving information that’s not truly relevant, repeating the same lookup work on every query, and producing inconsistent answers to the same question. Pinecone is a vector database that’s widely used to power semantic search and RAG at scale. The team recently developed Nexus, which is a knowledge engine that reframes context as a first-class, precomputed asset rather than something reassembled on the fly. The approach borrows the database concept of a materialized view, and curates context once into a versioned artifact that carries its own schema, metadata, permissions, and lineage. Jörg Schad is the VP of Engineering at Pinecone. In this episode, he joins Kevin Ball for an in-depth conversation about the frontier of retrieval technology. They discuss precompiled context, how context artifacts are curated and versioned much like code, how metadata and semantic layers help agents choose the right information, and much more.Sponsorship inquiries:sponsor@softwareengineeringdaily.com The post Moving Beyond RAG with Precomputed Context appeared first on Software Engineering Daily.

Can eggs be made from skin cells? Could a skin biopsy one day replace egg retrieval entirely? In this episode, Dr. Aimee sits down with Dr. Sherman Silber, a world-renowned pioneer in infertility and reproductive biology whose groundbreaking work in IVF, IVM, ovary freezing, and in vitro oogenesis has redefined what's possible in fertility medicine. In this episode, we cover: • What IVM is and how it differs from traditional IVF • The "give every egg a chance" philosophy • How IVM benefits PCOS patients without hormonal stimulation • Making eggs and sperm from skin cells (in vitro oogenesis) • Why a skin biopsy could one day beat egg retrieval • Ovarian transplantation to delay menopause and extend longevity • The regulatory landscape for these breakthrough technologies Resources Watch on YouTube  Read the show notes on Dr. Aimee's website Resources: Dr. Sherman Silber's website: https://www.infertile.com/ Dr. Silber on Instagram: https://www.instagram.com/silberinfertility Join The IVF Class: https://the-egg-whisperer-school.teachable.com/p/the-ivf-class/ Get Dr. Aimee's Fertility Essentials: https://www.draimee.org/fertility-essentials Would you like to learn more about IVF? Click here to join Dr. Aimee for The IVF Class. Join the class, and you'll get to join Dr. Aimee for a live class call where she will explain IVF and there will be time to ask her your questions live on Zoom. Subscribe to my YouTube channel for more fertility tips!Subscribe to the newsletter to get updates Dr. Aimee Eyvazzadeh is one of America's most well known fertility doctors. Her success rate at baby-making is what gives future parents hope when all hope is lost. She pioneered the TUSHY Method and BALLS Method to decrease your time to pregnancy. Learn more about the TUSHY Method and find a wealth of fertility resources at www.draimee.org.

This is a very special episode. Natalie sat down with her identical twin sister, Holly Wheeler, and guided her through a live Love Mapping. Two people who shared a womb, who lived the same childhood in the same house at the same time, one of them now witnessing the other go back through it.We built Liberate.Love partly for this. So much of what we carry was formed inside a family system, and so much of what could restore it lives there too. Holly and Natalie are demonstrating what's possible through their sisterhood.What started as a small trigger on a Monday evening didn't stay small. Holly came home from the studio after a full weekend of family and community, felt an emptiness open up, and reached for tobacco to fill it. That was the thread. Natalie followed it back.It led to a Christmas Eve phone call with her dad, Holly alone in her apartment while he was with her stepbrothers and sisters, sitting with an ache she never let herself feel at the time. It led further back to a DJ booth at a birthday party, where she felt most adored, most powerful, most seen, and completely separate from the dance floor she was playing to. Part of, and apart from.Then Holly saw the structure holding all of it together. The party environment was the closest thing to family she had ever felt. That's why she wanted to take the drugs and stay up all night and never let it end. There was such a scarcity around it. Every reckless way she tried to feel love traced back to the same place.And then she says something that changes the whole shape of it. She feels that same fullness now with her church family, and there is no comedown.What you'll hear on this episode:- What a Love Mapping & Retrieval actually looks like, guided start to finish- Numbing as a form of control, and what it protects- The loneliness that lives inside seeming connection- Why the party, the substances, and the DJ booth were all the same search- Being protected by the wrong person, and what that does to a child's sense of safety- What it means to pause a mapping when the nervous system says enough- The hungry ghost, and what she's actually hungry for- Restoring coherence inside a family system by going firstHolly has spent her life being the one holding the room. On this episode she's the one being held, by the person who was standing next to her for all of it.When we stop chasing the feeling of family and remember how to source it, the belonging we were reaching for outside of us was never actually out there. It was waiting to be restored.I hope you enjoy this episode.EXPERIENCE THIS FOR YOURSELFGet started at https://liberate.love/begin?hollyThe Love Map is a 45-minute guided conversation you can do from the privacy of your own home. Virgil, our AI guide, has been trained on thousands of live sessions, and maps your whole story with you to uncover the underlying geometry underneath your patterns. The superstructure holding your stories in place, repeating in your relationships, in your career, in your body, in every place you have ever wondered "why this keeps happening to me."The Love Retrieval is where the Love Map becomes an experience: a live, gently guided 90-minute session with Raj or Natalie to restore internal safety, trust, and love. Through our signature process, you revisit the moments where parts of your soul fragmented in order to stay safe. In this profound re-meeting, the younger you is met with restorative witnessing, and your nervous system learns it's safe to let go of the old story. Get started at https://liberate.love/begin?holly Hosted on Acast. See acast.com/privacy for more information.

Nathan's guest this episode is Pete Johnson, Field CTO of AI at MongoDB, and the conversation is really two conversations woven together: a history of database architecture, and a status report on the still-unsolved problem of agent memory. Pete opens with a framing device that recurs throughout — he was born in February 1970, four months before E.F. Codd's original relational-model paper that gave rise to SQL. The relational model, he explains, was built for a world where storage was the scarce resource, so normalization — splitting data across linked tables to avoid duplication — was the rational design choice. For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/write-change-recall-forget-mongodb-s-pete-johnson-on-how-retrieval-drives-agent-performance/ Sponsors: Mercury: Mercury is the banking platform loved by 300,000+ entrepreneurs, with virtual cards and Spend controls for granular budgets, receipts, and low-risk AI agent purchases. Learn more and apply in minutes at https://mercury.com Granola: Granola is an AI-powered notepad that securely transcribes meetings and turns rough notes into clean, structured action items. Try it free at https://granola.ai/tcr Diffusion: Diffusion helps organizations build custom AI software factories that scale business outcomes, not just outputs. Cognitive Revolution listeners get a 25% service credit on their first engagement at https://diffusion.io/tcr Deepgram Flux TTS: Deepgram Flux TTS brings lifelike AI voices with real personalities that handle interruptions, pauses, and natural conversation. Try all the voices free through September 12 at https://deepgram.com/keep-talking Claude: Claude is the AI collaborator for problem solvers, helping with writing, coding, financial models, strategy, and more. Get started with Claude and explore Claude Pro at https://claude.ai/tcr CHAPTERS: (00:00) About the Episode (03:15) Sponsor: Mercury (04:56) SQL versus NoSQL (11:24) Enterprise database choices (Part 1) (18:30) Sponsors: Granola | Diffusion (21:27) Enterprise database choices (Part 2) (21:27) Schema flexible search (33:50) Contextualized chunking tradeoffs (Part 1) (35:12) Sponsors: Deepgram Flux TTS | Claude (37:17) Contextualized chunking tradeoffs (Part 2) (46:22) Retrieval quality thresholds (53:17) Agent memory systems (01:05:51) Enterprise AI deployment (01:16:32) Voyage acquisition strategy (01:23:38) Global AI adoption (01:30:56) Episode Outro (01:34:41) Outro PRODUCED BY: https://aipodcast.ing

