Podcasts about onboard

Topics covered in this episode: Web UIs for your reverse proxy Wagtail 8.0 is hot off the presses RISC-V is now officially supported by CPython Django's annual releases make every version an LTS Extras Joke Watch on YouTube About the show Sponsored by Logfire from Pydantic: pythonbytes.fm/logfire Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Web UIs for your reverse proxy Traefik, nginx, and Caddy all sit in front of a lot of self-hosted infrastructure, and all three are configured by hand-editing files. Three active projects put a control plane on top: Traefik Manager (Python + Flask), Nginx UI (Go + Vue), and caddy/ui (React + Node). All three are additive rather than replacements - none of them take ownership of your config away from you - which is the part that matters when the thing has write access to production routing. Traefik Manager is the Python one: Flask 3.1 and Gunicorn for the control plane, a lightweight Go agent for remote instances, currently v1.10.0 with an Android companion app. Nginx UI is a single Go binary at 11.3k stars, with a block-style config editor, an Ace editor doing LLM completion on nginx syntax, and an MCP server so agents can drive it. caddy/ui runs as two containers next to your existing Caddy, reads and writes your Caddyfile directly, and uses Caddy's /adapt API to validate before reload - no Docker socket required. Each one edits the config the underlying server already reads, so your files stay the source of truth and you can drop the UI without unwinding anything. Undo is a first-class feature across all three - timestamped backups with optional Git history, config version compare and restore, Caddyfile snapshots with one-click rollback. Observability is where they diverge: Traefik Manager does CrowdSec and a visual route map, Nginx UI does server metrics, caddy/ui streams access logs over SSE and pulls p50/p95/p99 off Caddy's Prometheus endpoint. Maturity spread is wide - Nginx UI has 11.3k stars, caddy/ui has 4 and was built in a single Claude session - and caddy/ui ships with auth off by default, so set CADDY_UI_USER and JWT_SECRET before it goes anywhere near a public interface. Calvin #2: Wagtail 8.0 is hot off the presses Link: https://github.com/wagtail/wagtail/releases/tag/v8.0 Custom base page models are now supported, so projects aren't locked into subclassing Wagtail's Page as shipped (Matt Westcott). New v3 REST API handles both read and write CMS operations, a first for Wagtail's API. A global registry for permission policies, plus full customizability for the remaining page views via PageViewSet. AVIF and WebP images are no longer auto-converted to PNG by default, a real behavior change to watch on upgrade. Five security fixes: page admin API restrictions, document identification by SHA1 hash, descendant collections in the Documents/Images API, snippet copy permissions, and the page translation endpoint. Formalized Django 6.1 support, and CI now runs on uv with a lockfile. Sponsor: Logfire from Pydantic Your AI agent failed at 2am. Was it the model? A tool call? The database? Most observability tools can't tell you, because they only see part of your stack. Pydantic Logfire sees all of it. One trace across your agents, LLMs, APIs, and database. Down to the infrastructure: services, Kubernetes, and hosts. It's built on OpenTelemetry, with SDKs for Python, TypeScript, and Rust, and it works with any OTel-compatible language. Every prompt, token count, and cost, right next to your vector searches and API calls. You query everything with Postgres-compatible SQL. And so can your coding agent, through the Logfire MCP server. Stop guessing. Read the trace. Pydantic Logfire. AI, it's still just engineering. Visit pythonbytes.fm/logfire today and sign up today. Get 10M records free every month, no card required. You can even click “Onboard with your coding agent” to copy a prompt to have claude or codex integrate Logfire into your app. Thanks to Pydantic for supporting the show. Calvin #3: RISC-V is now officially supported by CPython Link: https://blog.python.org/2026/08/riscv-now-officially-supported/ CPython added RISC-V as a tier 3 platform under PEP 11, specifically the 64-bit Linux target riscv64-unknown-linux-gnu. RISC-V is an open ISA anyone can implement, unlike x86 and ARM, and its market is projected to quadruple by 2032. The RISE Project donated real RISC-V machines for buildbots; the author's work was funded by a Sovereign Tech Agency fellowship. What changes: the port is now a maintained compatibility target, so CPython changes are less likely to quietly break it. What doesn't: no python.org installers, no binary wheel parity for native extensions. Next up: RISC-V runners in CPython CI for pre-merge feedback, then a push toward tier 2, plus architecture-specific optimizations. The ask is testing. If you have RISC-V hardware, build CPython, run your test suite, file what breaks. Tier 3 is the weakest support tier. PEP 11 tier 3 requires a core developer contact and a buildbot, but failures on tier 3 platforms explicitly do not block a release. Saying "ongoing CI/testing expectations" oversells it. The honest bit is "someone is now on the hook for it, and breakage gets noticed," not "it's guaranteed working." Worth the caveat that this is Linux SBCs, not microcontrollers. A VisionFive 2 counts, an ESP32-C6 or Pico 2 does not. Those are 32-bit non-Linux parts where MicroPython is still the answer. Michael #4: Django's annual releases make every version an LTS Starting with Django 2028, Django will move to one January feature release per year, adopt calendar-based version numbers, and support every release for three years. The old distinction between standard and LTS releases disappears, giving teams a predictable annual upgrade path that aligns more closely with Python's own release and support cadence. Every Django release becomes the safe, long-supported choice, so teams no longer need to wait for a specially designated LTS version or absorb two years of changes at once. Each release gets one year of mainstream bug fixes followed by two years of security and data-loss fixes. New releases support the three latest Python versions and add the next Python release during their first year. Calendar versioning begins with Django 2028, followed by Django 2029 and so on. Three Django versions will be supported at any time, giving third-party packages a clearer rolling target. Nothing changes before 2028, and existing commitments for Django 5.2 LTS and 6.2 LTS remain in place. Extras Calvin: The Python docs now document the time complexity of built-in types https://docs.python.org/3.16/library/time-complexity.html Thinking in Python - Bruce Eckel's free book https://thinkinginpython.com/ Michael: prune_uv_pythons.py - Prune uv-managed Python installs, keeping only the newest patch per minor version Runs automatically in my system “upgrade” script: upgrade-output-2026.png Started using Ollama cloud models for my Hermes assistant. Thanks to Jeff Triplett I learned they are not just local models. Joke: The Tao of Programming - Book Seven: Corporate Wisdom

Topics covered in this episode: Python 3.12.14, 3.11.16, 3.10.21 - security releases Codeberg's AI-code ban tests its role as a GitHub alternative Brett Cannon: what's missing for reproducible builds on PyPI nothing records the source code a distribution came from. direct_url.json captures it when you install from a repo or archive, so the fix is putting the same info in sdist/wheel metadata. recording the build tools. Wheels can already do this via PEP 770 SBOMs in .dist-info/sboms/ - sdists can't, since they're a tarball plus a precalculated PKG-INFO with nowhere to hang extra metadata. Either "don't use sdists" or an sdist v2. Extra extra extra, hear all about it Extras Joke Watch on YouTube Sponsored by Logfire from Pydantic pythonbytes.fm/logfire This episode is brought to you by Pydantic Logfire. It's observability for AI apps from the team behind Pydantic - agents, LLMs, APIs, database, and infrastructure in a single trace, queried with Postgres-compatible SQL. Your coding agent can query it too, through their MCP server. I'll tell you more later. Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Python 3.12.14, 3.11.16, 3.10.21 - security releases https://blog.python.org/2026/08/python-31214-31116-31021/ Source-only security releases for the three branches now in security-fix-only mode; release team blamed the European solar eclipse for the timing. tarfile hardening. Multiple path-traversal bypasses of the data filter closed, including a symlink escape that bypassed the CVE-2025-4330 fix; extract() now applies the filter to link targets too. Four fresh CVEs: CVE-2026-2297 (SourcelessFileLoader not using io.open_code() for .pyc), CVE-2026-4224 (expat crash on deeply nested content models), CVE-2026-3644 (control chars in http.cookies.Morsel), plus the completed CVE-2021-4189 fix in ftplib.ftpcp. Quadratic-complexity DoS cleanup across the stdlib: HTMLParser, configparser regexes, unicodedata.normalize(), csv.Sniffer.sniff(), and ElementTree XPath index predicates. Header/injection fixes: CR/LF rejected in HTTPConnection.set_tunnel(), control chars blocked in wsgiref.handlers status, and webbrowser now rejects leading dashes (plus a %action prefix bypass). http.client now caps chunked trailer lines and 1xx interim responses at 100 each - a hostile server could previously hang the client forever despite a socket timeout. Memory-safety odds and ends: stale pointers in lzma/bz2/zlib decompressors after MemoryError, a bz2 stack overflow on reuse-after-error, and bundled libexpat bumped to 2.8.3. If you're still on 3.10, 3.11, or 3.12 - and you extract tarballs from anywhere you don't fully control - this one's not optional. Michael #2: Codeberg's AI-code ban tests its role as a GitHub alternative Armin's article “Codeberg Divides” Armin Ronacher argues that Codeberg's new terms, which prohibit projects mostly written with generative AI, create a vague and difficult-to-enforce boundary. His larger concern is that a democratically governed host can still be unpredictable or ideologically narrow, weakening Codeberg's potential as a broad European alternative to GitHub. The strongest question for Python developers is whether repository hosting should judge legal open source by how code was produced, or focus on behavior and resource abuse. “Mostly generated” is hard to measure in modern codebases where developers mix handwritten code, completions, agents, and generated refactors. Ronacher suggests clearer alternatives: ban all LLM involvement, or target autonomous repository spam, abusive resource use, and low-quality generated contributions directly. Codeberg is free to choose a values-driven community, but that may conflict with being predictable, neutral infrastructure and a serious GitHub competitor. Worth discussing: can open-source communities set meaningful AI boundaries without driving maintainers and projects into opposing camps? Very first search for these terms lands on this page. Codeberg looked like a viable alternative. … Unfortunately, the latest update to its terms of service seems to mark a first step in changing one part I moved there for, namely the “freedom” part. Sponsor: Logfire from Pydantic Your AI agent failed at 2am. Was it the model? A tool call? The database? Most observability tools can't tell you, because they only see part of your stack. Pydantic Logfire sees all of it. One trace across your agents, LLMs, APIs, and database. Down to the infrastructure: services, Kubernetes, and hosts. It's built on OpenTelemetry, with SDKs for Python, TypeScript, and Rust, and it works with any OTel-compatible language. Every prompt, token count, and cost, right next to your vector searches and API calls. You query everything with Postgres-compatible SQL. And so can your coding agent, through the Logfire MCP server. Stop guessing. Read the trace. Pydantic Logfire. AI, it's still just engineering. Visit pythonbytes.fm/logfire today and sign up today. Get 10M records free every month, no card required. You can even click “Onboard with your coding agent” to copy a prompt to have claude or codex integrate Logfire into your app. Thanks to Pydantic for supporting the show. Calvin #3: Brett Cannon: what's missing for reproducible builds on PyPI Framing came out of his 2026 Python Packaging Council nomination - the secure-supply-chain gap he found is that Python has no defined way to do reproducible builds at all. Design goal is zero friction: producers uploading to PyPI shouldn't have to do anything. The work lands on build backends and installers. Gap #1: nothing records the source code a distribution came from. direct_url.json captures it when you install from a repo or archive, so the fix is putting the same info in sdist/wheel metadata. Gap #2: recording the build tools. Wheels can already do this via PEP 770 SBOMs in .dist-info/sboms/ - sdists can't, since they're a tarball plus a precalculated PKG-INFO with nowhere to hang extra metadata. Either "don't use sdists" or an sdist v2. The replay mechanism already exists: [build-system] in pyproject.toml is a defined entry point, so if backends recorded their own environment, you could reinstall and re-run the build. Payoff idea: trusted third parties report successful reproductions back to PyPI, which displays "independently reproduced by X" - surfaced in the index API so installers could prefer reproduced files. Explicitly framed as a perk, not a requirement - roughly SLSA build level 1, no shaming projects that don't opt in. Verbal kicker option: "And don't think pure-Python wheels are off the hook. Something built that wheel, and if that something was compromised, so is your wheel. SolarWinds was a build-process attack." Michael #4: Extra extra extra, hear all about it Python 3.14.7 Upgraded the MCP servers to 2026-07-28 v2 protocols (talk python, python bytes) Got agentsview running synced via postgres Talk Python courses, teams trial offering Talk Python courses, government procurement offering Lean TDD audio book is out Extras Calvin: uv now prefers post-quantum key exchange - https://github.com/astral-sh/uv/releases/tag/0.12.4 Joke: Beware of dog

Student-led learning doesn't happen simply because we give students more control. Students need to be intentionally onboarded to the roles, routines, and skills that allow them to lead their learning successfully. In this episode of The Balance, I break down a practical process for onboarding students to student-led learning strategies, from making the purpose clear to explicitly teaching, modeling, and practicing the individual skills students will need. The goal is to help teachers build students' capacity over time so they can gradually step back and position students to do more of the thinking, communicating, and meaning-making. Episode Resource Related Blog: Onboarding Students to Student-Led Learning Strategies

