Podcasts about modular

Take a Network Break! It’s a Red Alert double feature for Oracle’s Internet Directory LDAP server and Cisco’s Secure Workload Software. In tech news, US law enforcement and intelligence agencies release a joint advisory warning of active threats against Siemens PLCs, Marvell wins a deal to make chips for Google that could bring billions in... Read more »

Take a Network Break! It’s a Red Alert double feature for Oracle’s Internet Directory LDAP server and Cisco’s Secure Workload Software. In tech news, US law enforcement and intelligence agencies release a joint advisory warning of active threats against Siemens PLCs, Marvell wins a deal to make chips for Google that could bring billions in... Read more »

Take a Network Break! It’s a Red Alert double feature for Oracle’s Internet Directory LDAP server and Cisco’s Secure Workload Software. In tech news, US law enforcement and intelligence agencies release a joint advisory warning of active threats against Siemens PLCs, Marvell wins a deal to make chips for Google that could bring billions in... Read more »

This interview is disseminated on behalf of Buffalo Potash Corp.Buffalo Potash Corp. (TSXV: BUFF | OTCQB: BLPTF | FRA: VU5) President and COO Quinton Hardage, P. Eng., PMP, presents the company's strategy for advancing the Disley Project in Saskatchewan.He discusses Buffalo's patented Horizontal Line Drive Selective Solution Mining technology, ongoing development of its 125,000-tonne-per-year Initial Production Module, and plans to scale production following its successful implementation.Learn more: https://www.buffalopotash.ca/Watch the full YouTube interview here: https://youtu.be/t0pIAWu0xDgAnd follow us to stay updated: https://www.youtube.com/@stockstowatchofficial 

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.

Guests:Rowan BarlowOrla Hegarty, Assistant Professor at the UCD School of ArchitectureCiara O'Brien, Business & Technology Journalist with the Irish TimesSarah O'Neill from shynesdesign.ie

In this episode we start with a noisy complex binary comparator patch and add layers to build an ambient/noise patch from there!Patreon: https://www.patreon.com/nullphiinfinity Bandcamp:  https://nullphiinfinity.bandcamp.com/Instagram: https://www.instagram.com/nullphiinfinity/Youtube: https://www.youtube.com/@Nullphiinfinity  ★ Support this podcast on Patreon ★

But can you be a champion of our goo-------------------------------------------------------Follow Modular on twitter: https://twitter.com/TheModularMediaFollow Modular on Bluesky:https://bsky.app/profile/modularmedia.bsky.socialFollow Modular on Tumblr:https://www.tumblr.com/modularmediaAll Modular Media Links:https://linktr.ee/TheModularMediaHub-------------------------------------------------------------------Co-hosted by Chris Gaston: https://www.youtube.com/@BoingoRider https://bsky.app/profile/boingorider.bsky.socialhttps://twitter.com/boingo_rider https://boingo-rider.tumblr.com/https://discord.gg/H83j5PGCo-Hosted by Cody Burke:https://www.youtube.com/@snowburke83https://twitter.com/snowcone83https://snowburke.tumblr.com/https://www.instagram.com/never_robot/https://www.twitch.tv/snowcone83Co-Hosted by Buster Corp: https://www.youtube.com/@BusterCorphttps://bsky.app/profile/bustercorp.bsky.socialhttps://twitter.com/BusterBluey3https://busterscorp.tumblr.com/Co-Hosted by Simeon Scotthttps://simeonslinks.carrd.co/

We find out that the modular construction of today is quite different than the modular home of yesterday year. Totally customizable and indistinguishable from a stick built home. Lots to learn on this episode of the Real Estate Roundtable! Join us!.

**Jeep Talk Show – New Jeep Scrambler Plans, Battery Trickle Charging Tips, Video Voicemails & More** Welcome back to the Jeep Talk Show, the longest-running and best Jeep podcast out there! Tony and Josh (back in the mix) dig into Jeep's upcoming Scrambler, why they think it's going to happen around 2028, the design details that have them both excited and pissed off (IFS front *and* rear? Side steps? Modular rear seats that flip for tailgating? A K5 Blazer-style removable hatch?), and whether this "halo" desert-racer two-door pickup is actually for Jeep people or just another attempt to chase the side-by-side market. They also cover: - Real-world trickle charger problems (and a better, simpler solution) - How to leave a proper video voicemail (and why the Camflare redirect is legit) - A listener video voicemail from Nathan in Pittsburgh - The new "Most Popular / Best Of" page on the website that remembers what you've already heard - Bonus segment teaser: Hard shackles vs soft shackles (YouTube members & Patreon only) This is pure Jeep Talk Show — honest opinions, zero corporate filter, and plenty of laughs. ### Timestamps 00:00 Intro and Call to Share 01:27 Scrambler Intro and Bonus Segment Tease 02:32 Scrambler Prospects and Voice Mail 03:27 Scrambler Concept and Naming Debate 04:58 Scrambler Timeline and Near‑Term Outlook 06:59 Scrambler Design and Utility Features 11:34 Scrambler Versatility, Halo Role, and Engines 13:05 Side‑Step Concerns and Design Critique 14:15 Mic Glitch and Quick Design Note 14:42 Editing Interlude and Halo Concept 15:27 Clarifying Halo Terminology 16:44 Engine Feasibility Discussion 18:10 Drivetrain Options and SRT Overview 19:21 Desert Racing Focus 20:13 IFS vs Solid Axle and Market Strategy 22:01 Front‑End Design and Styling Highlights 25:02 Cost Concerns and Trophy‑Truck Aspirations 26:17 Trophy‑Truck Speculation 27:29 Jeep Strategy vs Side‑by‑Side Market 30:46 Success Outlook and Listener Feedback 32:22 Listener Engagement Options 32:42 Interactive Listening Challenges 34:12 CamFlare App Explanation 38:51 CamFlare Installation and Submission Guidance 40:19 Personal Updates and Lockbox Mention 41:16 Trickle Charging Tips and New Charger 47:08 Bluetooth Load Tester Overview 48:16 Navigating Podcast Archive 50:07 Meta Discussion on Episode Length 53:26 Closing Remarks and Disclaimer ### Key Highlights - Jeep is finally admitting past mistakes and aiming the Scrambler at the high-speed desert / side-by-side crowd instead of pure rock crawling. - Expected powertrains include the 2.0 Hurricane, 3.6 Pentastar, and a possible 6.4 392 Hemi SRT version. - Tony's take: cool looking, interesting, but IFS (especially rear) means it's not a real Jeep for him. - Practical garage tip: many cheap trickle chargers draw power from the battery when "off." A simple automatic maintainer + smart plug schedule fixes it. - New website feature: jeeptalkshow.com/bestof (or "Most Popular" in the menu) ranks the top 50 episodes by downloads and remembers what you've already played. **Share the show.** Seriously. Even if you only half-enjoy it, tell somebody. We're not asking for much — just hit the share button. Drop a comment with your thoughts on the Scrambler (love it, hate the IFS, want a solid-axle version, etc.). Leave us a video voicemail at jeeptalkshow.com/voicemail (30 seconds, landscape mode, download Visit our website: https://jeeptalkshow.com/ Watch/Listen on Spotify https://jeeptalkshow.com/spotify Join our Discord Server: https://jeeptalkshow.com/discord Subscribe to our newsletter: https://jeeptalkshow.com/newsletter Help Support the show via Patreon: https://jeeptalkshow.com/patreon

Eirebus, a coach operator of the Swords Express in Dublin, has applied to Fingal County Council seeking to temporarily retain 19 modular accommodation units and six mobile home units at its depot in Dublin 15 for three years.It comes after new regulations surrounding modular homes and planning come into effect across the country.Joining Shane to discuss this is Brendan O'Sullivan, Head of University College Cork's Planning School.

superheroes are pretty good-------------------------------------------------------Follow Modular on twitter: https://twitter.com/TheModularMediaFollow Modular on Bluesky:https://bsky.app/profile/modularmedia.bsky.socialFollow Modular on Tumblr:https://www.tumblr.com/modularmediaAll Modular Media Links:https://linktr.ee/TheModularMediaHub-------------------------------------------------------------------Co-hosted by Chris Gaston: https://www.youtube.com/@BoingoRider https://bsky.app/profile/boingorider.bsky.socialhttps://twitter.com/boingo_rider https://boingo-rider.tumblr.com/https://discord.gg/H83j5PGCo-Hosted by Cody Burke:https://www.youtube.com/@snowburke83https://twitter.com/snowcone83https://snowburke.tumblr.com/https://www.instagram.com/never_robot/https://www.twitch.tv/snowcone83Co-Hosted by Buster Corp: https://www.youtube.com/@BusterCorphttps://bsky.app/profile/bustercorp.bsky.socialhttps://twitter.com/BusterBluey3https://busterscorp.tumblr.com/Co-Hosted by Simeon Scotthttps://simeonslinks.carrd.co/

The following article of the Tech industry is: “Modular Automation: Redefining Automotive Manufacturing in Mexico” by Chad Durkee, Global Fueling & Technology Scouting Innovation Manager, FORVIA.

