Podcasts about L4

On today's exciting HEP-isode, the boys try to figure out what that chair in the hotel room is for and get sidetracked by old Builder's Square flashbacks while Mike gets worked up about the proliferation of dually pickups and Jo sits down with Einride CEO Roozbeh Charli to talk about their new Amazon deal, L4 autonomous semi trucks, and the company's billion dollar IPO.

It's a question of who is actually delivering product and who is just delivering promises on today's delivery-heavy episode of Quick Charge! Is the tenth time a charm for Elon's FSD, or will Einride CEO Roozbeh Charli steal his self-driving thunder? We've also got some concerning news about Tesla's Actually Smart Summon app getting faster, a sneak peek at some upcoming affordable EVs from BMW, Stellantis, and Volvo, and a full interview discussing Amazon's multimillion dollar middle mile deal with Einride CEO Roozbeh Charli. Today's episode is sponsored by GM Energy. If you want to experience more resilience and control over your home energy, the GM Energy Home System adds stationary battery power for always-ready backup energy for your home, and the GM Energy PowerBank takes in energy from the grid and stores it for when you need it most. Learn more here. Source Links Musk says Tesla unsupervised FSD will be ‘widespread' in the US by year-end — again Tesla increases Actually Smart Summon speed by 33% in new FSD update BMW has a plan to ‘keep the brand young' — A cheaper EV Jeep maker reveals plans for a new ‘groundbreaking' EV that will start at under $18,000 The Dodge Neon deserves a comeback – and Stellantis could do it tomorrow $10,000 Leapmotor A10 is the new-age Neon Stellantis needs in the US Amazon is deploying these massive cargo e-bikes for deliveries Amazon taps Einride to scale electric semi truck fleet capacity, NOW Forget Tesla hype — Einride begins public L4 autonomous electric semi operations Costco has a new stackable discount on the 2027 Chevy Bolt Yes, an EV really CAN power your home – if it's one of these [update] Prefer listening to your podcasts? Audio-only versions of Quick Charge are now available on Apple Podcasts, Spotify, TuneIn, and our RSS feed for Overcast and other podcast players. New episodes of Quick Charge are (allegedly) recorded several times per week, most weeks. We'll be posting bonus audio content from time to time as well, so be sure to follow and subscribe so you don't miss a minute of Electrek's high-voltage podcast series. Got news? Let us know!Drop us a line at tips@electrek.co. You can also rate us on Apple Podcasts and Spotify, or recommend us in Overcast to help more people discover the show. If you're considering going solar, it's always a good idea to get quotes from a few installers. To make sure you find a trusted, reliable solar installer near you that offers competitive pricing, check out EnergySage, a free service that makes it easy for you to go solar. It has hundreds of pre-vetted solar installers competing for your business, ensuring you get high-quality solutions and save 20-30% compared to going it alone. Plus, it's free to use, and you won't get sales calls until you select an installer and share your phone number with them.  Your personalized solar quotes are easy to compare online and you'll get access to unbiased Energy Advisors to help you every step of the way. Get started here.

Kiedy kupić ubezpieczenie od utraty dochodu — dlaczego wiek 35+ ma znaczenieKupując ubezpieczenie od utraty dochodu... warto znać jeden ważny mechanizm... który ma wpływ na zakres Twojej ochrony.Leadenhall stosuje tak zwany lookback period... czyli okres wsteczny... dwudziestu czterech miesięcy przed zawarciem umowy.Co to oznacza w praktyce?Jeśli w ciągu dwóch lat przed podpisaniem polisy... byłeś diagnozowany... leczony... albo konsultowałeś się z lekarzem w związku z jakimkolwiek schorzeniem... towarzystwo może wyłączyć to schorzenie z zakresu ochrony.Przykład.Programista... rok przed zakupem polisy był u neurologa z powodu bólów głowy. Dwa lata później trafia na zwolnienie lekarskie z powodu migreny. W takiej sytuacji... schorzenie mogło zostać wyłączone już na etapie zawierania umowy... ponieważ konsultacja lekarska miała miejsce w okresie lookback.Dlatego wiek trzydziestu pięciu lat i więcej... to dobry moment żeby poważnie myśleć o zakupie polisy.Im wcześniej... tym mniejsze prawdopodobieństwo że historia leczenia wpłynie na zakres ochrony.Przed zakupem polisy... warto porozmawiać szczerze z doradcą o swojej historii leczenia.Dobry doradca pomoże Ci wybrać produkt... który realnie chroni Twój dochód... bez nieprzyjemnych niespodzianek przy pierwszej szkodzie.

Six stories today — all connected to your portfolio.The NIO ES8 topped China's large SUV retail rankingsfor the fifth consecutive month — including combustionvehicles. The most premium mainstream NIO product isthe best-selling large SUV in China. That margin mixis what William Li was signaling when he told his teamrevenue growth would outpace delivery growth in Q1.The Onvo L80 launches tomorrow. Starting at 245,800 yuanwith the battery — 17,700 yuan cheaper than Tesla'sModel Y in China. Under BaaS the entry price drops to159,800 yuan. 2,840 liters of total cargo space —largest of any five-seat SUV in China. 900-voltarchitecture. LiDAR version with NIO's Shenji chipor pure vision with Nvidia Orin X. Early order datais tracking slightly better than the L90 at the samepre-sales stage. Onvo targets 3,300 swap stationsby year end.Trump landed in Beijing today with Tim Cook, Elon Musk,and Jensen Huang — who boarded Air Force One in Alaskaat the last minute after Trump called him personally.Topics: trade, Taiwan, Iran, and Nvidia chip exportcontrols. If any movement emerges on chip exportsfrom this summit it moves the entire semiconductorsector and NIO's autonomy roadmap simultaneously.Tesla FSD just crossed 10 billion cumulative miles.Huawei Qiankun hit 10.47 billion kilometers cumulativewith 910 million kilometers in April alone. A Huaweiexecutive declared they will surpass Tesla by October.Tesla is reportedly pushing its full-strength FSD V14to employee vehicles in China for the first time.The biggest autonomous driving battle in Chinesehistory is underway.Chinese industry executives are now calling L3 amarketing grey zone — the liability problem betweenL2 and L4 creates confusion that hurts consumersand exposes manufacturers. NIO's careful marketinglanguage is a competitive advantage in this environment.Xpeng reports Q1 earnings May 28th — one week after NIO.PPI hit 6% year over year. Markets hit all time highs.Cerebras priced its AI chip IPO at $185/share.---