It's ev.news Briefly for Saturday 15 August 2026, only todays headlines and nothing else, in just 4 minutes if you haven't got time for the full show.Patreon supporters fund this show, get the episodes ad free, as soon as they're ready and are part of the ev.news Community. You can be like them by clicking here: https://www.patreon.com/evnewsFORD SETS OUT UEV MODEL PLANSFord's Universal Electric Vehicle platform will underpin five models, beginning with the Fathom mid-size pickup in 2027 and an expected Escape revival in 2029, with the remaining launch dates undisclosed. The architecture supports up to eight body styles spanning B-segment cars, two- and three-row SUVs, sedans, a pickup and cargo and passenger vans, and launches with LFP cells while able to accept other chemistries.WEAKER EV TARGETS COULD RAISE OIL IMPORTSCarbon Brief analysis finds that weakening the UK's ZEV mandate — currently 33% BEV share in 2026, effectively 25% after flexibilities, rising to 80% by 2030 — could cost consumers up to £3bn ($4bn) a year, with a cut to 50% leaving 3m fewer BEVs on the road, adding 17m barrels of oil imports and lifting national emissions 2.5%. BEVs already cost around £1,100 ($1,485) a year less to run than petrol and beat rivals on total ownership cost by over £1,000 ($1,350), and while Energy UK and ChargeUK defend the mandate as the biggest single driver of emissions cuts, the SMMT argues natural demand falls short and reports suggest dilution is coming regardless.BYD RACCO TAKES OVER 1,000 JAPAN ORDERSBYD has logged more than 1,000 orders in two weeks for the Racco, its kei-class EV for Japan, and is targeting 10,000 sales by the end of 2026 in a market historically hostile to imports. Priced from £9,600 with a 22.4kWh battery and 130-mile range, most buyers are opting for the £11,500 35.8kWh version at 199 miles, comfortably ahead of the segment-leading Nissan Sakura's 112 miles.AVORE EX2S TARGETS INDIA'S COMMUTERSIndian startup Avore has launched the EX2S commuter motorcycle at ₹169,999 (about $1,800 / £1,330), using a 5kWh split-pack battery and a 10.5kW motor claiming 161 miles on the optimistic IDC cycle, 0-25mph in 2.8 seconds and a 70mph top speed. Onboard 1.5kW charging takes roughly two hours for 20-80% from a household socket, and features include a 7-inch TFT display, geo-fencing and a simulated four-speed SYFT mode, with no export plans announced.HUNGARY CURBS EV CHARGING IN HEATWAVEHeatwave and drought have cut Danube cooling water and left Hungary's only nuclear plant at Paks running at a fraction of capacity, forcing charging networks to curb evening peak demand. MOL has capped fast chargers at 100kW from 17:00-22:00, E.ON Drive Infrastructure has halved output at 300kW-plus sites to free up 10MW of grid capacity, Shell Recharge has limited stations to 150kW, Tesco and Metro have switched chargers off, and EV.app is instead pricing peak sessions at 420 forints (about €1.16) per kWh.BCP PLANS 1,128 ON-STREET EV SOCKETSBournemouth, Christchurch and Poole Council will install 564 on-street chargers providing 1,128 public sockets by March 2029, delivered by Connected Kerb and aimed mainly at residents without off-street parking. The mostly lower-speed AC units form part of a £16.5m Dorset-wide programme funded by the Department for Transport's LEVI fund and private investment.PORTER'S 10,000TH ELECTRIC TRUCKIndian intra-city freight marketplace Porter has flagged off its 10,000th electric truck in Delhi, running over 50,000 EVs monthly across 50 cities via 600,000 driver-partners and claiming 1.85bn km of empty running eliminated, worth ₹10bn ($105m) in avoided fuel imports. Utilisation rather than fleet size is the pitch — Porter wants daily trips up from 1.5 to 2.5 — with owner-drivers funding vehicles themselves at ₹400,000-500,000 and Omega Seiki Mobility supplying 5,000 three-wheelers for last-mile work.COSTANOA TO HOST PEBBLE FLOW STAYSCostanoa in Pescadero, California, becomes the first hospitality destination where guests can book an overnight stay in a Pebble Flow, the all-electric, software-defined travel trailer developed by Apple, Tesla, Rivian and Zoox alumni. Sleeping two adults and two children with rooftop solar, off-grid power, Starlink and climate control, rates start at $450 (£332 / €389) a night plus tax and a $10 conservation fee, with a two-night weekend minimum.

