Podcasts about Retrieval

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

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.

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]

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.

Ep 254 “The Law-Enforcement Inspired Yogurt Retrieval System” – National Gun Violence Awareness Month (Bullet Poof Bulletins) Celebrating the launch of eco-fiction anti-gun novella Bullet Poof and National Gun Violence Awareness Month, Avis Kalfsbeek brings back beloved Kitty O'Compost with the Bullet Poof Bulletins. Tonight on the Spoke-Easy stage, Kitty O'Compost tears apart a leaked corporate branding deck that tries to turn a standard trip to the grocery store into a paramilitary operation. This bulletin satirizes the "duty-grade readiness" marketing trend, exposing the absurdity of designing high-capacity grocery bags and tactical cereal deployment systems to commodify domestic anxiety. Inspired by the transformative themes of Bullet Poof, this episode laughs at the hyper-vigilant lifestyle brand and reminds us of the true safety found in open, trusting neighborhoods. Resources: Bullet Poof is a hopeful eco-fiction novella about what happens when ordinary people refuse to accept the gun status quo. Get the book: https://www.aviskalfsbeek.com/bullet-poof National Gun Violence Awareness Month: www.wearorange.org Theme Music: "Turn the Steel" and punk intros produced by Avis Kalfsbeek (via ElevenLabs). Music Credits & Support: Buy LPs and music downloads directly from the bands' websites, or from platforms like Bandcamp where artists retain the majority of your purchase. This project is inspired by decades of punk ethos, raw energy, and the brilliant musicians who shaped the movement. The sonic landscape of this series was informed and inspired by: The Sex Pistols, Black Flag, Rites of Spring, The Buzzcocks, Minor Threat, The Clash, Social Distortion, Bad Religion, The Dead Kennedys, The Ramones, Jawbreaker, Fugazi, Rise Against, The Damned, The Stooges, Bad Brains, Bikini Kill, The Lawrence Arms, Husker Du, Pennywise, The Adicts, The Exploited, Descendents, Stiff Little Fingers, Crass, The Germs, Dropkick Murphys, Operation Ivy, Against Me!, Green Day, Blink-182, The Hives, Sleater-Kinney, The Violent Femmes, The Network, The Jam, The Gaslight Anthem, No Use For A Name, and The Interrupters.

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!

Episode 29 of Guardians of M365 Governance: Christian Buckley, Joy Apple, and Ragnar go off-script. No guest this month, just three MVPs working through a laundry list of the governance topics keeping them up at night, from Agent 365 and shadow AI to the real question underneath it all: what does it mean to be the human in the loop?In this episode we get into:00:59 The hottest news in the M365 governance space02:00 Lessons from Agent 365 customer workshops (delivered in Spanish!)03:25 What resonates: agent inventory and classification across Microsoft, third-party, and homegrown agents03:52 Shadow AI: OpenClaw, Cortex, Bedrock and why "observe or block" is the only lever today05:04 Don't be the department of "no": have the conversation first06:50 Coming soon to shadow AI discovery: Claude Code CLI, Codex CLI, Cursor, Llama and more07:19 Multi-model reality: Copilot, Grok, Claude and where each fits08:35 Mike Gennady's agent factory, nightly agent conferences, and #ClawPilot10:08 Microsoft Build preview and OpenClaw + Teams / Copilot integration11:05 New Agent 365 registry sync: Amazon Bedrock, Google Vertex AI, Databricks Genie, Salesforce Agentforce15:16 Cloud migration vs. AI: the governance parallels and the need for foundational cleanup18:00 The risk to Microsoft's strategy: enterprise vs. the developer space20:28 Licensing changes, Agent 365 pricing, and the true (unknown) cost of AI22:45 Why automating away junior roles handicaps your future talent pipeline24:01 Retrieval, semi-autonomous, and autonomous agents, and why nobody wants full autonomy yet25:33 Human in the loop on multiple levels: content cleanup, the publishing quality gate, and workflow escalation28:50 100 test cases for Power Platform alone: never underestimate the testing effort29:31 Productivity vs. effectiveness: redefining how humans work with AI31:17 AI-assisted writing done right: a 47-page doc drafted by AI, then days of human verification35:28 Handwriting vs. typing, stream-of-consciousness drafting, and thinking through the words36:36 Why the human mind can't be replicated, and Hegel on master and horse39:28 Finding your USP as a human in the loop, a daily new discoveryThe big takeaway: the discussion of the next two to three years won't be about productivity. It will be about effectiveness, and resetting the standard for what it means to keep humans meaningfully in the loop. Govern your agents as helpers, never the other way around.Guardians of M365 Governance is a monthly webcast dedicated to everything governance in the Microsoft 365 ecosystem. Got a topic you want us to cover, or want to join as a guest? Connect with Christian, Joy, or Ragnar on LinkedIn.Microsoft Build runs June 2-3, free online: https://build.microsoft.com

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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Welcome to JAIG EYES & JEDI – a podcast dedicated pretty much now to ALL STAR WARS! Join HOPE MULLINAX and CHRIS HONEYWELL as they watch to the tenth episode of season 2 – RETRIEVAL! THERE WILL BE  – MAUL TALK – MORE GONK NEEDED – PET TALK – HOPE [...]

263 | Ein überraschender neuer Player im AI-Rennen, warum will SpaceX Cursor kaufen und müssen alle Startups sterben, wenn Corporates sie kaufen? Samuel und Alex diskutieren die Tech-Themen der Woche.Partner dieser Folge:ebay.comPrüft, wie Live-Commerce euer Business voranbringen kann. Keine Verkaufsprovision für neue Live-Seller in den ersten 6 Monaten. Mehr Infos: ebay.de/startliveMach das 1-minütige Quiz und finde eine Geschäftsidee, die zu dir passt: digitaleoptimisten.de/quiz.Kapitel(00:00) Intro(01:29) DasTelefonbuch.de - under the radar over the top(08:14) Finds of the week: Offline is the new shit und SpaceX kauft Cursor (vllt.)(23:31) Sprach-Post von Optimisten - Mirja(38:12) Ankerkraut - was wollen Corporates mit Startups?(48:35) Das Große Digitale Optimisten Linkedin Quiz(52:25) Geschäftsidee von Samuel: Outfraction(1:00:06) Geschäftsdiee von Alex: Urban Sports Club für KinderSo erreichst du uns:Sprachnachricht senden: https://www.speakpipe.com/digitaleoptimistenEmail schreiben: alexander@digitaleoptimisten.deLearningsGanzheitliche AI-StrategieEndgame der AI-Entwicklung wird als holistisches Ökosystem aus LLM, Code-Tools und Hardware beschrieben. Die Diskussion nennt Beispiele wie Claude Code, Cursor, XAI und SpaceX, um diese Verknüpfung zu illustrieren. Hypothese: Wer AI über digitale und physische Systeme hinweg integriert, könnte Wettbewerbsvorteile erzielen.Fractional Ownership als neues ModellDie Idee, teure Ausrüstung per Fractional Ownership zu finanzieren, wird konkret diskutiert und auf Kategorien wie Camping- und Outdoor-Ausrüstung, Kletter- oder Wanderausrüstung sowie Rennräder übertragen. Beispiele nennen anteilige Beteiligungen von 10 Prozent bis 20 Prozent an Vermögenswerten wie Ferienhäusern oder Luxusuhren; Upfront-Kosten könnten pro Anteil bei ca. 500 Euro liegen, plus jährliche Servicegebühren. Die Plattform soll Käufer bündeln und zusätzlich durch Abonnements laufende Einnahmen generieren, was laut Diskussion etwa 20 Prozent des Umsatzes als Subscription-Revenue bringen könnte.Retrieval-gestützter Wissensbot aus TranskriptenEine Hörer-Idee (Mirja) schlägt vor, einen Retrieval-basierten Bot zu bauen, der Transkripte als Wissensbasis nutzt, um Folgewissen abrufbar zu machen. Kernidee ist, eine App zu bauen, die auf die Transkripte der Folgen zugreift und Fragen zu einzelnen Episoden beantworten kann, idealerweise ohne Halluzinationen. Die Umsetzung erfolgt über Cloud-Code, Kontext-Import der Transkripte und eine einfache Benutzeroberfläche, um relevante Passagen schnell nachzuschlagen.Unternehmens- und Markenakquise im AI-ZeitalterEs wird diskutiert, dass große Konzerne wie Nestlé Marken wie Ankerkraut kaufen, um unorganisches Wachstum zu generieren, und dass Gründer später oft den Verkaufsweg wählen. Der Fall Ankerkraut (Rückkauf durch die Gründer 2026) wird als Signal genannt, dass Markenwert durch Übernahmen kippen kann und Shareholder-Value-Denken eine treibende Kraft bleibt. Für Gründer und Unternehmen bedeutet das: Exit-Planung, M&A-Strategie und das Verständnis der Rollenspiele in Kapitalmarkt-basierten Wachstumsstrategien frühzeitig berücksichtigen.KeywordsClaudeCursorSpaceX XAINestlé Ankerkraut RückkaufClaude Coding-Tools Einsatz im B2B UmfeldBenzinpreisvergleich Telefonbuch.deFractional Ownership Camping AusrüstungSecond Order Investing Anwendung MärkteMiroFish Tool ReaktionsszenarienOffline AI ModelleRetrieval SystemeOpenAI Konkurrenz ClaudeKI Coding Tools Markt