Send us Fan MailWhat happens when AI models that perform almost perfectly on scattered cervical cells become less reliable than a coin flip on crowded cell groups?In DigiPath Digest #51, I examine what this performance gap tells us about artificial intelligence in cytopathology.The first paper evaluated six convolutional neural network models trained to distinguish benign from high-grade lesions using scattered cervical cytology cells. The models achieved AUCs ranging from 0.950 to 0.996 on the original images.When the same models were applied to hyperchromatic crowded cell groups without retraining or threshold recalibration, their AUCs fell to 0.385-0.683. Different architectures also failed differently. Some overcalled benign clusters, while others missed high-grade clusters.This isn't simply a technical problem. It illustrates a practical rule for pathology AI: a model should only be trusted for the morphology, specimen type, imaging system, and intended use on which it has been directly validated.I also review the current evidence for large language models in cytopathology. Potential applications include structured reporting, diagnostic support, quality control, education, research, and workflow integration.Some early results appear promising, but each comes with important limitations. One structured-reporting application achieved 99.4% accuracy at a single institution. A diagnostic-support model included the correct answer among its top 10 differentials in 59.1% of general medicine cases. A hybrid quality-control system flagged 84% of errors associated with amended reports, but its false-positive rate wasn't reported.Most importantly, the review found no language model specifically trained and clinically validated on cytopathology reports.The takeaway is straightforward: we're still working with narrow AI. Strong performance in one setting doesn't guarantee performance when the cells, preparation, scanner, institution, or clinical task changes.Low-risk applications may offer the most practical starting point. Text extraction, completeness checks, report consistency review, and quality-control flagging could reduce repetitive work without asking an unvalidated model to make the final diagnosis.Highlights with timestamps00:00 - Welcome to DigiPath Digest #51 and the new lunch-and-learn time01:40 - How two image models performed worse than a coin flip on cell clusters02:29 - Image models, language models, and vision-language models04:40 - Why AI adoption in cytopathology remains low05:25 - Scattered single cells versus hyperchromatic crowded cell groups08:14 - AUCs fall from 0.950-0.996 to 0.385-0.68310:02 - How ResNet-50 and GoogLeNet failed differently11:04 - What the attention maps revealed13:22 - The intended-use lesson for pathology AI17:02 - Current applications of large language models in cytopathology18:39 - Structured reporting and the 99.4% accuracy result19:32 - Diagnostic support, AMIE, and the top-10 limitation21:09 - Quality-control applications and the missing false-positive rate23:30 - Why cytopathology still needs domain-specific language models24:54 - Retrieval-augmented generation, education, and research support27:07 - Two AI families, one requirement: direct validation28:35 - Context of use and intended-use validation29:12 - Human-reviewed training data and destructive book scanning32:02 - FDA and European approaches to evolving AI models35:00 - Protecting patient and practitioner well-being36:21 - Cytopathology-specific benchmarks and shared test sets38:16 - Why low-risk AI applications should come first39:50 - Cytopathology's direct-to-digital advantage40:39 - Digital Pathology 101 and Pathology VisionsResources from this episodeWatch DigiPath Digest #51Diagnostic performance of AI models trained on scattered single-cell imagesLeveraging large language models to enhance cytopathologyFDA guidance on AI and context of useFDA guidance on predetermined change control plansGoogle Research: AMIE diagnostic medical AIEU In Vitro Diagnostic Medical Devices RegulationEU Artificial Intelligence ActDigital Pathology 101Pathology Visions 2026 Support the showGet the "Digital Pathology 101" FREE E-book and join us!

It's 0300 and you encounter a rapidly expanding zone 1 retroperitoneal haematoma. Time to enter “tiger country”. What can you do to make this a more familiar expedition?• Hosts: Bulleted list of host names, including title, institution, & social media handles if indicated1.     Mr Prashanth Ramaraj. ST5 General Surgery, George Hospital, Western Cape / Southeast Scotland Deanery. @LonTraumaSchool2.     Dr Roisin Kelly. Medical Officer, Emergency Medicine, Sydney, Australia. 3.     Mr Max Marsden. Consultant Trauma and UGI Surgeon, Royal London Hospital. @maxmarsden834.     Mr Christopher Johnston. Consultant Liver Transplant Surgeon and Transplant Surgery Fellowship Lead, Edinburgh Transplant Unit. @cjcjohnston• Learning objectives: Bulleted list of learning objectives.A)    To understand the rationale for thoracic aortic clamping in resuscitative trauma surgery and how this is performed.B)     To understand how to access the retroperitoneal structures by means of medial visceral rotation and how this is performed.C)     To understand the haemostatic ladder for liver trauma and considerations in hepatic exclusion.Please visit https://behindtheknife.org to access other high-yield surgical education podcasts, videos and more.  If you liked this episode, check out our recent episodes here: https://behindtheknife.org/listenBehind the Knife Premium: https://behindtheknife.org/premiumOral Board Review: https://behindtheknife.org/oral-boardOral Board Simulator: https://behindtheknife.org/oral-board/simulatorGeneral Surgery Oral Board Review Course: https://behindtheknife.org/premium/general-surgery-oral-board-reviewTrauma Surgery Video Atlas: https://behindtheknife.org/premium/trauma-surgery-video-atlasDominate Surgery: A High-Yield Guide to Your Surgery Clerkship: https://behindtheknife.org/premium/dominate-surgery-a-high-yield-guide-to-your-surgery-clerkshipDominate Surgery for APPs: A High-Yield Guide to Your Surgery Rotation: https://behindtheknife.org/premium/dominate-surgery-for-apps-a-high-yield-guide-to-your-surgery-rotationVascular Surgery Oral Board Review Course: https://behindtheknife.org/premium/vascular-surgery-oral-board-reviewColorectal Surgery Oral Board Review Course: https://behindtheknife.org/premium/colorectal-surgery-oral-board-reviewSurgical Oncology Oral Board Review Course: https://behindtheknife.org/premium/surgical-oncology-oral-board-reviewCardiothoracic Oral Board Review Course: https://behindtheknife.org/premium/cardiothoracic-surgery-oral-board-reviewOBGYN Oral Board Review Coures: https://behindtheknife.org/course/obgyn-oral-board-reviewEPA Playbook: https://behindtheknife.org/course/epa-playbookSurgical Instrument Flashcards: https://behindtheknife.org/course/surgical-instrument-flashcardsABSITE Review: https://behindtheknife.org/course/absite-2026-exam-reviewDownload our App:Apple App Store: https://apps.apple.com/us/app/behind-the-knife/id1672420049Android/Google Play: https://play.google.com/store/apps/details?id=com.btk.app&hl=en_US

For Southeast Asian CIOs, the window to merely experimenting with AI is fast closing. The imperative now is to industrialise intelligence, moving from proof-of-concept to production-scale agentic systems that deliver tangible business value. Yet, as the "token economy" dictates, success hinges on a foundational element: your data platform. The key to controlling costs and ensuring trusted, real-time outcomes lies not in the fragmented data stack that exists in many organisations today, but in a unified architecture that makes retrieval accuracy a competitive advantage. This demands a new strategic focus. In this PodChats for FutureCIO, Thorsten Walther, Managing Director, CXO Advisory Asia, MongoDB, shares the most prevalent issues facing CIOs and their enterprises in navigating their AI journey while managing the economics of AI integration and use.As we move from AI experimentation to production, how can we rationalise a fragmented data and retrieval stack to reduce latency and governance risk, particularly given the need to manage highly dynamic, unstructured data?Given that the "token economy" makes retrieval accuracy a direct cost-control lever, how can we implement a unified data platform to improve retrieval quality and reduce expensive LLM retry loops?How do we ensure our data architecture provides the schema flexibility needed for rapid AI iteration, avoiding the rigidity of relational models that slows development and creates technical debt?With the rise of agentic AI, how can we architect a system for "agent memory"—blending short and long-term context—that allows agents to act on the current state of data, not stale copies?How can we improve retrieval accuracy by natively combining semantic understanding with precise keyword search, while also using reranking to refine results, without adding external systems that create sync delays?For regulated enterprises, how can we bring these production-grade AI retrieval capabilities inside our compliance framework, avoiding the choice between innovation and data sovereignty?What is our strategy to move beyond the complexity of managing separate databases, search engines, and vector stores to a single, unified platform that reduces operational overhead?How do we build a flexible, "production-ready" data foundation that allows us to pivot quickly as model providers and agent frameworks evolve, without being locked into a rigid stack?How can we best equip our developer and agentic workflows with the necessary skills and best practices to avoid common pitfalls like over-normalisation, ensuring agents build on a robust data model?With a significant focus on the ASEAN market, how can we leverage local partnerships and expertise to accelerate our AI modernisation journey and address specific regional regulatory and data challenges?