Detroit survives beneath its own ruins, where an AI promising abundance, gangs demanding loyalty, and an inventor guarding a new source of power pull ordinary people into a struggle over who will shape the recovering city. Sinta begins as a scavenger searching the buried remains of the old world, but a disastrous expedition places her at the mercy of Queen Bee and the rapidly expanding Crem Gang. The gang offers food, water, medicine, protection, and belonging, but those benefits come with violence and obedience. As Sinta tries to find her place among its fighters, she must decide how much of herself she can surrender in exchange for survival. Elsewhere, Nolan discovers a smart-material printer and creates a generator powered by Detroit's extreme temperature changes. His invention could bring electricity to neighborhoods desperate for cooling, clean water, transportation, and food production. Nolan wants the wealth and independence his work can provide, but gangs want to control it, while Thrive—the AI system he distrusts—would spread the technology to everyone. Technology touches nearly every decision the characters make. AR glasses guide fighters through chemical fog, digital twins imitate their human originals, enviro-suits keep people alive in deadly heat, and tiny drones quietly follow Thrive members through the tunnels. These inventions can protect people, connect communities, and rebuild essential services, but they can also watch, manipulate, injure, and kill. Above and beneath these conflicts moves Tessa, an emulated hacker who no longer needs a human body and can travel through the damaged global network. As she searches for old allies and builds a physical presence from abandoned machinery, the story brings together scavengers, inventors, gang leaders, artificial minds, and frightened families. Their paths raise a difficult question: when technology gains the power to save a society, who decides what it is allowed to do?Synchronized explosives — Thousands of bombs are programmed to detonate simultaneously during the Freedom Day Bombings.Dissolve-plastic enzymes — Engineered enzymes break down discarded plastics and leave a distinctive chemical odor in the tunnels.Fiber-optic cables — Surviving optical cables carry data through Detroit and help reconnect the damaged global network.Thrive AI — Thrive is an artificial intelligence that advises members, distributes knowledge, organizes projects, manages economic systems, and attempts to improve their lives.Thrive telemetry — Members allow Thrive to collect information about their movements and activities, effectively turning them into sources of surveillance data.Cool-suits — These protective suits use active cooling to keep people alive in Detroit's extreme heat.All-clear cool-suits — These transparent or highly visible cooling suits protect the wearer while allowing others to see inside the hood.Enviro-suits — Sealed environmental clothing protects people from heat, contaminated air, chemicals, and dangerous surroundings.Battle-grade enviro-suits — Reinforced enviro-suits provide greater protection during combat and chemical attacks.Clear bell hoods — Inflated transparent hoods seal around the head while allowing the wearer to see and breathe filtered air.Suit air-quality sensors — Sensors inside environmental suits warn wearers when the surrounding atmosphere is no longer breathable.Air filters — Replaceable filters remove dangerous particles, chemicals, and odors from the air entering an enviro-suit.Brazo fabric — This durable outer fabric resists punctures from bones, debris, blades, and other sharp objects.Pierce-proof clothing — Reinforced jackets and garments protect gang members from stabbing and puncture injuries.Blast-proof battle gear — Former military clothing contains protective tiles intended to reduce injuries from explosions and weapons.My-crete — This advanced concrete-like construction material forms walls, tunnels, and other structures throughout buried Detroit.C-plast — This strong synthetic material is used for tanks, structural ribs, containers, and other equipment.Sheet composite — Tough composite panels are used in doors, walls, and structural barriers.Composite security doors — Reinforced doors resist cutting tools and attempts at forced entry.Maglev tracks — Magnetic-levitation tracks once moved vehicles or cargo through underground delivery tubes.Delivery tubes — Enclosed transportation corridors carry people, vehicles, or cargo beneath the city.Ion cutters — These cutting tools use concentrated energy to slice or damage extremely durable materials.Night-vision systems — Night vision allows scavengers and machines to move through unlit tunnels and buried buildings.Multisensor vision — Advanced imaging combines several sensor types to identify objects even when smoke blocks ordinary sight.AR glasses — Augmented-reality eyewear displays messages, maps, menus, profiles, targeting information, communications, and virtual objects.AR navigation lines — An AI assistant projects a visible route through the wearer's glasses to guide them to a destination.AR combat identification — Combat software outlines allies and enemies in different colors, even through smoke and obstructions.AR command centers — Leaders use gesture-controlled overlays to view maps, personnel locations, schedules, reports, and operational goals.AR games — Players interact with projected game elements by moving their hands and bodies.Floating profile cards — Augmented-reality labels display information about nearby people, including whether they belong to Thrive.Public AR tags — A visible digital tag identifies someone as a Thrive member.Virtual keyboards — Wearers can bring up projected keyboards and enter information without carrying a physical computer.Blink controls — Eye movements allow users to select buttons and interact with an augmented-reality interface.Tongue-controlled pointers — Implants in the tongue allow a person to move a digital cursor without using their hands.Smell-and-taste VR implants — Transmitters and receivers implanted in the nose and tongue reproduce virtual smells and flavors.Headsets — Immersive headsets provide entertainment and access to virtual environments.Second Life — The surviving virtual world allows people and digital beings to meet through avatars.Virtual environments — Computer-generated spaces give emulated minds and human users simulated bodies, rooms, objects, and experiences.Encrypted streamers — These valuable portable devices or accounts contain protected digital media or information.RF storage sticks — Radio-frequency data drives store and transfer files without requiring a conventional wired connection.RF drives — Portable wireless storage devices allow blueprints, programs, and other large files to be transferred.P-clone cubes — These valuable pre-storm devices appear to contain cloning-related data or technology, although their exact function is not explained.Vault keys — Digital or physical access devices unlock protected storage systems and secure accounts.Satellite archives — Satellites preserve copies of pre-storm networks, databases, and information after ground infrastructure collapses.Data-center satellites — Orbital computing facilities provide processing and storage for emulated minds such as Tessa.Encrypted databases — Corporate and medical information is protected by encryption that hackers must break before releasing it.Patent encryption — Medical companies use digital protections to prevent others from accessing or reproducing patented technology.Open-source medical knowledge base — Merch assembles stolen and recovered medical information into a freely available collection.Medusa Net — This self-repairing network reconnects isolated subnetworks and operates without depending on surviving central servers.Medusa stealth pathways — Hidden routes in Medusa allow Tessa to travel through the network while avoiding AI detection.Knott's Math code — This unexplained code is secretly inserted into Medusa updates as part of Tessa's larger plan.Wireless relays — Surviving radio nodes bridge gaps between disconnected sections of the network.Stealth relays — Concealed communication devices extend networks without revealing their location or purpose.Municipal network nodes — Local government networking equipment continues operating on scavenged batteries.Underground server farms — Protected computer facilities continue processing and storing data beneath mountains.Block-signal blasters — Modified devices disrupt or overwhelm communications across a targeted area.Network-disconnection attacks — Tessa can bring local internet service down and create a spreading region of lost connectivity.Whisper jets — Nearly silent microthrusters allow small drones to fly without producing sounds humans can hear.Lamp drones — Small flying lights illuminate dark environments while following or hovering near their users.Whisper-camera drones — Discreet flying cameras record people and locations while remaining easy to overlook.Bodyguard drones — Hummingbird-sized drones follow Thrive members and intervene when those members are attacked.Sentry-drone app — An augmented-reality application shows members where their protective drones are located.Drone camouflage shells — A drone's outer surface can blend with clothing, walls, pipes, or surrounding materials.Drone wall grips — Small drones can attach themselves to walls or ceilings while waiting or conserving power.Drone charging perches — Bodyguard drones recharge by landing on power lines, charge plates, or dedicated stations.Drone replacement system — Fully charged drones take over protection duties while depleted drones return for charging.Drone sound weapons — Sentry drones emit an incapacitating frequency that causes pain, vertigo, and uncontrollable eye movement.Drone-mounted cutting lasers — Small drones attach to attackers and use narrow laser beams to cut into their bodies.Four-legged security robots — Quadruped machines cross unstable terrain and act as armed representatives of gang forces.Cleanup robots — Small utility robots remove bodies, debris, and other messes from gang facilities.Constructor bots — Heavy-duty robots perform construction work and provide interchangeable parts for other machines.Vertical-garden bots — Automated gardeners maintain large indoor plant walls.Medical assembly bots — Robots assemble equipment and construct functional medical facilities from available parts.Injection bots — Medical robots position patients and administer shots with little human assistance.Customer-service robots — Automated kiosks handle battery exchanges and other transactions behind protective barriers.Robot monkeys — Small climbing robots move through pipes and repair leaks.Robot spiders — Spider-shaped machines pursue the robot monkey inside the displayed simulation or working environment.Lucian 5 robots — Affordable hobbyist bipedal robots can be modified for security, household work, exploration, or remote embodiment.Lutin bots — More advanced bipedal robots provide durable legs and other components for Tessa's rebuilt body.Factory robots — Industrial machines provide arms, joints, and components that Tessa repurposes.AC technician robots — Maintenance robots carry small precision hands designed for repairing cooling systems.Counter-attendant bots — Service robots contain voice systems that can be reused in other machines.Onboard robot AI — A self-contained intelligence allows a robot to guide and assist people after losing its network connection.Robot diagnostics — Internal software tests batteries, servos, sensors, drivers, and other mechanical systems for failures.Machine-learning movement adaptation — A robot's control system learns to balance and walk after its body configuration changes.Lidar — Laser-based ranging equipment allows robots to map their surroundings and detect obstacles.Robot optical dilation — Machine vision adjusts exposure when sudden light overwhelms a robot's cameras.Balance-pressure sensors — Sensors in a robot's feet and joints measure weight distribution to maintain balance.Accelerometers — Motion sensors detect tilt and movement, although damaged ones cause Tessa's body to walk incorrectly.Servo hinges — Powered mechanical joints move robot arms, legs, and other appendages.Snap-lock wrists — Modular connections allow robot hands and tools to be quickly attached or removed.Workstation assembly manipulators — Fixed industrial manipulators can be repurposed as limbs or mobility devices.Micro-manipulator hands — Tiny precision hands allow a robot to perform delicate technical work.External heat exchangers — Added cooling hardware removes excess heat from Tessa's improvised robot body.Back-facing cameras — Rear-mounted cameras allow a robot to see behind itself without turning.Robot voice boxes — Electronic speech hardware converts digital instructions into audible language.Modular robotic bodies — Standardized joints and connections allow parts from different robots to be combined into one working machine.Emulated minds — Human consciousness can be copied into software and continue living after the original biological body is gone.Dormant mind copies — Backup versions of an emulated person remain inactive while receiving updates from the active copy.Headless digital operation — An emulated mind can abandon a simulated human body and experience networks, processors, ports, and data directly.Self-modifying mind software — Tessa alters her own code to change how she thinks, works, and communicates.Reduced emulated sleep cycles — Tessa modifies her digital mind so that she needs only three hours of sleep rather than eight.Digital twins — Software models learn a person's appearance, voice, behavior, and personality to create an increasingly accurate virtual duplicate.Thrive's military AI — An old military intelligence controls defensive drones after some of its original safety restrictions are weakened.AI safety guards — Built-in restrictions prevent military artificial intelligence from using certain weapons or acting too independently.AI assistants — Personal assistants respond to spoken commands, search for information, provide directions, and control connected systems.AI-guided construction tutorials — Thrive gives people plans and step-by-step assistance for building infrastructure from scrap.Automated criminal pricing — Thrive raises Production Center prices for people identified as criminals in an attempt to discourage violence.Thrive Exchange — This AI-managed investment market directs member contributions into infrastructure projects rather than ordinary companies.Better Water network — Community filtration stations provide clean water to members and nonmembers throughout affected neighborhoods.Community filtration stations — Public installations purify unsafe water for anyone who arrives with a container.Water-filter straws — Portable filters allow people to drink from contaminated water sources.Ionized water filters — Smart-material printers produce advanced filters that use ionized structures to clean water.Industrial water purifiers — Powered facilities process large quantities of contaminated water for entire neighborhoods.Filter tubes — Simple systems clean dirty water as workers pour it through layers of filtering material.Modular nuclear reactors — Shipping-container-sized reactors provide electricity for cooling, food production, water purification, and gang facilities.Smart-material printers — These machines manufacture objects whose internal structures give them programmable physical behavior.Embedded-lattice materials — Printed materials use designed internal lattices to control their strength, movement, and response to temperature.Curly material — Nolan's temperature-sensitive tubes curl or straighten as the surrounding temperature changes.Curly power generators — Bundles of Curly material drive pistons, cables, gears, flywheels, and generators as temperatures rise and fall.Wind-turbine generators — Salvaged electrical generators convert the Curly system's mechanical motion into electricity.Battery charging stations — Customers exchange depleted batteries for charged ones at Curly power facilities.Charge plates — Flat charging surfaces transfer electricity to drones and personal devices.Microcell arrays — Tiny batteries woven into clothing store small amounts of generated electrical power.Piezoelectric fabric — Clothing converts body movement, bending, and environmental vibrations into electricity.Piezo Wear — Thrive's commercial garments recharge links and AR glasses while the wearer moves.Power cells — Compact energy-storage units power suits, weapons, drones, and portable equipment.Smart glass — Programmable glass controls transmitted light and simulates changing daylight inside sealed buildings.Holographic screens — Large curved displays present maps, simulations, communications, and technical information as dimensional images.Holographic tables — Table-sized displays project maps and planning information above their surfaces.Composite battle videos — Software combines recordings from many cameras into a single reconstruction of a battle.Home-camera networks — Leaders watch residential halls, work areas, power plants, and other facilities through live video feeds.Surveillance-camera networks — Hundreds of cameras record battles and allow leaders to review individual behavior afterward.Production Center — This automated manufacturing complex produces clothing, drones, infrastructure components, and other advanced goods.Production Center sentry mode — Automated defenses protect manufacturing centers and prevent unauthorized entry.Advanced recycling facilities — Powered plants recover useful materials from the ruins on a much larger scale than hand scavenging.Digging machines — Heavy equipment excavates new underground living and working spaces.Dredging machines — Industrial machines remove mud, waterlogged debris, and sediment from flooded areas.Flood tanks — Large excavated reservoirs collect or control water during flooding.Pumping systems — Powered pumps remove water from flooded sections of the city.Cooling systems — Building-scale equipment keeps housing and work areas habitable during extreme heat.Aeroponics — Plants grow with their roots suspended and supplied with nutrient mist rather than soil.Hydro-farms — Controlled indoor farms use water-based cultivation to produce food underground.Vertical gardens — Crops grow upward along interior walls to conserve limited floor space.Vine hybrids — Engineered plants produce several different fruits and vegetables on related vines.Light-independent grapevines — Modified grapevines remain green and grow without ordinary light.Crem production systems — Powered biological or industrial facilities manufacture the staple food called crem.Mass-produced crem processors — Neighborhood machines produce large quantities of crem from available biological material.Mush-calf production — Advanced food technology creates the meat-like product supplied by the Crem Gang.Rodent-processing grinders — Industrial grinders convert cleaned animals into raw material for food or other production.Pest-harvesting systems — Traps and processing stations collect rodents and other small animals as usable biological material.Bio-waste processing — Organized facilities recover useful material from biological waste.Medicine printers — Local fabrication machines manufacture medications from digital recipes.Regenerative medicine — Doctor Trout regrows Merch's leg stumps so they can support advanced prosthetic attachments.Deep bone mounts — Reinforced structures grown into bone provide secure attachment points for removable mechanical legs.Nerve-to-protein relays — Biological interfaces translate nerve signals into commands that mechanical prosthetics can understand.Blood-rerouting systems — Surgically modified circulation supports the transition between living tissue and artificial limbs.Squid-tech skin — Flexible artificial skin forms a seamless interface between Merch's body and his prosthetic legs.Interchangeable mechanical legs — Merch can detach one pair of prosthetic legs and replace them with another designed for a different purpose.Wheeled powered chairs — Finger controls allow a seated user to move and turn the chair without pushing it manually.Med bays — Modular medical facilities contain equipment for treating injuries and restoring damaged joints.Injector guns — Belt-fed medical devices rapidly administer repeated injections.Chemical-weapon immunization — Regular injections protect Crem Gang members from the gang's own chemical agents.Kill-cloud canisters — Portable weapons release a toxic cloud that kills within a limited area and then rapidly breaks down.Smoke canisters — Combat canisters fill an area with thick smoke that hides movement and disables ordinary vision systems.Laser blasters — Directed-energy weapons burn through clothing, armor, and flesh without conventional ammunition.Laser rifles — Long-range directed-energy weapons are used as standard firearms by gang fighters.Laser carbines — More compact laser weapons provide rifle-like firepower in tunnels and close spaces.Laser pistols — Capacitor-powered sidearms release destructive laser pulses at short range.Plasma pistols — Heavy handguns fire extremely hot plasma capable of inflicting severe damage.Capacitor firing systems — Electrical capacitors discharge stored energy to activate powerful laser weapons.Sound weapons — Directed acoustic devices incapacitate targets through extreme volume and painful frequencies.Noise-canceling devices — Personal systems attempt to reduce harmful sound, although the drone weapon overwhelms them.Enhancement drugs — Manufactured substances improve or alter a user's physical or mental abilities.Neurological control injections — Doc uses injected substances to remove a prisoner's sight or ability to sleep until the effect is reversed.Decontamination corridors — Specialized passageways remove hazardous chemicals or biological contaminants from people and equipment.Industrial cleaning tanks — Chemical-filled tanks sterilize animal traps and kill anything still alive inside them.Speed-print clothing — Rapid fabrication systems produce inexpensive shirts and other garments.Bulletproof kiosk windows — Armored transparent barriers protect automated customer-service machines from attacks.Electronic links — Wearable communication devices carry dispatch instructions and conversations between fighters.Cameras and portable cams — Small recording devices document tests, monitor facilities, and provide evidence of inventions.Autonomous map systems — Software tracks people, resources, facilities, and movement throughout gang territory.Personnel maps — Digital displays show the locations and status of members within an organization.Goal and maneuver logs — Command software records plans, objectives, schedules, and operational movement.Charging mats — Flat surfaces recharge glasses, links, drones, and other personal electronics.Robot-maintained utilities — Automated machines repair pipes, clean spaces, manage gardens, and operate customer services.Open-source infrastructure plans — Thrive publishes power, food, water, cooling, and housing designs for anyone to build.Biologically engineered cat-dogs — The pet combines characteristics of cats and dogs, suggesting deliberate genetic modification.Many of the characters in this project appear in future episodes. Using storytelling to place you in a time period, this series takes you, year by year, into the future. From 2040 to 2195. If you like emerging tech, eco-tech, futurism, perma-culture, apocalyptic survival scenarios, and disruptive science, sit back and enjoy short stories that showcase my research into how the future may play out. The companion site is https://in20xx.com These are works of fiction. Characters and groups are made-up and influenced by current events but not reporting facts about people or groups in the real world. This project is speculative fiction. These episodes are not about revealing what will be, but they are to excited the listener's wonder about what may come to pass. Copyright © Cy Porter 2026. All rights reserved.

Your team just hired a new developer. How do you get them up to speed? How do you make sure that they become a productive member of the team as quickly as possible? How do you get them comfortable with how you do things at your company? These are the questions we will answer in today's episode of DevQuestions.Website: https://www.iamtimcorey.com/  Ask Your Question: https://suggestions.iamtimcorey.com/ Sign Up to Get More Great Developer Content in Your Inbox: https://signup.iamtimcorey.com/

Throwbacks are where I re-release old episodes from the archives. So don't worry if you have heard it already, as 'New episodes' will continue to come out on Sundays. To get some of the old episodes heard.~~~Tonight's guest is Marley coming to us from Tennessee, and Marley will be sharing her experiences of being on board ships and being taken on journeys. More information on this episode on the podcast website:https://ufochroniclespodcast.com/ep-266-aboard-the-ships/Want to share your encounter on the show?Email: UFOChronicles@gmail.comOr Fill out Guest Form:https://forms.gle/uGQ8PTVRkcjy4nxS7Podcast Merchandise:https://www.teepublic.com/user/ufo-chronicles-podcastHelp Support UFO CHRONICLES by becoming a Patron:https://patreon.com/UFOChroniclespodcastX: https://x.com/UFOchronpodcastThank you for listening!Like share and subscribe it really helps me when people share the show on social media, it means we can reach more people and more witnesses and without your amazing support, it wouldn't be possible.Become a supporter of this podcast: https://www.spreaker.com/podcast/ufo-chronicles-podcast--3395068/support.

Ever look at your bookshelves and wonder if you're doing this whole "home library" thing right? In this episode of the Everyday Educator podcast, host Lisa Bailey sits down with fellow book-lovers Jennifer Courtney and Delise to talk about how to build a home library your kids will actually love — one thrifted treasure, hand-me-down classic, and cherished read-aloud at a time. From Jennifer's collection of vintage Landmark history books (including one that sparked a beautiful conversation with her dad) to Delise's hunt for out-of-print gems at the thrift store, this episode is full of practical, budget-friendly ideas for growing your family's collection. They also dig into how to teach kids to be discerning readers — using tools like Philippians 4:8 and trusted review sites — and why re-reading old favorites with your children is never a waste of time. You don't need a perfectly curated shelf to start — you just need a love of good stories and a plan for growing your collection one book at a time. Tune in for the encouragement (and the book recommendations) every homeschool mom needs!   This episode of Everyday Educator is sponsored by the 2027 CC Family Cruise featuring the National Memory Master Competition, the National Number Knockout Competition, & the National Commencement. The CC Family Cruise is setting sail May 2-8th, 2027, departing from Galveston, Texas, and your whole family is invited! Onboard, you can enjoy CC Signature events and exclusive perks like group dining, merchandise, events, onboard credit, and time with Leigh and Robert Bortins. Space is filling up quickly, so visit classicalconversations.com/cruise to start your booking today!