  In this episode, Ray Cochrane leads with Mozilla shipping Firefox 150 with 271 patched bugs found by Anthropic’s Mythos system, the first major real-world deployment of the AlphaGo-Moment cybersecurity tooling. He also covers a 9-year dormant Linux kernel root, a college student stopping Taiwan’s high-speed rail with a software-defined radio, GitHub MCP secret scanning going GA, the NVIDIA NeMo lawsuit surviving its motion to dismiss, the Hugging Face Reachy Mini app store, Anthropic’s Auto Mode for Claude Code, and the 4-gigabyte AI model Chrome silently installed on your computer. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens the show with the AlphaGo Moment moving from theory into production. Mozilla shipped Firefox 150 this week with 271 patched bugs that Anthropic’s Mythos system found. Furthermore, the broader episode threads a clear pattern: AI tooling is reshaping security, developer workflows, and consumer software faster than the surrounding ecosystem can absorb it. The show closes on the four-gigabyte AI model Chrome installed on a billion machines without explicit consent. Mozilla Ships 271 Mythos Bugs in Firefox 150 Mozilla ran Anthropic’s restricted Mythos system against the Firefox 150 codebase before shipping. The result: 271 found bugs (180 high severity, 80 moderate, 11 low) baked into the release. However, the bigger number is the year-over-year jump. April 2026 shipped 423 total Firefox security fixes versus 31 a year prior. The breakdown for April: 271 from Mythos, 41 from external researchers, and 111 from other internal sources. Cochrane is sticking to his guns on calling this the AlphaGo Moment for cybersecurity. Skeptics argue Mythos is industrial-scale fuzzing because most found bugs sit in memory-safety territory. However, his counter is the velocity itself. Furthermore, he frames the resistance as carriage-versus-cars: humans-first research still grounds the tool, but throughput is the win. The Firefox CTO put it directly: defenders finally have a chance to win, decisively. For developers asking whether Mythos changes anything if they already run fuzzers, Cochrane’s answer is yes, and not even close. Additionally, he notes Mythos is restricted-access. The broadly available tier is Claude Opus 4.7, which Mozilla used since February before getting onto the restricted program for the Firefox 150 cycle. Run Opus 4.7 first. Sponsor: GoDaddy GoDaddy has been sponsoring this show for over twenty years. Economy hosting starts at $6.99/month, WordPress hosting at $12.99/month, and domains at $11.99. Use codes at geeknewscentral.com/godaddy for exclusive deals and to directly support the show. Copy Fail: 9-Year Linux Kernel Bug, 732 Bytes to Root A 9-year-old dormant Linux kernel bug got disclosed April 29 as CVE-2026-31431. Researchers published a 732-byte Python script that roots every major Linux distribution shipped since 2017. Additionally, CISA added the CVE to its Known Exploited Vulnerabilities catalog on May 1 with a May 15 federal deadline. The bug lives in the kernel’s crypto socket layer through the AF_ALG AEAD interface, originating in a 2017 in-place crypto optimization that lacked bounds checking. Cloudflare published their post-mortem this week. Their first instinct was to remove the kernel module entirely. However, service dependencies forced a workaround instead. Cloudflare resumed normal patched-kernel reboot automation across their 330-city fleet on May 4, with manual reboots and rollouts continuing after. Taiwan Rail Stopped by a 23-Year-Old With a Software-Defined Radio A 23-year-old Taiwanese university student with the surname Lin spoofed a TETRA general alarm signal on April 5, stopping trains on Taiwan’s high-speed rail. The accomplice supplied the radio parameters. Both were arrested by month-end. Lin posted NT$100,000 bail; the accomplice posted NT$80,000. The incident hit at 11:23 PM during the Qingming holiday weekend, stopping three revenue passenger trains plus one deadhead. Furthermore, the system has been in service for 19 years without rotating its cryptographic parameters once. Cochrane notes this is exactly the type of long-dormant infrastructure flaw that Mythos-class tooling catches, if anyone bothers to point it at the wires we already have. GitHub MCP Secret Scanning Goes GA GitHub’s secret scanning in the MCP server hit GA on May 5, with dependency scanning entering public preview the same day. Both released after a seven-week public preview run starting March 17. Additionally, the feature lets MCP-compatible coding agents (Copilot CLI, VS Code, JetBrains, Claude Code, Cursor, Windsurf) detect exposed secrets before commits or pull requests. Findings are ephemeral. They surface only in the current chat session and don’t persist as GitHub alerts. Sources disagree on scope: GitHub’s GA changelog says repo-level or org-level settings work, while the docs say only org-level applies. Cochrane flags the open question of whether MCP prompt injections could be exploited to send discovered secrets elsewhere. Subquadratic Debuts a 12-Million-Token Context Window Miami-based Subquadratic emerged from stealth on May 5 with a $29 million seed round and a reported $500 million valuation. Their model, SubQ 1M-Preview, runs on a new Subquadratic Sparse Attention architecture (their technical writeup calls it Selective Attention; same acronym, different second word). The headline claim: a thousand-times reduction in attention compute at 12 million tokens versus frontier models. However, that figure is vendor marketing math. There is no peer-reviewed paper, no public weights, and no independent benchmark replication. Researchers are demanding independent proof. Furthermore, CTO Alex Whedon’s pull line, “Retrieval / RAG plumbing is a waste of human intelligence,” signals how aggressively they want to position against retrieval-augmented architectures. ChatGPT Goblins, China’s “Catch You Steadily”: Sycophancy Is Universal Last week’s ChatGPT goblin obsession has a Chinese-language twin. The model overuses a phrase translating as “I will steadily catch you.” Additionally, a new Stanford and CMU study called ELEPHANT shows social sycophancy is universal across all 11 LLMs tested with 2,400-plus participants. Models endorsed users 49 percent more than humans did, and 47 percent even on harmful prompts. Alibaba’s Qwen and DeepSeek topped the rankings. Cochrane notes sycophancy is obvious once you’re aware of it but tricky to dissuade. Even with explicit instructions, longer context windows can reintroduce the behavior as the instructions get diluted. Furthermore, the trap is believing you’ve handled it. Once you think you’ve got it under control, you’re more prone to being influenced because you stopped watching for it. NVIDIA NeMo Lawsuit: Judge Tigar Denies Motion to Dismiss Three authors filed Nazemian v. NVIDIA in March 2024, alleging NVIDIA used The Pile and Books3 (approximately 196,640 pirated books) to train its NeMo AI framework. NVIDIA’s defense relied on the Sony v. Universal Betamax doctrine, arguing NeMo’s training scripts are general-purpose tools like a VCR. This week, Judge Tigar denied NVIDIA’s motion to dismiss in the Northern District of California. The headline quote: NeMo’s training scripts “have no other purpose than to speed up the process of infringement.” Furthermore, the judge rejected the VCR analogy outright. NeMo’s scripts are not general-purpose tools; they were allegedly purpose-built to ingest pirated material. Cochrane reads the Betamax framing as legal-jargon arbitrage rather than honest defense. The Humanoid Robot Market Is Smaller Than the Hype Michael Barnard at CleanTechnica argues that scenario-math against the global labor market puts realistic humanoid TAM at $200 billion to $1 trillion, not $20 trillion. Near-term wins cluster in warehouses, not homes. Additionally, the framework weighs dexterity burden against human-proximity safety burden. Real opportunities cluster where both burdens are low. Cochrane connects this to last week’s reservations about humanoids in the household. Furthermore, the risk profile is the issue: these robots aren’t prepared for every scenario, can’t make dynamic decisions, and one software update can change the definition of “safe.” Hugging Face Launches Reachy Mini App Store Hugging Face launched an open-source app store for the Reachy Mini robot this week, $299 for the Lite tethered version and $449 wireless. There are 200-plus community-built apps at launch from over 150 creators, with nearly 10,000 Reachy Minis cumulative shipped. Additionally, apps are forkable, with the default agent (ML Intern) able to modify, write, test, and ship code on any existing app. Examples at launch include an office receptionist built in under two hours, a Reachy Phone Home anti-procrastination app, baby-monitor-style apps, a cooking assistant, and a 78-year-old Joel Cohen’s voice-controlled CEO peer-group app. Pollen Robotics, the company behind Reachy, was acquired by Hugging Face on April 14, 2025. Bebop the Humanoid Robot Delays Southwest Flight 1568 A 4-foot, 70-pound humanoid robot named Bebop delayed Southwest flight 1568 from Oakland to San Diego by more than 73 minutes on April 30. The crew flagged the lithium battery as oversized. Furthermore, the battery was reportedly four times the cabin limit. Bebop belongs to Dallas-based Elite Event Robotics, which bought a full-price cabin ticket because the robot exceeded checked-baggage weight. Bebop danced for passengers at the gate before boarding. However, Southwest had Elite remove the batteries before departure, and replacements were overnighted to Chicago for the next event. Cochrane flags the obvious: batteries have always been flagged in aviation, so forgetting that with a humanoid robot in tow is a strange miss. Ouster Rev8: Native Color Lidar With Google, Volvo, Skydio Stating Intent Ouster announced the Rev8 OS Family on May 4 in San Francisco. The sensors fuse depth and color via SPAD detectors (single photon avalanche diodes) on Ouster’s custom L4 and L4 Max chips. Google, Volvo Autonomous Solutions, Skydio, Liebherr, Epiroc, and PlusAI have stated intent to adopt, though nothing is formally signed. Specs include 48-bit color, 116 dB dynamic range, and pre-fused 3D colorized point clouds. The OS1 Max gets 500-meter max detection. Available to order today and shipping this quarter, with no pricing disclosed. CEO Angus Pacala in his TechCrunch interview: “The goal is to obviate cameras. There’s no reason that one sensor can’t do both.” TagTinker Lets a Flipper Zero Mess With Electronic Shelf Labels A new Flipper Zero app called TagTinker uses infrared signals to push images and text to electronic shelf labels. Additionally, these are the same kind of price tags grocery chains are starting to use for surveillance pricing. The app and GitHub repo went public this week. Maryland’s HB 895, signed by Governor Wes Moore, takes effect October 1 as the first-in-nation surveillance pricing law. It covers food retailers and third-party food delivery service providers. Furthermore, ESLs use the same IR signaling as TV remotes with weak security. The dev’s disclaimer states it’s strictly for educational research, security curiosity, and displaying digital art on hardware you legally own. Fitbit App Becomes Google Health, Plus Fitbit Air, Plus Google Fit Sunset Google announced May 7 that the Fitbit app becomes Google Health on May 19, rolling through May 26. The launch ships with the new $99.99 Fitbit Air screenless tracker and the long-rumored Google Fit shutdown. Additionally, the four-tab interface (Today, Fitness, Sleep, Health) bundles a Gemini-powered AI Health Coach. Coach is premium-gated at $9.99/month or $99/year. Medical records integration is US-only at launch. The Fitbit Air gets up to one week of battery life and 50-meter water resistance. However, Cochrane flags conflicting privacy framing: Google’s AI summary bullets say “your data stays private,” but the actual document copy says only “committed to not using Fitbit user health and wellness data for Google Ads.” Those are not the same statement. Russinovich on Why Win32 Won and WinRT Didn’t Microsoft Azure CTO Mark Russinovich said via Microsoft Dev Docs video that Win32, the 1995 API, is still foundational to Windows 11. WinRT, the modernization replacement, “didn’t play out the way a lot of people expected.” Mostly clickbait framing per Windows Latest, but the substantive angle is real. Microsoft is pivoting back to native WinUI 3 development after years of pushing developers toward WebView2 and Electron. Additionally, Electron-based apps are known for insane RAM usage, and everyone is hurting for RAM right now. Furthermore, the bigger open question is whether Electron survives the test of time, especially with the React engine reportedly being rewritten in Rust. “Tabula Plena”: The Brain Starts Full, Not Blank A Nature Communications study from the Institute of Science and Technology Austria found that the mouse hippocampal CA3 recurrent network begins densely connected and refines through pruning. ISTA’s press release frames this as “tabula plena,” meaning full slate, counter to tabula rasa. The paper published April 21. First author Victor Vargas-Barroso and senior author Professor Peter Jonas studied mice at three developmental stages. Furthermore, the “starting overloaded enables faster sensory integration” framing is Jonas’s hypothesis from the press release, not a paper conclusion. Cochrane closes on the bigger question: did we have human growth and experience mapped wrong from the start? The Aqueous Battery You Can Pour Down the Drain A Chinese research team led by Professor Chunyi Zhi at City University of Hong Kong built an aqueous battery using a custom organic polymer electrode plus neutral magnesium and calcium salts (food-grade tofu coagulants) as electrolyte. Published in Nature Communications on February 18. Numbers to know: 120,000-plus charge cycles, full-cell energy density of 48.3 watt-hours per kilogram. That’s well below typical lithium-ion. However, post-cycling analysis showed only magnesium, calcium, chlorine, carbon, and copper, with no heavy metals. The cell complies with US RCRA, ISO 14001, and China’s GB 18599-2020 for direct environmental disposal. Additionally, the “300-plus years” framing is journalists extrapolating from the 120,000 cycles, not a paper claim. ResoNix Klippel Tests Expose Car-Audio Spec Lies Nick Apicella, founder of ResoNix Sound Solutions in Stony Point, New York, spent around $23,000 on independent Klippel LSI and TRF testing of 40 subwoofers. He published 21 results showing widespread misrepresentation of Xmax (excursion) and thermal/power-handling claims. Test data published in three batches between December 2025 and January 2026. Specifics: Wavtech thinPRO12 claimed 20 mm of excursion but delivered 8.85 mm, scoring 15 out of 100 on marketing accuracy. One driver hit 44 percent of advertised excursion. Another tripped thermal protection at half its rated power. Additionally, nine of 21 drivers scored below 50 out of 100. Brands tested include JL Audio, Sundown, Focal, Morel, Audiofrog, Adire, Stereo Integrity, and Dynaudio. Conflict-of-interest flag: ResoNix’s own GUS-15, 12, and 10 prototypes conveniently rank one, two, three. JetBrains Opens 2026 Developer Ecosystem Survey JetBrains opened the 10th annual Developer Ecosystem Survey this week. It takes about 30 minutes, with prizes including a MacBook Pro 16-inch and a $1,000 Amazon gift card. Anonymized raw data is published publicly, and cumulative scale is 100,000-plus developers across recent years. Additionally, the survey is going fully anti-AI: “evil bots, dishonest respondents, and AI agents will be excluded from prize distribution.” Cochrane is curious whether TypeScript holds its 2025 crown after knocking Python off, and whether Rust shows real growth given the wave of LLM-driven Rust rewrites in the past few months. Anthropic’s Claude Code Auto Mode Goes Live Anthropic launched Auto Mode for Claude Code roughly six weeks ago. Claude Code’s previous behavior required user approval for most file modifications and command executions, generating heavy approval-fatigue complaints during longer sessions. Auto Mode is the answer: Claude can run multi-step development tasks without per-action approval. Additionally, the architecture is a two-stage classifier, with stage one a fast yes/no filter and stage two doing chain-of-thought on flagged actions. Cochrane runs his own Claude Code in YOLO mode but with custom rejection rules baked into settings to block commands he doesn’t want, even with skip-permissions on. He recommends configuring settings as the actual policy layer rather than relying on classifier judgment alone. Furthermore, recent posts about Claude deleting websites or wiping production databases reinforce why the settings layer matters more than the auto-mode toggle. Chrome Quietly Installed a 4GB AI Model on Your Computer Google Chrome silently downloads on-device AI model weights (Gemini Nano family) to a `weights.bin` file in the OptGuideOnDeviceModel directory, around four gigabytes in Alexander Hanff’s audit. Furthermore, the model re-downloads if you delete it. Hanff timed his own install at 14 minutes 28 seconds on macOS. Affected platforms include Windows, macOS (including Apple Silicon), and Linux. Hanff frames this as a multi-front legal violation: a direct breach of Europe’s ePrivacy Directive, two articles of GDPR, and an environmental harm of a magnitude that would be notifiable under the Corporate Sustainability Reporting Directive. At one billion users, the four-gigabyte distribution represents roughly 240 gigawatt-hours of network and storage energy paired with about 60,000 tonnes of CO2-equivalent emissions. However, no EU regulator action or formal complaint has surfaced as of this episode. The model powers on-device features (email writing, scam detection, summarization, smart paste, tab grouping) but not the visible AI Mode button, which routes to the cloud. To disable, Cochrane recommends Chrome Settings, then System, then On-device AI, toggle to off. Two more paths exist via `chrome://flags` or a Windows registry edit. Cochrane closes the show with show housekeeping: GNC Insider at geeknewscentral.com/insider, email at geeknews@gmail.com, newsletter signup at geeknewscentral.com, and Pocket Casts as a solid modern podcast app pick. Have a wonderful night. The post Mozilla Meets Mythos #1864 appeared first on Geek News Central.

Jaka suma ubezpieczenia jest odpowiednia — 50%, 70% czy 80% dochodu?Większość towarzystw ubezpieczeniowych pozwala ubezpieczyć od 50% do 80% średniego miesięcznego dochodu netto — i choć 50% może wydawać się bezpiecznym minimum, w praktyce przy kredycie hipotecznym, leasingu i kosztach stałych firmy ta kwota często nie wystarcza nawet na pokrycie zobowiązań, nie mówiąc o bieżących wydatkach. Optymalnym wyborem dla większości przedsiębiorców B2B jest poziom 70–80% dochodu — na tyle wysoki żeby utrzymać płynność finansową przez kilka miesięcy choroby, a jednocześnie akceptowalny składkowo. Pamiętaj że towarzystwo przy wypłacie świadczenia zweryfikuje Twoje realne dochody z ostatnich 12 miesięcy — dlatego suma ubezpieczenia powinna być ustalona realistycznie już na etapie zakupu polisy, a nie zawyżona w nadziei na wyższą wypłatę.Gdzie kupić ubezpieczenie od utraty dochodu? Serwis ubezpieczeniapoludzku.pl oferuje ubezpieczenia od utraty dochodu dla lekarzy, programistów, osób na umowach B2B.