Can organizations trust an AI recommendation when they cannot understand the evidence, relationships, and previous decisions behind it? In this episode of Tech Talks Daily, I welcome back Jim Webber, Chief Scientist at Neo4j, to discuss the company's acquisition of GraphAware and its move from graph database provider to graph intelligence platform. GraphAware has worked with Neo4j for many years and developed Hume, an intelligence analysis platform used to connect and examine complex information. Bringing the two companies together gives Neo4j a direct role in applications serving police forces, governments, intelligence agencies, and other organizations handling connected data. Jim explains why context has become one of the biggest requirements for dependable AI. Enterprises already possess enormous volumes of data, but facts alone provide endpoints rather than the complete path leading to a decision. An agent needs to understand the knowledge available, the conversation taking place, and the record of previous decisions. It also needs to know which actions produced good outcomes and which produced poor ones. Jim compares these information layers to SimCity. Each can be viewed separately, but their greater value appears when they are combined. Knowledge, conversations, and decision traces can then help an agent understand why something happened and learn from the result. This introduces an interesting lesson from scientific research. Positive outcomes are frequently published, while failed experiments receive less attention. An AI agent needs both. Recording the breadcrumbs behind good and bad decisions provides the material required to improve its future behavior. We also discuss why large language models cannot understand every organization by themselves. Jim describes a model as a lossy compression of the internet. It can generate impressive natural language, but it does not automatically understand a company's policies, customers, history, evidence, or operating environment. Retrieval augmented generation can introduce relevant organizational information into the process. Graph RAG adds relationships between facts, helping the system understand how people, events, products, accounts, and other entities connect. According to research Jim references from the National Innovation Centre for Data, Graph RAG can improve accuracy while reducing costs by using fewer, higher-quality tokens. Explainability becomes especially important when AI supports decisions across policing, cyber defense, taxation, intelligence, banking, and government. A fluent answer may sound authoritative while containing a serious technical mistake. Jim shares an example from his own work where an agent confidently warned him about a "committed minority" inside a fault-tolerant computing protocol. The statement sounded plausible, but only a majority could commit within that protocol. Someone without Jim's technical knowledge might have accepted the recommendation and removed working code. This leads us to human oversight. Jim argues that the correct level depends on the consequences of the action. Automating a routine banking process with monitoring and safeguards may improve the customer experience. Ordering someone's arrest based solely on an agent's conclusion demands human involvement. We also consider digital sovereignty and why control over data has become a strategic concern for governments and large enterprises. Geopolitical instability, overseas technology dependencies, privacy requirements, and changing national policies are forcing leaders to ask where their data resides and whether they can retrieve or move it. Jim explains how Neo4j intends to offer organizations flexibility over where their information is stored and how it is deployed. The discussion also examines the opportunity for Neo4j and Hume to provide an alternative within a market where Palantir has held a powerful position. Looking ahead, Jim imagines intelligence analysts directing swarms of digital agents. Those agents could search data, connect evidence, identify relevant patterns, and present findings while humans retain responsibility for consequential decisions. If AI can connect information at machine speed, how do we ensure the person making the final decision can inspect the evidence and challenge the conclusion? Listen to the episode and share your thoughts with me.

The video features a Love Retrieval where the Raj, revisits a difficult childhood memory to gain healing and insight. The session progresses through the following stages:Recalling the Memory: Raj describes moving to Southeast Texas shortly after 9/11. As a brown-skinned teenager, he faced intense bullying and racism. He recounts a specific, humiliating incident in a seventh-grade gym locker room where an eighth-grader physically harassed him, mocked his body, and used a slur.Initial Internalization: Raj explains that he did not feel anger toward his bully at the time; instead, he internalized the experience, feeling disgust, shame, and self-rejection. He reveals that this event drove him to "torture" his body through intense athletic training for years, using physical fitness as a protective mechanism to ensure he would never be a target again.Guided Healing and Reparenting: The guide facilitates a process where Raj connects with his 11-year-old self. Raj offers comfort to his younger self, promising that he will not abandon or reject him and that he is worthy of love regardless of his appearance or performance.Integration of the Divine Father: The guide introduces the imagery of a protective "divine father" figure into the memory to provide the safety Raj lacked at the time. This addition provides Raj with a profound sense of protection and validation, helping him realize that he did nothing wrong.Reflection and Realization: Following the session, Raj expresses feelings of tenderness and profound gratitude. He concludes that he is his own healer and that the core of his past fears and triggers often stems from an inner child who simply needs to feel safe and held.EXPERIENCE THIS FOR YOURSELFA Love Retrieval is a gentle, guided 90-minute experience of restoring internal safety, trust, and love in the presence of Raj or Natalie. Through our signature process, we revisit moments from your childhood where you didn't receive the care you needed, let go of the past, and write a more beautiful story in its place.Get started at https://liberate.love/retrieval?pc Hosted on Acast. See acast.com/privacy for more information.

Have you ever forgotten to text someone you deeply love, missed an appointment that genuinely mattered, or lost the important object you deliberately placed somewhere “safe”? It is often described online as an ADHD or AuDHD “object permanence” problem—but that is not quite what is happening.In this episode of Authentically ADHD with Carmen, Carmen breaks down the real neuroscience behind why AuDHD brains lose track of people, plans, tasks, and everyday objects. She explores context-dependent recall, prospective memory, attention, memory encoding, and working-memory interference—and explains why information can still exist in your brain without becoming accessible at the moment you need it.You will learn why walking into another room can erase your original mission, why future plans can disappear once the conversation ends, and why forgetting to contact someone is not automatically evidence that you stopped caring. Carmen also explores the important difference between having a neurological explanation and taking accountability when forgetfulness affects other people.With validating insights, practical AuDHD strategies, and the appropriate amount of dark humor, this episode offers realistic tools for externalizing memory, creating stronger retrieval cues, connecting tasks to existing routines, designing visible homes for important objects, and supporting relationships without relying entirely on spontaneous recall.Because memory is not a moral system. Your brain did not forget because nothing mattered—the present moment simply became louder than the thing you wanted to remember.Thank you for tuning in! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit carmenauthenticallyadhd.substack.com/subscribe

After sharing my live Love Retrieval in Episode One, I wanted to come back and talk about what actually happened in that experience and, more importantly, what has changed since.I recorded the original session in May. It's now August, and the emotional charge and pattern we uncovered that day no longer lives in my body in the same way. That's why I recorded this integration episode: to help you understand what you witnessed, how our present-day patterns are often connected to much older stories, and what can happen when those stories are finally met with safety, love, and presence.In this episode, I share:Why the thing we're fighting about is often not really the thing we're fighting aboutHow present-day triggers can lead us back to the emotional roots of our patternsWhat it means to follow an emotion through the body instead of trying to think our way out of itThe role of safe, loving human presence in helping an old experience finally completeWhat reparenting, the inner mother, and the inner father actually look like in practiceWhat has shifted for me in the months since my Love RetrievalWhy this podcast exists: to show you what this work actually looks like, through real humans, real stories, and real experiencesMore than anything, this episode is about hope: the past you had does not have to dictate the future you experience.EXPERIENCE THIS FOR YOURSELFGet started at https://liberate.love/begin?rajThe Love Map is a 45-minute guided conversation you can do from the privacy of your own home. Virgil, our AI guide, walks your whole story and traces your patterns back to the moments they were written.The Love Retrieval is where the Love Map becomes an experience: a live, gently guided 90-minute session with Raj or Natalie personally, returning to the moment a pattern was born, so the younger you can finally be met with the love that was missing.Get started at https://liberate.love/begin?raj Hosted on Acast. See acast.com/privacy for more information.