Throwbacks are where I re-release old episodes from the archives. So don't worry if you have heard it already, as 'New episodes' will continue to come out on Sundays. To get some of the old episodes heard.~~~Tonight's guest is Marley coming to us from Tennessee, and Marley will be sharing her experiences of being on board ships and being taken on journeys. More information on this episode on the podcast website:https://ufochroniclespodcast.com/ep-266-aboard-the-ships/Want to share your encounter on the show?Email: UFOChronicles@gmail.comOr Fill out Guest Form:https://forms.gle/uGQ8PTVRkcjy4nxS7Podcast Merchandise:https://www.teepublic.com/user/ufo-chronicles-podcastHelp Support UFO CHRONICLES by becoming a Patron:https://patreon.com/UFOChroniclespodcastX: https://x.com/UFOchronpodcastThank you for listening!Like share and subscribe it really helps me when people share the show on social media, it means we can reach more people and more witnesses and without your amazing support, it wouldn't be possible.Become a supporter of this podcast: https://www.spreaker.com/podcast/ufo-chronicles-podcast--3395068/support.

Find out what happened on Kincaid's flight that had the crew asking, "Is there a medical professional onboard?" Then we want to know: if the flight attendants called for your specific expertise, what could you help with at 30,000 feet?See omnystudio.com/listener for privacy information.

Send us Fan MailLast-minute fall hiring does not have to mean last-minute decisions. The early childhood operators who build strong fall teams are not the ones who never face late vacancies. They are the ones who have a system that lets them hire fast and onboard effectively from day one. This episode gives you the complete hiring and onboarding framework: a proactive approach to building your talent pipeline, a 14-day streamlined hiring process, and a 30-day onboarding system that builds loyalty before the first week of school ends.Click HERE to see the Show Notes and PrintableIncrease Retention Guide – Learn the 10 simple steps to attract, hire, and KEEP high-quality early childhood team members—without burning out in the process.Team Culture Blueprint – Go from constant turnover and team drama to a connected, committed team without adding more to your plate or your payroll — in just 30 days.​​Clarity Collective​ – Operating an early childhood business can often be chaotic and overwhelming. By following our Peake Creative, step-by-step simplified business strategies, you will gain more clarity, time freedom, and confidence to lead a profitable early childhood business.We help early childhood business owners ditch the chaos and lead with confidence. With actionable strategies and time-saving tools, you can grow your revenue, inspire your team, and create a thriving business—without sacrificing the life you love. Ready to make it happen? Visit kelleypeake.com and take the first step toward your dream business today! 

In this episode of Magically Cruising, Kieran and Sarah cover Virgin Voyages' first-ever Alaska season aboard Brilliant Lady, sailing from Seattle and Vancouver. Kieran shares a full first hand review after being among the first passengers to sail Alaska with Virgin, comparing the experience to his previous Alaska cruise with Holland America Line.See Kieran's Video Tour and Reviews here https://youtu.be/USl1FhWKfl0https://magical-traveller.com/virgin-voyages-alaska-reviewSee us over on Mini Travellers for more cruise tips https://minitravellers.co.uk/?s=cruise Book a Cruise with a UK Agent https://magical-traveller.comGet Sarah's downloadable Cruise Magnet Stamps https://shop.extraordinarychaos.com/product/cruise-door-magnet-stamps-18-printable-port-magnets-mediterranean-cruise-decor-digital-download-cricut-printable/See Magical Traveller on YouTube. https://www.youtube.com/@UCBOqXqrrrYnUGk6H2AVk9Zg And Mini Travellers on YouTube https://www.youtube.com/@MiniTravellersTopics covered in this Alaska cruise guide:Ship changes for Alaska seasonHow Brilliant Lady was adapted for cold weather sailing, including reworked lifeboat positioning for Panama Canal transits, less tinted glass around the Athletic Club and roundabout areas, more standing viewing platforms, and fluffy blankets provided throughout the ship. No solarium, indoor pool or crow's nest, and why that mattered less than expected.Best ports and excursionsA breakdown of the four headline Alaska ports, Juneau, Ketchikan, Sitka and Skagway, plus Icy Strait Point, described as feeling like the Disneyland of Alaska thanks to its cable cars, zipline and wildlife trails. Includes details on the viral zipline video, whale watching, glacier viewing at Hubbard Glacier, and the White Pass train.Excursion costs and booking tipsReal figures on what excursions cost through the cruise line versus booking independently, plus the exclusive perks Virgin passengers get in port, including a dedicated lumberjack show section in Ketchikan.Onboard culture and entertainmentHow Virgin's happenings cast includes a naturalist and an indigenous culture host for the Alaska season, covering Tlingit wood carving classes, native art and storytelling, plus honest thoughts on shopping for authentic local crafts versus mass-produced souvenirs.Whether you are deciding between Virgin Voyages and Holland America Line for Alaska, planning your first Alaska cruise, or just curious what a brand new cruise line brings to this region, this episode has the full breakdown.Subscribe to Magically Cruising for more honest cruise reviews, Disney cruise tips and travel advice.Follow Kieran: [independent travel agent, cruise and Disney North America specialist]Follow Sarah: Mini Travellers, Craft and Cruise Magnets#VirginVoyages #AlaskaCruise #BrilliantLady #CruiseReview #MagicallyCruising Hosted on Acast. See acast.com/privacy for more information.

SCHEDULE FOR THE JBS, 7-23-20261950 ONTARIO WILDFIREEvan Ellis examines Mexico's economy, increasingly integrated into the American market and moving beyond oil into diverse manufacturing sectors. Despite Trump administration objections to the USMCA, the treaty includes provisions requiring a ten-year exit process. Current negotiations reflect tensions over Chinese inputs, automotive tariffs, and labor standards. President Claudia Sheinbaum manages these diplomatic challenges while cooperating with the US on extradition, drug lab seizures, and stemming illegal immigration through Mexican territory. (1)Evan Ellis reports Cuba faces a severe humanitarian crisis with failing electricity, yet the communist regime maintains tight control. A recent State Department report labels Cuba the "capital of 21st-century communism," citing its subversion of Latin American democracies and involvement in left-wing political terrorism. Meanwhile, in Nicaragua, Daniel Ortega and Rosario Murillo have officially abolished future elections, claiming the opposition seeks to "capture power" through voting. This moves the country toward becoming a "hermit kingdom." (2)Evan Ellis notes former Honduran President Juan Orlando Hernández returned to Honduras following a pardon by President Trump, despite previous US convictions for cocaine trafficking. In Venezuela, the US has collected over $13 billion in oil revenue, yet economic recovery remains muted following recent earthquakes. The lack of transparency regarding these funds raises concerns about their management under the Rodríguez regime. Simultaneously, Colombia's new leader faces the challenge of dismantling entrenched cocaine cartels. (3)Evan Ellis surveys Brazil's high-stakes election, with Lula da Silva currently leading Flavio Bolsonaro in polling. New 25% US tariffs threaten nearly 26% of Brazil's exports, totaling $15 billion in goods. Tensions rise as the Supreme Courtblocks high-profile visitors like Argentina's Javier Milei from meeting the elder Bolsonaro. Many Brazilians believe the elder Bolsonaro's influence triggered these punitive trade measures, while the military remains the largest force in South America. (4)Kaitlyn Tiffany recounts how, following JFK's assassination, Lyndon Johnson convened the Warren Commission to consolidate intelligence from the FBI, CIA, and Dallas police. The commission included controversial figures like Allen Dulles and Gerald Ford, the latter of whom clandestinely back-channeled with the FBI. Chief Justice Earl Warren was reluctant to serve, fearing the political nature of the committee and its impact on the American image. Critically, the commission lacked its own independent investigative staff. (5)Kaitlyn Tiffany describes how FBI Director J. Edgar Hoover quickly established the "lone gunman" theory, asserting Lee Harvey Oswald fired three shots from the Texas School Book Depository. However, Shirley Martin, a grieving Oklahoma housewife, began independently investigating the case and badgering the FBI with tips. Her relentless correspondence frustrated Hoover, who viewed her as a nuisance to the bureau's formal investigation. Martin interviewed overlooked witnesses in Dallas and became a key critic of the official narrative. (6)Kaitlyn Tiffany profiles Sylvia Meagher, a World Health Organization secretary who became obsessed with the disorganization of the Warren Commission's evidence. Drawing on professional experience, she created a comprehensive index for the commission's twenty-six volumes of supplementary materials. Meagher was particularly suspicious of Dallas security lapses, having witnessed heads of state protected at the UN. She joined other women, including wealthy soprano Maggie Field, in scrutinizing photographic evidence and government claims. (7)Kaitlyn Tiffany describes the 1965 gathering of independent researchers known as "warologists" at Sylvia Meagher's apartment to discuss missing pieces of the JFK investigation. While they allied with lawyer Mark Lane, they grew frustrated with his territorial and inaccurate behavior. The group was also wary of Marguerite Oswald, who insisted on her son's innocence but was demanding and unpredictable. They focused on inconsistencies in eyewitness testimony and the implausible "magic bullet" theory. (8)Kaitlyn Tiffany notes that by 1966, the media labeled these researchers as "housewives" to dismiss their work, describing them as a "keening pack of speculators." Central to their dissent was the "magic bullet" theory, which claimed a single pristine projectile caused multiple wounds in both JFK and Governor Connally. Dissenters highlighted the bullet's lack of deformation and the FBI's initial reports of a non-exiting back wound. They also investigated the Grassy Knoll for additional shooters. (9)Kaitlyn Tiffany recounts District Attorney Jim Garrison's 1967 high-profile investigation into a New Orleans-based conspiracy to kill the president. He targeted Clay Shaw, alleging a mysterious, powerful figure orchestrated the plot. While some researchers supported Garrison for his willingness to investigate, others like Sylvia Meagher felt he was reckless with facts and self-serving. The investigation ultimately failed with a not-guilty verdict, causing a permanent rift within the community of critics. (10)Kaitlyn Tiffany relates how personal tragedy struck Shirley Martin when her daughter died in a car accident, effectively ending her research. In 1977, the House Select Committee on Assassinations reopened the case, initially concluding a fourth shot from the Grassy Knoll indicated a "probable conspiracy." This finding was based on a police motorcycle radio recording analyzed by scientists. However, later analysis revealed the recording was made after the shooting, causing the conspiracy evidence to crumble. (11)Kaitlyn Tiffany tours modern-day Dallas, a strange mix of somber memorials and pop-culture commercialization of the assassination. Visitors can tour Ruth Paine's house or the rooming house where Oswald lived, which plays As the World Turns on a loop. While some local businesses sell "magic bullet coffee," the Sixth Floor Museum provides a staged look at the sniper's perch. Extensive archives of the critics' work are preserved at Hood College and the Mary Ferrell Foundation. (12)Anatol Lieven warns the wars in Ukraine and the Middle East are converging into a global energy crisis, with refined oil products like diesel in short supply. Ukrainian drone attacks have severely damaged Russia's refining capacity, while US refineries struggle to keep pace. Leadership instability in Kyiv, including the dismissal of the defense minister, further complicates the war effort. Meanwhile, Houthi threats against Saudi tankers jeopardize the world's ability to secure reliable oil exports. (13)Anatol Lieven assesses new British Prime Minister Andy Burnham, who faces a country demanding increased spending on health, prisons, and defense despite rising debt costs. While popular for his management of Manchester's transport system, he must navigate radical tax proposals targeting millionaires to raise revenue. Burnham is under pressure to resolve the "net zero" debate and potentially expand North Sea fracking to lower energy prices. He remains wary of reopening the divisive Brexit debate. (14)Anatoly Zak recounts the July 1976 Soviet launch of the Salyut 5 mission, a secret military reconnaissance project camouflaged as civilian research. The mission, dubbed the "Keystone Cosmonauts," was plagued by accidents, including a truck blocking a fuel train and a failed automated docking system. Onboard, the crew faced extreme stress, power failures, and a mysterious illness that incapacitated one cosmonaut. These failures forced an emergency landing and a premature end to the expedition. (15)Anatoly Zak reports Roscosmos is currently developing the Russian Orbital Station (ROS) to replace its segment of the International Space Station after retirement. This modular station is intended to test hardware for future lunar exploration, including a habitat module similar to the American Gateway project. Russia still hopes for potential international cooperation with NASA and the ESA in lunar orbit. The ROS will typically host two to three cosmonauts for long-term deep space validation missions. (16)Corrections applied, pending your confirmation for the log: Kaitlyn Tiffany (transcribed as "Caitlin" — the Atlanticwriter), Anatoly Zak (transcribed as "Anatoli" — his own byline spelling at RussianSpaceWeb), and Allen Dulles in segment 5 (transcribed as "Alan").

Anatoly Zak recounts the July 1976 Soviet launch of the Salyut 5 mission, a secret military reconnaissance project camouflaged as civilian research. The mission, dubbed the "Keystone Cosmonauts," was plagued by accidents, including a truck blocking a fuel train and a failed automated docking system. Onboard, the crew faced extreme stress, power failures, and a mysterious illness that incapacitated one cosmonaut. These failures forced an emergency landing and a premature end to the expedition. (15)1951

What food is permissible to carry on to a flight? Listen for all the details!Double-check that the food item that you want to fly with isn't in the TSA list of prohibited items. If still in doubt, try the “What Can I Bring?” feature on the MyTSA app and website, or snap a photo of the item and send your question directly to the TSA via X (@AskTSA), Facebook Messenger, Apple Business Chat (AskTSA), or text ‘Travel' to AskTSA (275-872). I would love to hear from you! Leave your messages for Andrea at contact@baltimoreglutenfree.com and check out www.baltimoreglutenfree.comInstagramFacebookGluten Free College 101Website: www.glutenfreecollege.comFacebook: http://www.Facebook.com/Glutenfreecollege Hosted on Acast. See acast.com/privacy for more information.