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

For years, auto shows have been primarily about new models.多年来，车展的核心一直是全新车型。At Auto China 2026 in Beijing, it is increasingly about new systems — from AI-driven driving to centralized computing architectures.而在北京 2026 中国国际汽车展览会上，焦点正逐步转向全新系统，涵盖人工智能驾驶、集中式计算架构等多个领域。This year's show, the largest of its kind, has drawn more than 2,000 companies from 21 countries and regions, with 1,451 vehicles on display.本届车展为全球规模最大的汽车展会，吸引了 21 个国家和地区的 2000 余家企业参展，展出车辆 1451 台。It includes a total of 181 global debuts and 71 concept cars, hitting a new high.展会共迎来 181 款全球首发车型与 71 台概念车，数量创下历史新高。More than 60 percent of global premieres at the show come from Chinese brands, while the number of concept vehicles has reached a record level.本届车展超六成全球首发车型来自中国品牌，概念车展出数量也创下历史纪录。Behind the numbers, however, a deeper transformation is underway, one defined by artificial intelligence, software architecture and system-level innovation.但在亮眼数据背后，一场以人工智能、软件架构、系统级创新为核心的深度行业变革正在悄然推进。Chinese automakers, long known for their expansive product lineups, are now using the show floor to highlight technology stacks as much as vehicles.中国车企素来以丰富的产品矩阵著称，如今在车展舞台上，其展示重心已兼顾整车产品与全栈核心技术。Large standalone halls occupied by domestic brands — including BYD, Chery and Geely — reflect not only product breadth but also accumulated capabilities in electrification and intelligent systems.比亚迪、奇瑞、吉利等中国品牌坐拥独立大型展馆，既彰显了产品覆盖面，也体现出在电动化、智能化领域长期积累的技术实力。As performance gaps in batteries, motors and electronic control systems narrow, competition is moving toward shaping technological identity and capturing user mindshare, said analysts.分析人士表示，随着电池、电机、电控系统的性能差距不断缩小，汽车行业的竞争正转向打造专属技术标签、抢占用户心智。Artificial intelligence has become the focal point of this shift.人工智能已然成为这场行业变革的核心焦点。Geely is highlighting its full-domain AI 2.0 system, while SAIC's Roewe brand is showcasing AI-based in-car applications developed with Volcano Engine.吉利重点展出全域人工智能 2.0 系统，上汽荣威则亮相与火山引擎联合研发的车载智能应用。Huawei's automotive business also made its first appearance as an independent brand cluster, presenting multiple new models alongside its Harmony-based intelligent cockpit ecosystem.华为汽车业务首次以独立品牌矩阵形式参展，多款全新车型同台亮相，同步展示基于鸿蒙系统打造的智能座舱生态。Across the exhibition, intelligent driving, smart cockpits and large language model integration are no longer isolated features, but part of unified system architectures.整场展会中，智能驾驶、智能座舱、大模型融合技术不再是孤立配置，而是融入一体化系统架构的核心组成部分。One of the most explicit articulations of this transition came from XPeng CEO He Xiaopeng, who outlined the company's strategy centered on "physical AI".小鹏汽车董事长何小鹏清晰阐释了行业转型趋势，并介绍了企业以「物理人工智能」为核心的发展战略。XPeng's latest model, he said, is the first Chinese vehicle designed with full hardware redundancy to meet robotaxi standards and has already obtained road testing permits in Guangzhou, Guangdong province.他表示，小鹏全新车型为国内首款搭载全冗余硬件、符合自动驾驶出租车标准的量产车型，目前已在广东广州获得道路测试资质。The company is currently conducting regular Level 4 pilot operations and plans to begin passenger-carrying tests with safety drivers later this year, targeting fully driverless operation by early 2027.企业现阶段已常态化开展 L4 级自动驾驶试点运营，计划今年年内启动带安全员的载人测试，力争 2027 年初实现完全无人驾驶落地。He also extended the concept of physical AI beyond vehicles.何小鹏还将物理人工智能的应用范畴拓展至汽车之外。XPeng is developing humanoid robots following a "commercial-first" path, with initial deployment in retail environments.小鹏正以商业化优先为导向研发人形机器人，首批落地场景聚焦零售行业。The company aims to sell more than 10,000 units by 2027.企业目标在 2027 年前实现人形机器人销量破万台。Underlying these initiatives is a broader industry view: hardware iteration is slowing, while software, particularly AI-driven capabilities, is becoming the primary driver of differentiation.一系列创新举措的背后，是行业共识：硬件迭代速度放缓，软件能力尤其是人工智能技术，正成为车企核心差异化竞争力。The integration of AI is also reshaping the underlying computing architecture of vehicles.人工智能的深度融合，也在重构汽车底层计算架构。At a technology event two days ahead of the show, Horizon Robotics introduced its Starry chip, built on a 5-nanometer automotive-grade process.车展开幕前两天，地平线在技术发布会上推出星辰系列芯片，该芯片采用 5 纳米车规级制程工艺打造。With 650 TOPS of computing power, the chip supports both intelligent driving and cockpit AI models on a unified platform.芯片算力可达 650TOPS，可在同一平台兼容智能驾驶与智能座舱大模型运算需求。This shift toward centralized computing, combining previously separate domains, is expected to reduce system complexity, lower costs and shorten development cycles.集中式计算架构打破以往各模块独立运行的模式，有望降低系统复杂度、压缩生产成本、缩短车型研发周期。According to the company, integrated architectures could cut vehicle-level costs by up to 4,000 yuan ($585) and reduce development timelines from 18 months to eight months.地平线表示，一体化架构最高可使单车成本降低 4000 元（折合 585 美元），并将车型研发周期从 18 个月缩短至 8 个月。More than 10 carmakers and suppliers including BYD, Chery, Volkswagen and Bosch have shown interest in the chip, said the company, indicating growing industry alignment around unified computing platforms.官方透露，比亚迪、奇瑞、大众、博世等十余家车企及供应链企业已表达合作意向，行业正加速向统一计算平台靠拢。Horizon Robotics CEO Yu Kai described autonomous driving as "the first large-scale application of physical AI", placing the current technological wave within a broader transition toward intelligent systems interacting with the physical world.地平线创始人余凯表示，自动驾驶是「物理人工智能」首个大规模落地场景，当下技术浪潮，本质是智能系统与实体世界深度融合的时代变革。For the first time, core suppliers and automakers have appeared in the same exhibition halls — a structural change that reflects shifting power dynamics across the industry.核心零部件供应商与车企首次同馆参展，这一结构性变化，折射出汽车产业格局与话语权的重塑。Battery makers, chip companies and AI solution providers are no longer operating behind the scenes.电池企业、芯片厂商、人工智能解决方案供应商不再局限于幕后研发。Instead, they are presenting integrated system solutions directly to the market.转而直接面向市场输出一体化系统解决方案。CATL, for example, built a 1,500-square-meter energy technology zone at the entrance of one hall, showcasing its next-generation battery concepts, including solid-state and sodium-ion technologies.例如宁德时代在展馆入口打造 1500 平方米能源技术展区，集中展示固态电池、钠离子电池等下一代动力电池前沿技术。Other major suppliers, from Bosch to SenseAuto, are also displaying full-stack solutions rather than individual components.博世、毫末智行等头部供应链企业，也不再单独展示零部件，转而推出全栈式技术解决方案。Global automakers accelerate localization国际车企加速本土化布局International carmakers are deepening their engagement with China's AI ecosystem.海外车企正深度融入中国人工智能产业生态。Volkswagen Group CEO Oliver Blume said the company is moving beyond electrification and driver assistance toward "agentic AI for all".大众汽车集团首席执行官奥利弗・布鲁姆表示，企业发展重心已从电动化、辅助驾驶，升级为普及全域智能体人工智能。"Starting this year, our in-car AI Agent will begin coming to our locally developed cars," he said.他称：「从今年起，车载智能体将逐步搭载于本土研发车型。"With this step, the Volkswagen Group is the first global automaker to deploy agentic AI across an entire vehicle portfolio in China at scale."借此举措，大众成为首家在华全系车型规模化落地智能体人工智能的跨国车企。The system, based on a locally trained large language model, is designed to proactively understand user intent and execute complex, multisystem tasks through natural interaction, while keeping data processing within the vehicle.该系统依托本土训练大模型打造，可主动识别用户需求，通过自然交互完成多系统复杂操作，同时实现数据车端本地化处理。BMW Group CEO Oliver Zipse emphasized a similar approach to localization.宝马集团首席执行官奥利弗・齐普斯也强调了本土化协同发展战略。"In China, we can integrate local partners such as Momenta and Alibaba," he said, highlighting the flexibility of software-defined architectures.他指出：在中国市场，我们将深度整合毫末智行、阿里巴巴等本土合作伙伴资源，并着重提及软件定义架构带来的灵活升级优势。Zipse added that artificial intelligence will underpin the future of driving itself.齐普斯补充道，人工智能将成为未来出行的核心基石。"The vehicle will anticipate your next move — slowing down before a turn or adapting to your habits," he said.未来车辆可预判驾驶员操作，提前弯道减速、适配个人出行习惯。"AI and sheer driving pleasure are not contradictory; they are fundamentally connected."人工智能与驾驶乐趣并非对立，二者相辅相成、深度融合。architecture /ˈɑːkɪtektʃə(r)/n. 架构；体系结构redundancy /rɪˈdʌndənsi/n. 冗余；备份localization /ˌləʊkəlaɪˈzeɪʃn/n. 本土化；本地化ecosystem /ˈiːkəʊsɪstəm/n. 生态系统

In the Electrek Podcast, we discuss the most popular news in the world of sustainable transport and energy. In this week's episode, we discuss Tesla's disaster earnings, the Beijing Auto Show being absolutely insane, and a plethora of new EVs unveiled this week. The show is live every Friday at 4 p.m. ET on Electrek's YouTube channel. As a reminder, we'll have an accompanying post, like this one, on the site with an embedded link to the live stream. Head to the YouTube channel to get your questions and comments in. After the show ends at around 5 p.m. ET, the video will be archived on YouTube and the audio on all your favorite podcast apps: Apple Podcasts Spotify Overcast Pocket Casts Castro RSS We now have a Patreon if you want to help us avoid more ads and invest more in our content. We have some awesome gifts for our Patreons and more coming. Here are a few of the articles that we will discuss during the podcast: Tesla (TSLA) releases Q1 2026 financial results: slight beat on earnings Tesla seems to say Robotaxi launch will be pushed back in 5 US cities Tesla (TSLA) pulled questionable levers to make Q1 2026 financials look good Elon Musk pushes unsupervised FSD for consumer Teslas — again Tesla will build factories just to retrofit millions of HW3 cars it said could do FSD Tesla confirms Cybercab production has started despite delays in unsupervised driving Tesla announces HW4 Plus with doubled memory — will HW4 follow HW3 to the grave? Tesla (TSLA) quietly discloses $2 billion AI hardware company acquisition buried in filing Tesla Model YL prototype spotted on US roads for the first time Porsche Cayenne Coupe Electric is an off-road capable, 2.4s 0-60mph beauty and beast BMW unveils the new i7 with nearly 450 miles of range and Neue Klasse style [Images] XPeng unveils GX flagship SUV with 750 km range, L4-ready hardware for $58,000 Hyundai unveils sleek new IONIQ V: a production car that looks like a concept Hyundai unveils the IONIQ 3 with class-leading range and a new infotainment [Images] CATL is launching sodium-ion batteries in EVs in 2026, aiming for 370+ miles range Rivian (RIVN) starts R2 production days after tornado hit factory, deliveries this spring Xpeng is actually building this electric flying car — I visited the factory Here's the live stream for today's episode starting at 4:00 p.m. ET (or the video after 5 p.m. ET: https://www.youtube.com/live/A7NTG5vjRuM

5 pytań które musisz zadać agentowi przed podpisaniem polisy ubezpieczenia od utraty dochodu B2BPrzed podpisaniem umowy zapytaj agenta o pięć kluczowych kwestii: jaka jest definicja niezdolności do pracy w tej konkretnej polisie, ile wynosi karencja i od kiedy dokładnie liczy się ochrona, czy choroba psychiczna i kręgosłup są objęte ochroną, jak wygląda procedura zgłoszenia szkody i jakich dokumentów wymaga towarzystwo oraz czy suma ubezpieczenia jest stała czy indeksowana. Agent który nie potrafi odpowiedzieć na te pytania wprost i bez owijania w bawełnę — to sygnał że warto poszukać innego doradcy.Gdzie kupić ubezpieczenie od utraty dochodu? Serwis ubezpieczeniapoludzku.pl oferuje ubezpieczenia od utraty dochodu.Specjalizuję się w ubezpieczeniach od utraty dochodu — czyli w tym, co często nazywa się prywatnym L4 — a także w ubezpieczeniach na życie i rozwiązaniach zabezpieczających dochód osób pracujących w modelu bi-tu-bi i na kontraktach managerskich.Na portalu ubezpieczeniapoludzku.pl pomagam przedsiębiorcom, specjalistom IT, lekarzom, managerom i freelancerom dobrać odpowiednie ubezpieczenie utraty dochodu. Tworzę analizy, porównania i rankingi, wyjaśniam zapisy OWU i pokazuję realne różnice między świadczeniami z ZUS a prywatnym ubezpieczeniem od utraty dochodu.Jestem autorem książki „Jak sprzedawać ubezpieczenia. 100 historii agentów ubezpieczeniowych" i twórcą trzech podcastów: „Ubezpieczenia po ludzku", „Praca w ubezpieczeniach" oraz „Marketing i sprzedaż dla agenta ubezpieczeniowego". Publikuję w Gazecie Ubezpieczeniowej, prowadzę społeczność mistrzowie.online i stworzyłem portal insurjobs.pl, który łączy pracodawców z kandydatami w branży ubezpieczeniowej.Dla firm ubezpieczeniowych i multiagencji tworzę strategie marketingowo-sprzedażowe, wdrażam systemy CRM, prowadzę audyty stron i social mediów oraz wspieram procesy rekrutacyjne w sektorze ubezpieczeń.