Training is not successful because we covered the material.It is successful when people remember what they learned, make sense of it, and actually do something differently afterward.Katrina Kennedy returns to Lessons from Learning Leaders for a conversation about what trainers can do inside the learning experience to make that more likely.Katrina describes herself as an accidental trainer. She entered learning and development because a manager noticed she could communicate, then built a career spanning nearly three decades helping subject matter experts, facilitators, and trainers design and deliver better learning.A major focus of our conversation is reflection, but Katrina makes an important distinction between reflection, retrieval, and the traditional review that trainers often use.A review usually means the trainer repeats the important points.Retrieval makes the learner do the work of pulling the information back out of memory.Reflection then asks the learner to move forward with it.What does this mean for me?What am I going to do?How will I apply this when I return to work?That distinction led us into a conversation about one of my favorite Bob Pike activities, Give One, Get One. Participants retrieve the most important ideas from the session, get up and share them with other people, add useful ideas they hear, and then return to their seats.After talking with Katrina, I realized the activity needed one more step: ask participants to choose one of those ideas and identify exactly how they are going to use it.Now the activity combines retrieval, reflection, movement, social learning, and application.Katrina also shares some of the thinking behind her upcoming ATD Core 4 session, “Your Training Is Missing This and You Don't Even Know It.”She has created a 16-card diagnostic that helps trainers examine whether important elements of effective learning are missing from their sessions. Some of the questions are deceptively simple:Do participants move?Do they have opportunities to reflect?Are they given breaks from cognitive load?Are they connecting with one another?The point is not to add activities simply to make training more entertaining. Each element serves a learning purpose.Movement can help reset attention. Reflection gives people time to process what they have learned. Retrieval strengthens memory. Connection allows participants to learn from the experience already present in the room.That last point led us somewhere I think trainers sometimes overlook.When participants interact with each other during training, we may be creating value that extends beyond the course itself.Someone discovers that a colleague has expertise they did not know about. A relationship develops. Months later, when a problem appears on the job, that participant now knows someone they can call.Training can strengthen the informal network inside an organization while it develops individual capability.We also talk about doing this virtually. Connection does not disappear simply because the training happens through a screen. Chat, breakout rooms, questions, introductions, and opportunities for participants to contribute can all make a webinar feel more like a learning experience and less like watching someone talk through slides.Underlying the entire conversation is a larger challenge for our profession.Too much training is still judged by whether people attended, completed the course, or enjoyed the experience. Those measures may be useful, but they do not tell us whether people changed their behavior or whether organizational performance improved.If learning and development wants to be viewed as an investment rather than an expense, trainers have to think beyond delivery.We need to create the conditions that help learning survive after participants leave the room.That means giving people time to retrieve, reflect, connect, practice, and decide what they are going to do next.Katrina's Book: Learning That LastsKatrina is also the author of Learning That Lasts: Reflection Activities for Trainers and Designers, a practical collection of more than 45 reflection activities designed to help trainers intentionally build reflection into learning before, during, and after the session. Katrina organizes the activities around eight outcomes, including stronger motivation, connection, memory, critical thinking, and improved performance.Get Learning That Lasts on AmazonConnect with Katrina KennedyYou can learn more about Katrina's work, workshops, resources, and newsletter at her website.Visit KatrinaKennedy.comYou can also connect with Katrina on LinkedIn.Connect with Katrina Kennedy on LinkedInBring This Conversation to Your OrganizationIf this episode has you thinking about what happens between delivering information and actually changing performance, that is a conversation worth having with your training team.Better learning does not necessarily require more content. Sometimes it means creating more opportunities for people to retrieve what they know, reflect on what it means, learn from one another, and leave with a clear idea of what they will do differently.If you would like help creating learning experiences that produce more participation, stronger transfer, and better performance, I'd be glad to talk.Learn more at DuaneLester.com.Share this episode with a trainer, facilitator, or learning leader who wants to create learning that lasts, and subscribe to Lessons from Learning Leaders so you don't miss the next conversation. Get full access to Lessons from Learning Leaders at lessonsfromlearningleaders.substack.com/subscribe

Philip Clark and the world of trauma and emergency medicine, latest research and science with Professor Brian Burns, trauma specialist at Royal North Shore Hospital in Sydney. 

In this Somewhere in the Skies Classics episode, Filmmaker, James Fox discusses the years of investigation behind the documentary, The Program, which explores the bipartisan Congressional effort to uncover the truth about UFOs, alleged crash retrieval programs, and claims of non-human biologics. As the UAP conversation continues to evolve, this episode provides essential context behind one of the most talked-about documentaries on the subject. Watch The Program HERE Join us at ANOMACON on September 12th: http://www.anomacon.com Send us a voicemail: https://www.speakpipe.com/SomewhereSkiesPod Patreon: http://www.patreon.com/somewhereskies ByMeACoffee: http://www.buymeacoffee.com/UFxzyzHOaQ Substack: https://ryansprague.substack.com/ All socials and books: https://linktr.ee/somewhereskiespod Email: ryan.sprague51@gmail.com Opening theme song by Septembryo Closing song by Per Kiilstofte Copyright © 2026 Ryan Sprague. All rights reserved. #UAP #UFOs #JamesFox #TheProgram #UFOcases #Documentary #Documentaries Learn more about your ad choices. Visit megaphone.fm/adchoices

AI isn't just transforming the way we work, but also the way we write the software that people use for work. In this episode, we talk to two engineering productivity leads at Dropbox: Uma Namasivayam, senior director of software engineering productivity, and Anuradha Agarwal, director of software engineering. Whether it's writing tests, fixing bugs, tackling tech debt, or accelerating migrations, they explain how Dropbox engineers are using agentic AI—including in-house tools like Nova—to build the future of Dropbox, and create more space to do impactful work. ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck.  Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

Dr.. Eric Davis reveals the truth behind the 1947 Roswell UFO crash and the alien biologics discovered on-site. As White House UAP files hit the public domain, hear the astrophysicist and AAWSAP/AATIP science adviser who briefed Defense Department agencies on off-world vehicle retrievals. This Q&A covers government cover-ups, declassified evidence, and what officials knew about extraterrestrial contact.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-good-trouble-show-unidentified-flying-objects-ufo-disclosure--5808897/support.JOIN US:  https://www.thegoodtroubleshow.com

Men with non-obstructive azoospermia may undergo microscopic testicular sperm extraction, or mTESE, without knowing whether sperm will be found. Kun Tan, Ph.D., of UC San Diego explains how molecular semen analysis could provide a noninvasive way to predict the procedure's outcome. The assay uses stage-specific germ cell markers identified through single-cell RNA sequencing, then measures those markers in semen with qPCR. Because semen contains germ cells from different stages of sperm development, their molecular signals may reflect what is happening inside the testes. Tan shows how the assay predicts whether sperm retrieval was successful and can identify where sperm development may be blocked. The research could help couples planning IVF or ICSI avoid unnecessary surgery, reduce financial and emotional burdens, and receive more detailed information about male infertility before treatment. Series: "Motherhood Channel" [Health and Medicine] [Show ID: 41630]

Men with non-obstructive azoospermia may undergo microscopic testicular sperm extraction, or mTESE, without knowing whether sperm will be found. Kun Tan, Ph.D., of UC San Diego explains how molecular semen analysis could provide a noninvasive way to predict the procedure's outcome. The assay uses stage-specific germ cell markers identified through single-cell RNA sequencing, then measures those markers in semen with qPCR. Because semen contains germ cells from different stages of sperm development, their molecular signals may reflect what is happening inside the testes. Tan shows how the assay predicts whether sperm retrieval was successful and can identify where sperm development may be blocked. The research could help couples planning IVF or ICSI avoid unnecessary surgery, reduce financial and emotional burdens, and receive more detailed information about male infertility before treatment. Series: "Motherhood Channel" [Health and Medicine] [Show ID: 41630]

Men with non-obstructive azoospermia may undergo microscopic testicular sperm extraction, or mTESE, without knowing whether sperm will be found. Kun Tan, Ph.D., of UC San Diego explains how molecular semen analysis could provide a noninvasive way to predict the procedure's outcome. The assay uses stage-specific germ cell markers identified through single-cell RNA sequencing, then measures those markers in semen with qPCR. Because semen contains germ cells from different stages of sperm development, their molecular signals may reflect what is happening inside the testes. Tan shows how the assay predicts whether sperm retrieval was successful and can identify where sperm development may be blocked. The research could help couples planning IVF or ICSI avoid unnecessary surgery, reduce financial and emotional burdens, and receive more detailed information about male infertility before treatment. Series: "Motherhood Channel" [Health and Medicine] [Show ID: 41630]

