Podcasts about prototypes

Traditional Product Management Is Ending in 2026For years, product management followed a familiar playbook.Gather requirements. Write PRDs. Prioritise backlog. Run sprint rituals. Coordinate across teams. Ship features.That model worked in a world where building software was slow, expensive, and engineering bandwidth was the biggest constraint.But in 2026, that world is changing fast.AI is reducing execution time. Prototypes can be built in hours. Research can be synthesized in minutes. Code generation is accelerating delivery. Design iterations are faster than ever.Which means something important is happening:Traditional product management, built around coordination and process, is losing relevance.And a new version of product management is taking its place.How to connect with AgileDad:- [website] ⁠⁠⁠https://www.agiledad.com/⁠⁠⁠- [instagram] ⁠⁠⁠https://www.instagram.com/agile_coach/⁠⁠⁠- [facebook] ⁠⁠⁠https://www.facebook.com/RealAgileDad/⁠⁠⁠- [Linkedin] ⁠⁠⁠https://www.linkedin.com/in/leehenson/

If you've been saying you want to buy a business for years, your next move is here. Get your ticket to Main Street Millionaire Live and learn how to find deals, evaluate them, finance them, and own the upside: https://contrarianthinking.biz/MSML26_BDYT You've been planning to start that business for months. Maybe years. But you still haven't made the first move. Here's the truth: you're not lazy. You're stuck in a neurological loop that's hijacking your brain before you even realize it. This episode breaks that loop and gives you the exact protocol to rewire your brain for execution instead of avoidance. In this episode, you'll learn: The procrastination equation: why your motivation equals expectancy times value divided by impulsiveness times delay, and how to solve for the variables that are killing your progress The four traps that disguise procrastination as productivity: planning theater, research mode, waiting to feel ready, and the future self illusion Why your brain's anterior cingulate cortex and dorsolateral prefrontal cortex fire less when you procrastinate and how that becomes your default wiring The shrink, specify, stack protocol: how to make tasks so small your brain stops fighting them, anchor them to exact moments, and ride existing habits Why Phil Knight sold shoes out of his trunk before he built Nike, and why I launched my newsletter on a random Tuesday with zero plan How James Dyson built 5,127 prototypes over 15 years and why refusing to stay stopped is the only skill that matters ___________ (00:00:00) Introduction: You're Not Lazy, You're Stuck in a Neurological Loop (00:01:05) The Procrastination Equation: Why You Don't Start (00:02:48) Your Brain Is Wired to Avoid: The FMRI Study (00:04:19) Trap One: Planning Theater and the Artifact Illusion (00:06:08) Trap Two: Research Mode Is Mental Masturbation (00:08:04) Trap Three: Waiting to Feel Ready Is Biologically Impossible (00:10:10) Trap Four: The Future Self Illusion (00:11:51) The Fix: Shrink, Specify, Stack Protocol (00:11:57) Move One: Shrink the Action Until It Feels Stupid Small (00:13:23) Move Two: Specify With Implementation Intentions (00:14:45) Move Three: Stack It on Something You Already Do (00:15:30) The Real World Example: How One Sentence Became a New York Times Bestseller (00:17:13) The Rule for Missed Days: Don't Make It Bigger (00:17:52) The Dyson Story: 5,127 Prototypes and 15 Years of Failure (00:18:21) The 51 Calls: How Cody Bought Her First Business (00:18:59) Closing: Every Action Is a Vote for Who You Become ___________ MORE FROM BIGDEAL

Why do some working prototypes still fail when they reach production? This is episode 329 and the second part of our discussion on this topic, and Adrian and Paul move from the general prototype-to-production gap into real-world failure patterns that can derail a product launch. They look at 3 common scenarios: Component swaps made for cost reduction Firmware clean-up before release And transferring production from one factory to another You'll hear why a cheaper component that looks identical on paper can still cause major problems, why every firmware change needs to be tested and documented, and why a factory transfer should never be treated as a simple handover. The episode also explains how a structured NPI/MPI process, production-representative builds, configuration control, phase gates, pilot runs, and factory process audits help reduce the risk of production failure. The key message: a prototype proves the concept, but production proves the process. Before approving production, you need to know exactly what was validated, what configuration it applied to, and what has changed since.   TIMESTAMPS 00:00 - Introduction: why working prototypes still fail in production 01:32 - Failure pattern 1: component swaps and hidden validation risks 06:26 - Failure pattern 2: firmware tidy-up before production release 08:53 - Failure pattern 3: transferring from prototype shop to production factory 13:20 - How to bridge the prototype-to-production gap 13:48 - Why a structured NPI process matters 14:51 - Production-representative builds, EVT, DVT, tooling, and PVT 16:49 - Controlled ramp-up instead of jumping straight to mass production 17:32 - Configuration control: validation only applies to what was tested 20:29 - Practical decision framework for managers 22:03 - Setting a configuration baseline from DVT onward 23:05 - Using NPI phase gates and change assessment before moving forward 24:29 - Factory process audits: why an audit is not just a factory tour 27:09 - Pro tips: quality standards, NPI discipline, and validation tracking 30:39 - Factory transfers and why pilot runs are essential 33:05 - Final recap: what changed, what was validated, and what is now unknown   Related content Get help with your project from Sofeast. These services cover the topics discussed today: New Product Introduction Support NPI Deliverables Review DFM Review for Manufacturing in Asia Reliability Engineering & Testing Process Management Audit (PMA) First Article Inspection Get in touch with us Connect with us on LinkedIn Contact us via Sofeast's contact page Subscribe to our YouTube channel Prefer Facebook? Check us out on FB

A prototype works. The team signs it off. Everyone feels confident. Then production starts, and unexpected failures appear. Why does this happen? In this episode, Adrian is joined by Paul Adams, the Sofeast Group's Head of New Product Development, to discuss the gap between prototype and production. This is part one of a two-part discussion on why working prototypes can still fail once products move toward mass production. Paul explains why prototypes and production units are often not the same thing, even when they look identical. The episode covers five areas where important changes can creep in: Components Firmware Suppliers and factories Tolerances and process variation Validation basis The key point is simple: A prototype proves the concept. Production proves the process. Understanding that difference helps hardware teams, product developers, and importers avoid painful surprises when moving from a successful prototype to production. In part two, next week, we'll continue the discussion by looking at common real-world failure patterns, including component swaps, firmware tidy-ups, factory transfers, and how a structured NPI process helps close the gap.   TIMESTAMPS 00:00 Introduction: why working prototypes can still fail 02:09 Prototypes and production units are not the same thing 03:46 The gap between prototype and production 04:23 Five things that change before production 04:36 1 - Components: prototype parts vs production parts 09:17 2 - Firmware: why prototype code is not production-ready 12:03 3 - Suppliers and factories: why process knowledge gets lost 16:50 4 - Tolerances and process variation 19:54 5 - Validation basis: What exactly was tested? 22:22 Key takeaway from part one 23:17 What to expect in part two   Related content How Many Prototypes Are Needed Before We Get ‘Perfection?' Process Management Audit (PMA) An Effective New Product Development Process for Electronics From Prototype to Production: 7 Pitfalls for Tech Products Get in touch with us Connect with us on LinkedIn Contact us via Sofeast's contact page Subscribe to our YouTube channel Prefer Facebook? Check us out on FB

