Podcasts about Verification

Josh chats with Sal Kimmich about the current state of everything, and what we can expect next. Sal has some incredible insight into what we can expect to see due to the current wave of security bugs and incidents. There are some new features we will need in both our hardware and software to ward off the state of things. Since those features are years away, what we need in the short term is shoring up our SDLC programs. Sal has some really good medical examples and analogies for this one. It's a huge problem but not insurmountable. The show notes and blog post for this episode can be found at https://opensourcesecurity.io/2026/2026-06-verification-sal-kimmich/

News and Updates: OS Age Verification Laws: California's Digital Age Assurance Act (2027) requires operating systems to collect and share user age ranges with apps, sparking major privacy concerns nationwide. Vanguard Bricks Cheaters: Riot Games' latest Vanguard anti-cheat update permanently disables DMA cheat firmware on PCs, forcing full OS reinstalls — Riot's response was unapologetic and blunt. Waymo Flooding Woes: Waymo suspended robotaxi operations in Atlanta and San Antonio after vehicles drove into flooded roads, prompting a voluntary recall of nearly 4,000 vehicles for software fixes. China's Underwater Data Center: A $226 million, 24-megawatt subsea facility off Shanghai houses 2,000 servers, using passive ocean cooling and offshore wind power to achieve exceptional energy efficiency. Data Centers in Space: SpaceX, Blue Origin, and Google are pursuing orbital AI data centers powered by massive solar arrays, but engineers warn the economics remain extremely challenging and unproven.

Timestamps: 0:00 Corsair's New Memory Supplier 1:14 HP's Broken BIOS Updates 2:09 California Age Verification Law Backlash 4:24 QUICK BITS INTRO 4:37 China's Underwater Data Center 5:12 TSMC Employee Bonuses 5:44 Rippable GameCube, Wii, and Xbox Games 6:12 Drone Speed Record 6:44 The Pope Weighs in on AI NEW SOURCES: https://lmg.gg/gZoBL Learn more about your ad choices. Visit megaphone.fm/adchoices

Interview with Xin Yan is the Co-Founder and CEO of Sign, a sovereign-grade digital infrastructure for national systems of money, identity, and capital. By Selva Ozelli Esq., CPA, Author of "Sustainably Investing in Digital Assets Globally" Xin Yan is the Co-Founder and CEO of Sign, a sovereign-grade digital infrastructure for national systems of money, identity, and capital. Under his leadership, Sign has raised a total of $55 million. Other major backers include YZi Labs, IDG Capital, Sequoia and Circle. Trends to watch with Xin Yan An electrical engineer by profession, before co founding Sign in 2021, Xin served as an investor at Huobi Group. What started as an e-signature tool (EthSign) Sign has expanded into Sign Protocol, an omni-chain attestation protocol, and TokenTable, a platform for managing and distributing tokenized assets that bridge the gap between traditional legal agreements and blockchain technology. Yan advocates digital identity and sovereign technology, arguing that the next stage of blockchain adoption will be driven by real-world utility and revenue rather than just speculation. He often refers to the community and movement surrounding the protocol as the "Orange Dynasty". Xin's work currently centers on digital sovereignty, onchain verification, and building infrastructure for nation-states, including digital IDs and Central Bank Digital Currencies (CBDCs). Yan is actively working with governments (e.g., in the UAE and Sierra Leone) to implement blockchain-enabled national infrastructure. Tell us about your educational and professional journey leading up to co-founding Sign. I was an electronic engineer by training, secured over 10 patents at school before dive-dropping into crypto by building my own mining rigs. That hands-on experience led me to a leading VC, where I spent three years as an investment manager and engineer backing cornerstone projects like Polkadot and Avalanche. In 2021, I combined that technical grit with my VC insights to co-found Sign. Tell us about Sign Sign builds secure infrastructure for digital money, identity, and capital. Sign has five years of production deployments and has reached a valuation of $1.3billion. Its systems support governments and regulated institutions in delivering secure, large-scale digital transformation, reaching more than 50 million people in production. Sign works with countries like UAE, Thailand, Kyrgyzstan, Singapore, Barbados and Sierra Leone. Most recently, Sign partnered with the Blockchain Center Abu Dhabi and has raised over $55M across three funding rounds. Your work at Sign currently centers on digital sovereignty, on-chain verification, and building infrastructure for nation-states, including digital IDs and Central Bank Digital Currencies (CBDCs). Which countries are you actively working with? Thailand, Kyrgyzstan, Singapore, Barbados and Sierra Leone The United Arab Emirates (UAE) is a leading global cryptocurrency hub, currently ranked third globally in crypto adoption behind only Singapore and Hong Kong. Its status is defined by a "pro-innovation" regulatory environment, zero personal income tax on crypto gains, and the presence of over 1,800 crypto companies as of early 2026. The UAE's central bank digital currency (CBDC) project, known as the Digital Dirham, has transitioned from an experimental pilot to a formal legal reality as of early 2026 with the Digital Dirham officially recognized as legal tender under Federal Decree-Law No. 6 of 2025. Managed by the Central Bank of the UAE (CBUAE), this initiative is a core pillar of the nation's multi-year Financial Infrastructure Transformation (FIT) program. How is Sign involved with UAE's CBCD project? Sign and ADBC recently partnered to accelerate sovereign blockchain infrastructure in Abu Dhabi. In 2026, the tokenization of the world financial market is rapidly advancing through stablecoins and Central Bank Digital Currencies (CBDCs), which function as programmable, on-chain cash for ...

Mandy Wiener speaks to EWN Reporter , Nhlanhla Mabaso about Home Affairs starting its verification process of immigrants in Durban. The Midday Report with Mandy Wiener is 702 and CapeTalk’s flagship news show, your hour of essential news radio. The show is podcasted every weekday, allowing you to catch up with a 60-minute weekday wrap of the day's main news. It's packed with fast-paced interviews with the day’s newsmakers, as well as those who can make sense of the news and explain what's happening in your world. All the interviews are podcasted for you to catch up and listen to. Thank you for listening to this podcast of The Midday Report Listen live on weekdays between 12:00 and 13:00 (SA Time) to The Midday Report broadcast on 702 https://buff.ly/gk3y0Kj and on CapeTalk https://buff.ly/NnFM3Nk For more from The Midday Report, go to https://buff.ly/BTGmL9H and find all the catch-up podcasts here https://buff.ly/LcbDdFI Subscribe to the 702 and CapeTalk daily and weekly newsletters https://buff.ly/v5mfetc Follow us on social media: 702 on Facebook: https://www.facebook.com/TalkRadio702 702 on TikTok: https://www.tiktok.com/@talkradio702 702 on Instagram: https://www.instagram.com/talkradio702/ 702 on X: https://x.com/Radio702 702 on YouTube: https://www.youtube.com/@radio702 CapeTalk on Facebook: https://www.facebook.com/CapeTalk CapeTalk on TikTok: https://www.tiktok.com/@capetalk CapeTalk on Instagram: https://www.instagram.com/ CapeTalk on X: https://x.com/CapeTalk CapeTalk on YouTube: https://www.youtube.com/@CapeTalk567See omnystudio.com/listener for privacy information.

Catch Up on the latest leading news stories around the country with Mandy Wiener on Midday Report from 12:00 to 13:00. The Midday Report with Mandy Wiener is 702 and CapeTalk’s flagship news show, your hour of essential news radio. The show is podcasted every weekday, allowing you to catch up with a 60-minute weekday wrap of the day's main news. It's packed with fast-paced interviews with the day’s newsmakers, as well as those who can make sense of the news and explain what's happening in your world. All the interviews are podcasted for you to catch up and listen to. Thank you for listening to this podcast of The Midday Report Listen live on weekdays between 12:00 and 13:00 (SA Time) to The Midday Report broadcast on 702 https://buff.ly/gk3y0Kj and on CapeTalk https://buff.ly/NnFM3Nk For more from The Midday Report, go to https://buff.ly/BTGmL9H and find all the catch-up podcasts here https://buff.ly/LcbDdFI Subscribe to the 702 and CapeTalk daily and weekly newsletters https://buff.ly/v5mfetc Follow us on social media: 702 on Facebook: https://www.facebook.com/TalkRadio702 702 on TikTok: https://www.tiktok.com/@talkradio702 702 on Instagram: https://www.instagram.com/talkradio702/ 702 on X: https://x.com/Radio702 702 on YouTube: https://www.youtube.com/@radio702 CapeTalk on Facebook: https://www.facebook.com/CapeTalk CapeTalk on TikTok: https://www.tiktok.com/@capetalk CapeTalk on Instagram: https://www.instagram.com/ CapeTalk on X: https://x.com/CapeTalk CapeTalk on YouTube: https://www.youtube.com/@CapeTalk567See omnystudio.com/listener for privacy information.

Today in the business of podcasting:Spotify is expanding its Verified by Spotify badge to podcast shows, adding a green checkmark to identify official creator, publisher, and brand presences on the platform based on listener activity, platform standing, and audience authenticity.The 2026 Podnews Report Card collected 779 pieces of community feedback on major podcast platforms, with YouTube reaching second place in the overall rankings for the first time, just behind Apple Podcasts and ahead of Spotify.A new WARC report, The Multiplier Playbook, surveying more than 200 senior marketers finds that 60% say their C-suite does not fully understand the role of advertising, and only 21% say advertising objectives align with broader corporate goals.New Omeda research finds 70% of publishers consider audience data critical, yet only 9% of their organizations use it effectively, a gap writer Brian Morrissey links to decentralized and siloed data infrastructure.YouTube is rolling out its AI-powered likeness detection technology to all eligible creators over 18, expanding access beyond select YouTube Partner Program members and allowing users to set up protection via ID verification.To find links to these, and every article covered in today's episode, click here. You can also subscribe to The Download's newsletter to receive the full issue straight to your email inbox every day.

Today in the business of podcasting:Spotify is expanding its Verified by Spotify badge to podcast shows, adding a green checkmark to identify official creator, publisher, and brand presences on the platform based on listener activity, platform standing, and audience authenticity.The 2026 Podnews Report Card collected 779 pieces of community feedback on major podcast platforms, with YouTube reaching second place in the overall rankings for the first time, just behind Apple Podcasts and ahead of Spotify.A new WARC report, The Multiplier Playbook, surveying more than 200 senior marketers finds that 60% say their C-suite does not fully understand the role of advertising, and only 21% say advertising objectives align with broader corporate goals.New Omeda research finds 70% of publishers consider audience data critical, yet only 9% of their organizations use it effectively, a gap writer Brian Morrissey links to decentralized and siloed data infrastructure.YouTube is rolling out its AI-powered likeness detection technology to all eligible creators over 18, expanding access beyond select YouTube Partner Program members and allowing users to set up protection via ID verification.To find links to these, and every article covered in today's episode, click here. You can also subscribe to The Download's newsletter to receive the full issue straight to your email inbox every day.

Have you claimed your Google Business Profile (previously Google My Business)? If not, you might be missing out on free visibility in your local area. As a photographer, that could mean fewer clients finding you.In this episode, I'm joined by Lydia Fine, local SEO expert and photographer, to walk us through the current landscape of Google Business Profiles, what's changed, and exactly what you need to do to make yours work harder for you. Whether you run a studio, do in‑home shoots, or travel for sessions, this episode breaks down the window into local search that many creatives ignore.Find It Quickly00:27 - Meet Lydia Fine02:24 - Importance of Google Business Profiles for Local SEO04:01 - Verification and Privacy Concerns08:00 - Optimizing Your Google Business Profile12:55 - Leveraging Google Business Profile Services17:29 - SEO Strategies and Google Business Profiles20:22 - The Importance of Google My Business21:27 - Adding Photos and Videos to Your Profile24:12 - Managing Reviews and Updating Content25:26 - Exploring Bing Places and SEO Strategies27:16 - Asking for Reviews: Best Practices31:07 - Prominence and Local SEO TipsMentioned in this EpisodeGoogle Business ProfileConnect with LydiaWebsite: apolloandivy.comGrab the Guide: apolloandivy.com/quickfixInstagram: instagram.com/lydia_apolloandivy

Hardin Ratshisusu– Acting commissioner, National Consumer Commission SAfm Market Update - Podcasts and live stream

Healthcare is undergoing a serious transition away from hardware-centric systems toward software-defined and physical AI-enabled healthcare environments. Healthcare is also moving toward a paradigm that is proactive, personalized, and ambient, leveraging intelligent technologies to improve patient care, operational efficiency, and clinical outcomes. The conversation explores how technologies such as advanced diagnostics, surgical robotics, remote monitoring, and smart medical devices are reshaping healthcare delivery. Gopal also shares practical insights into the challenges the industry faces around regulation, cybersecurity, data interoperability, verification and validation, and operational transformation. Most importantly, the discussion highlights how healthcare is gradually moving from reactive treatment models toward more proactive, personalized, and patient-centric care by 2030. This is a valuable conversation for healthcare leaders, technology executives, and anyone interested in the future of healthcare innovation. Watch this insightful interaction Ramachandran S, HFS Research, had with Gopalratnam VC, Executive Vice President and Global CIO at Philips, on the move toward software-defined healthcare. Key TakeawaysSoftware is becoming the key differentiator in modern medical devices. Intelligent systems and robotics are improving precision and operational efficiency in healthcare. Verification, validation, and compliance remain critical in healthcare technology adoption. Data fragmentation and lack of interoperability continue to slow innovation across healthcare ecosystems. The healthcare industry is moving toward more proactive and preventive care models. Personalized healthcare experiences will become increasingly important by 2030. Automation can help reduce administrative burdens for clinicians and healthcare staff. Future healthcare systems are expected to become more connected, efficient, and patient-focused.