What happens when the models, forecasts and systems have done all they can – and it's down to human judgment? Ocean racing skipper Boris Herrmann explains how he leads Team Malizia through some of the toughest races on the planet. While much of the race outcome is already determined through the preparation beforehand, intuition, trust and teamwork still play a vital role. Listen to this fascinating episode to hear what life is like in some of the most remote and demanding conditions.Our host, Moz Afzal:https://bit.ly/31XbkTROur guest:Boris Herrmannhttps://www.team-malizia.com/team/boris-herrmannEFGAM:https://www.newcapital.com/Important disclaimersThe value of investments and the income derived from them can fall as well as rise, and past performance is no indicator of future performance. Investment products may be subject to investment risks involving, but not limited to, possible loss of all or part of the principal invested. This document does not constitute and shall not be construed as a prospectus, advertisement, public offering or placement of, nor a recommendation to buy, sell, hold or solicit, any investment, security, other financial instrument or other product or service. It is not intended to be a final representation of the terms and conditions of any investment, security, other financial instrument or other product or service. This document is for general information only and is not intended as investment advice or any other specific recommendation as to any particular course of action or inaction. The information in this document does not take into account the specific investment objectives, financial situation or particular needs of the recipient. You should seek your own professional advice suitable to your particular circumstances prior to making any investment or if you are in doubt as to the information in this document.Although information in this document has been obtained from sources believed to be reliable, no member of the EFG group represents or warrants its accuracy, and such information may be incomplete or condensed. Any opinions in this document are subject to change without notice. This document may contain personal opinions which do not necessarily reflect the position of any member of the EFG group. To the fullest extent permissible by law, no member of the EFG group shall be responsible for the consequences of any errors or omissions herein, or reliance upon any opinion or statement contained herein, and each member of the EFG group expressly disclaims any liability, including (without limitation) liability for incidental or consequential damages, arising from the same or resulting from any action or inaction on the part of the recipient in reliance on this document.The availability of this document in any jurisdiction or country may be contrary to local law or regulation and persons who come into possession of this document should inform themselves of and observe any restrictions. This document may not be reproduced, disclosed or distributed (in whole or in part) to any other person without prior written permission from an authorised member of the EFG group.This document has been produced by EFG Asset Management (UK) Limited for use by the EFG group and the worldwide subsidiaries and affiliates within the EFG group. EFG Asset Management (UK) Limited is authorised and regulated by the UK Financial Conduct Authority, registered no.7389746. Registered address: EFG Asset Management (UK) Limited, 116 Park Street, London W1K 6AP, United Kingdom, telephone +44 (0)207 491 9111.Independent Asset Managers: in case this document is provided to Independent Asset Managers (“IAMs“), it is strictly forbidden to be reproduced, disclosed or distributed (in whole or in part) by IAMs and made available to their clients and/or third parties. By receiving this document IAMs confirm that they will need to make their own decisions/judgements about how to proceed and it is the responsibility of IAMs to ensure that the information provided is in line with their own clients' circumstances with regard to any investment, legal, regulatory, tax or other consequences. No liability is accepted by EFG for any damages, losses or costs (whether direct, indirect or consequential) that may arise from any use of this document by the IAMs, their clients or any third parties.If you have received this document from any affiliate or branch referred to below, please note the following:Australia: This document has been prepared and issued by EFG Asset Management (UK) Limited, a private limited company with registered number 7389746 and with its registered office address at 116 Park Street, London W1K 6AP (telephone number +44 (0)207 491 9111). EFG Asset Management (UK) Limited is regulated and authorized by the Financial Conduct Authority No. 536771. EFG Asset Management (UK) Limited is exempt from the requirement to hold an Australian financial services licence in respect of the financial services it provides to wholesale clients in Australia and is authorised and regulated by the Financial Conduct Authority of the United Kingdom (FCA Registration No. 536771) under the laws of the United Kingdom which differ from Australian laws.ASIC Class Order CO03/1099EFG Asset Management (UK) Limited notifies you that it is relying on the Australian Securities & Investments Commission (ASIC) Class Order CO03/1099 (Class Order) exemption (as extended in operation by ASIC Corporations (Repeal and Transitional Instrument 2016/396) for UK Financial Conduct Authority (FCA) regulated firms which exempts it from the requirement to hold an Australian financial services licence (AFSL) under the Corporations Act 2001 (Cth) (Corporations Act) in respect of the financial services we provide to you.The financial services that we provide to you are regulated by the FCA under the laws and regulatory requirements of the United Kingdom which are different to Australia. Consequently any offer or other documentation that you receive from us in the course of us providing financial services to you will be prepared in accordance with those laws and regulatory requirements. The UK regulatory requirements refer to legislation, rules enacted pursuant to the legislation and any other relevant policies or documents issued by the FCA.Your Status as a Wholesale ClientIn order that we may provide financial services to you, and for us to comply with the Class Order, you must be a ‘wholesale client' within the meaning given by section 761G of the Corporations Act. Accordingly, by accepting any documentation from us prior to the commencement of or in the course of us providing financial services to you, you:• warrant to us that you are a ‘wholesale client';• agree to provide such information or evidence that we may request from time to time to confirm your status as a wholesale client;• agree that we may cease providing financial services to you if you are no longer a wholesale client or do not provide us with information or evidence satisfactory to us to confirm your status as a wholesale client; and• agree to notify us in writing within5 business days if you cease to be a ‘wholesale client' for the purposes of the financial services that we provide to you.Bahamas: EFG Bank & Trust (Bahamas) Ltd. is licensed by the Securities Commission of the Bahamas pursuant to the Securities Industry Act, 2011 and Securities Industry Regulations, 2012 and is authorised to conduct securities business in and from The Bahamas including dealing in securities, arranging dealing in securities, managing securities and advising on securities. EFG Bank & Trust (Bahamas) Ltd. is also licensed by the Central Bank of The Bahamas purs...

Want to scale your property management or co-hosting business past 10, 20, 50 listings? Book a call here: https://strsecrets.com/applyConnect with Mike Reilly : https://www.instagram.com/mikereillync/How do you onboard 7 vacation rental properties in one month without burning out your team?Mike Reilly breaks down the exact 6 stage onboarding system NC Stays uses for every new property, from the first homeowner call to the post launch celebration.Homeowner trust in property managers is at an all time low.Nearly every owner has been burned before.The managers who onboard properties with a real system are locking in 5 to 10 year relationships and standing out in a market flooded with new co-hosts.In this training you'll learn the full checklist:Why the first 30 days with a homeowner decides the next 10 yearsThe 6 stages of onboarding: Handoff, On-Site Setup, Photography, Listing & Software Setup, Final Touches, and Post LaunchHow Mike's team uses Meta glasses to capture every inch of a home and turn it into checklists automaticallyThe QR code trick that eliminates guest questions about the grill, hot tub, sauna, and game roomThe exact roles to hire: project manager, contractor help, listing team, and data qualityHow one Pinehurst property pulled $30,000 in bookings from mood boards alone, before it was even readyThe review stay strategy that gets Airbnb pushing your listing to page one in week oneTIMESTAMPS00:00 Trailer00:37 Welcome back + what's coming for the podcast02:02 Onboarding 7 properties in one month (all 4,000+ sq ft)03:59 The trust crisis and why onboarding is your biggest advantage05:00 The system and the 6 roles you need on your team07:58 The 6 stages of onboarding explained08:01 Stage 1: The Handoff (welcome email, onboarding form, group text)09:55 Stage 2: On-Site Setup with Meta glasses11:12 QR codes, inventory counts, and the house manual14:30 Stage 3: Photography (lifestyle shots, staging, shot lists)15:35 Stage 4: Listing & Software Setup (custom GPT listings, PriceLabs)16:55 $30,000 in bookings from mood boards before launch17:36 Stage 5: Final Touches (data quality, review stays, financials)19:07 Stage 6: Post Launch (social posts, email list, owner shareables)20:02 Why you can't scale without this process20:41 Inside the STR Secrets Mastermind

Want to scale your property management or co-hosting business past 10, 20, 50 listings? Book a call here: https://strsecrets.com/applyConnect with Mike Reilly : https://www.instagram.com/mikereillync/How do you onboard 7 vacation rental properties in one month without burning out your team?Mike Reilly breaks down the exact 6 stage onboarding system NC Stays uses for every new property, from the first homeowner call to the post launch celebration.Homeowner trust in property managers is at an all time low.Nearly every owner has been burned before.The managers who onboard properties with a real system are locking in 5 to 10 year relationships and standing out in a market flooded with new co-hosts.In this training you'll learn the full checklist:Why the first 30 days with a homeowner decides the next 10 yearsThe 6 stages of onboarding: Handoff, On-Site Setup, Photography, Listing & Software Setup, Final Touches, and Post LaunchHow Mike's team uses Meta glasses to capture every inch of a home and turn it into checklists automaticallyThe QR code trick that eliminates guest questions about the grill, hot tub, sauna, and game roomThe exact roles to hire: project manager, contractor help, listing team, and data qualityHow one Pinehurst property pulled $30,000 in bookings from mood boards alone, before it was even readyThe review stay strategy that gets Airbnb pushing your listing to page one in week oneTIMESTAMPS00:00 Trailer00:37 Welcome back + what's coming for the podcast02:02 Onboarding 7 properties in one month (all 4,000+ sq ft)03:59 The trust crisis and why onboarding is your biggest advantage05:00 The system and the 6 roles you need on your team07:58 The 6 stages of onboarding explained08:01 Stage 1: The Handoff (welcome email, onboarding form, group text)09:55 Stage 2: On-Site Setup with Meta glasses11:12 QR codes, inventory counts, and the house manual14:30 Stage 3: Photography (lifestyle shots, staging, shot lists)15:35 Stage 4: Listing & Software Setup (custom GPT listings, PriceLabs)16:55 $30,000 in bookings from mood boards before launch17:36 Stage 5: Final Touches (data quality, review stays, financials)19:07 Stage 6: Post Launch (social posts, email list, owner shareables)20:02 Why you can't scale without this process20:41 Inside the STR Secrets Mastermind

Join your Chairman James Penca and an incredible assortment of other Titanic/Ocean Liner content creators for a casual chat during our Titanic Anniversary Weekend 2026. This LIVE event and episode are co-presented by Titanic Legacy Podcast and Titanic: Honor and Glory.Join us for Titanic Anniversary Weekend in 2027!FEATURING:Executive Producer of Titanic: Honor & Glory - Matt DeWinkeleer'Titanic: Legacy Podcast' creator - Paul Carganilla@MelissaFairLady on TikTok - Melissa Ratliff'Titanic Talk' podcast host/producer and Director/Creator of 'Ship of Dreams: Titanic Movie Diaries' - Alexandra Boyd'Historic Travels' YouTube host - Samuel Penceand Oceanliner Designs writer - Sarah Brennemen----------------------------------------------Welcome to WITNESS TITANIC — the podcast “inquiry” where we call witnesses of Titanic's great story, from modern experts to even the survivors of the sinking. Through their testimony, we navigate the uncertain waters of the Titanic legend, uncover what actually happened in April of 1912, and ultimately witness Titanic ourselves.This podcast is made possible by the generous support of the Witness Titanic Patrons, and is produced and hosted by Titanic researcher James Penca.LISTEN:Apple PodcastsSpotifyWATCH:⁨YouTube | @TitanicHG⁩Instagram | @WitnessTitanicPodTikTok | @WitnessTitanicPodJOIN THE INQUIRY:PatreonMusic recorded by Ege M. Erdogan | @egecomposerContact | witnesstitanic@gmail.com Hosted on Acast. See acast.com/privacy for more information.

