Podcasts about loops

“When you listen to your mentor, things will work.” - Cathy Schwartz Collecting people, finding your hottest leads, building a foundation for your subscription box launch… It's all part of the Waitlist Plan I teach inside Launch Your Box. And by following that Waitlist Plan, Cathy Schwartz of Decorator Crafts sold out the launch of her Luxury Loops subscription box in only 24 hours.  Cathy's subscription box journey started when she learned to make wreaths and work with ribbon as a way to deal with a stressful corporate job. She fell in love with ribbon, particularly high-end, designer ribbon. She soon realized she wasn't alone in her love for luxury ribbon - there were a LOT of people who wanted to learn how to decorate with high-end ribbon. Soon, Cathy decided to take the passion and knowledge and launch a monthly subscription box. She also started a private Facebook Group where subscribers go to learn how to decorate with their ribbons and build community.  Cathy is a member of Launch Your Box and Scale Your Box. She is a dream student who soaks up the training and implements it in her business. This includes the Waitlist Plan.  The Waitlist Plan is all about consistently driving traffic to your subscription box waitlist. It's a list of people who have said “Yes, I'm interested in what you're selling.” They are your warmest leads. A waitlist is not only for your first launch. You need to consistently build your waitlist between launches as well. Just like Cathy does.  Due to the quantities Cathy's exclusive ribbons are available in, she adds new subscribers in groups of 48. When she recently launched to new subscribers, she of course started with her waitlist. Just like I teach inside Launch Your Box. But, unlike most subscription box owners, Cathy never had to move on to the next phase of her launch. She sold out her launch to her waitlist in only 24 hours!  Cathy had a waitlist of 300 people when it was time to launch. 300 very warm leads. She grew that waitlist - and continues to grow it consistently. Recently, Cathy posted a video of a Christmas Urn project. The response was beyond anything she could have imagined. She gained 6,000 Facebook followers in 72 hours and the video has more than 150,000 views on TikTok. Cathy realized she needed to capture all these people's emails to bring them into her world. She quickly put together an opt-in for the project and has been busy driving people to her waitlist.  Cathy serves her audience. She teaches them, she engages with them, and she builds excitement and FOMO with the help of her subscribers. She's doing all the right things, especially when it comes to following the Waitlist Plan.  I'm so proud of all Cathy has accomplished in her business. She now has over 500 subscribers and I know that number will continue to grow.  Join me for this episode and be inspired as Cathy tells us what can happen when you get consistent, follow the plan, and curate an exclusive experience for your subscribers.  Find and follow Cathy:  ⁠Decorator Crafts on Instagram⁠ ⁠Decorator Crafts on Facebook⁠ ⁠Luxury Loops Subscription Box ⁠  ⁠Decorator Crafts Website⁠ Join me in all the places:  ⁠Facebook⁠ ⁠Instagram⁠ ⁠Launch Your Box with Sarah Website⁠  Are you ready for ⁠Launch Your Box⁠? Our complete training program walks you step by step through how to start, launch, and grow your subscription box business. ⁠Join ⁠today!

ClickLock stealer uses kill loops to force password entry TELEPUZ malware uses ClickFix to steal data and run commands 1Password's new Agentic Mode lets Claude log into accounts Notes: https://cisoseries.com/cybersecurity-news-clicklocks-kill-loops-telepuz-clickfix-tricks-1passwords-agentic-login/ Huge thanks to our sponsor, ThreatLocker Every security leader is being asked the same question right now: How do we enable innovation without creating unnecessary risk? That's the challenge behind cloud adoption. Behind AI. Behind automation. And behind every major technology decision. ThreatLocker helps organizations take a Zero Trust approach to that challenge—giving them greater control over what can execute, what can access their environment, and what users and applications are allowed to do. That's why ThreatLocker is proud to support Cyber Security Headlines. Because security works best when innovation and control move together.

Tony Holdstock-Brown is the co-founder and CEO of Inngest, the durable execution platform that quietly powers your favorite AI agents.We get into why agents work in a demo and die in production, building their own cloud to get 20x lower cost, growing 35x after AWS and Cloudflare copied them, growing a dev tools company without a personal brand or Twitter account, why he thinks evals today are like “asking the criminal if they committed the crime”, and the thing they built to score 100% of your production agents without paying for LLM as a judge.Thank you to Numeral, Flex, Amplitude, Merge, and Monaco for supporting this episode.Numeral: Sales tax on autopilot https://www.numeral.comFlex: Premium banking, 60-day credit, 0% APR https://home.flex.one/referral/bananacapitalAmplitude: AI analytics https://www.amplitude.comMerge: Every model, one API https://www.merge.dev/turnerMonaco: The revenue engine for startups https://www.monaco.com/Timestamps:(0:00) The hidden infra layer every AI agent runs on(1:46) Building complex chains of logic(3:31) Why agent SDK's don't go far enough(4:49) Healthcare was the original event-driven nightmare(6:32) Storing traces on your infrastructure enables self-improving loops(14:26) Why Inngest was already in the right place for AI(15:49) Score agents off product events, not LLM's(17:31) The OpenAI copy-paste signal(21:24) Swap in LLMs and cut costs 20x(23:44) How customers pulled the product forward(25:41) Orchestration belongs outside the sandbox(29:48) Building a neocloud to cut costs 20x(32:09) Most neoclouds just resell AWS(32:54) All AI infrastructure is converging(34:49) Why Claude can't just build your backend(36:44) How to build a software factory(39:12) Agents are a lottery you get addicted to(42:44) Loops must exist until AGI hits(45:38) If models keep getting better, why orchestrate?(48:28) When incumbents steal your features(52:30) Why you can't vibe code infrastructure(55:54) Why Tony has no personal brand(59:38) Dev tools GTM without Twitter(1:03:20) Lessons from the founder of DuckDuckGo(1:10:39) Truth as a company value(1:13:08) Taking too long adapting to AI(1:15:10) Startups are 100% R&D(1:17:19) Ali from Databricks(1:19:03) Writing his own code, Voice-to-text with local models(1:23:53) Evals are batshit insaneReferencedInngest: https://www.inngest.com/Principles by Ray Dalio: https://www.amazon.com/dp/1501124021?lv=shuf&channelId=500&plpRedirect=mhFallbackTraction - How Any Startup Can Achieve Explosive Customer Growth: https://www.amazon.com/dp/1591848369?lv=shuf&channelId=500&plpRedirect=mhFallbackFollow TonyTwitter: https://x.com/itstonyhbLinkedIn: https://www.linkedin.com/in/tonyhb/Follow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

Fishing Fails, Target Startup Nightmares & Chicago Fireman's Dump Lines | Advanced Refrigeration PodcastBrett Wetzel and Kevin Compass open with fishing stories and then jump into a rough Target CO2 startup plagued by miswired comm loops, mixed case-control hardware, and constant walking in a multi-level downtown layout. They discuss troubleshooting methods for crisscrossed MODbus wiring, common causes of shorts and opens, and an electrician mistake that blew an XM controller apart. The hosts explain Chicago's “fireman's dump line” code requirement and how piping it off the bottom of a suction header can trap oil and cause recurring low-oil issues. They also flag a backwards high-pressure control/relay setup on a Target backup CO2 unit, share practical tips for S3C/Corelink/CC200 case controllers, Ethernet jumpers, termination, protecting solid-state outputs, transducer orientation, and water-damaged controllers from case washing.

Sporlan S3C, Comm Loops, Fireman's Dump Line, Brett's Retiring, and Noodling Episode 528Fishing Fails, Target Startup Nightmares & Chicago Fireman's Dump Lines | Advanced Refrigeration PodcastBrett Wetzel and Kevin Compass open with fishing stories and then jump into a rough Target CO2 startup plagued by miswired comm loops, mixed case-control hardware, and constant walking in a multi-level downtown layout. They discuss troubleshooting methods for crisscrossed MODbus wiring, common causes of shorts and opens, and an electrician mistake that blew an XM controller apart. The hosts explain Chicago's “fireman's dump line” code requirement and how piping it off the bottom of a suction header can trap oil and cause recurring low-oil issues. They also flag a backwards high-pressure control/relay setup on a Target backup CO2 unit, share practical tips for S3C/Corelink/CC200 case controllers, Ethernet jumpers, termination, protecting solid-state outputs, transducer orientation, and water-damaged controllers from case washing.

Why do we keep repeating the same patterns, even after years of personal growth? In this episode, Jen explores the four unconscious loops outcomes, entitlement, making meaning, and judgment—that keep us stuck, and how awareness can help you break free and step into a new identity.

In this episode, Anna sits down with Kati Morton, LMFT, bestselling author and one of the internet's most trusted mental-health educators. Kati has spent more than a decade helping millions of people understand their emotions, navigate trauma, and break unhealthy patterns. Her latest book, Why Do I Keep Doing This?, unpacks the emotional habits we fall into — and what it actually takes to change them from the inside out.In this episode, we explore:• What inspired her new bookHow Kati sees “doing this” — the patterns, habits, and emotional cycles we repeat — showing up in her own life and in her clients.• The truth that even therapists get stuckWhy mental-health professionals aren't immune to autopilot patterns… and the early signs Kati looks for in herself when she's drifting away from alignment.• Burnout cycles & nervous-system loopsEspecially for sensitive creatives, caretakers, and over-functioners:o why burnout often comes from emotional over-responsibilityo how chronic self-abandonment masquerades as “being capable”o the biological and psychological loops that keep people stucko how to tell the difference between capacity and compulsion• What it means to shift safelyKati shares the gentlest places to begin — including micro-bids for rest, nervous-system resets, interrupting old narratives, and slowly rebuilding trust with yourself so change doesn't feel threatening.• Why shame blocks every kind of healingAnd what happens when we bring curiosity instead of self-criticism to our patterns.• Practical tools for coming back to yourselfFrom checking in with your body before your to-do list, to noticing emotional “flare alerts,” to using supportive structure without falling into perfectionism.This conversation is validating, clarifying, honest, and deeply hopeful.If you're tired of your own loops, this one's for you. Connect with Kati• Website: https://www.katimorton.com/• YouTube: https://www.youtube.com/user/katimorton• Instagram: https://www.instagram.com/katimorton/• Her books, including Why Do I Keep Doing This?, Are U OK?, and Traumatized Connect with Anna• Instagram: https://www.instagram.com/anna_holtzman/• Website: https://www.annaholtzman.com/• Free workshop — Let Yourself Be Seen: https://www.annaholtzman.com/beseen

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Si no haces bucles de auto-mejora ya vas tarde - California blinda legalmente que los empleados escolares sean humanos - El 2 de Agosto de la AI Act se acerca amigos - GPT 5.6 hoy, Fable unos días más - Las medidas provisionales para la Administración de los Servicios de Interacción Antropomórfica de Inteligencia Artificial en China Patrocinador: Los másteres en Inteligencia Artificial de la Universidad Comillas ICAI, pensados para cualquier tipo de profesional: - Tienen el MUDIA, un máster en Digitalización e IA para recién graduados y profesionales junior de perfiles no técnicos (como empresa, derecho o salud) que proporciona un diferencial clave en automatización y análisis para impulsar su movilidad laboral entre diversos sectores. - También ofrecen otros programas puramente técnicos enfocados en machine learning y modelos generativos para quienes buscan programar y desarrollar estas soluciones. El objetivo de estos programas es aprender de forma práctica y con casos reales, sin olvidar el uso ético y la regulación de la tecnología. Las clases se imparten de forma presencial en Madrid, empiezan en septiembre de 2026 y la admisión está abierta hasta julio. Tienes toda la información disponible en su web comillas.edu monos estocásticos es el pódcast de inteligencia artificial presentado desde Málaga por Antonio Ortiz (@antonello) y Matías S. Zavia (@matiass). Hay un episodio nuevo cada jueves. Puedes unirte gratis a nuestro club social de Telegram y seguirnos en redes sociales: - Telegram https://t.me/monosclub - Twitter https://x.com/monospodcast - LinkedIn https://www.linkedin.com/company/monos-estoc-sticos/ - Instagram https://www.instagram.com/monosestocasticos - TikTok https://www.tiktok.com/@monosestocasticos - Bluesky https://monosestocasticos.bsky.social - Threads https://www.threads.com/@monosestocasticos - Facebook https://www.facebook.com/profile.php?id=61584654541061 Todos los episodios en YouTube: https://www.youtube.com/playlist?list=PL-6s6cUsxTnsY_V0rqQFURaHDYuXD0AXj Más enlaces al pódcast: https://cuonda.com/monos-estocasticos/links

Are you feeling wired but tired? Connected yet lonely? Inspired but completely stuck? In this episode of Soul Sessions, Amanda Rieger Green explains why we are experiencing a wild energetic recalibration in 2026. The frequencies of our planet are accelerating, and our physical bodies are struggling to keep up. This can cause us to get sucked back into "recycled energetic loops"—old narratives, resentments, and outdated identities. Instead of digging deeper into psychoanalysis or therapy to fix it, Amanda shares a powerful, highly effective physiological hack involving your subconscious daily habits (like brushing your teeth or driving) to instantly shift your brain hemispheres, create new neural pathways, and elevate your energetic baseline. In this episode, you’ll learn: Why you are feeling massive polarity in your energy field right now. How to become the observer of your consciousness without judgment. The "non-dominant hand" physiological trick to break negative thought loops. Why it’s time to put psychoanalysis on the shelf and focus on physics and physiology. What physical detox symptoms (ringing ears, sleep disruptions) mean for your field. We want to hear from you! What is shifting in your energy field? What are you experiencing telepathically or psychically right now? Are your dreams changing? Host: Amanda Rieger Green Subscribe to Amanda's YouTube HERE! Follow Amanda on Instagram: @soulpathology Check out Amanda's Website: SoulPathology.com Send Amanda an Email: Podcast@soulsessions.meFollow Amanda on Instagram: https://www.instagram.com/soulpathology/See omnystudio.com/listener for privacy information.

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

Are you actually healing, or are you just addicted to the idea of fixing yourself? Welcome back to Season 4 of Let's Not Sugar Coat It!About the Episode: In this unfiltered season premiere, Bella and Lee sit down with Kirsten Davidson, a Registered Psychotherapist and founder of Mind the Gap Psychotherapy. We are diving deep into the uncomfortable truths that mainstream self-help completely glosses over.Kirsten breaks down why traditional mental health diagnoses can sometimes keep us trapped in boxes, how talk therapy can accidentally become a loop to justify staying the same, and the hidden danger of the "strong, independent" identity that leads to emotional numbing. If you're tired of surface-level advice and want to understand how to actually move the needle in your marriage, your trauma recovery, and your self-awareness—this episode is for you."Therapy isn't about becoming this perfect version of yourself. It's about unbecoming all the things you are not." — Kirsten Davidson

Most people are still using AI like a prompt box. They ask a question, get an answer, copy a few lines, and then the work comes right back to them.In this podcast, I show a different way to think about AI. I wanted a public ClickUp chat where I could share AI discoveries and training updates. Instead of brainstorming forever, I told Codex the outcome, rejected unnecessary platforms, and had it build the form, welcome flow, instructions, and email handoff.That is the difference between a prompt and a loop. A prompt answers. A loop keeps moving until the outcome exists.To find out more go to www.talktoaninja.com today.#AIForBusiness #AILoops #BusinessAutomation #ManuelSuarez

  As life changes we may be tempted to think about how good things "could" have been. We all can still change the future, and get control of those negative thoughts  #SupportUkraine

What does it actually take to move an entire company from handwriting PRDs to shipping features with agents in two years?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Alex Meyers, Principal Product Manager at Gusto, to trace how the company became AI-native from the inside. Alex shares how his own quiet tool use turned into a C-suite demo, why that demo made clear that you cannot simply mandate “be AI native,” and how PM AI hackathons became the ritual that moved the org forward.They explore why dedicated, uninterrupted build time beats an hour here and there, why the ritual has to recur as the tools keep changing, how roughly three-quarters of the PM org now merges pull requests, and where Alex thinks the work is heading as loops and goals let PMs spend more time on customers and taste.If you're a product leader trying to raise your team's AI fluency, a founder deciding how much structure to put around learning, or an operator wondering what the PM role becomes when agents handle the busywork, this episode is for you.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

Dr. Vitz talks about how our thoughts make our emotions linger much longer than they need to, and sometimes even increase their intensity. (Originally aired 10-01-2025)

Talking about prompts and chatbots won't help you talk about AI strategy in 2026. You've gotta know the ins and outs of loops, plans, goals, subagents and more. In this episode of Everyday AI, we're breaking down the agent lingo and how the key terms play out in systems like Codex and Claude Desktop. Desktop Agent Lingo Simplified: Goals, Loops, Plans, Subagents and how it works in Codex and Claude Code -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Desktop Agent Vocabulary PrimerAgent Harnesses: Codex vs. Claude CodeDesktop Agent Plans: Features and WorkflowGoal Setting in Codex and Claude DesktopPlan vs. Goal: Key DifferencesAgent Loops: Automation and VerificationSub Agents: Parallel Task ManagementContext Windows and Task DelegationGuardrails, Verification, and Cost ControlTransition from Chatbots to Autonomous AgentsTimestamps:00:00 Shifting focus to AI agents03:28 Accessing the Start Here series09:31 Using plan mode in clawed desktop12:04 Understanding plan vs. goal mode14:25 Setting project goals and planning19:33 Accessing Start Here series22:03 Building effective training loops26:48 Managing sub agents effectively27:30 Setting up sub-agent system30:47 Closing and subscription reminderKeywords: desktop agent, desktop AI agent, agent lingo, agent vocabulary, long running agent, autonomous agent, codex, Claude Code, Claude desktop, AI harness, agentic harness, agentic tools, super app, Microsoft super app, OpenAI codex, long running desktop agents, plan mode, planning phase, agent plan, goal setting, AI goal, agent goals, loop mode, agent loops, scheduled automations, sub agents, agent subagents, context windows, parallel work, context hygiene, verification steps, approval points, skills, automations, API token usage, project threads, co work tab, code tab, work trees, checkpoints, file access, browser automation, human in the loop, token efficiency, agent delegation, AI supervision, knowledge work automation, AI subagent management, desktop agent mental model, computer control, AI project management, AI workload delegation, remote steering, front end chatbot, proactive AI, AI context sharing.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

Gnarly is a British Sri Lankan producer, finger drummer, and performer based in London. In this clip from episode 025, she makes the case for building a small, engaged audience over chasing viral numbers — and explains why she's never once tried to go viral.This is a short one. But it might reframe how you think about the whole game.Listen to the full conversation: Episode 025 — Gnarly Part 1: Working Quickly, Not Frantically.Subscribe to ProducerHead on Substack at producerhead.substack.com and get access to Sonic Stimulus Vol. 1, a curated sample pack, Invisible Instruments, a collection of writing on the creative and psychological side of making music, and The Pocket, a video library of guests sharing behind the scenes of their sessions. Get full access to ProducerHead at producerhead.substack.com/subscribe

The evolution of software development toward a loop-driven era, where autonomous AI agents transition from simple code generation to independent system orchestration. Central to this shift is compound engineering, a methodology that treats every development task as a reusable investment to achieve massive productivity gains. This paradigm emphasizes that code verification, rather than generation, is now the primary bottleneck in engineering velocity. To address these risks, the texts advocate for a robust execution harness—such as those developed by Harness AI—which provides the necessary memory, governance, and real-time context for safe deployment. Furthermore, the documents highlight community innovations like Lore, a tool designed to extract developer judgment from session histories into reusable agent skills. Ultimately, the materials illustrate a transition from manual programming to the design of sophisticated autonomous verification platforms that operate within live cloud-native environments.

#939 Show Notes: https://wetflyswing.com/939 Presented By: Stonefly Nets, Jackson Hole Fly Company, Yellowstone Teton Territory - Visit Idaho, Fly Fish with me Utah Sponsors: https://wetflyswing.com/sponsors Paul Arden, founder of Sexyloops and one of the most respected fly casting instructors in the world, returns to the podcast to share lessons from more than three decades of teaching anglers how to cast more efficiently. From beginners learning loop control to experienced anglers searching for more distance and accuracy, Paul breaks down the fundamentals that separate average casters from great ones. The conversation covers fly casting plateaus, loop control, back-cast awareness, the 170 cast, double-haul mechanics, fly line selection, and common misconceptions that hold anglers back. Paul also shares practical drills, coaching insights, and why understanding what happens behind you may be the fastest path to improving what happens in front of you. #939 Show Notes: https://wetflyswing.com/939

Crocheting started as a hobby for Deniz Evangelista in 2024, and quickly grew into a passion for her. The full-time nurse at Ann and Robert H. Lurie Children's Hospital of Chicago says she was just looking for an escape from the stress of life and work. "I took one class, and after that, I was hooked. No pun intended. I was addicted," she recalled with a laugh. The married, mother of two says it's hard to put her finger on what drew her to the craft. "I don't know if it's the repetitive movement or what it was, but it just put me in this little bubble. It was a de-stresser, and it was just something that I suddenly loved to do," she said. And that love just kept growing, and she kept getting better and better. "I started making gifts. I would make blankets for baby showers. I would make little plushies for some of my friends kids," she recalled of her early days. Evangelista's skills surprised not just her, but everyone who saw her finished creations. "A lot of my friends and family would comment and motivate me and say, ‘hey this is really good. I think you could take this somewhere,” she remembers. So Evangelista did, launching her business, Little Loops and Layers in 2025. At first, she sold her creations on social media sites including facebook, Instagram and TikTok, and eventually she expanded to outdoor markets and spaces at AngMir in Pilsen and the Dollup Coffee location in the Prudential Building where we caught up with her. "I crochet all my plushies. I crochet blankets, some wearables and accessories. They're all handmade by me," she said proudly, adding, "it makes me really happy to see that it sparks joys in those who receive or even take a look at my products." Evangelista has gotten so good, she now takes custom orders. "I'm currently making a second order of 'The Very Hungry Caterpillar.' It's literally the caterpillar with an open mouth, and I made all the food items that get stuffed into the mouth so it's really interactive for kids and it's something that's a good addition to follow along with the book when you're reading to them," she explained. It's still hard for Evangelista to believe how far Little Loops and Layers has come in less than a year. "I'm just proud of myself. It is hard to be a working mom in this day and age so to have this and grow it into the business that it is today. It means everything to me," she said. Evangelista is also very grateful to all her family members, friends and fellow crafters who helped and encouraged her every step of the way. "I wouldn't be where I am today without them," she said, adding "it's brought me so much joy, especially when I see the reactions of how other people receive this." And she hopes to keep creating and spreading joy for years to come.

Crocheting started as a hobby for Deniz Evangelista in 2024, and quickly grew into a passion for her. The full-time nurse at Ann and Robert H. Lurie Children's Hospital of Chicago says she was just looking for an escape from the stress of life and work. "I took one class, and after that, I was hooked. No pun intended. I was addicted," she recalled with a laugh. The married, mother of two says it's hard to put her finger on what drew her to the craft. "I don't know if it's the repetitive movement or what it was, but it just put me in this little bubble. It was a de-stresser, and it was just something that I suddenly loved to do," she said. And that love just kept growing, and she kept getting better and better. "I started making gifts. I would make blankets for baby showers. I would make little plushies for some of my friends kids," she recalled of her early days. Evangelista's skills surprised not just her, but everyone who saw her finished creations. "A lot of my friends and family would comment and motivate me and say, ‘hey this is really good. I think you could take this somewhere,” she remembers. So Evangelista did, launching her business, Little Loops and Layers in 2025. At first, she sold her creations on social media sites including facebook, Instagram and TikTok, and eventually she expanded to outdoor markets and spaces at AngMir in Pilsen and the Dollup Coffee location in the Prudential Building where we caught up with her. "I crochet all my plushies. I crochet blankets, some wearables and accessories. They're all handmade by me," she said proudly, adding, "it makes me really happy to see that it sparks joys in those who receive or even take a look at my products." Evangelista has gotten so good, she now takes custom orders. "I'm currently making a second order of 'The Very Hungry Caterpillar.' It's literally the caterpillar with an open mouth, and I made all the food items that get stuffed into the mouth so it's really interactive for kids and it's something that's a good addition to follow along with the book when you're reading to them," she explained. It's still hard for Evangelista to believe how far Little Loops and Layers has come in less than a year. "I'm just proud of myself. It is hard to be a working mom in this day and age so to have this and grow it into the business that it is today. It means everything to me," she said. Evangelista is also very grateful to all her family members, friends and fellow crafters who helped and encouraged her every step of the way. "I wouldn't be where I am today without them," she said, adding "it's brought me so much joy, especially when I see the reactions of how other people receive this." And she hopes to keep creating and spreading joy for years to come.

Crocheting started as a hobby for Deniz Evangelista in 2024, and quickly grew into a passion for her. The full-time nurse at Ann and Robert H. Lurie Children's Hospital of Chicago says she was just looking for an escape from the stress of life and work. "I took one class, and after that, I was hooked. No pun intended. I was addicted," she recalled with a laugh. The married, mother of two says it's hard to put her finger on what drew her to the craft. "I don't know if it's the repetitive movement or what it was, but it just put me in this little bubble. It was a de-stresser, and it was just something that I suddenly loved to do," she said. And that love just kept growing, and she kept getting better and better. "I started making gifts. I would make blankets for baby showers. I would make little plushies for some of my friends kids," she recalled of her early days. Evangelista's skills surprised not just her, but everyone who saw her finished creations. "A lot of my friends and family would comment and motivate me and say, ‘hey this is really good. I think you could take this somewhere,” she remembers. So Evangelista did, launching her business, Little Loops and Layers in 2025. At first, she sold her creations on social media sites including facebook, Instagram and TikTok, and eventually she expanded to outdoor markets and spaces at AngMir in Pilsen and the Dollup Coffee location in the Prudential Building where we caught up with her. "I crochet all my plushies. I crochet blankets, some wearables and accessories. They're all handmade by me," she said proudly, adding, "it makes me really happy to see that it sparks joys in those who receive or even take a look at my products." Evangelista has gotten so good, she now takes custom orders. "I'm currently making a second order of 'The Very Hungry Caterpillar.' It's literally the caterpillar with an open mouth, and I made all the food items that get stuffed into the mouth so it's really interactive for kids and it's something that's a good addition to follow along with the book when you're reading to them," she explained. It's still hard for Evangelista to believe how far Little Loops and Layers has come in less than a year. "I'm just proud of myself. It is hard to be a working mom in this day and age so to have this and grow it into the business that it is today. It means everything to me," she said. Evangelista is also very grateful to all her family members, friends and fellow crafters who helped and encouraged her every step of the way. "I wouldn't be where I am today without them," she said, adding "it's brought me so much joy, especially when I see the reactions of how other people receive this." And she hopes to keep creating and spreading joy for years to come.

Risk is the point – that's the whole reason you make music in the first place.Yes, it is scary to make something and share it with people. Yes, it is extra work to tell people about it. But wanting people to hear your music isn't selfish. It represents why you make music in the first place: to connect with others.If you don't risk vulnerability, you will avoid the pain of rejection, but you also completely seal yourself off from the possibility of connection. We are fixated on how many people or views something gets and we forget there are people on the other side. This fixation distracts from the who, the unquantifiable nature of a new fan willing to buy your next record, your next collaborator, or someone who wants to remix your song.There's a lot of space between being a content creator and telling people about your music. Becoming someone you're not is not required nor recommended. But, if you were vulnerable enough to distill your life in song, go one step further and tell someone. Get full access to ProducerHead at producerhead.substack.com/subscribe

John welcomes former Reuters head of West Coast news and global technology coverage Jonathan Weber to discuss his new book “City on the Edge: Technology, Politics, and the Fight for the Soul of San Francisco”—along with last week's California primary, the rivalry between (and presidential ambitions of) Kamala Harris and Gavin Newsom, and the rise of San Francisco's popular new mayor, Daniel Lurie. To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

Megan and Erin kick off their summer with shorter episodes. Listen now or save them up and listen when you get back to school in the fall. This summer Erin will focus on building skills that will help students in the present and are essential to transition to a functioning adult. Today's focus: habits. Learn How do you make change?  How do you know you are ready?  How do we form habits?  Here are some resources mentioned: The Power of Habit – Charles Duhigg Atomic Habits – James Clear The post 632: Summer of Skills: Habit Loops appeared first on The College Prep Podcast.

What if one simple mindset shift could transform your relationships, health, faith, and happiness? In this powerful episode, Nicole Phillips shares how kindness, gratitude, and intentional thinking helped her overcome negative thought patterns, addiction, self-criticism, and life's toughest challenges.Join me as Nicole reveals how our thoughts shape our reality, why "what you look for is what you'll see," and practical ways to rewire your brain for greater joy, peace, resilience, and connection. If you've ever felt stuck in worry, negativity, overwhelm, or self-doubt, this conversation will encourage and equip you to move forward with hope.Nicole also shares her personal journey through addiction, breast cancer, family struggles, forgiveness, and discovering God's purpose through kindness.If this episode encouraged you, be sure to subscribe, leave a review, and share it with someone who needs hope today.PODCAST CHAPTERS[00:00] Podcast Preview[01:20] Topic and Guest Introduction[03:20] Gratitude vs Kindness: Which Comes First?[05:58] Think About What You're Thinking About[08:00] Nicole's Battle with Negative Self-Talk[10:18] Rewiring the Brain Through Faith[12:00] The Ripple Effect of Kindness[14:50] How Kindness Changed Her Family[16:18] Living with Purpose, Not Agenda[18:15] Adventures with the Holy Spirit[21:00] The Kindness Project Journey[24:30] Breaking Free from Negative Thought Loops[26:15] The Hurry, Worry, Jury Trap[28:47] Feel the Pain Without Feeding the Panic[32:50] Growing Up Visiting a Prison[37:33] A Message of Hope for Anyone Feeling StuckResources mentioned:Nicole Phillips' Website: nicolejphillips.comConnect with today's guest:Facebook: https://www.facebook.com/NicoleJPhillipsNicole C. Phillips is a speaker, author, podcast host, and kindness advocate dedicated to helping people transform their lives through the power of positive thinking, resilience, and intentional kindness. A breast cancer survivor, recovering addict, former hospital chaplain, and founder of Kindness Is Contagious, Nicole draws from her personal experiences to inspire hope and meaningful change.She is the host of The Kindness Podcast, recognized by Oprah Magazine as one of the top happiness podcasts, and the author of five books, including The Negativity Remedy. Through her engaging storytelling, practical wisdom, and signature blend of humor and heart, Nicole has inspired more than 30,000 people to break free from negative thought patterns, strengthen relationships, and cultivate lives filled with purpose and joy.Whether speaking on mindset, emotional wellness, faith, or personal growth, Nicole empowers audiences to embrace kindness as a catalyst for transformation and discover the extraordinary impact of everyday actions.P.S. If you're just checking out the show to see if it's a good fit for you, welcome!If you're really serious about becoming Visibly Fit, you'll get the best experience if you download the worksheets available at https://wendiepett.com/visiblyfitpodcast.

El episodio 119 llegó con framework, números y oportunidades que no te podés perder.Arrancamos con lo más accionable del episodio: los 7 principios de Y Combinator para construir una empresa en la era AI. No es AI como herramienta, es AI como sistema operativo de toda la organización. Loops cerrados en cada proceso, empresas legibles para los modelos, fábricas de software donde los humanos definen los specs y la AI construye el código, y equipos lo más flat posible donde cada persona tiene una responsabilidad directa y no hay lugar para esconderse. Si estás construyendo algo hoy, este es el episodio.Después el número que más sorprendió de la semana: Uber gastó 500 millones de dólares en tokens en tres meses y su CFO admitió públicamente que no vio ningún resultado. La contracara fascinante es que los propios modelos de AI no saben cuánto les cuesta producir cada token. Están vendiendo algo a un precio que ellos mismos no entienden todavía. Y mientras tanto, la mayoría de las empresas está descubriendo que un modelo open source más chico instalado en sus propios servidores les da el 90% del resultado al 2% del costo.También hablamos de la nueva métrica que define esta era: el EBITDA ya tiene una T nueva. Ya no es solo Before Interest, Taxes, Depreciation and Amortization. Ahora es Before Tokens también. Si tu empresa no está midiendo cuánto gasta en tokens, no está midiendo bien.En el frente de IPOs, Anthropic hizo su filing privado, OpenAI apunta a septiembre y SpaceX está cada vez más cerca. Tres movimientos que van a redefinir el mercado en los próximos meses.Cerramos con dos temas más personales. Primero, el debate entre Oura, Whoop y Fitbit — cuál sirve, para quién y por qué el nuevo monitor de glucosa Lingo de Abbott a 30 dólares puede ser uno de los dispositivos más importantes para entender tus hábitos. Segundo, cómo filtrar el spam de family offices y brokers de secundarios que llega por LinkedIn todos los días sin perder tiempo.

Everyone is chasing AI software, but the biggest opportunity may actually be services. In this video, Eric explains why top investors are betting on services-as-software, how AI is reshaping agency and consulting business models, and why the future belongs to companies that sell outcomes instead of labor. He breaks down managed growth loops, AI-powered operating systems, and the new organizational structures that will separate winners from everyone else. If you're building an agency, consulting firm, service business, or AI startup, this video will change how you think about growth, valuation, and the next decade of opportunity. Chapters (00:00) Why Services Beat SaaS (01:13) The $1 Software vs $6 Services Opportunity (02:52) Why Managed Growth Loops Matter (04:49) Agents, Loops, and Human Judgment (06:43) How Single Brain Powers AI Service Businesses (07:22) The Services-as-Software Manifesto (08:41) The New AI-Native Org Chart (10:13) Building Outcome-Based Offers (11:13) Final Thoughts

Have you been feeling more frustrated, emotionally drained, or confused, even though nothing is technically "wrong"? That feeling isn't a sign you're failing. It might actually be a sign you're growing. So many high-achieving leaders hit a point where things that used to energize them start to feel flat. Small things suddenly bother them. They go quiet in conversations, pulled inward by a desire they haven't let themselves admit. And instead of recognizing these as signs of outgrowing the life they've built, they wonder what's wrong with them. In this episode, Blake shares what growth actually looks and feels like before the clarity arrives, and why the discomfort you're experiencing may not be dysfunction at all.   Episode Highlights   Why Growth Rarely Starts With Clarity  [01:02] – The emotional signs you've outgrown your current way of living or leading  [02:45] – Why frustration, confusion & emotional sensitivity are so often misunderstood  [04:10] – "You pay the full price emotionally on your unused potential"   Why High Achievers Get Stuck in Loops  [06:30] – You can't see the label from inside the bottle  [08:15] – Why going to friends & family often keeps you more stuck  [10:00] – When to stop spinning and seek outside perspective   The Blender at the Bottom of the Ocean  [12:20] – Why forcing clarity creates more confusion  [14:05] – How to let the sediment settle so you can actually see  [15:30] – The difference between discomfort and unnecessary suffering   Whispers, Knocks & Bangs  [17:10] – Why misalignment gets louder the longer you ignore it  [19:00] – Susan's story: 20 years of pushing through before the house came down  [21:15] – How to start hearing the signals earlier and move through them with more ease Powerful Quotes "One of the most misunderstood parts of growth is that it rarely starts with clarity. It usually starts with frustration." –Blake Schofield "You pay the full price emotionally on your unused potential." –Randy Massengale "The longer you push through in misalignment, the worse it gets. It starts as a whisper, then a knock, then a bang, and then the whole house comes down." –Blake Schofield "There is a discomfort in growth, but there doesn't need to be suffering." –Blake Schofield   Resources Mentioned Let's explore what's possible for your team: If your company is investing in burnout, wellness or adaptability initiatives, but seeing rising burnout, disengagement, or retention risk, it may be time to address the root cause.   We identify & diagnose organizational risk - surfacing the key drivers of burnout, leadership capacity and adaptability strains impacting your team; reduce leadership attrition, disengagement and preventable turnover; equip your leaders with the skills to increase their productivity & lead effectively during pressure and uncertainty.    Explore Workshops, Leadership Capacity Risk Assessments, Leadership Development or Consulting at https://impactwithease.com/corporate-training-consulting/   Executive Coaching:  For founders, executives, and senior leaders who are successful but feeling drained, stagnant, or uncertain about their next step. Whether you're burned out, standing at a crossroads, or simply know you're meant for more—you don't have to figure it out alone.  Go to impactwithease.com/coaching to apply!   Discover what is driving your burnout:  In just 5 minutes, learn your unique burnout type™ & how to restore your energy, fulfillment & peace at www.impactwithease.com/burnout-type  

Buried in the process, your habits take over. Your automatic selection of instruments and sounds leads to predictable patterns.If you begin with your fingers you are immediately filtering your imagination through your chops. Instead, ask yourself: What do I hear? As you hum a melody or beatbox a groove you hear in your head, you are not thinking about whether or not you can play it. This is an honest map of your idea. Record it, not because it will necessarily become part of the song, but because it provides you with direction. The way that you sang this melody will imply more than the notes themselves. You will hear tone, timbre, and texture. That can guide which instrument you use, instead of forcing a part through the instrument you already loaded.When you begin from your imagination and what you hear, you provide a path for fingers to follow, instead of the other way around.Full Episode: 049. Ideas Over Everything | feat. Moo LatteSubscribers get access to The Practice — a growing archive of in-studio sessions from guests on the show. Watch how they work. Free to subscribe. Get full access to ProducerHead at producerhead.substack.com/subscribe

Today, we're diving into the four biggest thought loops that are likely costing you serious money in your business every single month, and more importantly, how to break out of them. Because the things that cost us the most in business are often the stories (aka LIES) we tell ourselves! And don't worry, this isn't an episode that's going to leave you feeling like sh*t. We're keeping it real, but we're also keeping it positive!In this episode, we talk about:Why "it won't work because it hasn't worked in the past" is the most expensive story you can tell yourself and how to separate data from your identityThe mindset shift that explains why your energy feels flat even when you're "going all in" on a launchHow to use other people's results as proof that it's possible for you instead of evidence that it isn'tThe "proof list" exercise that gives your brain the safety it needs to believe in a new outcomeWhy your content ideas feeling "too basic" or "too boring" might actually mean they're exactly right to postThe sneaky thought loop that's quietly killing your consistency and costing you clientsWhy you don't know other people's money stories and how that's keeping you from charging what you're worthThe pricing framework: 70% safe, 30% stretchy and why your clients need to be stretched tooThe mental reframe that will change EVERYTHINGEvery single situation in front of you is shaped not by the reality of it, but by the story you tell yourself about it. This episode is here to help you start telling a new one.If you have a desire, it just means the thing wants you back. You wouldn't want something if it doesn't want you back!Listen to Similar Episodes: 235. How to Identify & Remove the Subconscious Block Keeping You From Your Next Level206. It Cost Me $15K to Gain Clarity: I'm Pivoting!166. GlowTFU Fridays: The Top 4 Manifestation BLOCKS & How To Bust Through Them156. GlowTFU Fridays: 3 Not-So-Obvious Things Keeping You Stuck at the Same Income LevelP.S. When you rate and review the podcast, you'll receive my Connect to your Higher Self Visualization as a thank you! Click here to claim your gift. Ways to Work with Nora:1:1 Coaching Waitlist – Add your name to the waitlist to be the first to learn when spots open.90-Minute Intensives Waitlist – Limited openings for deep-dive, high-impact sessions. Join the waitlist to be notified when spots become available.Courses – Explore Nora's signature programs:Full Throttle – The ultimate business strategy courseElite – Business energetics + identity work coursePodcasting for Business Growth – Turn your podcast into profitConnect with Nora – Follow her on Instagram @iamnoravirginia for updates, tips, and inspiration.

Want more leads from YouTube Shorts? Wondering how to create YouTube Shorts that get more than a thousand views? I interview John Scott to discover techniques to produce YouTube Shorts that people will watch, rewatch, and share.What Marketers Need to Know About the Shorts FeedThe Audio-Visual-Text Hook FrameworkCuriosity LoopsObstacle + Solution StorytellingGuest: John Scott | Show Notes: socialmediaexaminer.com/719Review our show on Apple PodcastsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Mark Dowdle ran 306.6 miles in 73 hours, drove 20 hours home, picked up a puppy, and was back umpiring youth baseball the next week. That's either the most unhinged post-race recovery plan in endurance sports history, or it's the most honest thing anyone's said about who he actually is.This is the conversation Dominic was saving for after the race—and it delivered on the hype. Mark walks through the G1M Ultra from the inside: the moment on the first night at 2 or 3 a.m. where he made the irreversible decision not to quit; the loop where he noticed Kim and Harvey were off their timing and knew what was coming; and the final miles walking with Kendall as both men quietly sensed the race was ending. The 13-second lap finish wasn't a dramatic sprint—it was two men who'd been through three days of mud and rain and dark deciding, together, to keep going one more time.What makes this conversation different from a typical winner's debrief is what Mark keeps returning to: the idea that who you are at a youth baseball game is exactly who you are at mile 290. His sister-in-law Lily's voice was in his earbuds pulling him through the low loops.The internal battle between wanting the race to end and wanting to see how far two people can actually go together. And the realization, standing upright after 73 hours, that he didn't have to perform for anyone.He also quietly drops that he's now officially a BPN athlete. The chapter he'd title What It Looks Like to Walk in Faith is just getting started.Tap into the Mark Dowdle Special.If you enjoy the podcast, please consider following us on Spotify and Apple Podcasts and giving us a five-star review! S H O W  N O T E S  -The Run Down By The Running Effect (our new newsletter!): https://tinyurl.com/mr36s9rs-Our Website: https://therunningeffect.run  -THE PODCAST ON YOUTUBE: https://www.youtube.com/channel/UClLcLIDAqmJBTHeyWJx_wFQ-My Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/therunningeffect/?hl=en⁠⁠⁠⁠⁠⁠-Take our podcast survey: https://tinyurl.com/3ua62ffzInstagram: @mark.dowdle 

Walt Disney World Resort hotels include their own medley of music loops, from the cozy acoustic sounds of Disney's Wilderness Lodge to the whimsical, Disney-inspired songs at the Disneyland Hotel. In this episode, we analyze the music loops of the Disney Resort hotels in the EPCOT, Magic Kingdom, and Disneyland resort areas. We also discuss how Walt Disney Imagineering selects music to match the theme of each Disney resort hotel. Get ad-free episodes, bonus episodes, in-depth news analysis, and premium content at patreon.com/imaginationskyway. To plan a trip, be sure to work with KMV Travel.   View virtual room tours of Aulani: https://disneyvacationclub.disney.go.com/destinations/list/us-hawaii/aulani-hawaii/points-rooms   Read Matt's Imagineering column in WDW Magazine.   Imagination Skyway is a Disney Parks and Imagineering podcast. Episodes explore attraction design, recap Disney news, and dive into the stories behind the magic, including interviews with Disney Imagineers, Disney Legends, and other Disney creators. Not affiliated with or endorsed by The Walt Disney Company. Disney is a trademark of The Walt Disney Company.   Tag me and join the conversation below. Instagram: www.instagram.com/imaginationskyway Facebook: www.facebook.com/imaginationskyway YouTube: https://www.youtube.com/@imaginationskyway Email: matthew.krul@imaginationskyway.com  How to Support the Show Share the podcast with your friends Rate and review on Apple Podcasts or Spotify Join our Patreon Group - https://www.patreon.com/imaginationskyway Enjoy the show!

Dr. Troy Spurrill of the Synapse Center for Health and Healing joins Susie to talk about on fear, anxiety, and trauma loops. He also answers some listener health questions. Listen to the Dysregulated Nervous System show here Dr. Troy's non-profit is State of Grace Foundation. Find out more here.  Check out Susie's new podcast God Impressions on Apple, Spotify, or wherever you listen to podcasts! Faith Radio podcasts are made possible by your support. Give now: click here

Antonia and Roger Vanoro are husband and wife, soul collaborators, and the creators of Ontodelic Inquiry™, a somatic and subtle-field approach to transformation that bridges depth psychology, body-centered therapy, and the lived wisdom of altered states. Ontodelic draws from the Greek roots for "being" and "revealing," pointing to a revelation of being that works at the level of the body, the psyche, and the karmic storehouse beneath. Antonia is an intuitive energy healer and guide with over 22 years of experience in the sacred medicine space, trained in Hakomi, IFS, depth psychology, and energy work (CHt, CPTC). She works with the body as a direct route to the unconscious, midwifing clients through their natural healing processes with love, compassion, and skill. Roger is a somatic therapist and embodiment guide whose work begins in the present moment, using co-regulated presence to support clients as the deeper body awakens, heals, and reveals its histories. Together they developed Ontodelic Inquiry to meet what conventional somatic and parts-based work cannot: multi-lifetime and ancestral material, experiences of unity and expanded states, and the deep patterns that keep people looping regardless of how much inner work they've done. Their Ontodelic Inquiry Training is for therapists, coaches, facilitators, and healers ready to go deeper in their own process and their work with others. It is, as participants have described it, a psychedelic experience without the psychedelics. Antonia and Roger are based in the Hudson Valley, New York, and offer individual sessions, couples work, mentorship, and immersive trainings. Episode Highlights ▶ What Ontodelic Inquiry is and where the name comes from, rooted in the Greek words for "being" and "revealing" ▶ How somatic and parts-based modalities like Hakomi and IFS can become their own kind of loop, and what becomes possible when you work at the level of the karmic storehouse ▶ Why peak experiences alone rarely produce lasting change, and what shifts when deep somatic preparation and integration are in place ▶ How multi-lifetime and ancestral material shows up in sessions regardless of belief, and why a therapeutic frame that can hold it matters ▶ The role of pattern interruption and real-time somatic mapping in unwinding what talk therapy and medicine work can't fully reach ▶ How this work connects to ancient wisdom traditions from Andean Cosmovision to Vajrayana Buddhism, and why that lineage matters ▶ Why bringing light down into the body rather than bypassing shadow is the alchemy at the center of their approach ▶ What AI reveals about the irreplaceable nature of human consciousness and the transmission field only living beings can carry ▶ Who the Ontodelic Inquiry Training is for, including therapists, coaches, facilitators, and healers ready to go deeper   Antonia & Roger Vanoro' Links & Resources ▶ https://wheeloflife.us ▶ https://www.illuminateyourtruth.com/ ▶ https://rogervanoro.com/ ▶ Instagram: https://www.instagram.com/antoniavanoro   Download Beth's free trainings here: Clarity to Clients: Start & Grow a Transformational Coaching, Healing, Spiritual, or Psychedelic Business: https://bethaweinstein.com/grow-your-spiritual-businessIntegrating Psychedelics & Sacred Medicines Into Business: https://bethaweinstein.com/psychedelics-in-business▶ Beth's Coaching & Guidance: https://bethaweinstein.com/coaching ▶ Beth's Offerings & Courses: https://bethaweinstein.com/services▶ Instagram: @bethaweinstein ▶ FB: / bethw.nyc + bethweinsteinbiz

ELPHNT is an Ableton authority. His extensive experience as a producer, performer, and educator has allowed him to see a common trope play out over and over again.It's accepted as the norm, but as he explains here, it doesn't have to be.On the one hand, scrolling endlessly for the perfect kick drum is funny.On the other, it is creative self-sabotage by disorganization.Here, ELPHNT provides a clear and direct path forward.Hear the full conversation in Episode 035 — Soul-Crushing Success feat. ELPHNT.What are ProducerHead Loops?Gems from past conversations worth running back. Perfect for when you need a quick hit of inspiration.ProducerHead is free to subscribe. Subscribers get access to The Practice — an ongoing video archive of in-studio sessions from guests on the show. Get full access to ProducerHead at producerhead.substack.com/subscribe

Walt Disney World Resort hotels include their own medley of music loops, from the African medleys of Disney's Animal Kingdom Lodge to the contemporary jazz played at Disney's Saratoga Springs Resort. In this episode, we analyze the music loops of the Disney Resort hotels in the Disney's Animal Kingdom, ESPN Wide World of Sports, and Disney Springs resort areas. We also discuss how Walt Disney Imagineering selects music to match the theme of each Disney resort hotel. Get ad-free episodes, bonus episodes, in-depth news analysis, and premium content at patreon.com/imaginationskyway. To plan a trip, be sure to work with KMV Travel.   View virtual room tours of Aulani: https://disneyvacationclub.disney.go.com/destinations/list/us-hawaii/aulani-hawaii/points-rooms   Read Matt's Imagineering column in WDW Magazine.   Imagination Skyway is a Disney Parks and Imagineering podcast. Episodes explore attraction design, recap Disney news, and dive into the stories behind the magic, including interviews with Disney Imagineers, Disney Legends, and other Disney creators. Not affiliated with or endorsed by The Walt Disney Company. Disney is a trademark of The Walt Disney Company.   Tag me and join the conversation below. Instagram: www.instagram.com/imaginationskyway Facebook: www.facebook.com/imaginationskyway YouTube: https://www.youtube.com/@imaginationskyway Email: matthew.krul@imaginationskyway.com  How to Support the Show Share the podcast with your friends Rate and review on Apple Podcasts or Spotify Join our Patreon Group - https://www.patreon.com/imaginationskyway Enjoy the show!

Join My Email List⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Say hi on TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Say Hi on Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Henry@vibeabundant.com---This is the final step.If you've carried grief… trauma… loss… or a version of yourself that feels heavy and stuck — this episode helps you release it for good.In this guided transformation session, you'll align with your higher self, reconnect with your inner strength, and step into the version of you that already exists beyond pain.You are not broken. You are not behind. You are not alone.Through powerful visualization, breathwork, and identity alignment, this episode helps you:• Release emotional weight from the past• Stop repeating old pain patterns• Connect with your higher-frequency self• Reclaim joy, clarity, and direction• Step into abundance without fearYour next life chapter doesn't start later.It starts the moment you decide the old story is over.Listen all the way through — this experience is designed to shift you.Subscribe for more mindset activations, identity upgrades, and transformation tools that help you become the version of you that was always meant to rise.

This episode was sponsored by Cardiff & whooptriggerz.com   LightSpeed VT: https://www.lightspeedvt.com/ Dropping Bombs Podcast: https://www.droppingbombs.com/   Today's Dropping Bombs episode features Carlton "C-Dub" Whitfield, who went from playing piano in his dad's church to building a multi-app ecosystem used by musicians and churches worldwide — no funding, no label, no gatekeepers.   In this episode, Carlton breaks down how he made the leap, what almost stopped him, and what it actually takes to build recurring revenue as a creator.   Creators, musicians, and faith-based entrepreneurs — this one was made for you.   __