Show Notes - https://forum.closednetwork.io/t/episode-57-age-verification-is-becoming-digital-border-control/192Website / Donations / Support - https://closednetwork.io/support/BTC Lightning Donations - closednetwork@getalby.com / simon@primal.netThank You Patreons & Direct Supporters! - https://www.patreon.com/closednetworkhttps://xmrchat.com/closednetworkDirect Support - https://closednetwork.ioSubscribe Without Patreon - https://closednetwork.io/#/portal/signupMichael Bates - Privacy Bad AssDavid - Privacy Bad AssTK - Privacy Bad AssDavid - Privacy Bad AssTrying - Privacy Bad AssVO - Privacy Bad AssMrMilkMustache - Privacy SupporterHutch - Privacy AdvocateInferno_Potato Privacy SupporterDolores YTOP LIGHTNING BOOSTERS !!!! THANK YOU !!!@bon thousands and thousands and thousands of SATs sats!!@fireflygow - 5,000 sats!!frigolay - 34,540 SATs.. HOLY SHITEwardemoff - 5,000 SATsSilas ThornbrookThank You To Our Moderators:Unintelligentseven - Follow on NOSTR primal.net/p/npub15rp9gyw346fmcxgdlgp2y9a2xua9ujdk9nzumflshkwjsc7wepwqnh354dMaddestMax - Follow on NOSTR primal.net/p/npub133yzwsqfgvsuxd4clvkgupshzhjn52v837dlud6gjk4tu2c7grqq3sxavtJoin Our CommunityClosed Network Forum - https://forum.closednetwork.ioJoin Our Matrix Channels!Main - https://matrix.to/#/#closedntwrk:matrix.orgOff Topic - https://matrix.to/#/#closednetworkofftopic:matrix.orgSimpleX Group Chat - https://smp9.simplex.im/g#SRBJK7JhuMWa1jgxfmnOfHz7Bl5KjnKUFL5zy-Jn-j0Join Our Mastodon server!https://closednetwork.socialFollow Simon On The SocialsMastodon - https://closednetwork.social/@simonNOSTR - Public Address - npub186l3994gark0fhknh9zp27q38wv3uy042appcpx93cack5q2n03qte2lu2 - primal.net/simonTwitter / X - @ClosedNtwrkInstagram - https://www.instagram.com/closednetworkpodcast/YouTube - https://www.youtube.com/@closednetworkEmail - simon@closednetwork.ioSpecial Thanks to - EloquentWinter for creating - A Linux guide on MAC address randomizationhttps://forum.closednetwork.io/t/a-linux-guide-on-mac-address-randomization/189TOPICSSection 702 reauthorization bill still lacks a warrant requirementTop Priority Stories for On-Air1. Utah targets VPNs around age verification — age gates become privacy-tool restrictions.2. Canvas/Instructure breach — centralized school platforms become student dossiers.3. Citizen Lab: adtech powers Webloc surveillance — commercial tracking becomes state surveillance.4. DHS sought Google data over anti-ICE speech — platform-held data becomes political surveillance leverage.5. GM privacy settlement — cars are data brokers with wheels.6. Meta removes opt-in encrypted Instagram DMs — defaults are policy; optional privacy dies quietly.7. New Orleans live facial recognition — local biometric surveillance outruns public consent.8. LinkedIn GDPR paywall complaint — platforms monetize access to your data while resisting legal access rights.

This week we're talking all about the future of embedded software development with TASKING Co-CEO Christoph Herzog. Christoph and I explore how Large Language Models and agentic AI are moving from novelty to necessity, directing external agents within the TASKING toolchain to automate critical verification and validation tasks. We also discuss the Model Context Protocol (MCP) and how it helps maintain adherence to strict industry standards. 

Doesn't it seem like the older you get, the sadder visiting Las Vegas becomes? Bryan just went there and returned with some wonderful examples of the sadness. Let's talk about that, having a giant black sex toy glued to your forehead for five years, drawing on fake mustaches in order to bypass age requirements online, raising money for brain cancer treatment through a lemonade stand, and more on today's episode of Can You Don't?!*** Wanna become part of The Gaggle and access all the extra content on the end of each episode PLUS tons more?! Our Patreon page is LIVE! This is the biggest way you can support the show. It would mean the world to us: http://www.patreon.com/canyoudontpodcast ***New Episodes every Wednesday at 12pm PSTWatch on Youtube: https://youtu.be/YbHL_2K6678Send in segment content: heyguys@canyoudontpodcast.comMerch: http://canyoudontpodcast.comMerch Inquires: store@canyoudontpodcast.comFB: http://facebook.com/canyoudontpodcastIG: http://instagram.com/canyoudontpodcastYouTube Channel: https://bit.ly/3wyt5rtOfficial Website: http://canyoudontpodcast.comCustom Music Beds by Zach CohenFan Mail:Can You Don't?PO Box 1062Coeur d'Alene, ID 83816Hugs and tugs.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

We look at the role of Authorised Corporate Service Providers (ACSPs) in the new regime at Companies House and explain why understanding both its strengths and weaknesses is important to anyone responsible for onboarding or monitoring UK companies, wherever in the world you work.Send us Fan MailSupport the showFollow us on LinkedIn at https://www.linkedin.com/company/the-dark-money-files-ltd/ on Twitter at  https://twitter.com/dark_files or see our website at https://www.thedarkmoneyfiles.com/

What happens when the government decides to protect your kids online — and ends up with your ID, your face, and your data in the process?In this episode, Josh and Will dig into the growing push for age verification laws in the US and around the world. What starts as a conversation about protecting kids on social media quickly spirals into a much bigger question: what are we actually trading away when we hand our digital identities over to Instagram, PlayStation, or the government? Will walks through his deep passion for digital privacy and anonymity, and Josh pushes back (and sometimes agrees) on where personal responsibility ends and government regulation begins. It's their most politically adjacent episode yet — and honestly, one of their best.They cover everything from facial recognition in dating apps to surveillance state worst-case scenarios, VPNs and Brave browser, the death of online anonymity, and whether parents — not laws — should be the ones calling the shots on their kids' screen time.Buckle up. This one gets real.--♣️Want to become a HiTech Club member, support the pod, and get all of the extras on our episodes? Head over to our Buy Me a Coffee to subscribe: buymeacoffee.com/hitechpodcast.

> I almost don't read code now. My approach with Roborev is it's like my code reader. The mantra is: Roborev reads every line of code that is generated. It gets read multiple times. And so, whenever I push up a pull request, the branch gets re-reviewed. And so by the time I'm merging a pull request into a repository, the code has all been read by agents four or five times minimum. I look at the code in terms of structural detail: does it look right?— Wes McKinney (creator of pandas, POSIT)Wes, Jeremiah Lowin (Prefect), and Randy Olson (Good Eye Labs) join Hugo and his cohost Thomas Wiecki (PyMC Labs) for the premiere of Show Us Your Agent Skills, a live session where guests walk us through the exact skills, workflows, and setups they use to work with agents every day.We Discuss:* Wes McKinney on why he barely writes, or even reads, code anymore, his “software factory” of parallel agents, and RoboRev, the background reviewer that reads every line four or five times before he merges;* The shift from “vibe coding” to agentic engineering, and why verification, not reading, is the part that actually matters;* Jeremiah Lowin on years of context engineering: trickling voice memos, recorded meetings, and morning briefs into his agent's memory substrate as a true “second brain”;* Why Jeremiah picked OpenCode specifically for how deeply he can customize its memory, and what he's building with FastMCP, Prefab, and Cardboard;* Randy Olson on encoding human judgment, like Tufte's rules for data visualization, directly into agent skills, so the agents themselves perform the verification;* The “digital twin” Randy loads into his agents as a thought partner that pushes back instead of agreeing;* Skills as thin drivers, progressive disclosure, and managing context rot across extended sessions;* The rise of ephemeral, “just for me” software that agents finally make viable.Skills and workflows discussed and shown in the episode:* Wes's RoboRev background code reviewer, his “software factory” dashboard, and his agentic engineering setup built on the Superpowers skills framework;* Jeremiah's “explain” skill (which anchors every other skill he has), his voice memo memory pipeline, his FastMCP and Prefab projects, and Cardboard, his ephemeral presentation tool;* Randy's data visualization verifier skills, his digital twin thought partner prompt, his cron job reports for colleagues, and his reflect and improve skill design pattern.Check out the GitHub repo where we're starting to drop some of these skills and workflows for you to grab and try yourself.You can also find the full episode on Spotify, Apple Podcasts, and YouTube.You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!Up next on Show Us Your Agent Skills: Hilary Mason (CEO, HiddenDoor), Bryan Bischof (Theory Ventures), Eric Ma (Research DS lead, Moderna Therapeutics), and Tomasz Tunguz (Theory Ventures). Register on lu.ma to join live, or catch the recording afterwards.

Send us Fan MailAfter a hearing test, patients should not have to imagine what better hearing could sound like. In this episode of the Hearing Matters Podcast, Blaise Delfino, M.S. - HIS explains why the in-office hearing aid demo is such an important part of the patient journey, and how hearing technology can help patients better understand their hearing loss, their options, and the real-life value of prescription hearing aids.You can't expect someone to wait years to address hearing loss, walk into a clinic for the first time, and feel confident based on an audiogram, a chart, and a price tag alone. The in-office hearing aid demo is one of the most powerful tools in hearing healthcare because it turns “you're a candidate for hearing technology” into a moment the patient can actually hear, feel, and understand.In this episode, we break down what happens after a hearing test and why patients should have the opportunity to hear hearing aids before they buy. From a clinician's point of view, we discuss how to keep the audiogram review simple, use speech-in-noise testing to connect results to real life, and avoid overwhelming patients with brand names, technical jargon, or too much information too soon.We also share a repeatable in-office hearing aid demo setup that simulates a restaurant or noisy listening environment using background noise, hearing aids programmed to the patient's hearing test, and a familiar voice, such as a spouse, friend, family member, or coworker, to make that first unmuted conversation meaningful. Hearing aids are typically programmed based on the patient's audiogram, and the first listening experience can sound different, especially for new users adjusting to amplified sound. The episode also explains normal acclimatization, why your own voice may sound different with hearing aids, and how an in-office demo can create a helpful frame of reference before moving forward with treatment. We make it clear that a demo is not a replacement for best practices like real ear measurement, but it can help patients better understand what hearing technology may offer before making a decision. Verification, orientation, and validation are key parts of the hearing aid fitting process. From there, we zoom out to the added clinical wins: counseling patients on adaptive directionality in plain English, learning more about lifestyle needs beyond intake forms, and using the demo to observe dexterity, vision, comfort, and device-handling ability. These details help hearing care professionals recommend hearing aids that actually fit the patient's life, not just their hearing test.We also cover hearing aid trial periods, the importance of consistent wear time, what patients should ask before choosing a hearing care provider, and why the best hearing aid experience is about more than the device itself. It's about education, counseling, verification, follow-up care, and helping people reconnect with the conversations that matter most.If you Visit our website and take our quick online hearing screener. And if you're ready to take the next step, our online hearing care provider locator can help you find a trusted hearing care professional near you. Taking that first step can make a meaningful difference, helping you stay connecting to the people and moments that matter most.  Connect with the Hearing Matters Podcast TeamEmail: hearingmatterspodcast@gmail.com Instagram: @hearing_matters_podcast Facebook: Hearing Matters Podcast

Send us Fan MailWatch the video!https://youtu.be/20HQbX6AKXcIn the News blog post for May 8, 2026https://www.iphonejd.com/iphone_jd/2026/05/in-the-news827.html 00:00 Band Leaders24:30 2 Letters from Steve25:52 Education Verification33:16 Apple Enterprise Report Card37:52 HomePod Home44:46 Woven Woot!47:38 Foiled Again!51:04 London Calling53:09 Social Casinos57:04 In the Show! Dinking on Lasso1:04:00 Brett's Gadget: Lego Game Boy1:06:48 Jeff's Site: Bandbreite app (and website)Joe Rossignol | MacRumors: Apple Announces 2026 Pride Band, Watch Face, and iPhone WallpaperJohn Gruber | Daring Fireball: 2 Letters from SteveChance Miller | 9to5Mac: Apple now requires verification for Education Store, adds Apple Watch with discountsJason Snell | Six Colors: Apple in the Enterprise: A 2026 report cardWesley Hilliard | Apple Insider: Owning an Apple Home: HomePods as a whole-home audio systemWoot! Apple USB-C to USB-C Woven Charge CableDavid Brown | The Times: Afghan masterminded pickpocket gang who shipped £180m of iPhones abroadDan Moren | Six Colors: My tech travel experience, 2026 editionPeter Robison and Vernon Silver | Bloomberg: The $11 Billion Casino-Style Economy Built on PLayers Who Can Never Cash OutRyan Christoffel | 9to5Mac: Apple TV has its best summer lineup ever, here's what's comingBrett's Gadget: Lego Game Boyhttps://amzn.to/3Oz6cBB Jeff's Site: Bandbreite app (and website)https://bandbreite.watch/ Support the showBrett Burney from http://www.appsinlaw.comJeff Richardson from http://www.iphonejd.com

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AI Everywhere hosts Lindsey Naples and Alexia Morgan, together with Keypoint Intelligence's Kris Alvarez, explore the growing debate surrounding age verification laws, online privacy, and the role artificial intelligence (AI) plays in digital identity systems. Here, they set the stage for deeper conversations unpacking the balance between protecting users online and concerns around surveillance, data storage, consent, and the long-term implications of handing personal information over to AI-driven systems.