Special discounts up for AIE Melbourne (LS discount) and AIE World's Fair (group discounts up to 25% - CFPs still open for Autoresearch and Vertical AI) Cya there!Abridge did not start as an “GPT wrapper”. It was founded in 2018, years before the Cambrian explosion of AI application layer companies. OpenAI launched ChatGPT publicly on November 30, 2022 and by then, Abridge had already spent years doing the unglamorous work of building trust for one of the highest context, most important workflows in healthcare: the conversation between a patient and a clinician.Abridge's original wedge was clinical documentation. Listen to the visit, generate the note, reduce the clerical burden, and let clinicians spend more time with patients instead of the EHR. By focusing on how doctors actually document, how health systems actually buy, how EHR integration actually works, how clinicians verify outputs, and how missing context during a visit turns into downstream friction across billing, prior authorization, quality, and follow-up, the adoption of LLMs became a force multiplier on a workflow already optimized for sensitive context gathering.The company has scaled fast: Abridge says it is projected to support 80M+ patient-clinician conversations this year across 250 large and complex U.S. health systems, with support for 28+ languages and 50+ specialties. It raised $300M at a $5.3B valuation in June 2025, after a $250M round earlier that year.Today, Janie Lee and Chaitanya “Chai” Asawa of Abridge join us for another crossover pod with Redpoint's Jacob Effron (who is on the board of Abridge) to dive into how Abridge is building the clinical intelligence layer for healthcare starting with ambient documentation, then expanding into clinical decision support, prior authorization, payer/provider/pharma workflows, and eventually real-time agents that act before, during, and after the patient conversation. We go inside the product, data, infra, evals, workflow, privacy, and org design choices behind bringing AI into one of the highest-stakes enterprise environments from 100M+ medical conversations and specialty-specific evals to real-time alerts, EHR integration, de-identification, clinician-scientist teams, and why healthcare may solve some of the hardest AI problems first.We discuss:* Why Abridge started with clinical documentation, “pajama time,” and saving clinicians 10–20 hours a week* The transition from ambient scribe to clinical intelligence layer: save time, save money, and save lives* Why conversations between patients and clinicians may be the most important workflow in healthcare (patient visit summary feature)* Chai's “healthcare-coded Glean” framing: context is king, but healthcare raises the stakes on safety, evals, and rollout* Why Abridge wants AI to feel like “air conditioning”: always in the background, but only interrupting when it truly matters* The prior authorization example: turning a denied MRI weeks later into real-time guidance while the patient is still in the room* Why payer policies, EHR data, medical literature, and hospital-specific guidelines make the problem hard, and also create the moat* How Abridge thinks about ambient form factors: mobile, desktop, in-room devices, nursing workflows, multimodality, and future AR* The multi-sided healthcare customer: CMIOs, CFOs, CIOs, clinicians, patients, payers, and pharma* The hardest AI problem at Abridge: high-quality, low-latency, low-cost real-time support in a high-stakes clinical setting* When Abridge uses frontier models vs proprietary models, and why its unique data from medical conversations matters* Why “every agent is a coding agent underneath,” and how the EHR can be thought of as a filesystem for healthcare agents* How Abridge approaches personalization across individual doctors, specialties, and health systems* Why “AI slop” is AI without context, and how edits, memories, and clinician preferences create a data flywheel* Abridge's eval stack: LFDs, LLM judges, in-house clinicians, third-party evaluators, specialty-specific evals, and progressive rollout* HIPAA, PHI, de-identification, one-way anonymization, customer contracts, and learning from healthcare data safely* What changes when you operate at 100M+ conversations: reliability, cost, post-training, model routing, and infrastructure optimization* Why the same clinical conversation can serve doctors, patients, payers, pharma, and future clinical-trial workflows* How Abridge works with EHRs, and why deep interoperability is table stakes for clinician adoption* Why healthcare AI has regulatory tailwinds, why 80/20 does not work here, and why high-stakes domains may drive AI forward* Why Abridge embeds “clinician scientists” into product and eval teams* What Chai learned from Glean about search, quality, and durable AI infrastructure* Why the future of AI infra may look like context layers, event-driven systems, Kafka, Temporal, sockets, CRDTs, and tools built for humans* Why Janie changed her mind on “PRDs are dead,” and why crisp written clarity matters more in complex AI products* How Abridge uses Claude Code, Cursor, and coding agents internallyAbridge:* Website: https://www.abridge.com/* X: https://x.com/AbridgeHQJanie Lee:* LinkedIn: https://www.linkedin.com/in/janiejleeChaitanya “Chai” Asawa:* LinkedIn: https://www.linkedin.com/in/casawaTimestamps00:00:00 Introduction and what Abridge does00:02:05 From ambient documentation to clinical intelligence00:04:04 Clinical decision support and context as king00:06:57 Alert fatigue, proactive intelligence, and prior authorization00:12:36 Ambient AI form factors and healthcare customers00:16:59 The hardest AI problems in healthcare00:18:26 Frontier models, proprietary data, and model strategy00:21:07 The EHR as a filesystem for agents00:24:03 Personalization, memory, and clinician preferences00:30:40 Evals, LLM judges, and progressive rollout00:36:47 HIPAA, de-identification, and privacy00:39:21 100M conversations and operating at scale00:44:10 EHR integration and the clinical intelligence layer00:46:39 Healthcare regulation, latency, and high-stakes AI00:50:11 Clinician scientists and long-tail quality00:53:04 Lessons from Glean and durable AI infrastructure00:57:03 The future of agentic healthcare workflows00:57:34 PRDs, product clarity, and building serious AI products01:03:11 AI coding tools at Abridge01:04:06 OutroTranscriptIntroduction: Abridge, Clinical Intelligence, and the Latent Space x Unsupervised Learning CrossoverSwyx [00:00:00]: Okay. This is a special crossover Latent Space Unsupervised Learning pod.Jacob [00:00:07]: Very excited to do this.Jacob [00:00:08]: At this point, we get together once a year.Swyx [00:00:10]: Once a yearJacob [00:00:11]: And this is a fun occasion to get to do it on.Swyx [00:00:13]: I really wanted to talk to Abridge but I felt very underqualified because healthcare is not something we cover very intensely. It just so happens that Redpoint's our big investors and supporters of Abridge.Jacob [00:00:27]: Anytime you want to have a portfolio company on your podcastJacob [00:00:29]: Please, by all means.Swyx [00:00:31]: So we'll introduce our guests. Chai and Janie, welcome to the pod.Janie [00:00:34]: Thanks for having us.Chai [00:00:35]: Thank you.Janie [00:00:35]: We're excited to be here.Chai [00:00:36]: Thank you.Swyx [00:00:36]: So for listeners, what do you guys do, just to situate you guys in the company?Janie [00:00:42]: Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians and as we think about reducing the burden that clinicians have, they're spending 10 to 20 hours a week on documentation. There's a massive doctor shortage in the country. We also think that conversations between patients and clinicians are probably the most important workflow in healthcare. It's where care is given and received but if you think about the 20% of our GDP that goes towards healthcare, almost everything is a derivative of that conversation, whether it's the claim, the payment, the actual diagnosis given, the treatment. And we've started with a conversation to reduce the burden for doctors on documentation but we're really excited about the path ahead as we become this broader clinical intelligence layer.Chai [00:01:34]: I'm Chai. I work on clinical decision support at Abridge.Swyx [00:01:37]: Yes.Chai [00:01:37]: And so as Janie said, we're uniquely situated where we started off with the clinical note. What I'm really excited about and where we're expanding towards is what are all the things you can do before the conversation, during the conversation and after the conversation if you did have access to all the context about patients, payer guidelines, medical literature and put that together and to serve, how healthcare could look fundamentally different.Swyx [00:02:01]: And that's the context engine that you guys have?Chai [00:02:04]: Yes.Swyx [00:02:04]: Is that what it's called? Okay.Swyx [00:02:05]: So historically, as I understand it, the company started in 2018. A lot of people would be familiar with the AI voice notes form factor that doctors would be “Well, do you consent to being recorded?” It replaces handwriting and what have you. But it sounds like more recently there's been a big transition in the company. Tell me about the broader transition.From Documentation to Clinical Intelligence: Save Time, Save Money, Save LivesJanie [00:02:26]: So from a transition perspective, we really think about our journey as The first act was: how do we help save time? And that's where a lot of that original product was.Swyx [00:02:37]: By the way, one of those interesting statsSwyx [00:02:39]: On your landing page was, doctors spend time after hours.Janie [00:02:43]: They call it pajama time.Swyx [00:02:44]: Why is that pajama time?Janie [00:02:46]: Doctors after work in their pajamasSwyx [00:02:48]: In their pajamas. OhJanie [00:02:49]: At home are just writing and catching up on their notes every day.Janie [00:02:53]: Some of our favorite customer love stories, we have a Slack channel called Love Stories. We have clinicians telling us, “Abridge has helped us, from retiring early or we're now finally able toJanie [00:03:06]: go home and eat dinner with our kids for the first time.”Chai [00:03:08]: Save the marriage in some cases.Swyx [00:03:10]: One of the quotes was “We're not divorcing anymore.”Swyx [00:03:12]: I'm asking, “Why?”Swyx [00:03:14]: Because they're working too much.Janie [00:03:16]: But, in terms of where we're going and where we're expanding, we really think about our second and third acts around how do we help health systems save and make more money. Health systems are operating with record-low operating margins. It's getting harder and harder to serve patients and they have regulatory, some tailwinds but also a lot of headwinds coming their way and AI is ripe for helping on the saving and make-more-money piece. And then ultimately, how do we help save lives? The fact that our software and our product is open millions of times a week before, during and after a patient walks in the room, gives us massive opportunity with products like clinical decision support, which Chai is building but so many others to improve patient outcomes and probably one of the most important workflows and problems to be going after right now.From Glean to Healthcare: Context Is KingJacob [00:04:04]: One thing that's interesting, Chai, is you came over to Abridge from Glean and clinical decision support, which for our listeners is, in the context of a visit, helping a doctor figure out the right type of care. It's really a search problem in many ways, going through lots of different data sources. Very analogous to your previous role as one of the earliest engineers over at Glean. I'm sure a lot of our listeners are curious what's similar about the problems that you're going after now and what feels different, now that you're in healthcare.Chai [00:04:33]: Very similar. Taking a step back, with every wave, there's a lot of very similar patterns that happen across different products. A lot of social networking products look the same. A lot of credit-based products look the same. And we're seeing that very similar in the agent era with many companies, of course, in Redpoint's portfolio and so forth. And the key insight between both companies is that you have amazing models but context is king. Context is what puts them to work. So I see it in a lot of ways, a lot of similarities in this is a healthcare-coded version of Glean but the differences are really interesting. A couple things that come to mind. First and foremost, the rigor of the setting we're in. The downside risk is extremely high here in healthcare. It can be fatal in some cases. You prescribe something that the patient is allergic to for example. Whereas at Glean, it's “Oh, you got the question wrong.” It wasn't the end of the world in most cases. And so what does that mean? That shapes our evaluation strategy, both offline evaluation, progressive rollout and there's a lot more we could go into there. Second thing that comes to mind is, vertical versus horizontal. In both cases, there's a large variance but when Glean is, it's a much more horizontal company, there's a variance of personas, companies that you're working with. We also have a variance of personas, different types of specialties, different hospital systems. But the variance is a little more narrow. So from a product perspective, you're able to focus far more, especially when you have a maturing technology and you're building new products that never existed before. It lets you go after them much more easily and especially in healthcare where so many problems were solved with labor and process, that it's extremely ripe for AI to keep helping augment and enable. And the final thing that's really interesting, Abridge specifically compared to many other companies in the AI area, is the modality we started with where we're ambient and we're always listening in the background. And many more AI products will go that way but it's how we started. And that's the greatest form of AI we can create, AI that's seamless. You're not looking at your screen. It's always there. It's always helping you out and being proactive. The Jarvis vision that, every hackathon I went to over the past decade, there was always a Jarvis competitor. But Abridge very much started from the opportunity and continues to go that way.Ambient AI and Alert Fatigue: When Should the Product Interrupt?Jacob [00:06:57]: One thing that is super interesting then from a product perspective is you have this always-on seamless in the background and then you have to decide when you break the wall almost and say, “Hey, clinician, you might not have thought about X,” or whatever it is that you want to do. And in healthcare traditionally there's been this idea of alert fatigue and a million pop-ups and then a doctor just ignores all of them. It's probably a pattern that a lot of builders are thinking through now. How do you think about the right way to intervene or to pop up in a doctor visit?Janie [00:07:26]: It's such a good question. Alerts are notorious in healthcare specifically. Over 90% of alerts are ignored. The first and most important thing is context is everything, as Chai alluded to and I also think about how do we go from being reactive alerting to really proactive intelligence at the point at which it matters most. One thing we like to say is we want our product to feel like air conditioning. It should be in the background just making things better and if there is something that has great clinical risk and we're acutely aware that intervening now and not later is incredibly important, we should decide to act. But if you think about proactive versus reactive, instead of alerting a clinician during a visit when they're with their patient having a pretty serious and sensitive conversation, how do we prep a clinician before they walk into the room with that patient? And so historically, clinicians might have to manually go through charts with a patient that they've had over the course of months or years and they'll try to suss out what are the things they should be doing. You can imagine a world with Abridge. We'll summarize all of the most recent context for you, tell you based on the reason for a visit the patient is coming in for the types of things you should be discussing. And so you're going into that conversation prepped rather than walking in cold to that patient visit and then having this product interrupt you five or 10 times throughout the visit. And there might be times where it's really important to interrupt. We have a product called Prior Authorization and so this is when you may go into a doctor's office with knee pain. They'll prescribe you an MRI and so many of us have had this experience before, where in four weeks you'll get a call saying, “Hey, Sean, that MRI that you were prescribed wasn't approved and why don't you come back in? We'll figure it out.” In a world with Abridge, we might choose to quietly but still alert a doctor in that visit. And alert is probably not even the word we would want to use. Before a patient leaves, we would want to tell the doctor, “Hey, Doctor, before Sean leaves, you should ask him, has he had physical therapy and has his pain lasted for more than six weeks? Because the Aetna plan that he's on in California requires six things. We've already confirmed four of them have been met ‘cause we have all the context. But these two last criteria, if you can address with Sean before he leaves the room, we could guarantee that your MRI is approved before you leave.” And so when you think about clinical usefulness, impact to the patient, there are instances in which if we can catch a doctor while the patient is still in the room, as we think about save time, save money, save lives, we get to check all of those boxes. But when doctors have 15 minutes between visits, we have to be really thoughtful about when it matters.Prior Authorization: Reducing Latency in CareChai [00:10:23]: There's this interesting product opportunity AI has is reducing latency in the world. For example, prior authorization is an example of where care gets delayed and so great AI can reduce that. And the problem with alerts before partially is a technical problem: the quality of your alerts really matters. They're going to get ignored if you get alerts that... Similarly in engineering, where they're noisy alerts that you can't act on. But if you can make really high-quality alerts with both the context, as Janie said, and really high-quality models, then you can create a whole other game.Janie [00:10:53]: And I really like that experience because it starts to tease apart, what makes this so hard and unique. One, to make that prior authorization example possible, think about all the data that you need to have. You need to integrate with the electronic health record to know all of the patient context. Do we have access to your previous labs, previous imaging? And then to match you and to know that you're on Aetna, we have to collect all of the different payer policies and they vary by state. Some of these payer policies live on websites. Some of them live in unstructured 50-page PDF files.Jacob [00:11:31]: I thought this episode wasJacob [00:11:31]: To make sure we didn't scare people from healthcare.Janie [00:11:34]: But when you think about the things that make it hard, it also gives you the moat.Janie [00:11:39]: And then the second is the AI and the model quality we need to be able to hang our hat on. And so the bar, similarly when I worked at Opendoor, I worked on pricing models. Every outlier wiped out the margins of 30 and so similarly here in healthcare, the bar for accuracy is so high. And then I'd say the last is workflow is everything. If insurance companies deploy AI, it typically happens too late and this is when you have the notorious comical examples of AI just fighting each other when it's too late. But if we can pull forward the use of both the AI but also the ability to solve problems when the patient's in the room, you can start to collapse what typically takes weeks or months after your visit, ideally down to minutes or real-time. And it's where healthcare is both very difficult but also extremely rewarding if you can crack it.Product Form Factors: Mobile, Desktop, In-Room Devices, and ARSwyx [00:12:36]: Just to get some baseline on the form factors, because I've seen some videos on your website and stuff. You guys talk a lot about ambient AI. Is it primarily on the phone? Is there any other form factor that people get Abridge in? Is there an Abridge room setup where it's always on? I don't know.Jacob [00:12:55]: An Abridge podcast studio.Janie [00:12:58]: Primary form factor is mobile and desktop. UsuallyJanie [00:13:00]: Clinicians are walking in and out of rooms with mobile but at the end of the day, when they're closing out their notes or wanting to prep for the day ahead, they might use desktop. We have been having a lot of really interesting partnership conversations with a lot of these in-room device companies as you think about the power of multimodality and even more data, as you think about all of what is not captured today. It is fascinating to think about, especially even as we go into building and scaling our nursing product. It's one where nurses constantly, as they're walking in to check in on a patient for two minutes or maybe even 30 seconds,Janie [00:13:43]: Starting an Abridge experience is probably going to take longer than the visit. And so what can we do with in-room devices that are always on starts to raise really interesting and fun product questions.Swyx [00:13:54]: I was thinking, the way in tech companies we have all these Google MeetSwyx [00:13:58]: And other things, we might as well set up entire rooms with just Abridge tech.Chai [00:14:02]: Very much. AR glasses and related form factors are also relevant: how do we bring the information to the clinician in real-time without a screen, while still letting them focus on the patient?Swyx [00:14:18]: Do you think they want that? I'm skeptical of AR, but I'm curious what you've tried.Chai [00:14:26]: Admittedly, it's not a near-term product roadmapChai [00:14:29]: By any means. I'm being far-fetched.Jacob [00:14:31]: There's some sick AR stuff for surgeries.Swyx [00:14:33]: Really?Jacob [00:14:33]: When people are trying to visualize, you're about to make an incision but you want to see, what the cut might look or what the body might look like inside and they can layer in imaging.Swyx [00:14:43]: That's cool.Chai [00:14:45]: At some point in the future.Janie [00:14:46]: But there are a lot of our largest customers and at the largest health systems integrating already and so even as we think about building into it, unlocks a lot of product capabilities.Swyx [00:14:57]: And just to establish the terminology. Sorry, and I know I'm asking basic questions somewhat for myself but also for the audience who might beHealth Systems, Buyers, Clinicians, Patients, and PayersSwyx [00:15:05]: Less integrated. When you say health systems, it's like the Johns Hopkins, the Kaiser Permanentes.Janie [00:15:09]: Mayos, the Kaisers of the world.Swyx [00:15:10]: These are your customers, right? And the outcome that you deliver for them is happier doctors, reduced cost of processing, reduced mistakes. It's weird in a sense that I feel like there's also, a secondary customer, the customer of the customer and I don't know if you — do you think about it that way?Janie [00:15:28]: The other interesting and complex part of building product is we have our buyers, who are the chief medical information officersJanie [00:15:39]: The chief financial officers, the CIOs of these large health systems. Our users today are clinicians but if you think about who downstream is impacted, it's patients. And so as we build, with every product in mind, we think about who we're building for, who the secondary user is and what does that mean either in terms of experience, security compliance, ROI that we have to make tangible. And so like you said, time savings is one of them. But for CFOs, they care a lot more than just time savings. We have to show for every dollar you put into Abridge, because you have more compliant documentation or because you have fewer queries coming from your billing team, we save or add real dollars to your bottom line or top line, are things that we're constantly thinking about because of the dynamic across all three sets of users.Chai [00:16:32]: There's a whole other axis too with the payers and pharmaChai [00:16:35]: as well. Connecting all these three big stakeholders in healthcare isSwyx [00:16:39]: Do the payers ever see your data? Sorry, the payers meaning the insurers, right?Chai [00:16:44]: Yes.Swyx [00:16:44]: They also see Abridge data?Chai [00:16:47]: NoSwyx [00:16:47]: Like the direct integration to you guysChai [00:16:48]: They wouldn't see the raw Abridge data but when you're working together on something like prior authorization, whatever information they need, we'd communicate to them.Jacob [00:16:59]: That's cool. I would love to dig into the AI side. You still have a lot of problems on the AI side. And so maybe to start at the highest level, what's one of the hardest problems you have to solve in AI at Abridge today?The Hardest AI Problems: Quality, Latency, and CostChai [00:17:11]: To make things simple, let's take, building off the prior auth example. So one thing Janie talked about is okay, this data is all over the place and there's this combinatorial explosion of procedures, payer policies and even sometimes different health systems. There can be some cross-product of all of these different considerations you have to take into account. But what's really hard about this problem is doing it real-time in the conversation. So, in any AI product, usually the three KPIs you care about are quality, latency and cost. Now, what we're saying is we want you to do this real-time in the conversation, guiding the clinician. How do we do it in a way that does not break the bank? But we're using — But we also need very intelligent models because you're working with this cross-product of data and this, all this context layer as well. So you need high intelligence and high-quality because you don't want the alert fatigue but you also need to be fast and cost-effective. And so that's where a lot of clever engineering goes. It's okay, without getting into all the details here, can you model these policies in some intermediate representation or other things that you can do that can make this problem tractable? And of course, the Pareto frontier is always changing but we are also trying to do this now.Model Strategy: Third-Party Models, Proprietary Data, and Medical ConversationsJacob [00:18:26]: What implications has that had for what you take off-the-shelf and say, “ what? We don't need to be world-class at X. We'll just take this from the model providers or from some infrastructure player,” and what you're “No, this is where we spend most of our time focused on”?Chai [00:18:38]: This is, the fun challenge in AI?Jacob [00:18:42]: It changes every three months? SoChai [00:18:42]: Of course, with the shifting landscape, we try to be extremely thoughtful on predicting the trends of where third-party models are going and where we can uniquely go. And, sometimes when you talk about AI models, we're the models are just going to get infinitely better. But I don't think... It may be in the grandness of time you could say that but, within every month, every quarter, there's specific ways they're getting better. They're training on a lot more, coding data to be better coding agents, for example. And soChai [00:19:14]: We have to think about where are the things that won't — unique data that we're uniquely training on or to step back a little, where is a proprietary model bringing advantage to us is if it can give higher quality or lower cost and latency for similar quality, very similar to many other companies. And when we can do that is when we have proprietary data. So, for example, we have on the order of eighty million or hundreds of millions now getting close to of medical conversations.Jacob [00:19:44]: It's insane.Chai [00:19:45]: This is a unique data set. And this data set, it's very interesting because this data set is effectively a large part of the trace between the patient and the provider. That's where the quote-unquote debugging happens in healthcare. We have these traces at scale, as in as, our CEOs even called it, an exhaust that comes out of our product. And so when you have these traces, that's how you can train better agents on certain use cases, whether it's your transcription diarization use cases or so on or like note generation models and we can do that much cheaper and faster. But we're always also working with these third-party model providers. We closely collaborate with them and that's how we predict where the trends are going. The thing that I think about a lot is that, I know that the model providers are going to train much more on agentic workflows and so forth, so that's great, so that you have a better agentic harness. But the other thing that's interesting is that the model providers, because a large class of the consumer model providers is healthcare queries, that they might, optimize to train a lot of healthcare data to encode the knowledge in its weights. And this is just a great thing for us as well, where the off-the-shelf models can keep bett-getting better at general healthcare information, such that what our strategy is, we have a constellation of models, we can use something for this, that and, we only care about, at the end of the day, the best product experience.EHR as File System: Agentic Workflows and Real-Time InterfacesJacob [00:21:07]: And, you have, overall capabilities improving. I'm curious, as these models get better, is there something you look at and you're “, three months ago, we really couldn't do that but God, the the latest models really allow us to do it”?Chai [00:21:19]: So here's something interesting that I've, been toying with. So all models are... This wasn't super obvious a year ago but now it's become clear and clear that almost every agent is a coding agent underneath the hood? So you give it whatever file system, it can write its own code and so forth. So when you think about within healthcare and the use case that we have, you can think of the EHR effectively like a file system. It's just — it's a storage of all this information. It's a lot of information there that cannot fit into the context window, at least of today's models and you want to use that context effectively for all these product use cases we're talking about. And so if you have better agents that can, manipulate data, read that data, treat it as a file system as we see they're going and we know model companies are investing this way, then that very directly benefits us.Swyx [00:22:09]: Yeah. Okay, cool. Again, just establishing basic things. But we're going back to the model stuff. I'm really interested in double-clicking more on the real-time, element, which is pretty important for both of you. Is it — Is real-time just batches of every one minute, every five minutes? Is that how we do it? Or is there some more native, genuinely real-time in the sense that OpenAI has a real-time API or Gemini has a real-time API?Chai [00:22:35]: Yeah. Yeah. So today it is more on the on the batch basis but there's interestingChai [00:22:41]: Prototypes that we have that we're still not fully, full time, voice in text out or in that sense. But, can you trigger your models, your agents or agentic workflows, depending on the right times in the conversation?Chai [00:22:58]: And so you can imagine, different techniques to bring this latency down and, you want to bring the feedback loop down as much as you can. And so a lot of clever engineering there without fully... Maybe one day we'll do full voice in and text out, train a model to do something like that.Swyx [00:23:15]: You do — People don't want voice in voice out?Chai [00:23:18]: Now we aren't creating experiences that are, during the conversation, inter — It's almost likeSwyx [00:23:25]: Might be too disruptiveChai [00:23:26]: Too disruptive until, who knows, maybe eventually you could have full voice agents once we — the quality and we improve the comfort of the technology. But right now gra — that change is much more gradual and it's more text focus, text out.Janie [00:23:42]: And so much of currently what our product is trying to do is allow a clinician to focus on their patient and maybe at some point but right now patients, clinicians don't want a third voice, at least in a literal voice in that room. And so how do we be there with all the contacts and information ready at hand when there's the right moment?Personalization: Individual Doctors, Specialties, and Health SystemsJacob [00:24:03]: Jenny, one thing I'm curious about is how you think about, personalization in the product. I imagine, every doctor is a special snowflake in their own way, has their own way they like to do things. There are probably a bunch of different approaches you could take to doing that, both within the model layer itself but then also just with clever prompting or engineering. How do youJacob [00:24:20]: Deliver on that?Janie [00:24:21]: It's such a good question. Personalization is massive for us. We think about personalization at three levels. The first is at the individual, the second is at the specialty level and then the third is at the health system or the organization level. To your point, there are a lot of individual preferences. You-When a note is produced, it almost is a reflection that is so deeply personal of a doctor's work and how they give care. And so do they have preferences on things like style? They might want bullets versus paragraphs, really concise versus comprehensive. They also might have phrases that they really like to use or the templates that they want every note to be structured. And, we see it in our feedback all the time. We want two spaces in between sentences or I refuse to use this tool. And so that's something that we've had to build in. And the tricky part is how do you make sure that stylistic preferences don't interrupt accuracy and quality and that's something that we've really had to refine and hone over time. Second is at the specialty level. A cardiologist note or workflow is going to look very different from a dermatologist workflow.Jacob [00:25:32]: I assume cardiology notes are the highest stakes for you guys, given your CEO is a cardiologist.Jacob [00:25:36]: It's “Oh my God, make sure we get this one.”Janie [00:25:37]: Shiv, our CEO, is still a practicing cardiologist. He rounds once a month. And so, first call when we want just quick and easy user feedback too.Janie [00:25:46]: But, specialties require a lot of personalization, both in terms of what does the product look and so we make sure that as new users onboard, we catch that and the product proportionally reflects that. But also on the back end, evals at the specialty level, they are hard-earned to calibrate and get. What does a really great dermatology note look like? What makes it complete? What makes it compliant and billable is very different than a primary care doctor. And so it's not just about what does the product experience look but on the back end tuning and really deepening our understanding for the specialists. What does great output look like? And that's, a problem that we need to calibrate internally, externally, online, offline but, takes lots of cycles but is necessary in a high-stakes environment. And then at the health system level, for products like clinical decision support, you have health systems who've spent years or decades refining their best practices and they want to know, “Hey, we love your clinical decision support product but how do we embed our own hospital guidelines into them to inform clinicians before, during or after a visit what brest — best practices should look like?” And as you think about, deepening moats as well, when health systems, trust us with that data, allow us to productize it and directly into the clinical workflow, makes us a really great partner to health systems who want to build something that truly meets their needs, their practicing guidelines.AI Slop, Memory, and Product Data FlywheelsChai [00:27:23]: And I want to add onto that. The for the clinical documentation problem, it's very similar to AI writing that doesn't feel like your own and then we call that slop. But the way I describe one framing of slop is like AI without context. But we have all that context and both the clinicians, can have it and can guide it. And so part of the other interesting exhaust for us is, memory is, one of these new systems recordsChai [00:27:49]: Almost.Janie [00:27:50]: And we also have all the edits people make on our product and when you think about a data flywheel and how we get better over time becomes really powerful as a mechanism to just going deeper in personalization.Jacob [00:28:04]: It's interesting. I love this idea of working with systems on the guidelines they built up over a long time. I feel like so many of the best AI app companies today are... The question is: How do you take the expertise that a law firm or a bank has built up over many years and then add that as context and also a special sauce over, a an AI tool? And so seems like y'all are really doing that very effectively.Janie [00:28:24]: We're now starting to have our customers ask, “What are other customers doing?”Janie [00:28:28]: “And how are they doing it?”Janie [00:28:30]: And as we think about having visibility across such a large set of care being delivered right now, a really interesting place we could also partner.Swyx [00:28:40]: I'm just curious. I — This may be a nothing question but, how different are health system guidelines from each other? Don't they all converge to the same thing? And if not, where do they differ?Chai [00:28:52]: At a really high level, they're going to talk about very similar things but the difference is probably in some more of the details. “Oh, you should refer to specialists only when XYZ conditions are met,” or so forth and maybe different organizations have different practices and guidelines around that. But high level, talking about similar things but the details are what, of course, that shapes the context and the decisions you make.Swyx [00:29:15]: And this all goes into the context engine and it might affect the notes but maybe not.Chai [00:29:21]: The — For these local pathways, we're definitely thinking about it a little more for our clinical decision support product.Chai [00:29:26]: So yeah.Swyx [00:29:27]: Which is your stuff, yeah.Swyx [00:29:28]: And then the memory which you raised, let's just tell us more about that. What have you tried in memory? What's the structure of the memory? What works? What doesn't work?Chai [00:29:38]: There's, of course, many different ways you could do memory, where it's okay, can you bake it into the model weights or can you do it in some external store? For us, what's interesting is, of course, when you think the models are rapidly changing, whether it's in-house or third-party, baking into the model weights, sometimes you worry that it could be a little throwaway. And so, how do you... You need to find a way that you decompose the problem, the preferences from the underlying models and so forth. The thing we're right now most both that's easiest to start with and we're excited about is having, a separate store for memory, where you have, for example, a memory sub-agent that's, working in the background, figuring out what are the important parts of the clinician's actions that we want to remember for the long term. And then you can also imagine, other things where in the — you have background jobs that are running that are collating these, memories similar to Sleep, of course and what other pattern, patterns products do as well. Learning over all these action, all the action data we have, again, note edits, the conversations they did and the actual transcripts.Evals: LFD, LLM Judges, and Clinical SafetyJacob [00:30:40]: What about evals? How in the world do you... It is such a complex product surface area. We would love to hear you riff on that and also how has that evolved? I'm sure you've gotten better at it, so any learnings along the way.Janie [00:30:50]: From an evals perspective, we, from day one when we build any new product or feature, we think about, what does good look like? And there are table stakes things like clinical safety but then you start to get deeper into what does good quality look like. And when you go into something like our core product, there's stuff like style and completeness and there's things like does this note become something that can be billable, which is very high stakes for a health system. We have a number of ways in which we get confidence for this. We have, internal in-house clinicians who do what we call an LFD process to give us our very first pass at is this or isn't this a good enough output, look at the effing data.Jacob [00:31:41]: LFD?Chai [00:31:42]: That's why I was smiling. I was “Is Janie going to mention what it stands for?”Jacob [00:31:46]: I was not... There's like a million acronyms.Jacob [00:31:48]: How am I supposed to know that I don't? So “Oh yeah, of course, an LFD.”Swyx [00:31:51]: I've never heard of LFDs.Chai [00:31:53]: It's a bridge for sure.Janie [00:31:55]: I got through three days and then I had to ask someone.Janie [00:31:58]: I thought it was just me that didn't knowJanie [00:32:01]: It's our internal process.Swyx [00:32:02]: But look at the data as a meme in ML, ‘cause you tend to not look at it. You just want to look at number go up.Chai [00:32:06]: Exactly.Swyx [00:32:07]: But yes.Janie [00:32:08]: But so, we make sure we look at the data and then as we think about all of the components of good output, we, one, create LLM judges across all of these and we make sure with annotated data and either internal or external evaluators, we feel like these judges are calibrated. And then depending on the stakes, we also work with in-house and third-party evaluators across all of these before we ship any big change. And the goal is, in terms of evolution, how do you go from this process taking months, down to weeks, down to days? Some of it is, a true science and ML problem. A lot of it's also just, hard operational work. Have you planned ahead in terms of what you need? Have you really optimized the capacity that you need across all of the different specialties you need? Have you gotten a really good sense of which third parties are great to work with for what use cases? This takes a lot of domain, expertise and, lots of mistakes and errors in figuring that out. And so as much of it is an ML problem, so much of it has also been operational gains that are hugely important, where domain-specific expertise is everything.Specialty-Level Evaluation and Progressive RolloutsJacob [00:33:23]: But it's funny, ‘cause I feel like people talk about healthcare like it's one giant market and the reality isJacob [00:33:26]: It's, dozens and dozens of sub-markets. And so it feels like in your evals you have to build that up across the board, probably.Swyx [00:33:34]: And is specialization the primary cardinality at... That's the word that comes to mind.Janie [00:33:40]: Sometimes, depending on the product or the use case. And so if we're making a note improvement or feature for a particular specialty, definitely but we have products that are for nurses. We have products that, are really aimed at making the document or the output a lot more billable. And so we'll want to work with coding teams and not necessary clinicians. And so likeJacob [00:34:05]: Coding meaning healthcare coding.Janie [00:34:06]: Yes. Yes.Jacob [00:34:07]: NotChai [00:34:07]: Yes. I see you.Swyx [00:34:07]: Other kinds.Janie [00:34:09]: But is this output proportional to the work that was delivered? Is there sufficient documentation to justify the amount that a health system may end up charging? And so, specialty sometimes but also domain, very different across all of the different products that we're working for. And building out that network is, not easy and is where a lot of our operational investments have gone into.Chai [00:34:35]: And I view a lot of analogies to self-driving cars here, where, part of it is we really want progressive rollout of features to test in the real world is this useful? Is this going to work? One big difference compared to past lives is before I'd build a product, maybe I'd alpha it and then I'd like GA it the next week, ‘cause I'm “Go, move fast, ship,” and whatnot. But the mentality is like you... I want to make contact with the reality as quick as possible but I want a progressive rollout. Because as much as I get as large of an offline eval set, I want the distribution of that to match real-life distribution. And over time, by rolling out early, similar to Waymo has a tagline, “The world's most experienced driver,” another thing that can, at least linearly increase for us is, both the size of our evaluation offline and online, that and it all feeds back.Janie [00:35:25]: Something that's been earned over time, speaking of evolution, is just the trust we've gotten with customers. Historically, a lot of these health systems, when they bring on new vendors, their release cycles are quarters, sometimes twice a year. We've gotten our customers onto monthly release cycles, which is pretty fast for health systems but what is more exciting over the last, call it, few quarters, has been, a subset of our customers have said, “We want to innovate with you. We trust you,” and we have a pretty, decent chunk of our customers who say, “We'll develop with you outside of these monthly release cycles. We have a higher tolerance. We know that the stakes are very high but we want to be the first ones using these products, giving you feedback.” And so for a pretty substantial set of our customers, we've been able to convince them to be able to ship, in this gradual way before GA. Something we talk about a lot internally is, trust is earned in drops, earned in buckets and so we still can't do what I used to do when I worked at Loom. We had 30 million users. I'd just be, rolling out experiments left and. The bar is still quite high for iterative rollout but because of the trust we've earned, we're able to learn at pretty high volume very quickly.Privacy, HIPAA, and De-IdentificationSwyx [00:36:45]: Your scale is still pretty huge.Swyx [00:36:47]: One thing I want to... We were going to go into scale? In a sec. One thing I wanted to call up, follow up on evals, which, again, just coming from a generalist engineer point of view, just thinking through what would people be scared of in doing this, the privacy and HIPAAJacob [00:37:00]: Elements of this. I have zero experience in that. What do you have to do? What is surprisingly not that bad?Chai [00:37:06]: So one thing that's really important here from a compliance perspective is very much that any of the data we use needs to be de-identified, any real-world data we use as a basis of online eval sets we're learning from. And so you have to — And there's, very clear, government guidelines, what counts as PHI. And so we've even have built models that can take, for example, a clinical transcript and remove all the key PHI indicators and so you have a scrubbed/de-identified version. And then once you... And so one thing that's important is first you've got to get confidence in that model in the first place? And prove that out. Because, now you have, multiple probabilistic systems on top of each other.Chai [00:37:46]: But once you have that, then you can train on it use it for evaluation and so forth, provided one of the cool things also that you can do from a business side is the right data contracting as well with your partners.Jacob [00:37:57]: Is the anonymization one way? Once it's done, you cannot undo it? Or is there someoneChai [00:38:01]: YesJacob [00:38:02]: Who holds the master key that can... Yeah, okay. So it's one way.Chai [00:38:05]: It's one way. Yeah.Jacob [00:38:06]: That's how it works. I just wanted to... Because, there's a lot of this, learning from feedback and everything that, you would want to debug more but you can't because you just physically don't allow yourself to.Janie [00:38:17]: Some of it's also written in our customer contracts in terms of who can or can't access PHI data, how long do we retain it,Jacob [00:38:27]: Very goodJanie [00:38:27]: Before it gets de-identified. And so we have a pretty high bar for who can access that PHI data, just to make sure that we always respect our customer data and privacy. But that's something that we partner with our customers on too, to make sure that as we want full, as close to precision as possible in that qualityJanie [00:38:48]: We can still use it.Jacob [00:38:50]: But it'll be fascinating to see how that space evolves? Because you think about, I used to work at a company that, did a lot of healthcare data in the cancer space and if you asked, the average cancer patient, “Hey, do you want people, do you want other patients to be able to learn-”Chai [00:39:03]: Take it.Jacob [00:39:03]: “... Learn from your experience?”Chai [00:39:04]: Take it all.Jacob [00:39:05]: They're “Please.”Jacob [00:39:06]: “I'd love, nothing more than for other people to be able to learn fromJacob [00:39:10]: The experience that I had.” And so in the past it was a lot harder to do that learning. But with this technology, that might really be practical and so it'll be fascinating to see how that continues to evolve.Chai [00:39:21]: There's so much in our data set of 100 million conversations.Chai [00:39:26]: You can imagine things like insights that you can give to the clinician. How could you, oh, how could you have reacted to this? In coaching or insights around, which treatments are effective or, like... Because you have this, again, this data source that was never captured before but that's, where, intuition or experience is created from, going back to this idea that the conversation is the agent of truth.Operating at Scale: Reliability, Cost, and Token EfficiencyJacob [00:39:46]: Back to the 100 million conversations, I feel like you have this insane scale that maybe only a few other AI app companies have and everyone else dreams of. So not everyone has had to confront this yet but maybe just talk about some of the challenges of operating at that scale and what, our listeners have to look forward to if they ever get to this level of scale.Chai [00:40:05]: At large and larger in scale, so of course there's a general, infrastructure reliability. When you... In any given startup, you're building the plane while it's flying. So there's some notion of that. But what gets interesting on the AI and ML side for sure is this, as you get at more and more scale, so one, you have the data to first and foremost do this. But, you start thinking about costs or infrastructure in a whole different way at scale versus, a prototype.Chai [00:40:34]: You can use the most expensive model, you can burn as many tokens as you want but when you're doing 100 million conversationsJacob [00:40:41]: Token max on leaderboards are less upsetting than that context.Chai [00:40:45]: . When you're doing that and so that comes for we have the data and we also have the team that's able to post-train based on this and you can optimize for efficiency, especially in areas where you believe that maybe a lot of the quality headroom is less so and you don't expect the other off-the-shelf models to go that way, such that you want to do, efficiency maximization, in terms of compute and tokens.Jacob [00:41:08]: I feel like you guys live in the future in some way where most use cases today are really just in use case discovery mode, where it's “God, I really hope I can find something that can get to scale,” and so you're always going to use the most powerful model. And then the few things that do get to this level of scale, you start to do those optimizations.Chai [00:41:22]: It's a natural trajectory where it's like zero-to-one, we're not talking about any of these optimizations.Chai [00:41:26]: But when maybe we're in the one-to-100 or so forth, then we're in optimization mode and, what works out really well is you've got all this data from zero-to-one that lets you do this.What Comes Next: The Conversation as the Shared Healthcare PlatformJacob [00:41:36]: That's fascinating. I feel like one thing that's so interesting about the Abridge footprint is that you're in the doctor-patient visit in real-time. I always like to say, there's like probably 50 years' worth of product you could build on top of that. What gets each of you, I don't know, what are you most excited about building, either in the short term or medium term or even, long down the line?Janie [00:41:53]: Something that I get really excited about is that the same conversation can serve so many stakeholders. If you think about the conversation, a doctor needs to know what is the documentation, how do I make sure that this fully represent the care I gave? A patient needs to know, “What the heck just happened? This was really overwhelming. What are my next steps?” A payer needs to know, was this the proper and appropriate care given? A pharma company might want to know why isn't this drug being properly used or is there a good candidate for this clinical trial that I'm about to run? And where I get excited is that our product and our platform and our infrastructure can be the same product across all of those things and start to what's today, separate, very expensive, complex systems that serve each one of these stakeholders in very different ways, start to collapse all of that into a singular platform that enables not just more efficiency across the board but also better outcomes for everyone. And, all of us experience healthcare in probably very painful ways and knowing that there is a world in which we can simplify a lot is really exciting to me and it all starts with the conversation.Chai [00:43:15]: It's interesting. Of it very similar to going back to the KPIs that any AI product cares about. How do you increase quality of care? How do you reduce latency to care? And how do you reduce costs? Which is a huge, in healthcareJacob [00:43:28]: They call it the triple aim in healthcare.Chai [00:43:30]: But very similar to building AI products and the thing that really excites me is when we talk about that latency piece, we talked about one example earlier of prior authorization, can you reduce the latency to care? But you can imagine so much more. Oh, as soon as the lab value gets updated, do you have like a background agent that, kicks off and uses all the context to be “Oh, hey, the patient should do this next,” for example. And of flagging that to the clinician who's always in the loop but reducing that latency, to care. And then you can imagine this is much further down the road but it's like even connecting that to the direct patient and the consumer. And so how can you, how can you build a bridge to all of these things?EHR Partnerships and the Clinical Intelligence LayerJacob [00:44:10]: Very cool. The connections piece is just an ever-growing thing. And one of the key partners is the EHR and I wonder what that relationship is like. Will they, look at this as, something that is valuable enough that they want to own someday?Janie [00:44:29]: Our partnerships with the EHR is, we know that we have to be extremely close partners with all the EHRs who we partner with. Being able to not only pull and push all of the data into the right places is, not only table stakes, if we can't do that, health systems don't want to use us. The second and the reality of today is clinicians spend a lot of their days in the EHR. So much of what allowed us to win in the largest health systems was pretty direct and, very close partnerships with some of the largest electronic health records that allowed us to pull and push data with APIs that weren't ready out of the box. And clinicians want to save clicks. Anytime we introduce a new product that, adds two clicks for them in their day, they're “We're not going to use it.”Janie [00:45:21]: They have 15-minute back-to-back appointments with their patients. They're spending, hours during pajama time doing documentation. Every second and every minute counts and so we really think about being deeply integrated into the EHR as also table stakes to getting real usage and adoption. And anything that we build or introduce, we really talk about earn the right internally a lot, which is we have to provide so much value or save so much time that people will use us. But those are the two things that are close to us, is we know that the product won't be used unless it is deeply interoperable.Chai [00:46:01]: And strategically, to your point, it's like what does EHR want to own versus us? EHRs are really focused on the clinical workflows and so forth but some of the things that we're talking about here, I do these traditionally are outside of the domain where it's oh, connecting pairs and providers together with provider policies or the clinical trial matching, as Janie brought up. And so these are, entirely — we position ourselves as building this entirely new intelligence, clinical intelligence layer across, again, providers, pharma and, payers.Chai [00:46:33]: And so that's a it's a whole different ballgame that we try to playChai [00:46:36]: In combination with them.Jacob [00:46:37]: But it's like a different layer of scope.Healthcare AI Regulation, Technical Depth, and What Changed Their MindsJacob [00:46:39]: I'm curious, you are both relatively newcomers to healthcare. People have these, there's lots of futuristic healthcare AI takes of “Oh, everything will look different.”, now that you've been in healthcare for a bit, you live at the edge of AI, what have you, changed your mind on around this, as you think about what healthcare looks like in ten, 20 years? Any updates to your mental model from the time being close to the problems?Chai [00:47:02]: One thing that IChai [00:47:04]: Was hesitant about before and it's a common thing when I'm trying to recruit engineers that people ask me around, is definitely oh, healthcare, heavily regulated space. And it is, rightfully so. You want to keep, the patients at the end of the day safe. But one of the interesting things that, is a that surprised me how much it is coming to the company is there's a lot of really favorable regulatory tailwinds as well. Where you think about, government really wants interoperability between all these systems that we talked about and so agents can access this information. The government just in January, the FDA released updated guidance on clinical decision support, what I work on in such a way that they used to have guidance from like 2022 that required you to have, mention all these options and do all these other things but it's a very forward and forward-looking way. And so for me, what's been really cool to work on is this, there's this very special moment both in AI in general, we all know that but there's a special moment also regulatory in healthcare as well.Janie [00:48:05]: One thing I would call out is for the very reasons things are higher stakes or, potentially considered more difficult in healthcare, it's where some of the hardest AI problems will get solved first, just because the bar is so high. When I first joined, I was “Oh, this is where we'll be on the tail end of where, all of the AI innovation will be able to be applied.” But when you think about, zero error evals or multi-step workflows that have really low tolerance, a lot of the innovation will happen here just because we have to or else we can't ship.Jacob [00:48:42]: ‘Cause like in other domains, you'd much rather just solve the 80%-is-good-enough problems firstJanie [00:48:46]: 80/20 doesn't work hereChai [00:48:48]: And building off that, traditionally, there was a bit of stigma that, oh, healthcare companies are not that interesting from a technical perspective or I've seen that or faced that myself. But these are really hard and fun problems from a pure technical perspective beyond just the impact. How do you bring the latency of this thing down and make it really high-quality?Reducing Latency: Clinical Workflows, Agents, and Implementation RealityJacob [00:49:07]: How do you bring the latency of things down?Chai [00:49:10]: Yeah. Yeah. Yeah. So okay, let's answer the latency question. And maybe hopefully not too redundant with some of the things I've said earlier but some part of it is with any latency, you have to like what is, what is really your bottleneck. In a lot of workflows, it's sometimes it's the model itself. And so that's where like our data flywheel, our post-training team and so forth come in so that can you make the models far more efficient. So that's one aspect of latency. But there's whole other aspects of latency where it's okay, on top of that, if you use a constellation of different models, can you use — can you first use like a — it's like thinking fast and slow. Can you use a cheap, fast model that triages and hands it off to a larger model where you get more intelligence and so forth and so all theseChai [00:49:56]: Clever tricks to make it work.Chai [00:49:58]: And by the way, we are totally — we also realize that the parameter frontier is changing and so these tricks will — may not get us to where we want to be in five years but we need to if we want to build a useful product right now.Jacob [00:50:11]: Should we go to the quick-fire or you want to ask more about Abridge? We can stuff everything that's not Abridge into the quick-fireSwyx [00:50:16]: I don't mind. I was — I feel like Janie was on the topic of more long tail stuff, which isSwyx [00:50:21]: Not the eighty/twenty thing and that really matters. And I'll —, if you have any tips or cool stories or just general approaches that have worked for you that's interesting to dig into.Janie [00:50:32]: One of them is even just how we staff our teams looks different than a traditional software engineering team, I'd say.Swyx [00:50:40]: Let's go.Clinician Scientists, Edge Cases, and Evals at ScaleJanie [00:50:41]: We have a bunch of folks with different roles who are clinicians and so we have this role called the clinician scientist and I heard one of our leaders refer to them as mutants recently. But they are people who've had clinical backgrounds, so MDs typically, who are also deeply technical, somewhere, on the spectrum of like a full stack engineer all the way to like extremely scrappy prompter. But having each of these people embedded within our teams instantly raises the bar for everything that we build because not only are they determining, is this product clinically useful but they're deeply embedded in our whole evals process. And so when we talk about LFDs, when we talk about what is our actual evaluation criteria, you don't want Chai or me creating what those are because we don't have clinical background. But is probably unique to Abridge but has been game changing. And when you think about where the puck is going, you have people build with clinical backgrounds who are technical and where AI tools are going, they just becomeJanie [00:51:53]: More and more, critical and like the killers of the team. And so that's one. And then the second is just the scale at which we do evals to catch that long tail up front before anything ever gets into production is something that we've pretty much like really started to fine-tune, both from a scale but when do we know we need to get several hundred versus several thousand offline responses, what helps us make that quick decision and make this less of an art and as much of a science as possible. But that's also been something we've had to tune over time.Swyx [00:52:27]: And you have partners who opted in to give you those evals.Janie [00:52:31]: So we work either internally or with third-party for offline evals and then we have customers who also agree to give us, whether it's like thumbs up, thumbs down to like choose this or that, a lot of data to get us to what is as close to fully confident as possible.Swyx [00:52:51]: The term that comes to mind isSwyx [00:52:53]: Like active learning on things where you're weak. I feel like it's a lost artSwyx [00:52:58]: Is a lot of the polish that comes into doing something like this.Janie [00:53:02]: Really.Chai [00:53:03]: Hundred percent.Lessons from Glean: Technical Foundations and AI App InfrastructureJacob [00:53:04]: Maybe, on a totally unrelated note, Chai, you had a very, storied run at Glean b