Today, we check in a year after the first Unsupervised Learning x Latent Space Crossover special to discuss everything that has changed (there is a lot) in the world of AI. This episode was recorded just after AIE Europe, but before the Cursor-xAI deal.Unsupervised Learning is a podcast that interviews the sharpest minds in AI about what's real today, what will be real in the future and what it means for businesses and the world - helping builders, researchers and founders deconstruct and understand the biggest breakthroughs.Thanks to Jacob and the UL production team for hosting and editing this!Jacob Effron* LinkedIn: https://www.linkedin.com/in/jacobeffron/* X: https://x.com/jacobeffronFull Episode on Their YouTubeWe discuss:* swyx's view from the center of the AI engineering zeitgeist: OpenClaw, harness engineering, context engineering, evals, observability, GPUs, multimodality, and why conference tracks now reveal what matters most in AI* Whether AI infrastructure has finally stabilized: why “skills” may be the minimal viable packaging format for agents, why infra companies have had to reinvent themselves every year, and why application companies have had an easier time surviving model volatility* The vertical vs. horizontal AI startup debate: why application companies can act as the outsourced AI team for enterprises, why some horizontal companies still matter, and why sandboxes may be the clearest reinvention of classic cloud infrastructure for the AI era* The “agent lab” playbook: starting with frontier models, specializing for your domain, then training your own models once you have enough data, workload, and user behavior to justify the cost and latency savings* Why domain-specific model training is real, not just marketing: how companies like Cursor and Cognition can get users to choose their in-house models, and why search, domain specialization, and distillation are becoming more important* Open models, custom chips, and alternative inference infrastructure: why swyx has turned more bullish on open source, why non-NVIDIA hardware is suddenly getting real attention, and why every 10x speedup can unlock new product experiences* What it means to sell to agents instead of humans: why agent experience may mostly just be good developer experience by another name, why APIs and docs matter more than ever, and how pretraining-data incumbents are compounding advantages in an agent-first world* Why memory and personalization may become the next big wedge: today's models mostly reward frequency of mentions, but in the future, swyx expects product choice to be shaped much more by personalized memory systems* The state of the AI coding wars: why coding has become one of the largest and fastest-growing categories in AI, how Anthropic, OpenAI, Cursor, and Cognition have all ridden the wave, and why the category may still have more room to run* Capability exploration vs. efficiency: why the industry is still in a token-maxing, experiment-heavy phase where people are rewarded for spending more rather than less* Claude Code vs. Codex and the strange stickiness of coding products: why first magical product experiences may matter more than expected, and why the bigger mystery may be why only a few names have emerged as real winners so far* What the end state of the coding market might look like: two major players, a longer tail of niche products, and possible disruption if Microsoft, Mistral, xAI, or the Chinese labs push harder into coding* Where application companies still have room against the labs: why frontier labs are trying to expand into verticals like finance and healthcare, but still leave space for focused companies that own the workflow and the last mile* Why coding may be a preview of every other AI market: the first category to truly go parabolic, the clearest example of foundation model companies colliding with application companies, and a template for how future vertical AI markets may develop* Why AI valuations now feel unbounded: from billion-dollar ARR products built in a year to trillion-dollar market caps, swyx and Jacob unpack how the AI market has broken traditional startup intuitions about scale and durability* Consumer AI vs. coding AI: why ChatGPT's consumer category may have plateaued on frequency and product design, while coding continues to feel like a daily-use category with real momentum* The next product frontier beyond coding: consumer agents, computer use, and “coding agents breaking containment,” with swyx's thesis that 2025 was the year of coding agents and 2026 may be the year they begin to do everything else* Whether foundation models are really killing startup categories: why swyx is less worried for early founders, more worried for mid-size startups and traditional SaaS, and why building something ambitious may now be the best job interview for a frontier lab* AI vs. SaaS and the internal culture war around adoption: the tension between AI-native employees who want to rip out expensive software and skeptics who think quick AI-built replacements create fragile systems* Why traditional SaaS may be under real pressure: swyx's own experience spending six figures on event and sponsor management software, the temptation to rebuild it cheaply with AI, and the broader question of whether teams will trust custom AI-native replacements* Biosafety, security, and frontier model access: why swyx raised biosafety at a dinner with Anthropic's Mike Krieger, why Krieger argued security is the bigger issue, and what restricted model releases reveal about Anthropic vs. OpenAI* The era of giant models: why 10T+ parameter systems may only be a temporary rationing phase before bigger clusters arrive, why labs may increasingly keep their most powerful models private for distillation, and why scale alone no longer feels like a complete answer* Memory as the slowest scaling factor in AI: why context windows have improved far more slowly than people hoped, why million-token context still has not changed most real workflows, and why memory may be the key bottleneck for the next generation of systems* What swyx changed his mind on in the past year: becoming more bullish on open models, more convinced that the top tier of agent startups behaves very differently from the median AI company, and more optimistic about fine-tuning and specialized model adaptation* “Dark factories” and zero-human-review coding: the next frontier after zero human-written code, where models not only write the code but ship it without human review, forcing companies to rethink testing and verification from first principles* Why RL and post-training may matter more than people assumed: even if the resulting models get thrown out every few months, the data, workflows, and domain-specific improvements persist* Synthetic rubrics, Doctor GRPO, and multi-turn RL: why reinforcement learning is becoming much more domain-specific and multi-step than many people realize, opening the door to much deeper customization* The next frontier after coding: memory, personalization, and world models, including why swyx thinks world models matter not just for robotics or gaming, but for giving AI something closer to lived understanding* Fei-Fei Li, spatial intelligence, and the Good Will Hunting analogy: the idea that today's LLMs may know everything by reading it all, but still lack the lived experience that turns knowledge into a deeper kind of intelligenceTimestamps* 00:00:00 Intro preview: AI coding wars, startup pressure, and market structure* 00:00:28 Welcome to the Latent Space × Unsupervised Learning crossover* 00:01:17 What AI builders are focused on now: OpenClaw, harnesses, and infra* 00:04:33 Why AI infra is harder than apps, and where startups can still win* 00:06:39 Should companies train their own models?* 00:09:28 Open models, custom chips, and the new inference race* 00:11:25 Designing products for agents, not just humans* 00:16:49 The state of the AI coding wars in 2026* 00:19:27 Capability exploration, token-maxing, and why coding is going parabolic* 00:21:41 What the end state of the coding market could look like* 00:23:50 Where app companies still have room against the labs* 00:27:02 Why AI valuations and market swings feel unprecedented* 00:28:56 Consumer AI vs. coding AI, and why sticky products still matter* 00:32:28 What the next breakthrough product experience might be* 00:32:53 2026 thesis: coding agents break containment and eat the world* 00:35:27 Are foundation models wiping out startup categories?* 00:37:33 AI vs. SaaS, vibe coding, and internal team tensions* 00:40:01 Biosafety, security, and the politics of restricted model releases* 00:42:19 Giant models, compute constraints, and the limits of scale* 00:44:30 Memory as the real bottleneck in AI* 00:44:57 Why swyx changed his mind on open models* 00:47:44 Dark factories and the future of zero-human-review coding* 00:49:36 Why post-training and RL may matter more than people think* 00:51:50 Memory, world models, and the next frontier of intelligence* 00:53:54 The Good Will Hunting analogy for LLMs* 00:54:21 OutroTranscript[00:00:00] swyx: Isn't that crazy? That number is just mind boggling.[00:00:03] Jacob Effron: What is the state of the AI coding wars today?[00:00:05] swyx: We're in a phase of sort of like capability exploration. The general thesis that I have been pursuing now is that the same way that 2025 was a year coding agents 2026 is coding agents breaking containments to do everything else.[00:00:16] Jacob Effron: Do you worry about the foundation models just getting into a bunch of these startup categories?[00:00:21] swyx: Mid-size startups. Yes.[00:00:23] Jacob Effron: What do you think the end state of this market is[00:00:25] swyx: for the market structure to, to significantly change? There would be[00:00:28] Jacob Effron: today on unsupervised learning. We had a, a fun episode and what's really become an annual tradition, a crossover episode with our friends at Latent space.Swix and I sat down and we talked about everything happening in the AI ecosystem today. What we thought of the various changes at the model layer, what's happening in the infra world, the coding wars, and a bunch of other things. It's a ton of fun to do this with someone I really respect and another great podcaster in the game.Without further ado, here's our episode. Well switch. This is, uh, super fun to be back with another unsupervised learning, uh, latent space crossover episode.[00:01:02] swyx: Yeah,[00:01:02] Jacob Effron: I feel like a lot of places we could start, but you know, one thing I always find fascinating, uh, about the way you spend your time is you obviously are like at the epicenter of this engineering movement and community, and you run these events and conferences and put on these.Awesome talks and, and I think just have a great pulse on the zeitgeist of what's going on.[00:01:16] swyx: Yeah.[00:01:17] Jacob Effron: Maybe to, to start just what are the biggest topics people are thinking about right now?[00:01:21] swyx: Yeah, so I just came back from London, uh, where we did a IE Europe and we're doing roughly one per quarter now, which Yeah, you've[00:01:27] Jacob Effron: really up[00:01:27] swyx: the, hopefully[00:01:28] Jacob Effron: up the, up the pace.[00:01:29] swyx: It's trying. We're trying to match AI speed, youknow?[00:01:30] Jacob Effron: Yeah, exactly. The tops would be completely different, I imagine. Uh,[00:01:33] swyx: yeah. You know, I definitely curate the tracks, like you can see what I think. When you see the track list and the, the speakers that I invite, obviously Open Claw is like the story of the last four or five months, and then be, be just below that.I would consider harness engineering, context engineering to be two related topics in agents and rag. And then there's a long tail of Evergreen stuff like evals, observability, GPUs, uh, and uh, LM infra and just general, just in general. We also have other updates on like multimodality and, uh, generative media, let's call it.Um, but I definitely, the, the first three that I mentioned are top of mind people. Yeah.[00:02:13] Jacob Effron: I think harness is particular like, so interesting. Um, you know, there was this tweet from Harrison Chase, the, the lane chain, CEO, that, that caught my eye recently where he said, you know, it finally feels like we have stability, uh, around the infrastructure for, uh, you know, around ai.And I think what. He basically was implying his like, look over the past two, three years as a company at the epicenter of AI infrastructure, it was a bit like playing whack-a-mole, right? You were constantly moving around with, however, the building patterns were evolving[00:02:36] swyx: for Harrison for sure. Right? Like he's basically had to reinvent the company every year since he started Lang Chain.Right? It was Lang chain, Ang graph and LP agents and like, uh, I think he's like one of the most nimble, adept sharp people about this. Yeah. Yeah.[00:02:49] Jacob Effron: Saying now, now is finally the time stability[00:02:51] swyx: this. Yeah.[00:02:52] Jacob Effron: Yeah. Um, do you buy that or what have you kind of make of that take?[00:02:56] swyx: I think that. It, it's very expensive to say this Time is different sometimes, but when you're just writing code, like it's actually okay to just like try to make a call and I think it may not even matter if this call is right or not.Like I just don't even care that much because you can be right on a thesis, but if you don't, you don't figure out how to monetize the thesis, then who cares if you said something first that said, um, it does feel like, for example. Uh, we went through a lot of different ways of passion packaging integrations up with, uh, with agents.And it feels like we've landed at skills, which is like the minimal viable format. Yeah. Which is just a markdown file, uh, with some scripts attached to it, and I don't see how it can be more simple than that. And so there is some justification for. The stability around harnesses. I feel like there may be more adaptation with regards to maybe like the real time elements or subagents or memory or any of those like agent disciplines, let's call it in, in agent engineering.Uh, but if, if the thesis is that, okay, you just want agents are LMS with tools in the loop with a file system, what they can do. Retrieval with, with skills and all these like standard tooling that now seems to be relatively consensus then probably. That makes sense. Um, I just think like there's no point trying to stake your reputation on this thesis that we're there because if it changes again, just change with it.It's fine.[00:04:33] Jacob Effron: Yeah. It's always, you know, I've always been struck by how that is. Much more challenging for infrastructure companies and application companies. Like obviously I think, yeah. You know, on the application side you've seen, you know, Brett Taylor from Sierra Max, from Lara. Like, they're like, look, we build, you know, what's ahead of the models and we're willing to throw everything out every three months, you know, as the models get better and better.Exactly. Yeah. But the thing you at least have there is you have. Uh, you have an end customer, right? That's like decently sticky. Um, you know, they will mostly stick, you know, they'll, they'll give you a shot at least of, of building these things. What I've always found more challenging, uh, at, at the kind of like, you know, reinvent yourself every three months of the infrastructure layer, it's like, you know, developers are definitely a, a pickier audience maybe than an accounting firm or, uh, you know, a bank.Yeah. And so it's definitely a, a, a more challenging position to be in to, to have to constantly reinvent yourself.[00:05:17] swyx: Yeah. Yeah. Yeah. And, and like when they turn, it's like. Very complete. Like, they'll leave to like the, the hot new thing, uh, because there's like no defensibility, I guess. Like e even, even if you are a database, like, uh, people can migrate workloads off databases.Like it's, it's a, it's a known thing. Uh, so I think like basically what we're talking about is the vertical versus horizontal, uh, debate in, in AI startups. And uh, the way I think about it also is just that like when you are. Um, Lara, when you are a bridge, like you are the outsource AI team, right? You, you are, your job is to apply whatever state ofthe art AI methods.[00:05:55] Jacob Effron: Yeah. Like this translation layer between model capabilities and your[00:05:57] swyx: own customers. Yeah. To, to the end customers and like, well, if they didn't have you, they would've to hire in house and they're not gonna hire in house so they have you. And like, I think that's like a reasonable, like very robust to any whatever trends and, and discoveries that people make in, in the engineering layer.I do think like there is, um. It like sort of useful horizontal companies being built, but they're all. Very much like, sort of like the reinventions of classic cloud in the AI era and the, the primary one being sandboxes. Yeah. Um, which like, it's another form of compute guys, like, let's not get too excited about it.But I mean, like the, the workloads are enormous.[00:06:38] Jacob Effron: Right.[00:06:38] swyx: Yeah.[00:06:39] Jacob Effron: It's interesting, and I feel like as, as part of this, you know, the questions that folks are asking around infrastructure, there's a lot around, you know, the extent to which companies should have their own AI teams and what they should be doing in-house.And, you know, uh, I think there's questions around should people be training their own models? Should people be doing, you know, rl, uh, in-house based on the data they have? I feel like, you know, one has to evolve their takes on this every, every three months with paces. But where, where are you at on this today?[00:07:00] swyx: I think, well, I mean actually all models have gone up. Um, and obviously I'm involved in cognition and also cursors doing, doing, uh, a lot of own model training. And I think that that is some part of the, what I've been calling the agent lab playbook, where you start off with the state of the art models from, uh, from the big labs and you, uh, specialize for your domain.But once you have enough workload and enough high quality data from your users, then you can obviously train your own models and like save a lot on cost and latency and all that, all that good stuff. Um, you also get like a marketing bonus of like calling it some fancy name and putting out some research[00:07:38] Jacob Effron: from my seat.I can't tell how much of it is like actual, you know, value that's provided to the end user. And how much of it is that marketing bonus? Right. It seems some combination of the[00:07:45] swyx: I think it's both.[00:07:46] Jacob Effron: Yeah.[00:07:46] swyx: Um, no, no. There, there actually is real value. Um, and you, you know that for a number of reasons. Like one, even when it's not subsidized, people do choose it as like one of the top four or five.This is both composer two and, uh, suite 1.6 I one of the top five models. Like in a, in a fair market? In a free market, yeah. In a, in a, in a model switch. Or people do choose it and like, it's not subsidized. Like, so that's as good as it gets. Uh, but beyond that, like domain specific models, for example. For search with, with both, which both companies have absolutely makes, makes a ton of sense.Everyone says like, yeah, we should always, always do this. And honestly like, I think the infrastructure for that is becoming easier with, um, like thinking machines tinker thing as well as primary like, uh, lab stuff. Yeah, I mean like, this is one of those like reversal of the, the bitter lesson where you first bootstrap on the large models and the general purpose models to get big.And as you get very well-defined workloads that are just high quantity but not high variance, um, then you just distill down to a smaller model and run that on your own. Right. Which like totally makes sense.[00:08:50] Jacob Effron: What I'm less clear on is the kind of DIY RL use case, which I think is really mostly around, you know, improved, uh, quality for, for different things.Obviously there's probably like more efficient ways to, you know, get a smaller model that's that's faster and cheaper. And it'll be interesting to see whether. You know, obviously you had, you know, uh, two, three years ago this whole case of companies that were, you know, pre-training and claiming better outcomes in, in their domains than getting kind of cooked as each model iteration improved.You know, I wonder whether that's a, a similar story plays out in the, uh, in, in the, our all space. Yeah, for the focus on, on on pure outcomes and quality, not the cost side, which clearly your own models for cost at scale makes a ton of sense.[00:09:28] swyx: I think there are this, there are two sides of the same coin.Like you basically always want to hold, uh, quality constant or trade off a little bit of quality for a drastic decreasing cost. And that's true for everyone. Uh, one element I wanted to bring out, which is very much in favor of open models, is custom chips. So this would be cereus, but also talu. And then there's a huge range of stuff in between.This has been a huge story this past year on just like everything non Nvidia is getting bid up, including like freaking MatX is working for, which is very, which is very rewarding for me, but I think one of those things where like, oh, like the suddenly, because the number of alternative. Hard, uh, hardware is increasing and the inference that you can get is insanely high.Like, um, we're talking thousands of tokens per second instead of less than a hundred. So the trade off for qua quality doesn't hold as much anymore because the speed is so high.[00:10:24] Jacob Effron: Have you seen a lot of companies go all in on the alternative chip?[00:10:26] swyx: So cognition has Yeah. On Cerebras, uh, and, and so has OpenAIUm, uh, and so no, I don't think so beyond that, uh, and that, do you think that's like a, that's mostly, that's foreshadowing of, that's, yeah. I used to be kind of a skeptic in terms of like, okay, so what if I get my inference at a hundred to a hundred tokens per second sped up to 200 tokens per second. It's only two X faster.It's not that big a deal. Um, but when you, uh, I think every 10 x does unlock a different usage pattern. Um, and you, we have proof in Talas and, and some of the others. That you can actually, um, drastically imp improve inference speed and what happens from there? I don't even really know, like it's, it's so hard to predict when entire applications just appear at once.Yeah. Uh, and it also isn't that expensive, right? So like, um, this is one of those things where like, I, I think the, the investment cycle is gonna be multi-year. Um, and I. Would caution people to not dismiss it too, too quickly.[00:11:25] Jacob Effron: Yeah. I mean, one other like infra question I was curious to get your thoughts on is obviously it seems increasingly a lot of the cutting edge infra companies are building for agents as the buyers of their product or users of their product, right?[00:11:35] swyx: Ooh,[00:11:36] Jacob Effron: and[00:11:37] swyx: another huge theme. Yeah. Yeah.[00:11:38] Jacob Effron: And I'm trying to figure out like what. What, what do you have to do differently about selling into agents? Um, are they just the ultimate rational developers? Uh, or is there, you know,[00:11:46] swyx: no, absolutely not. Um, I think they are easily prompt, injected and, uh, very tuned towards like, basically com compounding existing winners.[00:11:57] Jacob Effron: Yeah,[00:11:57] swyx: so like if, like, congrats if you won the lottery for getting into the training data right before 2023, because now you're like installed in there for the foreseeable future. But yeah. Uh, you know, one stat that Versal, uh, CTO Malta dropped at my conference was that there are now, uh, 60% of traffic to Elle's, um, like app arch, like admin app architecture for like configuring versal applications, uh, is bought.It's not, it's not human. Uh, so like your primary customer is agents now. Um, and it's mostly co like mostly coding agents, mostly people using CLI on CP or whatever. But yeah, I mean, I think. More. I, I think step one, if it doesn't exist as an API that agents can use, it doesn't exist. Right, right. Which I think is like, uh, it's a good hygiene thing anyway, to, to make everything API available, but not as like an extra, um.Push on like products, people to not only work on the ui, um, you should probably work on the on SCLI stuff. Beyond that, I think honestly there is like, so I, I come from the sensibility of, I think everything that you are trying to do for agents experience now, which is the term that Matt Bowman and Nullify is trying to coin, is the same thing that you should have been doing for developer experience.That you should have had good docs, you should have had a consistent API, uh, that is. Mostly stateless. Um, you should have, I guess, discoverable or progressive disclosure or like search or like whatever. And so now that people have energy in like finding these customers to do that, that's great. Um, do I believe in.Extending beyond that into something like a EO, um, for gaming The chatbots? Not necessarily, but obviously there's gonna be huge advantages when people who figure out the short term wins. Yeah. And short term wins can compound.[00:13:43] Jacob Effron: Do you think these compounding advantages to like the, the pre-training data cutoff companies, like, you know, obviously over some period of time, I imagine that doesn't persist.And so as you think about like. I dunno, three, four years from now what the, you know, selection criteria end up being. Do you think it still mirrors exactly what you were saying before? Like it's exactly what you should have been doing all along to sell a good product to developers?[00:14:01] swyx: It could be, except that I think in three, four years we'll probably have much better memory and personalization.So then general a EO or GEO doesn't really matter as much. So I think whatever memory or personalization system we end up with will probably d determine what you end up choosing much more. Than, than what is currently the case, which is just frequency of mentions, let's call it. Yeah,[00:14:26] Jacob Effron: yeah.[00:14:26] swyx: Uh, so you just spa quantity and I think that's, I mean, that's something I'm looking forward to.I do think, like, like, you know, I, I think that the fundamental exercise to work through for yourself is if you start a new, um, sort of. Uh, disruptor company. Now there's a, there's a big incumbent that everyone knows, like, like superb base. Super base is like, kind of like the Postgres, like database, uh, incumbent.If you wanna start like new superb base, how would you compete with them? And I don't necessarily have the answer, but I, I, I do think like people, like resend like relatively new. I think they would start like 20, 23 and still there was, there was a recent survey where like, people. Checked what Claude recommends by default.If you just don't prompt it with anything, just say, gimme an email provider and says, resent as in like 70, 70% of each cases. Like the fact that you can get in there with like such a relatively short existence, I think is, is encouraging.[00:15:14] Jacob Effron: Yeah.[00:15:14] swyx: I do think like. Um, you do want to do whatever it is to, to like to, to get in that Very short mentions this because, um, it's not gonna be 20 of them, it's gonna be like three.[00:15:26] Jacob Effron: No, definitely. It feels like, uh, you know, probably more, more consolidation than ever. Uh, or, or kind of like, you know, uh, a winner take most market than maybe the, the, the physics of go-to market in the past. Yeah. Might have, uh, enabled.[00:15:38] swyx: The other thing also is like, semantic association is gonna be very important, uh, in the sense that like, you want to do like the combo articles where you're like, use my thing with for sale, with blah, blah.And like that all gets picked up in a, in a corpus. And so that's. Probably one thing that you, you wanna do? Well, I don't know what else. Uh, it's, it's, it's, it's one of those things where like, I think I feel, I feel I'm behind, uh, I don't know how you feel about this, but like,[00:16:04] Jacob Effron: I think AI is just everyone constantly feeling like they're behind some, uh,[00:16:08] swyx: yeah.With,[00:16:09] Jacob Effron: I wanna meet the person that doesn't feel behind,[00:16:11] swyx: but like with, with ax, right? Like, so, so like, my, my stance was that exactly what I said before, like everything that you, that you should do for agents is something that you should have done for humans anyway. Yeah. And so. To the extent that you're just getting it more energy to, to do things for agents, great.But like, uh, it's hard to articulate what new thing apart from just like more spam, um, that you should be doing. Anyway, that would be my take right now. Um, I I, I do think like there, there will be more turns at this. I think the personalization turn that is coming, um, will be big. And I don't know what that looks like because like basically we're kind of, we feel kind of tapped out on the memory side of things.[00:16:49] Jacob Effron: Yeah. I, I guess since we last chatted, you know, you, you took this role over at cognition, um, and you've obviously have a, have a front row seat to the AI coding space today. You know, I feel like coding in many ways. You know, people view it as this, like, I mean, besides being like the, the mother of all markets and this massive opportunity, I think it's kinda a preview of like, what's to come for many other spaces.Both. Yeah. You know, I feel like agents are most advanced in coding. I also feel like the, you know, competition between foundation models and application companies, you know, and, uh, mirrors what we may see in other spaces. And so maybe for our listeners, can you just lay out like what is the state of the AI coding wars today?[00:17:25] swyx: Um, it is massive, right? Like, uh, and I don't think necessarily, last time we talked about this, we appreciated the size of what[00:17:32] Jacob Effron: No, I wish we did.[00:17:33] swyx: I state of AI coding wars today, um, both opening eye philanthropic have made it their p serials to competing coding. Um, and. Tropic is like 2.5 billion in a RR just from Cloud Code.The way they recognize a RR is. Opt for debate, uh, open ai. I don't think the, a public number is known, but let's call it 2 billion as well. And then cursor is like, rumored to be 2 billion, you know? And, and those, those are like the public numbers that are known? Yeah. Um, so like huge markets that have just been created in the past one year.Like, like anthropic, just like Claude Code just recently celebrated their one year anniversary, which is, yeah, pretty nice. Um, so, and then I think, like the other thing that I see is there's, there's some other people who are like, oh, here's like the, the sort of relative penetration of, uh, Claude use cases, right?Like, and it's like coding 50% and then legal, whatever. Health, uh, it's like the, the remaining ones. And there was a very popular tweet that was like, okay, I'll look at the, the empty space and all these other use cases. If you are a new founder today, you should be betting on the other stuff because on, on a sort of catch up Yeah.Theory and my. Consider my, my pushback is the same pushback that, uh, I had on app over Google, which is like, well, well why is this time different? Like, why, if it went from let's say 10 to 50% in the past year, why can't I keep going? Uh, and like getting that wrong is actually a very painful one because you could have just did, did the momentum bet.Instead of the mean reversion bed. So I, I, I think that that is the, the state of things now that people are very, very much into psychosis. Um, they're are getting rewarded for spending more rather than spending less. And I think we're not in that phase of efficiency. We're in a phase of sort of like capability exploration.So I think people who are more crazy, who are more. Uh, creative, um, get rewarded comparatively. Yeah.[00:19:27] Jacob Effron: Well, it's interesting. I mean, it feels like behind these like token maxing, leaderboards and whatnot is this, it's like the first phase of this transition from a workforce perspective is you just gotta show your employer like, Hey, I, I use these tools.