Jimmy Barrett takes you through the stories that matter the most on the morning of 05/07/26.

Sony wants to scan your face to prove your age, and UK gamers aren't having it. Age verification goes into full effect on June 1, and PlayStation 4 and PlayStation 5 owners in the UK will need to verify their age with a driver's license or facial ID scan to be able to unlock chat, multiplayer and more features. Can we just go back to OFFLINE consoles? Watch the podcast episodes on YouTube and all major podcast hosts including Spotify. CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. Get more news, views and reviews on Clownfish TV News - https://more.clownfishtv.com/ On YouTube - https://www.youtube.com/c/ClownfishTV On Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvg On Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629 MORE CLOWNFISH TV - Official Merch Store: http://ClownfishMinus.com Facebook - https://facebook.com/ClownfishTV X - https://x.com/ClownfishTVcom Clownfish TV subreddit: https://www.reddit.com/r/ClownfishTVOfficial/ Disclaimer: This series is produced by Clownfish Studios and WebReef Media, and is part of ClownfishTV.com. Opinions expressed by our contributors do not necessarily reflect the views of our guests, affiliates, sponsors, or advertisers. ClownfishTV.com is an unofficial news source and has no connection to any company that we may cover. This channel and website and the content made available through this site are for educational, entertainment and informational purposes only. These so-called “fair uses” are permitted even if the use of the work would otherwise be infringing. #Podcast #Commentary #News #Reaction #Gaming #Comedy #Entertainment #Hollywood #PopCulture #Tech #Anime #FYP Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Model Verification: West Rogers Park Temperature Forecast Review  This episode analyzes the accuracy of various weather models (ICON, Meteo Blue, HR DPS, HRRR, NAM) in predicting temperatures for West Rogers Park and West Ridge from Friday night to Sunday morning. While no single model was perfect, ICON and Meteo Blue demonstrated the best overall performance due to their steady guidance. HR DPS excelled in predicting Saturday's daytime warm-up but struggled with overnight lows, while HRRR and NAM consistently underpredicted daytime temperatures.   #WeatherForecast #ModelVerification #WestRogersPark #TemperatureAccuracy #WeatherAnalysisBecome a supporter of this podcast: https://www.spreaker.com/podcast/weather-with-enthusiasm--4911017/support.This episode includes AI-generated content.

HotelKey's self check-in kiosk scans an ID, runs face verification, and issues keys without a front desk line. I talk with Aditya Thyagarajan, co-founder of HotelKey, and Fareed Ahmad, co-founder and CEO of HotelKey, about why kiosks only work when they connect directly to the #PMS.

Join the Progressive Cattle crew as they discuss the current cattle market and the best pick-ups on the market. Tyrell visits with Where Food Comes From's Mindi Birkeland about getting ranchers involved in third-party verification programs. Abby sits down with Boehringer Ingelheim's D. L. Step to address antimicrobial resistance in beef cattle.

In this week's episode, I'm sitting down with Leann Saunders, president of Where Food Comes From. We talk about something that impacts every single one of us in agriculture, whether we realize it or not: third-party verification.Leann has seen the beef industry from every angle, from working on the buyer side with McDonald's to building one of the earliest USDA process verified programs, and now working directly with producers across the country. We break down what verification actually means, why it matters, and how it connects ranchers, consumers, and the entire food supply chain.We also talk about what this looks like from the actual verification process to the financial return, and why this isn't about changing your operation but validating what you're already doing. From direct-to-consumer brands to global export markets, this conversation really highlights where the industry is headed and why building trust with consumers is only going to become more important moving forward.Be sure to subscribe/follow the show so you never miss an episode!Connect with Leann:Visit Where Food Comes FromCall (866) 395-5883Connect with Jessie:Follow on Instagram @ofthewest.co and @mrsjjarvFollow on Facebook @jobsofthewestCheck out the Of The West websiteResources & Links:2026 Power of Meat Study (Ann-Marie Roerink)The Nightingale by Kristin HannahJoin The Directory Of The WestGet our FREE resource for Writing a Strong Job DescriptionGet our FREE resource for Making the Most of Your InternshipGet our FREE resource: 10 Resume Mistakes (and how to fix them)Get our FREE resource: How to Avoid the 7 Biggest Hiring Mistakes Employers MakeEmail us at hello@ofthewest.coSubscribe to Of The West's ⁠NewslettersList your jobs on ⁠Of The West