Podcast Chapters – Board Game Snobs: Cysmic Distilleries 0:00 – Intro & Banter Welcome, car washing, driving stories, near-misses, YouTube rabbit holes, cruises, flying anxiety, concerts, music tastes, Shazam, etc. 22:57 – Distilled Full discussion and review of Distilled: market / distill / sell-age phases, push-your-luck card shuffle, money tightness, recipes, upgrades, set collection vs aging, ratings. 34:09 – Cysmic Full discussion and review of (C-Y-S-M-I-C). Modular collapsing board, asymmetric factions, rocket-building / blueprints, action-card burn mechanic, combat, artifacts, player-count issues, theme delivery, overall rating. 56:01 – Outro  

In this episode, Carla Goddard shares her journey from construction to building Vicinity, a platform revolutionizing new home sales by focusing on lifestyle and transparency. Discover how her innovative approach is transforming the real estate industry and what trends to watch for in the coming years.   Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true 'white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a "mini-mastermind" with Mike and his private clients on an upcoming "Retreat", either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas "Big H Ranch"? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

What are the benefits of choosing or owning a modular home? The stringent factory specifications will ensure consistent quality, better timing, and stable pricing… Which can eliminate many problems associated with building a new home. Join this episode of the Real Estate Roundtable to learn more.

An update on life inside a modular home - with its designer: 19-year old Londoner Ribal Zebian.

Barbara Cohen, Chief Information and Innovation Officer at Brandeis Marin, joins the podcast to discuss independent school technology department staffing and leadership. Cohen shares strategies for modular job design, aligning technology initiatives directly with school mission, and facilitating constructive parent dialogues regarding student device usage and social health.Brandeis Marin — An independent K–8 Jewish day school in San Rafael, California, where Barbara Cohen serves as Chief Information and Innovation Officer.The Anxious Generation — Book by Jonathan Haidt utilized by Brandeis Marin for community-wide reading and dialogue on youth technology use and supervision.The Six Types of Working Genius — Team assessment framework by Patrick Lencioni referenced for identifying individual natural talents and balancing organizational team dynamics.DISC Profile — Workplace behavior assessment tool used by technology leaders to analyze team communication preferences and working styles.Prizmah: Center for Jewish Day Schools — Network supporting Jewish day schools through peer cohorts, leadership development, and educational technology collaboration.

Today for "Rails Report #4":-- When people talk about AI agents, they usually focus on what the agent can do. Can it plan? Can it execute? Can it make decisions? Can it complete a task end to end?But once agents start interacting with real-world systems, another question becomes just as important: how do we know what, or who, we are trusting? That is where modular verification matters.ERC- 8004, in simple terms, points toward a more structured way for agents and systems to prove things about themselves. Instead of relying on vague assumptions or one-off integrations, the idea is to create a modular layer where identity, capabilities, permissions, and verification can be checked more cleanly. Why does that matter?Because agents are only useful if they can operate in environments where other systems are willing to trust them enough to let them act. If an agent is going to move value, access services, trigger workflows, or interact with financial rails, then the system around it needs some way to verify that it is legitimate, authorized, and behaving within bounds.Today, that kind of trust is often handled manually, or through fragmented point solutions. One platform does one check. Another platform does another. A third layer tries to enforce policy. It works, but it is brittle. Modular agentic verification is interesting because it suggests a more composable future. Instead of forcing every application to reinvent trust from scratch, you can build a shared framework where different components handle different parts of the verification problem.That could mean proving an agent's identity. It could mean checking what permissions it has. It could mean validating that it is allowed to perform a specific action. And it could mean making those checks interoperable across systems.The big picture is simple: as agents become more capable, verification becomes more important. Without verification, agents are just software that can act. With verification, they become software that can act inside systems that care about trust, compliance, and accountability.So when we talk about ERC-8004 and modular agentic verification, we are really talking about the plumbing that makes autonomous systems usable in the real world. Not just smarter agents, but trustworthy ones.And once that layer exists, a lot of what comes next in finance, infrastructure, and automated workflows becomes much easier to imagine.-- The podcasts are authored, edited and produced by Raph Grieco (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠raphael-grieco.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠olivecapital.vc⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠).

John Cummins, Minister of State for Local Government and Planning, discusses new planning exemptions which will allow modular homes in gardens.

This week will see regulations on the building of modular homes relaxed as the Government attempts to fix the housing crisis. However, some are concerned about how the new laws could affect Ireland's most vulnerable. Labour TD for Limerick City and spokesperson for Housing, Conor Sheehan, joined Jonathan Healy to discuss this

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)1952

In this episode we start out with Strega delay circuit noise through the VC F3DB and create an atmospheric textural patch from there!Patreon: https://www.patreon.com/nullphiinfinity Bandcamp:  https://nullphiinfinity.bandcamp.com/Instagram: https://www.instagram.com/nullphiinfinity/Youtube: https://www.youtube.com/@Nullphiinfinity  ★ Support this podcast on Patreon ★

In this episode, Juan Hernandez shares his innovative real estate projects in San Bernardino, including the first modular home community, and offers insights on community impact, law changes, and building investor relationships.   Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true 'white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a "mini-mastermind" with Mike and his private clients on an upcoming "Retreat", either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas "Big H Ranch"? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

The Alan Cox Show

The Alan Cox ShowSee omnystudio.com/listener for privacy information.

Qualcomm just held its 2026 Investor Day, and the headline number is a new $15 billion AI data center revenue target for 2029 — up from essentially zero two years ago. We break down whether this fabless smartphone-royalty giant can actually pull off a pivot into AI data centers.We cover the AlphaWave Semiconductor acquisition and why Arm-based data center networking IP matters to hyperscaler customers, Qualcomm's 3D-stacked silicon packaging approach designed to work around the high bandwidth memory bottleneck amid the 2026 memory shortage, and the Modular acquisition — an AI-native software platform positioned as a potential CUDA alternative. On fundamentals, we walk through the revenue mix as smartphone growth slows to a 5% CAGR following the Apple wind-down, and why automotive and IoT are beating original targets. We close with a reverse DCF on the ~$190 stock price to see how much growth is already priced in, and why QCOM still holds a place in our portfolio as a diversified, value-oriented semiconductor stock.Content is for general information and entertainment only, not individual investment advice. All investing involves risk, including loss of principal. CSI owns shares of Qualcomm.Semi Insider members get full access to our research platform and tools. Join at chipstockinvestor.com.

In this episode we start with a guitar/gameboy sample in the Morphagene and build a beat-based patch around it!Patreon: https://www.patreon.com/nullphiinfinity Bandcamp:  https://nullphiinfinity.bandcamp.com/Instagram: https://www.instagram.com/nullphiinfinity/Youtube: https://www.youtube.com/@Nullphiinfinity  ★ Support this podcast on Patreon ★