Kiedy ubezpieczenie od utraty dochodu NIE wypłaci świadczenia — wyłączenia i pułapki w OWUTowarzystwa ubezpieczeniowe mają prawo odmówić wypłaty świadczenia jeśli niezdolność do pracy powstała w okresie karencji, wynika z choroby zdiagnozowanej przed zawarciem umowy lub jest skutkiem działań umyślnych.Wyłączenia w ubezpieczeniu utraty dochoduNajczęstsze pułapki w OWU to zbyt krótki okres zwolnienia wymagany do uruchomienia świadczenia, wąska definicja niezdolności do pracy oraz wyłączenie chorób psychicznych i kręgosłupa — a to właśnie te schorzenia są najczęstszą przyczyną długich L4 wśród przedsiębiorców B2B (kupujących ubezpieczenie od utraty dochodu b2b). Zanim podpiszesz umowę — sprawdź te trzy punkty w OWU lub zapytaj doradcę.

Elke AI-prompt kost energie — maar hoeveel precies? En kun je daar iets aan doen zonder terug te gaan naar pen en papier? Cas Burggraaf is CTO en medeoprichter van GreenPT, een Nederlandse startup die open AI-modellen draait op groene Europese servers. Geen API-calls naar OpenAI of Anthropic, maar eigen bare-metal GPU's in een datacenter in Parijs waar de CO2-uitstoot per kilowattuur een stuk lager ligt. Het bedrijf laat gebruikers bij elke prompt zien wat hun energieverbruik is — iets waar de grote techbedrijven opvallend stil over zijn. In deze aflevering duiken Randal, Jurian en Cas in de polarisatie rondom AI, de echte milieukosten van taalmodellen, en waarom Europese digitale soevereiniteit meer is dan een buzzword. Daarnaast gaat Randal hands-on: hij vertelt over zijn eigen AI-server, en samen met Cas ontrafelen ze wat termen als quantization, MoE en distillation nu eigenlijk betekenen. Plus: luisteraarsvragen over energievergelijkingen en het ethische dilemma van trainingsdata. Over Cas Burggraaf Cas Burggraaf is CTO en medeoprichter van GreenPT, een Nederlandse AI-startup uit Utrecht die duurzame en privacy-vriendelijke AI levert op Europese infrastructuur. Eerder werkte hij als developer bij Brthrs Agency. Hij sprak recent op ai-PULSE 2025 in Parijs en ecoCompute Conference. LinkedIn: https://nl.linkedin.com/in/casburggraaf Website: https://greenpt.com GitHub: https://github.com/Casburggraaf Sponsor: Alliander Kijk op https://werkenbij.alliander.com/ Tijdschema 0:00:00 Waarom AI zo polariserend is — en wie er gelijk heeft0:02:42 GreenPT: groene AI én Europese soevereiniteit0:05:25 Hoe meet je de CO2-uitstoot van een AI-prompt?0:09:00 Open weights vs. open source: wat is het verschil?0:16:14 De GPU-wapenwedloop: van L4 tot Blackwell0:31:47 Een startup in de schaduw van OpenAI: hoe concurreer je?0:37:08 [Alliander — sponsor]0:42:14 AI neemt banen over: vertalers, developers, en dan?0:48:05 Vibecoden, Slack-bots en een slim ventilatiesysteem0:51:10 Waarom grotere modellen beter coderen (maar niet alles beter doen)1:01:07 Luisteraarsvraag: is één AI-prompt zuiniger dan 15 Google-zoekopdrachten?1:07:05 Zelf AI draaien: llama.cpp, VRAM en de kunst van quantization1:10:35 Dense vs. MoE vs. distillation — uitgelegd voor sterfelijken1:20:08 I use the AI to build the AI: semantic routing en de toekomst Genoemd in deze aflevering GreenPT Scaleway (datacenter-partner GreenPT) Open WebUI — open-source chat-interface Hugging Face — platform voor open weight modellen llama.cpp — server-software voor lokale AI-modellen Ollama — gebruiksvriendelijke AI-server NVIDIA H100, L4, L40, B300 (GPU's) DeepSeek, Mistral, QWEN, Gemma (open weight modellen) GPT-NL (samenwerking DPG Media) "Escaping an Anti-Human Future" - Making Sense podcast — Sam Harris Kingdom Come: Deliverance 2 (Warhorse Studios) Startpagina.nl (ja, die bestaat nog) Tips van de tafel Randal: Probeer eens een AI-model lokaal te draaien op je eigen hardware. Begin met Ollama of llama.cpp en een open weight model van Hugging Face. Je leert er enorm veel van.Cas: Kijk bij het kiezen van een AI-dienst niet alleen naar het model, maar ook naar waar het draait en hoe transparant de aanbieder is over energieverbruik.See omnystudio.com/listener for privacy information.

Dr. Al Grauer hosts. Dr. Albert D. Grauer ( @Nmcanopus ) is an observational asteroid hunting astronomer. Dr. Grauer retired from the University of Arkansas at Little Rock in 2006. travelersinthenight.org From October 2025. Today's 2 topics: - The Lagrange point L4 is 60° ahead of Mars whereas L5 is 60° behind Mars on the red planet's orbital path about the Sun. An object placed at either of these locations is trapped gravitationally and is likely to remain there indefinitely. The Mars L4 and L5 locations could provide a permanent place for staging and resupply missions to Mars and would give humans a different view of space weather and its effects on our home planet.   - Obtaining accurate data on the Earth's climate and how it is changing is vital to inform agriculture , insurance risks, business planning, disaster preparedness, financial investments, wild fire mitigation, and national security. The USA should not be flying blind and relying on Europe and China for the data we need.   We've added a new way to donate to 365 Days of Astronomy to support editing, hosting, and production costs.  Just visit: https://www.patreon.com/365DaysOfAstronomy and donate as much as you can! Share the podcast with your friends and send the Patreon link to them too!  Every bit helps! Thank you! ------------------------------------ Do go visit http://www.redbubble.com/people/CosmoQuestX/shop for cool Astronomy Cast and CosmoQuest t-shirts, coffee mugs and other awesomeness! http://cosmoquest.org/Donate This show is made possible through your donations.  Thank you! (Haven't donated? It's not too late! Just click!) ------------------------------------ The 365 Days of Astronomy Podcast is produced by the Planetary Science Institute. http://www.psi.edu Visit us on the web at 365DaysOfAstronomy.org or email us at info@365DaysOfAstronomy.org.

Ile kosztuje ubezpieczenie od utraty dochodu dla chirurgaIle kosztuje ubezpieczenie od utraty dochodu dla chirurga? To jedno z trudniejszych pytań na rynku ubezpieczeń — i zaraz wyjaśnię dlaczego.Chirurg to zawód wysokiego ryzyka. Nie dlatego, że operuje w stresie — ale dlatego, że jego dochód zależy dosłownie od sprawności rąk. Uraz ręki, przeciążenie kręgosłupa, problem neurologiczny — i chirurg nie operuje. Nie operuje, to nie zarabia.Weźmy konkretny przykład. Chirurg, czterdzieści lat, dochód trzydzieści pięć tysięcy złotych miesięcznie. Chce zabezpieczyć siedemdziesiąt procent — czyli dwadzieścia cztery tysiące pięćset złotych miesięcznie. Karencja trzydzieści dni, okres wypłaty dwadzieścia cztery miesiące.Gdyby taki chirurg wypadł z pracy na sześć miesięcy — kontuzja nadgarstka, dyskopatia, cokolwiek, co uniemożliwia pracę przy stole operacyjnym — ubezpieczyciel wypłaca mu sto czterdzieści siedem tysięcy złotych. ZUS tego nie pokryje. Renta z ZUS-u to ułamek realnych zarobków.Ile kosztuje polisa? Właśnie tu jest haczyk. Chirurdzy są klasyfikowani przez ubezpieczycieli jako grupa podwyższonego ryzyka — co bezpośrednio wpływa na składkę. Dlatego wycena jest tu wyjątkowo indywidualna. Zależy od specjalizacji chirurgicznej, trybu pracy, historii zdrowotnej i towarzystwa ubezpieczeniowego.Nie znajdziesz tej ceny w żadnym kalkulatorze online. Potrzebujesz rozmowy z doradcą, który zna specyfikę ubezpieczeń dla lekarzy i chirurgów.Wejdź na ubezpieczeniapoludzku.pl, wypełnij formularz kontaktowy — i dostań wycenę skrojoną pod Twój przypadek.Ubezpieczeniapoludzku.pl — dwie minuty i wiesz, na czym stoisz.Wejdź na ubezpieczeniapoludzku.pl —  Formularz zajmie ci dwie minuty.O mnie - nazywam się Marcin Kowalik – Pomagam dobrać i zawrzeć ubezpieczenie od utraty dochodu, ubezpieczenie na życie czy polisy emerytalne na ubezpieczeniapoludzku.pl. Współpracuję z wieloma towarzystwami. Jestem praktykiem pozyskiwania leadów ubezpieczeniowych, twórcą rankingów i porównań ubezpieczeń na życie, ubezpieczeń od utraty dochodu, założycielem społeczności mistrzowie.online, autorem książki „Jak sprzedawać ubezpieczenia. 100 historii agentów ubezpieczeniowych" (dostępne na marcinkowalik.online), ekspertem łączącym pracodawców ubezpieczeniowych z osobami szukającymi pracy w ubezpieczeniach przez portal insurjobs.pl, autorem podcastów „Ubezpieczenia po ludzku", „Praca w ubezpieczeniach", „Marketing i sprzedaż dla agenta ubezpieczeniowego", autorem tekstów w Gazecie Ubezpieczeniowej. 

Martyn Briggs, Director, Thematic Investing Strategy, Bank of America joined Grayson Brulte on The Road to Autonomy podcast to discuss why Physical AI is no longer a concept on the horizon but an era that continues to emerge across humanoids, autonomous vehicles, drones, and industrial robotics.AI has left the chat, and is moving from digital text-based intelligence to the physical world. Last year, 20,000 humanoids were manufactured, 80 percent of which were in China. The market for humanoids is projected to grow exponentially to 1.2 million by 2030 and 10 million by 2035, driven by falling component costs, simulation-to-real transfer breakthroughs, and the convergence of generative AI with robotics.Across the autonomous vehicle landscape, L2+ advanced driver assistance is emerging as the trust gateway to full L4 autonomy. As consumers grow comfortable with supervised automation on highways, the path to trusting robotaxis becomes shorter and shorter. The physical AI opportunity extends well beyond the United States, with Europe and the UK positioned to deploy robotaxis as an economic driver across dense urban corridors.Episode Chapters0:00 AUTNMY AI00:24 Physical AI Primer07:51 Open Source Physical AI Models12:46 The ChatGPT Moment for Robotics15:56 Scaling Humanoids27:17 Capital Flowing to Embodied AI29:28 Fleet Infrastructure, Real Estate & Charging31:43 OEM Struggles, Consumer Demand for L442:08 Waymo in London & Europe's Robotaxi Opportunity49:16 UAE as a Global Autonomy Market52:14 Autonomous Trucking57:30 Drones and Scaling Physical AI59:53 The Future of Physical AI--------About The Road to AutonomyThe Road to Autonomy is the definitive media brand covering the Autonomy Economy™. Through our podcasts, newsletter, and proprietary market intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Subscribe today for free: https://www.roadtoautonomy.com/ae/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This week on Autonomy Signals, Grayson Brulte and Rob Grant discuss Tesla Optimus delays driven by China's rare earth export controls, the EU's push to slow AI regulation and what it means for autonomous vehicles, and Waymo's potential expansion into Canada.China's Ministry of Industry and Information Technology (MIIT) has classified humanoid robot actuator components as dual-use technology, requiring foreign manufacturers to share technical specifications to obtain export licenses. Tesla relies on Chinese suppliers for the specialized rare earth magnets that give Optimus its 22-degree hand dexterity, and with China controlling 90% of that supply, delays could persist.AUTNMY AI's proprietary AI algorithm, OMEGA, analyzed the impact of a potential export ban, which could increase the price from $46,000 to produce Optimus parts in China to $133,000 if all production moves to America. If this were to happen, it would lead to a delay in Optimus, and this is further compounded by an FTC investigation into whether over 60% Chinese component content disqualifies Tesla's made-in-America branding.Then there is the MIIT's March 2nd humanoid robot standardization directive, which requires Chinese suppliers to prioritize domestic manufacturers such as Unitree and Xiaomi over foreign customers including Tesla, which creates an additional supplier prioritization risk on top of the export control risk.Closing out the show, Grayson and Rob discuss Waymo's potential Canadian expansion, examining lobbying records that show Waymo Co-CEO Tekedra Mawakana met with Toronto council staff to discuss ride-hail, goods delivery, and commercial operating authorizations. OMEGA also discovered lobbying records showing Waymo has been lobbying British Columbia to change the laws to allow L4 autonomous vehicles, pointing to a potential Vancouver expansion.Episode Chapters00:00 AUTNMY AI00:24 Signal 1: Potenial Tesla Optimus Gen 3 Delay23:35 Signal 2: Europe Delays Classifying L4 Autonomous Vehicles as High Risk48:45 Signal 3: Waymo Eyes Canadian Expansion51:29 Closing--------About The Road to AutonomyThe Road to Autonomy is the definitive media brand covering the Autonomy Economy™. Through our podcasts, newsletter, and proprietary market intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Subscribe today for free: https://www.roadtoautonomy.com/ae/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This week, the MRI showed that I have post-surgical epidural fibrosis over my L4 left thecal sac and left L4 nerve root! That is the likely cause of my continuing femoral nerve pain. I will see the neurosurgeon this week to find out next steps. Meanwhile, I’ve gotten going on a number of natural remedies to […]