From building Applied Intuition from YC-era autonomy tooling into a $15B physical AI company, Qasar Younis and Peter Ludwig have spent the last decade living through the full arc of autonomy: from simulation and data infrastructure for robotaxi companies, to operating systems for safety-critical machines, to deploying AI onto cars, trucks, mining equipment, construction vehicles, agriculture, defense systems, and driverless L4 trucks running in Japan today. They join us to explain why “physical AI” is not just LLMs on wheels, why the real bottleneck is no longer model intelligence but deployment onto constrained hardware, and why the future of autonomy may look less like one-off demos and more like Android for every moving machine.We discuss:* Applied Intuition's mission: building physical AI for a safer, more prosperous world, powering cars, trucks, construction and mining equipment, agriculture, defense, and other moving machines* Why physical AI is different from screen-based AI: learned systems can make mistakes in chat or coding, but safety-critical machines like driverless trucks, autonomous vehicles, and robots need much higher reliability* The evolution from autonomy tooling to a broad physical AI platform: starting with simulation and data infrastructure for robotaxi companies, then expanding into 30+ products across simulation, operating systems, autonomy, and AI models* Why tooling companies came back into fashion: Qasar on why developer tooling looked unfashionable in 2016, why Applied Intuition still bet on it, and how the AI boom made workflows and tools central again* The three core buckets of Applied Intuition's technology: simulation and RL infrastructure, true operating systems for vehicles and machines, and fundamental AI models for autonomy and world understanding* Why vehicles need a real AI operating system: real-time control, sensor streaming, latency, memory management, fail-safes, reliable updates, and why “bricking a car” is much worse than bricking an iPad* Physical machines as “phones before Android and iOS”: Peter explains why today's vehicle and machine software stack is fragmented across many operating systems, and why Applied Intuition wants to consolidate the platform layer* Coding agents inside Applied Intuition: Cursor, Claude Code, internal adoption leaderboards, and how AI tools are changing engineering workflows even in embedded systems and safety-critical software* Verification and validation for physical AI: why evals get harder as models improve, how end-to-end autonomy changes simulation requirements, and why neural simulation has to be fast and cheap enough to make RL practical* From deterministic tests to statistical safety: why autonomy validation is shifting from binary pass/fail requirements toward “how many nines” of reliability and mean time between failures* Cruise, Waymo, and public trust: Qasar and Peter discuss why autonomy failures are not just technical issues, how companies interact with regulators, and why Waymo is setting a high bar for the industry* Simulation vs. reality: why no simulator perfectly represents the real world, how sim-to-real validation works, and why real-world testing will never disappear* World models for physical AI: hydroplaning, construction equipment, visual cues, cause-and-effect learning, and where world models help versus where they are not enough* Onboard vs. offboard AI: why data-center models can be huge and slow, but onboard vehicle models need millisecond-level latency, low power, small size, and distillation-like efficiency* Why physical AI is not constrained by model intelligence alone: the hard part is deploying models onto real hardware, under safety, latency, power, cost, and reliability constraints* Legacy autonomy vs. intelligent autonomy: RTK GPS in mining and agriculture, why hand-coded path-following worked for decades, and why modern systems need perception and dynamic intelligence* Planning for physical systems: how “plan mode” applies to robotaxis, mining, defense, and multi-step physical tasks where actions change the state of the world* Why robotics demos are not production: the brittle last 1%, humanoid reliability, DARPA Grand Challenge-style prize policy, and the advanced engineering gap between research and deployment* Applied Intuition's hard-earned lessons: after nearly a decade, Peter says they can look at a robotics demo and predict the next 20 problems the company will hit* Qasar's advice to founders: constrain the commercial problem, avoid copying mature-company strategies too early, and remember that compounding technology only matters if you survive long enough to see it compound* Why 2014 YC advice may not apply in 2026: capital markets, AI company dynamics, and the difference between building in stealth with a deep network versus building as a new founder today* What Applied is hiring for: operating systems, autonomy, dev tooling, model performance, evals, safety-critical systems, hardware/software boundaries, and engineers with deep curiosity about how things workApplied Intuition:* YouTube: https://www.youtube.com/@AppliedIntuitionInc* X: https://x.com/AppliedInt* LinkedIn: https://www.linkedin.com/company/applied-intuition-incQasar Younis:* X: https://x.com/qasar* LinkedIn: https://www.linkedin.com/in/qasar/Peter Ludwig:* LinkedIn: https://www.linkedin.com/in/peterwludwig/Timestamps00:00:00 Introduction: Applied Intuition, Physical AI, and 10 Years of Building00:01:37 Physical AI vs. Screen AI: Why Safety-Critical Changes Everything00:02:51 The Origin Story: Tooling, YC, and the Scale AI Comparison00:05:41 The Three Buckets: Simulation, Operating Systems, and Autonomy Models00:11:10 Hardware, Sensors, and the LiDAR Question00:14:26 The Operating System Layer: Why Vehicles Are Like Pre-Android Phones00:19:13 Customers, Licensing, and the Better-Together Stack00:21:19 AI Coding Adoption: Cursor, Claude Code, and the Bimodal Engineer00:26:41 Verifiable Rewards, Evals, and Neural Simulation00:31:04 Statistical Validation, Regulators, and the Cruise Lesson00:40:25 World Models, Hydroplaning, and Cause-Effect Learning00:43:34 Onboard vs. Offboard: Latency, Embedded ML, and Distillation00:50:57 Plan Mode for Physical Systems and Next-Token Prediction Universally00:53:04 Productionization: The 20 Problems Every Robotics Demo Will Hit00:58:00 Founder Advice: Constraints, Compounding Tech, and Mature-Company Mimicry01:05:41 Hiring Philosophy: Hardware/Software Boundary and Engineering Mindset01:08:50 General Motors Institute, Education, and the Curiosity MindsetTranscriptIntroduction: Applied Intuition, Physical AI, and 10 Years of BuildingAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: And today we're very honored to have the founders of Applied Intuition, Qasar and Peter. Welcome.Qasar [00:00:17]: You guys really know how to turn it on to podcast mode. That was, you guys are real pros at this.Qasar [00:00:23]: They were just joking around right before this, and then they flipped it pretty quick.Alessio [00:00:29]: Oh, yeah, it's good to have you guys. Maybe you just wanna introduce yourself so people know the voice on the mic and they'll know what they're hearing.Peter [00:00:33]: Oh, sure. Yeah, I'm Peter Ludwig. I'm the co-founder and CTO of Applied Intuition.Qasar [00:00:38]: And my name is Qasar Younis. I am the CEO and co-founder with Peter.Alessio [00:00:42]: Nice. Can you guys give the high-level overview of what Applied Intuition is? And I was reading through some of the Congress files, when you went out there, Peter, and eighteen of the top twenty global non-Chinese automakers, you two guys, you have customers in agriculture, defense, construction. I think most people have heard of Applied Intuition tied to YC when it was first started, and then you were kinda in stealth for a long time, so maybe just give people the high-level overview of what it is today, and then we'll dive into the different pieces.Peter [00:01:10]: Yeah. So at Applied Intuition, our mission is to build physical AI for a safer, more prosperous world. And so we work on physical AI for all different types of moving systems, everything from cars to trucks to construction and mining equipment, to defense technologies. And we're a true technology company, so we build and sell the technology, and we sell it to the companies that make the machines. We sell it to the government, really anyone that wants to buy a technology to make machines smart.Physical AI vs. Screen AI: Why Safety-Critical Changes EverythingQasar [00:01:38]: Yeah. And I think in the broader AI landscape, a lot of the focus, rightfully so in the last, three years has been on large language models, and so everything fits in a screen. Like, whether it's code complete products or things like that. And what's different about us is we're deploying intelligence onto a lot of things that don't have screens. they're physical machines. There are sometimes screens within the cabin or for example of a car or a truck or something like that, but most of the value we provide is putting intelligence that is in safety critical environments. So that those two words are really important because learn systems can make mistakes if you're asking for, like, some, so something like, “Tell me about these podcast hostsQasar [00:02:28]: that I'm about to go meet.” But you can't do that obviously when you run, like, as an example, we run driverless trucks in Japan right now, as we speak. We can't have errors. Those are L4 trucks. Yeah.Alessio [00:02:40]: Yeah. Was that always the mission? I remember initially, I think people put you and Scale AI very similarly for some things about being kinda like on the data infrastructure side of things. What was the evolution of the company?The Origin Story: Tooling, YC, and the Scale AI ComparisonPeter [00:02:51]: Well, from the very beginning, we always wanted to, really be a technology company that helped generally push forward the industrial sector. And so we started off working in autonomy. Our very first customers were robotaxi companies. And we started off doing a lot of work in simulation and data infrastructure. And then over the years, we've expanded our portfolios. Now we have, over thirty products, and it's a pretty broad technology play within the landscape of physical AI.Qasar [00:03:19]: Yeah, I think the Scale reason is because we're all YC Universe companies. But it was a very different company. Scale, was, is more of a services company, data labeling company fundamentally. We started and still are, do a lot of tooling. So like, you think developer tooling is now in vogue again, thanks to the AI boom. But honestly, ten years ago, it was out of vogue. It w Like, doing a tooling company in 2016, 2017 was not, like, the thing to do because, I don't know if you remember, the VCs generally, their views was that toolings are They're just workflows, and workflows ultimately are not really interesting. And we've gone and come, full circle with that. But when we started the company, our kind of it's kinda like in the periphery of what the company wants to be. It was like, from our earliest days, like, we wanna deploy software on physical machines, like on cars and on trucks and things like that. And obviously, we didn't know that the transformer boom was gonna happen. We didn't know that autonomy systems would become end-to-end. Those things we didn't know. And why that's important when autonomy systems become end-to-end, it is just now those models can be generalized to, multiple form factors. And so back nine, ten years ago, tooling was a great way, and still is a great way to, build the technology and sell technology to our end customers, a lot of them who wanna build this stuff themselves. And so we just offer like a spectrum of solutions from you can just use like one part of a development suite of tools all the way to buying the full thing. The way to think about the company, or at least the way we think about the company is, as Peter said, a technology provider. It's kinda like, what NVIDIA does or what an AMD, but we just don't do chips.Qasar [00:05:06]: We don't do silicon. But we're a technology provider fundamentally. And I think even, we used to joke when we started the company, like, we're not the guys to build, like, Instagram. Like that was just towards That's not our That's just not us in a most fundamental way. IAlessio [00:05:20]: You have thoughts.Qasar [00:05:21]: Yes.Qasar [00:05:22]: Well, it's, it's I mean, I think it's just like what And I mean, we worked on Maps and stuff, Google Maps. Consumer products are extremely difficult for a lot of different reasons. It just, I think doesn't scratch the itch. I think we're like Michigan guys who are kind of more of that traditional engineering kind of a realm, or lineage. we used to jokeThe Three Buckets: Simulation, Operating Systems, and Autonomy ModelsPeter [00:05:41]: I gotta say, though, what was clear ten years ago was that there was so much more that was possible with software and AI in vehiclesPeter [00:05:47]: and that was generally the space that we started in ten years ago.Peter [00:05:51]: And the precise path that we've taken over the years, I think we've been strategic, and we've adjusted to make sure that we're actually building stuff that's valuable to the market. And like, the technology has changed so much. Like our own technology stack has completely changed, I would say, roughly every two years. And so now we've probably done, let's say, four complete evolutions of our own technology stack. And I sort of see that cadence roughly keeping up.Peter [00:06:13]: And so the way even we think about engineering is almost on this two-year horizon, we're preparing ourselves that, hey, like, we wanna invest the appropriate amount, but then also be very dynamic as the research gets published and as our research team figures out new advancements and adapting to that.Qasar [00:06:27]: Yeah. One thing that has been consistent is the type of people we've, we've recruited. It's engineers who are fall into the sometimes very traditional, like, GoogleQasar [00:06:38]: -gen suite, but way different from, other companies. We are hiring folks who really know the intersection of hardware and software, who know really low-level systems. Obviously, traditional ML researchers and folks who've, actually, put ML systems into production. That's been pretty consistent. I think that, like, you look at the mix of our engineering, eighty-three percent of the company is engineering, so it's, like, a giant list.Qasar [00:07:05]: A lot of engineers.Alessio [00:07:06]: Which, by the way, a thousand engineersQasar [00:07:07]: Yeah. A thousand engineers.Alessio [00:07:08]: that's on your website, so I imagine it's up to date.Qasar [00:07:11]: It is, it is up to date, yes. Yes.Alessio [00:07:12]: okay. And then forty-plus founders.Qasar [00:07:15]: Yeah. We would tend to also, This was more luck than strategy. But we've recruited a lot of ex-founders. It's been a great place for founders, YC and non, ‘cause obviously I know a lot of the YC folks. It's kind of like we recruit a lot of Google people.Qasar [00:07:33]: For them to exercise both their technical and non-technical skills because, we're, we're, we're on the applied side. We have a research team that we do fundamental research, we publish, and we've, we've had great traction there. But fundamentally, the business wants to take this intelligence and deploy it into production and there's, like, a certain type of person that's more interested in that.Alessio [00:07:54]: Yeah. You mentioned the tech stack, Peter, so I just wanted to give you some rein to just go into it. I'm interested in where Wayve Nutrition, starts and ends in some sense, what won't you do? What, do you do that's common among all the verticals that you cover?Peter [00:08:10]: There's a few buckets of work that we do, and we've been at this for almost ten years now, so the technology's pretty broad. But we got startedQasar [00:08:17]: Yeah, with a thousand engineers, like, you could work on lots of things.Peter [00:08:19]: There's lots of stuff, yeah, espe-especially with AI tools to help.Peter [00:08:22]: So we got our start in simulation and simulation tooling and infrastructure. And so generally, if you're trying to build a very complex software system that involves moving machines, you need to test that, and the best way to test it is it's a combination of virtual developments, a simulation, and then also obviously real world testing.Peter [00:08:39]: And then there's a very careful process of that correlation between the simulation results and the real world results and ensuring that the simulator is in fact accurate to that. Simulation's a very deep topic.Peter [00:08:49]: We have a whole suite of products in that, and we could talk for many hours about that specifically. But that is one part of what we do as a company. Reinforcement learning as a subpart of that is also super critical. I think a lot of the a lot of the best advancements happening in a lot of these AI systems right now in some way relate to reinforcement learning, and with now we have lots of compute, and you can do tons of interesting things for reinforcement learning. The second bucket of work that we do is on operating systems technology. true operating systems. Like, think about, schedulers and memory management and middleware and message passing and highly reliable networking and data links. Like, the reality is, if you want to deploy AI onto vehicles, you need a really good operating system. And when we were getting deeper into that space, there wasn't really anything that we were happy with.Peter [00:09:39]: Like, things existed, absolutely, and we were using what was available in the market, and as an engineering organization, we roughly realized these things aren't great. We think we can do this better, and so let's, let's build something. And that was then the that was the moment of inspiration that started our operating systems business, which is now a very real business for us. And in order to write and run great AI, you need a great operating system, and so that-that's what got us into that. And then the third bucket that we work on, it's, it's true fundamental AI technology. Models, we do a lot of work in, as mentioned, the foundational research, but then the also the world models and the actual autonomy models that are running on these physical machines, and that's across cars, trucks, mining, construction, agriculture, and defense, and so that's both land, air, and sea.Qasar [00:10:31]: And also, a smaller subsector of that third bucket is the interaction of humans with those machines.Qasar [00:10:38]: So that's a multimodal, experience. Historically, if you're moving a dirt mover or any of these machines, there are, like, buttons you press, whether they're actual physical tactile buttons or something like a touch screen. That's just That fundamentally is changing to where you're just talking to the machine and the machine and you're teaming with the machine.Alessio [00:10:58]: Voice?Qasar [00:10:59]: Yeah, voice, absolutely, yeah.Alessio [00:11:00]: Oh.Qasar [00:11:00]: And also the machine just being aware of who is in the cabin, what their state is. you can think from a safety systems perspective, the most simple version of this is, like, the driver is tired, right? They're, they're if you get those alerts when you're driving your car and saysHardware, Sensors, and the LiDAR QuestionQasar [00:11:15]: -maybe take a coffee break, that take that times, a couple of order of magnitudes up. But this concept of teaming man and machine is important. When you think about running agents or just running, different instances of, Claude and doing work for you in the background, you can take that analogy out, almost copy and paste and put it into, like, a farm, where you have a farmer who's running a number of machines. So where they interact with the machine is where there's maybe a critical decision or a disengagement or something like that, but generally speaking, the agent on the physical machine is running and making decisions on the behalf of the farmer until there's something maybe critical. And that's also what we work on. So that's not pure autonomy. It's a little bit of a mix, but it falls under, autonomy. In the automotive sense, that's typically defined in SAE levels as an L2++ systemQasar [00:12:05]: -with a human in the loop. But just take that idea, to other verticals.Alessio [00:12:09]: Yeah. You've not mentioned hardware at all, like sensors or obviously we you mentioned you don't do chips. I think even in AV there's, like, a big, cameras versus lidars. Like, what are, like, in your space maybe some of those design decisions that you made, and are they driven by the OEM's ability to put things on the machinery? And like, how much influence do you guys have on co-designing those?Peter [00:12:32]: Yeah. So we don't make sensors. Like, we're, we're not a manufacturer. Obviously, we use a lot of sensors in our autonomy products. in terms of what actually goes on the vehicles, we have a preferred set of sensors that we, let's say fully support, and then our customers, they can sort of choose from those. And obviously if there's a very strong opinion on supporting something else, we'll add that to the platform as well. And the lidar question is at this point sort of the age-old,Peter [00:12:59]: topic in autonomy, and the state of the industry right now is lidar is hands down a useful sensor, specifically for data collection and the R&D phase of autonomy development. if you see, for example, a Tesla R&D vehicle, it actually has lidar on itPeter [00:13:17]: to this day, right? In the Bay Area we see these. you'll see, like, Model Ys or Cybercab that have lidars on them just driving around. So it's, it's useful because it gives you per pixel depth information. So if you can pair a lidar with a camerand you can say that, well, this camera's looking this direction, this lidar's looking this direction, and now for each pixel of the camera I can see how far away is that pixel. you can actually then use that as a part of your model training, and then the that depth information then becomes a learned, a learned state of the camera data. And then when you're doing the production system, you can now remove the lidarPeter [00:13:52]: and now you can actually get depth with just the camera. And so that difference between, like, a highly sensored R&D vehicle and then the down-costed production vehicle, we use that across our whole portfolio of products. And of course the end goal is you want super low cost and super reliable.Peter [00:14:08]: And then in certain use cases you have some more, bespoke things. Like in defense as an example, you do things at night oftentimes, and so you care about sensors like infrared, more so than And you don't, you don't wanna be putting energy out, so you don't wanna use lidar or radar.Peter [00:14:23]: but you still need to be able to see at nighttime. So yeah, we work the whole gamut.The Operating System Layer: Why Vehicles Are Like Pre-Android PhonesAlessio [00:14:27]: Cool. So that's kinda like on the hardware level. Then on the OS level, how does that look like? What is, like, unique? my drive- I drive a Tesla. Whenever I drive some other car that has a screen, it always sucks.Alessio [00:14:38]: It's on, like, cheap Android tablet. It's like, it's laggy and all of that. What does the OS of, like, the autonomy future look like?Peter [00:14:46]: When most people, it's really what you just described. When you think about operating system in a vehicle, you're thinking about the HMI, right? The human machine interface, and absolutely that's a an important part of it, but that's actually only one thin layer on top. So when we talk about operating systems for, like, AI in vehicles, there's many layers that go deep into the CPU critical realm and embedded systems, and you're talking about the real time control ofPeter [00:15:13]: let's say the electric motors or the engine and the actuators, and you have different redundancies for different, let's say, the steering actuation in the vehicle. And all of these things, need very core support in the in the operating system. And then of course for autonomy you have real time sensor data that's streaming in, and the latencies there are really important, right? If you try to Imagine you try to run Microsoft WindowsPeter [00:15:35]: like streaming your sensor data in or controlling the vehicle. Like, the latencies are gonna be absurd. Like, you can never do that. And so what's special about what we do is we really have this system level thinking, right? So we're looking at, we care about every performance characteristics of the entire system, and then we also, because we're doing a lot of the software or all of that software, we can fine-tune and control all of those things. So we can very carefully tune in the latencies for every aspect of the system. We can carefully tune in the memory management. We can have the right, fail-safes and fallbacks, for different things. ‘Cause you have to account for what if, what if there is a critical failure? What if there's a cosmic ray that flipsPeter [00:16:14]: a bit in the middle of the processor that causes some, malfunction? And you have to have a fail-safe to all of that, and so the core operating system is a part of that. And then the one last thing, which is a lot less exciting but is, actually a very big topic, is reliability of updates.Peter [00:16:30]: so the I have a Tesla and you get updates fairly frequently, right?Peter [00:16:36]: Once a month. Most companies that are making vehiclesPeter [00:16:40]: are basically never doing updates, and they're And even if they are doing updates, they're usually only updating maybe one module. Maybe they're updating the HMI module. But they're not able to update, let's say, the CPU critical parts of the system.Peter [00:16:51]: You have to go into the dealer for that. And so with our operating system now we can actually enable highly reliable updates of any system in the vehicle, and that's way easier said than done. Like, there's lots of technical, technically deep stuff, in the tech stack to do that in a way that you're not going to accidentally brick a vehicle.Peter [00:17:08]: And right? If, imagine yourAlessio [00:17:10]: That would be bad.Alessio [00:17:11]: Bad.Peter [00:17:11]: Bricking a car is a very expensivePeter [00:17:13]: and honestly, like across the industry maybe one of the most just pure impactful things that we've done is we've just, we're, we're now enabling the industry to actually do software updates.Alessio [00:17:22]: Just to clarify as well, who is the customer for this? Like, I assume a lot of hardware manufacturers have their own firmware, and I'm sure some of them would just have you write it for them because you're experts. And others would have their own. Like, who pays for this? Who invites you into the house? Is it, is it the end user, or is it, is it the manufacturer?Peter [00:17:41]: Yeah. So let me make an analogy firstly on the on the fragmentation of software. So physical machines today are more akin to the state of the phone market before Android and iOS existed, right? So I worked on Android at Google by the way many years ago, and part of the reason that Larry at Google decided to get into Android was they wanted to run Google products on a bunch of phones, and they bought all of these phones from the industry, and it turned out they had like 50 different operating systems on these phones. And it was virtually impossiblePeter [00:18:17]: for Google to make their app run on all 50 devices equally well. And so the solution was, well, actually what if, what if they created-A really great operating system and made it attractive to all of these phone makers, and that was sort of the genesis for what Android was and why Android existed. It was a way for Google to get their products onto really wide diversity of devices. The state of the physical, industry right now, it's a little bit like that. Like, there's yes, these companies have firmware, but they have so many different operating systems, it's so fragmented, and to actually get a modern AI application to run on these vehicles, you actually, you first have to consolidate the operating system, and so that's, that's why we've done that. And then, your specific question was who are our customers? It's, it's, generally it's the companies that are making these machines.Peter [00:19:06]: And we're, we're, we're selling our technology to them to really simplify the architecture and then enable these AI applications to run on them.Customers, Licensing, and the Better-Together StackSwyx [00:19:13]: How much is reusable across? Like, do you have, like, one OS that is just configured for everything, or is there some more customization that is needed?Peter [00:19:22]: Yeah, highly reusable. So the fundamental technology is quite universal, right? So things that we do have to think about though are, like, chipset support. And so if you're, if you're coding, let's say, an LLM and you have start with an assumption that, “Hey, oh, I'm gonna, I'm gonna use CUDA, and I'm gonna run this, on an NVIDIA chip,” then you don't really have to think about the hardware in that sense. Like, you're just, “Okay, I'm just I'm in the CUDA/NVIDIA ecosystem, and I'm, I'm going to use that.” But the hardware, especially in safety critical systems, it's a lot more diverse. There's not one or one or two players. There's a bunch of different chipsets that we have to support. And so our operating system doesn't just run on, like, the equivalent of X86. It has to, it has to run on a number of different architectures from chips from a bunch of different companies. But again, we've been working on this for a long time now, so we have, we have support for all of those chipsets. And then when you want to then run the AI applications, we can then do that reliably across now a variety of providers.Qasar [00:20:19]: And I think that is, like, heavily inspired by Android, right? Android has a huge suite of testing and it's a reliable operating system that runs on thousands of devices. And we think we can, we can do the same in all these physical moving machines, with the difference that we're really in a safety critical realm. Android isn't.Alessio [00:20:40]: So on Android, I don't need to use Gmail, I can use Superhuman. Like, what about your machinery? Like, can people bring somebody else's automation to it, or is it kinda like all-in-one?Qasar [00:20:50]: You have to use us. No. Yeah. we're If, Yeah. Yeah, it's totally open. Yeah.Peter [00:20:56]: Yeah. our philosophy is that we are a technology company, and so we license our technology to customers to use how they want. And so if a customer wants to If they wanna license our autonomy tech and our operating system, then great, we'll license those. If they just wanna license the operating system and then use different autonomy tech, that's fine also, and we have great documentation andSwyx [00:21:17]: Or if they wanna use developer tooling.Peter [00:21:18]: Yeah, exactly.AI Coding Adoption: Cursor, Claude Code, and the Bimodal EngineerSwyx [00:21:19]: It's, like, a better together if, obviously, if you, if they work together. Is it all C++ I assume is with different compile targets?Peter [00:21:27]: We use a lot of C++.Peter [00:21:28]: Rust is sort of a hot, the new hot kid on the blockPeter [00:21:32]: for a bunch of things as well. But yeah, the lower level you get, especially when you get to real-time constraints, you hit C++ at some point, and at some point maybe you work your way into assembly when needed.Swyx [00:21:44]: Oh, damn.Alessio [00:21:46]: I'm curious about the coding agent adoption, just, like, since you're mentioning more esoteric languages. Like, what's the adoption internally? What have you learned?Peter [00:21:55]: Yeah. We use everything. So Cursor was, I think the hottest tool in the company for a good while. Now Claude Code, I think has taken the reign on that. We have a internal leader, leaderboard that we use just to sort of encourage adoptionPeter [00:22:09]: with-within the company. And yeah, it's, they're phenomenally useful. it's, Honestly, we take inspiration from some of those tools also in how we're adapting some of that mindset of thinking to the physical realm. Like if it's so easy to build an app for this or that thing