We've been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we're excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.The cloud was built for developers. But agents are now changing that.The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.However, agents don't have that luxury. Now in this new era of agents, everything has to be tighter.They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:At the time, Modal was just a teeny little company with a $17M Series A.Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don't fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.We go deep on Modal's AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal's capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.We discuss:* Why Kubernetes wasn't built for bursty AI workloads* How Modal started as a better runtime before becoming an AI cloud* Why Modal added GPUs before ChatGPT* The shift from developer experience to agent experience* Why observability matters when agents are writing the code* Elastic inference for custom models across audio, video, robotics, and comp bio* GPU snapshotting, cold starts, and why inference workloads are so bursty* Why RL rollouts can require 100,000 sandboxes* DeFlash, speculative decoding, and frontier-level inference performance* Auto Endpoints and making optimized inference easier to deploy* What Modal adds beyond vLLM, SGLang, and raw GPU rental* Modal's 17-cloud capacity pool and supercloud strategy* Networked sandboxes, sidecars, private IPv6, and RDMA* Serverless multi-node training for post-training and research workloads* Auto-research, model-guided sweeps, and agents launching GPU experiments* Compute strategy, capacity planning, and batch tiers* Why production agents need specialized sandboxes and hard guardrails* Modal's take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal BenchAkshat Bubna* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103* X: https://x.com/akshat_bModal* Website: https://modal.comTimestamps00:00:00 Introduction00:00:39 Modal's origin and why Kubernetes wasn't enough00:04:32 Developer Experience → Agent Experience00:06:21 Modal's AI cloud primitives00:09:14 Sandboxes, agent loops, and proto-Cognition00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes00:15:24 DeFlash, speculative decoding, and Auto Endpoints00:19:59 Production-grade inference beyond raw GPUs00:22:00 Background agents, Ramp Inspect, and the agent lifecycle00:24:08 Modal's 17-cloud supercloud strategy00:26:40 Networked sandboxes, private IPv6, and RDMA00:32:48 Multi-node training, post-training, and auto research00:37:36 Compute strategy, capacity planning, and batch tiers00:40:55 Open models, real-time AI, and production agent infra00:43:06 Hard guardrails, managed agents, and specialized sandboxes00:46:06 Why AI made infrastructure exciting again00:48:30 Model APIs, differentiated products, and agentic video00:51:50 CI, coding-agent infra, SDKs, and Modal Bench00:57:28 Closing ThoughtsTranscriptIntroduction: Modal, Series C, and the Art PartySwyx [00:00:00]: We're here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.Akshat [00:00:10]: Thank you.Swyx [00:00:11]: Your party yesterday was amazing.Akshat [00:00:15]: Yeah.Swyx [00:00:15]: From all the photos and all the swag.Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.Modal's Origin: A New Runtime Beyond KubernetesAkshat [00:00:39]: I first met Eric, who's the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It's because you have to run them on Kubernetes. Kubernetes is hard to manage. It's not built for burstiness and, custom images,Swyx [00:01:03]: YeahAkshat [00:01:03]: It has a terrible developer experience.Swyx [00:01:05]: And I'll, I'll interjectAkshat [00:01:06]: YeahSwyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there's a bit more of the story there from Spotify and all those things.Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.Akshat [00:01:26]: Yeah.Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it's a very useful primitive in itself. It's There's a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would've been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.From Serverless Containers to GPU WorkloadsSwyx [00:02:19]: Nice.Akshat [00:02:19]: We just didn't think it would be that big of a deal.Swyx [00:02:22]: Yeah, just like add A100.Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?Akshat [00:02:28]: Yeah. Primarily it's just, none of the tooling that was out there was built for, one, a really great developer experience, and also there's a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there's just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.Software-Defined Infrastructure and Decorator-Based DXSwyx [00:03:13]: Yeah. Yeah. Be nice. I don't know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.Akshat [00:03:22]: Yeah, the self-provisioningSwyx [00:03:23]: Self-provisioning.Akshat [00:03:24]: Yeah.Swyx [00:03:24]: Yeah. I can't even remember my own post.Swyx [00:03:26]: And then you put me on the landing page.Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?Akshat [00:03:37]: Yeah.Swyx [00:03:37]: Was that a big part of the originalAkshat [00:03:39]: YesSwyx [00:03:39]: Story or it was just like a DX layer?Akshat [00:03:41]: That was, really important because we really didn't want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you're doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that's more expressive and dynamic. and so yeah, that was always a very important part.Swyx [00:04:04]: Then the pushback is this is a DSL.Akshat [00:04:07]: Yeah.Swyx [00:04:07]: It's you're closed source. I am locked into Modal.Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you're using, how you're scaling things up, but you still own the code.Akshat [00:04:27]: And that's, that's been an important, part of our story, even as we do inference now.Swyx [00:04:32]: Yeah.Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there's very agent native primitives that are different than if I'm doing this myself, right?Developer Experience → Agent ExperienceAkshat [00:04:54]: We've changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that's not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.Akshat [00:05:38]: Yeah.Swyx [00:05:38]: Well, the negative thesis now is that nobody's looking at their code anymore, so there's no point.Akshat [00:05:44]: Yeah, people aren't looking at code. one thing we still see is really important is observability.Swyx [00:05:51]: Yeah.Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what's going on and, make judgment calls and whatnot. and that's I feel like, Maybe more important now than looking at the code itself.Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.What Modal Is For: AI Cloud PrimitivesAkshat [00:06:21]: Yeah.Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.Swyx [00:06:31]: People ask you, “What are you for?” You're like, “ I don't know. We can do this, we can do that.”Vibhu [00:06:36]: Well, I'd be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There's a lot you guys do, sandboxes, GPUs, everything. How do you answer?Akshat [00:06:46]: Modal is a cloud platform that's built for, where we've built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.Akshat [00:07:00]: But we're building a lot moreSwyx [00:07:02]: I noticed you didn't say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.Akshat [00:07:09]: Yeah, absolutely. We're, we're not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they're, they're, they're just shaped differently.Working Alongside Frontier StartupsVibhu [00:07:26]: I think you're building a lot of it alongside the startups, right? They're innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they're, they're innovating with you, right? And that's not something AWS is doing directly with.Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We're a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They're like, “Yeah, I just. I am embedded inside of Cog.”Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him overSwyx [00:08:07]: Yeah.Akshat [00:08:07]: Because, the latency of communication was too high otherwise.Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.Vibhu [00:08:16]: Yeah.Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.smol developer, Sandboxes, and Proto-CognitionAkshat [00:08:39]: Yeah, you blew up on Hacker News and,Swyx [00:08:41]: YeahAkshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.Akshat [00:08:55]: Right.Swyx [00:08:56]: If only I had, like, stuck to it.Swyx [00:08:58]: Like, that was like, if - did you say draw the tech treeAkshat [00:09:00]: AbsolutelySwyx [00:09:00]: You're just like, “Yeah, like, probably this will happen.”Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon usSwyx [00:09:04]: I just didn't realize.Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.Swyx [00:09:14]: Yeah.Akshat [00:09:14]: This is like twenty-three.Swyx [00:09:15]: Yeah.Akshat [00:09:16]: So we builtSwyx [00:09:17]: You introduced a new API right after that.Akshat [00:09:18]: Yeah.Swyx [00:09:19]: Yes.Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developerSwyx [00:09:28]: Smol developerAkshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.Swyx [00:09:33]: Loops are hot these days.Vibhu [00:09:34]: It's the looper.Akshat [00:09:34]: Yeah.Vibhu [00:09:35]: Loops in. When was this, twenty-three?Akshat [00:09:38]: Yeah.Vibhu [00:09:39]: A small check.Akshat [00:09:39]: Yeah.Swyx [00:09:39]: It's like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?Swyx [00:09:46]: Like, you're just trying to like. They're not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.Akshat [00:09:55]: Yeah.Akshat [00:09:55]: I don't remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answerVibhu [00:10:08]: Of course they will get betterSwyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environmentAkshat [00:10:15]: RightSwyx [00:10:15]: Sell it for like ten billion dollars to Meta.Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this isAkshat [00:10:23]: Yeah, of courseSwyx [00:10:23]: The funny machine. Like, it's like, it's about the hardware.Akshat [00:10:28]: It's hard to have that inherent conviction that the stuff will get that much better.Swyx [00:10:33]: In retrospect, it's so f*****g obvious.Akshat [00:10:36]: Fair enough.Swyx [00:10:37]: Like, what else were we doing back then? I don't know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn't blow up until, like, last year.Akshat [00:10:49]: Yeah.Swyx [00:10:50]: So there was like a couple years of quietness.Akshat [00:10:52]: Exactly, yeah. We wereVibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he's like, “ there's this cool company, Modal. They'll like spin up a GPU sandbox, we can throw it on there. They'll take a Hugging Face link.” And like there's so much value just right there, right? Like instant hosting, spin it up, spin it down. It'll stay cold, but we run the demo a few days later, it'll come back up and like all this stuff in retrospect, like it's still what we needed like today.Akshat [00:11:27]: Yeah, it's still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it's it's not about scaling from zero to one, but it's how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It's the same shape problem.Elastic Inference, GPU Autoscaling, and Custom ModelsVibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?Akshat [00:11:53]: Yeah.Vibhu [00:11:53]: They had a. “We'll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.Vibhu [00:12:01]: When you look at workloads like that, you're doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That's the example for we do need it, right?Akshat [00:12:12]: Yeah. Well, so I'll, I'll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it's like diurnal. It's Some days, like the company will do a launch and, they'll need like, way more. And it's not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.Akshat [00:13:20]: So that's like our sortVibhu [00:13:22]: And thatAkshat [00:13:22]: YeahVibhu [00:13:22]: That in and of itself is a huge category. There's a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that's carved into its own niche for language models, at least right now.Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.Vibhu [00:13:41]: Yeah.Akshat [00:13:41]: Because we found that it's not universally true that everyone else can autoscale, and we've gone deeper into it on the tech side by, we've incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it's That's why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we'll see, a lot of companies, before they have a training run, they'll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you're doing RL. RL is justRL, Batch Jobs, and 100,000 SandboxesVibhu [00:14:28]: Or commerceAkshat [00:14:28]: Insanely bursty.Vibhu [00:14:29]: Yeah.Akshat [00:14:30]: Yeah. Like when you're doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.Vibhu [00:14:37]: Yeah. I'm curious if you've seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced thisAkshat [00:14:45]: YeahVibhu [00:14:45]: They're, they're trying to do training. That also seems like a different workload, right? If you're doing training twenty-four/seven per se, there's a very weird dynamic of how you're using GPUs between people and whatnot, but seems like something you guys would work for.Akshat [00:15:00]: As you said, we're, we're fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It's possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we're, we're just waiting to seeVibhu [00:15:23]: YeahAkshat [00:15:23]: How it shakes out.Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?LLM Inference, DeFlash, and Speculative DecodingAkshat [00:15:32]: I guess we've been going much deeper into LLM inferenceVibhu [00:15:35]: YeahAkshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren't, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we've been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we've open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we're thinking about hereVibhu [00:16:23]: I thought this wasAkshat [00:16:24]: YeahVibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.Akshat [00:16:33]: Yeah.Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.Vibhu [00:16:44]: Yes.Akshat [00:16:44]: I will, yes.Vibhu [00:16:45]: I think, likeAkshat [00:16:46]: YeahVibhu [00:16:46]: So we've covered like Eagle and all thisAkshat [00:16:47]: YeahVibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.Akshat [00:16:51]: Yeah.Vibhu [00:16:51]: I think it doesn't hurt, right?Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it's faster is if you're predicting, one token at once, you're bound by memory bandwidth. But if you can batch the verification of, the draft model, then you're much more efficient using compute, and it's faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that's, multiple times of, the original model speed. and well, that's what we highlight here. It's Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decreaseVibhu [00:17:47]: Like two to four X.Akshat [00:17:48]: Yeah, exactly.Vibhu [00:17:48]: Without much head-on performance.Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,Vibhu [00:17:57]: I meant quality performanceAkshat [00:17:58]: Probably not by muchVibhu [00:17:58]: But yeah. I thinkAkshat [00:17:59]: So there's no drop in quality performanceVibhu [00:18:01]: YeahAkshat [00:18:01]: Because you're always. You're never accepting a token that the big modelVibhu [00:18:04]: It's strictly betterAkshat [00:18:05]: YeahVibhu [00:18:05]: Or it's same.Akshat [00:18:06]: Exactly.Vibhu [00:18:07]: Right. Yeah.Akshat [00:18:08]: And so we've been working a bunch on DeFlash, which is a block-based speculator. so it's instead of predicting, one token at a time, it's predicting a block. And we've been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it's it's something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we're, we're launching is, as you run an auto endpoint, we shadow trafficAuto Endpoints and Frontier-Level PerformanceVibhu [00:18:54]: Do you want to explain what auto endpoints are?Akshat [00:18:57]: Yeah.Vibhu [00:18:57]: I lovely, yeah.Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don't wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we've made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there's full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It's it's not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that's compatible, you serve open model. If someone was to do this themselves, what's the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What's the delta that plugging into something this, like this offers outside of the benefit of, scaling?Production Inference Beyond Raw GPUsAkshat [00:20:34]: It's interesting because we've taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.Vibhu [00:21:20]: Yeah. And I will say it's not that straightforward to just. like what I said is easier said than done, right?Akshat [00:21:26]: Yeah.Vibhu [00:21:27]: It's I think still for the average person, still hard to just gut check using different. There's, there's quite a bit of combinations you can make there. the trade-offs aren't really known at face value.Akshat [00:21:40]: Yeah. it's it's not just that. I think it's it's that running production-grade inference is a hard infer problem.Vibhu [00:21:49]: YeahAkshat [00:21:49]: Even if you subtract out the autoscalingVibhu [00:21:50]: YeahAkshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.The Model and Agent LifecycleVibhu [00:22:00]: There's a lot of innovation that you can do here. I think, it's very interesting that you're starting to encroach on, like as you become a full cloud, you're starting to encroach on other people's turf.Vibhu [00:22:09]: What will you not do?Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we're focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let's say, sandbox, do persistent storage, a whole bunch of other stuff.Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.Akshat [00:22:42]: Yeah.Vibhu [00:22:42]: And RealInspect also is on Modal.Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.Ramp Inspect and Background AgentsVibhu [00:23:02]: Yeah. That's the new CTO of, Ramp right there.Akshat [00:23:05]: Yeah, Rahul.Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guysThe Inference Inflection: CPU, GPU, and Co-LocationVibhu [00:23:19]: You weren't that much in the GPU game, and now you're all about, inference. And one of the points that I hinged on for Jensen's keynote at GTC this year was, what we're calling like the inference inflection, right? That let's say in AI workloads or machine learning workloads, it used to be like, let's call it eight to one GPU to CPU, and now it's more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.Akshat [00:24:01]: Yeah.Vibhu [00:24:02]: GPU, CPU. And now it's like just constantly, and you just have to locate everything.Seventeen Clouds and the Supercloud StrategyAkshat [00:24:08]: Yeah. And that's one of the things that, again, we see as, something appealing about Modal, which is we've built this capacity pool that spans, 17 cloud providers, so we're, we're very good at Running on various kinds of cloud capacity across the worldSwyx [00:24:24]: You don't have your own data centers?Akshat [00:24:25]: We don't have our own data centers. We just run across a lot of neo cloudsSwyx [00:24:29]: Yeah. AreAkshat [00:24:30]: Metal providers.Swyx [00:24:30]: Yeah. Question mark.Swyx [00:24:31]: Yeah. You're, you're running the math, and you're like, “What's the cutover point where you're like.”Akshat [00:24:36]: Yeah, it's a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it's worked out well because there are so many other people building data centers that we're able to work effectively with them, and again, focus on what makes us, special.Swyx [00:24:55]: Yeah.Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. LikeAkshat [00:25:00]: The,Swyx [00:25:01]: Which was the most interesting one?Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that's why it's something we've invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,Swyx [00:25:30]: YeahAkshat [00:25:30]: You as a user would be able to.Swyx [00:25:32]: It's a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.Akshat [00:25:41]: Yeah. That's, that's, that's the idea. and so I guess when you mentioned colocation, that's, that's another interesting thing where, one thing we've seen is people come to us when they want, very specifically located, CPUs or GPUs, like they wantSwyx [00:25:57]: Oh, they pin it in likeAkshat [00:25:58]: YeahSwyx [00:25:58]: EU?Akshat [00:25:59]: Exactly. Or EU, US.Swyx [00:26:01]: Right. Data resiliencyAkshat [00:26:02]: AustraliaSwyx [00:26:02]: Locality thing or performance or what?Akshat [00:26:04]: It's either data locality or latency, yeah.Swyx [00:26:07]: Yeah.Akshat [00:26:07]: Like, you want your. They're running sandboxes and model. They want them to be right next to aSwyx [00:26:10]: Yeah, it's easy thenAkshat [00:26:11]: YeahSwyx [00:26:12]: To. That is important in all those things. and so, like, you've accidentally, I don't know if it's accident, but, like, you've built the perfect primitive for agents to express themselves. And then, like, it's almost very funny how every extra development just involves more file system, just involves more CPU.Akshat [00:26:30]: Yeah.Swyx [00:26:31]: Just like the things that you already have. I don't know much about, if there's any, like, networking usages that are interesting, but you've also done some good work on networking.Networking, Sidecars, Private IPv6, and SandboxesAkshat [00:26:40]: Yeah, that's exactly right. Like, we're just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.Swyx [00:26:49]: YeahAkshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we haveSwyx [00:26:57]: For like a Docker cluster type thing.Akshat [00:26:59]: Yeah.Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?Akshat [00:27:02]: Compose.Swyx [00:27:03]: Compose type thing.Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.Swyx [00:27:23]: Yeah.Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we've, we've had to build a lot of that stuff ourselves.Swyx [00:27:38]: Yeah.Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we're seeing. We have support for that for a different reason, and yeah, we'll see if that becomes stable.Swyx [00:27:52]: Like, just an open socket. It's a. This is directly like mTLS.Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.Swyx [00:28:01]: Yeah.Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven't talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.Akshat [00:28:28]: So it's like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that's truly serverless. and we did the overlay network for that. But then we've seen that people are using it for other reasons, and, I'm intrigued to yeah, what would people do with it.Swyx [00:28:59]: Build primitives and let people figure it out, right?Akshat [00:29:01]: Yeah, exactly.Swyx [00:29:02]: You put out a pretty interestingAkshat [00:29:03]: They're like, they read the docs webpage. Let me use thatSwyx [00:29:06]: YeahAkshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they're using it.RDMA, Memory Movement, and Distributed TrainingSwyx [00:29:12]: Huh.Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I'm sure someone found it. It's found it to be a lot more efficient before you made a thing out of it, right?Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me becauseAkshat [00:29:51]: YeahSwyx [00:29:51]: So I review 2,200 submissions for the World's Fair.Akshat [00:29:56]: Yeah.Swyx [00:29:57]: And then I got this from John OsterhoutAkshat [00:29:58]: HuhSwyx [00:29:59]: Who I don't know if. Do John Osterhout by name?Akshat [00:30:01]: The name sounds familiar.Swyx [00:30:02]: He published a. He's a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I'm like, you wouldn't think that this guy, who is like operating systems guy, would care about RDMA.Akshat [00:30:20]: I, it makes sense to me because I,Swyx [00:30:24]: This is the cloud, right? YeahAkshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there's a lot of degrees of freedom, and it is a systems problemSwyx [00:30:41]: YeahAkshat [00:30:41]: Moving memory aroundSwyx [00:30:42]: YeahAkshat [00:30:43]: Scheduling.Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.Swyx [00:30:46]: Is this like the domain of WireGuard as well?Akshat [00:30:50]: Not quite.Swyx [00:30:51]: It's adjacent?Swyx [00:30:53]: Explain everything.Akshat [00:30:54]: Sure.Swyx [00:30:56]: How do we move memory around GPUs?Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you've set up.Swyx [00:31:09]: Yeah.Akshat [00:31:09]: Is it like it's a VPN?Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you're right. It is,Akshat [00:31:16]: Right. Yeah, you already moved on to new topicsSwyx [00:31:17]: A similarAkshat [00:31:18]: OkaySwyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,Akshat [00:31:23]: And you don't need encryption.Swyx [00:31:23]: Yeah.Akshat [00:31:24]: Yeah.Swyx [00:31:24]: This is not encrypted. that's the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you're allowed to do it.Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.Swyx [00:31:39]: Yeah.Akshat [00:31:40]: Yeah. I don't know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that's the bottleneck, is your networking fast enough?Swyx [00:31:59]: Yeah. So I guess you're talking about fully distributed training like, Dialog or something which is like cross data centerAkshat [00:32:06]: That would be, yes.Swyx [00:32:07]: That's the extreme.Akshat [00:32:08]: Yeah.Swyx [00:32:08]: You're in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it's a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networkingSwyx [00:32:40]: OkayAkshat [00:32:40]: Which is the standard that's needed.Swyx [00:32:42]: Okay. So I misunderstood whatAkshat [00:32:43]: 50Swyx [00:32:43]: What part of the stack you wereAkshat [00:32:44]: 50 gigs overSwyx [00:32:45]: YeahAkshat [00:32:45]: If you wentSwyx [00:32:45]: YeahAkshat [00:32:46]: RDMA.Swyx [00:32:46]: Okay.Swyx [00:32:48]: Yeah. I, very impressive work.Multi-Node Training, Post-Training, and Auto ResearchSwyx [00:32:52]: So effectively you're extending like the model philosophy to the training cluster, like, yeah.Akshat [00:32:59]: Yeah. And we're, we're not going for like large scale training runs. the thing that we've built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.Akshat [00:33:31]: Yeah. The other use case we've seen for multi-node training is even if you have a big cluster, your researchers are still doing small runsSwyx [00:33:38]: YesAkshat [00:33:39]: Having elasticity thereSwyx [00:33:40]: Right, sureAkshat [00:33:40]: Matters a lot more.Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.Akshat [00:33:51]: We have a blog post on auto resource and model is,Swyx [00:33:55]: YeahAkshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.Swyx [00:33:59]: So my impression is auto research means many things, likeAkshat [00:34:01]: YeahSwyx [00:34:01]: Anything that Andrej coins. Right now it's still science fair, right? Like not like, I don't know how many people are doing this.Akshat [00:34:08]: We're having a golf.Swyx [00:34:08]: Yeah.Akshat [00:34:09]: I thought the same thing.Swyx [00:34:11]: Yeah, you would know.Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we've automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It'll even run like, NVIDIA inside profiler and it'll like tweak configs and it'll arrive the right thing. it'll change your GPUs both from H200 to B200, and works really well.Swyx [00:34:47]: Nice.Akshat [00:34:47]: So yeah.Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.Swyx [00:34:52]: It's very different from forward-deployed engineering from other people.Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they're like applied inference researchers or applied training researchers.Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they're good, they're just like post-sale type of thing?Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with themSwyx [00:35:13]: YeahAkshat [00:35:13]: Matters a lot.Swyx [00:35:14]: They want the same thing.Akshat [00:35:15]: Yeah.Swyx [00:35:15]: ?Akshat [00:35:15]: But it's it's not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.Swyx [00:35:23]: Okay. Let's spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there's all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.Auto Inference and Modal BenchAkshat [00:35:51]: Yeah, like,Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.Akshat [00:35:54]: Yeah, - So the stuff I've seen people do with it is nowhere on the architecture level. It's pretty much tweaking parameters, but it's it's a hyperparameter sweep that's guided by some model intuition, so it's like much more efficient than, whatever other, sweep you would have.Swyx [00:36:12]: Yeah, it's just, it's just a question of where you want to spend your compute?Akshat [00:36:16]: Right.Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you'll bang out Shakespeare?Akshat [00:36:22]: Yeah, infinite monkey.Swyx [00:36:24]: Yeah, so like the very good for model. and I think it's also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.Akshat [00:36:42]: Yeah. They're, they're surprisingly good. I think like pre Cloud 4 they were not, and then now they're able to shot, stuff out of the box. But we're playing around with releasing like a Modal Bench for like the harderSwyx [00:36:55]: YeahAkshat [00:36:55]: Things, that the LLMs cannot do yet and maybeSwyx [00:36:59]: What's an example of that?Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It's reasoning about that. But they're able to shot, likeSwyx [00:37:23]: Yeah. You can just add a skill to it?Compute Strategy and Capacity PlanningAkshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It's to find things like that, so we can address them in our tool.Swyx [00:37:35]: Tune a skill. Yeah.Akshat [00:37:36]: Yeah.Swyx [00:37:36]: No. it's it's good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.Swyx [00:37:44]: Yeah.Akshat [00:37:45]: We have had a lot of growth, which means that, there's - we've had to be much better aboutSwyx [00:37:53]: PlanningAkshat [00:37:54]: Proactive capacity planning.Swyx [00:37:55]: Yeah.Akshat [00:37:55]: So we have,Swyx [00:37:57]: Which by the way, like it's like a MBA's like dreamAkshat [00:38:00]: YesSwyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on thatSwyx [00:38:13]: Compute strategy?Akshat [00:38:13]: Yeah.Swyx [00:38:14]: I think,Akshat [00:38:14]: I feel like,Swyx [00:38:15]: I think the normies call it FP&A or something.Akshat [00:38:18]: Well, it's more It's it's not FP&A. It's it's There's a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,Swyx [00:38:49]: YeahAkshat [00:38:49]: Based on that.Swyx [00:38:50]: Tokenomics.Akshat [00:38:50]: Yeah.Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.Akshat [00:38:59]: Yeah.Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost becauseAkshat [00:39:12]: OhSwyx [00:39:12]: Compared to everyone else.Akshat [00:39:14]: Yeah. I hadn't thought about that.Vibhu [00:39:16]: We're at a fun time too?Akshat [00:39:18]: Yeah. It's. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it's how you can unlock more value for customers. Like, one of the things we're building now is like a way for customers to get, If they don't care about latency, like get much cheaper pricing and they'll get results back in like next 24 hours or something, like a batch tier essentially.Batch Tiers and Latency-Insensitive WorkloadsSwyx [00:39:47]: Yeah.Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficientSwyx [00:39:53]: Yeah. I feel like they're not as popular. Like those, like the Frontier Labs have all those APIs. They're not as popular as they should be.Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals andSwyx [00:40:08]: OkayAkshat [00:40:08]: Synthetic data prep and there it makes sense.Swyx [00:40:10]: Okay.Akshat [00:40:11]: But it's from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don't care about when they get it back.Swyx [00:40:22]: Yeah. And like they have a reasonable. It's it's also like a cousin to the stopping problem of like, will this finish in time?Akshat [00:40:30]: Yeah. You can bound it.Swyx [00:40:33]: Yeah.Akshat [00:40:33]: Like you can give peopleSwyx [00:40:34]: YeahAkshat [00:40:34]: SLAs on it.Swyx [00:40:35]: Yeah. I think what's, what's interesting is like the next phase of model.Swyx [00:40:38]: Like what, do people expect from you, now that you're established and you're like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?What Modal Builds NextAkshat [00:40:55]: We are building primitives that make our users' lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we're thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven't talked to anyone. It looks somewhat different on other verticals. Like, we're also seeing a lot of real-time, audio-video stuff in there, which is why like, we're working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it's,Akshat [00:41:52]: We're still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there's a lot of other things people will need from this agent stack as they build production agents. So yeah, we're thinking about those other things that fit in there.Swyx [00:42:13]: I want to ask what the other things are.Akshat [00:42:15]: Yeah. I probably should share right now.Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.Akshat [00:42:25]: Yeah.Swyx [00:42:25]: Because so far for me, it's fine. so far for the. the first couple generations of cloud, it's fine. What's different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I'll just kinda spew tokens at you until it like hopefully sparks something.Akshat [00:42:43]: Yeah.Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they're like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.Hard Guardrails vs. LLM-Mediated PermissionsVibhu [00:43:03]: Now you're looping it with a goal and letting it roll.Akshat [00:43:06]: Yeah, I'm, I'm skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.Swyx [00:43:16]: Yeah.Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.Swyx [00:43:20]: But likeAkshat [00:43:20]: YeahSwyx [00:43:20]: Maybe that's old school thinking. Maybe we're the dinosaurs.Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.Swyx [00:43:30]: Like it makes you feel uncomfortable.Akshat [00:43:31]: Yeah, I'm, I'm toldSwyx [00:43:32]: But that's what trusting the LLM is. Like imagine a spherical cow perfect LLM.Akshat [00:43:36]: Right.Swyx [00:43:37]: That it.Akshat [00:43:39]: Maybe.Swyx [00:43:41]: I wanna test the boundaries, right?Akshat [00:43:42]: Yeah.Swyx [00:43:42]: Like, and I don't believe that, but I wanna see where I'm wrong ‘cause that's, that's the consensus.Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that's gonna be a lot of mediated.Managed Agents and Specialized SandboxesSwyx [00:44:00]: There. I'll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.Akshat [00:44:17]: Yeah.Swyx [00:44:17]: What's going on?Akshat [00:44:19]: Yeah, we're, very excited to partner with Anthropic and some of the other foundation labs, will not name who we're also working with. the way we see it is the manage agent thing is a great place to start if you're starting out building an agent and, But then when you get to, building something more production grade, like you're a company that's like Ramp that's building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that's the role that we are trying to play.Swyx [00:45:15]: YeahAkshat [00:45:16]: We don't really have an opinion on the harness, whether it runs - it's a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We'll see where people converge with that.Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?Akshat [00:45:31]: You mean like the OpenPipeSwyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can't remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it's kinda pseudo agent cloud type things.Akshat [00:45:50]: I personally have not played around with them.Swyx [00:45:53]: Yeah.Akshat [00:45:53]: Build agents with them.Swyx [00:45:54]: Everything's bullish Modal, as long as it consumes more infra.Akshat [00:45:57]: That's why we're focusing on the infra layer. It's somewhere where our, relative competence is and, also it's a hard problem to solve.Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don't know if - if there's other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn't really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.Why Infrastructure Became Exciting AgainAkshat [00:46:39]: Yeah.Swyx [00:46:40]: And like now everyone gives a crap.Akshat [00:46:42]: That's true. It is a very exciting time, and I think a lot of that's driven by just the amount of scale all of this stuff needs.Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn't necessarily have thought about it myself, which.Akshat [00:47:00]: We need the predictions.Swyx [00:47:02]: I think there's a lot that you just don't even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?Akshat [00:47:10]: What else is coming up for usSwyx [00:47:11]: Yeah. Where do you see things going?Akshat [00:47:13]: Yeah. I, in generalBiotech, Robotics, and Non-LLM AI WorkloadsAkshat [00:47:15]: It's it's clear that there's there's a huge shift happening. I think one thing that's not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?Akshat [00:47:50]: No. We,Swyx [00:47:51]: You should cloud only.Akshat [00:47:51]: Yeah.Swyx [00:47:52]: Yeah. Okay. But yeah, so what you're saying is like because you're focused on primitives and they're good primitives, you find use cases in all these kinds of things.Akshat [00:48:01]: Yeah.Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.Akshat [00:48:05]: Yeah, absolutely. We're, we'- our goal isn't to only serve the LLM inference market.Swyx [00:48:10]: There are a lot just on the website, the audio,Akshat [00:48:12]: Yeah. We said both onSwyx [00:48:14]: Computational bio images. Yeah, there's a lot here. There's QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.Akshat [00:48:24]: Okay. Yeah.Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.Model APIs vs. Differentiated AI ProductsSwyx [00:48:31]: What's your postmortem on what happened?Akshat [00:48:34]: This is one thing we've stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.Swyx [00:48:50]: Yeah.Akshat [00:48:50]: And we've always wanted to build for companies that are building products and need more flexibility that's not just an API.Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you're saying, you can wrap it into a more fully functioning back end that you run.Akshat [00:49:06]: Yeah. So all of our examples, it's not that spin up this model, here's an API token, use it. They're all code.Swyx [00:49:13]: Okay.Akshat [00:49:13]: And so the point is that this is just an example.Swyx [00:49:16]: Starter code.Akshat [00:49:17]: Yeah. But you can tweak it however you want.Swyx [00:49:20]: Yeah.Akshat [00:49:21]: And if you're like a company building a product, like, computational bio whatnot, yeah.Swyx [00:49:26]: I guess I'm trying to tease out for listenersAkshat [00:49:28]: YeahSwyx [00:49:28]: When does it stop becoming, oh, you're just an API call and you're just a wrapper on API to becoming what you call a product, right?Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?Akshat [00:49:46]: I think there's a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that's more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I'm not saying that's, that successful, in that case. But a better example is like, let's say Suno. because Suno, does not use Modal for training.Swyx [00:50:26]: Mikey on the pod. Yeah.Akshat [00:50:27]: But they use Modal for all their inference and that's because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.Swyx [00:50:41]: It's interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it's a better model or agent that orchestrates video models.Video Agents and Production WorkflowsAkshat [00:50:56]: Oh, interesting.Vibhu [00:50:56]: Language model backbone that can use toolsAkshat [00:50:58]: RightVibhu [00:50:59]: And write code.Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.Akshat [00:51:06]: And I'm not going there through normal video gen.Swyx [00:51:10]: Yeah, that's interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.Swyx [00:51:23]: Run FFmpeg. But likeAkshat [00:51:25]: That's not enough.Swyx [00:51:25]: Yeah.Akshat [00:51:26]: You need to give it Adobe.Swyx [00:51:27]: Yeah, I hadn't put it together with like it would be a video production thing. in my mind these things were going more towards editingAkshat [00:51:36]: Yeah.Vibhu [00:51:36]: Well, shout out Mantis.Akshat [00:51:37]: I think about this a lot.Swyx [00:51:38]: .Akshat [00:51:41]: Yeah. Sorry.Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it's a off.Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happensGitpod/Ona, CI, and Runtime SandboxesSwyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very li