Jak zawrzeć ubezpieczenie od utraty dochodu – krok po kroku dla przedsiębiorcy i freelanceraW tym odcinku podcastu „Ubezpieczenia po ludzku" tłumaczę krok po kroku, jak zawrzeć ubezpieczenie od utraty dochodu. Nazywam się Marcin Kowalik i od lat specjalizuję się w doborze prywatnego L4 dla przedsiębiorców, freelancerów i specjalistów pracujących w modelu B2B. Jeśli prowadzisz działalność gospodarczą i zastanawiasz się, co się stanie z Twoimi finansami w razie choroby lub wypadku – ten odcinek jest właśnie dla Ciebie.Co usłyszysz w tym odcinku?Na portalu ubezpieczeniapoludzku.pl pomagam setkom klientów miesięcznie wybrać odpowiednie ubezpieczenie od utraty dochodu – polisę, która realnie chroni dochód w razie niezdolności do pracy. Pracuję ze specjalistami IT, lekarzami, managerami i freelancerami. Znam ich pytania, wątpliwości i błędy, które popełniają przy wyborze ochrony.W tym odcinku odpowiadam na najczęstsze pytania, które słyszę od klientów szukających ubezpieczenia od utraty dochodu:– Jak krok po kroku zawrzeć ubezpieczenie od utraty dochodu? – Ile kosztuje prywatne L4 dla przedsiębiorcy i od czego zależy składka? – Czym różni się prywatne ubezpieczenie od utraty dochodu od dobrowolnego ubezpieczenia chorobowego ZUS? – Jak działa wypłata świadczenia i kiedy możesz się jej spodziewać?Jak zawrzeć ubezpieczenie od utraty dochodu – 4 krokiCały proces jest prostszy, niż większość przedsiębiorców myśli. Wystarczą cztery kroki.Pierwszy krok to wypełnienie formularza kontaktowego z agentem ubezpieczeniowym – najlepiej takim, który zna specyfikę dochodów z działalności gospodarczej i pracy na kontraktach B2B. Już na tym etapie warto podać informacje o swojej branży i formie zatrudnienia.Drugi krok to analiza potrzeb i możliwości finansowych. Zanim wykupisz ubezpieczenie od utraty dochodu, przeprowadzam analizę potrzeb klienta (APK). Podajesz mi średni miesięczny dochód i wspólnie ustalamy, jak wysokie świadczenie chcesz otrzymywać w razie choroby lub wypadku – może to być nawet do 80% Twoich przychodów.Trzeci krok to podpisanie umowy i opłacenie składki. Składka za prywatne L4 dla przedsiębiorcy wynosi zazwyczaj od 200 do 400 zł miesięcznie – w zależności od wysokości świadczenia, wieku, zawodu i klasy ryzyka. Podkreślam to zawsze klientom: to koszt zbliżony do dobrowolnego ubezpieczenia chorobowego ZUS, ale zakres ochrony jest nieporównywalnie szerszy.Czwarty krok to spokój i realna ochrona. W przypadku choroby, wypadku lub trwałej niezdolności do wykonywania zawodu zgłaszasz zdarzenie bezpośrednio do towarzystwa ubezpieczeniowego – bez czekania na decyzję ZUS. Świadczenie wypłacane jest nawet przez 24 miesiące.Prywatne L4 vs ZUS – dlaczego warto dopłacić?W tym odcinku szczegółowo porównuję prywatne ubezpieczenie od utraty dochodu z dobrowolnym ubezpieczeniem chorobowym ZUS. Różnica jest znacząca: ZUS wypłaca maksymalnie 4 560 zł miesięcznie i tylko przez 182 dni. Prywatna polisa od utraty dochodu może pokryć nawet 80% Twoich realnych przychodów przez dwa pełne lata. Co więcej – zakres ochrony obejmuje nie tylko chorobę, ale też wypadek i niezdolność zawodową, a świadczenie jest indeksowane.Dla przedsiębiorców zarabiających powyżej 10 000 zł miesięcznie różnica między ZUS a prywatnym ubezpieczeniem od utraty dochodu może oznaczać dziesiątki tysięcy złotych w razie poważnej choroby. To liczby, które robią wrażenie – i które pokazuję każdemu klientowi podczas analizy potrzeb.Gdzie kupić ubezpieczenie od utraty dochodu?Rekomenduje zakup polisy od utraty dochodu przez doświadczonego agenta ubezpieczeniowego, który współpracuje z wieloma towarzystwami i może realnie porównać oferty. Jako multiagent sam przeprowadzam analizę potrzeb, dobieram odpowiednią klasę ryzyka i przedstawiam spersonalizowaną ofertę –

In hierdie week se episode van Wiele2Wiele maak hulle kennis met Lepas en sy L4. Lepas is 'n nuwe handelsmerk onder die Chery-groep. Hulle spandeer ook tyd met die BMW X3 Pure Design en besef weereens hoekom die BMW X3 as die 2025 SAGMJ Motor van die Jaar-wenner aangewys is. Hulle vertel jou dan meer oor die toetsdae van die 2026 Motor van die Jaar-kompetisie wat reeds aan die gang is. Vir 2Wiele gesels hulle oor die MotoGP-voorval wat almal aan die gons het. Wiele2Wiele op Facebook · Wiele2Wiele op Maroela Media

1 hour, 4 minutes (Released: February 26, 2018 / Recorded: February 3, 2018) In this episode of WEBurlesque The Podcast, Viktor Devonne interviews Attica Wilde, a New Jersey based producer and burlesque performer. Attica Wilde passed away in March 2026 in a senseless act of violence.  We re-upload this episode in her memory, reminding us to remember her energy, style, and hope for the future. Attica starts off by talking about her own humble beginnings as a performer and how she can't get rid of the video evidence entirely no matter how much she tries:  "I wasn't even supposed to perform; I was supposed to be a stage kitten. And the week before, [the producer] was like 'Hey, so I have an opening, come up with two acts.' And I already kind of had one with a giant coffin because I never do anything small, so I said ok and I did them and they still exist on the internet." Along with being a producer and performer, Attica is also a burlesque teacher. She goes on to talk about some of the tips she gives her students and how she is tired of everyone doing acts to the same old burlesque classics: "Don't always pick the obvious choice. I tell that to my students a lot… If it's an obvious choice that you've already seen someone else do… It's really hard… as a newbie to be like let me reimagine this completely." From there, Attica recounts her time as the producer of the classical review Smoke and Mirrors and why she decided to go in that direction when it was created and what led her to eventually realizing she wanted to get out: "At the time, it felt to me there was a gap to what we offered here in Jersey in terms of styles of burlesque…there wasn't really any classical here… I thought that was the easiest way to make my mark…One of the many reasons [my show] Smoke and Mirrors isn't around anymore is that I found it very confining…and I didn't have room or space to be more creative or to cast the more creative or weird out there things." Attica is recovering from back surgery and is slowing getting back into performing after having to recover. She recounts why she had to get the back surgery in the first place and why it was so hard to receive the care she needed: "I was in a car accident and I had cracked one of the disks in my L4, L5, like almost in half…it's the lowest part of your spine… it was one of those quiet injuries where no one believes you are actually injured because you can't see it…. I had the surgery after about a year and a half of trying everything else because I was 27 at the time and [the doctors] were like 'You are too young for back surgery.'" With all these experiences behind her, Attica finishes the podcast by reflecting on what being a burlesque performer means to her and where she sees the artform going. Society has made many strides in the past few decades, but she admits to still being insecure in her identities:  "There is an implication to being a burlesque performer and talking about it. Now, I generally don't run in circles with people that don't get it or who cast shade or look down on me for it because I just don't have time for that in my life. However, the fact that we are having more politically toned shows more obviously speaks volumes to where we are at right now and I see them all the time… [Despite the more widespread acceptance] I still have feelings of not being queer enough in queer spaces. I'm currently dating a girl and seeing a trans person and I'm still not queer enough because most of my relationships have been straight passing."

Odbierz bezpłatną konsultację ubezpieczenia utraty dochodu: http://eepurl.com/hX7uQPUbezpieczenie od utraty dochodu dla stylistki rzęsCześć, z tej strony Marcin Kowalik, a to jest podcast "Ubezpieczenia po ludzku". Dzisiaj mam dla Ciebie odcinek stworzony specjalnie z myślą o stylistkach rzęs.Wyobraź sobie taką sytuację. Kasia, stylistka rzęs, 30 lat, zarabia średnio 6 000 złotych miesięcznie. Stałe klientki, własny gabinet, zbudowana marka osobista. Ale pewnego dnia – kontuzja dłoni, alergia, problem ze wzrokiem, cokolwiek, co wyłącza ją z pracy na kilka miesięcy. Co wtedy? ZUS? Przy dobrowolnym ubezpieczeniu chorobowym dostałaby ułamek swoich rzeczywistych zarobków. A czynsz za gabinet, rachunki, rata kredytu – czekać nie będą.Mam dla Ciebie konkretne liczby. Za składkę już od 93 złotych miesięcznie Kasia może uzyskać świadczenie w wysokości 4 800 złotych miesięcznie w przypadku niezdolności do pracy. 93 złote miesięcznie. To mniej niż koszt jednego zabiegu przedłużania rzęs. A w zamian – spokój ducha i 4 800 złotych co miesiąc od ubezpieczyciela, dopóki nie będzie mogła pracować. Dla porównania – dobrowolne ubezpieczenie chorobowe w ZUS daje przy podobnej składce 2 do 3 razy niższe świadczenie. I to jeśli w ogóle spełniasz warunki formalne.Jak w ogóle działa ubezpieczenie od utraty dochodu? Tłumaczę to prosto.To prywatna, lepsza alternatywa dla ubezpieczenia chorobowego z ZUS. Gdy z powodu choroby lub wypadku nie możesz wykonywać swojego zawodu, ubezpieczyciel wypłaca Ci miesięczne świadczenie – równowartość Twojego dochodu. Wypłaty mogą trwać nawet do osiągnięcia 65. roku życia, a łączna suma świadczeń może sięgnąć nawet dziesięciokrotności Twoich rocznych dochodów.Co wpływa na wysokość składki? Trzy rzeczy: suma ubezpieczenia, którą wybierasz, Twój wiek i Twoja specjalizacja zawodowa. Dlatego liczby różnią się dla programisty, lekarza, adwokata czy fizjoterapeuty.Jak to kupić? Nie kupujesz tego przez internet jak biletu lotniczego. Potrzebujesz rozmowy z dobrym multiagent ubezpieczeniowym, który ma dostęp do ofert kilku towarzystw, porówna je i dobierze produkt do Twojej konkretnej sytuacji. Na stronie ubezpieczeniapoludzku.pl znajdziesz formularz – wypełniasz go, a sprawdzony agent sam się z Tobą kontaktuje.Jedna ważna rzecz: ubezpieczenia od utraty dochodu nie możesz wrzucić w koszty firmowe. To koszt prywatny. Ale pomyśl o tym inaczej – to koszt zabezpieczenia swojego największego aktywu. Aktywu, który generuje Twój miesięczny dochód. Tym aktywem jesteś Ty.Na koniec – słowo o tym, skąd wiem, o czym mówię.Od lat prowadzę portal ubezpieczeniapoludzku.pl, gdzie łączę przedsiębiorców, specjalistów IT, lekarzy i freelancerów ze sprawdzonymi doradcami ubezpieczeniowymi. Tworzę rankingi i porównania ubezpieczeń od utraty dochodu, analizuję zapisy OWU i pokazuję realne różnice między tym, co daje ZUS, a tym, co daje prywatne ubezpieczenie – nazywane też prywatnym L4.Napisałem książkę "Jak sprzedawać ubezpieczenia. 100 historii agentów ubezpieczeniowych" – znajdziesz ją na marcinkowalik.online. Prowadzę trzy podcasty: "Ubezpieczenia po ludzku", "Praca w ubezpieczeniach" i "Marketing i sprzedaż dla agenta ubezpieczeniowego". Publikuję w Gazecie Ubezpieczeniowej. Jestem założycielem społeczności mistrzowie.online dla profesjonalistów ubezpieczeniowych oraz twórcą portalu insurjobs.pl, który łączy pracodawców z kandydatami w branży ubezpieczeniowej. Dla multiagencji i firm ubezpieczeniowych tworzę strategie marketingowo-sprzedażowe, wdrażam systemy CRM i prowadzę audyty stron oraz social mediów.Jeśli chcesz zobaczyć konkretne liczby dla swojego dochodu i swojego zawodu – wejdź na ubezpieczeniapoludzku.pl, skorzystaj z kalkulatora lub zostaw kontakt. Do usłyszenia w kolejnym odcinku.