that lives just on a screen, we can We're taking now a lot of the same ideas and applying that to, “Okay, well, if you wanted a physical machine to do something, how easy can we make that, using our own tooling and platform as well?”Alessio [00:22:40]: Are you changing any of, like, the OS architecture, kinda like the way you expose services to, like, be more AI friendly or?Peter [00:22:48]: Yeah, absolutely. The in the early days of our tools infrastructure work, it was a lot about, You had engineers that were experts in certain topics, but the things that you're dealing with, they're oftentimes more mathematical or more abstract, where actually GUI tools are very useful for certain things. Like as an example, we have a product we call Sensor Studio, which is, it helps you design the sensor suite for your autonomous vehicle, whether, again, it could be a car, it could be a drone, could be a mining equipment, could be a robot. And you place sensors in different places. You There's different, There's a library. You can understand what are the trade-offs that you're making in the design of that system, and that was, like, a very, a very GUI intensive, thing ‘cause it's a little more like a CAD tool in that senseSwyx [00:23:37]: YepPeter [00:23:37]: if you've seen CAD tools. Nowadays, though, right, we expose all of the underlying APIs for that and now using, AI agents, you can actually configure a sensor suite with just text and likely reach a better result than you could've through the GUI in the past, and we're taking that thinking now through the whole product portfolio.Swyx [00:23:57]: Another thing I was thinking about is just in terms of, like, AI, adoption, does it change your hiring at least a little bit, or how do you, how do you sort of manage engineers, differently?Peter [00:24:08]: Yeah. absolutely, it does. we, I think like every company in the Valley right now, are evolving our hiring practicesPeter [00:24:16]: because the skills required to be effective are changing so fast, right? you used to really select for just rote implementation ability and now it is more the AI engineer skill set, right? Where it's like, yeah, how to implement, but actually-Just banging out code is no longer the core job, right? It's, it's actually knowing what questions to ask, knowing how to tie, how to tie together these different AI tools. And so the interviews that we give now I think are way harder than they've ever been.Peter [00:24:46]: But we also allow, right, selective use of AI tools to solve the problems. And I think in that you start to see more of a bimodal distribution of engineers, right? You start to see like wow, there's, there's this subset of people that they really get it. Like they're, they're all in and they've, they've clearly invested the hours needed to learn these tools and how to be effective.Peter [00:25:09]: And then there's sort of the group of people that haven't done that, and that the productivity gap is just enormous. And so we're, we're trying to obviously select for the people that are really into this.Qasar [00:25:20]: I first wrote the my AI engineer piece three years ago, and when I first wrote about it, I was like, “Actually, not everyone should be an AI engineer,” ‘cause I think there's a there's an extremist stance where well, every software is an engineer is an AI engineer. And my actual example of people who should not be adopting AI was embedded systems and operating systems, and database people. Are they adopting AI?Peter [00:25:41]: I think it's the classic bitter lesson, topic, which is the Six months ago I would've said the same thing, but it's, it's becoming super useful for every domain.Qasar [00:25:53]: I'm sure.Peter [00:25:54]: Right? Like,Peter [00:25:56]: there was, I think six months ago, or maybe a year ago, if you tried to use, let's say the latest Claude model for writing shaders, GPU shaders, the results were probably underwhelming. And if you use the latest model now to do that kind of task, you're a little bit blown away, like, “Wow, that actually worked. That's amazing.” And we see the same thing in the embedded realm. No question though, especially when you get into safety critical systems, the human validation isPeter [00:26:25]: is 100% key. Like I You're not gonna trust your life to a an AI written software that's, that's not been very carefully, checked by humans. And so I think now the really the challenge is about that appropriate level of human validation for these safety critical systems.Verifiable Rewards, Evals, and Neural SimulationAlessio [00:26:41]: How do you think about, yeah, touching on the simulation side, I think verifiable reward and reinforcement learning is, like, the hottest thing. What have you done internally to build around that? And like, what gives you What makes you sleep at night? Like, if somebody's like, just web coding something or likeAlessio [00:26:57]: wants to try something new, you have like a good enough system. Because I think the opposite is also true, is like if it's super easy to write anythingAlessio [00:27:04]: then it puts a lot of work on like the verifiableAlessio [00:27:07]: side of it. Like, what does that look like for people?Peter [00:27:10]: Yeah. So verifiability, a broader bucket of like evaluations, right? Like how do you evaluate the results that you're, you're getting? I think this is probably the hardest problem right now, because the As the models get better, it can be harder and harder to find the faults on the system.Peter [00:27:29]: And so like the problem of doing proper eval to find those faults, like that problem also keeps getting harder as the models get better. But it's no less important than it's ever been, right? You still there are still going to be edge cases that are not met and whatnot. And so it's, it's a big area of investment for us. On the reinforcement learning topic, the key thing is there's all these new requirements that come to be in the latest generation of these technologies. So for example, end-to-end is the big thing right now in autonomy and physical AI, which is you can now train these models that can effectively take sensor data in and then put control signals out, and get really good results out of that. But the way that you train and improve those models is really different from the previous generations. And so to do reinforcement learning on an end-to-end model, you now need to actually simulate all the sensor data, right? So then this becomes a we call our, work in this neural simulation, but it'sPeter [00:28:26]: think of it like a hybrid of Gaussian, splatting and diffusion methods, and where you really care about performance. Like performance is everything. If you can't do enough simulation fast enough and cheap enough, you actually can't get results that are worthwhile, in the end. It also gets to a lot of our work in embedded systems, which is like performance critical work, and that performance optimization, performance criticality, it carries over to a lot of the model training work. because, like, the only way to make it affordable is it has to be really fast.Qasar [00:28:58]: I think it's worth a few minutes talking about our own, evolving thoughts on verification and validation withinQasar [00:29:05]: kind of, traditional simulators, which are, you can think of like vehicle dynamics or something like that, which you're just taking textbooks and taking those formulasQasar [00:29:13]: and putting them into software, to like now this neural sim/world model universe. I think that's an interesting topic.Peter [00:29:20]: Yeah. So in more traditional development, right, you oftentimes would have, more black-and-white answers to questions.Peter [00:29:28]: And so the in Europe as an example, there's, a regulatory, system, it's called Euro NCAP. It's the European New Car Assessment Program, and as part of that, the vehicles have to pass a bunch of tests, and those tests actually, include, safety systems. So automatic emergency braking for a child that runs in front of a carPeter [00:29:51]: or let's say an occluded child that runs out and you hit it. And so you have You end up with sort of these binary answers of like, well, did the car under test pass this specific test? And there's a very well-known set of test casesPeter [00:30:05]: that the vehicle has to pass. And that was how the industry worked, let's say, until 10-ish years ago. But what's changed now is with these models, everything is statistics, right? Like you no longer have a black-and-white answer, but it's like, well, how many orders of magnitude or how many nines of reliability can I get in the system, and how can I, how can I prove that to be true? And the big unlock honestly for physical AI as an industry is that these models are just becoming much more reliable. Right? Things like things actually work a lot better. It's like the number of nines you can get out of these systems are now good enough that it actually becomes cost effective to really deploy these things. And so the big shift in, so verification and validation has been from a little bit more of a Again the past it was strictly requirements, and are you meeting or not? And now it's more of a statistical, verification and validation case where it's all about how many nines of reliability and meantime between failures, that sort of thing.Statistical Validation, Regulators, and the Cruise LessonSwyx [00:31:04]: And is the target audience regulators or even the customers are yeah, if you I imagine the customers are bought in, and it's mostly regulators that need to be satisfied.Peter [00:31:15]: We do work with the US government, we do work of course with the European governments and the government of Japan, and the government is not like an AI lab by any means.Peter [00:31:25]: So Swyx [00:31:26]: They just care about the outcome.Peter [00:31:27]: They care about the outcome.Peter [00:31:28]: And so we do education, in that regard, and like so sort of teaching about, “Hey, this is how we think validation should be done, and this is an approach that we think is reasonable,” and how to think about like when is a driverless system actually safe enough to go on the roads and that sort of thing. But I wouldn't say that the government is asking for it. It's like we're more teaching the government in that, in that sense. It's honestly, it's more so for our own, our own comfort, right? Like, we want to build very safe systems, and then of course our customers care deeply about that as well. But in that context we're also typically educating our customers.Qasar [00:32:01]: Yeah. Our first, our first core value is on round safety. So I think we can't underline enough that, us also verifying and validating that the systems that we're deploying are safe to us is probably as important as, like, some regulator or a customer saying,Swyx [00:32:19]: Of course. Okay. Yeah.Swyx [00:32:20]: You have to satisfy yourselves.Peter [00:32:22]: As I say, as a whole across the world, regulation oftentimes it's like a almost lowest common denominator. But like, you really have to substantially exceed what the regulators are expecting to make good products.Swyx [00:32:33]: Yeah. One thing I often talk about, I think and I try to make this relatable to the audience also, is Cruise, where they had an accident that basically ended the company. I wonder if people overreact to single incidents, because incidents are going to happen regardless, right? ‘Cause it's a statistical thing, but as long I don't know if regulators understand that, you cannot extrapolate from a single incident, but we do because that's all we have to go on. And your sample sizes are necessarily gonna be lower than, I don't knowSwyx [00:33:00]: consumer driving.Qasar [00:33:01]: Yeah. I think the Cruise example wasn't a technology failure. there was The real, compounding issue there was just how did the company talk to the regulators and what was their kind of behavior, and I think that became more of the issue. If you look,Peter [00:33:19]: It isn't It definitely was a technology failure, but it was made much worse by theSwyx [00:33:23]: Put the car back on the woman.Qasar [00:33:25]: Yeah. And let me put it another way. There is a version where Cruise still exists.Swyx [00:33:29]: right. Right.Qasar [00:33:30]: Right. It'sSwyx [00:33:30]: It was like the last strawQasar [00:33:31]: ItSwyx [00:33:31]: in like a long chain ofSwyx [00:33:33]: like issues.Qasar [00:33:33]: So do you feel like ATG had that horrific accident or someone actually dying, because, that was a homeless person crossing the street? So yeah, I think we can't understate enough that ultimately, like, statistical validation of something, that's one part of it, but it's not the only part of it. Like, consumer and let's say, mainstream adoption of these technologies is also gonna be part of that conversation. I think companies like Waymo are doing a lot of service positively to the industry in the sense of they're, they're setting a high benchmark and they're showing, kind of in a very responsible way how to, how to deal with these. There have been Waymo incidences as well. They've just not been as significant as the Cruise one that you mentioned. But yeah, so I think you'll just continue to see that. I think probably the long term question is really gonna be, again, around Like it is very clear humans are way worse drivers statistically.Qasar [00:34:29]: Like, there's no, there's no debate. And so at what point But we're emotional animals.Swyx [00:34:34]: Yeah. So my thing is, like, we have to get to a point as a society where we accept horrific accidents that would never happen by a human because statistically we understand that it is safer overall. In the same way that planes, they're safer, than I think they're the safest mode of transport that we have.Qasar [00:34:50]: Yeah. it's more dangerous to drive to the airport than it is to get on a flight.Qasar [00:34:53]: So if you're everQasar [00:34:54]: if you're ever getting nervous about getting on a plane, just think “I just gotta get to the airport.”Swyx [00:34:58]: Yes, we're flying.Qasar [00:34:59]: If I get to the airportQasar [00:35:00]: I'll be good.Swyx [00:35:00]: But then it's, planes also concentrate the tail risk if planesQasar [00:35:03]: Yeah. AndPeter [00:35:04]: And I was, I don't think we honestly have to worry about there ever being, accidents from these systems that are like much worse than what humans would cause, ‘cause humans do terrible things.Peter [00:35:14]: Like, people fall asleep at the wheel all the time.Swyx [00:35:16]: I have.Swyx [00:35:17]: Like, I'll call, I've been a drowsy driver.Peter [00:35:19]: Kinda drunk drivers, and that'sPeter [00:35:20]: that's the extreme end of the example. But these AI systems, you have redundancies, you have fallbacks. Like, there's many things have to go wrong for there to actually be a something catastrophic because there's, there's so many, fallbacks that these systems have.Alessio [00:35:36]: your simulation is like so vast because there's so many use cases. What are, like, maybe things that worked in a simulation and then you put it out and it's like, “F**k, this isAlessio [00:35:45]: this just did not work at all?”Peter [00:35:47]: Yes.Alessio [00:35:47]: IsPeter [00:35:47]: That's maybe a bit of a misconception, about simulation there. So let me go a little bit, more technical on this. So at first go, no simulation is going to represent the real world. There's always a process of this, sim to real matchingPeter [00:36:02]: where you actually, you need the real world feedback to basically feed into the parameters that are being used in the simulator, and you have to do that, it's like this validation flow, a number of times until you can get some confidence that, like I think the simulator is now accurately representingPeter [00:36:19]: what's gonna happen in the real world. Now, if you have a situation where you've done that full validation and you thought that it was accurate and then there's something different, those are much trickier cases, and that's, that absolutely can happen, but really I think the validation process is a really important part. You can never skip the simulation validation process, like where you're actually ensuring that, hey, the actual, my sim to real gap here is small enough that I can trust these simulation results. And there's, there's so many fun things that you can do when you get into it. Like, I'll, I'll give one fun example that came up recently is like in these humanoid robotics, systemsOverheating actuators is a real problem, right? So obviously phenomenal demos. IPeter [00:37:01]: The most amazingAlessio [00:37:02]: For 10 minutes.Peter [00:37:03]: The most amazing I can get. I love, I love watching robots do acrobatics like everybody but the these systems actually overheat, right? If, like, And one of the ways you can use simulation though is you can actually have that, the temperature of those actuators be one of the parameters that's representedPeter [00:37:18]: in the simulation. And if you're doing reinforcement learning over a certain task, then the robot can actually adjust its motions in the simulation to account for the fact that, oh, it knows that as it's moving, it's actually beginning to overheat this motor. But if you didn't have that parameter of, let's say, the heat of that motor represented in the simulation initially, then your RL policy might It will disregard that. And now you run that on the robot and the robot will overheat and fail.Alessio [00:37:43]: I guess the question is, like, how do you have all of these parameters taken care of while also understanding the deployment environment? Like, temperature is like a great example, right? WellAlessio [00:37:53]: why did you make my robot worse when it runs in like a freezer?Alessio [00:37:57]: So it actually shouldn't worry about that. it's like, yeah, how do you design these simulations?Peter [00:38:02]: This is honestly the This is what makes simulation so hard, right? it's because you Simulation is fundamentally about you're trying to optimize the development of a system, right? Like, how can I build this system faster and better and cheaper and what are all the levers that I have to actually accomplish that? And because simulation's just a software program, you can, you can change it a lot more easily than you can hardware systems. And then what's particularly awesome about the let's say, world models and using that as a part of simulation is now the simulation doesn't just scale with, let's say, adding new math equations inPeter [00:38:36]: but we can actually scale the simulation environment now with additional real world data and that also unlocks a whole new field of robotics.Qasar [00:38:46]: There is a meniscus line where you cross where still doing real world testing is better. there's, in this, sim-to-real gap, you can reproduce reality at exceedingly expensive costs and this So nothing is free. So really you have to you're finding that line where you're getting great performance, you're getting great feedback, whether it's on the training side or on the eval side, but it's way cheaper than doing it in the real world. At some point it, that doesn't make sense. And so even, from our earliest days in autonomy, our view was you're still gonna do real world testing. You There's, there's not, there's not this, magical land where you're not gonna do that. And maybe even like a more nuanced version of this in like traditional software development is, most of your testing for software in a vehicle, 95% of that can be like traditional CI/CD kind of, flows that you would have in traditional web development. But once you have Now you, let's say you have a truck. Well, you can do like 4% of those in like a rig which has all the components, the electrical and electronics of a truck, but doesn't have, it doesn't have the tires and it doesn't have the And then you have the 1%, which is actually the vehicle. There's something There's a similar analogy in terms of using simulation for intelligent systems. You can do a lot in a simulator, but in using world models, but ultimately it's, it's physical AI. So you're gonna deploy it on physical machines andQasar [00:40:17]: the freezer example comes to, comes to light.Alessio [00:40:20]: The world model thing has been to me the hardest thing toAlessio [00:40:22]: wrap my head around. Like we have Faith Eliyon on the podcast.World Models, Hydroplaning, and Cause-Effect LearningQasar [00:40:25]: We've been doing a small series with like another Intuition company, General Intuition as well.Qasar [00:40:31]: yeah, and I mean, lots of, lots of coverage on NeRFs and yes.Alessio [00:40:34]: Yeah. It feels like we talk with about, the heliocentric system, right? It's like in a world model, if you just feed visual data, the model might learn that the sun spins around the Earth. It makes sense, right? And it's like, well, not really. And I think what are like some of these other things that like hydroplaning is one thing I think about, is like can a world model understand hydroplaning and like what amount of water like causes it to happen? And it's like, yeah, to me it's like I don't understand how you guys do it. I guess it's like the real thing is like when you're doing both cars and the highway in Japan versus the excavator in a mine in,Qasar [00:41:13]: ArizonaAlessio [00:41:13]: wherever you're Arizona, wherever you're deploying them.Alessio [00:41:15]: How much of it are you relying on the world models to like generate the simulations for you and then try and close the gap after versus like giving the world models as a tool to your engineers to like curate the simulations if that makes sense?Peter [00:41:28]: Yeah, totally. So yeah, I can say at a pure engineering level, I think if you're hoping to do real world deploys and you're purely relying on a world model approach, you probably won't get to something that works, before you go bankrupt. So there is just a very practical mindset of like, world models are amazing and they're extremely useful for a lot of use cases, but there are a lot of other things that you need to do to actually get something started and something deployed and working. most fundamentally, world models are all about It's understanding the world, but also understanding what's going to happen. It's like the cause-effect relationship.Peter [00:42:01]: Right? And so like it, right, if you have a take some sort of construction tool, and that construction tool is gonna be doing some work on the Earth in some way, it's gonna be moving earth, the world model needs to understand that cause-effect relationship. Like, okay, when I, when I take this material from here and put it over there and now I have things that are over here and not over there anymore and that cause-effect, relationship. data obviously is a is a big problem. The hydroplaningPeter [00:42:26]: one is actually a really great example because it's actually quite non-obvious sometimes. Right? It's like, well, it's, it's raining and well this road, has, let's say the appropriate curvature to it so the water is running off the road and cars are driving faster here and then you approach a road that's very flat and water is now puddling on that road and all of a sudden cars are driving slower because when they were driving faster they were starting to lose control. And there are a lot of visual nuance, very nuanced visual cues in the scene and so I do think in the world model concept there's a good chance that the model actually would learn that you should just drive slower when these visual cues exist, and that's obviously the beautiful-The beauty of, these kinds of models where they just, they learn these non-obvious things.Swyx [00:43:14]: It doesn't need to know about hydroplaning to know that it needs to drive slower.Peter [00:43:17]: Yes.Swyx [00:43:17]: I guess it's Yeah. I wanna ask questions about, also deploying models. I presume, like, you use a lot of these world models for training data and simulation, but what about deploying it onto the systems in production? Presumably you have you have, like, GPUs on deviceOnboard vs. Offboard: Latency, Embedded ML, and DistillationSwyx [00:43:36]: but they're I keep saying on device. What's the what's the right term for that?Peter [00:43:40]: On machine.Swyx [00:43:41]: On machine.Peter [00:43:41]: Or embedded, yeah.Swyx [00:43:42]: Yeah. What is the embedded world like? because for people who are not used to that world, this is very alien.Peter [00:43:49]: Yeah. So it's actually We call it onboard and off board.Peter [00:43:52]: So like, onboard software and off board software.Peter [00:43:54]: And the great thing about off board software is you don't have to care about time, and you can run really large models, right? So you can, you can say, “Well, this model, I don't care if it takes one second for it to give me a result or 10 seconds for it to give me a result, because we have time.” And the models can be really big, and they can run, in a data center or on a on a huge GPU and you can obviously have distribute to compute, et cetera. But onboard you don't have any of those benefits. You're like, “Well, I need I have this many milliseconds where I need an answer from this model.” And so a lot more of the energy then is about, think of it more like distillation and it's like truly efficiency and like, literally every fraction of a millisecond counts. And you can't have a situation where the model takes too long because then the vehicle can't actually function.Peter [00:44:42]: And so you can, you can still use a lot of the same techniques, and the models themselves you can think of as like a derivative of larger models that you can run offline, and then you're, you're trying to just get a model that is still performs really well but it's, it's a it's smaller, small enough version that you can then run on this embedded system where you care about latency and power.Qasar [00:45:03]: Yeah. And I think like, the broader point I think which, maybe is not obvious but it's worth saying is in physical AI world, we're not really constrained right now by, like, the intelligence of the models. It's actually what Peter's talking about, it's actually deploying them inSwyx [00:45:19]: The hardware they give you.Qasar [00:45:21]: Yeah. On the hardware you give you.Qasar [00:45:22]: And so And there's just a reality is of safety critical systems. So those end up being the your limiting factorsQasar [00:45:29]: rather than, let's say, a limiting factor for, a foundation model companyQasar [00:45:34]: is gonna be just capital maybe or researchers.Qasar [00:45:38]: So we're, we're in that way dealing with, for us as people who kind of come in that realm with like a very interesting Those constraints force creativity.Swyx [00:45:47]: And I imagine, nobody was deploying or giving you the hardware for transformers back in 2018, whatever, but now they are. What's the evolution like? just peel back the curtains a little bit.Peter [00:45:59]: Yeah. Transformers first off, I think the paper was originally published in 2017.Swyx [00:46:02]: 2017.Swyx [00:46:02]: So there's no time.Peter [00:46:04]: And ISwyx [00:46:05]: But I'm just saying I guess I'm saying, like, embedded ML systems usually, like, a lot less parameters, a lot less compute, and now, like, orders of magnitude more.Peter [00:46:14]: Yeah. absolutely. what I was gonna say though was I think in the in the original paper in 2017, maybe it's in the last paragraph, somewhere in the paper they talk about, like, “Oh, by the way, this technique might be useful for, like, images and videos as well.”Peter [00:46:30]: These last subjects.Peter [00:46:31]: And it took a few years for that impact to really hit. But like, now, we're seeing transformers are everywhere.Swyx [00:46:39]: Yeah. Vision transformers.Peter [00:46:40]: And then then the compute just keeps getting better and better. But you do have this fundamental trade-off, right? It's like you have power, you have cost, and performance and like, getting the right, getting the right mix of those things in an embedded package that can also be, like, shaken and baked in all thePeter [00:47:00]: conditions that these things have to have to operate in. But yeah, I think that they're only going to keep getting better and so we also try to plan our strategy understanding that, we know the rate of improvements of these systems.Swyx [00:47:11]: Yeah. So like, Google just released the Gemma 2B modelSwyx [00:47:15]: that effective 2B model. Is that useful to you guys or is that too big?Peter [00:47:18]: You can run that model on an embedded system, definitely.Peter [00:47:21]: the So yes, it's, it's useful in that regard. The bigger question is, like, what do you use it for in an embedded system? Like, you actually need to customize it quite a bit to make it useful for something. But yeah, you could run a two billion parameter model, definitely.Swyx [00:47:35]: It also interesting, like, what percent is a custom ML model that only does that thing versus a generalist LLMSwyx [00:47:41]: which probably is not that useful actually for your context.Peter [00:47:46]: Like, you, like, you can imagine different use cases, right?Peter [00:47:48]: So theSwyx [00:47:49]: The voice stuff, yes.Peter [00:47:49]: Yeah, the voice test. Totally, yes.Peter [00:47:51]: So for the actual, autonomy elements, that's 100% in-house. We do every bit of that, the data simulation, the model, everything. But when you get into the more generic use cases like voice or voice assistant kind of thing, that's where these more generalist models like Gemma actually can be quite, can be quite useful.Swyx [00:48:09]: Yeah. And then there's also obviously a trade-off between, like, what percent must you do on machine, versus just call home.Peter [00:48:16]: Yeah. It's all about latency.Swyx [00:48:17]: Latency.Peter [00:48:17]: It's all about latency. Yeah.Swyx [00:48:18]: Yeah. Well, like, I think actually in a lot of contexts, especially in the US, you can just have a connection to the web.Qasar [00:48:26]: Yeah. I think though most of our universe is everything has to be fairly, embedded and local because just the nature of Even in the US there's a lot of likeSwyx [00:48:39]: PatchinessQasar [00:48:40]: don't haveQasar [00:48:41]: have coverage, right? And if you look at, like, the old world of autonomy within mining, which is, like, long before transformers and kind of, neural networks, in the like CNN and kind of a universe, they were really just hand-coded, systems. They were just like, this machine is gonna run to that place with thisPeter [00:49:03]: That was our GPS, like very accurate GPS.Qasar [00:49:05]: Yeah. And so that worked, and that worked for 20 years, so why would we actually need to use transformers or kind of more modern end-to-end systems? Mainly because you can only really run a path and run backwards. That provided a lot of value, but m-Not as much as you get when the machine is actually intelligent. It's, it's seeing, it's perceiving, it's acting in a dynamic world.Alessio [00:49:28]: I looked up RTK, real-time kinematic, one to two-centimeter accuracy.Qasar [00:49:32]: Yeah. Fantastic. But the and fantastic in faraway lands where there's not gonna be cell phone coverage.Peter [00:49:39]: Yeah, so it's widely used on the legacy mining and agricultural autonomy systems today. So like, for example, a combine that can be precise within one or two centimeters as it's driving down the field, they use RTK.Qasar [00:49:53]: Yes.Peter [00:49:53]: But it's, it's expensive.Qasar [00:49:54]: Yeah. And it's, it's, it's autonomy, but it's not intelligent in the way that I think all of usQasar [00:49:58]: if in twenty-six we'd be talking about intelligence.Alessio [00:50:00]: In one of your blog posts, you mentioned research on large scale transformers that are similar to those doing modern generative AI. What are, like, the big differences other than, “You're absolutely right. I should steer the car, so you probably wanna remove that?”Peter [00:50:14]: We have a diversified bet strategy internally, and the reason we've done that is because we operate in now a bunch of industries, a bunch of geographies, and each of the approaches has, obviously a different risk to them.Peter [00:50:27]: And so like, we're not going to put all of our eggs in a single basket for a single approach because that approach may no