In this Solutions Spotlight episode of Control Amplified, sponsored by Honeywell Process Automation, Keith Larson reports from the 50th Anniversary Honeywell Users Group meeting in Phoenix, Arizona. He was joined by Russ Ford, Honeywell president of projects and automation solutions, to discuss modular approaches to energy resilience and how they can help industrial organizations protect themselves from production interruptions.

James and Frank dig into Slate's modular electric pickup — a $24,950, 205‑mile, no-frills truck you can customize later — and unpack how factory vs dealer options, aftermarket mods and build quality will make or break the idea. They also cover real-world charging (level 1/2/3), fleet and daily-use practicality, and why a DIY, upgradeable EV could reshape affordable vehicle ownership. Follow Us Frank: Twitter, Blog, GitHub James: Twitter, Blog, GitHub Merge Conflict: Twitter, Facebook, Website, Chat on Discord Music : Amethyst Seer - Citrine by Adventureface ⭐⭐ Review Us ⭐⭐ Machine transcription available on http://mergeconflict.fm

Send us Fan MailA modular schedule can collapse over one thing: late decisions. That's why cross-laminated timber (CLT) and modular construction fit together so well and why they can also magnify each other's coordination mistakes if you treat them like conventional builds. Sidney Filippis, Director of Design at Sterling Structural, details what it really takes to bring mass timber into a factory-driven workflow without surprises. Sidney explains how modular teams should think about CLT, whether as a substitute for familiar assemblies or as a structural system that changes how loads, tolerances, and interfaces behave. Sidney also shares the first design assumptions to revisit when moving from steel to mass timber, including lighter weight impacts and the reality that wood, steel, and concrete each bring different tolerance expectations. Additionally, Sidney discusses costs: CLT can be more expensive up front, so the smarter comparison is total project cost, where exposed wood finishes can reduce drywall, paint, and labor while faster installs cut schedule risk. From there, Sidney details aspects that can make or break projects: MEP penetrations, connection engineering, acoustics planning, and moisture protection on site, and more.If you're considering a first step, Sidney explains why a plant tour and a pilot project can de-risk adoption, plus what success could look like for mass timber and commercial modular construction in the coming years.Support the showListen to all episodes of MBI's Inside Modular podcast at https://www.modular.org/inside-modular-the-podcast-of-commercial-modular-construction/