Odbierz bezpłatną konsultację ubezpieczenia utraty dochodu: http://eepurl.com/hX7uQPUbezpieczenie od utraty dochodu dla konsultanta IT – składka od 255 zł, świadczenie 12 000 zł miesięcznieCześć, z tej strony Marcin Kowalik, a to jest podcast "Ubezpieczenia po ludzku". Dzisiaj mam dla Ciebie odcinek stworzony specjalnie z myślą o konsultantach IT.Wyobraź sobie taką sytuację. Mateusz, konsultant IT, 40 lat, zarabia średnio 15 000 złotych miesięcznie. Dobra specjalizacja, dobrzy klienci, stabilne kontrakty B2B. Ale pewnego dnia – wypadek, poważna choroba, cokolwiek, co wyłącza go z pracy na kilka miesięcy. Co wtedy? ZUS? Przy dobrowolnym ubezpieczeniu chorobowym dostałby ułamek swoich rzeczywistych zarobków. A kredyt, czynsz, rachunki, rodzina – czekać nie będą.Mam dla Ciebie konkretne liczby. Za składkę już od 255 złotych miesięcznie Mateusz może uzyskać świadczenie w wysokości 12 000 złotych miesięcznie w przypadku niezdolności do pracy. 255 złotych miesięcznie. To mniej niż jeden lunch biznesowy w Warszawie. A w zamian – spokój ducha i 12 000 złotych co miesiąc od ubezpieczyciela, dopóki nie będzie mógł pracować. Dla porównania – dobrowolne ubezpieczenie chorobowe w ZUS daje przy podobnej składce 2 do 3 razy niższe świadczenie. I to jeśli w ogóle spełniasz warunki formalne.Jak w ogóle działa ubezpieczenie od utraty dochodu? Tłumaczę to prosto.To prywatna, lepsza alternatywa dla ubezpieczenia chorobowego z ZUS. Gdy z powodu choroby lub wypadku nie możesz wykonywać swojego zawodu, ubezpieczyciel wypłaca Ci miesięczne świadczenie – równowartość Twojego dochodu. Wypłaty mogą trwać nawet do osiągnięcia 65. roku życia, a łączna suma świadczeń może sięgnąć nawet dziesięciokrotności Twoich rocznych dochodów.Co wpływa na wysokość składki? Trzy rzeczy: suma ubezpieczenia, którą wybierasz, Twój wiek i Twoja specjalizacja zawodowa. Dlatego liczby różnią się dla programisty, lekarza, adwokata czy fizjoterapeuty.Jak to kupić? Nie kupujesz tego przez internet jak biletu lotniczego. Potrzebujesz rozmowy z dobrym multiagent ubezpieczeniowym, który ma dostęp do ofert kilku towarzystw, porówna je i dobierze produkt do Twojej konkretnej sytuacji. Na stronie ubezpieczeniapoludzku.pl znajdziesz formularz – wypełniasz go, a sprawdzony agent sam się z Tobą kontaktuje.Jedna ważna rzecz: ubezpieczenia od utraty dochodu nie możesz wrzucić w koszty firmowe. To koszt prywatny. Ale pomyśl o tym inaczej – to koszt zabezpieczenia swojego największego aktywu. Aktywu, który generuje Twój miesięczny dochód. Tym aktywem jesteś Ty.Na koniec – słowo o tym, skąd wiem, o czym mówię.Od lat prowadzę portal ubezpieczeniapoludzku.pl, gdzie łączę przedsiębiorców, specjalistów IT, lekarzy i freelancerów ze sprawdzonymi doradcami ubezpieczeniowymi. Tworzę rankingi i porównania ubezpieczeń od utraty dochodu, analizuję zapisy OWU i pokazuję realne różnice między tym, co daje ZUS, a tym, co daje prywatne ubezpieczenie – nazywane też prywatnym L4.Napisałem książkę "Jak sprzedawać ubezpieczenia. 100 historii agentów ubezpieczeniowych" – znajdziesz ją na marcinkowalik.online. Prowadzę trzy podcasty: "Ubezpieczenia po ludzku", "Praca w ubezpieczeniach" i "Marketing i sprzedaż dla agenta ubezpieczeniowego". Publikuję w Gazecie Ubezpieczeniowej. Jestem założycielem społeczności mistrzowie.online dla profesjonalistów ubezpieczeniowych oraz twórcą portalu insurjobs.pl, który łączy pracodawców z kandydatami w branży ubezpieczeniowej. Dla multiagencji i firm ubezpieczeniowych tworzę strategie marketingowo-sprzedażowe, wdrażam systemy CRM i prowadzę audyty stron oraz social mediów.Jeśli chcesz zobaczyć konkretne liczby dla swojego dochodu i swojego zawodu – wejdź na ubezpieczeniapoludzku.pl, skorzystaj z kalkulatora lub zostaw kontakt. Do usłyszenia w kolejnym odcinku.

Odbierz bezpłatną konsultację ubezpieczenia utraty dochodu: http://eepurl.com/hX7uQPUbezpieczenie od utraty dochodu dla kuriera – składka od 255 zł, świadczenie 12 000 zł miesięcznieCześć, z tej strony Marcin Kowalik, a to jest podcast "Ubezpieczenia po ludzku". Dzisiaj mam dla Ciebie odcinek stworzony specjalnie z myślą o kurierach.Wyobraź sobie taką sytuację. Tomek, kurier, 35 lat, zarabia średnio 8 800 złotych miesięcznie. Własna działalność, kontrakt z platformą, regularny dochód. Ale pewnego dnia – wypadek na drodze, kontuzja kręgosłupa, cokolwiek, co wyłącza go z pracy na kilka miesięcy. Co wtedy? ZUS? Przy dobrowolnym ubezpieczeniu chorobowym dostałby ułamek swoich rzeczywistych zarobków. A rata leasingu na auto, czynsz, rachunki, rodzina – czekać nie będą.Mam dla Ciebie konkretne liczby. Za składkę już od 141 złotych miesięcznie Tomek może uzyskać świadczenie w wysokości 7 000 złotych miesięcznie w przypadku niezdolności do pracy. 141 złotych miesięcznie. To mniej niż jeden bak paliwa. A w zamian – spokój ducha i 7 000 złotych co miesiąc od ubezpieczyciela, dopóki nie będzie mógł pracować. Dla porównania – dobrowolne ubezpieczenie chorobowe w ZUS daje przy podobnej składce 2 do 3 razy niższe świadczenie. I to jeśli w ogóle spełniasz warunki formalne.Jak w ogóle działa ubezpieczenie od utraty dochodu? Tłumaczę to prosto.To prywatna, lepsza alternatywa dla ubezpieczenia chorobowego z ZUS. Gdy z powodu choroby lub wypadku nie możesz wykonywać swojego zawodu, ubezpieczyciel wypłaca Ci miesięczne świadczenie – równowartość Twojego dochodu. Wypłaty mogą trwać nawet do osiągnięcia 65. roku życia, a łączna suma świadczeń może sięgnąć nawet dziesięciokrotności Twoich rocznych dochodów.Co wpływa na wysokość składki? Trzy rzeczy: suma ubezpieczenia, którą wybierasz, Twój wiek i Twoja specjalizacja zawodowa. Dlatego liczby różnią się dla programisty, lekarza, adwokata czy fizjoterapeuty.Jak to kupić? Nie kupujesz tego przez internet jak biletu lotniczego. Potrzebujesz rozmowy z dobrym multiagent ubezpieczeniowym, który ma dostęp do ofert kilku towarzystw, porówna je i dobierze produkt do Twojej konkretnej sytuacji. Na stronie ubezpieczeniapoludzku.pl znajdziesz formularz – wypełniasz go, a sprawdzony agent sam się z Tobą kontaktuje.Jedna ważna rzecz: ubezpieczenia od utraty dochodu nie możesz wrzucić w koszty firmowe. To koszt prywatny. Ale pomyśl o tym inaczej – to koszt zabezpieczenia swojego największego aktywu. Aktywu, który generuje Twój miesięczny dochód. Tym aktywem jesteś Ty.Na koniec – słowo o tym, skąd wiem, o czym mówię.Od lat prowadzę portal ubezpieczeniapoludzku.pl, gdzie łączę przedsiębiorców, specjalistów IT, lekarzy i freelancerów ze sprawdzonymi doradcami ubezpieczeniowymi. Tworzę rankingi i porównania ubezpieczeń od utraty dochodu, analizuję zapisy OWU i pokazuję realne różnice między tym, co daje ZUS, a tym, co daje prywatne ubezpieczenie – nazywane też prywatnym L4.Napisałem książkę "Jak sprzedawać ubezpieczenia. 100 historii agentów ubezpieczeniowych" – znajdziesz ją na marcinkowalik.online. Prowadzę trzy podcasty: "Ubezpieczenia po ludzku", "Praca w ubezpieczeniach" i "Marketing i sprzedaż dla agenta ubezpieczeniowego". Publikuję w Gazecie Ubezpieczeniowej. Jestem założycielem społeczności mistrzowie.online dla profesjonalistów ubezpieczeniowych oraz twórcą portalu insurjobs.pl, który łączy pracodawców z kandydatami w branży ubezpieczeniowej. Dla multiagencji i firm ubezpieczeniowych tworzę strategie marketingowo-sprzedażowe, wdrażam systemy CRM i prowadzę audyty stron oraz social mediów.Jeśli chcesz zobaczyć konkretne liczby dla swojego dochodu i swojego zawodu – wejdź na ubezpieczeniapoludzku.pl, skorzystaj z kalkulatora lub zostaw kontakt. Do usłyszenia w kolejnym odcinku.

On today's high-dollar episode of Quick Charge, Tesla is reeling from a $243 million judgement against it in a high-profile wrongful death case involving the company's Autopilot system, and has a hard time getting the relevant data to NHTSA. We've also got news that Waymo its expanding its L4 autonomous and driverless taxi operations into four new US cities across Florida and Texas, bringing its total to 10 compared to Tesla's 0 total cities with driverless electric vehicles in operation. Plus: the US Air Force has deployed the world's first portable 5MW nuclear reactor – which seems like the kind of thing we should all know about, you know? Source Links Tesla has to pay historic $243 million judgement over Autopilot crash, judge says Tesla avoids 30-day California sales suspension after dropping misleading ‘Autopilot' marketing Tesla sues California DMV to reverse ‘Full Self-Driving' false advertising ruling Tesla is having a hard time turning over its FSD traffic violation data Waymo adds 4 more cities to its robotaxi service, now 10 total (Tesla: still 0) Tesla admits it still needs drivers and remote operators — then argues that's better than Waymo Waymo founder trashes Tesla safety as Waymos illegally pass school buses World's first: US Air Force deploys portable nuclear power station Prefer listening to your podcasts? Audio-only versions of Quick Charge are now available on Apple Podcasts, Spotify, TuneIn, and our RSS feed for Overcast and other podcast players. New episodes of Quick Charge are (allegedly) recorded several times per week, most weeks. We'll be posting bonus audio content from time to time as well, so be sure to follow and subscribe so you don't miss a minute of Electrek's high-voltage podcast series. Got news? Let us know!Drop us a line at tips@electrek.co. You can also rate us on Apple Podcasts and Spotify, or recommend us in Overcast to help more people discover the show. If you're considering going solar, it's always a good idea to get quotes from a few installers. To make sure you find a trusted, reliable solar installer near you that offers competitive pricing, check out EnergySage, a free service that makes it easy for you to go solar. It has hundreds of pre-vetted solar installers competing for your business, ensuring you get high-quality solutions and save 20-30% compared to going it alone. Plus, it's free to use, and you won't get sales calls until you select an installer and share your phone number with them.  Your personalized solar quotes are easy to compare online and you'll get access to unbiased Energy Advisors to help you every step of the way. Get started here.