AI is entering residential real estate appraisal faster than most appraisers expected. Many professionals now ask whether using AI violates USPAP. Fear often drives that question, but clarity requires careful thinking. USPAP does not ban tools of any kind. Instead, USPAP governs behavior, judgment, and credibility. Confidentiality remains a real concern. Appraisers must protect private data at all times. Entering sensitive property details into unsecured systems creates risk. Lack of transparency also creates problems. Some AI tools produce answers without showing their reasoning. Appraisers must always explain and support their conclusions. Competency matters as well. USPAP requires appraisers to understand the tools they use. Blind reliance on any system creates exposure. However, not all uses of AI create problems. Many applications simply improve writing, organization, and clarity. Those uses resemble spellcheck or templates and carry minimal risk. The key issue is judgment. Appraisers may use tools, but they cannot outsource decision-making. Value conclusions must always come from the appraiser's own reasoning. Responsibility never shifts to software or automation. Every signed report still reflects the appraiser's professional opinion. Think about a courtroom setting. An attorney may ask how AI influenced the report. Clear, confident answers protect credibility. Weak or uncertain answers create serious problems. Verification and understanding become essential safeguards. Smart appraisers follow three rules. Protect confidential information carefully. Verify every AI-assisted output thoroughly. Disclose meaningful use when appropriate. These steps maintain credibility and compliance. AI is not the violation. Surrendering professional judgment creates the real danger. Appraisers who stay in control will adapt successfully. Those who do not risk losing both credibility and trust.