[00:19:37] swyx: Here's my nu number of tokens I cost, and that's it. They don't care about the quality. Right. It is, uh, maybe distasteful to someone who cares about the craft and, and all that. Um, but directionally everyone just wants you to go up regardless. And so, um, there it is not very discerning. It's, and it's probably very sloppy, but I think it's net fine because we're still probably underusing ai just in generally.Yeah. Um, and so I think that's like very interesting. Like we had on the podcast, uh, Ryan La Poplar from OBI, who spends a billion tokens a day. Yeah. Um, and that's for those county home, it's like something like 10,000 worth, $10,000 worth a day of API tokens. If they, they did market rates, um, and like most of us can't afford that.Yeah. But like. And, and, and probably a lot of what he does is slop.[00:20:25] Jacob Effron: Right.[00:20:25] swyx: But like, he's going to dis, he's like, if there were a new capability, he would discover it first before you because he was, he was trying and you were not trying. Right. And like, you only do things that work like, well, good for you.But like the, the people who are going to discover the next hot thing are living at the edge.[00:20:42] Jacob Effron: Right and increase in living at the edge of just having the compute budget to like run these experiments. I mean, kind of similar to what living at the edge on the research side has always been. You know, it was constrained in many ways by the amount of compute you had to run these experiments.It feels similarly on the, almost on the builder or like actualizing these tools now.[00:20:56] swyx: Yeah. The other thing that's, I mean, very obvious is philanthropic is kind of like the high price premium player. Um, that where, you know. Restricting limits or restricting model releases even is like the name of the game.Whereas Codex is like, come on in guys, use our SDK, use our login and we don't care. We're gonna reset limits. Whatever you do want to try to exploit the subsidies where you can get it. And definitely Codex is super subsidized right now. Gemini also very subsidized. Um, and. Comparatively, like, I think you should make, Hey, I guess while, while that's going on, it's not that bad to be a capabilities explorer on just the $200 a month plan from Cloud Code or from OpenAI.Um, and, uh, I I, I, my sense is that people aren't even there yet.[00:21:41] Jacob Effron: How do you think this, like, market ultimately plays? I mean, it's obviously such a big market that, you know, any slice of that market is interesting for, for anyone going after it. But I think what, what makes people so interesting in the coding market particularly is it feels like it's kind of this.Foreshadowing of what will happen in other, you know, any other kind of application market that the foundation models eventually turn to and are all their models against and gather data around. And so how do you think, you know, like does there end up being room for lots of different kinds of players or like, what do you think the end state of this market is and is that, do you think that's applicable to other markets?[00:22:10] swyx: I feel like there will be, I mean. Status quo is probably the most likely outcome, which is there are two big players and there's a small range of longer tail people that, um, fit other use cases that the, the two big players don't. That feels right to me. I think that, um, for it to, for the market structure to, to significantly change there would be, there needs to be significant change in like the economics or like the, the brand building or like the, the, the, the value propositions of the, of the companies involved and I.Haven't seen any in the last six months that, that have really changed the stories materially. So I feel like they would just keep going until something, something else happens. Something else happens, meaning like Microsoft wakes up and like goes like. Guys, we have GitHub, we have, uh, you know, we, we, we'll, we'll do something much bigger here than other, other than just copilot.Um, and, uh, that would be a big change. Um, MSL has put out a model now, and I was in a breakfast with, uh, Alex Wang, where they were like, yeah, like, we, we really, really want to go after the coding use case. We haven't done anything yet, but like, don't underestimate them. Right. Um, and, and similarly for the Chinese labs.Um, I think they're trying to go after it. Like ZAI is doing stuff. GLM uh, ZI and GLM is same thing. Um, uh, and, and so it's, so like everyone's trying to get a piece of that pie. I, I feel like the, the status quo has been pretty stable for the past, like almost a year I'll say.[00:23:39] Jacob Effron: Yeah. And is the room for the, not like, you know, for, for the application companies more on like the enterprise side or like where do the, where do the, like what surface area do the model companies leave for application companies?[00:23:50] swyx: Yeah, that's a good one. Um. It's very much evolving. Um, it, I, I, I will say because opening I did not have this, the, this level of attention on coding. Yeah. Uh, a year ago. We just don't have that much history. Right. Um, and it seems like, for example, so the big push at Open I now is the Super app. Um, is that a consumer thing?Is that like a products like. Portfolio rationalization thing, how much is that gonna take away attention from coding at the time when they actually do want to put more coding? I think it's, it's very unclear. So I do think like there's, there's all these, like in both big labs, there's. Uh, sorry. Both of the, and, and drop and, and deep minus and XAI are are separate cases.Um, they are trying to see the other time expansion areas. So cloud code for finance. Yeah. Um, uh, cloud cowork, all those, all those things. Whereas I think cursor and cognition are like comparatively just focused on coding and so I, I do think they leave space and I do think for the other verticals that also means the same thing.Right. That, uh, that they're not gonna be that. Um, intensely focused on, on, on that domain. Except for, I, I think I would mark out finance and healthcare as like the next ones, um, that they're clearly going after. Uh, I, I would say comparatively, healthcare seems more thorny. There, there, there've been some announcements about it, but like, I would respect the, the finance work a lot more just because like the, the path to money is a lot clearer.[00:25:12] Jacob Effron: Yeah, no, I mean, obviously like, I, I think, you know, maybe similar to, to the space that's being left in these other domains, you know, there's obviously. Uh, a lot that's required to actually implement these tools in enterprises, uh, versus, you know, maybe just giving them, uh, giving model access to, to folks outta the box.[00:25:27] swyx: Yeah, yeah. Yeah. So the, the agent lab thing is like, we'll do the last mile for you. Whereas I think the model labs tend to just trust the model and, and be minimalist about it. Both of them work.[00:25:38] Jacob Effron: Yeah.[00:25:38] swyx: I, I don't, I don't necessarily think one, uh, beats the other, uh, for every, for every use case. Um, all I, all I do know is that it does seem like.Uh, the large enterprises do want a dedicated partner that isn't just the model labs, which is kind of interesting.[00:25:55] Jacob Effron: We, we've been in this phase of, of pure capability exploration. And so I think nothing has been, you know, better for the large labs, right? I mean, they're always gonna be, uh, uh, the frontier of, of capability exploration.And so I think have a very good relationship with a lot of these enterprises. But ultimately over time, like. The, uh, the incentive structure of these labs is always gonna be maximal, you know, token consumption for, uh, for the end customers they work with. And there's just, I think, so few companies that have actually gotten to massive scale.Maybe coding again is the most interesting. So it's the first space that really is just completely gone, you know? Yeah. You must love it every day. Like absolutely insane. And. I think it[00:26:32] swyx: gets even. Okay. I mean, like, I think we, we say good things about crystal cognition, but the sheer liftoff of like both end UPIC and open ai.‘cause they, they, they have independent valuations. I mean, let's throw an XEI in there because it's now I ping at 1.2 trillion. That number is just mind boggling. Like I, I feel like in normal investing or normal startups, there's kind of like a ceiling market cap or valuation. Totally. That, that like you, you reach and you go like, all right, let's, it's gonna be chiller from now on.And these guys are not slow down. No.[00:27:02] Jacob Effron: Well, I also think the dynamic is fascinating about some of these later stage companies is, is, you know, in the past, I feel like in, in venture world, if you got to a certain level of scale, the question around you was really more a valuation question. And this is like why there was different phase, like, you know, types of venture people did and like the late stage growth people were just incredible at like, you know, a little bit of what's the ultimate market opportunity of this company, but also what's the right way to, to value it.Like we know it's, it's in some bands of an outcome that is like. Sure there's some variance to it, but it's like relatively understood what that bands is and then maybe you get over time surprised to the upside. Whereas any kind of like later, even the labs themselves, any later stage company, the bands of which that company might be worth right now, even in a year or two years are so massive because of how fast the ecosystem changes that it's like.Even for later stage companies, every three months could be an existential level event to the upside to the downside. Yeah. Um, and I think that, like, you are obviously seeing it in the, in the positive with code, which, you know, if you think about a company like philanthropic, you know, that. For a while, it was like unclear if they were going to have access to enough capital, um, to really stay in the, in the race, right?And then coding hit at the exact right time. They had the perfect model for it. They executed brilliantly. Um, and you know, now are, are, you know, uh, you know, one of the most valuable companies in the world.[00:28:13] swyx: Uh, at the same time, I, I don't find, I, I have zero sympathy for opening eye because they're crushing it and they're all rich.You know, this is like a high class champagne problem to have to, uh, to be number two at coding or whatever. Like, who cares? Like, you're, you're doing great.[00:28:27] Jacob Effron: Yeah. It's funny though. I can't even, I mean, you would be closer to this, uh, you know, even that you're in the AI coding space, but it's like a lot of people I talk to think Codex is just as good, if not better than Claude Code.Right. I think one thing that I've been really surprised by, and maybe, maybe Cloud Code is a better product in some ways, I'm curious your thoughts is just in consumer AI with chat GBT. You saw this big first mover advantage, right? Where admittedly today, like, I don't know, Claude Gemini. Great products.Not sure, not abundantly clear chat GBTs any better, but like. People stick with chat, GBT, it's the first thing to introduce them.[00:28:56] swyx: They stay, but they're not growing anymore. I don't know if you've seen[00:28:59] Jacob Effron: Right. But that to me is more of like a, a, a product problem than it is. They're not like, it's not like they've like lost share to someone else.My understanding is the overall problem with consumer AI today is much more of a how do you take this tool and, you know, for, for folks like us, like knowledge workers, it's like this incredible magic tool, but it's not necessarily a daily active use tool for a lot of people around the world today. And what are the like products?It's, it's kind of a category wide problem. Like in coding, for example, like. The entire space has gone parabolic. There may be some relative growth in, uh, in other consumer AI players, but it's not like consumer AI as a category is like going parabolic and they're not capturing most of that thing. I think it's actually the larger problem is much more, hey, the category has kind of hit a bit of a plateau of people haven't figured out how to bring, you know, tons more users on board.Yeah, yeah. Or increase the frequency of those users. And so it seems more of a category wide problem than it is, you know, a massive market share of change. I was gonna draw the comparison to, to the coding space where Claude Co is the first product, obviously, to introduce people to this magical experience.You know, by all accounts, codex is, is pretty damn close to as good, if not better. Um, but like still that first product, you, you would've thought that would not be a super sticky, uh, you know, product surface area. And it actually has, it turns out, I, it feels like the first lab to introduce you and experience really does, uh, keep a lot of, uh, a lot of the focus.[00:30:12] swyx: I, I think. M maybe it's like still, still early days. You know, Chad, BT is like three plus years old and Yeah. Cloud code is only one. Just turned a year. Yeah. So give it time, you know? Yeah. Like, yeah. I mean, definitely sometimes a lot of people have switched from to Codex. Maybe that will keep going. I, it's like really hard to tell.Uh, yeah. I, I, I do, I do think that. Because we are in this like, high volatility, high temperature phase. Um, the loyalty and stickiness to first movers and category creators, I don't think is as high as it might be in some other, uh, areas in our careers that we've looked at.[00:30:47] Jacob Effron: Yeah. Though, I mean, I've been surprised by the cloud code thing.I, I would've thought that, like, in many ways I always worried about the[00:30:52] swyx: enterprise. You think you would've been gone by now?[00:30:53] Jacob Effron: Not gone. But I would've, I I always worried that the, that the consumer business of these companies would be quite sticky. And then the enterprise API business. Uh, was actually like, you know, in some ways like your least loyal buyers, like they would, they would move to,[00:31:05] swyx: right, right.But, but they worked out that it wasn't the enterprise API it was enterprise product.[00:31:09] Jacob Effron: Totally. And maybe that was the, that was the secret that like, but the amount of lock-in or just default behavior that has happened in that space, uh, is, is more than I might've imagined with two products that by all accounts are pretty damn similar.Yeah.[00:31:22] swyx: No fight there. Uh, I will say I do think that Codex is still in like a catch up. Like in terms of personal experience. Um, the only thing I like out of, out of Codex is the, is like Spark and like yeah. Uh, the, I, I feel like the skills integration is a little bit better. I feel like, uh, the, the speed is a bit better.Maybe ‘cause it's in, is written in rust or whatever. Um, very minor things that you like. Almost like telling yourself rather than like objectively assessing between two, two of them. I, I, I do think, like vibes wise, I think that's going on. Um, the, the, you know, I, I feel like the, the missing questions, uh, in, in this whole debate is like, why is this so concentrated in only two names, right?Yeah. Like, um, how, where, like, where is the Gemini? You know, presence, where's the Xai presence? Um, and like they are trying, it's just they haven't made that much progress yet.[00:32:12] Jacob Effron: But what the, what the Claude Co moment does show, and it actually in some ways makes you a little more bullish on the potential for someone else to catch up because it does feel like if you're the first person to introduce some magical net new product experience, that that actually might be stickier than one might have imagined.[00:32:27] swyx: Right, right, right. Okay. Yeah.[00:32:28] Jacob Effron: And so it's, everyone can believe they have shot[00:32:29] swyx: that. What do you think that new product experience might be like? I, I, it's, it's like, and this is a failure of imagination on my part. Like, I always wonder, like, people always say this like, well, the, the thing that will save us is like being first to the next new thing.Like what is it?[00:32:41] Jacob Effron: Yeah.[00:32:42] swyx: It's like,[00:32:45] Jacob Effron: I dunno, something around like, uh, consumer agent, computer use, like hybrid. I think, obviously, I think we're like scratching the surface on the consumer side.[00:32:53] swyx: So my, my current theory is like the. Open claw is like a vision of things to come.[00:32:58] Jacob Effron: Totally.[00:32:58] swyx: Um, and uh, it's good that O open I has like the association with open claw, but by no means do they have the rights to win it.The general thesis that I have been pursuing now is that the year the same way that 2025 was the year of coding agents, 2026 is coding agents breaking containment to do everything else. Um, and so coding agents continue to still win, but because they generate software and software eats the world, so like, it's kind of like the trans.Associated property of like software, eat the world, coding agents, eat software, therefore coding agents eat the world. Um, which is like an interesting,[00:33:30] Jacob Effron: yeah, and breaking containment always an easier phase phrase in the consumer context than the enterprise one. You've seen people run these really cool, uh, experiments in their own personal lives.I think like,[00:33:37] swyx: yes.[00:33:38] Jacob Effron: Figuring out, you know, how you, obviously everyone's focused, you know, on the enterprise side now around how you create these experiences. I feel like the vibes, you know, people love to have these narratives of like, everything is completely shifted. It's like I actually, you know, open AI.Organizationally, uh, you know, volatility aside is, you know, great products, great team, great models like everyone else in the world is incentivized for there to be. Two, three more. Everyone would love more like great model companies. And so I feel like the, the natural forces of the world revolt when any one company, you know, is too much the star of the show, right?There's so many people in the ecosystem that are incentivized for that not to happen. And so I think I'd be shocked if we don't have. Uh, uh, reversion of vibes, not maybe completely the other way, but at least a little bit more equal at some point over the next six, 12 months.[00:34:24] swyx: I, I think there's just a kind of different stages when, when you talk about the world, one wanting more model companies, I talked think about like the neo labs.[00:34:30] Jacob Effron: Yeah.[00:34:31] swyx: And I mean, I don't know, is it fair to say none of them have really broken through in the past year?[00:34:35] Jacob Effron: I think that's totally fair,[00:34:37] swyx: which is rough. Um, and well, how are we gonna, how are we gonna grow that diversity in, in, in choice, like. Um, that's, this is it.[00:34:46] Jacob Effron: Yeah. It'll be really interesting to see what, what, what ends up happening with that.And you've seen, you know, folks like Nvidia, you know, very incentivized to make sure there's, there's a broader platform of, of other model providers.[00:34:57] swyx: I think, uh, I don't know people say this, but I, I, I don't think they try it hard. Nvidia tries harder to build neo clouds[00:35:05] Jacob Effron: Yeah.[00:35:06] swyx: Than neo labs.[00:35:07] Jacob Effron: Well, they try pretty damn hard to build neo Cloud, so[00:35:09] swyx: that's,[00:35:09] Jacob Effron: yeah.[00:35:10] swyx: But like, you know, let's call it like the, the core weaves of the world, much happier place in the, you know, than any neo lab built on top of them.[00:35:18] Jacob Effron: Yeah. That one might argue it's, it's easier to, to enable a neo cloud to be successful than it is. Uh, you can't will a neo lab into existence the same way you, soNvidia[00:35:25] swyx: has more direct control over it.Uh, for sure.[00:35:27] Jacob Effron: What else is kind of catching your eye today on the startup side? I mean, you worry, there's obviously this whole narrative of like, you know, the foundation models, you know, they announced a product and every stock goes down 15%. Like[00:35:36] swyx: Yeah.[00:35:37] Jacob Effron: Do you, do you worry about the foundation models just kind of eating into to a bunch of these startup categories?[00:35:43] swyx: Not really. I, I think actually like. As, uh, there's, there's, okay, there's, there's, there's the, there's the point of view of like being an investor in startups, and there's a point of view of like, do you wanna start something? And I think honestly, like the, the downside for all these is so. Minimal in, in a sense of like, the worst you do is you just get hired into one of these labs anyway.So I, I think the, the market for people who just do things and try things and try to execute in like a competent way, even if like it doesn't work out commercially, even if it just wasn't that great anyway. Like, but like that's your job interview to go into, into one of these things anyway, so, um, I don't feel that.From a, from a very, very small startup perspective, mid-size startups. Yes. Uh, I will say there's been a lot of dead, um, LM Infra, a lot of LM infra consolidation like the, the, uh, lang fuses of the world getting absorbed into, into click house. And I, I think. Like people have maybe worked out the domain specific playbook, uh, and like, I think that's okay.Um, and, and yeah, I'm not that, not that worried about, uh, okay. So, um, I, I would say I'd be more worried about traditional SaaS, like low NPSS. This is the whole AI versus SaaS debate that has, that's been going on. Uh, and, and like literally I'm going through that exact thing in my company where, so I like kind of.Thinking through this on a very visceral, visceral level, right? On one hand you have the people who say you vibe coders don't appreciate the amount of work that goes into A-A-C-R-M and like, yeah, you think you can rip out Salesforce? So did the 30 entrepreneurs before you, right? Like, like, you know, you classically underestimate the things that you don't.Deeply, no. And, and, and target audience is not you. Uh, at the same time, like we have never been able to build software so easily and customize software so easily and like Yeah, you're not gonna use 90% of the things in Salesforce. So like, yeah. What's the typical, so what have you, what[00:37:33] Jacob Effron: have you done internally?[00:37:34] swyx: So we have there the main SaaS that we do for event management and sponsor management. That's, and we paid 200 KA year for that. Not, not huge, but like chunky for, for, for my, my scale. Um, and like, yeah, I could probably spend 2000 and, and build like a custom version of that. Um, the, the, the trick has been dealing with my, the rest of my team and getting them on board.Yeah. ‘cause I'm the most ethical person on my team, but like, I can't make that decision myself. And I think in the same way I've been telling with other CEOs team leaders as well, it's like, well you can be super cloud pilled. You can be super LM psychosis and that you think that's okay, but you like you have to bring your team with you.And I think like there, the sort of widening disparity in LM psychosis in companies is causing real s real riffs because. And on one hand, on one hand, the people who are less AI native are not getting with the picture. They're not, they're actually like behind, they're actually not waking up to the fact that like you, everything you think is necessary is not actually that necessary.And in fact, exactly would be better of you if you just like held your nose and went in and when came out the other side. Yeah, only talking to agents in natural language and like your life would actually be better and you just, you're just like close-minded. There's that perspective. The other perspective is, oh, you vibe coder.You, you did this in a weekend and you got the 80% solution and now the rest of your employees. Have to pick up the rest of your s**t, right, that you, that you thought you were, you were such hot, amazing, uh, uh, at, but like, actually you didn't figure it out. And like, actually LMS are still useless at this and blah, blah, blah.So like, I think there's this huge debate going on in every company right now. Um, and like, um, you know, I have a small microcosm of it, but like, yeah, it, it's making me hesitate to, to pull the trigger. But like I will at some point, it's like maybe I've put it off for one year, but not like five. Yeah, but like, so, so like SaaS is definitely getting squeezed.Um, it does make me wonder, like, I, I do think that there's an opportunity for a more AI native, um, system of record thing that is not just Postgres. Um, or not just MongoDB, although both are very good. Maybe it's like a convex or like people Yeah. Bring up convex a lot. I don't know, like, like, I, I just feel like the sort of quote unquote firebase of, of AI apps isn't really a thing yet.Um, beyond what we have. Uh, which, which is fine. It's, it's, it's just. We could probably start in a more sort of rapid iteration cycle first before scaling up to like a Postgres or MongoDB, which are more sort of old tech. I was at a dinner with, uh, Mike Krieger, the CPO of en philanthropic, and, and he, we were just kind of going around the room going like, what are people most worried about?Yeah. And, uh, for me, uh, I, instead of security, I brought up biosafety. Yeah,[00:40:21] Jacob Effron: classic.[00:40:22] swyx: Um, actually, like I said, it was. Cliche and classic, and the rest of the table were, were like, what do you mean? Someone sitting at home can manufacture a virus that wipes out half of humanity,[00:40:32] Jacob Effron: almost like the OG Jeffrey Hinton.Like, this is why you should be scared.[00:40:35] swyx: I'm like, yeah, like the read the, you know, risk reports. Like this is like the thing. Um, I think, and Mike was just sitting there knowing he was sitting on Mythos and going like, actually it's security. Um, and I think like, um, I think the, there's, there's, part of it is.A very good marketing. Like too good. Yeah, like I would actually advise and topic to tune down the marketing because also it's, it is just a very good model and you don't have to make so many marketing claims around it. At the same time, it is not really a private model. If you give it to 40 companies.Each of whom have like 10,000 employees or whatever. Right. It's not, it's not private, it's, it's like there's bad actors in there.[00:41:18] Jacob Effron: Yeah. Hopefully, hopefully not as, uh, as bad as releasing it widely, but, uh, no, I mean, it's an interesting. You know, it's an interesting case study for how all, I mean, many model releases might, I mean, you know, this might be the first model release that looks like the rest of ‘em from from now on, right?[00:41:31] swyx: It, it, so it's, it's the, there's an overall product strategy, uh, for anthropic of like bundle, uh, you know, restrict access bundle, uh, product with model maybe.Whereas, uh, OpenAI has definitely been a lot more sort of. Philosophically aligned on like, we will just enable access everywhere and we don't know what you, what will come out of it. Right.[00:41:51] Jacob Effron: Right. Though, I mean, this current moment, uh, obviously the cynical take is also just ties to the amount of compute that both companies[00:41:56] swyx: Yeah.Right, right, right. Yeah, I think, I think that's true. I I do think like the, the, this is the, the, the scale, the dawn of like larger than 10 trillion parameter models is very interesting. I don't think it, I think it's a temporary phenomenon because we have much larger compute clusters coming online for everyone over the next like three, five years.It's, and this is like already written in, in the cards.[00:42:18] Jacob Effron: Yeah.[00:42:19] swyx: So to the extent that like, you know, will we have rationing of models, uh, above 10 trillion, uh, in like two years? I don't think so. I think everyone will have no, we'll just[00:42:29] Jacob Effron: have rationing of the next phase.[00:42:30] swyx: Right. Right. But like, that's as it should be almost like, um.My, my classic example, which I, this is just me theorizing, not anything confirmed by Google. When Google announced Gemini, they actually announced three sizes, which was Flash Pro Ultra. They never released Ultra. They only have Pro and Flash. Um, so my theory is they have ultra sitting in a basement and they just could distilling from it for, for flashing pro.Um, which like, yeah, I mean, I, I actually think that's. As it should be for any lab that they, that they do that.[00:43:02] Jacob Effron: Yeah. Just because those are the models that people actually wanna end up using. And it's just like cost prohibit.[00:43:06] swyx: It is more, yeah, it's cost. Yeah. It's, it's not the want, it's just, just, just the cost.Um, I do think, like, uh, it is interesting that, uh, for a while I was, I was considering the theory that models capped out at two, 2 trillion, and I think that's proving to be wrong. And well then if I'm wrong, how wrong? How wrong am I? Do we do 200 trillion? Do we do two quarter trillion, whatever? Um, and I don't think we have the straight answer to that, but like, uh, it's interesting that we are continuing to scale number of pers when everyone kind of assu like can see that we're not going to get like the next thousand or 1 million x from this paradigm.So like the others, like the alias of the world are working on other. Um, model architecture improvements. We need a different scaling law, I guess, because like, we're, I, I feel like people already already feel like we're tapped out on this. Like the, the end, the end state of this is we turn most of the world into data centers and like, I don't know.I don't know if we want that.[00:44:08] Jacob Effron: Yeah, I mean, uh, if the, if, if, if the return of intelligence are there, maybe, uh, maybe not so bad.[00:44:13] swyx: I, I, I think there, there's just a sheer amount of like, like un scalability that like is wrangling people's sensibilities right now. Um, especially in terms of like context lengths.Um, my classic quote is that context length is like the slowest scaling factor in, in lms.[00:44:30] Jacob Effron: Yeah.[00:44:30] swyx: Um, we, like, we took maybe. Three years to go from like 4,000 context length to a million and that's about it. Yeah. Like Gemini has had a million token context length for two years now. Um, and no one's using it.Like, so like yeah, it's memory. Memory is probably gonna be the, the biggest limiting constraint on all these things.[00:44:50] Jacob Effron: Yeah. Certainly seems that way. I guess I'm curious over the last year since you recorded last, like what's one thing you've changed your mind on?[00:44:57] swyx: I feel like I was kind of bearish on open models like last year.Um, in a sense of, like, I, I had just done the podcast with an Al[00:45:07] Jacob Effron: Yeah.[00:45:08] swyx: Of Braintrust where he, and he, I mean, you know, he has a good cross section of all the top AI companies and he says market share of open source is 5% and going down. Um, I think that's changed. I think it's going up. Um, and even if,[00:45:22] Jacob Effron: even though the capability gap does seem to be increasing.Spending on the[00:45:26] swyx: time. It's hard to tell. Yeah, it's, it's really hard to tell. ‘cause like, okay, for, for listeners, capability gap increasing is like on public benchmarks. And let's say you're comparing mythos versus like, I don't know, G-T-O-S-S or like GLM 5.1. And, um, it's, it is really hard to tell. ‘cause even if they were closing, you will also not believe that they were closing that much because it's very easy to gain the benchmarks.Yeah. So you just don't really, really know. Um, all you know is like. Uh, there's somewhat objective open router stats on like what people choose in a free market. And people do choose some of these open models in significant volume, except that a lot of them are heavily discounted. So you need to kind of like price adjust, uh, these things.So even if, even if that were true, which I, I'm not sure, like I, I, I feel like the numbers just up now instead of down. Uh, I think the. Separation between what the top tier agent labs

Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM Cas de Morree and Mark Smith unpack why applied AI skills now matter more than prompts. They explore Copilot's real strength inside work data, the rapid shift towards agents and outcome‑driven systems, and how low‑cost tooling is reshaping productivity. The conversation challenges привычные workflows, highlights why more output does not equal better work, and shows how professionals can rethink email, meetings, automation, and software building using AI that actually integrates into daily work.

Episode 131 How to design read aloud lessons that build understanding—not just engagement The difference between read aloud that supplements vs. supplants your instruction Using read aloud to teach reading skills like character motivation and author's craft How to connect knowledge building and accountable talk into one cohesive lesson Embedding learning science strategies like retrieval practice and interleaving into read aloud Designing literacy instruction so students remember and apply what they learn over timePractical Strategies Mentioned• Modeling character motivation during read aloud using sentence stems • Using repetition in a text to teach author's craft • Retrieval prompts like “What happened yesterday?” • Interleaving skills (character traits + motivation in one question) • Echo, choral, and partner reading followed by comprehension checks • Planning intentional stopping points and think-alouds • Using text sets (poems, articles, videos) to deepen understandingThese are all strategies grounded in the science of reading and learning science that help students move from understanding in the moment to learning that actually sticks.As you listen, consider this question:What is my read aloud actually doing in my literacy block?Is it:Filling time?Reinforcing skills?Or driving instruction and building understanding over time?Instructional leadership starts with teachers who are willing to move from doing the lesson to designing the learning experience.Earthquake Terror (used as a mentor text example for author's craft)Wonder by R.J. Palacio (used for text connections and deeper thinking)Episode 129: Why Read Aloud Still Matters in Upper Elementary Episode 130: How Accountable Talk Builds Thinking in Your Literacy ClassroomIf you're ready to strengthen your instruction and design literacy lessons that actually stick, you can learn more about coaching and professional development below:In This Episode We DiscussSelf-Leadership ReflectionResources MentionedPrevious Episodes ReferencedWork With EvaGrab my free guide: How to Keep Your Mini Lesson Mini  Book a discovery call for 1:1 coaching or school professional development