Men with non-obstructive azoospermia may undergo microscopic testicular sperm extraction, or mTESE, without knowing whether sperm will be found. Kun Tan, Ph.D., of UC San Diego explains how molecular semen analysis could provide a noninvasive way to predict the procedure's outcome. The assay uses stage-specific germ cell markers identified through single-cell RNA sequencing, then measures those markers in semen with qPCR. Because semen contains germ cells from different stages of sperm development, their molecular signals may reflect what is happening inside the testes. Tan shows how the assay predicts whether sperm retrieval was successful and can identify where sperm development may be blocked. The research could help couples planning IVF or ICSI avoid unnecessary surgery, reduce financial and emotional burdens, and receive more detailed information about male infertility before treatment. Series: "Motherhood Channel" [Health and Medicine] [Show ID: 41630]

Men with non-obstructive azoospermia may undergo microscopic testicular sperm extraction, or mTESE, without knowing whether sperm will be found. Kun Tan, Ph.D., of UC San Diego explains how molecular semen analysis could provide a noninvasive way to predict the procedure's outcome. The assay uses stage-specific germ cell markers identified through single-cell RNA sequencing, then measures those markers in semen with qPCR. Because semen contains germ cells from different stages of sperm development, their molecular signals may reflect what is happening inside the testes. Tan shows how the assay predicts whether sperm retrieval was successful and can identify where sperm development may be blocked. The research could help couples planning IVF or ICSI avoid unnecessary surgery, reduce financial and emotional burdens, and receive more detailed information about male infertility before treatment. Series: "Motherhood Channel" [Health and Medicine] [Show ID: 41630]

Men with non-obstructive azoospermia may undergo microscopic testicular sperm extraction, or mTESE, without knowing whether sperm will be found. Kun Tan, Ph.D., of UC San Diego explains how molecular semen analysis could provide a noninvasive way to predict the procedure's outcome. The assay uses stage-specific germ cell markers identified through single-cell RNA sequencing, then measures those markers in semen with qPCR. Because semen contains germ cells from different stages of sperm development, their molecular signals may reflect what is happening inside the testes. Tan shows how the assay predicts whether sperm retrieval was successful and can identify where sperm development may be blocked. The research could help couples planning IVF or ICSI avoid unnecessary surgery, reduce financial and emotional burdens, and receive more detailed information about male infertility before treatment. Series: "Motherhood Channel" [Health and Medicine] [Show ID: 41630]

AI makes it easier than ever to find and act on information—especially now that teams can connect to and search across all the apps they use for work. So how do you ensure that only the right people and the right tools can access your team's most sensitive content? In this episode, we talk with Jess Jimenez, the head of security at Dropbox, about what security looks like in the age of AI at Dropbox-scale—from building AI products securely to building trust with the people who use them. Jess talks about the importance of access control lists, defending against the latest AI threats, and how Dropbox Protect helps teams securely share content with both humans and AI so they can collaborate more safely. ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck.  Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

Will The Bad Batch get their home back?! In this episode of Dark Side Divas we discuss the Star Wars - The Bad Batch episode "Retrieval" (s2e10). In a episode that is often labeled as a filler episode, Stef and Chris explain why it is not, and why this strange mad max like story is very important to the overall meta plot. Meanwhile we pop off about George Lucas and his opinion on AI. Listen to hear what the divas have to say!

Dr. Jaclyn Ford is a Research Assistant Professor in the Cognitive and Affective Neuroscience Laboratory (https://Bclearningmemory.com) in the Department of Psychology and Neuroscience at Boston College. Her research examines the effects of emotion and social relevance on memory retrieval processes, focusing on how individual differences in retrieval goals and context may modulate these effects. She utilizes behavioral and neuroimaging methods to characterize these changes in an attempt to better support memory retrieval in individuals with memory impairments.   If you would like to suggest a topic for us to cover on the podcast, please send an e-mail to DownSyndromeCenter@chp.edu. If you would like to partner with the Down Syndrome Center, including this podcast, please visit https://givetochildrens.org/downsyndromecenter. We are thankful for the generous donation from Caring for Kids – The Carrie Martin Fund that provides the funding for the podcast recording equipment and hosting costs for this podcast.

Vice President Vance was on Joe Rogan show recently and talked about various topics but the topic of UFOS came up. his answers were interesting. Kristian Harloff gives his thoughts. #ufo #uap #ufos #uaps #news  

SummaryIn this episode of the Trainer's Bullpen, host Dan Fraser chats with learning researcher, practitioner expert and author of “Powerful Teaching” Patrice Bain. Patrice shares evidence-based learning strategies rooted in cognitive science, including retrieval practice, spacing, interleaving, and feedback-driven metacognition. These powerful tools can transform teaching and learning, making knowledge more accessible and durable. The interview contains not only the scientific basis of how these tools greatly enhance long term learning, but also practical tips and examples of application in the learning environment. Stay around after the interview as Dan and Chris discuss how they use these principles in the Method of Instruction Course and give advice on how trainers and coaches can easily apply these principles to accelerate learning and retention in their students.Key topics• Retrieval practice as a core learning tool• Spacing and interleaving to enhance retention• Metacognition and feedback for self-assessment• Priming and pretests to set the stage for learning• Teaching students how to learn and take ownership• Why forgetting is essential for learning• The importance of errors and how they shape the conditions for rich learning• How the coach must ‘choreograph' the learning• Practical application in classroom and practical skills environments• Law enforcement specific applicationResources:Powerful Teaching by Patrice Bain - https://www.amazon.com/s?k=Powerful+Teaching+Patrice+Bainretrievalpractice.org - https://retrievalpractice.org/patricebain.com - https://patricebain.comA Parent's Guide to Powerful Teaching - https://www.amazon.com/s?k=A+Parent%27s+Guide+to+Powerful+TeachingMake It Stick by Brown, Roediger, McDaniel - https://www.amazon.com/s?k=Make+It+Stick+Brown+Roediger+McDaniel

Why are companies investing heavily in AI, analytics, and data platforms while business leaders still struggle to see what is happening across their operations quickly enough to make confident decisions? In this episode of Tech Talks Daily, I speak with Massimo Merlo, Vice President for UK, Iberia, and Italy at Elastic, about why the next stage of enterprise AI adoption will depend less on who deploys the most advanced models and more on which companies can give people and AI systems access to relevant, trusted, and secure information when decisions need to be made. Massimo describes the problem as a lack of decision-grade visibility. Most large companies are not short of data. They have spent decades building data platforms, analytics systems, dashboards, cloud infrastructure, and reporting tools. Yet information remains fragmented across departments and applications, insights arrive too late, and employees often struggle to find the small amount of information that matters among enormous volumes of data. The result is a growing gap between having information and being able to act on it. Massimo explains why simply adding an AI model to this environment does not solve the underlying problem. If an AI system is connected to fragmented, outdated, poorly governed, or irrelevant information, it can produce convincing answers without providing reliable business outcomes. The quality of an AI model matters, but the context available to that model increasingly determines whether AI becomes a useful business asset or an operational liability. This leads to one of the biggest technology conversations emerging around enterprise AI: context engineering. Massimo explains how context engineering provides AI systems with the relevant data, tools, permissions, organizational knowledge, and guardrails required to complete a task safely. Rather than sending ever-larger volumes of information to AI models, companies need infrastructure capable of retrieving the right information and making it available at the moment a person or software agent needs to act. Fraud detection provides a practical example. An AI agent evaluating a transaction needs more than access to a powerful model. It requires customer history, behavioral patterns, company risk thresholds, permissions, compliance requirements, and the ability to recognize activity that falls outside normal behavior. Without that context, the system could block legitimate customers or approve fraudulent transactions while presenting its decision with complete confidence. We also discuss why digitally mature companies can still struggle with real-time decision-making. Massimo shares lessons from Elastic's work with organizations including Reed, the Met Office, and Rightmove, explaining why having sophisticated technology systems does not automatically make a company context mature. Information can still remain trapped between applications, teams, and databases, preventing employees and AI agents from seeing the complete picture when it matters. The conversation challenges another long-standing enterprise technology habit: adding more dashboards. Massimo explains why dashboards often provide visibility into what has already happened without helping people decide what to do next. Companies can continue adding reporting layers while employees become overwhelmed by information and remain unable to identify the actions that will improve customer experience, productivity, security, or business performance. A healthcare example demonstrates what becomes possible when companies solve this problem. Massimo shares how CogStack at King's College Hospital brought together unstructured patient information during the COVID-19 pandemic and made it searchable using natural language processing. Clinicians could find relevant information without waiting for technical teams to build new queries or systems, helping medical professionals access information when patient decisions needed to be made. For CEOs, CIOs, CTOs, data leaders, and technology teams trying to improve AI ROI, Massimo offers practical advice on where to begin. Do not start with another model, tool, or dashboard. Start with a business decision or workflow that is currently too slow, unreliable, or difficult to execute. Identify what information that decision requires, where the data is stored, who or what system needs access to it, which permissions should apply, and where information currently becomes delayed or disconnected. That process can reveal the visibility gaps preventing companies from turning their existing data and AI investments into measurable results. We also examine why search and retrieval are becoming infrastructure concerns for companies introducing AI agents. As software agents begin making recommendations and taking actions across business systems, their performance will depend on whether they can securely retrieve relevant information at scale. For business and technology leaders facing pressure to demonstrate returns from AI investment, this conversation provides a practical framework for improving enterprise search, context engineering, AI agent reliability, real-time operational visibility, and decision-making. The companies that gain the greatest value from AI may not be those collecting the most data or deploying the most models. They will be the companies capable of finding what matters, understanding its context, and getting trusted information to people and AI systems quickly enough to act on it. That is where better visibility can become better decisions, stronger productivity, and business growth.