Introduction What happens to your health coverage the day you leave a job, go independent, or pick up seasonal work? For more than 90 million Americans who earn outside a traditional employer plan, the answer is usually that it disappears. Felix Ortiz, founder and CEO of Smirk Health, joined host Joshua Hollander to explain why the benefits system built in 1929 for full-time employees no longer fits the way people work, and what it takes to build something that does. The conversation covers portable coverage that moves with the worker, modular plans that start at $19, and an AI layer that does more than answer questions. Guest Bio Felix Ortiz is the founder and CEO of Smirk Health, an AI health benefits infrastructure company in Austin building portable coverage for 1099, part-time, hourly, and seasonal workers, underwritten and insured by Chubb. He is a repeat founder whose earlier companies spanned education technology, talent intelligence, and a banking-and-insurance platform for Americans of modest means. He is also a U.S. Army veteran and a marathoner who has finished five of the seven World Marathon Majors. Much of Smirk traces back to watching insurers decide his younger brother's care during a childhood heart condition, and to his own coverage gap leaving the military. Key Topics The 1929 problem - Group benefits were designed for full-time employees, so the fastest-growing part of the workforce gets priced out or left ineligible. Coverage that follows the worker - When a member changes employers, the plan, the price, and the benefits stay the same instead of ending with the job. Modular plans from $19 - Smirk rebuilt the plan chassis so members can add or remove coverage and see what is covered and what it costs before they buy. From supplemental to fully insured - Smirk took a product category damaged by bad actors, re-engineered it into a fully insured medical plan, and stacked it on an AI layer. An AI layer, not a chatbot - Because the plan is tied into Smirk's infrastructure, the concierge can take a member from a question all the way to a booked appointment with known costs. The AI divide - Ortiz argues a gap is opening between people on free AI models and people on paid ones, and that health is the wrong place to let that gap decide outcomes. Getting a carrier to say yes - How Smirk earned Chubb as an underwriting partner, and what Ortiz tells founders about approaching a large carrier. Notable Quotes "Somebody will have a Toyota, somebody else will have a Mercedes-Benz, but ultimately they still have access to a car. And when it comes to health, they don't. That's the big pain point." "We've gutted the whole thing and re-engineered it to be a fully insured medical plan." "You actually are starting to get a divide in AI infrastructure. That's a topic no one talks about." "Three years out, Smirk will be the infrastructure stack for AI within the health and financial intersection." Resources Guest: Smirk Health: https://www.smirkhealth.com Smirk Health on LinkedIn: https://www.linkedin.com/company/smirk-health Felix Ortiz on LinkedIn: https://www.linkedin.com/in/fwoiii/ Host & Organization: Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/ Horton International (USA): https://www.horton-usa.com/ Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show Subscribe & Review If this episode was useful, subscribe and leave a review. The Insurtech Leadership Podcast is on YouTube, Podbean, Apple Podcasts, and Spotify.

Big Tech and the space industry are completely rewriting the rules of infrastructure, from low-Earth orbit down to the safety guardrails of generative AI. In this episode of the Tech Field Day News Rundown, Tom Hollingsworth and Alastair Cooke tackle Rocket Lab's massive $8 billion acquisition of satellite operator Iridium and its play for total space infrastructure dominance. They also dive into Qualcomm's strategic buyout of Modular to challenge enterprise AI giants, China's deployment of its first fully renewable-powered AI data center, and the Trump administration's cybersecurity-centric AI Executive Order.Finally, the team breaks down Couchbase's new AI Data Plane, Microsoft's surprising decision to extend free Windows 10 security updates until 2027, and a controversial shift from Anthropic that might just turn AI safety into a premium, paid feature.Time Stamps: 0:00 - Cold Open 0:27 - Welcome to the Tech Field Day News Rundown1:29 - Rocket Lab Buys Iridium in $8 Billion Space Industry Deal4:20 - Qualcomm Buys Modular to Expand Open AI From Edge to Cloud6:57 - China Opens First Fully Renewable-Powered AI Data Center10:14 - Trump AI Order Signals New Focus on Cybersecurity14:11 - Couchbase Expands Database Platform for AI Agents17:30 - Microsoft Extends Windows 10 Security Updates to 202722:26 - Anthropic's New AI Models Raise Big Questions About Safety30:50 - The Weeks Ahead: Upcoming Events32:37 - Thanks for Watching the Tech Field Day News RundownFollow our hosts ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tom Hollingsworth⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Alastair Cooke⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Stephen Foskett⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow Tech Field Day ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X/Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bluesky⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mastodon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.

Today's show is sponsored by The Cost Segregation Guys. To learn more, click on the link and you can qualify for a discount on your cost seg study. -----------I'm usually pretty skeptical when someone tells me they've found a breakthrough in homebuilding.We've heard this story many times before. Modular construction was going to change everything. 3D printing was going to make housing dramatically cheaper. Mass timber was going to replace steel and concrete. In each case, there may be a place for the technology, but the promised revolution often fails to account for the entire building process.Construction is not one problem. It is a collection of hundreds of small problems, each with code requirements, scheduling constraints, labor dependencies, financing implications, and supply chain risks.But every once in a while, something comes along that deserves a closer look.That's the case with Plantd, a North Carolina company founded by former SpaceX engineers, including Nathan Silvernail and Huade Tan. Their target is not the whole house. They're not trying to replace every trade on the job site. They're going after one of the most common materials in homebuilding: OSB, or oriented strand board.Traditionally, OSB is made from wood strands pressed together with resin under heat and pressure. Plantd is making a direct alternative from fast-growing perennial grass. According to the source material, the grass can grow about six inches a day, can be harvested twice a year, and has a much higher carbon sequestration rate than pine. The company's internal testing claims the panels are two times more moisture resistant and one and a half times stronger than traditional softwood OSB. A pine forest takes 40 years to mature. These grasses can supply the same home building in 90% less acreage. And you can grow the grass next to the factory. ------------**Real Estate Espresso Podcast:** Spotify: [The Real Estate Espresso Podcast](https://open.spotify.com/show/3GvtwRmTq4r3es8cbw8jW0?si=c75ea506a6694ef1)   iTunes: [The Real Estate Espresso Podcast](https://podcasts.apple.com/ca/podcast/the-real-estate-espresso-podcast/id1340482613)   Website: [www.victorjm.com](http://www.victorjm.com)   LinkedIn: [Victor Menasce](http://www.linkedin.com/in/vmenasce)   YouTube: [The Real Estate Espresso Podcast](http://www.youtube.com/@victorjmenasce6734)   Facebook: [www.facebook.com/realestateespresso](http://www.facebook.com/realestateespresso)   Email: [podcast@victorjm.com](mailto:podcast@victorjm.com)  **Y Street Capital:** Website: [www.ystreetcapital.com](http://www.ystreetcapital.com)   Facebook: [www.facebook.com/YStreetCapital](https://www.facebook.com/YStreetCapital)   Instagram: [@ystreetcapital](http://www.instagram.com/ystreetcapital)  

In this episode, we explore innovative modular systems for safe and efficient offshore well abandonment, focusing on James Fisher Energy's SeaBass system and global regulatory frameworks.

 This episode of The Circuit covers a mix of tech industry tributes and major semiconductor financial updates. Hosts Ben and Jay begin by paying their respects to pioneering tech blogger and journalist Om Malik, who recently passed away. They then dive into Cerebras's first earnings report as a public company, highlighting strong top-line demand for AI inference but noting investor concern over their complicated gross margins. Next, the hosts unpack Qualcomm's Investor Day, focusing on the company's aggressive $15 billion data center revenue guidance for fiscal 2029, their "Dragonfly" custom ARM CPU roadmap, and their strategic acquisition of software startup Modular. Finally, they analyze Micron's massive earnings report, detailing a staggering 60% quarter-on-quarter memory price surge that has caught major buyers like Apple by surprise, concluding with a lively discussion on the "Game of Thrones" style market dynamics driving the memory industry. 