By the time farmers in Haryana were ready to head to the anaj mandis (grain markets) to sell their Rabi harvest, the rules of entry had been revised. The Haryana State Agricultural Marketing Board's March 28 directive introduced a new set of conditions for crop procurement — mandatory registration on the 'Meri Fasal Mera Byora' portal, Aadhaar-based biometric verification, and vehicle number registration via the e-Kharid app. The State government says the measures are aimed at curbing fraud and improving transparency. Farmers see it as a bureaucratic maze that, whether by intent or effect, slows the procurement process to a point where the Minimum Support Price (MSP) becomes harder, and sometimes impossible, to claim.On April 11, farmers staged demonstrations across the State, calling for the compliance burden to be re-evaluated. Can digital governance work if it places the weight of reform on the farmer? When the State modernises to fix systemic fraud, who pays the price? Host: Vibha B. Madhava Guest: G.S. Mann, member of the India chapter of the Global Farmer Network and a former journalist. Producer: Jude Francis Weston Learn more about your ad choices. Visit megaphone.fm/adchoices

We're back! This time we have Nick from @TheLinuxEXP to speak with us about age verification laws. ==== Special Thanks to Our Patrons! ==== https://thelinuxcast.org/patrons/ ===== Follow us

Rotavirus surges across NYC and the Northeast... Columbia students and faculty want leadership with ties to Jeffrey Epstein kicked out.... TikTok's age verification not working full 480 Thu, 23 Apr 2026 09:44:22 +0000 V2zew1sZycBa2lSndnj1lOrtuKd2miPs news 1010 WINS ALL LOCAL news Rotavirus surges across NYC and the Northeast... Columbia students and faculty want leadership with ties to Jeffrey Epstein kicked out.... TikTok's age verification not working The podcast is hyper-focused on local news, issues and events in the New York City area. This podcast's purpose is to give New Yorkers New York news about their neighborhoods and shine a light on the issues happening in their backyard. 2024 © 2021 Audacy, Inc.

Arriva l'app per la verifica dell'età made in EU! Ursula la lancia in pompa magna definendola sicura, privata e open source. Risultato: è un colabrodo. Ecco perché la privacy è fondamentale nell'era digitale. Inoltre: la DeFi cola a picco, Tether lancia un suo wallet, congelati carte e conti a un magistrato francese della Corte Penale Internazionale, e che cos'è Cryobrick.It's showtime!

Imagine being forced to prove how old you are - and having it verified by a third party -  before unlocking your phone or signing up for a social media site. That's the goal of legislation introduced or passed in some U.S. states, the UK and Australia. Age verification requirements for social media and “adult content” are being proposed across the US, Europe and beyond with the justification of protecting children. But verifying your age basically means scanning your ID, and therefore tying your online presence with your real-world identity. Not only will this exclude millions of people who don't have government IDs, it attacks people who need to speak out anonymously against injustice, including whistleblowers. Even beyond age verification for social media, California's Assembly Bill 1043 goes into effect on January 1st, 2027 and requires operating systems - Windows, macOS and Linux among others, to perform age segmentation at the base level of the phone or computer system.The surveillance state loves the idea of age verification, which does very little to protect children as it claims to do - but does a lot to silence dissent, prevent the spread of information and silence free speech.Support the show

London's open-science crowd takes over the Francis Crick Institute, UCL and UCLH share a seriously encouraging bowel cancer trial follow-up, and Sony starts nudging UK PlayStation users toward age verification ahead of June. Plus, Oppo's next flagship tees up its UK arrival, and Fallout 76 gets its latest tune-up. Read more at standard.co.uk — and follow Tech and Science Daily from The Standard for your weekday briefing. Hosted on Acast. See acast.com/privacy for more information.