Mike Gray's path to dentistry was anything but straightforward — and that's precisely what makes this conversation so compelling. A former semi-professional mountain biker who raced the World Series across three disciplines, a musician who once had the head of Universal Publishing sitting in his living room in rural Wales, and a dentist who spent years doing everything he could to avoid dentistry, Mike has lived several lives before arriving at the one he clearly loves. Payman and Mike cover the full sweep — grief, therapy, surgical war stories, and an obsessive, self-taught approach to digital restorative dentistry that culminates in his POISE Protocol: a no-prep veneer workflow that he believes makes truly minimally invasive ceramics available to the vast majority of patients, not just a lucky five per cent.In This Episode00:00:55 – Introductions and first impressions00:01:20 – Mountain biking career00:09:15 – A friend's suicide, guilt and stepping back from maxfax00:12:15 – Therapy00:14:10 – Life on the World Series circuit00:19:25 – From maxfax to music00:28:10 – Blackbox thinking00:33:45 – Music career — Alabama Three, Peppa Pig and Covid00:49:25 – NHS dentistry debate00:51:50 – Falling in love with dentistry00:54:40 – Self-taught restorative and the digital workflow01:00:25 – Ditching the articulator01:01:20 – Prototypes, not temporaries01:05:10 – Into implants01:11:00 – Compassion fatigue01:13:40 – POISE protocol and no-prep ceramics01:25:10 – The Lodge and the course01:29:05 – Resilience and failure01:34:20 – Practice ownership01:41:10 – Instagram01:49:20 – Fantasy dinner partyAbout Mike GrayMike Gray is a dentist based in Wales, working at Parkway Clinic in Swansea and The Lodge — a referral and education centre where he hosts his sold-out POISE Protocol course on minimally invasive ceramic veneers. His background spans maxillofacial surgery, semi-professional mountain biking at World Series level, and a music career that attracted interest from Universal Publishing and, improbably, Peppa Pig. He teaches himself CAD, machines his own surgical instruments, and has spent five years developing a digital workflow for no-prep ceramic restorations that he believes renders feldspathic and heavy preparation largely redundant.