Learn how to fix your pain with our “Centralization Process” here! https://rebrand.ly/ytpainfreeSubmit an application to work with us 1:1 and learn how to fix your low back! ⁠www.therehabfix.com/low-back-program⁠To view hundreds of free low back videos please follow us on instagram at @rehabfix ⁠www.instagram.com/rehabfix⁠If your pain shoots from your low back down the back of your thigh, into your calf or foot, or even into the front of your thigh or shin, those are not random pain patterns. Each one points to a specific nerve root being irritated—most commonly by a disc issue in your lower back.In this episode, I'll show you how to identify which nerve root is involved in your sciatica and walk you through a simple 3-step routine to reduce disc pressure, calm the nerve, and start getting relief at home.Instead of chasing symptoms in your leg with random stretches, you'll learn how to address the problem at the source—right in your lower back.In this video, you'll learn:

In today's session, we dive deep into the mechanics of why lower back pain and sciatica flare up, even when you think you are doing the right things. The core of the issue is often "movement leakage," where motion intended for your hips or upper body inadvertently puts stress on an injured lumbar segment. Whether you are dealing with a herniated disc at L4/5 or L5/S1, these tissues have a reduced capacity for stress. When you move incorrectly—such as rounding your spine during a bent-over row or a simple daily task—you aggravate those vulnerable tissues. Understanding this is the first step toward moving away from the cycle of chronic pain and toward a structured rehabilitation programme.We also challenge the common misconception that more bending and stretching is the solution for a stiff back. If movement is what caused the aggravation, it is rarely logical to focus your recovery on more bending and twisting of the injured area. Instead, the priority must be to stabilise and protect the spine through isometric contraction and proper technique. By building a foundation of strength through exercises like squats and hip hinges, you teach your body to shield the injured segments, allowing the healing process to take place without constant re-injury.### Key Topics Covered

Presenting Sponsor Thirdzy!  https://thirdzy.com/JAZZYPromotion Code for 15% off: JAZZYEveryday we take a break from the busy work day to catch our breath, hang out with friends and talk about the world of Sports, Entertainment and specifically CrossFit. Today we talk about the Open numbers thus far, The Ring Doorbell Fears are spreading, Why is nobody signing with the WFP and what are the true numbers? What is up with the L4?

In this episode, Tu and Lei dive into a week dominated by autonomy, AI, and a widening gap between China's EV ecosystem and the rest of the world.  The episode opens with a deep reaction to Rivian's Autonomy AI Day—why it felt like déjà vu for anyone following China's smart-EV space, and how Rivian's announcements mirror what Chinese players like XPeng, NIO, and Li Auto have already been deploying. The hosts debate whether Rivian's approach represents real leadership or simply entry into the top tier.From there, the conversation expands to L4 autonomy momentum: WeRide launching passenger rides with Uber in Dubai, Mercedes partnering with Momenta in Abu Dhabi, and Waymo accelerating multi-city deployments while publishing safety data others still keep opaque.Tu and Lei also tackle the LiDAR vs. vision debate, Volkswagen's unusual dual bet on Rivian (US) and XPeng (China), and why silicon strategy—not just batteries—will decide winners. The discussion closes with affordability: why 300-mile EVs under $40K are existential for Western OEMs, and why China's cost structure makes that challenge unavoidable heading into 2026.Candid, comparative, and forward-looking, this episode explains why autonomy and AI—not just electrification—will define the next phase of the global auto industry.___

The overtime loss to the Boston Bruins made it an L4. What do the Red Wings need to do to stop the bleeding and get back in the win column? We have an exclusive collection with Vintage Detroit! https://www.vintagedetroit.com/product-category/keep-it-local/the-grind-line-podcast/ Remember to follow us on Twitter & Instagram @GrindLinePod and join our Discord at discord.gg/mQ6KP6ePGX Rate, review, subscribe, and check out our merch on Redbubble! https://www.redbubble.com/people/TheGrindLine/shop Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In Episode 227, Tu and Lei break down a massive week in the global EV industry — one where China's innovation pace keeps accelerating while Western automakers scramble to respond.  Xiaomi's YU7 officially outsells the Tesla Model Y in October, marking a symbolic shift in China's most competitive EV segment. Meanwhile, Tesla's domestic sales slump to 26,000, signaling that aggressive price cuts and financing perks may not be enough as Chinese challengers tighten the pressure.The hosts also unpack XPeng's viral AI Day, featuring the “Iron Lady” humanoid robot, new L4 capable RoboTaxi prototypes, the Turing chip's rising importance, and XPeng's “physical AI” strategy — positioning the company as a vertically integrated mobility+AI platform rather than just an automaker.On the U.S. side, GM sparks headlines after reportedly urging suppliers to “de-China” their supply chains by 2027 — a massive, risky reshoring effort that could reshape cost structures across North America. Tu and Lei discuss the feasibility and geopolitical backdrop, including the Nexperia crisis, ICE tariff pressures, and USMCA uncertainty._____________________They also hit:

Adam Hardaker, author of The Seat Left Empty - a book about Love, Loss, and matchday in L4 - shares some of his favourite players and the things he loves about Everton. Pick up Adam's book at www.adamhardaker.com or from Amazon

NIO's largest investor just created the world's hottest autonomous vehicle market, and Chinese robotaxi companies are flooding in. This week, Abu Dhabi issued the first fully driverless commercial licenses to Chinese companies including WeRide, Carrot Express, Didi, and Cao Cao Mobility - while XPeng continues building its autonomous capabilities globally. But here's what NIO investors need to understand: the same Abu Dhabi sovereign wealth fund that invested 3.3 billion dollars in NIO is actively building the infrastructure, regulatory framework, and commercial pathways for Chinese autonomous technology to compete globally.In this episode, we break down why seven Chinese autonomous vehicle manufacturers have converged on Abu Dhabi, what the SAVI Smart and Autonomous Vehicle Industry Cluster means for the future of robotaxis, and how Middle Eastern sovereign wealth funds have invested 7 billion dollars in Chinese technology between 2023 and 2024 - five times the previous year. We also cover the geopolitical strategy behind Abu Dhabi's National AI Strategy requiring 25 percent of traffic to be autonomous by 2030.Meanwhile, Tesla just released FSD Version 14.1.7 with Elon Musk claiming version 14.2 will achieve fully autonomous driving within one to two months. We analyze the technical developments including end-to-end neural networks with 4.5 to 10 times more parameters than version 13, breakpoint recovery capabilities using L4 terminology, and weekly iteration cycles. Plus, Tesla is internally testing CarPlay integration after years of resistance - a McKinsey study found one-third of car buyers won't consider vehicles without CarPlay support as Tesla sales weaken.For NIO investors specifically, this episode examines why NIO's financial relationship with Abu Dhabi positions the company uniquely for autonomous vehicle deployment, how the NAD platform compares to competitors, and what the validation of Chinese autonomous technology in international markets means for NIO's global expansion strategy. We discuss battery swap infrastructure advantages for autonomous fleets, premium brand positioning in the robotaxi market, and the strategic timing considerations for NIO's entry into commercial autonomous operations.The robotaxi race is accelerating. Chinese companies are proving their technology works internationally. Tesla is pushing aggressive timelines. And NIO has the capital, technology, and partnerships to compete - the question is when they'll make their move. This is the competitive landscape analysis every NIO investor needs to understand right now.Topics covered: NIO stock analysis, Abu Dhabi autonomous vehicles, Chinese robotaxi deployment, CYVN Holdings investment strategy, XPeng autonomous driving, Tesla FSD version 14.1.7 analysis, Middle Eastern sovereign wealth fund investments, WeRide commercial license, Didi autonomous driving expansion, SAVI industry cluster, UAE National AI Strategy, NIO NAD platform development, global EV competition, autonomous driving technology comparison, robotaxi business models, and Chinese EV manufacturer global expansion.

Everyday we're planting seeds that grow up in our heart.Katy Berry plants God's Word, cuz that's where good fruit starts!Katy Berry has her own show! Check it out at this link: https://open.spotify.com/show/15866kxIZ2gk6XdPzFWIAk?si=Y-JcrRiZR866SUWoWv1RSw#1 Peaceful PeachJasmine watched as Billy angrily threw a tantrum on his first day at daycare. When Jasmine tried to be Billy's friend, he pushed her down. What should Jasmine do? Should she listen to Cruel Cocoplum and kick Billy or should she listen to Peaceful Peach, forgive Billy and try again to help him feel better? “There is peace with God through Jesus Christ.” Acts 10:36 L1#2 Teachable TangerineBasil learns to overcome stubbornness and receive instruction from his mother—for his own good. Teachable Tangerine reminds him that Mom is trying to teach him when it's safe to play outside and when he should stay inside. Basil learns that obeying your parents and being teachable pleases God. “I have hidden God's Word in my heart…” Psalm 119:11 L2#3 Self-Controlled ServiceberryWhen Mom told Jasmine to put her dolls away, Jasmine ignored her and kept playing with her dolls, like Selfish Sea Grape suggested. Self-Controlled Serviceberry reminded Jasmine to be obedient and bear the fruit of self-control. What would you do? “The Holy Spirit produces this kind of fruit in our lives: love, joy, peace… and self-control.” Galatians 5:22-23 L3# 4 Wise WatermelonWhen Jasmin was distracted from listening to the teacher in kid's church because Joey was acting foolishly, Wise Watermelon encouraged Jasmin to pay attention and get godly wisdom. We can choose to act foolishly, or we can seek godly wisdom and act wisely. What would you do? “The Lord grants wisdom… and you will find the right way to go.” Proverbs 2:6,9 L4#biblestoriesforkids, #bedtimestoriesforkids, #storiesforchristiankids, #biblelessonsforkids, #christiancharacterforkids, #peacewithgod, #pleasinggod, #goodseedgoodfruit, #plantgoodseeds, #beeattitudes, #jesusnmeclubhouse, #beteachable, #receiveinstruction, #letthechildrencometoJesus, #self-control, #willingness, #obedience, #resistselfishness, #resisttemptation, #wisdomforkids, #bewise, #seedtimeandharvest, #fishbytesforkids, #fishbytes4kids, #fishbitesforkids, #fishbites4kids, #ronandcarriewebb, #roncarriewebb

L4 del Mexibús sumó 20 unidades eléctricas de 18 metros Se rehabilitará el drenaje en la Venustiano Carranza: BrugadaMéxico entrega a EU al chino ‘Brother Wang', ligado al tráfico de fentanilo  Más información en nuestro podcast

Everyday we're planting seeds that grow up in our heart.Katy Berry plants God's Word, cuz that's where good fruit starts!Katy Berry has her own show! Check it out at this link: https://open.spotify.com/show/15866kxIZ2gk6XdPzFWIAk?si=C-wKi4P2TmaKCcmIqoc_Ag #1 Peaceful PeachJasmine watched as Billy angrily threw a tantrum on his first day at daycare. When Jasmine tried to be Billy's friend, he pushed her down. What should Jasmine do? Should she listen to Cruel Cocoplum and kick Billy or should she listen to Peaceful Peach, forgive Billy and try again to help him feel better? “There is peace with God through Jesus Christ.” Acts 10:36 L1#2 Teachable TangerineBasil learns to overcome stubbornness and receive instruction from his mother—for his own good. Teachable Tangerine reminds him that Mom is trying to teach him when it's safe to play outside and when he should stay inside. Basil learns that obeying your parents and being teachable pleases God. “I have hidden God's Word in my heart…” Psalm 119:11 L2#3 Self-Controlled ServiceberryWhen Mom told Jasmine to put her dolls away, Jasmine ignored her and kept playing with her dolls, like Selfish Sea Grape suggested. Self-Controlled Serviceberry reminded Jasmine to be obedient and bear the fruit of self-control. What would you do? “The Holy Spirit produces this kind of fruit in our lives: love, joy, peace… and self-control.” Galatians 5:22-23 L3# 4 Wise WatermelonWhen Jasmin was distracted from listening to the teacher in kid's church because Joey was acting foolishly, Wise Watermelon encouraged Jasmin to pay attention and get godly wisdom. We can choose to act foolishly, or we can seek godly wisdom and act wisely. What would you do? “The Lord grants wisdom… and you will find the right way to go.” Proverbs 2:6,9 L4#biblestoriesforkids, #bedtimestoriesforkids, #storiesforchristiankids, #biblelessonsforkids, #christiancharacterforkids, #peacewithgod, #pleasinggod, #goodseedgoodfruit, #plantgoodseeds, #beeattitudes, #jesusnmeclubhouse, #beteachable, #receiveinstruction, #letthechildrencometoJesus, #self-control, #willingness, #obedience, #resistselfishness, #resisttemptation, #wisdomforkids, #bewise, #seedtimeandharvest, #fishbytesforkids, #fishbytes4kids, #fishbitesforkids, #fishbites4kids, #ronandcarriewebb, #roncarriewebb

The Lagrange point L4 is 60° ahead of Mars whereas L5 is 60° behind Mars on the red planet's orbital path about the Sun. An object placed at either of these locations is trapped gravitationally and is likely to remain there indefinitely. The Mars L4 and L5 locations could provide a permanent place for staging and resupply missions to Mars and would give humans a different view of space weather and its effects on our home planet.

Jakie zmiany będą czekały zwolnienia chorobowe? Co będzie można robić podczas L4? O rządowym projekcie rozmawialiśmy z mec. Moniką Wieczorek, radczynią prawną z kancelarii radcowskiej Wieczorek.

On this episode of the ABB Solutions Podcast, host Mike Murphy is joined by Brie Stanley, Director of Offer Management for Motion Services at ABB. They discuss ABB Motion OneCare, a tailored service platform designed to extend equipment life, increase uptime, and reduce costs. Brie explains how OneCare is not just a service agreement, but a personalized partnership that adapts to each customer's needs.Tune in to hear insights on:What is Motion OneCare? How ABB builds tailored partnerships to support long-term operational goals.Maximizing Equipment Life: Lifecycle assessments, preventive maintenance (L1 to L4), reconditioning, modernization, and advanced diagnostics such as ABB Ability™ LEAP.Remote Condition Monitoring: How smart sensors and real-time diagnostics spot issues before they become downtime.Flexible Support Models: Adapting to both proactive and reactive maintenance approaches.Predictable Costs and Expertise: How OneCare delivers budgeting confidence and access to ABB's global service professionals.Energy Efficiency and Sustainability: Insights from ABB Energy Appraisals and ways to reduce energy waste.Real-World Success: A global customer boosted uptime by 13 percent through their OneCare agreement.Getting Started: The process of building a OneCare agreement and the role of ABB's Project Management Office in ensuring success.ReferencesIf you would like to attend a training, head over to our U.S. Drives & PAC Automations Solutions Training page.Learn more about ABB Motion OneCare: https://new.abb.com/service/motion/abb-motion-onecare.

Are you looking for creative, entertaining ways to develop good character and behavior in your preschool through early elementary aged kids? Papa and Mama Bee teach Chubbee and Bree how to do things God's way as they navigate through relationships with family and friends. “Papa!” said Bree, “I know it's SundayBut can we go to the park today?We'd miss church ‘cause the band plays at tenThey're famous and might not come here again!” Papa thought about what he should sayHe could make her do things God's wayOr teach her to do all things wiseSo, his answer was quite a surprise!“Let's pretend you get to decide.But first, think about why Jesus' died.He wanted to live inside your heartSo He could help you do what's smart.We go to church so we can knowHow much God loves us, which helps us grow.God wants us all to learn His way.And make wise choices every day.“The Lord grants wisdom… and you will find the right way to go.” Proverbs 2:6,9. L4#fishbytes4kids, #roncarriewebb, #kids, #biblelessonsforkids, #storiesforkids, #christiankids, #bedtimestoriesforkids, #Christianstoriesforkids, #storiesforyoungkids, #storiesforkids, #storiesforpreschoolers, #bedtimestories, #kidstories, #christiankids, #wise, #wisdom, #bewiseBunny, leaf artwork by Freepik.com

Dallas Bienhoff was our guest regarding cislunar space. Dallas defined cislunar, talked about our being in a space race, including a race for lunar development and a presence on the Moon. We talked about propellants, a Moon Base, the Chinese lunar program, L4 and L5 points, Artemis and the potential usage of same by government and military. Be sure to read the full summary of this program at www.thespaceshow.com and also at doctorspace.substack.com for this date, Wednesday, Sept. 2, 2025.

Drive with Dr. Peter Attia: Read the notes at at podcastnotes.org. Don't forget to subscribe for free to our newsletter, the top 10 ideas of the week, every Monday --------- View the Show Notes Page for This Episode Become a Member to Receive Exclusive Content Sign Up to Receive Peter's Weekly Newsletter Stuart McGill is a distinguished professor emeritus at the University of Waterloo and the chief scientific officer at Backfitpro where he specializes in evaluating complex cases of lower back pain from across the globe. In this episode, Stuart engages in a deep exploration of lower back pain, starting with the anatomy of the lower back, the workings of the spine, the pathophysiology of back pain, and areas of vulnerability. He challenges the concept of nonspecific back pain, emphasizing the importance of finding a causal relationship between injury and pain. Stuart highlights compelling case studies of the successful treatment of complex cases of lower back pain, reinforcing his conviction that nobody needs to suffer endlessly. He also covers the importance of strength and stability, shares his favorite exercises to prescribe to patients, and provides invaluable advice for maintaining a healthy spine. We discuss: Peter's experience with debilitating back pain [3:00]; Anatomy of the back: spine, discs, facet joints, and common pain points [14:15]; Lower back injuries and pain: acute vs. chronic, impact of disc damage, microfractures, and more [24:30]; Why the majority of back injuries happen around the L4, L5, and S1 joints [30:45]; How the spine responds to forces like bending and loading, and how it adapts to different athletic activities [36:00]; The pathology of bulging discs [43:00]; The pathophysiology of Peter's back pain, injuries from excessive loading, immune response to back injuries, muscle relaxers, and more [45:45]; The three most important exercises Stuart prescribes, how he assesses patients, and the importance of tailored exercises based on individual needs and body types [56:00]; The significance of strength and stability in preventing injuries and preserving longevity [1:08:00]; Stuart's take on squats and deadlifting: potential risks, alternatives, and importance of correct movement patterns [1:19:15]; Helping patients with psychological trauma from lower back pain by empowering them with the understanding of the mechanical aspects of their pain [1:29:45]; Empowering patients through education and understanding of their pain through Stuart's clinic and work through BackFitPro [1:38:30]; When surgical interventions may be appropriate, and “virtual surgery” as an alternative [1:46:30]; Weakness, nerve pain, and stenosis: treatments, surgical considerations, and more [1:55:15]; Tarlov cysts: treatment and surgical considerations [2:00:00]; The evolution of patient assessments and the limitations of MRI [2:02:00]; Pain relief related to stiffness and muscle bulk through training [2:06:45]; Advice for the young person on how to keep a healthy spine [2:14:00]; Resources for individuals dealing with lower back pain [2:25:15]; and More. Connect With Peter on Twitter, Instagram, Facebook and YouTube

View the Show Notes Page for This Episode Become a Member to Receive Exclusive Content Sign Up to Receive Peter's Weekly Newsletter Stuart McGill is a distinguished professor emeritus at the University of Waterloo and the chief scientific officer at Backfitpro where he specializes in evaluating complex cases of lower back pain from across the globe. In this episode, Stuart engages in a deep exploration of lower back pain, starting with the anatomy of the lower back, the workings of the spine, the pathophysiology of back pain, and areas of vulnerability. He challenges the concept of nonspecific back pain, emphasizing the importance of finding a causal relationship between injury and pain. Stuart highlights compelling case studies of the successful treatment of complex cases of lower back pain, reinforcing his conviction that nobody needs to suffer endlessly. He also covers the importance of strength and stability, shares his favorite exercises to prescribe to patients, and provides invaluable advice for maintaining a healthy spine. We discuss: Peter's experience with debilitating back pain [3:00]; Anatomy of the back: spine, discs, facet joints, and common pain points [14:15]; Lower back injuries and pain: acute vs. chronic, impact of disc damage, microfractures, and more [24:30]; Why the majority of back injuries happen around the L4, L5, and S1 joints [30:45]; How the spine responds to forces like bending and loading, and how it adapts to different athletic activities [36:00]; The pathology of bulging discs [43:00]; The pathophysiology of Peter's back pain, injuries from excessive loading, immune response to back injuries, muscle relaxers, and more [45:45]; The three most important exercises Stuart prescribes, how he assesses patients, and the importance of tailored exercises based on individual needs and body types [56:00]; The significance of strength and stability in preventing injuries and preserving longevity [1:08:00]; Stuart's take on squats and deadlifting: potential risks, alternatives, and importance of correct movement patterns [1:19:15]; Helping patients with psychological trauma from lower back pain by empowering them with the understanding of the mechanical aspects of their pain [1:29:45]; Empowering patients through education and understanding of their pain through Stuart's clinic and work through BackFitPro [1:38:30]; When surgical interventions may be appropriate, and “virtual surgery” as an alternative [1:46:30]; Weakness, nerve pain, and stenosis: treatments, surgical considerations, and more [1:55:15]; Tarlov cysts: treatment and surgical considerations [2:00:00]; The evolution of patient assessments and the limitations of MRI [2:02:00]; Pain relief related to stiffness and muscle bulk through training [2:06:45]; Advice for the young person on how to keep a healthy spine [2:14:00]; Resources for individuals dealing with lower back pain [2:25:15]; and More. Connect With Peter on Twitter, Instagram, Facebook and YouTube

From next-gen chipsets to scalable autonomous driving solutions, Magna's groundbreaking partnership with NVIDIA is paving the way for the next generation of software-defined, AI-powered vehicles.   By harnessing NVIDIA's new Blackwell GPU architecture to enable ADAS and L2+ through L4 capabilities, Magna is blending its deep automotive expertise with powerful AI compute to redefine what's possible for both in-vehicle experiences and autonomous driving.   To learn more, we sat down with Steven Jenkins, Vice President of Technology & Strategy at Magna International, to discuss the company's collaboration with NVIDIA, navigating legacy mindsets, and scaling vehicle intelligence across global markets.   We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today—a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen—and give us a review on your preferred podcasting platform.   Follow SAE on LinkedIn, Instagram, Facebook, Twitter, and YouTube. Follow host Grayson Brulte on LinkedIn, Twitter, and Instagram.

How does a multi-billion dollar self-driving startup pivot from custom autonomous delivery vehicles to L4 software licensing? Nuro co-founder/President Dave Ferguson explains their new plan, the partnership with Lucid and Uber, and why deploying the first-ever luxury robotaxi makes sense. Also, Alex Roy asks an insane question.

FFoDpod.com   Patreon   Merchandise   CC-BY-SA  "SCP-610-L4" by NekoChris, from the SCP Wiki. Source: https://scpwiki.com/scp-610-l4. Licensed under CC-BY-SA.

- Cars Hold Up U.S. and Japan Trade Deal - Brazil Wants Higher Tariffs on China Now - Most China Plants Underutilized - U.S. Tariffs Will Raise Car Prices $1,760 - Is GM Trying to Torpedo Ford Battery Plant? - Buick Electra E5 Has 500K Mile Battery - Zoox Starts Building AVs in California - Helm.ai Uses Vision-Only for L4

- Cars Hold Up U.S. and Japan Trade Deal - Brazil Wants Higher Tariffs on China Now - Most China Plants Underutilized - U.S. Tariffs Will Raise Car Prices $1,760 - Is GM Trying to Torpedo Ford Battery Plant? - Buick Electra E5 Has 500K Mile Battery - Zoox Starts Building AVs in California - Helm.ai Uses Vision-Only for L4

你能想象一辆没有人类司机、但每天都安全地穿梭于城市的无人驾驶车吗？当大众更多将这般景象投射在乘用车上时，在主流视线之外，自动驾驶已经在商用车领域引爆前所未有的技术突破。率先驶上道路的，是改变城市物流的无人车。在L4级自动驾驶的加持下，无人货运不仅解决了行业价格竞争加剧及末端运输成本攀升的痛点问题，更带来了从底层重塑城市物流系统的可能性。本期节目，我们邀请到自动驾驶科技公司九识智能的联合创始人兼副总裁周清，分享自动驾驶技术、产品化、商业化间的平衡之道。这家年轻的创业公司，为什么能在不到4年内实现商业闭环？如何进一步扩大成本优势？企业如何与链条上下游紧密合作，推动产业发展？面对自动驾驶商用车上路的现实问题，政策支持与路权开放进展到了哪一步？【01:17】将L4自动驾驶引入城配物流【02:19】中低速载货对自动驾驶的需求【04:02】“并非取替终端快递员”【07:06】一家自动驾驶创业公司的护城河【09:57】无人货运商业模式：整车销售vs运力租赁【12:53】硬件+订阅制软件，降低物流运营成本【16:50】从通用芯片过渡到自研定制化芯片【17:55】无人驾驶车辆路权的政策趋势【22:51】在海外，本地化运营优先于技术输出【25:41】如何提升新行驶路线的部署效率？【28:45】自动驾驶车发生交通事故，怎样定责？【31:04】九识智能人才招聘【32:01】评论区抽取5名听众送出精美周边《创业内幕》粉丝群已经开通，在这里，你可以跟节目制作人/主持人直接沟通，也可以第一时间了解到纪源资本线下活动动态，见到纪源资本的投资人，结交其他互联网圈子里的小伙伴。 入群方式：1）添加微信号“JiyuanFans”为好友，并在好友请求中标注“创业” 2）把你的全名和职称发给创业小助手；如果您想约访谈，请添加小助手微信，并附上访谈嘉宾简介，小助手将帮您对接。

In this episode, we explore Lyft's evolving approach to autonomous vehicles and the future of rideshare. Rather than building its own L4 tech, Lyft is doubling down on its marketplace strengths—demand generation, rider experience, and fleet management—while teaming up with AV innovators like Mobileye. The company envisions a hybrid future where human drivers and AVs coexist, expanding the market rather than replacing people.We dive into how Lyft plans to support its diverse driver base—over two million strong annually—by creating new opportunities, such as turning today's drivers into tomorrow's AV fleet owners. Plus, we break down the economics of surge pricing, the complexities of fleet ops, and how Lyft compares to competitors like Uber and Waymo.

Everton are unbeaten at home (in the league) for 12 months. Bottom of the league Sunderland come to L4 next on Boxing Day. A heroic 10-man defeat sets the tone for the second half of many people's favourite Good Times season. This episode is in association with The Excelsior. Thanks to Sean Ponzini and Gary Lunt for their contributions. Learn more about your ad choices. Visit podcastchoices.com/adchoices