As I've said on here, and others have said repeatedly, the bottleneck in research is probably less so production as it is verification now that researchers have access to AI Agents like Claude Code, Gemini and Codex. But how to verify, and what to verify, is largely something all researchers will have to bumble through themselves. We did it the old fashioned way — we asked Hannah to check Claude's work by independently going through the marriage records we found herself. In today's episode, Hannah comes back to tell us what she found. And after today, we are now one episode closer to actually running some regressions! Famous last words. Thanks again for your support of the Odd Couple podcast, as well as my substack. Scott's Mixtape Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to Scott's Mixtape Substack at causalinf.substack.com/subscribe

Timestamps: 0:00 Intro 0:10 YouTube Allows Users to Hide Shorts 1:05 EU Age Verification App Hacked 1:57 Adobe Under New Pressure 3:47 QUICK BITS INTRO 3:59 NVIDIA GeForce 3060 Returns 4:24 Microsoft Sweepstakes 4:53 Meta Quest 3 Price Hike 5:15 Europol Targets DDoS Attackers 5:47 Whales Speak in Vowels NEWS SOURCES: https://lmg.gg/spsjI Learn more about your ad choices. Visit megaphone.fm/adchoices

Anthropic launches Claude Design, Mac mini gets harder to buy, TCL expands its Mini LED TV lineup. MP3 Please SUBSCRIBE HERE for free or get DTNS Live ad-free. A special thanks to all our supporters–without you, none of this would be possible. If you enjoy what you see you can support the show on Patreon,Continue reading "Cybersecurity Experts Find Major Flaws in European Commission's Age-Verification App – DTH"

(April 16, 2026) Jared Kushner's Mysterious Role in the Trump Administration. America’s self-storage craze has reached a tipping point. More young men are attending religious services regularly, poll finds. Age verification is coming for the internet, and it’s already raising red flags.See omnystudio.com/listener for privacy information.

The Mick In The Morning team is on holiday for another couple of days, but you can relive some of our favourite bits right here.Including: Mick Reviews Hamnet, Roo reviews Wurthering Heights, Aussie Tennis Superstar Pat Cash drops in, What's your most used emoji Good Chat and Comedian Nath Valvo talks about his time "camping" in the jungle. Mick in the Morning, with Roo, Titus & Rosie will be back again LIVE next week Monday April 20th 6am on 105.1 Triple M Melbourne or via the LiSTNR app. Mick In The Morning Instagram: https://www.instagram.com/molloy Triple M Melbourne Instagram: https://www.instagram.com/triplemmelb Drop us a voice memo: https://www.mickinthemorning.comSee omnystudio.com/listener for privacy information.

The European Union Commission readies a ‘zero-knowledge proof’ cryptography age verification app for deployment, Spotify will eliminate 1,000 jobs (16% of workforce), and the FCC grants Netgear conditional import approval for future routers and devices. MP3 Please SUBSCRIBE HERE for free or get DTNS Live ad-free. A special thanks to all our supporters–without you, noneContinue reading "EU Commission Readies Open-Source Age Verification App – DTH"

Google's Live Video Verification is quickly becoming one of the most important and misunderstood parts of managing a Google Business Profile.In this episode, Adam walks through what to prepare, what the rep asks to see, and how the process unfolds in real time, from showing exterior signage and active workspace to switching to desktop and completing the verification.For any business facing verification or trying to avoid getting stuck in a suspension loop, this episode gives a clear picture of what to expect and how to pass the first time.About Adam Duran, Digital Marketing Expert⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Local SEO in 10⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ is helmed by Local SEO expert Adam Duran, director of Magnified Media. With offices in San Francisco, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Los Angeles⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ & Walnut Creek, California, Magnified Media is a ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠digital marketing agency⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ focused on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠local SEO for businesses⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠marketing strategy⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠national SEO⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠website design⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠and qualified customer lead generation for companies of all sizes.Magnified Media helps companies take control of their marketing by:• getting their website seen at the top of Google rankings,• getting them more online reviews, and• creating media content that immediately engages with their audience.Adam enjoys volunteering with CoCoSAR, hiking and BJJ.About Jamie Duran, host of Local SEO in 10Local business owner Jamie Duran is the owner of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Solar Harmonics⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Northern California⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠'s top-rated solar company, which invites its customers to “Own Their Energy” by purchasing a solar panel system for their home, business, or farm.  You can check out the website for the⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ top solar energy equipment installer⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Solar Harmonics, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Thanks for joining us this week! Want to subscribe to Local SEO in 10? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Connect with us on iTunes and leave us a review.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Have a question about Local SEO? Chances are we've covered it! Go to our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠website and sign up for our Newsletter!

In a world that profits from your confusion, real clarity is an act of rebellion. Today we're sitting down with a man who has spent his life in the arena (as a soldier, a statesman, and a straight-talker) to cut through the noise on masculinity, truth, and what it actually means to be free, Nick Freitas. We're talking logical fallacies, the Marxist oppressor/oppressed framework, and why so many men today are disgusted, deflated, and dangerously close to giving up. But this episode isn't a pity party, it's a plan of attack. We're going to talk about why masculinity is under assault, how to stop painting yourself as a victim, and why there is no virtue in suffering, only in overcoming it. If you're ready to trade your grievances for a mission and your excuses for a legacy, this one's for you. SHOW HIGHLIGHTS 00:00 - Reconnecting and General Reflections on Culture 00:54 - Post-Modernism and the Rejection of Objective Truth 01:50 - The Marxist Framework of Oppressor and Oppressed 03:43 - The Concerted Effort to Target Masculinity 04:45 - Debating Traditional Gender Roles and Credentials 06:41 - The Hijacking of Academia and Institutions 09:38 - Understanding the Appeal to Authority Fallacy 12:05 - Christianity and the Epistemological Question 13:57 - Miracles as Evidence for the Truth of Christ 16:21 - Historical Scrutiny and the Verification of Scripture 19:10 - The Relationship Between Love, Freedom, and Justice 21:27 - Logical and Philosophical Arguments for the Cross 22:28 - Responsibility as the True Path to Freedom 24:45 - Self-Actualization Within a Correct Worldview 26:01 - Integrity of Belief and the Misuse of Moral Words 28:34 - Gender Conversion and Affirming Biological Reality 31:52 - Intellectual Manipulation and the Victim Dynamic 34:39 - Critical Theory and the Expansion of State Power 36:54 - The Shift Toward Labeling Speech as Violence 38:56 - State-Run Healthcare and the Devaluation of Life 41:16 - Addressing the Denigration of Modern Men 45:18 - Reclaiming Leadership and Building Godly Families 49:35 - Overcoming Victimhood vs. Identifying as a Victim 51:40 - Overcoming Unjust Circumstances Through Resilience 53:56 - Finding True Identity Through Service to God 56:44 - The Radical Power of Forgiveness and Grace 59:45 - Humility, Maturity, and Leading as a Father 01:01:33 - "The Man Book": A Practical and Philosophical Guide 01:04:37 - Closing Encouragement for the Modern Man Battle Planners: Pick yours up today! Order Ryan's new book, The Masculinity Manifesto. For more information on the Iron Council brotherhood. Want maximum health, wealth, relationships, and abundance in your life? Sign up for our free course, 30 Days to Battle Ready

a16z general partner Erik Torenberg speaks with Balaji Srinivasan, angel investor and entrepreneur, about why AI simultaneously reduces the cost of creation and increases the cost of verification, and what that tension means for the shape of the AI economy. They discuss why AI drives companies toward the "trusted tribe" model of the Chinese internet, why physical world tasks are easier to automate than digital ones, why shortcuts only work for experts, and why AI makes everyone a CEO rather than making CEOs obsolete.   Resources: Follow Balaji Srinivasan on X: https://twitter.com/balajis Follow Erik Torenberg on X: https://twitter.com/eriktorenberg Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this week's Fish Fry, I'm excited to welcome back Rob Knoth from Cadence Design Systems! Rob and I dive into a major shift in electronic design automation: the direct integration of artificial intelligence into the design workflow. Rob and I discuss the details of Cadence's newest innovation - the ChipStack AI Super Agent and Cadence's overarching strategies of 'design for AI' and 'AI for design. We also explore the core challenges currently facing verification engineers, and examine how the ChipStack AI Super Agent is set to bring predictability and acceleration to your next design process.

In episode 2033, Miles and guest co-host Jamie Loftus are joined by co-host of The Future of Our Former Democracy, Colin Cole, to discuss… Ruh Roh - Aint Nobody F**kin With The US-Israel War? Those Social Media Verdicts Are Bad, Actually... and more! VOTE IN THE WEBBY AWARDS for 'WE THE UNHOUSED' for Public Service & Activism Hegseth: "The president was clear this morning in his Truth that there are countries around the world who ought to be prepared to step up on this critical waterway as well." Trump Hits Out at France for Closing Airspace During Iran War Spain closes airspace to US military over Iran war, widening rift with US Trump Tells Aides He’s Willing to End War Without Reopening Hormuz The Big Tech Verdict Everyone Got Wrong: Social Media Addiction Trial Everyone Cheering The Social Media Addiction Verdicts Against Meta Should Understand What They’re Actually Cheering For Section 230 is the best protection we have from Trump’s censorship What is Section 230 and why does Donald Trump want to change it? Social Media Addiction Lawsuits (2026): KGM Trial, MDL 3047, and TikTok & Snapchat Settlements Explained Reddit User Uncovers Who Is Behind Meta’s $2B Lobbying for Invasive Age Verification Tech Age Verification Is A Windfall for Big Tech—And A Death Sentence For Smaller Platforms What's next in social media legal battles after a New Mexico jury finds Meta platforms harm children Hackers Expose Age-Verification Software Powering Surveillance Web Blackburn’s TRUMP AMERICA AI Act Repeals Section 230, Expands AI Liability, and Mandates Age Verification New Documents Show First Trump DOJ Worked With Congress to Amend Section 230 Meta, Google lose US case over social media harm to kids Landmark lawsuit finds that social media addiction is a feature, not a bug Meta ordered to pay $375m after being found liable in child exploitation case Jury finds Meta liable in case over child sexual exploitation on its platforms AI & Tech Brief: Forecasting an AI deal Walmart Pulls Cosmo From Checkout. Plus! Guess Who’s Claiming Victory. Revealed: how a US far-right group is influencing anti-gay policies in Africa Here’s Why Not Everyone Is Celebrating Meta’s Landmark Losses: ‘The Legal Precedent Being Set Is Terrifying’ This Bill Purports to Protect Kids from Big Tech. For LGBTQ+ Youth, It’s a Grave Danger Addictive potential of social media, explained Trump Administration Takes Major Steps Toward Comprehensive Federal AI Regulation LISTEN: Vapour by MildlifeSee omnystudio.com/listener for privacy information.

In this episode, Stephanie Cheng, Associate Director of Client Success, explains why eligibility and benefits verification in the physical therapy space still requires more than automation alone. She walks through how a hybrid model of payer integrations, workflow technology, and human support can help organizations scale, reduce bottlenecks, and improve visibility across patient access workflows.

MIT economist Christian Catalini joins Ryan and David to unpack his new paper, "Some Simple Economics of AGI," which argues that the scarce resource in the AI economy is no longer intelligence but verification: the human capacity to check, judge, and certify that AI output is correct. Christian walks through the two cost curves reshaping every industry (cost to automate vs. cost to verify), explains why entry-level jobs are collapsing first through what he calls the "missing junior loop," why even top experts are unknowingly training their replacements (the "codifier's curse"), and maps out the three roles that survive the transition: Directors, Meaning Makers, and Liability Underwriters. ---

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Welcome back everyone to the second of the Deep Dive episodes. In this new format the intention is to bring complexity back into the conversations around regenerative agriculture. Myself and many of my peers have been observing the discourse online, and especially on social media devolve into catch phrases and buzz words with little meaning and I want to embrace the complexity and many perspectives around many of the topics that get debated online. We'll be testing out a mix of investigative journalism, key interview snippets, and narrative weaving, not to assert a single stance on any issue, but rather to guide listeners through the fact that there are rarely any easy answers and that there's so much more to these conversations than the over-simplified arguments that we gloss over on click-bait titles and polarizing debates. You may remember in the last Deep Dive, we looked into the question of WHO has the authority and credentials to say what Regeneration is. As a continuation to this question, today we'll be exploring HOW to measure the journey of regeneration. One thing is to establish standards and validity, but as we'll see in these discussions, this is much harder to do than to talk about. This is a subject that is very relevant to my own work here with Climate Farmers because I helped to work on the creation of our our Monitoring, Reporting, and Verification program (MRV for short) and I continue to think about how such a complex and nuanced journey can be measured and communicated as I build and refine the educational programs in the Climate Farmer's Academy.  The question of HOW to measure regeneration also contains many sub-questions, such as what is the end goal? When does the timeline for measurement start and stop? What tools and resources are available for measuring? Where do we set the parameters for observation? I mean, is it just the ecology of the farm that needs improvement, or do we need to look at the economy of the farm business and the state of health of the people involved and the community around them? It's also very important to ask why we're bothering to measure this at all. Who gets the data? What are they going to do with it, and how will this information affect the relationship between farmers, policy makers, and the end customer?