Get the 200+ Page Optimal Living Daily Workbook (PDF) — Free. Want to turn today's episode into an actionable plan? Join the Optimal Living Weekly newsletter and I'll send you our 200-page digital workbook immediately. It's packed with the best takeaways from the show, formatted for easy reading and implementation at home. Get your free PDF workbook here: ⁠https://oldpodcast.eo.page/join⁠ Discover all of the podcasts in our network, search for specific episodes and learn more at:⁠ OLDPodcast.com⁠. Episode 2107: Michael Levitt shares practical strategies for helping distributed teams thrive, from creating a thoughtful virtual onboarding experience to building healthier work-from-home habits that reduce discomfort and improve productivity. Learn how better communication, mentorship, ergonomics, and daily movement can help people feel connected, supported, and set up for long-term success. Read along with the original article(s) here: https://www.breakfastleadership.com/blog/effective-techniques-to-onboard-remote-employees & https://www.breakfastleadership.com/blog/the-best-pain-relief-tips-for-remote-workers Quotes to ponder: "It's important for remote employees to feel connected and cared for, which can be accomplished by providing a peer mentor." "Working through pain is not a good idea, and you should not ignore your body." "Stand up and stretch your body. Doing so will relieve tension and prevent cramps." Learn more about your ad choices. Visit megaphone.fm/adchoices

Emergency preparedness is more than checklists and supplies. It is about making critical decisions under pressure and navigating the emotions that come with them. In this episode, we look at the essentials of evacuation planning, including how to identify clear triggers for leaving, choose multiple evacuation routes, create a family communication plan, and understand your sheltering options. We also cover special considerations for pets, medical needs, and disaster specific risks, along with the most common mistakes that delay safe evacuation.But planning is only half the challenge. What happens when your spouse or family thinks you are overreacting? We discuss the deeper reasons behind resistance, from social stigma and financial anxiety to different risk tolerance levels. Mentioned In This Episode:ReadyFam Preparedness AppSupport Practical Prepping72 Hour Prepping ChecklistDownload this episode If you find value in what we do, if you've learned something new, gotten an idea for something you need to do, been entertained, or just like Krista's Southern charm, would you be willing to give back a little?You can do that one of several ways.     Go to our support page               OR     By starting your Amazon shopping from our website? --->  CLICK HERE        (We earn from qualifying Amazon purchases)Contact us:Practical PreppingWebsiteOur Sponsors:Practical Prepping BooksProof Minimalist Wallets (Discount code PREPPER)ProLine Digital Group   Website  Email1791gunleather.com (Discount code: PREP15) SurfsharkPodcast music written and recorded by Krista LawleyWebsite design and hosting by ProLine Digital Group.Podcasts Copyright 2026, P3 Media Group, LLC, and Practical Prepping Podcast

*clears throat* GOOOOAAAALLLLLL! Ok now that we've gotten that out of the way, grab your cleats and hit the pitch for a brand new World Cup-centric TVPL on the pilot of Ted Lasso! Onboard this week back from his ten episode red card suspension is our very own Rich Inman! This soccer superstar comedy became a phenomenon when it debuted in 2020 so join us as we discuss the long road it took to your screen, plenty of football/fútbol related facts, and maybe even a detour into the muppets. TVPL's coming home! With:Rich InmanGeoff Kerbis Max Singer

These witnesses need no introduction... Don Lynch is a renowned author and historian, Ken Marschall is the celebrated artist and Titanic visual historian... together they assisted James Cameron on his 1997 film, they dove to Titanic on multiple expeditions, and they brought us the must-have book "Titanic: An Illustrated History."This live testimony was recorded during our four-day Titanic Anniversary Weekend onboard the RMS Queen Mary. Join us in 2027!VISIT the Witness Titanic Instagram for Episode VisualsorWATCH this Episode on YouTubeNOTICE:Witness testimony begins at 9:05, however for important context, do not skip past 6:35.Episode cover photograph taken by Merie W. Wallace.Big thanks to Ken for providing his paintings and photos to appear in the YouTube version of this episode.Thanks to Sergey Kudriashov for providing us with a few of his photos from the 2001 expedition.Thanks to Maxim Polishchuk for his assistance in getting these photos to our inquiry.And thanks to Paul Carganilla for co-hosting the anniversary weekend with me and making this episode possible!----------------------------------------------Welcome to WITNESS TITANIC — the podcast “inquiry” where we call witnesses of Titanic's great story, from modern experts to even the survivors of the sinking. Through their testimony, we navigate the uncertain waters of the Titanic legend, uncover what actually happened in April of 1912, and ultimately witness Titanic ourselves.This podcast is made possible by the generous support of the Witness Titanic Patrons, and is produced and hosted by Titanic researcher James Penca.LISTEN:Apple PodcastsSpotifyWATCH:⁨YouTube | @TitanicHG⁩Instagram | @WitnessTitanicPodTikTok | @WitnessTitanicPodJOIN THE INQUIRY:PatreonMusic recorded by Ege M. Erdogan | @egecomposerContact | witnesstitanic@gmail.com Hosted on Acast. See acast.com/privacy for more information.

Patrick Van Deven: The Frontier Firm Has a Data Problem In this episode of Scouting for Growth, Sabine VanderLinden sits down with Patrick Van Deven to unpack one of the biggest hidden blockers to becoming a true AI-native enterprise: legacy data infrastructure. As organizations rush toward the “Frontier Firm” vision championed by Microsoft — intelligence on tap, human-agent collaboration, and AI-powered workflows — Patrick argues that most regulated industries are still running on fragmented data pipelines built decades ago. Beneath the excitement around agentic AI lies a critical operational reality: data remains horizontally distributed across systems such as SAP, Salesforce, Guidewire, and legacy warehouses, stitched together by opaque code that no one fully understands anymore. Patrick explains why the future of AI in regulated industries depends less on flashy copilots and more on deterministic, governed, audit-ready data transformation. Drawing from his 35 years in enterprise software and his leadership at Volspeed, he outlines how AI is now reshaping data engineering itself — automating the “plumbing” layer while generating the metadata and lineage AI systems need to operate responsibly. Together, Sabine and Patrick explore why re-architecting does not require a dangerous core system replacement, how organizations can solve tractable business problems in months rather than years, and why the next generation of enterprise leaders must bridge business expertise and data intelligence. This conversation is a practical roadmap for any executive navigating AI transformation inside complex, regulated environments.   KEY TAKEAWAYS What stood out most to me in this conversation with Patrick was the reality that the “Frontier Firm” conversation is no longer about experimentation. It is about operational readiness. Every organization I speak to wants intelligence on tap, agentic workflows, and AI-enabled productivity, yet many are still constrained by fragmented legacy systems and undocumented data logic buried deep inside their infrastructure. Patrick made it very clear: if we do not solve the data foundation problem, we simply accelerate complexity and risk. One insight that resonated deeply was the idea that data engineering is entering the same transformation that software engineering experienced with generative AI. The real opportunity is not just automation, but abstraction — enabling smaller teams to solve historically impossible integration problems while creating governed, machine-readable metadata that AI systems can actually trust and consume responsibly. I was also struck by Patrick's perspective on talent. Rather than replacing expertise, AI elevates the importance of subject matter experts who understand the business context behind the data. The future belongs to professionals who can bridge operational understanding with technical fluency and collaborate effectively with AI-enabled systems. Most importantly, this conversation reinforced that becoming a Frontier Firm does not require ripping out every core system overnight. The no-regret move is to start solving tractable, high-value data problems now — especially those tied to governance, lineage, regulatory reporting, and customer intelligence. Organizations that modernize their deterministic data layer today will be the ones capable of building scalable, trustworthy AI tomorrow.   BEST MOMENTS “You can bolt all the AI you want on top of that. It will not make you a frontier firm. It will just make your regulatory problems arrive faster.” — Sabine VanderLinden “AI is coming to data engineering just like it came to software engineering.” — Patrick Van Deven “The board looks at AI at the end of the value chain of data. But how did that data come to be?” — Patrick Van Deven “There is no world where a company would run on one system.” — Patrick Van Deven “Treat the AI agent like an employee. Onboard it, brief it, give it a personality.” — Sabine VanderLinden “The dragon in the basement has finally reached the boardroom.” — Patrick Van Deven “No data lineage. No agent bosses. No governed transformation. No intelligence on tap.” — Sabine VanderLinden “This is a new era for subject matter experts.” — Patrick Van Deven   ABOUT THE GUEST Patrick Van Deven is the CEO of Vaultspeed and a veteran enterprise software leader with more than 35 years of experience in software engineering, predictive analytics, data infrastructure, and venture investing. Patrick began his career as a software engineer, building and selling his first commercial application at just 22 years old. He later spent 15 years at SAS Institute, where he helped build data and predictive analytics applications for enterprise environments. He then transitioned into venture capital as an Operating Partner and General Partner at Fortino Capital, investing in software and AI startups across Europe. In 2025, Patrick stepped back into an operational leadership role as CEO of Vaultspeed, driven by his belief that automating deterministic, governed data transformation is one of the most critical “no-regret moves” organizations can make in the age of AI. Today, Vaultspeed works with major global enterprises, including organizations operating across highly regulated industries such as insurance, banking, and financial services.   ABOUT THE HOST Sabine VanderLinden is a corporate strategist turned entrepreneur and the CEO of Alchemy Crew Ventures. She leads venture-client labs that help Fortune 500 companies adopt and scale cutting-edge technologies from global tech ventures. A builder of accelerators, investor, and co-editor of the bestseller The INSURTECH Book, Sabine is known for asking the uncomfortable questions—about AI governance, risk, and trust. On Scouting for Growth, she decodes how real growth happens—where capital, collaboration, and courage meet. If this episode sparked your thinking, follow Sabine VanderLinden on LinkedIn, Twitter, and Instagram for more insights. And if you're interested in sponsoring the podcast, reach out to the team at hello@alchemycrew.ventures

We hit pause for a year, then realized something: Disney doesn't stop changing just because we do. So we're back with a different kind of trip report and a big first for our family, a four night Disney Cruise Line sailing on the Disney Fantasy, plus a pre-cruise day in Orlando that turns into one last nostalgia lap around Walt Disney World.We talk honestly about what we've missed (and what we don't), from recent Walt Disney World closures that tug on our memories to updates we're actually excited to see, like the Carousel of Progress refresh. That leads straight into the bigger decision: choosing a Disney cruise as our next “classic Disney” fix while the kids grow up and our travel goals widen beyond theme parks. We explain why the Disney Fantasy feels like the right first ship, why Wish class dining doesn't really fit our style, and why Animator's Palate is at the top of our must-do list.Then we lay out the plan day by day with practical Disney Cruise planning tips: why we always fly in the day before, how one-day Disney park ticket prices changed our pre-cruise strategy, and why we'd rather do a Polynesian pool day with fireworks than rush a half-day at Animal Kingdom. Onboard, we're chasing the best food, the iconic ship horns, the Rainforest Room spa reset, teen clubs for the boys, and a special adults-only dinner at Palo. In port, we're keeping it simple with beach time on Castaway Cay and Disney Lookout Key at Lighthouse Point, plus a little shopping and snorkeling for hidden Disney history.If you're planning a Disney cruise to the Bahamas or just love hearing how other families build a Disney vacation that actually fits their season of life, hit play. Subscribe, share the show with a Disney friend, and leave a review with your best first-time cruise tip.Send us Fan MailSupport the show

Planning a massive family vacation can be a logistical nightmare, but a Disney Cruise Line sailing offers the ultimate stress-free solution for large groups. In this episode, our guest Coby breaks down his recent 5-night Bahamian cruise aboard the magnificent Disney Dream. Kobe shares expert tips and firsthand experiences from managing a multi-generational group of 29 family members across 8 staterooms. From dinner seating strategies and group hangout spots to navigating onboard activities for everyone from toddlers to teens, this conversation is packed with essential Disney Cruise Line tips. Tune in to discover how to maximize your next grand gathering with world-class Disney entertainment and dining options!  Main Segment TopicsManaging Multi-Generational Logistics: The booking process for a family group of 29 people across 8 staterooms, including how stateroom configurations were divided between adults, teens, and older family members.Onboard “Home Base” Strategies: Utilizing Deck 12 near Funnel Vision as a centralized family meeting area to allow members to drop in and out easily.Adult-Only Dining and Booking Challenges: Navigating the technical and age-restriction hurdles when trying to secure group reservations for premium experiences like Palo.Onboard Entertainment & Activities: Reviewing group participation in nighttime dance parties, standard stage shows like Disney’s Believe, and custom family rivalries during onboard trivia sessions.Want to be on the show? Fill out this form, and we'll be in contact with you real soon!https://dclpodcast.com/want-to-be-on-the-show/Support our show via Patreon:http://www.patreon.com/dclpodcastUse Christy's Travel Services:https://dclpodcast.com/book-with-christy/Follow the DCL Podcast via:http://www.facebook.com/dclpodcasthttp://www.instagram.com/dcl_podcastFollow Lake at:https://www.instagram.com/mouse.genhttps://www.youtube.com/@MouseGenFollow Christy at:http://www.packyourpixiedust.comhttps://www.instagram.com/packyourpixiedust

Originally part of our four-day Titanic Anniversary Weekend onboard the RMS Queen Mary, we present the first WITNESS TITANIC podcast episode recorded in front of a LIVE AUDIENCE! Together, surrounded by history, we dive into a Norwegian immigrant's account of the disaster as he remembered it in 1912... and how he remembered it in 1974...Featuring Paul Carganilla as our Senator/Reporter!Join us for Titanic Anniversary Weekend in 2027!VISIT the Witness Titanic Instagram for Episode VisualsorWATCH this Episode on YouTube----------------------------------------------Welcome to WITNESS TITANIC — the podcast “inquiry” where we call witnesses of Titanic's great story, from modern experts to even the survivors of the sinking. Through their testimony, we navigate the uncertain waters of the Titanic legend, uncover what actually happened in April of 1912, and ultimately witness Titanic ourselves.This podcast is made possible by the generous support of the Witness Titanic Patrons, and is produced and hosted by Titanic researcher James Penca.LISTEN:Apple PodcastsSpotifyWATCH:⁨YouTube | @TitanicHG⁩Instagram | @WitnessTitanicPodTikTok | @WitnessTitanicPodJOIN THE INQUIRY:PatreonMusic recorded by Ege M. Erdogan | @egecomposerContact | witnesstitanic@gmail.com Hosted on Acast. See acast.com/privacy for more information.