Origami blablabla to finish 24 pieces modular cube

In this episode, Corwyn Melette shares his insights on real estate development, affordable housing, and building a legacy through strategic investments. Discover how he balances community service with business growth and the importance of systems and leadership in scaling success.   Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true 'white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a "mini-mastermind" with Mike and his private clients on an upcoming "Retreat", either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas "Big H Ranch"? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

Guest host Robin Gill talks to Paul Binotto, director of Modular B.C. Learn more about your ad choices. Visit megaphone.fm/adchoices

Plus: Qualcomm to acquire AI software firm Modular in $3.9 billion stock deal. And Zoox debuts redesigned robotaxi for large-scale production. Julie Chang hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this reactor deep dive, Chris Keefer is joined by Navid Badie, Chief Nuclear Engineer at Candu Energy Inc, to explain the inner workings of the distinctive CANDU reactor. They trace how Canada's decision to use natural uranium led to heavy-water moderation, horizontal pressure tubes, separate coolant and moderator systems, and the ability to refuel continuously while operating. Navid breaks down the reactor's fuel channels, steam generators, online fueling machines, and the remarkable operating experience behind a technology that has completed nearly a million online refuelling operations.The conversation also examines CANDU safety and longevity: large inventories of water, natural thermosiphon cooling during a station blackout, and the engineering behind reactor refurbishment. Chris and Navid discuss pressure-tube metallurgy, robotic inspection and maintenance, cobalt-60 and other isotope production, and why locally manufacturable natural-uranium fuel can offer importing countries greater fuel-cycle sovereignty. A technically grounded introduction to one of the world's most unusual and capable reactor lineages.Listen to Decouple on:• Spotify: https://open.spotify.com/show/6PNr3ml8nEQotWWavE9kQz• Apple Podcasts: https://podcasts.apple.com/us/podcast/decouple/id1516526694?uo=4• Overcast: https://overcast.fm/itunes1516526694/decouple• Pocket Casts: https://pca.st/ehbfrn44• RSS: https://anchor.fm/s/23775178/podcast/rssWebsite: https://www.decouple.media

In this episode, real estate investor Erik shares his journey in land development, modular homes, and building strategic relationships to grow his business. We explore opportunities, challenges, and key insights for aspiring investors.   Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true 'white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a "mini-mastermind" with Mike and his private clients on an upcoming "Retreat", either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas "Big H Ranch"? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

Preview for Later Today: Amelia Bruno discusses modular electro-spray thrusters about the size of a postage stamp. Utilizing green monopropellant and solar power, this compact technology allows small satellites, like CubeSats, to change orbits or planes efficiently. It offers an affordable propulsion solution for universities and researchers exploring space using modular, scalable components.1962

Key TakeawaysThe biggest value-add opportunity in self-storage isn't always raising rents—it's adding units. Expanding a facility can create significantly more value than operational improvements alone.Look for excess land when buying self-storage. Vacant land, truck parking, RV storage, or underutilized areas can often be converted into additional storage units.Modular storage containers allow you to expand in phases. Instead of investing heavily upfront, operators can add units as demand grows, reducing risk and vacancy.Simple site designs often outperform maximized layouts. Customer experience, ease of access, safety, and traffic flow can be more valuable than squeezing in a few extra units.Small business customers are often the best tenants. Contractors, HVAC companies, home stagers, and other service businesses tend to stay longer and expand into additional units over time.Unit mix matters. Offering a combination of different sizes can help attract a broader customer base and maximize occupancy.Appearance affects leasing. New, well-maintained units create a better customer experience and can command stronger demand than older, worn containers.Run the numbers before expanding. In Tyler's example, a relatively small capital investment in additional units had the potential to create hundreds of thousands of dollars in additional property value.Think beyond cash flow. Every dollar of NOI created through expansion can dramatically increase a property's value through cap rate compression and future refinancing opportunities.The best self-storage deals often have hidden expansion potential. What looks like excess parking, RV storage, or unused land today may become the highest-return portion of the investment tomorrow

Amy Lokken shares insights on self-respect, self-confidence, and navigating heavy seasons of life, especially for women and caregivers. Amy Lokken is the founder of The Amy Factor™ andcreator of The Sophisticated Caregiver™ and designer of Müd Modular™. She serves as Private Counsel to high-performing women navigating heavy seasons, helping them live and lead in a way that makes their self-respect visible. With a background in industrial design, psychology, spatialintelligence, and decades of experience shaping environments and professional presence, Amy brings a distinct perspective to leadership, credibility, and theunseen weight many women carry while still showing up for everyone else. Her work centers on the belief that self-respect is not aluxury. It is the standard. And when a woman's standards remain intact during pressure, transition, caregiving, grief, or change, her credibility, steadiness, and leadership become unmistakable. Amy's philosophy is simple: Self-Respect Is the Standard.Credibility Is the Result. Grace Is Strength.GET IN TOUCH WITH AMY:https://theamyfactor.com/amy-factor/ https://theamyfactor.com/the-sophisticated-caregiver/LinkedIn

Tony Martens, co-founder of Plantible Foods, joins Neal to walk through the company's eight-year arc - from a free greenhouse in San Marcos to a commercial-scale rubisco protein facility in West Texas. They get into the science of duckweed and why rubisco is both the most abundant and most bioavailable protein on the planet, the modular “crawl, walk, run” scaling philosophy that kept Plantible from getting buried under capex, and how landing in El Dorado, Texas lifted the surrounding county's median household income by 62%. Plus: why the Taco Stand in Encinitas remains the most-mentioned spot on the pod.Key Topics* Why our food supply chain hasn't been updated in 3,000 years* How rubisco from duckweed competes with eggs, dairy, and meat* The “crawl, walk, run” approach to commercial scale-up* Why avian flu volatility is driving bakery and egg-replacement demand* Modular agriculture vs. billion-dollar capex projects* Living on the San Marcos farm in RVs through COVID lockdown* Lifting a West Texas county's median household income by 62%* Where Plantable products are showing up on shelves todayLinks & ResourcesPlantible Foods Connect on LinkedInTony MartensNeal Bloom This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit risingtidepartners.substack.com/subscribe

On this week's episode, NK News Senior Analytic Correspondent Colin Zwirko discusses a busy week of developments in North Korea. He examines Pyongyang's latest missile test of a new modular launcher and tactical cruise missile system, as well as what these weapons could mean for military planning near the inter-Korean border. He also talks about satellite imagery suggesting North Korea's Choe Hyon-class destroyer may be undergoing final testing near Nampho ahead of possible deployment. The episode also looks at signs Pyongyang may be preparing to welcome a foreign leader visit amid reports of a possible Xi Jinping trip, and at how North Korea's first large-scale solar farm aims to address energy shortages. About the podcast: The NK News Podcast is a weekly podcast hosted by Alannah Hill exclusively for NK News, covering the latest developments in and around North Korea. Each episode breaks down the week's news cycle with NK News journalists, analysts and expert guests.

The newspaper comic strip didn't go extinct — it evolved. But if your work doesn't keep up, your career may be fossilized! From Reddit-ready square comics to vertical-scroll storytelling, they explore how creators are adapting to phones, social media, and changing reading habits while keeping the heart of the comic strip alive. Topics covered The evolution of newspaper comic strips Why horizontal strips existed in the first place How phones changed comics formatting Square-format comics on Reddit and social media Vertical-scroll storytelling Why readers won't rotate their phones Charles Schulz and the flexible-format origins of Peanuts Newspaper syndication vs. modern web distribution YA graphic novels as the next evolution for newspaper strips Lincoln Peirce and the success of Big Nate books Why comic strips are still thriving online Modular comic formatting for webcomics The launch of The Comic Scout  Dave Kellett's Hugo Award nomination anticipation Tips for maintaining visual consistency in comics Workflow advice for newer cartoonists   You get great rewards when you join the ComicLab Community on Patreon$2 — Early access to episodes$5 — Submit a question for possible use on the show AND get the exclusive ProTips podcast. Plus $2-tier rewards.If you'd like a one-on-one consultation about your comic, book it now!Brad Guigar is the creator of Evil Inc and the author of The Webcomics Handbook. He is available for personal consultations. Dave Kellett is the creator of Sheldon and Drive. He is the co-director of the comics documentary, Stripped.

And at Google Next, Google splits its TPUs and unleashes more powerful workplace agents. Plus, did Anthropic's Mythos escape containment?Starring Tom Merritt, Sarah Lane and Andy Beach.Links to stories discussed in this episode can be found here. Hosted on Acast. See acast.com/privacy for more information.