This week @adafruit we're chatting about Pedro's BLE project using CLUE. Checking out prototypes from Noe. This week's timelapse features a tintin rocket. Adafruit CLUE: https://www.adafruit.com/product/4500 Feather RP2040 Propmaker: https://www.adafruit.com/product/5768 NeoPixel Pebble Strand: https://www.adafruit.com/product/6024 Gamepad QT Stemma: https://www.adafruit.com/product/5743 Timelapse Tuesday Tintin Rocket By dolu3D https://makerworld.com/en/models/2743403-tintin-rocket https://youtu.be/SWks9YADiRg

The NEW Prince Tour racquet line is here and we're breaking down everything — specs, playability, who they're built for, and whether they live up to the hype. If you've been asking questions about the new Prince Tour racquets, this is the episode you've been waiting for. Take a closer look at the Prince Tour Racquets & Specs:  Watch the YouTube Episode: https://youtu.be/mS1Zd8zyAo0 0:00:00 Tim from PRINCE is here & we are talking all things 2026 PRINCE TOURS 0:01:26 What Does the 2026 Prince Tour Lineup Look Like - What has been updated?! What has changed?! New technologies & layups explained 0:02:31 Prince Tour ATS were "too dampened" 0:03:50 Completely New Construction EXPLAINED 0:07:10 Cosmetic Inspo 0:0:9:52 Our Favorites From the Line 0:11:33 Michelle is obsessed with the new Tour 100 310g 0:13:50 Lucas Pouille x PRINCE Tennis 0:16:20 Prototype Hitting for 2027 0:18:57 We Answer YOUR Questions: What are the Most Notable Changes? 0:19:56 Foam filled frames? & Prototypes explained 0:22:12 How Does Prince Decide How or When To Make Large Changes In Racquet Lineup 0:26:24 Bring Back the EXO95; Bring Back the OPort Rebel 0:27:55 How Does Prince Adapt to the Market & Modern Tennis 0:29:55 Where is the Tour 95?! 0:32:30 Any alternative colors coming in the Prince Tours?! Stay tuned... 0:33:50 String Suggestions for New Tours & Poly / Poly Hybrid Chat! 0:37:38 We NEED MORE POWER - Will I get It With the New Tours?! 0:40:30 16x20?! Will we get a Prince 16x20 Racquet?! String Spacing Explained! 0:44:30 Will There Be a Retro Bag Collection Coming?! Any new bags?! 0:46:36 What Racquets Will Come From Japan?! 0:47:54 Zylon Tech in Tours & Japanese Market Explained 0:50:23 Difference between Ripstick & Tours; Prince Family of Racquets Explained 0:54:00 Keep an eye out on fun things to come!

A couple weeks ago, Michał Nedoszytko placed third globally at Anthropic's hackathon out of 13,000 participants. He was the head of cardiology at a hospital in Brussels at the time. Now he is Clinical Scientist at Abridge. On this episode of Vital Signs, Nikhil and I sat down with Abridge's CEO Shiv Rao and Michal to chat about how the hire came together, what changed with Opus 4.6 that let a cardiologist ship a working MVP in 40 minutes, and where they both think clinical AI goes next.   (0:00) Intro (0:19) Hackathon Fame (3:52) Shiv Recruits Mahal (6:30) Doctors Who Code (9:07) Prototypes vs Production (10:26) Regulation and Partnerships (13:00) Customization vs Reliability (15:59) AI Native Company Ops (19:29) Healthcare in 10 Years (21:08) Admin vs Clinical AI (24:47) Payers and Prior Auth (29:48) Training Doctors for AI (35:19) Context, Autonomy, and Demand (40:40) Pre-visit Workflows and Triage   Out-Of-Pocket: https://www.outofpocket.health/

What if a prototype built in two hours is worth more than a six-month IT project that delivers the wrong thing?Marc Gasser sits down with Valentin Binnendijk and Reto Laemmler (both Lovable Ambassadors) to break down the shift reshaping product teams: vibe coding with Lovable. Three blocks, no theory.What you take away:Why positioning comes from customer interviews, not from the meeting roomWhy product sense becomes the bottleneck once development speed stops being oneWhere vibe coding ends and engineering takes over, and why that line mattersBuilt for Founders, GTM and Tech Leaders rebuilding their teams for what comes next.

Tout commence avec un livre de vulgarisation scientifique et la passion des phénomènes qu'on ne peut pas tout à fait expliquer. Cette fascination pour la science et l'espace guide depuis toujours Kevin Nocentini, Technology & Innovation Manager chez Capgemini Engineering. Dans cet épisode du Lab, il revient sur son parcours d'ingénieur et sur ce qui l'anime depuis l'enfance : comprendre l'univers et ses systèmes complexes. Il partage son cheminement, de ses premières curiosités scientifiques à son rôle actuel au cœur de la recherche appliquée, où il travaille sur des sujets variés mêlant spatial, ingénierie système et durabilité. L'épisode aborde notamment la question des débris spatiaux, un enjeu largement invisible, mais aux conséquences très concrètes, qui illustre parfaitement son approche de la recherche : explorer, modéliser et progressivement faire émerger des solutions ancrées dans le réel.Un épisode éclairant, pour découvrir le parcours d'un ingénieur passionné par la science et convaincu que la recherche est avant tout une aventure humaine.

This module helps students understand how ideas move from abstract concepts to tangible learning tools. The focus is on prototyping as a way of exploring assumptions, testing as a way of generating evidence, and iteration as a disciplined process of improvement. Students should leave the module with a clear understanding that prototypes are not unfinished failures, but purposeful instruments for learning under uncertainty.

RaiderNation und alle Freaks, ihr seht es schon an der Länge der Folge: Es ist wieder Draft Time! Mit Patric und Gavin bespreche ich die aktuelle Offensiv-Klasse von Quarterbacks bis Wide Receivers. In einer Ausführlichkeit, bei der nur die wenigsten durchhalten ;) Wir labern über Fernando und seine zukünftigen Weapons, über Bust-Gefahren und Diamond Tags, über Prototypes und Gadget Players, wir sezieren die Klasse nach Strich und Faden - und geben unsere Raiders Draft-Tipps. Ist Jeremiah Love ein Generational Talent? Was kommt nach Mendoza? Gewinnt Makai Lemons Agility gegen Carnell Tates Ball Skills? Vertrauen wir Jordyn Tyson trotz seiner Verletzungen? Ist Germi Bernard ein möglicher Juwel? Und brauchen die Raiders überhaupt noch O-Liner? All das und tausende Beobachtungen mehr, hört ihr heute in der #114 vom LocoFootball Podcast. For Freaks only!