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Mauro Schilman, CTO and Co-founder of Tuki, the distribution standard for the AI agent era in travel, for a wide-ranging conversation that moves from the joys of international travel and the beauty of mathematics to the fast-evolving world of AI and large language models. Mauro shares his background as a math Olympiad competitor and later a coach, his time training coding models at the AI company Cohere, and his thoughts on how frontier models are progressing — or plateauing — at the foundational level while innovation accelerates at the application layer. The two also get into the mechanics of agentic AI, MCP and agent-to-agent protocols, hierarchical memory systems, red-green test-driven development as a powerful coding workflow, and the philosophical murkiness of open-source AI. They wrap up discussing Tuki Travel's mission to build AI-ready infrastructure for the travel industry, connecting hotels, suppliers, and online travel agencies to prepare for the coming wave of agentic commerce. You can learn more about Tuki Travel and reach out to the team at tukiclub.com.Timestamps00:00 - Stewart welcomes Mauro Schilman, CTO and Co-founder of Tuki Travel, who shares how traveling since age 15 through high school exchanges opened his mind to cultural similarities and differences.05:00 - Mauro explains Math Olympiad coaching culture and mentorship, noting LLMs now solve competition-level problems while Terence Tao explores AI assisting frontier unsolved mathematics.10:00 - Discussion turns to ChatGPT revealing Mauro's birthdate unprompted, exposing opaque application layers, preference tuning, and system prompts hidden within closed models.15:00 - Mauro argues true open source AI requires full training data, annotation protocols, and alignment processes, not just model weights, while scaling laws appear to be slowing.20:00 - Hierarchical memory models replace flat vector databases, using three-level retrieval systems improving context accuracy as knowledge management becomes AI's core challenge.25:00 - Mauro describes travel's fragmented infrastructure of aggregators, bed banks, and intermediaries, explaining Tuki builds agent-ready unification protocols for AI commerce.30:00 - MCP versus API debate clarifies natural language capability descriptions help agents consume services, while agent-to-agent communication embeds negotiating agents inside supplier systems.35:00 - Hallucinations and consumer trust block agentic payments, industries must build mistake-resilience into bookings before autonomous agent transactions become viable.40:00 - Mauro reveals red-green test-driven development methodology where agents write failing tests first then implementations, creating Oracle verification loops dramatically improving code quality.45:00 - Blockchain's potential for transparent distributed AI training discussed, distinguishing democratization from decentralization while stable coins and regulatory momentum build toward agentic commerce infrastructure.Key Insights1. Travel broadens perspective by revealing both universal human similarities and deep cultural differences. Mauro Schilman began traveling at fifteen through math olympiad competitions and found that people across the world share fundamental traits while also being shaped in profoundly different ways by their cultures. This tension between sameness and difference is what makes travel meaningful.2. Mathematics transitions from structured problem-solving in olympiads to genuine uncertainty in graduate school and research. Olympiad problems are carefully designed with elegant solutions meant to encourage creative thinking, but once a mathematician enters academia, the answers are unknown and the work becomes navigating that uncertainty.3. AI is now assisting mathematicians at the frontier, not just solving olympiad-level problems. Terence Tao, one of the greatest living mathematicians, has written publicly about how AI tools can help tackle unsolved problems, though the role of AI remains assistive rather than independent at the research level.4. Large language models are not truly transparent even when described as open source. Releasing model weights alone does not reveal the training data, annotation protocols, alignment tuning, or system prompts that shape model behavior. Real openness would require access to the entire pipeline.5. Memory and retrieval remain core unsolved challenges in AI systems. Researchers are moving from flat vector database approaches toward hierarchical memory structures with roughly three layers, which improves retrieval accuracy and reduces how much context gets consumed with each search.6. The travel industry is structurally unprepared for AI agents. A hidden web of bed banks, aggregators, and aggregators of aggregators sits between hotels and consumers, each taking a fee. Tuki Travel is building infrastructure to unify this distribution layer and make it consumable by AI agents through protocols like MCP and emerging agent-to-agent communication standards.7. Test-driven development using a red-green approach significantly improves AI-generated code quality. By asking the model to write failing tests before writing any implementation, developers create a verification oracle that guides the model toward correct solutions and avoids the bias of writing tests that simply confirm existing flawed code.

Join Walter Sterling for a late-night ride through the strange and unexplained! In this episode, journalist Ross Coulthart drops a bombshell about 11 missing aerospace and nuclear scientists who may be tied to a secret government UFO crash retrieval program. Retired NYPD detective Vic Ferrari also stops by to share the hilarious saga of a 300-pound serial car thief named Spud who made a terrible police informant. Plus, Walter and his callers debate whether a weaker gravitational pull is the real secret behind the construction of the pyramids and giant dinosaurs, argue about the physics of water on a spinning globe, and ponder if shape-shifting reptilians and mermaids secretly live inside the Earth. Finally, Matias "Boom Boom" delivers the Hollywood scoop with a review of Bob Odenkirk's bloody new action-comedy Normal. Tune in for aliens, anti-gravity, and absolute late-night chaos! Learn more about your ad choices. Visit megaphone.fm/adchoices

For all those who missed out on London, see you in Miami next week!Notion, the knowledge work decacorn, has been building AI tooling since before ChatGPT, with many hits from Q&A in 2023 and unified AI in 2024 and Meeting Notes in 2025. At the end of their last Make user conference, Ryan Nystrom teased Notion 3.0's Custom Agents - and they are finally embracing the Agent Lab playbook!Sarah Sachs and Simon Last of Notion join us for a deep dive into how Notion built Custom Agents, why it took years and multiple rebuilds to get right, and what it means to turn a productivity tool into an agent-native system of record for enterprise work.We go inside the product, engineering, evals, pricing, and org design decisions behind one of the most ambitious AI product efforts in software today — from early failed tool-calling experiments in 2022 to agent harnesses, progressive tool disclosure, meeting notes as data capture, and the long-term vision for software factories and agentic work.We discuss:* Sarah and Simon's path to launching Notion Custom Agents, and why the feature was rebuilt four or five times before it was ready for production* Why early agent attempts failed: no tool-calling standard, short context windows, unreliable models, and too much complexity exposed to the model* The “Agent Lab” thesis: not just wrapping a model, but understanding how people collaborate and building the right product system around frontier capabilities* How Notion thinks about roadmap timing: not swimming upstream against model limitations, but also building early enough that the product is ready when the models are* Why coding agents feel like the kernel of AGI, and how Notion is thinking about “software factories” made up of agents that spec, code, test, debug, review, and maintain codebases together* How Sarah runs AI engineering at Notion (“notes from Token Town”): objective-setting over idea ownership, low-ego teams comfortable deleting their own work, and a culture designed to swarm around fast-changing opportunities* The “Simon Vortex,” company hackathons, and why security gets pulled in early rather than late* How Notion organizes AI: core AI capabilities and infrastructure, product packaging teams, and a broader company mandate that every product surface must increasingly work for both humans and agents* Why prototypes have become much easier to build internally, and how “demos over memos” changes product development inside a tool the whole company already uses every day* Notion's eval philosophy: regression tests, launch-quality evals, and “frontier/headroom” evals that intentionally only pass ~30% of the time so the company can see where model capabilities are going* What a “Model Behavior Engineer” is, and why Notion treats eval writing, failure analysis, and model understanding as a distinct function rather than just software engineering* The changing role of software engineers in the age of coding agents, and why the new job looks less like typing code and more like supervising a rigorous outer system of agents, PRs, and verification loops* How the “software factory” should work: specs, self-verification, bug flows, subagents, and minimizing human intervention while preserving the invariants that matter* A live walkthrough of a Notion Custom Agent handling coworking space tenant applications by triaging email, enriching applicants with web search, and writing structured data into a Notion database* How agents compose inside Notion: shared databases as primitives, agents invoking other agents, “manager agents” supervising dozens of specialized agents, and memory implemented simply as pages and databases* Notion's take on MCP vs CLI: why Simon is bullish on CLI's self-debugging nature, where MCP still makes sense, and how Sarah thinks about capability, determinism, permissioning, and pricing alignment* The evolution of Notion's internal agent harness: from early JavaScript coding agents, to custom XML, to Markdown and SQL-like abstractions, to tool definitions, progressive disclosure, and a much shorter system prompt* Why Notion cares about teaching “the top of the class,” building for sophisticated operators rather than abstracting away too much capability for everyone* How agent setup works today: agents that can configure themselves, inspect their own failures, and edit their own instructions — with guardrails around permissions* How Notion prices Custom Agents: credits as an abstraction over tokens, model type, serving tier, web search, and future sandbox costs; why usage-based pricing was necessary; and how “auto” tries to match the right model to the right task* Why Notion is not eager to train a foundation model, where they do fine-tune and optimize today, and why retrieval/ranking is one of the most important investment areas as more searches come from agents rather than humans* Why Meeting Notes became one of Notion's strongest growth loops: not just as transcription, but as high-signal data capture that powers search, custom agents, follow-up workflows, and the broader system of record for company collaboration* Why Notion is more interested in being the place where collaboration data lives than in building hardware themselves — and how wearables or other capture devices may eventually feed into that systemSarah SachsLinkedIn: https://www.linkedin.com/in/sarahmsachsX: https://x.com/sarahmsachsSimon LastLinkedIn: https://www.linkedin.com/in/simon-last-41404140X: https://x.com/simonlastFull Video EpisodeTimestamps* 00:00:00 Introduction and launching Notion Custom Agents* 00:01:17 Why Notion rebuilt agents four or five times* 00:03:35 Building for where models are going, not just where they are* 00:05:32 The Agent Lab thesis, wrappers, and product intuition* 00:08:07 User journeys, leadership, and low-ego AI teams* 00:13:16 The Simon Vortex, hackathons, and bringing security in early* 00:16:39 Team structure, demos over memos, and building for agents* 00:20:25 Evals, Notion's Last Exam, and the Model Behavior Engineer role* 00:27:37 Evals as an agent harness and the changing role of software engineers* 00:30:42 The software factory: specs, verification, and agent workflows* 00:32:18 Live demo: a custom agent for coworking space applications* 00:35:08 Composing agents, manager agents, and memory as pages* 00:38:15 Notion Mail, Gmail, native integrations, and tools* 00:39:43 MCP vs CLI and the cost of capability* 00:44:13 When Notion uses MCP vs building its own integrations* 00:47:43 The history of Notion's agent harness rebuilds* 00:55:35 Power users, public tools, and the setup agent* 00:58:01 Self-fixing agents, permissions, and “flippy”* 01:01:13 Pricing, credits, and choosing the right model automatically* 01:09:01 Why Notion isn't training its own frontier model* 01:14:07 Retrieval, ranking, and search built for agents* 01:17:27 Meeting Notes as data capture and workflow automation* 01:21:18 Wearables, hardware, and Notion as the system of record* 01:23:45 OutroTranscript[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast. This is Alessio founder of Kernel Labs and I'm joined by swyx, editor of the Latent Space.[00:00:11] swyx: Hello. Hello. We're back in the beautiful studio that, uh, Alessio has set up for us with Simon and Sarah from Notion. Welcome.[00:00:18] Sarah Sachs: Thanks for having us.[00:00:19] Alessio: Thanks for having us. Yeah.[00:00:20] swyx: Congrats on the launch recently the custom agents, finally it's here. How's it feel?[00:00:26] Sarah Sachs: We ship things slowly. So it had been in Alpha for a little bit and at the point at which is it's an alpha, um, there's a group of people that are making sure it's ready for prod, and then there's a group of people working on the next thing.So sometimes some of these launches are a bit delayed satisfaction, so it's quite nice to remind yourself all the work you did because we do have a habit of like. Being two or three milestones ahead. Uh, just ‘cause you have to be, you know, you can't get complacent. Um, but it's been great that people understood how this is helpful.And I think that's just easier in general building AI tools today than it was two, three years ago. People kind of get it and so that user education, um, there's just, it was our most successful launch in terms of free trials and converting people and things like that. It was really successful, so yeah.But there's a lot to build.[00:01:12] swyx: Making it free for three months helps.[00:01:16] Sarah Sachs: Yep.[00:01:17] Simon Last: It was definitely super exciting for me because it's probably the fourth or fifth time that we rebuilt that.[00:01:22] swyx: Yes.[00:01:23] Simon Last: And I mean,[00:01:24] swyx: you've been building this since like 20, 22.[00:01:26] Simon Last: Yeah, I mean, like, it was even right when we got access to like GPT four in late 20 22, 1 of the first ideas we had is like, oh, okay, let's make an agent that I, we used the word assistant at the time, there wasn't really the word, the word agent yet, but, oh, we'll give an access to all the tools the notion can do, and then it, we run in the background like, like do work for us.And then we just tried that many times and it just. Was too early. Um,[00:01:48] swyx: I need to force you to like double click on that. What is too early? What didn't work?[00:01:52] Sarah Sachs: We were fine to, like, before function calling came out. We were trying to fine tune with the Frontier Labs and with fireworks, like a function calling model on notion functions.This is right when I joined. I joined because, um, we needed a manager as Simon was needed to be able to go on vacation. So, uh, that's, that's around when I joined, so you can speak much more to it.[00:02:11] Simon Last: Yeah, we did partnerships with both philanthropic and open AI at different times, uh, to try to, at the time the, I mean, when we first tried, there wasn't even a constant of like tools yet.We, we sort of designed our own like, like tool calling framework and then we tried to fine tune the models to, uh, to use it over multiple turns. Um, and because it, it didn't work well out the box, I think. Yeah. The models are just too dumb and the context thing was also way too short.[00:02:37] Alsesio: Yeah.[00:02:37] Simon Last: Um, and yeah, we just kind of banged our head against it for a long time.Uh, unfortunately it was always like, there was always like sort of. Glimmers that it was working, but um, it never felt quite robust enough to be like a useful, delightful thing. Um, until I would say, uh, the big unlock was probably like Sonic 3.6 or seven, uh, early last year. And that's when we started working on our agent, which we shipped last year.Um, and then, and then uh, uh, custom agents, kinda a similar capability and that, that one just took longer because we, we just wanted to get the reliability up a lot higher. ‘cause it's actually running in the background.[00:03:14] Sarah Sachs: And the product interface of like permissions and understanding, you know, this custom agent is shared in a Slack channel with X group of people and has access to documents that are surfaced to Y group of people.And the intersect experts, Y might not be whole. And so how do you build the product around making sure administrators understand that permissioning took multiple swings.[00:03:35] Alsesio: Everything is hard back at the end of the day. Yeah. I'm curious, like when the models are not working, how do you inform the product roadmap of like, okay, we should probably build, expecting the models to be better at some reasonable pace, but at the same time we need to, you know, you had a lot of customers in 2022.It's not like you were a new company or like no user base.[00:03:54] Simon Last: Yeah, I mean I think there's always the balance of, you know, like you want to be a GI pilled and thinking ahead and building for where things are going. Uh, but also you wanna be like shipping useful things. And so we always try to like, like keep a balance there.You know, we. We try to take clear, like a portfolio approach. You know, we're always working on multiple projects and, and we're always trying to work on, you know, maintaining things where that have already shipped, like, like shipping new things that are like eminently working well and make them really good.And, and then we wanna always have a few projects that are a little bit crazy. Um,[00:04:23] Alsesio: and what are the a GI peel projects that you have today? I'm curious about, uh, you don't have to share exactly what you're working on, but I'm curious what are things today that maybe in 18 months people will be like, oh, obviously this was gonna work[00:04:35] Sarah Sachs: 18 months.[00:04:37] Alsesio: Yeah, 18 months is, you know,[00:04:37] Sarah Sachs: it's a long time and Yeah. Yeah.[00:04:39] Simon Last: I mean, there's a number of things happening. I think one thing that's becoming more clear is I think like, like, uh, coding agents are the kernel of EGI, sort of, everything is a coding agent. Mm-hmm. I think that's, that's sort of one, one direction.Um, and then, yeah, the exciting thing about that is sort of your agent can sort of bootstrap its own software and capabilities and actually debug and maintain them. And so yeah, we're, we're, we're thinking a lot about that. And then, yeah, like, like another category of things that I'm, I'm really excited about is like, uh, we call the software factory also.People are using this, uh, this, this sort of word. Um, basically it just means can you create sort of like a, as automated as possible, a workflow for developing debugging. Mm-hmm. Merging, reviewing, and maintaining a code base and a service where there's a bunch of agents working together inside, and like, like how does that work?[00:05:28] Sarah Sachs: If you think back to your initial question, like, why did this take so long? I think something,[00:05:32] swyx: I didn't say that, but Yes. Okay. Go ahead.[00:05:34] Sarah Sachs: Why, what, what changed over the three and half years of trying[00:05:37] swyx: it? Exactly. Right. Because most people always say like, it didn't work yet. Then reasoning models came, then it worked.I was like, okay, let's go a little[00:05:43] Sarah Sachs: bit. That's, I mean, that's part of it, but I think the other part of it that I actually think is really what will set notion apart for every new capability is we have like. Two skills that are crucial when it comes to frontier capabilities. One is not letting yourself swim upstream.So like quickly realizing if you're just pressing against model capabilities versus not exposing the model to the right information, not having the right infrastructure set up. That and of itself is the skill of intuition. And the second is to see, okay, you're not swimming upstream. Which direction is the river flowing and what is like, how do we think ahead about the product and start building it even if it's not great yet, so that when it is there, we're ready for it.Right? And like those can sometimes feel like counterintuitive things. Like we can be trying to fine tune a tool calling model when they don't exist yet. And that the trick is to not do that for too long, but realize that there was something there. And we've had a lot of things which like, um, we're just like not swimming in the right direction with the streams.I think we had multiple versions of transcription before we got meeting notes, right? Oh, I gotta talk[00:06:39] swyx: about that. Yeah.[00:06:40] Sarah Sachs: Yeah. Um, and so. I, I, I think that like we, we really closely partner with the Frontier Labs on capabilities and we also have to have strong conviction on, as those capabilities move.Notion is about being the best place for you to collaborate and do your work. And how does that narrative change if the way that we work changes?Yeah.[00:06:58] swyx: Yeah. You told me you were a fan of the Agent Lab thesis, and this is, this is kind of it, right?[00:07:02] Sarah Sachs: Right. I show that thesis to so many candidates. Like I have it as like micro chrome autofill.Um, at this point, like it's one of my most visitations[00:07:10] swyx: because like, is this the, here's why you should work in notion and not open, open eye. I, it's like,[00:07:14] Sarah Sachs: here's, here's what's different about it.[00:07:16] swyx: Yeah.[00:07:16] Sarah Sachs: And here's why. It's not just a rapper. I actually think more and more people understand it's not just a wrapper.[00:07:21] swyx: Yeah.[00:07:22] Sarah Sachs: Um, and by the way, like in the beginning, parts of what we build are wrappers on functionality. That works well, of course, but that's not really the most, um. I would say that's not the product that, that drives revenue. And that's not necessarily always what users need.[00:07:35] swyx: I mean, you know, notion is the AWS wrapper, but like the, the wrapper is very beautiful and like very, very well polished.So[00:07:40] Sarah Sachs: like the analogy,[00:07:41] swyx: like[00:07:42] Sarah Sachs: the analogy that I've been coming back to his Datadog in AWS[00:07:45] swyx: Yeah.[00:07:46] Sarah Sachs: So, uh, Datadog could not exist with, without cloud storage. Right. That it's kind of fundamental that that works. Um, and AWS has like a CloudWatch product, but Datadog is an expert on understanding how people want observability on the products they launch.And we're experts in understanding how people wanna collaborate, and that's really where our expertise lies.[00:08:04] swyx: Totally.[00:08:04] Sarah Sachs: Um, regardless of the tools that we use,[00:08:07] Alsesio: I'm kind of curious how you think about implicit versus explicit expertise. I feel like Datadog is half and half implicit and explicit. It's like they understand across markets and industries what engineering teams usually look for.With notion, it's almost like more of the expertise is at the edge because you as a platform, you're like so horizontal that the end user is not really the same. Mm-hmm. Like with Datadog, the end user is always like, yeah, an engineering lead, a kinda like SRE related person with notion. It can be anything.So I'm curious how you put that expertise into a product versus, you know, obviously it, WS cannot build notion. It's, that doesn't quite work in this case, but[00:08:44] Simon Last: it's, it's a little bit differently shaped. I think, you know, a classic vertical SaaS, like the data is kind of like that. They understand their individual customer very deeply.It's kinda a narrow slice, um, notion has always been super horizontal. And our, our task has always been to sort of balance these two somewhat opposing forces of like, we're listening to our customers and what they want us to build. It's a broad slice. And then also we're thinking about like, okay, how do we decompose what they want into, uh, nice primitives that are, that are really nice to use and we'll, we'll get us like as much bang for the buck as possible.And then, you know. Maintain the whole system, make it all like, like super clean and nice to use.[00:09:22] Sarah Sachs: We still have user journeys. I mean, we still focus on like core. I actually think the failure of our team is when we focus too much on what are cools that are, what are tools that are[00:09:31] Simon Last: mm-hmm.[00:09:31] Sarah Sachs: Cool tools. I actually think that's when we make have the least velocity because you still need some sort of focus on a user journey.So like for instance, we'll all sit down every Friday and look at the P 99 of like the most token exhaustive custom agent transcript and just look at why it didn't do well and cut a bunch of tasks. Like we still focus on like, this has, like this should work. Email triaging should work. Mm-hmm. Right. And similarly, like when we're talking about before building, um, chatting, um, before we started filming about, okay, how can I do PDF export?Well that's functionality that then merits. Maybe we should build a tool that has access to a computer sandbox in a file system and the ability to write code. Right? Right. Um, but it's because we're thinking about the fact that our users to do their, to do their daily work, need to export PDFs, not because we're like, Hmm, I think a computer tool could be cool.Like, let's just see what happens. Mm-hmm. Like we, we have to focus on some user journeys, otherwise we just don't have like, enough strategy to, to prioritize.[00:10:29] swyx: I think there's a lot of like really strong opinions that you've had. Do you have like sort of like a towel of Sarah Sachs? Like, you know, like what, how do you run your team?Like I feel like you just have accumulated all these strong opinions. Obviously part, part of this is your, your token town thing.[00:10:43] Sarah Sachs: I think the TAs working with Service X is, um, you'd have to, it depends who you ask. Um, I think it depends if you're on my team or a partner Right. Or a vendor.[00:10:54] swyx: Yeah. There other people want to run their teams the way that you're Yeah.You're like bringing these things. And then also similarly, uh, Simon, when you did the custom agents demo, you had like, well, we've been using custom agents and here's the super long list of everything that we do. No humans ever read it. Right? That's what you said. I was like,[00:11:07] Sarah Sachs: yeah. So I think for, for me, um, something that I learned very quickly and became very comfortable with was that my job was not to be the ideas per person or the technical expert.My job was to make it so that everybody understood the objective, had a resource to help prioritize what they should work on, and had an avenue to prioritize what they thought was important. And I think that's true with all, all leadership, but I think especially on the AI team. Almost all of our best ideas come from prototypes, from people that have a cool idea because they saw a user problem, and it's a huge disservice if all of those ideas have to pass, like the sniff test of what me and a product partner or Simon and Ivan decided were the direction, right?Because a lot of what we're doing is leaning into capabilities, so. I think that's the first thing is like, I don't really view like the role of engineering leadership as like, uh, hierarchical, nor has it ever been, but especially now, like very willing to change direction based on, um, like proof is in the pudding.Yeah. And like, and I think we have rebuilt our harness three or four times. And when you do that, then the second rule of engineering leadership is like you need to build a team that's comfortable deleting their own code and is very low ego and is driven by what's best for the company. And, um, doesn't write design docs because they think it's their promotion packet.Right. And that's a culture that notion had long before I joined, but like our willingness to just swarm on different problems and um, redo things that we've built before because something has changed. Like, there's a lot of friction that can happen at companies when you do that. And it doesn't happen at Notion.And because it doesn't happen when new people join. Like they don't wanna be the ones that are saying, we shouldn't do this. I wrote that code. So then it's, you know, you, you create a culture that everyone thoughts and that culture comes directly, I think from Simon and Ivan though, um, because they're very open-minded.[00:12:50] swyx: Anything that you,[00:12:50] Simon Last: you'd add? I'm not a manager, like, like, like Sarah is. Um, a lot of my role is really to try to think a little bit ahead, make sure that we're, we're building on the right capabilities and then like the prototyping stuff. And yeah, it's really, really critical to always just be starting again.It's like, okay, this is new thing. What does this mean? What if we just rethought everything or wrote everything? And so I, I'm, I'm basically just doing that in a loop every six months.[00:13:16] swyx: Yeah. Do you believe in internal hackathons for this stuff?[00:13:19] Sarah Sachs: I think there's like two different versions. So one is like, we just have a, a, a solid bench of senior engineers that come and go on what we call the Simon Vortex and Productionizing what we built, right?Because when you're in the Simon Vortex, the velocity is super high. The direction changes daily, and it's meant to be like the equivalent of a SC Works lab. We don't need to do hackathons for that. We need to have senior engineers that we trust to come in and out of those projects. For instance, like management boundaries are really loose.Like you report to him, but you work for her right now. Yeah. That's something that when we hire managers, it's important they don't care about because we tend to form more structures. Yeah. Don't be too[00:13:54] swyx: territorial.[00:13:55] Sarah Sachs: We form more. It's after we ship things, not not before, just historically. Um, the second thing is we do have companywide hackathons.Actually we just had our demos day for the hackathon we had last week this morning. That's more for people that aren't directly working on the project, feeling like they have the time to pause and learn how to make themselves more productive or how they would use notion custom agents to build something.Or part of the hackathon was actually encouraging everyone across the company to build their own agentic tool loop, calling from scratch. Follow like an every blog post on how to do what I think because we want[00:14:26] swyx: just with the compound engineering one. Yeah.[00:14:28] Sarah Sachs: We want everyone to use cloud code in the company or whatever the coding agent they please and understand that fundamental.So we set aside a day and a half. We're all leadership, encourage everyone on their teams across the company to do it. So we have hackathons like that. I would say like kind of facetiously, like everything we build is a little bit like a hackathon until it graduates and puts on big boy pants and as a product ops rollout leader and has a assigned data scientists and stuff like that,[00:14:54] swyx: security review enterprise stuff,[00:14:56] Sarah Sachs: actually security reviews one of the things that we bring in first because it just slows us down way more and, um, causes a lot of tension and they build better product if they're involved early.So, um, that is probably the first person to get involved in something that's the[00:15:09] swyx: right PR approved answer.[00:15:10] Sarah Sachs: No, but it's not just PR approved. It like, um, um, it's[00:15:13] swyx: actually real. It's actually real. It's like, um, I'm just saying scar[00:15:15] Sarah Sachs: tissue.[00:15:15] swyx: Yeah,[00:15:16] Sarah Sachs: because like, you know, my background's also, I worked at Robinhood for a number of years.Yes. So like, uh, compliance and things like that, um, are a little bit more, you learn the hard way when it doesn't come naturally.[00:15:26] Simon Last: Yeah. I think the. The hackathon is really important for uplifting the general population, but like, if that's the only way you can build new things, you're kind of toast. I mean, it, it has to be like the daily processes, like, you know, building these new things.Um, and it has to be about, I think like, I think in the AI era a lot more leverage accumulates to the most curious and excited people. And so it's like we're all about just like activating that energy. You know, like if someone's protesting something on the weekend that they're excited about and it's important, that should be the main thing that we're doing.Yeah. Um, it's not a hackathon that we schedule once a quarter, it's just like, yeah. Daily process. Part of the culture.[00:16:02] Sarah Sachs: I mean, that's how we shift image generation and notion now. It was always this thing that would be kind of nice to have, but it wasn't really clear where that was necessarily aligned in product priorities.It'd be a lot of work. And we had someone on the database collections team, Jimmy, who was like. I really wanna do image generation for cover photos and inside notion. And we're like, if you wanna build it, like it's, do it please. Like we encourage you. We gave ‘em all the resources of working directly with Gemini and being able to like track the token usage and it working through endpoints.We gave them eval, support, everything, and then became a, a full project.[00:16:34] Alsesio: Yeah.[00:16:35] Sarah Sachs: That's why you can't have like ego as a, a leader. Like that's, that's how we work.[00:16:39] Alsesio: What's the size of the team today, both engineering and overall?[00:16:43] Sarah Sachs: I manage, uh, the team. That's what we'll call it. Core AI capabilities and infrastructure.That's about 50 people. But then we have per i partner teams that do packaging. So how it shows up in the corner chat versus custom agents versus meeting notes, that's another 30, 40 people. And, and then every team that has a product service at Notion that a user can interface with owns the tool that the agent interfaces with the editor team.The team that did CRDT for offline mode is the same team that handles how two agents, um, edit competing blocks. Mm-hmm. Right? It's the same problem. The team that built the underlying SQL engine is the same team that owns how the agent asks it to run a SQL query, and it does it performantly. And so from that regard, anyone working on product engineering is tasked with making them work for customers that are humans and agents because over time the majority of our traffic will be coming from agencies using in our interface, not humans.And so. Our objective is to make it so that the whole product org is building for agents.[00:17:40] Alsesio: Yeah. How has it changed internally? The activation bar is kind of lowered a lot. Like anybody can kind of create a prototype very, somewhat easily, especially if you're like an existing code base. Have you raised the bar on like what type of prototype people need to bring forward to gonna be taken?Not like seriously, but like, you know what I[00:17:58] Simon Last: mean? Yeah. I think the bar is lowered in many ways. Be like, one thing our, uh, our team built that is really cool is our, uh, our, our design team made a whole separate GitHub repo, uh, called the, the design Playground. And it's basically just to create a bunch of like, like helper components and you, uh, for, for quickly a throwing together UIs.And it's become like actually quite sophisticated. Like it has like an agent in there and like, uh, that's pretty fun. So like, we pretty much, like, they don't do mocks, they just make like, like full, full prototypes.[00:18:27] swyx: Here it is. It works.[00:18:28] Simon Last: They give you like a u rl. They're like, okay, all right. So we have to make the, like the real production version of that.Um, and then for engineers. A prototype looks like just making it a feature flag that actually works. Like that's sort of the bar.[00:18:39] Sarah Sachs: Something to understand that's really unique about notion. One of the reasons I joined we're super lucky is no one uses Notion in their job as much as people that work at Notion.[00:18:46] Simon Last: Of course.[00:18:47] Sarah Sachs: So I think there's very few companies, maybe if you worked on Chrome I guess, but like everything that we ship, we ship internally first and get a lot of really quick feedback. And also sometimes our dev instance is totally borked and you have to change a bunch of flags to get things done. And that's kind of like, but everyone, so people that do it ticketing, people that do supply chain procurement, recruiting, everyone is using the same instance of notion with like a lot of flags on for these prototypes people build.Um, and so we have this, Brian Levin, one of the designers on our team, I think evangelize this concept of demos over memos.[00:19:18] swyx: Ooh, too[00:19:20] Sarah Sachs: good. Um, which has been, uh, very good for building demos, and I think it's put a big pressure point on us to have really strong product conviction, because if anything can be demoed, you really need a strong filter of making sure that if you know, you're doing X amount of work, you're making the, you're, you're focusing on one tower, you're not just building a really flat hill.Right. That's actually where I think there has to be more conviction from our PMs, um, and our designers and, and well, the company really to have conviction of what journey we're going on.[00:19:52] Simon Last: But overall, I feel like it works pretty well. Like people, almost all the engineers have good enough taste to realize that like, this prototype doesn't actually make sense in the product, or, or it does.So it's not that common that I would see a prototype. It's like, oh, this makes no sense. Mm-hmm. It's like, you know, people are doing reasonable things and, and, and then it's just a matter of. Which things we build first and then often just, just figuring out how to turn it on and off. There's our, in the, in our like experimental chat ui, there's this, there's probably like, like a hundred check boxes in there.[00:20:22] Sarah Sachs: Kills me[00:20:23] Simon Last: the things you could turn on and off.[00:20:25] Sarah Sachs: Uh, but I think that, okay, so that is kind of true, Simon, but like being the person that manages the evals team, like there is a level of intensity that it adds to the platform team. So, you know, if we're gonna do image generation and notion, all of a sudden the way that we do attachments and the way that we, um, our LLM completion like cortex talks and expects tokens back and now it's getting images back.Like there's a lot of platform work that we do need to, like solidify a little bit. So sometimes it'll be in dev for a couple weeks before it makes it to prod just because we still have to like, make it robust, make it HIPAA compliant, ZDR compliant, figure out the right contracting with the vendor, whatever it is.And we need to eval it because we want the team. To still maintain what they build. That's the one thing is like if we have a bunch of prototypes, it can't just be like a small group of people that then maintain whatever end prototypes. So we have invested a lot of people in an eval and model behavior understanding teams that, we call it agent dev velocity.So your dev velocity building agents can be faster if we invest in that platform. And so we have a whole org dedicated to Asian, um, platform velocity so that you can build your own eval and then maintain it once you ship it. So if a new model release comes out and we, every[00:21:38] swyx: team maintains their own eval,[00:21:40] Sarah Sachs: we maintain the eval framework.Every team owns their own evals and a lot of them we've integrated to Optin, to ci, or we run them nightly and we have a team, uh, a custom agent that triggers to a team to look at the major failures. That's really critical because if we have like all these different surfaces now, a lot of it's on the same agent harness, so it's easier to maintain.It's just packaging of different agent harnesses, but new functionality of the agent. Let's say that like we wanna update like. Uh, you know, they deprecated, sonnet, um, four or whatever it is and we need to auto update. Are[00:22:11] swyx: they already? That's so, okay. Yeah. Actually wasn't that long ago.[00:22:14] Alsesio: Theywere[00:22:14] Alsesio: just 3.5.[00:22:15] Sarah Sachs: 3.537. Just got deprecated.[00:22:18] swyx: 3 7, 5 0.2 or, yeah. No,[00:22:20] Sarah Sachs: it's not. 5.2 is five point. Five point no. Yeah, five four is 40% more expensive than five two. So if they deprecated five two, you would hear they can, you would hear from me about that one. Um, but, uh, another conversation to have.[00:22:35] swyx: I have a cheeky evals question for you.Have you noticed any secret degradation from any of the major model providers?[00:22:40] Sarah Sachs: Secret degradation,[00:22:42] swyx: like. During the War Bay, when it's high traffic, it suddenly gets dumber.[00:22:47] Sarah Sachs: Yeah. I mean, not just between the, I mean, we definitely notice flakiness, we've definitely noticed, particularly for some providers, that things are slower during working hours and[00:22:57] swyx: there's a latency argument.Yes. Not a quality argument.[00:22:59] Sarah Sachs: No. I think the quality difference that's interesting is, um, even though companies that say they're selling the same, a, it's really into like quanti quantization, but like companies that say they're selling the same model through different vendors, whether it be through first party or Bedrock, Azure, et cetera.We do see different qualities sometimes, and that's not necessarily what's advertised.[00:23:21] swyx: Yeah. Kidney went to the point of like, if we, they shipped like this, like eval across all the providers and it was like very obvious we were secret equalizing and it was very,[00:23:28] Sarah Sachs: yeah. But[00:23:29] swyx: that's very embarrassing.[00:23:30] Sarah Sachs: You know, um, we hire Subprocess to figure that out for us.So we just wanna understand where it's regressing or where it's optimized. And sometimes we're okay with regressions that optimize latency if they're the appropriate regressions. Our job is to make sure we have the evals to understand the changes that are important to us. And even like when we're partnering with labs on pre-releasees of models, they'll send us multiple snapshots.And this is less about quantization, but more just regressions. Like they have shipped models that were not the snapshots that we wanted, and they have changed the snapshots that they shipped based on the feedback that we give. Because our feedback tends to be more enterprise work focused and not coding agent focused.And definitely those can be bummers, like, you know, uh, we know that this wasn't the version you wanted, but we'll help you make it work. I mean, we always make it work, but that definitely happens.[00:24:16] Alsesio: Yeah. Do you have, um, failing evals that you're just hoping, oh, that will have success eventually when a good model comes out?[00:24:23] Sarah Sachs: Uh, I mean, yeah. So I think. I mean, I could talk about this for 60 minutes, so I will limit myself. I think it's a real issue when people say evals and it's just like, that's quality, that's like unit, I mean, it's like saying testing. It's not just unit tests, right? So. We have the equivalent of unit test.Regression test. Those live in ci, those have to pass a certain percent, you know, within some stochastic error rate. Then we have, as you're building a product, evals of these aren't passing right now, and this is launch quality. So we have a report card and we need to, on these categories, you know, be it 80 or 90% of all of these user journeys to launch, and then what we have what we call frontier or headroom evals, where we actively wanna be at 30% pass rate.And that's actually been a effort that we took in partnership with philanthropic and OpenAI in the past maybe two or three months, because we actually hit a point where our evals were saturated and we weren't able to really give insightful feedback other than it wasn't worse. And not only is that not helpful for our partners, it's not helpful for us to understand where the stream is going.You know, going back to that analogy. And so we spent a lot of time thinking about. What notions last exam looks like, right? Mm-hmm. Not just humanities, last exam. Ooh, notions last exam. Mm-hmm. And, um, there's a lot of, you know, dreams about what that would look like. I know we've talked a lot about benchmarking, um, swix, but, uh, yeah.Notions last exam is a big thing inside the company and we have people, full-time staff to it exclusively. Mm. We have a data scientist, a model behavior engineer, and an full-time, um, evals engineer just dedicated to the evals that we pass 30% of the time.[00:25:56] swyx: What you're hiring for[00:25:57] Sarah Sachs: MBEs? I am hiring[00:25:58] swyx: What is an MBEA[00:25:59] Sarah Sachs: model?Behavior Engineer Model. Behavior engineers started with a title data specialist before I joined when they were working with Simon on like, uh, Google Sheets and like Simon just needed someone to look through Google Sheets and say, yes, no, this looks bad. This looks good. Right? And so we hired people with kind of diverse linguistics background.We had like a linguistics PhD dropout. Mm-hmm. And a Stanford ate new grad. And they're amazing. And they formed a new function basically. And over time we've built a whole team, um, with a manager who's now kind of reinventing what that role is with coding agents. So they used to be kind of manually inspecting code.Now they're primarily building agents that can write evals for themselves or LLM judges. There's a really funny day I can send you the picture where Simon, about a year and a half ago, was teaching them how to use GitHub. Um, and they're on the whiteboard and it was like, okay, I think it would be so much faster if our data specialists learned how to use GitHub and like learned how to commit these things in Dakota.And, and that was then and now I think, you know, coding has been a lot more accessible. Um, but moving forward it's this mix of like data scientist PM and prompt engineer because there's craft in understanding like even like what models can and can't do things. How do we define like that headroom? How do we define like what a good journey is?Um, is this model better or not? Why is this failing? There's some qualitative work, but then there's also like a lot of instinct and taste to it, and that's not necessarily software engineering. And so we have like very firm conviction and we have had for a number of years now that that is its own career path and we have always welcomed the misfits, so to speak.So we really firmly believe that you don't need an engineering background to be the best at this job. And that's what's quite unique about this particular role.[00:27:37] Simon Last: Yeah, this is something that I've been pretty excited about recently is we made an effort basically to treat the eval system as like an agent harness.So if you think about it, like, you know, you should be able to have an agent end-to-end, download a dataset, run an eval, iterate on a failure, debug, and, and then implement a fix. And ultimately you should be able to, you know, drive the full time process with a human sort of observing the, you know, the outer uh, system.So yeah, we went, went pretty hard on that. And that's, that's worked extremely well so far. It's like basically just to turn it into a coding agent, uh, uh, problem.[00:28:11] swyx: Your coding agent or just whatever[00:28:13] Simon Last: harness No coding agent. Yeah, code, cloud code. It should be totally general. Yeah. I think if it would be a mistake to like, like fix it on any, any particular coding agent.At the end of the day, it's just like CLI tools.[00:28:21] Sarah Sachs: It's like the same way that you would've a coding agent write the unit test. You should have a coding agent write the eval.[00:28:26] swyx: Yeah.[00:28:26] Sarah Sachs: But there's a lot of supervision in that still. We just don't believe that supervision has to come from software engineers because a lot of it is like, um, kind of you XREE and whatever, and these are the people that also triage failures and tell us where we should be investing next.[00:28:40] swyx: Yeah. I'm gonna go ahead and ask a spicy question. Is there a data, there are no software engineers at Notion.[00:28:46] Simon Last: Um,[00:28:46] Sarah Sachs: what does it mean to be a software engineer?[00:28:47] swyx: Exactly.[00:28:48] Simon Last: I mean, I think the way things are going is like we're on some continuum where. If, if you look back three years ago, humans were typing all the code and then we had auto complete, you're typing list of the code.Then we had sort of like filling agents, filling lines, and now we're getting into like agents doing longer range tasks where you can debug and implement a fix and then verify it works and you know, get your, get your PR even like, like Merion deployed. I think we're sort of just moving up the abstraction ladder and then the human role becomes more about observing and maintaining the outer system.There's a string of agents flowing through, like me prs what's going off the rails. Like what do I need to approve? Is there like a learning or memory mechanism that that works? So it's kind of a hard engineering problem. There's a, you know, there's, there's a lot to do there. I think we're just sort of moving up stack[00:29:34] Sarah Sachs: the same transition machine learning engineers have made, right?Like I haven't looked at a PR curve in a while.[00:29:39] swyx: Yeah. You used to do this stuff and now, um, auto research can do it,[00:29:42] Sarah Sachs: right? Like I think it depends on what you define as a software engineer.[00:29:46] swyx: Yes. It's, that's changing for sure.[00:29:49] Sarah Sachs: I think every software engineer in notion this summer went through like this, um, sheer, um, one of our engineering leads of the company called it, like every software engineer is going through the, the, uh, identity crisis that every manager goes through, where all of a sudden they realize their ability to write code is less important than their ability to delegate in context switch.And I think that is a transition out of being a software engineer. But[00:30:12] Simon Last: yeah. Yeah, there's a critical difference to being a manager, which is that like, it is actually very deeply technical. The problem, you know, humans are very like, like, like fuzzy and you can't like treat a team of humans like a, like a rigorous system where like, you know, prs like, like flow through and can be in like a block status and then what happens when they're blocked, right.With a set of agents, you actually can do that. And, and, and I think it's actually, there's a lot of interesting technical rigor that that goes into that it's like it's a technical design problem. Ultimately.[00:30:42] Alsesio: What is the design of the software factory that you're building?[00:30:46] Simon Last: Yeah, I mean, I think we're. Trying a lot of different things.I mean, ultimately you want to design a system that requires as little human intervention as possible, but like still maintaining the in variance that, that you care about. So yeah, we're exploring a lot different ideas there. I mean, I think I could talk about a few things I think are important there.Like, one thing I think is really important is, um, having some kind of like specification layer you can just commit marked on files. Mm-hmm. That works pretty well, but[00:31:15] swyx: it's nice to be notion man. I'm just saying like the spec, like Yeah. The natural home for specs is notion.[00:31:21] Simon Last: Yeah. Right. It can be a database of pages.Yeah. I mean, it needs to be something that is, you know, human readable and I viewable and I think that's pretty key. Another really key component is like the, the self verification loop. Yes. You need really, really good testing layers, basically. And that's a really deep, uh, uh, problem. But by getting that right, you know, and then, and then it's kinda like the workflow of like.What happens when there's a bug? How does it flow into the system? Like, is it like a subagent working on it? How does it make a PR and how does that get reviewed? And me, and then, you know, so there's like the, the flow or process.[00:31:56] swyx: Yeah. Cool. Uh, you know, one thing we did work out before you guys came in was this demo or this[00:32:01] Simon Last: agents[00:32:02] swyx: agent demo.Uh,[00:32:03] Simon Last: so every,[00:32:04] Alsesio: every time we do an episode, we try the product. Right. I don't think there's ever been an episode that I haven't tried. Yeah. Um,[00:32:11] swyx: and we, we try, try is a, a big word. Like since day one lane space has been on Notion, but this is the, this is the net new thing. Yes.[00:32:18] Alsesio: So this is for Nel Labs, which is the space we're in.So next week we're opening applications for tenants. So there's a web form, let me, we got this form done here. Uh, so, uh, before. Uh, the workflow would be I get an email, then I look at the person. It was like, should I spend time talking to this person? Then I respond, they respond back. So I build this. So the name it came up for on its own.Can you maybe h how do, how does it come up with its own name?[00:32:43] Simon Last: Yeah, that's a pretty app name. It's, it, it is just a random, it's a random, a name generator.[00:32:47] Alsesio: Oh, that's funny. It just came,[00:32:49] Simon Last: the fact that it picked that is, is kind of hilarious. I'm pretty sure it's just determined,[00:32:54] Sarah Sachs: resilient collector. I, I think I've never looked at the code for that.I've never second guessed it. I think it's kind of like a madlib situation.[00:33:00] Simon Last: Yeah, I think you're right. Yeah. It's, it's totally a, a deterministic. Oh, I thought it was great. Yes. Although, although when the, if you use the AI to set itself up, it can update its own name, so. Okay. Um,[00:33:11] Sarah Sachs: how did you create it? It, did you just do[00:33:12] Alsesio: classroom?I,[00:33:13] Sarah Sachs: okay.[00:33:13] Alsesio: I did, yeah. I'll say just check my inbox for applications for a coworking space. Keep a people, so it created the database for me. Which I have here. And I guess database is like an notion table because everything is notion. Um, and then whenever um, an email comes in, like here, it just creates a new role for the person.Mm-hmm. And then it uses web search to enrich the mm-hmm. The profile. So it kind of like searches the web and it's like, this is who this person is, this is when they say they wanna move in and kind of updates everything else. This is, I mean, it's not a GI, but to me, I don't wanna do this work. So it feels like, I mean, it took me maybe like 15 minutes to set up the whole thing.Um, and I really like that most of the information should live here. You know, it is not like some other tool asking me[00:34:01] Sarah Sachs: Yeah.[00:34:01] Alsesio: To like, bring my stuff there. It's like I would've probably already created an ocean thing.[00:34:06] Sarah Sachs: Mm-hmm.[00:34:06] Alsesio: So[00:34:07] Sarah Sachs: most of our biggest use cases and gains are from. That extra layer of human involvement in the process to make it so right.And so like one of our biggest use cases is bug triaging. So if someone posts something in Slack, can you just have a custom agent that lives there that has its own routing constitution of what team this belongs to, creates a task in your task database and then posts in that Slack channel, right? Like that's like one of the first things that we built internally, I think.And it's completely changed the way that notion functions as a company. Nothing falls through, well, most things don't fall through the crack. We don't know what we don't know. But it's not replacing people, it's replacing processes.[00:34:44] Alsesio: Yeah.[00:34:44] Sarah Sachs: Right.[00:34:45] Alsesio: And I'm curious how you think about composability of these things.So the other one I was working on is like a. These filler. So whenever somebody signs up as a tenant, kind of he'll sell the lease for them. There should probably some agent that is like office manager agent mm-hmm. That can handle the request, make the lease, and then, uh, give them a ADA access to the office and all of that.How do you think about that feature?[00:35:08] Simon Last: Yeah, so I mean, there's, there's two ways you can compose. One way is by using like the data primitives. So you can, you know, you, you could give, you have one agent, uh, be writing to the database and there's another agent that's walked in the database. So that's, that's one way that they, they can coordinate that's like a little bit more decoupled and mm-hmm.Works really well. Or you, you can couple them. So I, I think it's actually not released yet. Releasing it like next week is, uh, in the settings for an agent, you can give access to invoke any other agent.[00:35:34] swyx: Hmm.[00:35:34] Simon Last: So you can have them just. Just, uh, uh, talk directly. So[00:35:37] swyx: you, was there a limit on like, number of recursions or just,[00:35:40] Simon Last: um, probably,[00:35:42] swyx: you know what I mean?Like, you can just get an infinite loop that way there's[00:35:45] Simon Last: some kind of Yeah,[00:35:46] Sarah Sachs: I think it's, there is actually a number somewhere.[00:35:49] swyx: I believe I'm just, you know, like, you're, you're, someone's gonna screw up. You[00:35:51] Simon Last: should you try to see[00:35:53] swyx: Yeah. I mean, everything's gonna be paperclips.[00:35:55] Simon Last: Oh, yeah. Yeah. But, uh, but, but that's really useful.Yeah. So we, you know, like I just, I, I helped, uh, someone internally the other day, they had, they had built like over 30 custom agents for, uh, for our go to market team doing all kinds of different things. You know, for example, like researching, you know, like, like filling information about, about a customer or like, like triaging customer feedback or like, uh, something like that.Literally over 30 of them. And, and then he, and then he even made like a database of all the agents and then he is like, okay, and, and now I'm getting 70, over 70 notifications per day with just the agents are blocked on various things. Uh, and then I was like, oh, okay, cool. You know, the obvious thing to do there is to make a manager agent,[00:36:32] Sarah Sachs: right?[00:36:33] Simon Last: That's gonna sort of blocks be another abstraction layer in between your, your, uh, uh, 30 agents. Uh, so yeah, we, we send out with like a manager agent and then has access to invoke all the other agents and it's sort of like, like watching and observing them and then it sort of, it just creates a layer of abstraction.So instead of 70 notifications per day, it's like, like five. And then, and then the manager agent can help like, uh, debug and fix any problems with the,[00:36:54] swyx: does this is a concept of like an inbox or something like piece, you're basically saying that they can message each other?[00:37:00] Simon Last: Yeah.[00:37:01] Sarah Sachs: Well[00:37:01] swyx: they use the system of record, which, which is[00:37:02] Sarah Sachs: notion, so we[00:37:03] Simon Last: actually, yeah, we didn't make any special concepts at all.[00:37:06] swyx: They're interested to the motion notifications that I would've got,[00:37:09] Sarah Sachs: they can just like write a task to a database that the other agent's task to listening to, or they can actually call a web book to the agent, like they can just add the agent. Okay.[00:37:17] Simon Last: Yeah, I mean, this is something that, that we're still working on.I, I think we, you know, like, like generally, generally the way we do these things is, you know, you first make it possible, maybe like a sort of janky way. So I, I, I think the way I set ‘em up is like, you know, we created like a new database that was sort of like issues mm-hmm. That the custom agents were, were experiencing, and then gave them all access to file an issue and then the manager has access to, to read the issues.Um, and that works pretty well, essentially like, like give it its own like internal issue tracker just for the agents. And then, you know, if that becomes a, a concept that seems useful, generally maybe we will think of how to package it in. But I mean, generally we try to just keep it to composing the primitive if we can.You know, another example of this is we have no built-in memory concept. Memory is, is just pages and databases. And so if you wanna give a memory, just give it a page and give it. Edit access to that page and the[00:38:03] swyx: human can edit it. Agent can edit[00:38:04] Simon Last: it. Yeah. And so that works, that pattern works extremely well on it.And you know, depending this case, you can have it be just a page or it could be an entire database with, you know, or, you know, I can have sub pages is is pretty on what you can do with that.[00:38:15] Alsesio: So when I was setting this up, uh, I connected my inbox and it was like, do you wanna use Gmail or Notion Mail? And I'm like, I don't wanna use Eater, I just want you to do it.I'm curious how you think about, you know, notion, mail, notion, calendar, all of these kind of ui ux interfaces, full stack[00:38:29] Simon Last: notion.[00:38:30] Alsesio: Yeah. When like at the same time you have the agents abstracting them away from you in a way, you know, how do you spend like the product calories so to speak?[00:38:37] Simon Last: Yeah, I mean, I think it's pretty important that you don't have to use, not your mail to connect to the mail capability.So we can just connect to Gmail or, or whatever you want, uh, to use. And we're thinking of the mail service as being really great to the extent that it's really agent built, right? So maybe the mail app is just sort of a prepackaged agent that helps you automate your, your inbox.[00:39:00] Alsesio: Yeah, the auto labeling is great.Think[00:39:03] Sarah Sachs: the, when we, um, integrate with Gmail for instance, we have a series of tools available that are available via MCP or API to Gmail. When we integrate with Notion Mail, we have the Notion Mail engineering team to build us the, um, exact right tools that optimize latency, optimize performance and quality.They own that quality. Um, there's product leads there. They're directly thinking about the user problems that happen in mail. So it tends to be when we build integrations and connections, we build natively first. Um, and then think about, um, extending them generally just because it's also easier. Mm-hmm. Um, um, to build natively first.Um, so that tends to be how we phase things out.[00:39:43] swyx: Talking about integrations, you prompted me, so I gotta ask. M-C-P-C-L-I. What's going on? What's the[00:39:48] Simon Last: Yeah. Opinion. I think, I mean, I'm, I'm definitely bullish and excited about cli. I think there's a few really cool things about cli. So one really cool thing is like, um, is that it's in the terminal environment, so it gets a bunch of extra power.So it, you know, for example, it can like, like paginating and cursor through like long outputs. Um, and it has a progressive disclosure inherently. Uh, so, you know, you don't see all the tools at once. It's just, you see the CLI wrapper and you can like use the, the help commands and, and, and read files. And then I think the most important thing that's, that's super cool is that there, it's also inherently a, a bootstrapped.So if there's an issue, uh, the agent can debug and fix itself within the same environment that it uses the tool.[00:40:30] swyx: Mm.[00:40:30] Simon Last: Right. Like, you know, I think I saw a tweet this morning. Someone said, you know, my agent didn't have a browser, so I asked it to make all a browser tool and within a hundred lines of code, it gave itself a little browser, like, like wrapping the, the, the chromium API, um.That's pretty incredible. And then if there was a bug, it would just immediately try to fix it. Mm-hmm. Right. On the other hand, if you use an, you know, if you use like of, of the Chrome dev tools, MCP, I've had this issue where like, like sometimes the transport gets like messed up. If it gets messed up, the agent has no way to fix itself.It, it no longer has a browser, it's, it's not broken. Right. I think that's, that's pretty fundamental, but I would say like a lot of the, the bad things about it can be fixed. Uh, so I think like, as a progressive disclosure, that can be fixed with, with right harness. Like, it, it obviously doesn't make sense to show it all the tools all the time.That's not really inherent to the MCP protocol. It's just like how you wrap it and use it.[00:41:16] swyx: There's many poorly built MCPs because we didn't know.[00:41:19] Simon Last: Yeah, yeah. I mean it was just early, like, like the obvious thing is, uh, you know, to start with is, is to just show it all the tools and it's like, okay, now we have a hundred tools.Yeah. And like the tool calling actually works. So let's of[00:41:28] swyx: your success[00:41:29] Simon Last: give it a way to like, like filter to source the tools. So yeah, I would say like broadly speaking, I'm really bullish on cli. I'm still bullish on CPS and in a certain environment. I think in, in particular, CP is really great for when you want sort of like a narrow, lightweight agent.I think there's, there's definitely a lot of use cases where, where you don't want like a full coding agent with a compute run time. And also you want it to be like more tightly permissioned. MCP inherently has a really strong permission model, like all you can do is call the tools. A CLI is a little bit murkier.It's like, can I access the, if PI token are you, like, properly sort of like re-encrypt the token so it can't like exfiltrate it, it introduce a lot of like, like new issues, which are. Real and hard to solve. And MCP is just like the dumb simple thing that works and it that it's pretty good.[00:42:12] Sarah Sachs: I'll add two more perspectives, not from it working well for Notion, but how notion like commits to both platforms.Notion is dedicated to being the best system of record for where people do their enterprise work. So we will always support our MCP and so far as other people are using cps, right? So regardless of our perspective, we've put a lot of effort into our MCP and we have a fantastic team that we're building, um, to do more there.And the second thing I'll say, I think, um, we all think a lot, but lately I've been thinking a lot about making sure there's a value alignment and pricing, um, with capability.[00:42:43] swyx: Literally our next question[00:42:44] Sarah Sachs: and. Needing language to execute deterministic tasks feels wasteful and requiring on a language model to interface with third party providers seems wasteful for tasks that don't require it.And particularly because our custom agents are using usage-based pricing. We think of pricing as like the barrier of entry for use of our product, and we're quite committed to making sure that it's not wasteful. Um, not just because it's a bad deal for our customers, but it's also bad business. We wanna have as many buyers, like there's a, there's an elasticity of demand and so if we can have our agents properly execute code that calls on CLI deterministically, it's a one-time cost, right?Versus constantly having a language model integrate with an MCP over and over and over and paying those like repeated token fees and it's happening outside the cash window, then you're paying for it over and over and over and it's just kind of unnecessary and less deterministic when it doesn't have to be.[00:43:36] Alessio: Yeah, the open-endedness I think is like, the main thing is like, well, if I go write code to just call an API, I would never use an MCP. But then you need an NCP sometimes when you know what to call, but you don't want it to restart versus like, I think the it built a browser from scratch is like, it's great when you're doing it on your own, but like if your customers were having your AI write a browser from scratch every time and you had to pay the token cost of that, yeah.You'd be like, no, no. The Chrome dev tools CP is actually pretty great. Just use that. I'm curious, how do you make that decision? Like should it be. Just straight API call very narrow. Should it be an MCP? Should it be super open-ended?[00:44:10] Sarah Sachs: Do you mean for when we ship notion capabilities or when we add capabilities to[00:44:13] Alessio: notion[00:44:14] Sarah Sachs: AI or,[00:44:14] Alessio: I mean, you might have a capability that the only way to do is an open-ended agent, like an agent with a coding sandbox.[00:44:21] Sarah Sachs: Yeah. In Notion ai they're not explicit, not We also ship an MCP.[00:44:24] Alsesio: Yeah. Yeah. In B,[00:44:25] Sarah Sachs: yeah.[00:44:26] Alsesio: Internally. Okay. Like is there ever a discussion of like, we're not gonna ship it because we're not able to tie it down? Or are you happy to just like,[00:44:33] Sarah Sachs: um, no. I mean, there are a lot of things where we choose not to use MCP because we wanna add more high touch to quality.I think search an agent to find is like the largest instance of that, where we have. Um, slack and linear and Jira search and notion that is not using necessarily the search MCP functionality that is provided by those companies. And that's because it's quite critical we think, to how our agent trajectories work is for us to have a little bit more control on the functionality of the search journey.And so it usually comes from quality and there's a long tail of things and that's why we built an MCP client or an MCP server, excuse me, so that people can connect whatever they want. There's that long tail, right. But we, for search particularly, I would say that's like the primary entry point, but there are other connections as well that it's a little bit of secret sauce a

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What is theological retrieval, and how should evangelicals approach it wisely?In this episode, Dr. Shawn Wright (The Southern Baptist Theological Seminary; Clifton Baptist Church) introduces the concept of theological retrieval and explains why it matters today. Drawing from a recent faculty address, he outlines six key cautions—including the need for humility, the danger of overreaction, and the central importance of justification by faith alone and assurance of salvation. We also discuss why some evangelicals are drawn to Roman Catholic views of the church, how Protestants should respond, and why tradition must never stand above Scripture. Dr. Wright closes with a pastoral warning for those considering Rome.Episode resource: https://www.sbts.edu/news/shawn-wright-faculty-address/Support the show

Retrieval-augmented generation, or RAG, has become a foundational approach to building production AI systems. However, deploying RAG in practice can be complex and costly. Developers typically have to manage vector databases, chunking strategies, embedding models, and indexing infrastructure. Designing effective RAG systems is also a moving target, as techniques and best practices evolve in step The post DeepMind's RAG System with Animesh Chatterji and Ivan Solovyev appeared first on Software Engineering Daily.