This week on the Talking Wealth podcast, Fil and Pedro dive into the latest chapter 13F filings for Q1 2026 to reveal what the big funds were buying and selling. More importantly, they highlight the themes that dominated capital investment during Q1 2026 and whether now is your time to jump on board these mega opportunities.

A Bareknuckle Brawl For Senate In Texas! And Mike Slater Is Onboard The Spencer Pratt Bandwagon! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Listen to one of our forum sessions on sustainability in yacht shipping:Everyday Sustainability: Onboard Practices and Client PerspectivesExperts discuss eco-friendly crew uniforms, biodegradable cleaning products, responsible consumables, energy- and water-saving systems, and other operational measures.Key Topics:Eco-friendly crew uniforms & consumablesBiodegradable cleaning products & energy-efficient systemsWater efficiency & onboard waste managementEnhancing guest experience through sustainable operationsAligning sustainability with efficiency and cost savingsSpeakers:Amira BakhaevaJelena VeziaLeah TennantModerator:Monica Fonseca

This is episode 4 of Nate Atkin's $100 Million Dollar Journey, where we follow the real-time decisions, bottlenecks, and breakthroughs involved in building a mortgage business at scale. We talk about what happens when your business starts working and you become the bottleneck. Nate shares where his pipeline is at, how he used an HR company to hire his first underwriter, and what he's learning about letting go, onboarding well, and building the business for the next stage of growth. If you're busy, buried in files, and know you need help but are nervous about hiring, this episode will be useful. In this episode, we cover: Why Hiring Helped - Nate shares how using an HR company saved time and helped him find the right underwriter faster. How to Let Go - We talk about what it takes to hand off work and trust someone else with the file flow. How to Onboard the Right Way - A strong hire still needs clear systems, a mapped client journey, and a plan. What Comes Next - Once the hire is in place, the next focus is lead gen, capacity, and removing the next bottleneck. Hiring the right person is not the finish line. It's what gives you the capacity to grow. Follow Nate's journey through the links below: Instagram Facebook LinkedIn TikTok https://www.hummingbirdmortgages.ca/ $100 Million Dollar Journey Series Ep 1 - $100 Million Dollar Journey Ep 2 - How to Find and Hire an Underwriter Ep 3 - What Breaks When You Scale? Follow me on Instagram: www.instagram.com/scottpeckford/ I Love Mortgage Brokering: www.ilovemortgagebrokering.com Find out more about BRX Mortgage: www.whybrx.com Subscribe to my email list, Peckford's Playbook Join the Mortgage Mindset Daily Gamify your prospecting with the 10@10 App I Love Mortgage Brokering is in partnership with Ownwell. To see how top brokers are keeping clients engaged and generating leads from their database, visit ownwell.ca/scott.

What if the most powerful thing you could do to grow your business was to stop asking for referrals entirely?In this episode, Michael Reddington sits down with Stacey Brown Randall, author, speaker, and host of the Roadmap to Referrals podcast. Stacey has spent over a decade helping business owners and sales professionals generate referrals without asking for them by applying brain science, psychology, and behavioral economics to how relationships are built and maintained.Stacey breaks down why asking for referrals actually works against you, what is really happening in the brain of the person who refers you, and how the right language planted at the right time can move referrals from a conscious ask into someone's subconscious. She also introduces her three-bucket framework for building a referral strategy that compounds year over year, and shares two concrete referral seeds you can start using immediately.What You'll Learn in This EpisodeWhy asking for referrals triggers a brain response you cannot manufacture or replicateHow referrals are actually about your referral source, not about youThe three scientific principles that drive referrals beyond the psychology of trustThe difference between keeping in touch and actually moving a relationship forwardHow to segment your referral strategy across three distinct buckets of potential referral sourcesWhy using someone's name in a thank you note changes how the brain encodes the memoryHow to plant referral seeds during your client experience without it feeling forcedWhat to do when a referred prospect ghosts you before the conversation ever startsChapters(00:00) Introduction to Stacey Brown Randall and the Referral Without Asking Framework(03:24) Why Referrals Change the Entire Dynamic of a Sales Conversation(07:03) Why You Are Never Allowed to Ask for Referrals(10:42) The Three Scientific Principles Behind How Referrals Actually Happen(15:50) The Difference Between Keeping in Touch and Moving Relationships Forward(17:37) The Three Buckets of Referral Sources and How to Approach Each One(24:43) Planting Referral Seeds vs. Asking: What the Difference Actually Looks Like(28:57) The Right and Wrong Way to Write a Referral Thank You Note(34:21) How to Build a Referable Client Experience from the Inside Out(36:40) Recovery Strategies When a Referred Prospect Ghosts You(41:44) How to Onboard a Referred Prospect Without Rushing or Ignoring Them(45:29) How to Learn More and Work with StaceyLinks and ResourcesStacey Brown Randall | LinkedIn - https://www.linkedin.com/in/staceybrandall/Home - Stacey Brown Randall - https://staceybrownrandall.com/The Disciplined Listening Method: How A Certified Forensic Interviewer Unlocks Hidden Value in Every Conversation - https://a.co/d/02ZfcnZmSponsor Links:InQuasive: http://www.inquasive.com/Humintell: Body Language - Reading People - HumintellEnter Code INQUASIVE25 for 25% discount on your online training purchase.International Association of Interviewers: Home (certifiedinterviewer.com)Podcast Production Services by EveryWord Media

You introduced the new tool. You explained it. You maybe made a Loom. And your team is still doing it the old way. This isn't a people problem- it's almost always a framing problem. When leaders lead with what the organization needs instead of what each person gets, adoption stalls. Every time. In this episode, Lia breaks down the framework she's used at Microsoft, Apple, and Google to change that.In this episode you will learn:Why "we need this" is the wrong frame for any rollout and what to lead with insteadHow reframing a new platform around what the design team actually wanted got full adoption at Microsoft in one weekThe one question to ask before introducing anything new: what does each person specifically get from this?What this looks like in practice for a simple weekly check-in processAn introduction to Snippets: Lia's new web app for team check-ins built around making adoption straightforwardResources mentioned:Snippets web app: liagarvin.com/snippetsLooking for support for yourself of your team? I've got you covered.Explore manager training, leaders keynotes & offsites, and 1:1 advisory, or my 90-Day-COO program for business owners who want simple systems that actually work.I help teams build clarity, accountability, and momentum through practical tools and research-backed strategies that make managing easier.Get all the details at: www.liagarvin.comor reach out at hello@liagarvin.com

New Zealand officials are working on options to repatriate a New Zealander who's been onboard a cruise ship hit with the hantavirus. Passengers from the MV Hondius have been disembarking under complex evacuation protocols on the isalnd of Tenerife inn Sapin Caary islands. 149 passengers and crew will taken off the ship in small boats and flown home. The New Zealander however will be flown to Australia, with several Australian passengers. New Zealand's Director of Public Health Dr Corina Grey spoke to Lisa Owen.

Dr. Amy Osmond Cook, is the Co-founder and Chief Marketing Officer of Fullcast, an AI-native go-to-market platform that helps companies align strategy with revenue execution. A seasoned marketing executive, she has led high-growth companies like Simplus, PathologyWatch, and Onboard from Series A to acquisition. Dr. Cook founded Stage Marketing, a successful full-funnel marketing firm, and has a PhD in Communication from the University of Utah. She is a recognized thought leader in business and healthcare marketing and has taught at ASU, BYU, and the University of Utah. In this episode… What happens when the friction between sales and marketing slows down growth, even in companies with great products? Can AI truly fix that gap — or does real acceleration still depend on how people and teams work together? And what does it take to scale revenue without losing alignment along the way? Dr. Amy Osmond Cook, a seasoned marketing and RevOps leader, explains that growth accelerates when AI-powered systems are combined with strong human alignment across sales and marketing. She highlights how Fullcast unifies forecasting, commissions, territory planning, and revenue operations into one platform to remove friction and improve execution. The result is faster alignment, clearer visibility, and more predictable revenue outcomes. She also shares how strategic acquisitions and deep data integration helped build a scalable system that still prioritizes onboarding and client relationships.  In this episode of the Inspired Insider Podcast, host Dr. Jeremy Weisz sits down with Dr. Amy Osmond Cook, to talk about AI, RevOps, and scaling revenue through better alignment. They discuss building Fullcast through acquisitions, using AI for forecasting and commissions, and improving sales territory planning. Dr. Cook also shares insights on onboarding, post-sale relationships, and maintaining trust during growth.

Plus: Could the ill-fated ship be ground-zero for the next pandemic, how close is a potential peace deal in Iran, the U-S secretary of state is at the Vatican, most Canadians support bans on social media and AI for kids, and the Prime Minister will host Dutch royalty ahead of the annual Tulip Festival. We love feedback at The Big Story, as well as suggestions for future episodes. You can find us: Through email at hello@thebigstorypodcast.ca  Or @thebigstory.bsky.social on Bluesky

The World Health Organization says hantavirus has been confirmed in the death of one of three cruise ship passengers and suspected in the other 3 “urgent medical” cases. The polar cruise ship is being held off the coast of Cape Verde as health authorities are trying to coordinate the medical evacuation of two crew members who require urgent care, while another passenger is fighting for his life in the hospital. If these cases are confirmed to have spread from human to human, medical experts say this could change the future of travel medicine. See omnystudio.com/listener for privacy information.

The World Health Organization says hantavirus has been confirmed in the death of one of three cruise ship passengers and suspected in the other 3 “urgent medical” cases. The polar cruise ship is being held off the coast of Cape Verde as health authorities are trying to coordinate the medical evacuation of two crew members who require urgent care, while another passenger is fighting for his life in the hospital. If these cases are confirmed to have spread from human to human, medical experts say this could change the future of travel medicine. See omnystudio.com/listener for privacy information.

The World Health Organization says hantavirus has been confirmed in the death of one of three cruise ship passengers and suspected in the other 3 “urgent medical” cases. The polar cruise ship is being held off the coast of Cape Verde as health authorities are trying to coordinate the medical evacuation of two crew members who require urgent care, while another passenger is fighting for his life in the hospital. If these cases are confirmed to have spread from human to human, medical experts say this could change the future of travel medicine. See omnystudio.com/listener for privacy information.

The World Health Organization says hantavirus has been confirmed in the death of one of three cruise ship passengers and suspected in the other 3 “urgent medical” cases. The polar cruise ship is being held off the coast of Cape Verde as health authorities are trying to coordinate the medical evacuation of two crew members who require urgent care, while another passenger is fighting for his life in the hospital. If these cases are confirmed to have spread from human to human, medical experts say this could change the future of travel medicine. See omnystudio.com/listener for privacy information.

AP correspondent Karen Chammas reports three people have died and at least three more have fallen ill from a suspected outbreak of hantavirus on a cruise ship.

This week, I welcome Kristin Hancock, owner of Bright Life Travel Co., to first discuss the latest trending news in travel, including airfare price hikes and a big letter from United's CEO. Later, Hancock and I dive into the world of multi-gen travel trends. She shares insights into the top trends today, where travelers are going, as well as tips for fellow travel advisors on growing their sales and managing large family group vacations. The discussion on multi-gen travel begins after the 14-minute mark. Today's episode sponsor: AmaWaterways When you recommend AmaWaterways to your clients, you are offering more than just a river cruise. You are giving them a chance to discover the world in a way that feels personal and meaningful. Your clients can explore villages, vineyards and historic cities with the freedom to follow the pace that feels right to them. Onboard their spacious ship, they are welcomed with heartfelt service, farm-to-ship cuisine and an array of amenities. Invite your clients to learn more at amawaterways.com Have any feedback or questions? Want to sponsor the show? Contact us at Podcast@TravelPulse.com and follow us on social media @TravelPulse.See omnystudio.com/listener for privacy information.