Send us Fan MailWatch the video!https://youtu.be/Q7Wl0_KBGbkIn the News blog post for April 3, 2026https://www.iphonejd.com/iphone_jd/2026/04/in-the-news823.html 00:00 41 Years of the ABA TECHSHOW!03:15 50 Years of Apple - In Between Jobs, Prototypes, and the Pogue Feature31:15 Satellite Wars37:23 iPhones in Space!40:37 Max Squared 45:56 Time-Zoned Medications48:52 Brett's iTip: Apple Credit Card Colors54:18 Jeff's iTip: Take a Full-Page ScreenshotDavid Pogue | Pogueman: Apple and Me: The First 50 YearsJoe Rossignol | MacRumors: Apple Celebrates 50th Anniversary in Seven WaysJohn Gruber | Daring Fireball: Apple Marks 50th AnniversaryKalley Huang | The New York Times: One of Apple's First Employees Looks Back at 50 YearsJason Snell | The Verge: Between JobsRyan D'Agostino | Esquire: Tim Cook (Still) Believes in Crazy IdeasZac Hall | 9to5Mac: Read Tim Cook's ‘Apple at 50' memo: ‘What excites me most is what comes next'Tim Hardwick | MacRumors: Amazon Reportedly in Talks to Buy Apple Satellite Partner GlobalstarRoman Loyola | Macworld: iPhones in space! NASA brings 17 Pros on board Artemis missionChance Miller | 9to5Mac: AirPods Max 2 review: High-end adds modern features at lastGlenn Fleishman | Six Colors: Time for your meds, Mr. FleishmanBrett's iTip: Apple Credit Card Colorshttps://www.creditcards.com/card-advice/apple-card-colors/ Jeff's iTip: Take a Full-Page Screenshothttps://support.apple.com/en-my/guide/iphone/iphc872c0115/ios Support the showBrett Burney from http://www.appsinlaw.comJeff Richardson from http://www.iphonejd.com

On this episode, Tom, Julie and Eric are live at Dice Tower West in Las Vegas taking questions from the audience and talking about the games they've played (with a brief Tom-less interlude from Eric and Julie). 01:01 - Dice Tower West Library and Most Played 03:59 - Julie in Demoville and Escape Rooms 06:06 - Julie Came With a List 08:24 - Star Wars: Battle of Hoth 09:28 - Tea Garden 11:01 - Orloj: The Prague Astronomical Clock 12:55 - Feierabend 15:21 - Merchant of Venus #100 16:47 - Fathom 18:38 - Shapely 21:03 - Tale of Boardgaming Horror 28:28 - Eric and Julie Interlude 29:10 - E&J: Stuff on Tables 31:39 - E&J: Strange Things to Carry 34:05 - E&J: What Shouldn't Stay in Vegas 36:51 - E&J: Drawing a Crowd 39:34 - Question: Game Opening Disasters 44:42 - Question: Escaping Jail 46:44 - Question: Comprehensive Rulebooks 52:01 - Minor Tom Rant: Authority to Answer Questions 52:58 - Question: Art for Prototypes 56:31 - Question: Deluxe Editions 58:37 - Question: Less or More of a Genre Questions? Tales of Horror? tom@dicetower.com

I've seen this pattern repeatedly with teams building analytics and AI products: the issue usually isn't the quality of the models or the sophistication of the data. The technology often works just fine. The real breakdown happens earlier—when teams begin with the data they already have and try to figure out what to build, instead of starting with the decisions their customers need to make.     That approach often produces polished dashboards and compelling features that generate interest, but fail to drive real action. The missing piece is context. Decisions in the real world depend on incentives, habits, risk tolerance, and uncertainty—not just clean data. If your product doesn't reflect that reality, it won't meaningfully change behavior.     Another common trap is assuming all available data is *evidence* worth surfacing. This “more is better” mindset leads to cluttered analytics tools that offload interpretation onto users. Even conversational AI interfaces can fall into this, encouraging open-ended exploration without helping users reach decisions.     The analytics and AI products that succeed take a different approach. They're designed around decision-making to reduce uncertainty, fit into real workflows, and guide users toward clear actions. In doing so, they bridge the gap between analytical capability and real-world value, making the product's intelligence tangible, usable, and worth paying for.     Highlights/ Skip to:   The core mistake I see people making during the discovery process of building an insights product (2:07) Improve your product strategy by working ‘backwards” and understanding what decisions customers are trying to make  (6:06) Insights don't equal decisions in the real world (7:39) Designing with a goal of improving the lives of users in mind  (11:17) Prototypes as a means of discovery (vs. product/solution validation) (13:48) The bias of data availability (20:39) Using AI and LLMs for discovery and product UX (24:17) Why AI-assisted analytics products should shape UX around making structured decisions (31:03) Overcoming the Invisible Intelligence Gap  (34:57) Final thoughts (37:21)   Links CED: My UX Framework for Designing Analytics Tools That Drive Decision Making https://designingforanalytics.com/ced   Need my help finding the right use cases for your analytics or AI product?  Book a complimentary 1x1 discovery call with me: https://designingforanalytics.com/contact/ 

In this episode of Wednesday's Wins and Wiffs, the crew shares their latest toy hauls and collecting victories. From high-grade vintage Star Wars figures to LEGO Ninjago flips and rare 90s Kenner prototypes, we dive into what's currently moving in the hobby. We also discuss suspicious "retro" molds appearing in the market and share some laughs over technical difficulties and white glove etiquette.☎️ Leave a question, comment, or show idea on our new FITT Voicemail line: (732) 800-197700:00 - Intro & Technical Difficulties02:40 - LEGO Ninjago 15th Anniversary Win05:12 - The White Glove Show: Luke Bespin AFA 8509:23 - NECA Remco-Style Universal Monsters11:20 - Rare 1990s Kenner Predator Prototypes13:43 - Warning: Suspicious Retro Molds Discussion15:51 - Outro & How to Support the Show#StarWarsCollecting #Vintagetoys #LEGO #ActionFigures #ToyCollecting #Kenner #Ninjago #FiveIdiotsTalkingToys #ToyHaul #ActionFigureNews #ToyPodcast-----------------------

In this episode, I speak with returning guest Dan Olsen, product management trainer, consultant, speaker, and author of The Lean Product Playbook. We go deep into the rise of "vibe coding" and what it means for product teams. Dan has gone deep into vibe coding, is offering training courses in it, and believes it firmly sits within his existing Lean Product Playbook process and supports the Product/Market Fit Pyramid. Episode highlights AI shifts the product bottleneck – As AI tools make engineers more productive, the limiting factor increasingly becomes product discovery and decision-making rather than development capacity. Product management isn't going away – AI can automate some tasks, but judgement, prioritisation, and making decisions under uncertainty remain core human responsibilities. The rise of the product builder mindset – New AI tools allow product managers to prototype ideas directly, giving them a more hands-on way to explore solutions. The vibe coding spectrum – AI development tools exist on a spectrum from simple browser-based tools through to full developer IDE integrations, letting teams adopt them at different levels of technical depth. Vibe prototyping vs vibe coding – For most product managers, the real opportunity isn't replacing engineers, but quickly generating interactive prototypes that help teams explore ideas before committing to production code. Divergent thinking still matters – AI tools often generate a single solution, so teams need to deliberately explore multiple directions and alternatives rather than blindly optimising the first result. Prototypes have four key audiences – Early prototypes help clarify ideas for the creator, align the product team, communicate concepts to stakeholders, and gather feedback from real users. Context beats clever prompting – The quality of AI-generated output depends far more on the context, requirements, and constraints you provide than on the prompt itself. Iteration beats one-shot builds – The real power of these tools comes from rapid experimentation and refinement rather than expecting a perfect result from a single prompt. ... and much more. Dan's stuff LinkedIn: https://www.linkedin.com/in/danolsen98/ Dan's Website: https://dan-olsen.com/ Dan's Vibe Coding Template: https://dan-olsen.com/vibe-coding/ YouTube: https://www.youtube.com/danolsen Lean Product Meetup: https://www.meetup.com/lean-product/ The Lean Product Playbook: https://amzn.to/1EYCUdP

Suresh Krishna, CEO of Protolabs (PRLB), a digital manufacturer that helps create prototypes, joins to discuss the company and its latest financials. “We work with almost all the startups and most of the Fortune 500 companies,” he says, “We are there in every industry.” He recently assumed the mantle of CEO and notes that their most recent quarter was the best in years. They are benefitting from more companies looking to manufacture in the U.S. Suresh explains how their speed makes them like the “Amazon” of prototypes, along with the wealth of materials they use. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

On today's show we look at HDTV Display Technologies that are no longer with us. Some had a short run and some never made it to the market. We also read your emails and take a look at the week's news. News: LG pulls the plug on 8K OLED and 8K LCD TVs Apple's home hub could finally arrive this spring with a rather unique design Roku is Testing a New Home Screen With A New Look Google Home update brings more automation controls HDTV Display Technologies That Are No Longer With Us Over the 21 years we have been doing the show we have seen numerous HDTV display technologies come and go. Some never made it to market and some had a good run but were eventually beat out by something better. These technologies competed during the transition from bulky CRTs to flat panels, but most lost out as LCD, later becoming LED-backlit LCD, then OLED, became dominant for reasons like cost, scalability, picture quality improvements, and manufacturing ease. Technologies That Were Proposed/Demonstrated but Never Commercially Released to Consumers SED (Surface-Conduction Electron-Emitter Display)Developed primarily by a Canon and Toshiba joint venture starting in the late 1990s/early 2000s. It was essentially a flat-panel evolution of CRT technology using electron emitters for each pixel, promising CRT-like motion handling, deep blacks, high contrast, fast response times, and low power in a slim form factor. Prototypes were shown around 2005–2007 with impressive demos. Why it didn't make it: Repeated delays due to manufacturing challenges (high production costs, difficulty scaling/vacuum sealing), patent disputes, and aggressive price drops in LCD/plasma panels. Then by 2009–2010, LCD had become too dominant and cheap; Canon officially froze consumer SED development in 2010, shifting any remaining efforts to niche professional uses. FED (Field-Emission Display)Similar to SED and sometimes grouped together or seen as a precursor/variant. FED used field-emission electron sources (like microtips) for CRT-style performance in a flat panel. Demonstrated in prototypes in the 2000s by companies like Sony and Motorola. Why it didn't make it: Development took too long; manufacturing complexity and yield issues made it unviable. It was overtaken by faster-scaling plasma and then LCD/OLED technologies before reaching mass production. Technologies That Reached the Market but Were Discontinued DLP (Digital Light Processing) Rear-Projection TVsUsed Texas Instruments' DMD (digital micromirror device) chips to reflect light, often with a color wheel for sequential color (or pricier 3-chip versions). Popular in the mid-2000s for large-screen (50–70+ inch) HDTVs from brands like Samsung, Mitsubishi, RCA, and Toshiba, offering good brightness, no burn-in, and sharp images at competitive prices. Why discontinued: Bulky depth (even if thinner than CRT rear-projection), lamp replacements needed, rainbow artifacts (on single-chip models), poor off-angle viewing, and vulnerability to ambient light. As flat-panel LCD and plasma prices fell dramatically in the late 2000s, consumers preferred slim, wall-mountable designs. Rear-projection DLP TVs largely vanished by around 2010. LCOS (Liquid Crystal on Silicon) / Variants like D-ILA (JVC) and SXRD (Sony)A reflective microdisplay tech using liquid crystals on a silicon backplane, often in rear-projection or some front-projection setups. Offered excellent contrast, deep blacks, and smooth motion (better than early LCDs). Available in HDTVs from JVC, Sony, and others in the mid-2000s. Why largely discontinued for direct-view TVs: High cost, manufacturing complexity, and lower brightness compared to emerging flat panels. Rear-projection versions suffered the same bulkiness issues as DLP. While LCOS survives today in high-end projectors mostly in JVC and Sony home theater models, it never scaled to mainstream direct-view flat-panel HDTVs and was eclipsed by LCD advancements. Plasma Display Panel (PDP / Plasma TVs)Used ionized gas (plasma) cells to create light, excelling in black levels, contrast, color accuracy, wide viewing angles, and no motion blur. Very popular for HDTV in the 2000s from Panasonic, Pioneer, Samsung, and LG. Why discontinued: High power consumption, heat generation, heavier panels, burn-in risk (though mitigated later), and difficulty scaling to 4K efficiently/cost-effectively. As LCD/LED prices dropped with better brightness, efficiency, and no burn-in, plasma couldn't compete economically. Production fully ended around 2014–2015. Other Notable Mentions LCD Rear-Projection TVs — Used transmissive LCD panels; suffered from similar bulk and light issues as DLP; discontinued early-mid 2000s. Direct-view CRT HDTVs — The original standard; fully discontinued by the late 2000s/early 2010s due to size, weight, and inefficiency. Key Reasons Technologies Fail in HDTV Market Regardless of how good a display technology is, the following will keep it from the mass market: Cost & Manufacturing Yield: Technologies requiring ultra-precise processes (SED, FED, LCoS) couldn't hit competitive prices.  Competing Technologies Improve Fast: LCD and later LED/OLED got cheaper and better quicker than rivals could scale. Form Factor Shift: Direct-view panels beat rear-projection (DLP, LCoS, laser) because consumers prefer thin TVs. Performance Tradeoffs: Issues like power use, burn-in, brightness, viewing angles, or reliability hurt consumer uptake.  In summary, the winners were technologies that scaled cheaply to larger sizes, became thinner/lighter, improved efficiency, and avoided major drawbacks like high costs or reliability issues. LCD/LED dominated the 2010s due to mass production advantages, while OLED took premium segments later for superior contrast/per-pixel lighting. Many promising "next-gen" ideas from the 2000s (like SED/FED) simply arrived too late or proved too hard to manufacture affordably.

I had a conversation recently with a web team at a college who were stuck in a painfully familiar trap. They had a sprawling, chaotic website that had grown like an untended garden over the years. They knew it was letting users down. They had plenty of ideas for how to make it better. And yet, every time they tried to improve things, they hit a wall.Sound familiar? I suspect it might.The team had been there for years, and they had developed what I call "institutional scar tissue." Every suggestion was met with an internal voice saying "we tried that once and it didn't work" or "I don't have the power to change that." They had been worn down by years of small defeats until the only option that felt possible was incremental improvement to what already existed.And incremental improvement, when applied to something fundamentally broken, is a bit like repainting a house with a crumbling foundation. Sure, it looks nicer from the street, but you're still one bad storm away from serious structural failure.The trap of fixing what existsWhen you try to fix an existing website, you inherit all the reasons it became broken in the first place. Every stakeholder who fought for their pet page is still there. Every "but we've always had that section" is still lurking. Every technical limitation that forced an awkward compromise is still constraining your options.Worse, you're starting from a position of defense. You have to justify why something should be removed or changed. The burden of proof is on you to explain why the current state is wrong, rather than on stakeholders to explain why their content deserves to exist.This is exhausting work. And it rarely produces genuinely transformative results.Wait, haven't I said the opposite?Now, if you've been reading my stuff for a while, you might be thinking "hang on, Paul. Haven't you spent years telling people not to do periodic website redesigns?" And you'd be right. I have. I've written at length about how the boom-bust cycle of website redesigns is damaging. How you end up with a shiny new site that slowly decays until someone throws a tantrum and the whole thing gets rebuilt from scratch.Incremental improvement is almost always the better path. Small, continuous changes based on real user data. No big-bang launches. No throwing out the baby with the bathwater.So why am I now suggesting we do exactly what I've warned against?Because sometimes the rot runs too deep. When you're dealing with thousands of pages of redundant, outdated, and trivial content, when every attempt at incremental change gets blocked by institutional politics, when the team has been so beaten down that they can't imagine anything better, you need a different approach. Not a traditional redesign where you migrate all the old problems into a new template. Something more radical.You need to imagine what you would build if you were starting from nothing.Start from nothingThe approach I suggested to this team was counterintuitive: stop trying to fix the website. Instead, imagine you're building from scratch.If you were launching this college's online presence tomorrow with no existing site, what would you build? What are the actual tasks people need to accomplish? What questions do they have at each stage of their journey? Strip away all the accumulated cruft and think about what a prospective student genuinely needs.For a college focused on student recruitment, it might be shockingly simple. Someone needs to find a course, understand if they can afford it, and apply. That's perhaps 200 pages of genuinely useful content. Not the thousands that currently exist.Frame it as a thought experimentDon't announce that you're redesigning the website. That triggers immediate defensiveness. Every stakeholder starts worrying about their territory. Before you've finished your sentence, half the room is already composing their objection.Instead, frame the whole exercise as a thought experiment. "We're not proposing anything. We're just imagining what perfect could look like. What would we build if we had no constraints? If we were starting fresh tomorrow?"This framing is disarming. People stop defending and start dreaming. They can engage with the vision without feeling threatened, because it's explicitly hypothetical. No one's being asked to commit to anything yet. It's like asking someone what they'd do if they won the lottery. They'll tell you all sorts of things they'd never admit to wanting otherwise.Make it a collective visionBut, don't do this thought experiment alone.Bring in a few trusted people from other departments early in the process. Ask them what excites them about what better could look like. Let them shape the vision alongside you.When you do this, something important shifts. It stops being "the web team's idea" and becomes a collective vision. Those collaborators become invested. They'll defend it in meetings you're not in. They'll sell it to their own teams. And if one of those collaborators happens to be a senior executive, you've just gained a powerful champion who can clear obstacles you couldn't even see.Think of it like rolling a boulder down a hill. The hardest part is getting it moving at all. You're pushing and straining and it barely budges. But once you've got a few people pushing with you, momentum builds. Energy creates more energy. Excitement spreads. What started as a small team's thought experiment becomes something the whole organization wants to see happen.Turn it into a prototypeThe output of all this imagining should be something tangible. Not a document. Documents don't generate momentum. Prototypes do.You can write the most beautifully reasoned strategy document in the world, and everyone who reads it will walk away with a slightly different interpretation of what it actually means. But show people a clickable prototype where they can move through the experience from beginning to end, and suddenly everyone is on the same page. There's no ambiguity. They can see it, click through it, and imagine themselves using it.I often recommend teams create what I call a "shiny thing." This is a functional prototype of the ideal experience, built quickly and without worrying about all the practical constraints. It's not meant to be launched. It's meant to excite.The UK Government Digital Service did exactly this when they were trying to transform government websites. They got a small budget to build a prototype of what better could look like, ignoring all the legacy systems and political constraints. When they published it and got public feedback, everyone loved it. That enthusiasm created the momentum to push through all the obstacles that had previously seemed insurmountable.Watch the burden of proof flipOnce you've got people excited about this collective vision, something interesting happens. You flip the burden of proof. Anyone who objects is now the one ruining the party."Our CMS can't support that" stops being a conversation-ender and becomes a question: why not? Shouldn't our systems be flexible enough to deliver what users actually need? "But we've always had it" no longer works as an argument either. If it doesn't serve the vision everyone now wants, it's the thing that needs justifying.Remember COVID? Working from home was impossible before 2020. Absolutely out of the question. IT couldn't support it, security was a nightmare, productivity would collapse. Then suddenly it wasn't impossible at all, because there was enough momentum and desire to make it happen. Organizations can change dramatically when they really want to. Your job is to make them want to.Separate everythingOne final piece of advice: keep your projects small and separate.When you're trying to create a new vision, scope creep is your enemy. Someone will point out that you also need to consider existing students. Someone else will mention that the CMS is being replaced next year. Another person will want to tie in the new CRM system. Before you know it, your focused vision has become a massive, unwieldy initiative that will take years and satisfy no one.When people try to expand the scope, don't fight them. Simply agree that their concern is important and deserves its own dedicated project. "You're absolutely right, existing student retention deserves as much attention as recruitment. We'll run that as a separate project and link the two together later."This way, you can actually make progress on one thing instead of being paralyzed by trying to solve everything at once. Perfect is the enemy of good, and "comprehensive" is the enemy of "actually getting shipped."Breaking freeIf you're stuck maintaining a website that feels like a lost cause, I'd encourage you to try this approach. Stop asking "how do we fix this?" and start asking "what would we build if we were starting fresh?"Map out what users actually need. Create a prototype of that ideal experience. Get stakeholders excited about the vision. Then, and only then, start figuring out how to make it real.The constraints that feel immovable today might prove surprisingly flexible once people genuinely want what you're proposing. The trick is giving them something worth wanting.If you're an in-house digital leader trying to drive this kind of change and finding the organizational politics overwhelming, I offer one-to-one coaching to help you build influence and lead with more confidence. Sometimes having someone in your corner who has navigated these waters before makes all the difference.

The Five Idiots dive into the seismic news shaking the Star Wars galaxy: Kathleen Kennedy's official exit as head of Lucasfilm. We discuss her legendary career, the fan reaction to recent content, and the high hopes for a transition of leadership to Dave Filoni, new President of Lucasfilm.☎️ Leave a question, comment, or topic idea on our new FITT Voicemail line: (732) 800-1977Plus, we provide an update on the polarizing world of modern collectible prototypes, debating whether the flood of high-priced, multi-colored Star Wars Retro Collection figures are legitimate factory samples or factory reruns designed to scam high-end collectors. This leads to a fiery discussion about the ridiculously high buyer fees and shipping costs at auction houses like Hakes!0:00 - Cold Open: Continuing the Prototype Saga0:29 - Welcome & Show Format0:40 - Prototypes Update: Are Modern Star Wars Retro Figures Real or Fakes?4:40 - Factory Control & The "Rerun" Scam Theory11:18 - Collector Warning: Why You Should be Careful Bidding12:11 - Kathleen Kennedy Steps Down as Head of Lucasfilm12:30 - Kennedy's Incredible Career vs. Recent Star Wars Content16:51 - Side Rant: The Insane Shipping Costs from Hakes Auction18:55 - Discussing Kennedy's Massive Movie Filmography (E.T., The Goonies, Schindler's List)22:40 - Why Star Wars Needs a Change in Leadership26:15 - The Han Solo Movie Mistake & Missing Obi-Wan and Lando Projects31:55 - Dave Filoni: Is He the Right Person to Steer the Star Wars Ship?38:51 - The Necessity of Theatrical Star Wars Movies43:52 - Wrap-Up & Outro#KathleenKennedy #DaveFiloni #StarWarsNews #Lucasfilm #StarWarsPrototypes #ToyCollecting #StarWarsRetro #FITT #FiveIdiotsTalkingToys #Podcast #MovieNews #StarWarsCanon #Disney #ETTheExtraTerrestrial #TheGoonies #SchindlersList #SoloAStarWarsStory #BackToTheFuture #StarWarsMovies #ET #StarWarsTheForceAwakens #StarWarsTheLastJedi #StarWarsTheRiseOfSkywalker #RogueOneAStarWarsStory #JurassicPark #TheSixthSense #TheColorPurple #WhoFramedRogerRabbit #CapeFear #Gremlins-----------------------

Jordan Taylor has won just about every major event in sports car racing including the Rolex 24 Hours of Daytona, 12 Hours of Sebring, Petit Le Mans and the 24 Hours of Le Mans in addition to winning multiple class championships in Prototypes and GT cars. He's currently racing in the IMSA GTP class for his dad's Wayne Taylor Racing Team in one of two Cadillac V-Series.R prototypes.  During the COVID pandemic he decided to do some triathlon training and has since competed in multiple Ironman full-distance triathlons. We caught up with Taylor at the 50th Anniversary of the Acura Grand Prix of Long Beach where he tells us about how he got started in triathlon, how it helps him prepare for his auto racing, while also giving advice to new and experienced drivers on how this type of training can help improve their results. This guy is impressive on the track and off and his insight into this other world of racing is fascinating. Enjoy!NOTE:  You can catch Jordan Taylor at the Rolex 24 this weekend with live coverage from the “World Center of Racing” on Peacock, NBC and IMSA.com.

This week, the Five Idiots' conversation quickly turns to the suspicious phenomenon of Star Wars Retro Collection "prototypes" flooding online groups for massive prices ($500+), all while coming from multiple sellers in different countries.The Five Idiots are back for another episode of Five Idiots Talking Toys! We're discussing if these colorful test shots are legitimate factory leaks from Hasbro or if the modern toy market is being scammed by high-bidders and shady private deals. We break down the questionable authenticity, the unbelievable prices, and the bizarre market tactics. Plus, we start the show with a quick chat about our favorite Lord of the Rings movies!In this episode:• Are collectors getting duped by a price bubble?• Why are modern figures selling for more than vintage prototypes?• The problem with non-public, private "deal made" prices.0:00 Introduction & Lord of the Rings Talk4:52 The Star Wars Retro Prototype Phenomenon7:58 What is the Retro Collection and Why Prototypes?14:00 Absurd Prices: Why $800 for a Modern Figure?25:55 Sketchy Private Deals & Market Manipulation33:59 Comparing Vintage & Modern Prototype Value40:34 Final Thoughts & Outro#RetroCollectionPrototypes #ModernToyCollecting #StarWarsTestShot #StarWarsCollector #ToyCollecting #ActionFigurePrototype #FITT #StarWarsRetro #ToyScam #ActionFigures-----------------------

1 Gathering Storm - Back In Time 2 Spare Limbs - Absolute Unity 3 Berzärk & Artheist - Savage 4 DJ Deepcore - This is War 5 Berzärk - Slaughter 6 Death - What to Fuck 7 FxGx - System Failure 8 Gathering Storm - Scattered 9 Hydroxide - Faces of War 10 Iridium - Godzilla (Zardonic & Enduser Remix) 11 Respawn - Damnation 12 Respawn - This is Respawn 13 Respawn - Viper 14 Agent Zero - Evil Gain VIP 15 Artheist & MAZA - The Awakening 16 Iridium - Already Dead 17 Iridium - Lies (Pluvio Remix) 18 Iridium & Access One - Reign In Chaos 19 NeoQor - Hide 20 Piecemaker - Come With Us 21 Vein & Rancor Spike - Ghost 22 Embrionyc - Defy The Prophecy 23 Frenesys - Get This Or Die 24 Iridium & Zerberuz - Get Clocked 25 Iridium - Cybervoid 26 Iridium - The Beast Inside (Detest Remix) 27 Iridium & Nagazaki - You're not real 28 Iridium & The Satan - The Chosen 29 Noisemonger - Adrenaline 30 Piecemaker - 24 Carat Balls Deluxe 31 Embrionyc & Nagazaki - Voxfract 32 Gathering Storm - Berserker 33 Iridium - Owari 34 Nagazaki - Bloodbath 35 Nagazaki & Arcade Trauma - BUSTHAT! 36 Arcade Trauma & Nagazaki - Paroxysm

In this episode, Sarah chats with Ben Peck, Director of Product Design & Global Strategy at nCino and a longtime community builder in the UX and product world, to demystify how UX hiring really works, from the perspective of someone who's hired again and again.Ben brings over 20 years of experience across agencies, tech, leadership, and community building. As the co-founder of Front Conference and former Executive Director of Product Hive, he's reviewed hundreds of portfolios, partnered closely with recruiters, and built high-performing design teams across industries.Together, Sarah and Ben unpack what actually happens after you click “apply,” how hiring managers scan portfolios, why storytelling matters more than polish, and how community and relationships quietly shape most UX careers.If you've ever wondered what's going on behind the scenes of UX hiring, or how to stand out without burning yourself out, this episode is for you.What You'll Learn in This Episode:✔️ What hiring managers actually look for in UX portfolios✔️ Why your portfolio needs a hook—and what that hook should be✔️ How recruiters and hiring managers split screening responsibilities✔️ The biggest mistakes candidates make when telling case study stories✔️ Why generalists are thriving in today's UX job market✔️ How to make industry or role pivots without starting over✔️ The smartest way to reach out to companies (and who not to DM)✔️ Why community—not cold applications—is the real career accelerant✔️ How hiring managers evaluate experience beyond “years on paper”Timestamps:00:00 Introduction and Purpose of the Podcast00:38 Guest Introduction: Ben Peck03:25 Ben Peck's Career Journey05:31 The Value of Being a Generalist10:22 Hiring Insights and Job Market Trends20:59 Portfolio Tips for Job Seekers28:57 The Importance of Storytelling in Portfolios30:42 Balancing Content and Design32:21 Effective Use of Prototypes and Videos40:00 Transitioning to a UX Career43:22 The Role of Community in Career Growth48:37 Advice for Job Seekers49:33 Lightning Round: Fun and Personal Insights53:13 Conclusion and Final Thoughts

You don't need a Cajun accent to enjoy this episode, when Jeremy Miller tells us all about his hometown of New Orleans. He explains what makes New Orleans unique, including its complex history as a city shaped by French, Spanish, Caribbean, and African influences, its world-renowned music genres, and the rich culinary tradition that distinguishes between urban Creole and rural Cajun cuisines. Jeremy emphasizes the strong sense of community fostered by the city's architecture with front porches that encouraged neighborhood connections, the friendly culture where strangers greet each other on the street, and the racial diversity he took for granted until moving to the Midwest. He shares what he misses most about New Orleans, and recommends hidden gems for visitors.Guest BioJeremy Miller (he/him) is a UX designer, strategist, and author of Beyond UX Design: Master Your Craft Beyond Pixels & Prototypes. Through his book and the Beyond UX Design podcast, he helps designers and teams turn complex ideas into meaningful products. His work focuses on mastering the parts of the craft that live beyond the screen, like curiosity, influence, and the human connections that make great software possible.LinksJeremy's website: https://www.beyonduxdesign.com/CreditsCover design by Raquel Breternitz.

Bob DeMarco returns in 2026 with an extensive examination of prototypes, fresh releases, and loaner knives that preview what collectors can expect in the coming months. Episode 649 of The Knife Junkie Podcast delivers on its title with detailed looks at pre-production models from Pinkerton Knives, Kansept, CRKT, Medford, Artisan Cutlery, and Marconi Blades.The pocket check reveals daily carries including the Kansept Deadite, Jack Wolf Knives Gateway Equal End, and combat-oriented fixed blades from Savage Creature Defense Tools and Spartan Blades. Bob highlights his affiliate partnership with 3 Dog Knife, offering 25 percent off hardcore Alaskan-made knives with coupon code "knifejunkie," and announces the January 2026 Gentleman Junkie Giveaway featuring a 3 Dog Knife Champion Blade.Knife Life News covers three standout releases: the Real Steel Enthusiast Grade Griffin with Vanax SuperClean steel, the We Knife Co. Skyneks with a 3.7-inch M390 blade, and the GiantMouse No. 14 Valetta limited edition. The First Tool segment examines the Barong, a leaf-shaped blade from the southern Philippines that served as both a weapon and a symbol of Moro identity and faith.His State of the Collection showcases the Work Tuff Gear Steadfast L with its 7-inch K329 blade, the HX Outdoors FALCILUX Folding Hatchet, the Garret Wade Bifold Knife, and the JW Kollab Backwoods FIXedc collaboration. The main event focuses on prototypes and loaners, including the morphing CRKT Provoke Tomahawk, multiple Pinkerton Knives designs optimized for utility and self-defense, Kansept prototypes named Incitatus and Navaja, an unnamed Artisan clip point with dramatic recurve, and a custom Pinkerton Khanjarli featuring a fully double-edged blade.The episode offers knife enthusiasts a rare glimpse into the design process, showing how makers refine concepts before committing to full production. From traditional slip joints to futuristic morphing tools, Bob demonstrates the breadth of innovation occurring across the knife industry.Find the list of all the knives shown in the show and links to the Knife Life news stories at https://theknifejunkie.com/649.Support the Knife Junkie channel with your next knife purchase. Find our affiliate links at https://theknifejunkie.com/knives. You can also support The Knife Junkie and get in on the perks of being a patron, including early access to the podcast and exclusive bonus content. Visit https://www.theknifejunkie.com/patreon for details.Let us know what you thought about this episode, and leave a rating and review. Your feedback is appreciated. You can also email theknifejunkie@gmail.com with any comments, feedback, or suggestions.To watch or listen to past episodes of the podcast, visit https://theknifejunkie.com/listen. And for professional podcast hosting, use our podcast platform of choice: https://theknifejunkie.com/podhost.

In this second part of my three-part series (catch Part I via episode 182), I dig deeper into the key idea that sales in commercial data products can be accelerated by designing for actual user workflows—vs. going wide with a “many-purpose” AI and analytics solution that “does more,” but is misaligned with how users' most important work actually gets done.   To explain this, I will explain the concept of user experience (UX) outcomes, and how building your solution to enable these outcomes may be a dependency for you to get sales traction, and for your customer to see the value of your solution. I also share practical steps to improve UX outcomes in commercial data products, from establishing a baseline definition of UX quality to mapping out users' current workflows (and future ones, when agentic AI changes their job). Finally, I talk about how approaching product development as small “bets” helps you build small, and learn fast so you can accelerate value creation.    Highlights/ Skip to: Continuing the journey: designing for users, workflows, and tasks (00:32) How UX impacts sales—not just usage and  adoption(02:16) Understanding how you can leverage users' frustrations and perceived risks as fuel for building an indispensable data product (04:11)  Definition of a UX outcome (7:30) Establishing a baseline definition of product (UX) quality, so you know how to observe and measure improvement (11:04 ) Spotting friction and solving the right customer problems first (15:34) Collecting actionable user feedback (20:02) Moving users along the scale from frustration to satisfaction to delight (23:04) Unique challenges of designing B2B AI and analytics products used for decision intelligence (25:04) Quotes from Today's Episode One of the hardest parts of building anything meaningful, especially in B2B or data-heavy spaces, is pausing long enough to ask what the actual ‘it' is that we're trying to solve. People rush into building the fix, pitching the feature, or drafting the roadmap before they've taken even a moment to define what the user keeps tripping over in their day-to-day environment.   And until you slow down and articulate that shared, observable frustration, you're basically operating on vibes and assumptions instead of behavior and reality.   What you want is not a generic problem statement but an agreed-upon description of the two or three most painful frictions that are obvious to everyone involved, frictions the user experiences visibly and repeatedly in the flow of work.   Once you have that grounding, everything else prioritization, design decisions, sequencing, even organizational alignment suddenly becomes much easier because you're no longer debating abstractions, you're working against the same measurable anchor.   And the irony is, the faster you try to skip this step, the longer the project drags on, because every downstream conversation becomes a debate about interpretive language rather than a conversation about a shared, observable experience. __ Want people to pay for your product? Solve an *observable* problem—not a vague information or data problem. What do I mean? “When you're trying to solve a problem for users, especially in analytical or AI-driven products, one of the biggest traps is relying on interpretive statements instead of observable ones.   Interpretive phrasing like ‘they're overwhelmed' or ‘they don't trust the data' feels descriptive, but it hides the important question of what, exactly, we can see them doing that signals the problem.   If you can't film it happening, if you can't watch the behavior occur in real time, then you don't actually have a problem definition you can design around.   Observable frustration might be the user jumping between four screens, copying and pasting the same value into different systems, or re-running a query five times because something feels off even though they can't articulate why.   Those concrete behaviors are what allow teams to converge and say, ‘Yes, that's the thing, that is the friction we agree must change,' and that shift from interpretation to observation becomes the foundation for better design, better decision-making, and far less wasted effort.   And once you anchor the conversation in visible behavior, you eliminate so many circular debates and give everyone, from engineering to leadership, a shared starting point that's grounded in reality instead of theory." __ One of the reasons that measuring the usability/utility/satisfaction of your product's UX might seem hard is that you don't have a baseline definition of how satisfactory (or not) the product is right now. As such, it's very hard to tell if you're just making product *changes*—or you're making *improvements* that might make the product worth paying for at all, worth paying more for, or easier to buy. "It's surprisingly common for teams to claim they're improving something when they've never taken the time to document what the current state even looks like. If you want to create a meaningful improvement, something a user actually feels, you need to understand the baseline level of friction they tolerate today, not what you imagine that friction might be. Establishing a baseline is not glamorous work, but it's the work that prevents you from building changes that make sense on paper but do nothing to the real flow of work. When you diagram the existing workflow, when you map the sequence of steps the user actually takes, the mismatches between your mental model and their lived experience become crystal clear, and the design direction becomes far less ambiguous. That act of grounding yourself in the current state allows every subsequent decision, prioritizing fixes, determining scope, measuring progress, to be aligned with reality rather than assumptions. And without that baseline, you risk designing solutions that float in conceptual space, disconnected from the very pains you claim to be addressing." __ Prototypes are a great way to learn—if you're actually treating them as a means to learn, and not a product you intend to deliver regardless of the feedback customers give you.  "People often think prototyping is about validating whether their solution works, but the deeper purpose is to refine the problem itself. Once you put even a rough prototype in front of someone and watch what they do with it, you discover the edges of the problem more accurately than any conversation or meeting can reveal. Users will click in surprising places, ignore the part you thought mattered most, or reveal entirely different frictions just by trying to interact with the thing you placed in front of them. That process doesn't just improve the design, it improves the team's understanding of which parts of the problem are real and which parts were just guesses. Prototyping becomes a kind of externalization of assumptions, forcing you to confront whether you're solving the friction that actually holds back the flow of work or a friction you merely predicted. And every iteration becomes less about perfecting the interface and more about sharpening the clarity of the underlying problem, which is why the teams that prototype early tend to build faster, with better alignment, and far fewer detours." __ Most founders and data people tend to measure UX quality by “counting usage” of their solution. Tracking usage stats, analytics on sessions, etc. The problem with this is that it tells you nothing useful about whether people are satisfied (“meets spec”) or delighted (“a product they can't live without”). These are product metrics—but they don't reflect how people feel. There are better measurements to use for evaluating users' experience that go beyond “willingness to pay.”  Payment is great, but in B2B products, buyers aren't always users—and we've all bought something based on the promise of what it would do for us, but the promise fell short. "In B2B analytics and AI products, the biggest challenge isn't complexity, it's ambiguity around what outcome the product is actually responsible for changing.   Teams often define success in terms of internal goals like ‘adoption,' ‘usage,' or ‘efficiency,' but those metrics don't tell you what the user's experience is supposed to look like once the product is working well.   A product tied to vague business outcomes tends to drift because no one agrees on what the improvement should feel like in the user's real workflow.   What you want are visible, measurable, user-centric outcomes, outcomes that describe how the user's behavior or experience will change once the solution is in place, down to the concrete actions they'll no longer need to take.   When you articulate outcomes at that level, it forces the entire organization to align around a shared target, reduces the scope bloat that normally plagues enterprise products, and gives you a way to evaluate whether you're actually removing friction rather than just adding more layers of tooling.   And ironically, the clearer the user outcome is, the easier it becomes to achieve the business outcome, because the product is no longer floating in abstraction, it's anchored in the lived reality of the people who use it."   Links Listen to part one: Episode 182  Schedule a Design-Eyes Assessment with me and get clarity, now.

Tonight, we have an opportunity to bring a piece of Le Mans to you, sharing in the Legend of Le Mans with guests from different eras of over 100 years of racing.  Patrick Long … widely recognized as one of America's most successful endurance racers, with an impressive legacy at the 24 Hours of Le Mans. As Porsche's only American factory driver for many years, Patrick competed in 15 attempts from 2004-2019 at the helm of Porsche GT-class entries. He achieved class victories in 2004 and 2007, showcasing his skill, consistency, and deep understanding of endurance racing. Known for his smooth driving style and strategic mindset, Patrick became a staple presence on the Circuit de la Sarthe, representing Porsche with distinction and helping solidify the brand's dominance in GT racing. His Le Mans career reflects not only personal success but also his vital role in strengthening the presence of American drivers on the world endurance racing stage. ===== (Oo---x---oO) ===== 00:00 Meet Patrick Long: America's Endurance Racing Star 01:42 Patrick Long's Early Racing Years 04:15 Racing in Europe and Early Challenges 06:17 The Unique Challenges of Le Mans 11:53 Teammates and Inspirations 21:33 The Porsche Legacy and Racing Career 26:40 Porsche vs Ferrari: A Respectful Rivalry 28:16 Prototypes and Other Opportunities 29:48 Driving the 963: A Modern Challenge 31:03 The Evolution of Le Mans 33:29 Driver's Role in Strategy 35:53 Reflecting on a 20-Year Career 37:35 Crowd Q&A: Social Media and Racing, The F1 Movie, and more! 47:30 Le Mans Legacy and Lessons 48:27 Historic Racing and Future Plans 50:40 Conclusion and Acknowledgements ==================== The Motoring Podcast Network : Years of racing, wrenching and Motorsports experience brings together a top notch collection of knowledge, stories and information. #everyonehasastory #gtmbreakfix - motoringpodcast.net More Information: Visit Our Website Become a VIP at: Patreon Online Magazine: Gran Touring Follow us on Social: Instagram To learn more about or to become a member of the ACO USA, look no further than www.lemans.org, Click on English in the upper right corner and then click on the ACO members tab for Club Offers. Once you become a Member you can follow all the action on the Facebook group ACOUSAMembersClub; and become part of the Legend with future Evening With A Legend meet ups.

If you've ever wondered why you're still “senior” after years of great work, this episode is for you. Catt Small joins me to unpack what it actually takes to step into a staff designer role—the skills, mindset shifts, and invisible work no one tells you about.You've nailed the craft, shipped great work, and mentored others. So why are you still stuck at senior?Getting promoted isn't always about skill gaps. Sometimes it's about visibility, influence, and how you show up. In this episode, I sit down with Catt Small, Staff Product Designer, developer, and author of The Staff Designer, to explore what separates a strong senior designer from a true staff-level one.Catt shares the lessons that inspired her book: the moments of frustration, the confusion around “influence,” and the realization that being good at your craft isn't enough. We talk about the transition from execution to strategy, how to set a vision, navigate organizational politics, and build the kind of social capital that makes people listen when you speak.If you're wondering what's next after senior, or how to stop spinning your wheels, this episode breaks down the hidden skills that actually move your career forward. It's a candid look at how to lead without managing, earn trust across disciplines, and find meaning in the messy middle of your career.Topics:• 04:19 - Cat Small's Journey in Design• 09:39 - Understanding the Transition from Senior to Staff• 12:02 - The Role of Influence in Career Growth• 14:14 - Navigating Titles and Organizational Structures• 30:31 - The Importance of Vision in Design• 36:22 - Enhancing User Experience with Prototypes• 38:01 - Inspiring Vision and Influence• 39:12 - Negotiating and Planning for Vision Execution• 41:55 - Building Cross-Functional Collaboration• 46:41 - Balancing Craft and Soft Skills• 50:57 - Delegation and Accountability in Design• 57:34 - Promoting Your Work and Final ThoughtsHelpful Links:• Connect with Catt on LinkedIn• The Staff Product Designer• Staff Designer: Influence & Lead as an Individual Contributor—Thanks for listening! We hope you dug today's episode. If you liked what you heard, be sure to like and subscribe wherever you listen to podcasts! And if you really enjoyed today's episode, why don't you leave a five-star review? Or tell some friends! It will help us out a ton.If you haven't already, sign up for our email list. We won't spam you. Pinky swear.• Get a FREE audiobook AND support the show• Support the show on Patreon• Check out show transcripts• Check out our website• Subscribe on Apple Podcasts• Subscribe on Spotify• Subscribe on YouTube• Subscribe on Stitcher

What if you could build a working prototype just by describing it?That's the idea behind vibe coding a new way of designing where intent replaces execution, and AI handles the details.In this episode, we explore:what vibe coding actually is and how it workshow it's transforming the UX process and team collaborationwhich new skills designers need to stay aheadand the real risks behind the hype — from creative sameness to fragile AI codeYou'll hear insights, real examples, and a look at how designers, PMs, and developers can work together in this new AI-powered workflow.✨ Join the Vibe Coding for UX live Training✨ From Intent to Interactive Prototype, a live training where you'll learn to turn your ideas into testable prototypes, no coding required.

This Farm4Profit Podcast episode takes listeners deep into one of the most dramatic rivalries in agricultural history: the tractor wars of the 1970s and 80s. Our guest, Lee Klancher, is an award-winning author, photographer, and publisher of Octane Press, whose new book Snoopy and the Spy tells the story of International Harvester vs. John Deere during the height of the farm crisis.We explore:Lee's Journey: How his early passions for machinery, photography, and storytelling led to a 30-year career publishing more than 30 books, teaching writing, and founding Octane Press from his garage in Austin.Adventures Behind the Lens: From climbing the Julian Alps to photographing 30 rare John Deere tractors in a custom-built studio, Lee shares wild experiences that shape his work.Snoopy & the Spy Storyline:The bold espionage of Bud Youle sneaking into John Deere's Superdome showcase in 1982.The innovative but short-lived IH 2+2 “Snoopy” lineage and the canceled Super 70 prototypes (7288, 7488, rumored 7888).Deere's counterpunch: the 15-speed PowerShift transmission and factory MFWD on high-hp row crops.How espionage fueled innovation but couldn't save IH from its financial collapse and eventual merger into Case IH.Wider Themes: Loyalty, desperation, and ethics during a time when the survival of whole companies—and farm livelihoods—hung in the balance.The conversation also highlights Lee's reflections: espionage and secret prototyping accelerated tractor innovation and shaped the machines we know today, even if IH itself didn't survive.We close with lighter fare: Lee's dream tractor test drive, favorite adventures from around the globe, and what hidden book projects might be next.This episode blends ag history, corporate drama, and personal adventure—showing how tractors and their stories are as much about people and passion as they are about horsepower. Want Farm4Profit Merch? Custom order your favorite items today!https://farmfocused.com/farm-4profit/ Don't forget to like the podcast on all platforms and leave a review where ever you listen! Website: www.Farm4Profit.comShareable episode link: https://intro-to-farm4profit.simplecast.comEmail address: Farm4profitllc@gmail.comCall/Text: 515.207.9640Subscribe to YouTube: https://www.youtube.com/channel/UCSR8c1BrCjNDDI_Acku5XqwFollow us on TikTok: https://www.tiktok.com/@farm4profitllc Connect with us on Facebook: https://www.facebook.com/Farm4ProfitLLC/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

00:00 - Intro  6:42 - Who is Andre Selvaggi  14:35 - Kyle McBride the Fastest Australian, but Retired  19:32 - Andrew's Home Track Keilor is all about that oil 25:15 -  Australia 1/10th Off Road Scene  37:05 - Chargers Rc BMX track surface 41:33 - Vaasa World's Last Real Off Road WC - History of Worlds Since 2011 58:03 - Hobby Action Spec Tire Amonut Rant  1:02:55 -  Raw Speed Spec Tire  1:07:45 - Tire Wear, Track Conditions  1:11:05 - New Dirt Bought in - Unknown Factor  1:14:20 - Lots of money spent by companies sending drivers for WarmUps  1:15:42 - Track Change & How Track will Evolve 1:24:04 - Track Maintenance during the race 1:28:20 - Setup, Prototypes, Science Mode & More  1:36:36 - Aero - Why it's important at this race 1:48:00 - Who is Not attending the Race  1:58:12 - Favorites & Predictions Driver Analysis  2:11:03 - Alex & Lefty's Top Ten 2:19:45 -Bench Racing Q& A & Conclusion  The 2025 IFMAR 1/10th Off-Road World Championships are almost here, and the RC world is buzzing with excitement! In this episode, Lefty is joined by Andrew Selvaggi, Team Associated Australia Manager, to break down everything you need to know before the racing begins in Sydney, Australia. We dive into the freshly rebuilt Hills Off-Road RC track, discuss the unique challenges of outdoor 1/10th racing, and tackle the hot topic of control tire vs. open tire formats. Andrew shares insider knowledge on track prep, setup strategies, and what to expect as grip levels change throughout the event. We also cover warm-up race results, possible prototype cars, and predictions for the drivers to watch—including Spencer Rivkin, Marcus Kaerup, Michal Orlowski, Davide Ongaro, Broc Champlin, and hometown favorite Lachlan Donnelly. Whether you're a racer, fan, or just love RC, this episode delivers expert analysis, race previews, and the stories behind the biggest outdoor 1/10th race of the year.

Get the book!The greatest innovations often begin with a simple question: "What if we tried this differently?" In this fascinating exploration of innovation mindsets, we unpack the two complementary approaches that fuel breakthroughs—design thinking and first principles thinking.hese very approaches are at the heart of my book Protection for the Inventive Mind, a practical fieldbook that helps inventors and creatives turn frustrations into prototypes and big ideas into protected strategies.From the Wright brothers' wind tunnel experiments at Kitty Hawk to SpaceX landing rockets upright, we trace how returning to fundamental truths allows inventors to rebuild solutions from scratch. These stories show first principles thinking as the "logic scalpel" that cuts through assumptions and tradition to reveal new possibilities.Alongside this analytical approach, we discover design thinking—the "empathy engine" that powers human-centered innovation. We see how watching an arthritic woman struggle with kitchen tools birthed OXO Good Grips, how children's tears transformed hospital MRI machines into pirate ships, and how PillPack revolutionized medication management by truly understanding patient frustrations.The episode reveals surprising connections between seemingly unrelated innovations. The kingfisher bird's perfect dive inspired Japan's bullet train nose design. Velcro emerged when a Swiss engineer examined burrs stuck to his dog under a microscope. These moments of biomimicry demonstrate how nature offers solutions to our most persistent challenges.What's particularly inspiring is how often world-changing ideas emerge from everyday annoyances—James Dyson's 5,000 vacuum prototypes, IKEA's flat-pack revelation from a stubborn table that wouldn't fit in a car, and Airbnb's humble beginnings with air mattresses on an apartment floor. These stories prove that frustration can be billion-dollar inspiration when viewed through the right lens.Ready to apply these mindsets to your own challenges? Listen for five actionable innovation principles distilled from these remarkable stories, and discover how combining empathy with fundamental thinking can transform not just products, but experiences, systems, and culture itself. Whether you're sketching on a napkin or aiming for the stars, the way you think might be your greatest invention yet.Send us a textSupport the show

 Order of Battle Podcast episode 136 ANOTHER TOTAL ACTION FORCE BOOK!  Well, you know Jason's excited already. Brian and Paddy come on to talk Total Action Force vol 3 and it's a big one. Prototypes, unreleased, multiple unseen archives of photos and documents from 40 years ago.  The boys have done it again. And their Kickstarter is well underway so now is the time to back it!  https://www.kickstarter.com/projects/brianthehick/total-action-force-volume-3 Website: www.orderofbattlepod.com Email: orderofbattlepod@gmail.com Twitter: @orderofbattlepd Instagram: @orderofbattlepod #gijoe

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Interesting Quest Apps1. BeamXR Live Lets You Stream To Twitch & YouTube From Quest Without A PC2. Adobe Substance 3D Reviewer Lets Quest Owners View 3D Models Together For FreeQuest SDKs & Development3. Meta's Quest Runtime Optimizer Helps Unity Developers Fix Performance Bottlenecks4. Niantic Spatial SDK Brings Outdoor VPS & Long-Distance Live Meshing To Quest 3M5 Vision Pro & Vivo's Clone6. New Vision Pro With M5 Chip Spotted In Apple Code7. Vivo's Apple Vision Pro Copycat Is An In-Store Demo Only, For Now8. Pico 4 Ultra Gets Enhanced Body Tracking With 5 Pico Trackers & Travel Mode9. PlayStation VR2's Eye Tracking Now Works On PC Via Open-Source Driver ModMeta's Boba 3 & Tiramisu Prototypes10. Meta Boba 3 Prototype Hands-On: Ultra-Wide Field Of View Without Compromise11. Meta Tiramisu "Hyperrealistic VR" Hands-On: A Stunning Window Into Another World

The Federal Communications Commission is planning a review of the US emergency alert systems. The announcement of this plan notes that the infrastructure underlying the EAS — which includes radio, television, satellite and cable systems — is 31 years old, while the framework underpinning the Wireless mobile device alert is 13 years old. Also, Meta previewed some of its latest virtual reality prototypes this week, with concepts that are compelling on the specs and long on the design. The company shared some details on its Tiramisu project, dubbing it "hyperrealistic VR." This set promises three times the contrast, 14 times the maximum brightness and 3.6 times the angular resolution of the Meta Quest 3. In actual stats, that's up to 1,400 nits of brightness and an angular resolution of 90 pixels per degree; and HBO Max will begin an "aggressive" messaging campaign about the practice beginning next month, according to an earnings report. Beyond stricter messaging, the company is looking to close any and all loopholes that allow users to share account passwords by the end of the year. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Interesting Quest Apps1. Zoom Is Now Available As A 2D App On Quest2. Putt Window Lets You Use Your Real Putter & Ball In Quest 3 Mixed Reality3. VR Android File Manager For Quest Lets You Install APKs Without A PC4. Varjo Will Stop Supporting XR-3 & Aero Next Year5. After One Key Change, Meta's Quest UI Overhaul Has Gone From Bad To Great6. ByteDance Reportedly Working On Lightweight Pico HeadsetSmart Glasses & Display Glasses7. Meta Invests €3 Billion In EssilorLuxottica, Taking 3% Stake8. Apparent Renders Of Next-Gen Ray-Ban Meta Glasses Leak9. Meta Details EMG Wristband Gestures You'll Use To Control Its HUD & AR Glasses10. Viture's 'The Beast' Display Glasses Have Industry-Leading FOV & Brightness11. Rec Room Plus Subscribers Get Roomie, A Floating AI Companion12. Billie Eilish Tease Suggests A 180° 3D Release Of Her Latest Concert13. Sharp Is Developing A Hybrid Haptic VR Glove & Controller14. M4 Apple Vision Pro Refresh With New Strap Reportedly Launching This Year15. Meta Shows Off Research Towards Practical Ultra-Wide Field Of View Headsets

Last week, we conducted a Great Search for a clear shaft linear potentiometer. This week, the prototypes arrived—and they work great! We're also beginning work on the FruitJam Tester. We'll be showing the finalized silkscreen, the cover plate we designed, and the expected form factor of the tester itself. Additionally, we wrapped up a library for a QMC5883 breakout. This chip serves as a drop-in replacement for the now-discontinued HMC5883. It's frequently found in quadcopter builds, so we thought it would be a great candidate for a simple magnetometer breakout.

Far too many software projects crash not because of poor coding, but because of poor planning. In this episode of Building Better Developers with AI, Rob Broadhead and Michael Meloche explore why requirements matter more than ever. They dive deep into the foundational role that clearly defined, testable, and outcome-focused requirements play in delivering successful software projects. With insights drawn from hands-on experience and AI-generated discussion points, the episode uncovers how misaligned expectations and incomplete planning can derail even the most promising initiatives. Whether you're a developer, product manager, or founder, this conversation reminds us that getting it right starts well before a line of code is written. Why Requirements Matter in Software Development Rob and Michael begin by revisiting a powerful truth: software requirements are the blueprint for everything that follows. Vague requests and incomplete specifications are the root cause of missed deadlines, blown budgets, and frustrated clients. Callout CEO: 70% of software project failures are tied to poor requirements, not bad developers. When everyone understands what's being built—and more importantly, why—teams align better, and projects succeed more often. Requirements Matter More Than Perfect Code Even flawless code can't rescue a project built on the wrong foundation. Rob highlights three common causes of failure: Misunderstood business goals Disconnects between stakeholders and developers Expanding scope from unclear requirements If the team can't agree on what success looks like, no amount of elegant code will save the effort. For more on aligning teams and expectations, check out our episode on Bridging Methodologies. Requirements Matter: Start with the Why Michael emphasizes starting with the business objective. Before diving into specs or wireframes, ask: Why does this solution need to exist? What problem is it solving? Many clients envision modern systems based on outdated workflows. Developers must educate while extracting needs—balancing modernization with functionality that still matters. Requirements Matter When Writing User Stories Rob and Michael advocate for user stories—clear, testable statements of what the system must do. A well-written story includes: A specific actor (e.g., user, admin) A goal (e.g., schedule an appointment) An expected result (e.g., receive confirmation) Michael puts it plainly: If a developer doesn't know when a requirement is “done,” it's not a requirement—it's a guess. Learn more about effective story writing with this Agile user story guide. Requirements Matter in Managing Scope and Budget Requirements aren't just lists—they're guardrails. Michael warns that unchecked feature creep can quietly drain resources and sink projects. A disciplined list of must-haves versus nice-to-haves keeps everything on track. Start with the core. A “calendar app” doesn't need AI-scheduling in version one. Build the basics first, validate them, and then iterate with purpose. Requirements Matter in Prototypes and Demos Rob is a strong advocate for visual requirements. Tools like Figma, PowerPoint, and internal “kitchen sink” demos help bring vague ideas into sharp focus. Stakeholders often struggle to articulate what they want—until they see it. Clickable mockups bridge the communication gap and reduce costly rework. As Rob puts it, “the more real it feels, the better the feedback you'll get.” Balancing Detail: When Requirements Matter and When They Don't Finding the balance between too little and too much detail is key. Rob favors lightweight specs for creative flexibility, while Michael leans on testable, bulletproof requirements. Their advice? Define what the system must do, but avoid locking in how it must be done—especially too early. The goal is clarity of intent, not rigidity in implementation. Make Requirements Matter on Your Team Before wrapping up, Rob and Michael pose a practical challenge to all teams: Can every requirement in your backlog be tested and tied to a business goal? If not, it may be time to revise or remove it. Unclear requirements aren't just annoying—they're expensive. By committing to clarity, your team reduces ambiguity, limits rework, and speeds up delivery. Every stakeholder benefits when expectations are grounded in reality. Final Thoughts From stakeholder interviews to wireframes and test-driven development, requirements matter at every stage of the software development lifecycle. Each assumption should be questioned. Each “nice to have” should be weighed carefully. Every essential feature must be validated. So the next time you're tempted to “just start coding,” take a step back and ask: Do we really understand what we're building—and why? Because when requirements matter, your software delivers. Stay Connected: Join the Developreneur Community We invite you to join our community and share your coding journey with us. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at info@develpreneur.com with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development. Additional Resources Software Development Requirements: Staying True to Specifications The Importance of Properly Defining Requirements Changing Requirements – Welcome Them For Competitive Advantage Creating Use Cases and Gathering Requirements The Developer Journey Videos – With Bonus Content Building Better Developers With AI Podcast Videos – With Bonus Content

The majority of the world's rechargeable batteries are now made using lithium-ion. Most rely on a combination of different rare earth metals such as cobalt or nickel for their electrodes. But around the world, teams of researchers are looking for alternative – and more sustainable – materials to build the batteries of the future.In this episode, we speak to four battery experts who are testing a variety of potential battery materials about the promises they may offer.Featuring Laurence Hardwick from the University of Liverpool, Robert Armstrong from the University of St Andrew's, Ulugbek Azimov from Northumbria University and Bill Yen from Stanford University. Applications are now open for early career researchers to submit their projects for the Prototypes for Humanity 2025 awards and showcase in Dubai.This episode was written and produced by Gemma Ware with assistance from Mend Mariwany and Katie Flood. Sound design and mixing by Eloise Stevens and theme music by Neeta Sarl. Read the full credits for this episode and sign up here for a free daily newsletter from The Conversation.If you like the show, please consider donating to The Conversation, an independent, not-for-profit news organisation.

Jacob interviews Karla and Zimonja from Ivy Road about their experience building the characters, world, and gameplay systems of their recent release, Wanderstop. PLAYERS: Jacob McCourt (Bluesky) SPECIAL GUESTS: Davey Wreden (Bluesky) Karla Zimonja (Bluesky) SHOW NOTES: 0:00​​ - Housekeeping 2:00 - Intro 4:30 - Prototypes & Inspirations 7:00 - Approach to Cozy Games 9:10 - How Wanderstop Supports Chill 13:00 - Nodes 15:10 - Monster 20:45 - Finding the Right Balance with Monster 24:30 - When Karla Met Davey 28:20 - Tea vs. Coffee and the Machine 32:50 - The Business Boys 36:00 - Pacing 37:55 - Dirk Warhard 40:30 - The Team at Ivy Road 44:30 - COVID 49:00 - Ren 56:15 - Nana 56:45 - Zenith 1:01:45 - The Last Choice and Why It Doesn't Matter 1:05:15 - Looking Back at Development 1:10:45 - The Risk of Hurting Players 1:16:45 - Despelote 1:19:30 - What Now? RESOURCES: Episode 4: Interview with Karla Zimonja Episode 196: Wanderstop BLUESKY: leftbehindgameclub.bsky.social DISCORD: The Left Behind Game Club is a monthly game club podcast that focuses on positivity and community. To talk to members of the community, join our Discord server!

In this episode of This New Way, Aydin sits down with Sukhpal Saini, founder of Engyne, to dive into how AI is reshaping the way we build products, market them, and even manage our personal networks. Sukhpal shares how he prototypes with AI, automates content creation, and turns conversations into distribution-ready assets. From building 30+ products to launching a Canva app for LinkedIn carousels, this episode is packed with actionable insights. Timestamps:0:00 — Intro: Welcome to This New Way1:30 — The AI curiosity wave and why people are hungry for tactical content4:00 — Suk's journey from IBM, Apple, and Saks to 30+ side projects and Engyne5:45 — The shift from building in Figma to building 5 real prototypes with A8:00 — Using Replit and ChatGPT to get fast, real customer feedback13:00 — How marketers can build lead gen tools without engineering16:30 — Will we have fewer engineers in the future? 19:00 — Demo #1: Nexus — Using AI to query your own network22:00 — Why personal productivity tools no longer require SaaS subscriptions24:50 — Demo #2: A Voice of Customer app to analyze transcripts and shape messaging29:00 — Demo #3: Carousel Studio — Turn your ideas into LinkedIn carousels with a Canva app35:00 — The power of creating from your unique opinion, not AI-generated fluff37:00 — Engyne's vision: Become a mini media machine39:00 — Closing thoughts and future predictions for AI-powered solo businessesTools and Technologies Mentioned:Replit – A browser-based coding environment that lets you write, run, and deploy software quickly. Suk uses it to rapidly prototype multiple product ideas in minutes.ChatGPT – OpenAI's conversational AI model, used to generate code, iterate on features, and assist in product development.Claude – An AI assistant developed by Anthropic, used similarly to ChatGPT for coding and ideation.Prisma – A modern ORM (Object-Relational Mapping) tool for Node.js and TypeScript, used in Suk's Voice of Customer app to manage the database.OpenAI API – The underlying API that powers GPT models like ChatGPT, allowing users to integrate AI functionality into their custom apps.Enjoyed the episode? Subscribe at thisnewway.com

Matthias Endler, Rust developer, open-source maintainer, and consultant through his company Corrode, speaks with SE Radio host Gavin Henry about prototyping in Rust. They discuss prototyping and why Rust is excellent for prototyping, and Matthias recommends a workflow for it, including what parts of Rust to use, and what parts to avoid at this stage. He describes the key components that Rust provides to help us validate ideas via prototypes, as well as tips and tricks to reach for.  In addition, the conversation explores type inference, unwrap(), expect(), anyhow crate, bacon crate, cargo-script, Rust macros to use, generics, lifetimes, best practices, project layout styles, and how to design through types. Brought to you by IEEE Computer Society and IEEE Software magazine.

Elaina Natario returns to talk with Joël about what makes good quality product design and the priorities that shape development. The pair discuss the importance of certain elements such as security and accessibility, maintaining certain standards throughout development, as well as judging the practical applications of prototypes within a project and the broad role they play. — The Sponsor for this episode has been Judoscale - Autoscale the Right Way (https://judoscale.com/bikeshed). Check out the link for your free gift! You can read more about about inaccessable prototypes here (https://localghost.dev/blog/ai-and-the-trouble-with-inaccessible-saas/), or listen to the episode Joël mentioned with Aji about different typescripts here (https://bikeshed.thoughtbot.com/458)! Your guest for this week has been Elaina Natario (https://www.linkedin.com/in/elainanatario/) and you host has been Joël Quenneville (https://www.linkedin.com/in/joel-quenneville-96b18b58/). If you would like to support the show, head over to our GitHub page (https://github.com/sponsors/thoughtbot), or check out our website (https://bikeshed.thoughtbot.com). Got a question or comment about the show? Why not write to our hosts: hosts@bikeshed.fm This has been a thoughtbot (https://thoughtbot.com/) podcast. Stay up to date by following us on social media - YouTube (https://www.youtube.com/@thoughtbot/streams) - LinkedIn (https://www.linkedin.com/company/150727/) - Mastodon (https://thoughtbot.social/@thoughtbot) - BlueSky (https://bsky.app/profile/thoughtbot.com) © 2025 thoughtbot, inc. — Credit: Ad-read music by joystock.org

How do you turn 5,127 failures into a multi-billion-dollar empire? James Dyson turned dust into possibility, failure into discovery, and frustration into revolution.     Dyson didn't just build a better vacuum; he redefined a whole industry. Facing thousands of failed prototypes, crushing financial setbacks, and a dismissive industry that insisted a superior vacuum was impossible, Dyson transformed doubt into fuel that created an empire he still owns and operates today. Dyson's genius stretched far beyond engineering. He was a contrarian thinker whose natural state was to defy the experts. From reinventing hand dryers to fans and hairdryers, Dyson repeatedly turned mundane frustrations into game-changing products. His relentless curiosity and willingness to fail publicly set new standards for innovation. When competitors mocked him, he stayed focused. When patents were threatened, he defended fiercely. Dyson's story is one of unwavering persistence, unorthodox creativity, and the courage to trust his own instincts—even when everyone else doubted.      This is the story of James Dyson. Learn how one decision can change everything for a whole family.    This episode is for informational purposes only and is based on Against the Odds: An Autobiography by James Dyson. Quotes from Against the Odds and James Dyson's Invention: A Life  (02:35) Prologue: The Kitchen Floor Experiment PART 1 - EARLY SPARKS OF TENACITY (05:05) A Childhood of Resilience and Determination (08:19) Gresham's School (11:25) From Art to Engineering: A Defiance of Convention (14:58) A Mentor: Jeremy Fry (17:37) Just Build It (19:23) The Sea Truck (22:16) Lessons From The Egyptians (24:16) Misfit Mentality PART 2: FIRST INVENTIONS AND HARD LESSONS (26:48) Reinventing The Wheel(barrow) (28:54) Popular Not Profitable (30:56) Leaving Ballbarrow with Nothing (34:09) History of the Vaccuum (36:23) Cyclone in a Sawmill (39:17) 5,127 Prototypes (41:57) Industry Rejection (44:14) Building the Business PART 3: BUILDING AN EMPIRE (48:15) Passion Over Profit (50:04) Beyond Vacuums (53:08) R&D Culture & Iterative Design (55:44) Patent Wars & Legal Battles (57:49) Value of Keeping Ownership (59:59) Recap of Dyson's Journey (01:02:55) SHANE'S REFLECTIONS Upgrade — If you want to hear my thoughts and reflections at the end of all episodes, join our membership: ⁠⁠⁠⁠⁠⁠⁠fs.blog/membership⁠⁠ and get your own private feed. Newsletter - The Brain Food newsletter delivers actionable insights and thoughtful ideas every Sunday. It takes 5 minutes to read, and it's completely free. Learn more and sign up at fs.blog/newsletter Learn more about your ad choices. Visit megaphone.fm/adchoices

Blister reviewers Jonathan Ellsworth, Luke Koppa, Sascha Anastas, Kristin Sinnott, and the strikingly handsome Justin Bobb discuss a bunch of new ski and snowboard gear they spent time on at last week's Blister Summit. They also talk about several prototypes they got on at the Summit, so if you want to get a little glimpse into the future, you're in luck.RELATED LINKS:Taos Ski ValleyBlister Rec Shop: Willi's Ski & Board ShopGet Covered: BLISTER+TOPICS & TIMES:Chile Rant / Taos Ski Valley (0:59)Deals from Willi's Ski Shop (1:53)Story from a BLISTER+ Member (3:11)Prototypes at the Summit (7:54)Giro Meetups (10:21) Icelantic Pioneer Prototype (10:55) J Skis Hotshot Prototype (15:45) Romp Zorro 100 Prototype (17:06)Salomon Addikt Pro 66 (18:35)Kaestle Marble 84 (21:17)Majesty HNX Ti (23:05)New Salomon QST Skis (25:55)DPS Carbon Pisteworks 79 (30:10)Mito Skis (34:59)ON3P Woodsman 100 (38:02)Liberty Radian 100 (41:09)Snowboard Gear (42:58)J Skis Escalator (46:00)Patagonia Powslayer & Nano-Air Ultralight Freeride (50:08)Apparel (54:37)CHECK OUT OUR OTHER PODCASTS:Blister CinematicCRAFTEDBikes & Big IdeasBlister Podcast Hosted on Acast. See acast.com/privacy for more information.

Logan Metesh of High Caliber History and Allen Forkner of GunBroker introduce Dwight Van Brunt from Sportsman's Legacy to discuss the significant impact of gun writers on firearm trends and industry standards. Have you ever wondered what a top gun writer has in his or her personal collection? Today, we discuss that very thing as well as ways that you can add these special firearms to your own collection. Main Topics Discussed: Legacy of Iconic Gun Writers: The hosts and Dwight discuss the historical influence of legendary gun writers like Jack O'Connor, Elmer Keith, and others who shaped firearm preferences through their published works in major magazines. Evolution of Media Influence: Examination of how the role of gun writers has evolved from print to digital, impacting how firearms are reviewed and perceived in the modern era. Dwight shares anecdotes from past experiences at SHOT Show Media Days, illustrating the shift from a few influential writers to a crowded field of digital content creators. Challenges of Modern Gun Reviewing: The conversation turns to the difficulties of maintaining integrity and accuracy in firearm reviews amidst the rise of digital platforms and influencer marketing. Impact of Prototypes and Media Samples on Brand Perception: Discussion on how prototype firearms are handled in the industry, including their distribution to writers for feedback and the potential mishaps that can occur if prototypes do not perform as expected. Closing Thoughts: Dwight, Logan, and Allen reflect on the enduring impact of gun writers in shaping firearm culture and consumer choices, noting the necessity of adapting to new media landscapes while maintaining journalistic integrity. Links and Resources: See all the fine products available from Sportsman's Legacy: https://www.gunbroker.com/all/search?sort=13&includesellers=86817&pagesize=48 Call to Action: Who were some of your favorite gun writers? Who are your favorites today? Let us know in the comments.  Share this episode with all your friends or anyone interested in the writers of the past. Be sure to tune in every Thursday when new shows are released on all your favorite podcast platforms.  Learn more about your ad choices. Visit megaphone.fm/adchoices