Not all work happens in writing. Teams that work with photos, videos, and audio need AI that works for them too. This is why, with Dropbox, you can search within multimedia content for key moments and important information—not just text. In this episode, we talk with Appu Shaji and Hicham Badri, two Dropbox machine learning engineers who are part of the team that makes all of this possible. They explain how multimodal search works—from understanding the context of the initial query, to identifying objects and actions in complex scenes—and how they ensure those models work fast, even at Dropbox-scale. ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck.  Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

Separated instrument retrieval remains one of endodontics' most technically demanding—and humbling—procedures. In this episode of Endo Voices, Dr. Marcus Johnson joins internationally recognized clinician, researcher, and educator Dr. Yoshi Terauchi for a highly technical yet practical exploration of the science and strategy behind predictable instrument retrieval. From cyclic and torsional fatigue to canal curvature, fragment location, and CBCT assessment, Dr. Terauchi explains why successful retrieval begins with diagnosis, visualization, and disciplined treatment planning.Dr. Terauchi shares the principles behind his systematic approach to retrieval, including strategic dentin removal, ultrasonic technique, bypassing, and the clinical application of the Yoshi Loop. The conversation highlights the delicate balance between persistence and preservation: when does continued retrieval become more damaging than the fragment itself? Along the way, Drs. Johnson and Terauchi discuss secondary fracture, apical displacement, pericervical dentin preservation, tactile sensation, and those difficult cases in which the separated instrument lies beyond the curvature.Episodes of Endo Voices may include opinion, speculation and other statements not verifiable in the scientific method and do not necessarily reflect the views of AAE or the sponsor(s). Listeners should use their best judgment in evaluating the merits of any content.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Ross Coulthart said he has heard that Lue Elizondo had more to do with the UFO legacy retrieval program than he has led on to. Lue responded. If protection is provided for whistleblowers, will people like Lue step forward with what they truly know? Kristian Harloff gives his thoughts.

Qdrant Roundtable episode: The Current State of Agentic RetrievalJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguideBig shout-out to Qdrant for the collaboration!// AbstractAI agents are only as good as the information they can find, retrieve, and remember. In this community roundtable with the Qdrant team, we explored the latest advances in agentic memory, vector search, retrieval systems, and production AI architectures.As AI agents move beyond simple chatbots into systems that can reason across large amounts of information, retrieval is becoming one of the most important layers in the AI stack. The discussion covered the real-world challenges of building agents that remember what matters, forget what doesn't, and consistently retrieve the right context at the right time.If you're building AI agents, RAG systems, or production AI applications, this conversation offers practical insights into where retrieval is headed and what it takes to build reliable, scalable agentic systems.// BioEwa SzyszkaEwa is a Developer Relations professional based in San Francisco with a background in Computer Science and Hardware Engineering, passionate about bridging the gap between technology and the developer community. She holds a BSc in Computer Science and an MSc in Electronics, bringing a strong blend of deep technical foundations and communication skills to her work.Dylan CouzonDylan is based in New York City, and he helps developers build better AI applications. He is passionate about AI, programming, open source, and robotics, and enjoys sharing what he's building and learning along the way.Neil KanungoNeil is an experienced professional with expertise in data science, developer relations, and product growth. Currently serving as the Head of Developer Relations at Qdrant, Neil previously held the position of VP of Product Led Growth & Developer Relations at KX, where significant increases in product registration and user activation were achieved. At TIBCO, Neil managed a team focused on enhancing the adoption of TIBCO Spotfire through various initiatives, including tutorial videos and live webinars. With a strong technical background, Neil has developed innovative solutions in analytics, machine learning, and data visualization across multiple roles, including Engineering Data Analyst and Asset Integrity Engineer at Enterprise Products. Neil holds a Bachelor of Science in Radiation Physics from The University of Texas at Austin, a Master of Science in Mechanical Engineering from Texas Tech University, and is pursuing a Master in Applied Data Science from the University of Michigan.Evgeniya SukhodolskayaDeveloper Relations at Qdrant with 8 years of IT experience across software engineering, machine learning, and technical management, and 4 years in Developer Relations. Holds a Master's in Machine Learning, Data Analytics, and Data Engineering. Passionate about NLP, data-centric AI, and the role of vector search in advancing AI technologies.Andrei CristeaAndrei is a Berlin-based Developer Relations Engineer at Qdrant, a prominent open-source vector database. With a Master's degree in Artificial Intelligence from TU Munich, his expertise bridges AI, data infrastructure, and knowledge engineering.Hosted by Demetrios// Related LinksWebsite: https://qdrant.tech/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]

When AI is at its best, the conversations can feel uncanny—almost magical in their accuracy, relevance, and speed. For that you can thank the AI agents that work together behind the scenes to search, reason, and sift through all your content to get you what you need to do your job. We talk with Jongmin Baek and Marta Mendez, two Dropbox machine learning engineers, about building conversational AI that's helpful, useful, and grounded in your team's shared context, so you can spend more time on the work that really matters. ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck.  Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

Send us Fan MailAre pathology foundation models actually ready for labs, or are they still stronger on paper than in practice?In this episode of DigiPath Digest #49, I unpack a timely review on pathology foundation models and ask the question that matters most to me: not just what these models can do, but what has to be true before they are genuinely useful in real pathology workflows.I walk through how pathology AI moved from narrow, task-specific models into the era of transformer-based foundation models. That shift matters because pathology is no longer only about looking at H&E in isolation. Today, pathologists are expected to integrate morphology, immunohistochemistry, molecular assays, genomics, and clinical context. That growing complexity is one reason foundation models are getting so much attention.In this discussion, I explain how transformers entered pathology, why image patches are treated like tokens, and how shared embeddings can support classification, regression, segmentation, and multimodal retrieval. I also go through the major pathology foundation models mentioned in the paper, including Virchow/Virchow2, Mayo Clinic Atlas, UNI, CONCH, H-Optimus, GigaPath, and TITAN, and why scale alone is not the full story.A big part of this episode is about the gap between benchmark performance and clinical readiness. I talk about the persistent limitations in training data diversity, the overuse of TCGA, and why public benchmarks can still miss what real pathology practice looks like. I also cover where foundation models still struggle, especially in cytopathology, hematopathology, and underrepresented disease areas, along with the real-world problems of artifacts, domain shift, concept drift, infrastructure burden, regulatory complexity, and workflow disruption.For me, one of the most important themes is this: AI in pathology should augment, not replace, pathologists. The future is not about handing diagnosis to a model. It is about building tools that support pathologists better, fit real workflows, and can be validated in ways that deserve trust.I also spend time on what comes next: explainable AI, counterfactual explanations, conversational interfaces, retrieval-augmented systems, multimodal fusion, and the need for deployment-centric validation rather than paper-only excitement.If you are trying to understand where pathology foundation models really stand today, this episode will help you separate the promise from the practical barriers.Episode Highlights00:01 – Why I chose this paper, what is changing at Digital Pathology Place, and why foundation models are worth paying attention to now.02:15 – The core questions: what pathology foundation models are, where they are, and how difficult they are to apply in pathology.04:50 – Why pathology is becoming more cognitively demanding, and how multimodal complexity is driving interest in scalable AI.07:02 – From narrow AI to transformers: how pathology moved beyond single-task CNN models.10:16 – How transformers work in pathology: image patches as tokens, self-attention, embeddings, and downstream tasks.14:16 – Why multimodality matters, and what kinds of data foundation models may eventually integrate.15:27 – Timeline of key model developments, from “Attention Is All You Need” to gigapixel-scale pathology foundation models.17:13 – The leading models and what scale really looks like: Virchow, Mayo Clinic Atlas, UNI, CONCH, H-Optimus, and GigaPath.19:51 – Why dataset diversity matters more than sheer volume, and why TCGA is not enough.23:17 – Where foundation models still struggle: cytopathology, hematopathology, rare disease, artifacts, scanner shifts, and pen marks.28:06 – Explainability, counterfactual explanations, and why trust in pathology AI needs more than attention maps.30:17 – The real deployment hurdles: regulation, infrastructure, workflow fit, and economics.36:32 – Why AI should augment pathologists, not replace them, and which tedious tasks pathologists would gladly hand over.38:36 – Retrieval-augmented and conversational AI in pathology: where interactive systems may actually help.40:51 – Vision-language models and multimodal fusion with histology, radiology, genomics, and clinical notes.42:16 – The path forward: deployment-centric design, prospective multi-site validation, and human-AI collaboration.44:08 – Closing thoughts on AI literacy, community learning, and what needs to happen next.Resources MentionedMain paper discussed:Pathology Foundation Models: Evolution, Current Landscape, Challenges and Opportunities from a Technical and Clinical Perspectivehttps://doi.org/10.3390/bioengineering13050577Review article / journal landing page:https://doi.org/10.3390/bioengineering13050577Benchmarks mentioned:PathoBench — discussed in the review paper; use the review link here for context until you want to swap in a canonical project page:https://doi.org/10.3390/bioengineering13050577PathBench — public benchmark paper:https://arxiv.org/abs/2505.20202MEDFAIR — benchmark paper:https://arxiv.org/abs/2210.01725MEDFAIR code repository:https://github.com/ys-zong/MEDFAIRModels mentioned:Model overview in the review (Virchow/Virchow2, UNI, CONCH, H-Optimus, GigaPath, TITAN, Mayo Clinic Atlas):https://doi.org/10.3390/bioengineering13050577Virchow:https://arxiv.org/abs/2309.07778UNI:https://arxiv.org/abs/2308.15474CONCH:https://arxiv.org/abs/2307.12914Mayo Clinic Atlas:https://arxiv.org/abs/2501.05409TITAN:https://arxiv.org/abs/2411.19666Dataset mentioned:The Cancer Genome Atlas (TCGA)https://portal.gdc.cancer.gov/Book mentioned:Digital Pathology 101: All You Need to Know to Start and Continue Your Digital Pathology Journeyhttps://digitalpathologyplace.com/Platform:Digital Pathology Placehttps://digitalpathologyplace.com/Support the showGet the "Digital Pathology 101" FREE E-book and join us!

It took DrB fifteen years to finish a four-year degree — not because she wasn't capable, but because she was searching for an education and kept finding a factory. In this episode, she shares her full story and connects it to a message she received from a high school student halfway around the world who asked the question she hears from almost every student: why can't I concentrate, and why doesn't studying work for me?The answer isn't willpower or discipline. It's about a system that teaches what to learn but never how — and a population of gifted, high-capacity brains that pay the heaviest price for that gap. DrB breaks down what concentration actually is, why re-reading your notes is largely a waste of time, and six strategies for finally working with your brain instead of against it.Key Topics:• DrB's personal story: 15 years, multiple schools, and a brain that refused to settle• Why gifted brains are most harmed by standard education• The truth about concentration — it's not low, it's discerning• Retrieval practice vs. re-reading — the most important switch you can make• Six evidence-based strategies for brain-aligned learning• The connection between this and the work of GSDLinks:Register for the June 23 Masterclass for free with optional VIP upgrade:   https://itwasneveryoumasterclass.com/ Learn about GSD: https://www.drbarbaracohen.com/gsdprogram Learn about the Gifted Underachiever: https://www.drbarbaracohen.com/blog?tag=gifted+underachiever 

How do you build AI that actually understands you and the work you do? It all starts with having the right context.  We talk with Dropbox staff product manager Noorain Noorani and principal engineer Sean-Michael Lewis about the art of context engineering and how Dropbox connects to all the tools your team needs for work—so you get AI that works wherever you do.  ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck. Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

The Office of Civil Defense (OCD), says the main focus and efforts are in Sarangani and General Santos City, South Cotabato, near the epicentre. - Ayon sa Office of Civil Defense o OCD, nakatutok sila sa lalawigan ng Sarangani at sa General Santos City sa South Cotabato kung saan malapit ang sentro ng pagyanig

In This Episode We DiscussWhy student thinking—not compliance—became a central theme throughout Season 12How read aloud, accountable talk, writing, and learning science all connect to helping learning stickThe difference between understanding something during a lesson and actually learning it over timeWhy retrieval practice matters and how simple instructional moves can strengthen memoryWhat high expectations look like in literacy instruction and why they are easier to maintain than rebuildHow writing serves as a powerful tool for thinking, organizing ideas, and demonstrating understandingThe overarching lesson from Season 12: learning is not accidental—it is designedAs you reflect on this school year, consider:What instructional practice had the biggest impact on student learning this year?What did you learn about yourself as an educator?What is one thing you want to stop doing next year?What is one thing you want to do more intentionally?What do you now know about teaching that you didn't know in August?Throughout the summer, we'll be revisiting some of the most impactful conversations from the podcast while reflecting on how to move forward with greater clarity and intention.Topics will include:Systems and teacher sustainabilityAvoiding survival modeTier 1 instructionDifferentiationGuided readingSupporting struggling readersSupporting advanced readersSelf-efficacy and transferBuilding instructional clarityThe goal isn't to add more to your plate.The goal is to help you reflect, refine, and rebuild before next school year begins.As you listen, consider this question:What idea from this season most changed the way you think about literacy instruction?Not your favorite strategy.Not your favorite resource.What idea changed the way you think?Because lasting instructional growth often starts with a shift in thinking before it shows up in practice.If you're looking for a thinking partner as you strengthen literacy instruction in your classroom, school, or district, I'd love to support you.Join the email list for summer reflections and resourcesLearn more about coaching and professional learning opportunitiesExplore literacy workshops and professional development optionsRemember:You don't need permission to teach well.You need the tools to lead your own practice.

Modern work can be frustrating and chaotic—if you don't have the right tools. From context engineering to multimodal search, go behind the scenes and hear how Dropbox engineers are building AI that actually understands you, so you can focus on the work that matters most. If you're new to Working Smarter, we've travelled from the F1 track to the bottom of a lake, and heard real stories from chefs, doctors, lawyers, and founders about how AI is helping them do more of what they love about their jobs. But in our third season, we're talking to the people behind the tools—the engineers and product leaders building helpful, time-saving AI features into the Dropbox experience you already know and trust. You'll hear all about their work on agents, inference, security, and, of course, how the people building AI use AI themselves. ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck.  Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

Dr F Scott Feil discusses the first in a mini series of study tactics that are evidence based. This first one is all about Self Quizzing and Self Retrieval.Works a lot better than reading and highlighting and re-reading and re-highligthing until the page is completely yellow.

Enjoy these back to back throwback episodes! Forbidden Knowledge Network https://forbiddenknowledge.news/ FKN Link Treehttps://linktr.ee/FKNlinksMake a Donation to Forbidden Knowledge News https://www.paypal.me/forbiddenknowledgenehttps://buymeacoffee.com/forbiddenTake control of your health now with Christian Yordanov's Live Longer Program https://www.livelongerformula.com/fknWe are back on YouTube! https://youtube.com/@forbiddenknowledgenews?si=XQhXCjteMKYNUJSjBackup channelhttps://youtube.com/@fknshow1?si=tIoIjpUGeSoRNaEsDoors of Perception is available now on Amazon Prime!https://watch.amazon.com/detail?gti=amzn1.dv.gti.8a60e6c7-678d-4502-b335-adfbb30697b8&ref_=atv_lp_share_mv&r=webDoors of Perception official trailerhttps://youtu.be/F-VJ01kMSII?si=Ee6xwtUONA18HNLZListen to Forbidden Knowledge News on clearair.fm every Tuesday, Thursday, and Saturday 12:15pm CSThttps://clearair.fm/Pick up Independent Media Token herehttps://www.independentmediatoken.com/Be prepared for any emergency with Prep Starts Now!https://prepstartsnow.com/discount/FKNStart your microdosing journey with BrainsupremeGet 15% off your order here!!https://brainsupreme.co/FKN15Book a free consultation with Jennifer Halcame Emailjenniferhalcame@gmail.comFacebook pagehttps://www.facebook.com/profile.php?id=61561665957079&mibextid=ZbWKwLWatch The Forbidden Documentary: Occult Louisiana on Tubi: https://link.tubi.tv/pGXW6chxCJbC60 PurplePowerhttps://go.shopc60.com/FORBIDDEN10/or use coupon code knowledge10Johnny Larson's artworkhttps://www.patreon.com/JohnnyLarsonSign up on Rokfin!https://rokfin.com/fknplusPodcastshttps://www.spreaker.com/show/forbiddenAvailable on all platforms Support FKN on Spreaker https://spreaker.page.link/KoPgfbEq8kcsR5oj9FKN ON Rumblehttps://rumble.com/c/FKNpGet Cory Hughes books!Lee Harvey Oswald In Black and White https://www.amazon.com/dp/B0FJ2PQJRMA Warning From History Audio bookhttps://buymeacoffee.com/jfkbook/e/392579https://www.buymeacoffee.com/jfkbookhttps://www.amazon.com/Warning-History-Cory-Hughes/dp/B0CL14VQY6/ref=mp_s_a_1_1?crid=72HEFZQA7TAP&keywords=a+warning+from+history+cory+hughes&qid=1698861279&sprefix=a+warning+fro%2Caps%2C121&sr=8-1https://coryhughes.org/Our Facebook pageshttps://www.facebook.com/forbiddenknowledgenewsconspiracy/https://www.facebook.com/FKNNetwork/Instagram @forbiddenknowledgenews1@forbiddenknowledgenetworkXhttps://x.com/ForbiddenKnow10?t=uO5AqEtDuHdF9fXYtCUtfw&s=09Email Forbidden Knowledge News forbiddenknowledgenews@gmail.comsome music thanks to:https://www.bensound.com/ULFAPO3OJSCGN8LDDGLBEYNSIXA6EMZJ5FUXWYNC6WJNJKRS8DH27IXE3D73E97DC6JMAFZLSZDGTWFIBecome a supporter of this podcast: https://www.spreaker.com/podcast/forbidden-knowledge-news--3589233/support.

Episode 136- Listen to this interview with Dr. Shane Saeed as we talk about:1.The role forgetting plays in learning. 2. Familiar vs Known and the role it plays in student learning.3.What retrieval strategies to use now as you wrap up the school year. Stay in touch with Dr. Shane Saeed: @drshanesaeed on everythingIf you're ready to strengthen your instruction and design literacy lessons that actually stick, you can learn more about coaching and professional development below:Work With EvaGrab my free guide: How to Keep Your Mini Lesson Mini  Book a discovery call for 1:1 coaching or school professional development

Most AI conversations focus on models. The better conversation focuses on systems. In this episode, we continue our interview with Matt Levenhagen, exploring a practical challenge many developers are facing: integrating AI into business operations without creating costly chaos. The answer is not buying more AI tools. The answer is building an intentional AI Workflow Architecture. About Matt Levenhagen Matt is the founder and CEO of Unified Web Design, a web development agency focused on custom solutions, WordPress development, e-commerce, memberships, and business systems. His background as both a builder and agency owner gave him a unique perspective on where AI creates real leverage instead of superficial automation. Follow Matt on LinkedIn. AI Workflow Architecture Starts with Context Control One of the most important operational realities Matt discussed was token usage. Businesses rushing into AI often underestimate cost scaling. Every interaction with large models consumes resources, and poorly managed context windows dramatically increase operational expenses. Instead of treating AI like unlimited compute, Matt focused on controlling context intentionally. That included: Monitoring token usage Limiting unnecessary memory loading Structuring retrieval systems Using different models for different tasks Preventing oversized prompts This is a systems-thinking problem, not merely a coding problem. Developers who ignore architecture end up with bloated workflows that become financially unsustainable. The fastest way to make AI unprofitable is to send unnecessary context into every request. Why Retrieval Matters More Than Raw Memory A major breakthrough Matt discussed was implementing Retrieval-Augmented Generation (RAG). This matters because AI systems do not need all the information all the time. They need the right information at the right moment. That distinction completely changes system design. Without retrieval architecture: Costs increase Performance slows Outputs become less accurate Hallucinations increase Operational complexity grows RAG allows systems to retrieve semantically relevant information instead of dumping entire databases into prompts. This transforms AI from brute-force processing into intelligent retrieval. The future of AI operations will likely depend less on giant models and more on efficient information orchestration. AI Workflow Architecture Requires Layer Separation Another valuable concept from the conversation involved separating operational layers. Matt described balancing: Local storage Business memory External AI APIs Workflow automation SaaS integrations This layered architecture creates flexibility. Instead of locking the business into one AI provider, workflows remain adaptable. Different models can handle different workloads depending on cost, complexity, and accuracy requirements. This becomes increasingly important as pricing models fluctuate. Businesses relying entirely on one provider risk operational instability if pricing changes dramatically. Layer separation reduces that risk. The businesses that survive AI cost volatility will be the ones architected for flexibility instead of dependency. Why Embedded AI Features Often Disappoint Matt also discussed the growing wave of SaaS AI integrations. Every platform now markets AI capabilities: Project management tools Communication platforms CRM systems Design software Documentation systems Yet many users feel underwhelmed. The reason is architectural isolation. These tools only understand limited slices of operational context. They automate micro-tasks but rarely improve larger workflows. That creates a false impression that AI itself lacks value when the real issue is fragmented systems. AI becomes more useful as the organizational context becomes more connected. This is why developers building custom operational layers still maintain an enormous strategic advantage. AI Workflow Architecture Is an Operational Discipline The strongest insight from these episodes may be that AI implementation is becoming operational engineering. Success now depends on: Information structure Retrieval design Workflow sequencing Context prioritization Cost management Human oversight This moves AI away from novelty experimentation and toward infrastructure planning. Businesses that treat AI casually will likely accumulate technical debt quickly. Businesses that approach AI architecturally will build scalable operational leverage. AI is no longer just a development tool. It is becoming an operational systems discipline. Developers Must Learn Economic Thinking One overlooked topic in AI discussions is economics. Matt repeatedly referenced balancing capability with cost. This becomes critical because AI pricing models are still evolving rapidly. Businesses that ignore usage economics may accidentally build systems that become financially impossible to scale. Developers now need to think beyond: Can this be built? They also need to ask: Can this be sustained? Can this scale economically? Can context costs remain controlled? Can cheaper models handle simpler tasks? This represents a major evolution in modern software architecture. Review your current AI workflows and identify where unnecessary context or oversized prompts may be increasing costs. Conclusion AI Workflow Architecture is rapidly becoming one of the most important technical disciplines for modern developers. Matt Levenhagen's approach demonstrates that successful AI implementation is less about chasing the newest model and more about designing sustainable operational systems. The companies that gain long-term advantage from AI will not necessarily be the companies using the largest models. They will be the companies with the best architecture. 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