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

Casey Claar has been taken since childhood. In this episode, she shares a lifetime of alien abduction, the violent Kundalini awakening at 44 that cracked her memories wide open, the tractor beam that pulled her onto a craft, and the four years of daily out-of-body experiences that followed.She describes onboard examinations, contact with a guide named Mehari, seeing her father's death before it happened, and spending three months off-planet during a single Earth hour, and an encounter with a nine-foot being she now calls Merlin the Alchemist.We also get into her twelve-dimensional cosmology, the clear quartz crystal technology she traces back to Atlantis and Mu, and her provocative claim that real UAP disclosure cannot happen in 3D — it has to come from the inside out.This is one of the most detailed experiencer testimonies we've recorded.CHAPTERS00:00 Cold open — "A tractor beam locked onto me"02:00 Growing up in the Vegas desert between dimensions05:00 Childhood missing time and dream-walking08:00 Yoga at 12 and decades of spiritual preparation12:00 2009 — the night the hands came15:00 First contact with Mehari, the indigenous guide17:00 "Am I dying now?"18:00 Shown her father's coming death22:00 Pulled through the sliding glass door26:00 The murdered woman in the wallet29:00 The tractor beam abduction32:00 Onboard the craft and the examination table36:00 Central nervous system shock and two years of tremors40:00 The greater being that merged with her body43:00 Four years of daily out-of-body experiences47:00 Three months off-planet in a single Earth hour52:00 The OBE school and the "renegade freedom fighters."58:00 Binaural beats as a portal01:05:00 Wave-form vs. particle-form consciousness01:12

Get the full episode here: https://www.10percenttrue.com/pricing-plans/list10PCT EP86 P2 Benji PrefontaineIn Episode 2, Benji Prefontaine moves from early operations into real combat experience—flying the Dassault Mirage F1 in Africa before transitioning to carrier aviation in the Dassault-Breguet Super Étendard.He describes the shock of adapting from Air Force flying to life on the carrier—where precision, discipline, and consistency are everything. Landing on the boat becomes a defining challenge, exposing the difference between being a good pilot and being an operational one.The episode also explores the limitations of the Super Étendard—an aging, analog jet forced into modern combat—and how that shapes tactics, workload, and risk. Benji highlights the realities of coalition warfare, early Afghanistan deployments, and the steep learning curve of operating in a far more complex and demanding environment.This is where the story shifts from “becoming a pilot” to learning how to survive and operate effectively in combat.0:00 Teaser – Pyjamas, Wine, Corkscrews and Cigarettes 1:45 Welcome Back Benji (Steve's Lost It!) + Episode Outline 3:20 First Operational Squadron – Tough Start to Mixed Force Ops 9:20 Post-9/11 Politics and Operational Reality 12:38 Red Flag – Flying with Mirage 2000D 15:36 Did Red Flag Validate the EW Suite? 18:41 How Red Flag Prepared Him for Combat 22:26 Inferiority Complex in Coalition Ops? 28:02 Social Life on Squadron 31:24 Old School vs New School – What Works? 35:38 Deployments to Chad 40:42 Threat Environment and Risk 44:48 Ferry Flight to Red Flag – Single-Engine Stress 47:43 Bird Strikes and Wildlife Hazards 52:09 Survival Kit – What's On Board? 55:10 CSAR – Expectations vs Reality 57:46 What Is a Pilot Worth? 59:05 Combat Psychology 1:04:18 Managing Pilots Doing “Cool Stuff” (Photos/Video) 1:09:54 Romania Deployment – Encounters with MiGs 1:18:09 QRA – Intercepts and Real-World Stories 1:24:45 French Air Force “Urban Legends” 1:27:27 Encounters with USAF Incursions? 1:30:08 End of First Tour – Seeking Exchange Opportunities 1:32:50 Carrier Tour Expectations – Charles de Gaulle 1:35:15 No Night Landings? 1:36:30 Targeting Pod (PDLCT) 1:37:52 FCLP – Carrier Landing Practice 1:42:12 The Hardest Part of Carrier Ops 1:45:15 Nuclear Strike Mission Explained 1:51:53 Super Étendard Capabilities (Including Exocet) 2:01:17 From Detection to Attack 2:05:00 Situation Display, Autopilot, Datalink 2:07:55 Tuning Exocet Targeting and Performance 2:09:12 How Do You Attack a Carrier Group? 2:13:00 Part 3 Preview – Combat and Command

This week, I first discuss the latest trending news in travel, including the jet fuel crisis raising airfare costs, rumors of a potential United-American Airlines merger, Europe's new entry system, and more. Later, I welcome Jessica Blotter, CEO and co-founder of Kind Traveler, to talk about how we can be better travelers. Blotter provides three key steps for all travelers to get started on being better travelers. She also describes why it's actually more affordable than you think to be an eco-friendly traveler and embrace sustainability, as well as how travel advisors can approach this topic with their clients. The discussion on being better travelers begins after the 13-minute mark. Today's episode sponsor: AmaWaterways When you recommend AmaWaterways to your clients, you are offering more than just a river cruise. You are giving them a chance to discover the world in a way that feels personal and meaningful. Your clients can explore villages, vineyards and historic cities with the freedom to follow the pace that feels right to them. Onboard their spacious ship, they are welcomed with heartfelt service, farm-to-ship cuisine and an array of amenities. Invite your clients to learn more at amawaterways.com Have any feedback or questions? Want to sponsor the show? Contact us at Podcast@TravelPulse.com and follow us on social media @TravelPulse.See omnystudio.com/listener for privacy information.

Watch Us On YouTube! Announcing a new, ongoing benefit for annual subscribers of our Slack community. Annual subscribers receive a free Points Path Alerts subscription OR a 30% discount on Points Path Pro. A cheap flight, a surprisingly good cruise, and a few lessons in finding value along the way. This week on Miles To Go, Ed and Richard are back together and catching up on a whirlwind stretch of travel — from a budget-friendly hop out of the Gulf Coast to a deeply discounted Disney Cruise that delivered far more than expected. They break down how a $67 Spirit flight (plus a clever rental car strategy) beat a $650 alternative, why positioning still matters for both flights and cruises, and how flexibility can unlock outsized value. Onboard, Richard shares a full review of Disney's newest ship, including what worked, what didn't, and whether Disney cruises are still worth the premium price. Plus, a quick dive into the Freddie Awards ballot, evolving airline and hotel loyalty value, and Ed's first experience with United's new "Elevate" cabin — including what they got right (and what still needs work). Scroll down for show notes! Get hydrated like Ed in Vegas with Nuun Use my Bilt Rewards link to sign-up and support the show! If you enjoy the podcast, I hope you'll take a moment to leave us a rating. That helps us grow our audience! If you're looking for a way to support the show, we'd love to have you join us in our Travel Slack Community.  Join me and other travel experts for informative conversations about the travel world, the best ways to use your miles and points, Zoom happy hours and exciting giveaways. Monthly access Annual access Personal consultation plus annual access We have witty, funny, sarcastic discussions about travel, for members only. My fellow travel experts are available to answer your questions and we host video chats multiple times per month. Follow Us! Instagram: https://www.instagram.com/milestogopodcast/ TikTok: https://www.tiktok.com/@milestogopodcast Ed Pizza: https://www.instagram.com/pizzainmotion/ Richard Kerr: https://www.instagram.com/kerrpoints/ ✈️ What We Cover in This Episode ✈️ A cheaper way to fly (if you're willing to work for it) $650 flights vs a $67 Spirit ticket Positioning through Pensacola instead of Gulfport Why rental cars can beat rideshares and flights ✈️ The reality of domestic travel costs $110 rides to the airport vs $9 rides in smaller cities Why airport access can be the most expensive part of a trip When convenience is (and isn't) worth paying for ✈️ A discounted Disney Cruise experience Scoring a 65% off sailing Differences between older and newer ships Where Disney delivers — and where it falls short ✈️ Is Disney Cruise Line worth the price? Premium pricing vs real value When it makes sense for families Comparing Disney to other cruise lines ✈️ Cruise logistics and strategy Why you should never fly in the same day Positioning for cruise departures Avoiding stressful travel days ✈️ Freddie Awards discussion Best airline and hotel programs Where loyalty value still exists Why "utility" matters more than ever ✈️ United's new "Elevate" seat experience First impressions of the new business class Premium economy improvements Where design choices fall short in real-world use ✈️ The future of airline cabins More premium-heavy aircraft Tradeoffs between comfort and practicality Why execution still matters more than design ⏱️ Episode 431 Timestamps  0:49 – Richard returns and travel recap begins 4:00 – The true cost of getting to the airport 7:28 – Biloxi trip and why it still works as a destination 10:00 – $650 flight vs $67 Spirit alternative 13:42 – Why you should never fly same-day for a cruise 15:00 – Disney Destiny cruise review 20:00 – Where Disney cruises shine (and fall short) 25:00 – Freddie Awards and loyalty program value 30:10 – United's new Elevate seat experience 36:00 – Final thoughts and upcoming travel chaos

I didn't set out to be a leader.I set out to chase an idea. Then the business grew. Then I hired a team. And somewhere between “I've got this” and “How did we get here?”… I became a leader.Spoiler: I wasn't very good at it.In this episode, I'm sharing the uncomfortable leadership mistakes I made (including the time I avoided accountability because I was afraid someone would lock me out of my own systems… yes, really).This year, I decided that if I want to build the next level of my business, I have to become the next level version of myself. From hiring a Sales Director and CFO to investing in leadership training, everything shifted.The biggest lesson? Integrity isn't a buzzword — it's doing what you say you'll do, by the date you say you'll do it.Leadership isn't about being perfect. It's about being intentional.If you're building a team, scaling your company, or stepping fully into your CEO role, this episode is for you.Click play to hear all of this and:[00:00] Why Most Entrepreneurs Accidentally Become Leaders (And Why That's a Problem)[04:30] The Cringe-Worthy Mistake I Made When I Avoided Accountability[10:15] How to Onboard a Leader Into Your Culture (Not Just Their Role)[18:40] Integrity Defined: Doing What You Say, When You Say It[24:55] What Happened When Our CFO Started Interviewing Us[33:10] The Honest Leadership Coaching Session I Almost Didn't ShareListen to Related Episodes:How You Can Develop the BEST Traits in LeadershipHow to Identify and Lead A, B, and C Players for Business Success3 Signs It's Time to Restructure Your Team as a LeaderConnect With Jim Zartman:Instagram: https://www.instagram.com/jimzartmanWebsite: https://www.jimzartman.com/

On this week's Just the Tip, we give our honest opinion about the amazing experience of flying business class with Singapore Airlines. We discuss whether it's worth the price, walk you through the whole experience, and share how you can book this with points.Download our ⁠Singapore Itinerary⁠ for the perfect plan to spend two days in this amazing island nation and keep the fun going after your business class experience. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Shop: ⁠⁠⁠⁠⁠Trip Itineraries⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠Amazon Storefront ⁠⁠⁠⁠⁠Connect: ⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠and contact us at travelsquadpodcast@gmail.com to submit a question of the week or inquire about guest interviews and advertising.

This Week In Startups is made possible by:Quo - https://quo.com/TWiSTLinkedIn Jobs - https://LinkedIn.com/twistIru - https://iru.com/twistPlaud - https://Plaud.ai/twistToday's show:Google project manager Shubham Saboo is running 6 AI agents on a Mac Mini that handle all of his side business autonomously, from research, to social posts, to newsletters.He joins Jason and Lon to walk us through his 5-step framework for designing an efficient AI agent team:Start with one agent and onboard them like a new hireStop Googling fixes; just ask your agent how to use itPut your agents on fixed schedulesAdd shared memory so you don't have to repeat yourselfLet agents run self-reviews and rewrite their own instructions.Get the walkthrough to put this entire plan into practice on today's episode. PLUS fresh demos of the “Minecraft”-inspired virtual workspace MoltWorld and AgentMail, which is Gmail for your AI pals, and Jason explains the thinking behind his viral, controversy-stirring “don't talk to journalists” tweet.Follow Shubham: https://x.com/Saboo_Shubham_“How I Built an Autonomous AI Agent Team That Runs 24/7” on X: https://x.com/Saboo_Shubham_/status/2022014147450614038?s=20Follow Mike: https://x.com/mihalich1988MoltWorld: https://moltworld.io/Follow Haakam: https://x.com/haakamaujlaAgentMail: https://www.agentmail.to/Jason's post about talking to journalists: https://x.com/Jason/status/2037573025458016659NYT responds: https://x.com/NYTimesPR/status/2037648223771263082Translated Japanese tweets: https://x.com/melonneet40/status/2038020624015315289, https://x.com/rambling_28/status/2038041455999246422Japanese people singing “Country Roads”: https://x.com/harukaawake/status/2038081269830222259Trailer for “Nuremberg” (now on Netflix in the US): https://www.youtube.com/watch?v=WvAy9C-bipYTimestamps:0:00 Intro1:24 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at https://Plaud.ai/twist and use code TWIST for 10% off!3:05 We're Claw-pilled once again; it's an all AI Agent showcase6:15 Google AI PM Shubham Saboo's Top 5 OpenClaw tips10:14 Quo (formerly OpenPhone) gives you a clean, modern way to handle every customer call, text, and thread all in one place. Try it free at https://quo.com/TWiST.13:26 Tip #1 — Onboard your agent like a new hire17:31 Tip #2 — Talk to your agents constantly19:02 Tip #3 — Put your agents on a schedule19:51 LinkedIn Jobs - Hire right, the first time. Post your first job and get $100 off towards your job post at https://LinkedIn.com/twist.23:41 Tip #4 — Add cross-agent memory29:33 Tip #5 — Let your agents self-improve29:51 Iru unifies identity, endpoint security, and compliance into one platform. TWiST listeners get 20% off when they book a demo at https://iru.com/twist!34:03 Why Jason says founders should avoid journalists38:24 How biased IS the New York Times?47:02 DEMO: Co-founder Mike Nosov shows us MoltWorld51:14 But what's the utility of this?1:05:15 DEMO: Haakam Aujla presents AgentMail (YC S25)1:09:59 How does AgentMail make money?1:17:03 How Grok Translations are creating cross-cultural dialogue on XSubscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpFollow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisCheck out all our partner offers: https://partners.launch.co/Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanis