Podcasts about Synthetic

Synthetic peptides are getting some serious hype, from fitness influencers on your FYP to people seeking new options for chronic conditions. And while the human body already makes peptides, like insulin and the hormone oxytocin, most of the viral synthetic peptides are not approved by the Food and Drug Administration. Scientists say that raises some big concerns about safety and efficacy. So ahead of this week's FDA meeting to discuss seven commonly used peptides, we get into the science with health reporter Sarah Boden.Interested in more science behind health trends? Email us your question at shortwave@npr.org.Support public media with NPR+ and enjoy perks for over 25 podcasts like this one. It includes perks like bonus episodes, early access, archive access, curated playlists and sponsor-free listening. Learn more at plus.npr.org. See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

In this solo episode, Paul F. Austin offers a candid introduction to 5-MeO-DMT, one of the shortest and most intense psychedelic experiences. Find full show notes and links here: https://thethirdwave.co/podcast/episode-365/?ref=278 Paul distinguishes synthetic 5-MeO-DMT from Bufo, compares it with N,N-DMT and psilocybin, and examines the risks that social media narratives often leave out. He also explores low-dose versus breakthrough work, nervous system readiness, medication interactions, facilitator screening, reactivations, ecological responsibility, and why integration may matter far more than the experience itself. Paul F. Austin is an entrepreneur, educator, and pioneer in the psychedelic field. He is the founder of Third Wave, a leading platform dedicated to psychedelic education, and the Psychedelic Coaching Institute, which trains coaches and practitioners to responsibly support psychedelic experiences and integration. As host of The Psychedelic Podcast, Paul has interviewed hundreds of researchers, clinicians, Indigenous wisdom keepers, entrepreneurs, and cultural innovators. His work focuses on psychedelics, leadership, personal development, mental health, and cultural transformation. Highlights: 5-MeO-DMT beyond the social media hype Synthetic 5-MeO-DMT versus Bufo How 5-MeO differs from N,N-DMT Low-dose work versus complete ego dissolution Why nervous system readiness matters Medication interactions and physical safety How to evaluate a 5-MeO facilitator Why integration outlasts the experience Episode Links: The Practitioner Pychedelic Practitioner Certification by Third Wave's Psychedelic Coaching Institute. Third Wave's Ultimate Guide to 5-MeO-DMT Disclaimer: This content is for educational, informational, and entertainment purposes only. We do not promote or encourage the illegal use of any controlled substances. Nothing said here is medical or legal advice. Always consult a qualified medical or mental health professional before making decisions related to your health. The views expressed herein belong to the speaker alone, and do not reflect the views of any other person, company, or organization.

CLONE CULTURE – C8h11nPANIC PRIEST – RuinessSINNERELLA – Eyes Without a Face (Billy Idol cover)CIRCA WAVES – Le BateauSOHODOLLS – Right and Right AgainHUMAN LEAGUE – Don't You Want Me (Blue Collar Bros. Synthetic mix)THE VENOMOUS PINKS – Broken Hearts ClubCOIN – Time MachineVIOLENTENE – Break the SilencePET SHOP BOYS – Bullet For NarcissusDEAD POSEY – Sorry I'm Not DeadTHE INTERRUPTERS – AfterthoughtCARVED SOULS – I AmLEAETHER STRIP – Effigy (Ministry cover)JET CEMETERY – Melt AwayARK IDENTITY - Closer

Today's episode with Mitch and Amanda on the Holy Health podcast talks all about caffeine and nicotine. Mitch has been on a deep dive into the world of natural vs. synthetic nicotine and caffeine. He breaks down the differences and the couple talk about the impacts both of these substances have on health.Connect with us!YoutubeEmailInstagramMitch - SubstackMitch - InstagramMitch - FacebookAmanda - WebsiteAmanda - YoutubeAmanda - InstagramAmanda - Substackholyhealth222@gmail.comPlease share the show and leave a rating and review!

Hannah Roeschlein (@handerpumprules) joins Jesse and Matt for half the episode before the computer gods get angry, forcing the two guys to finish the show solo. But between us we talk about Filipino divers! Synthetic cells! And Hobbits and Komodo dragons! In the Patreon bonus, we get into space fires, and hear how Matt got served by a legendary astronaut and Jesse angered BBQ fans. Click here to support Probably Science via Patreon Click here to subscribe in Apple Podcasts Click here to subscribe in Stitcher

The beauty industry has spent decades arguing about natural versus synthetic ingredients. Most of that argument, Lorraine Dallmeier has long suspected, is about marketing rather than science. A carbon atom doesn't remember where it came from. But in this episode of Green Beauty Conversations, something shifts. Following her recent interview with Vikram Pandit of Phycus Biotechnologies, Lorraine asks whether published irritation data on fermentation-derived glycolic acid might finally give the natural versus synthetic debate a scientific footing it has never had before. If you listened to episode 325 and want to know what Lorraine actually made of it, start here. Free Resources Free formulation course | Green Beauty Conversations Podcast | Blog | YouTube Socials: Formula Botanica on Instagram | Lorraine Dallmeier on Instagram

The illicit drug market is evolving faster than ever before. Novel psychoactive substances (NPS), synthetic opioids and increasingly potent club drugs are creating new challenges for pre-hospital clinicians, emergency departments and critical care teams. Patients are presenting with unfamiliar toxidromes, unpredictable physiology, and rapidly deteriorating clinical conditions that demand evidence-based assessment and management. The episode begins with insights from Dr Caroline Copeland, Senior Lecturer in Pharmacology and Toxicology at King's College London and Director of the National Programme on Substance Use Mortality. Caroline explains how the illicit drug market has transformed over the past decade, discussing the emergence of synthetic opioids, nitazenes, novel benzodiazepines and the ever-expanding range of novel psychoactive substances. Drawing on national surveillance data and forensic toxicology, she highlights the trends every frontline clinician should understand and the public health implications of these rapidly evolving substances. Building on that foundation, Owen Bowden-Jones, Consultant Psychiatrist and one of the UK's leading experts in club drugs and addiction medicine, takes us into the practical realities of managing patients who have taken MDMA, ketamine, GHB/GBL, synthetic cathinones, synthetic cannabinoids ("Spice"), nitrous oxide and other emerging recreational drugs. The discussion focuses on recognising toxidromes, avoiding common pitfalls and delivering safe, evidence-based supportive care in the pre-hospital and emergency setting.How the illicit drug landscape has changed over the last decade.Novel psychoactive substances (NPS) and why they continue to emerge.Synthetic opioids, nitazenes and the growing public health threat.MDMA toxicity and serotonin syndrome.Ketamine: recreational use, toxicity and chronic complications.GHB/GBL overdose and withdrawal.Synthetic cathinones ("bath salts") and severe behavioural disturbance.Synthetic cannabinoids ("Spice") and unpredictable toxicity.Nitrous oxide misuse and neurological injury.Recognising toxidromes rather than chasing an exact drug diagnosis.Managing agitation, hyperthermia and cardiovascular instability.Harm reduction and communicating with patients who use recreational drugs.Emerging drug trends every pre-hospital clinician should know.This episode brings together and expands on two previous conversations from the Pre-Hospital Care Podcast:Novel Psychoactives and The New Drug Landscape: A Conversation with Dr Caroline Copelandhttps://podcasts.apple.com/gb/podcast/novel-psychoactives-and-the-new-drug/id1441215901?i=1000748836782Club Drugs, Illegal Highs and Novel Psycho-active Substances with Owen Bowden-Joneshttps://creators.spotify.com/pod/profile/phcp/episodes/Club-drugs--illegal-highs-and-Novel-Psycho-active-Substances-with-Owen-Bowden-Jones-ejk7rfYou can find excellent online eLearning resources on Club Drugs and NPS here: https://elearninghub.rcpsych.ac.uk/products/Module_1_Introduction_to_club_drugs_and_NPSThese are empirical studies conducted and published by Caroline Copland: https://www.kcl.ac.uk/people/caroline-copeland

Chapter 1: The Four-Year Pivot & Automation (00:01 – 03:43) A realistic assessment of the modern voiceover landscape. The conversation breaks down why doubling down on specific performance sectors combats the rise of generative AI, and shatters the panic surrounding long-form narration. Chapter 2: The IVR Evolution & NAVA (03:44 – 06:58) An analysis of the sectors most impacted by digital cloning, particularly telephony and IVR. The chapter covers historical warnings regarding voice data and introduces the operational growth of NAVA. Chapter 3: Corporate Blueprints & Intuition (06:59 – 11:24) A look into how a corporate career in insurance claims adjusting hands entrepreneurs the ultimate blueprint for business autonomy and time tracking. The discussion covers handling conflicting advice from industry mentors. Chapter 4: The LinkedIn Relationship Engine (11:25 – 14:49) The exact steps required to transform a standard profile into an organic, inbound lead-generation machine without spending money on premium platform upgrades. Chapter 5: Unconferences and Content Longevity (14:50 – 18:20) A breakdown of how simple, de-mystified business structures resonate best with creatives, featuring a look into long-term product modernization and content upkeep. Chapter 6: Paywalls & Parallel Accountability (18:21 – 28:04) Tactical advice on how to maintain dominant search functionality even as features get locked behind corporate paywalls, alongside a unique virtual co-working strategy that crushes procrastination. Chapter 7: Outsourcing & Dropping the Stress (28:05 – End) How to maintain tight creative control over a brand while strategically outsourcing administrative labor. The episode wraps with a reality check on social media posting schedules and child voice talent safeguards. Top 10 Boss Takeaways Human nuance remains un-clonable: Synthetic models cannot replicate the organic emotional transitions that define elite commercial performance. Continuous education is structural protection: Entrepreneurs must treat business development and tech training as non-negotiable daily habits. Run your business on intuition: Rely on common sense rather than chasing low-cost, race-to-the-bottom marketplace trends. Treat networking profiles like a mini-website: A professional profile must operate as a sleek landing page complete with clear social proof. Master the three pillars of direct outreach: High-converting marketing relies on a streamlined sequence: Optimize, Connect, and Message. Leverage parallel co-working sessions: Bypass administrative procrastination by setting up parallel focus blocks with an accountability partner. Retain vision when outsourcing: Scaling a business requires delegation, but the owner must always retain absolute control over the brand's trajectory. Give yourself permission to breathe: Focus entirely on the substance of marketing rather than the arbitrary frequency of a social media posting schedule. Audit service agreements closely: Talent must actively protect their biological voiceprints by ensuring explicit protective clauses are embedded into every contract. Hold corporate standards for child talent: Treat child performances as a legitimate business by enforcing standard industry rates and following official best practices.  

How has new research changed the way we see nutritional health? What role do ultra-processed foods, fibre, gut microbes, inflammation and insulin resistance play in the new paradigm? Why does the gut microbiome affect our mental health? How can simply diversifying the quantity of plants in your diet change health outcomes radically?In this episode we have the complex topic of nutrition to get up to date on. There's been so much research over the last 20 years that has contradicted some of our most deep seated preconceptions about food, so today we clarify science's new take. We discuss the rise of lifestyle medicine as a solution to western health crises; individualised diet; ultra processed foods; fibre; glycemic index issues; the gut microbiome; inflammation; a variety of foods over restrictive diets; how to support the use of new GPL-1 weight loss drugs; and what the nutritional organisation Zoe is doing to make this all easier for the public to put into practice.Fortunately our guest is a master at untangling all this nuance in a way that's so easy for us Joe public to understand - medical scientist and clinical nutritionist, senior lecturer and researcher at Imperial College London and King's College London, Dr. Federica Amati. She's also Head Nutritionalist at ZOE and Head of Nutrition Science at WellFounded Health. She's written 12 scientific papers and 3 books, “EveryBODY should know this”, “Recipes for better menopause”, and her brand new book “The Appetite reset”, which we discuss today.What we discuss: 00:00 Intro06:30 Worldwide pandemic of previously western-only diet related chronic diseases.07:00 Western diet, exported to the rest of the world is a big part of the problem.08:45 Sedentary lifestyle is also part of the metabolic dysfunction crisis.09:15 The “Diabesity” crisis.10:15 Dysfunctional food environments.10:55 One size dos NOT fit all for diet.11:45 Influencers are confusing the public.16:25 Personalised diet issues: more required from the patient, more expensive.18:00 Ultra-processed foods damage 1: quick energy uptake.20:30 U.P.F. damage 2: High palatability - more delicious, harder to stop eating.21:45 U.P.F. damage 3: Synthetic additives - unknown consequences & cocktail effect.23:35 Processed food risk scale from Zoe nutrition can help people consider.24:15 %60 of Uk calories are coming from ultra processed food.30:45 Fibre - How much do we eat and should we eat?32:45 Plant diversity: 30 per week, leads to Intestinal microbe diversity.35:15 Mapping the microbiome. 39:05 Microbiome and mental health - Vagus gut-brain axis, 42:55 Inflammation theory of depression - withdraw from society to heal.43:50 Chronic inflammation caused by western diet and stress.46:45 Your habits are more important than exceptions.48:15 Restrictive calorie-counting diets aren't sustainable for losing weight because of metabolic adaptation.52:05 “The Appetite Reset” book - GLP1 weight loss drugs.56:45 Adult obesity has doubled, adolescent obesity has quadrupled.References:Federica Amati, “The Appetite reset”.Federica Amati, “EveryBODY should know this”.Francesco Asnicar et al.,'Gut micro-organisms associated with health, nutrition and dietary interventions' paper. Zoe Nutrition - microbiome testing, glycemic monitoring, and personalised diet guidance.The microbiome, mental health & rethinking hygiene - Dr. Graham Rook episodeMood Food: The gut, diet and inflammation - Alex Laird episodeNot all U.P.F's are equal - Zoe Nutrition article

Astronomy Daily — S05E140 | Tuesday 14 July 2026 | Hosts: Anna & Avery Space mirrors are officially cleared for launch — and astronomers are sounding the alarm. In today's episode, Anna and Avery unpack the FCC's approval of Reflect Orbital's Eärendil-1, the first of a proposed constellation of sunlight-reflecting satellites, and what tens of thousands of orbital mirrors could mean for the night sky. Then it's off to the geostationary belt, where astronomers using clever image-stacking — with help from Siding Spring Observatory and the ANU — have revealed a minefield of invisible debris, most of it in no public catalogue. Also on the show: physicists in New York recreate black hole energy extraction on a benchtop, validating a 50-year-old Penrose prediction with 'synthetic rotation'; the Extremely Large Telescope in Chile turns on its axis for the very first time — 3,500 tonnes floating on 80 microns of oil; and University of Sydney's Dr Manisha Caleb and colleagues lay out how the Square Kilometre Array will turn fast radio bursts into a survey tool for the invisible universe. Plus a quick Starship Flight 13 update (now NET Thursday 16 July after a full 33-engine static fire), the story of Avi Loeb's appointment to chair the White House UAP Science Advisory Council, and a skywatch built around tonight's super new Moon. Stories & sources •       01. FCC approves Reflect Orbital Eärendil-1 space mirror — SpaceNews / Space.com / Engadget •       02. Faint debris "minefield" in geosynchronous orbit (DebrisWatch II, Journal of the Astronautical Sciences) — University of Warwick / Phys.org / Space.com •       03. Penrose superradiance via synthetic rotation (Nature) — CUNY ASRC / EurekAlert / Phys.org •       04. ELT completes first full rotation of its 3,500-tonne structure — ESO Picture of the Week / Space.com •       05. Fast radio bursts as cosmological probes with the SKA (Caleb et al.) — Universe Today / arXiv •       06. Starship Flight 13 slips to NET 16 July; full 33-engine static fire complete — Space.com •       07. Avi Loeb appointed chair of White House UAP Science Advisory Council — Space.com / AP coverage Skywatch highlights •       Tonight (14 July): super new Moon — darkest skies of the month; Milky Way core overhead for southern observers •       Evenings: Venus blazing in the west in Leo; crescent Moon joins Regulus & Venus on 16–17 July •       Pre-dawn: Saturn high with rings tilted ~9°; Mars passes 5° north of Aldebaran on 14 July Visit astronomydaily.io for all episodes and the free newsletter. Follow @AstroDailyPod. Astronomy Daily is part of the Bitesz.com Podcast Network.Become a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-the-latest-space-news--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.bitesz.com/nordvpn. You'll be glad you did!Become a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

This episode is sponsored by Notion. Learn more about Notion's Developer Platform today at https://notion.com/mlstBritain's most capable coding model can't be exported, and that ban is the whole reason Cosine set out to build one from scratch. Alistair Pullen, CEO and co-founder of Cosine, sits down with Tim Scarfe to explain how a frontier system he calls Fable, locked behind US export controls, became the founding case for a UK sovereign model trained on the Isambard supercomputer in Bristol.The bet underneath it is economic. Pullen argues that an inference company, rather than a training-first lab, doesn't need billions to compete: millions, a national compute allocation, and a consortium feedback loop can be enough. From there it gets into the machinery, why open-weight models still trail the frontier on size, active parameters and data, the mixture-of-experts versus dense trade-off and why active params dominate how a model actually feels, and the edge that real coding trajectories confer.The back half is about making agents trustworthy. Pullen makes the case for beating "slop" by rewarding the process instead of the final answer, reframes code review as runtime proof (spin the bug up in a VM and force the agent to actually exploit it), and walks through Swarm, Cosine's system running hundreds of sub-agents in one shot. It ends on why memory is still an unsolved hack, how synthetic graders let you run RL on tasks with no built-in test, and why Pullen reads US export controls as an accidental gift, with a supply-chain sting in the tail.---TIMESTAMPS:00:00:00 The sovereign mandate and the Fable ban00:04:02 Millions vs billions: the inference-company model00:07:19 The consortium feedback loop00:07:40 Why open models lag the frontier00:14:59 MoE vs dense, and why active params matter00:16:29 Trajectories: the process-data advantage00:19:48 Beating slop: reward the process, not the answer00:26:06 Reusable abstractions and the epistemic wall00:29:56 Code review becomes runtime proof00:37:32 Do agentic harnesses still matter?00:40:35 Swarm: orchestrating hundreds of sub-agents00:45:14 Why memory is still unsolved00:48:25 Synthetic data and graders for RL00:53:09 The US export gift and supply-chain risk---REFERENCES:organization:[00:01:15] Cosinehttps://cosine.sh[00:04:14] Mistral AIhttps://mistral.ai[00:05:50] Anthropichttps://www.anthropic.com[00:07:42] Coherehttps://cohere.com[00:08:36] DeepSeekhttps://www.deepseek.comtool:[00:02:52] Isambard-AIhttps://isambard.ac.uk[00:05:56] Colossus (xAI)https://en.wikipedia.org/wiki/Colossus_(supercomputer)[00:07:52] GLM (Z.ai)https://z.ai[00:11:52] NVIDIA B300https://www.nvidia.com/en-us/data-center/dgx-b300/[00:15:37] gpt-oss-120bhttps://huggingface.co/openai/gpt-oss-120b[00:15:52] Devstral 2https://mistral.ai/news/devstral[00:16:01] Llama 70bhttps://www.llama.com[00:17:05] Claude Codehttps://www.anthropic.com/claude-code[00:26:23] ARC-AGI (Francois Chollet)https://arcprize.org[00:40:38] Swarm (Cosine)https://cosine.sh[00:40:50] OpenAI Codexhttps://github.com/openai/codex[00:41:16] Lumen Outpost (Cosine)https://cosine.sh[00:41:18] Kimi K2 (Moonshot)https://huggingface.co/moonshotai/Kimi-K2-Instruct[00:49:55] SWE-benchhttps://www.swebench.com[00:52:40] SystemVeriloghttps://en.wikipedia.org/wiki/SystemVerilogperson:[00:23:40] Andrej Karpathyhttps://karpathy.aipaper:[00:27:10] GRPO (DeepSeekMath)https://arxiv.org/abs/2402.03300[00:27:13] GSPOhttps://arxiv.org/abs/2507.18071Incompressible Knowledge Probes, Bojie Lihttps://arxiv.org/pdf/2604.24827Estimating the Size of Claude Opus 4.5/4.6https://unexcitedneurons.substack.com/p/estimating-the-size-of-claude-opus---ReScript:https://app.rescript.info/session/5852d2b884c4ce4b?share=10b9799160845bb11779f8ac6cd3124f

For a number of years now, we've been hearing that the future of transport is electric, in particular due to the impact of fossil fuels on global warming. But there are a number of drawbacks, including cost, battery life, battery recycling, charging time which mean some people are reluctant to get on board. There are also fears that the switch to electric vehicles won't be quick enough to meet environmental objectives. Some experts believe that the fastest way to reduce CO2 emissions from transport is to use fuels synthesised from organic materials that are carbon-neutral and can power existing vehicles. How are synthetic fuels made? Why are they a good alternative to fossil fuels? Will it be available to the general public? In under 3 minutes, we answer your questions! To listen to more episodes, click here: ⁠⁠Could job enrichment make your work more rewarding?⁠⁠ ⁠⁠What is Ulysses Syndrome?⁠⁠ ⁠⁠What are nepo babies?⁠⁠ A Bababam Originals podcast. Written and produced by Joseph Chance. First broadcast: 16/01/2023. Learn more about your ad choices. Visit megaphone.fm/adchoices

Hour 1 for 7/9/26 Drew and Fr. Tad Pacholczyk discuss a recent development in synthetic cells (1:00), the dangers it might pose (9:56), and fertility (12:55). Then, Wendy Wilmowski discusses housing prices, immigration and foreclosures (33:34). Other topics: Chinese investors (37:26) and positive developments in the market (41:22). Links: https://www.ncbcenter.org/ https://www.fathertad.com/ https://www.twocrownhome.com/

Associate Professor Aaron Engelhart joins Adam and Jordana.

The volume problem in AI is solved. Now it's all about data quality, and who gets to define it. Enzo Blindow is VP of Data & AI at Prolific, a platform that connects hundreds of thousands of people worldwide to the frontier labs and enterprises training and evaluating AI models. In this conversation with Liam, Enzo breaks down what actually goes into building high-quality training data, why models lean too hard into stereotypes, and the research Prolific published showing how easily AI can be nudged toward commercially motivated, and sometimes harmful, suggestions. They discuss why synthetic data hits a ceiling that only human data can break through, how a single mistranslated instruction can quietly corrupt an entire dataset, and why "good taste" might be one of the hardest things for AI to ever replicate. Key Topics Covered: Why data volume is a solved problem and quality is everything now How RLHF actually shaped early versions of ChatGPT Why AI models lean too heavily into stereotypes The asymmetry and hidden bias baked into internet-sourced training data Prolific's ICLR research on commercial pressure in AI models Who's responsible when AI models cause harm: labs vs. data providers Synthetic data's ceiling, and why humans still have to validate it What actually defines "taste" and why it's nearly impossible to model The risk of AI flattening nuance and marginalized perspectives Why human data is one of the most defensible moats in AI Enzo's own definition of what "data" really means Episode Timestamps: 00:00 Intro 00:21 What Prolific actually does 02:48 MCP vs. API vs. CLI access 04:19 How frontier labs started working with Prolific 06:40 Data volume vs. quality, and the role of RLHF 10:58 Who Prolific's biggest customers are 13:12 Why labs choose Prolific over other data vendors 16:13 Fact vs. opinion in AI training 19:02 Stereotypes and bias in AI models 21:15 Prolific's ICLR research on commercial pressure 23:36 Who's responsible: labs, governments, or data companies 27:22 How Prolific's data collection actually works 31:59 Synthetic data vs. human data 36:04 What defines "taste" in AI-generated content 39:33 Good taste vs. bad taste, and the risk of AI regression to the mean 42:36 Why Enzo joined Prolific 45:56 Blind spots most people have about training data 47:22 The "SaaSpocalypse" and data as a business moat 51:38 How Enzo visualizes "data" in his own mind 54:22 Why Enzo does what he does 57:16 Where to find Enzo and Prolific Connect with Enzo on LinkedIn: https://www.linkedin.com/in/enzoblindow/ Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices

Recorded live at SocialWest 2026 in Calgary, Sabaa Quao, venture studio advisor and AI strategist, joins host Lindsay Smith to explore how synthetic data is about to change the way every marketer builds customer models.Sabaa spends his days advising startups at the intersection of AI and fintech, and points to Arima as one of the clearest examples of where this is headed: a synthetic model of society built on 25,000 to 50,000 data points per person, layered on top of a brand's first-party data to fill in the gaps that surveys, forms, and imperfect customer records leave behind.Sabaa breaks down why ecosystems and compounding data models are the two biggest predictors of startup success, how synthetic data stays privacy-first while still getting richer over time, why marketing is behind industries like autonomous driving and fraud detection in adopting it, and what happens when predictive modeling becomes accessible to businesses of every size, not just the ones with a head of data and analytics.Guesswork was never a strategy. Now it doesn't have to be the default either.Edited and Produced by TAKT.

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

Heather Zeiger of Center for Bioethics and Human Dignity talks about the "spud" cell created by scientist at the University of Minnesota that can divide and reproduce itself, well kinda.  Is it really "alive?" Also, she delves into the science behind soccer, plus what happened in Venezuela that caused the earthquakes.  Licensed counselor Debra Fileta of the Talk to Me, author of "People Skills," talks about how empathy is important in understanding others, even those who may have hurt you.  While certain behaviors should not be excused, they can at least be understood where they came from.  The Reconnect with Carmen and all Faith Radio are made possible by your support. Give now: Click here

Episode Highlights With MichaelWhy he thinks synthetic fragrance is the new secondhand smoke and how he can back this up with dataSecondhand smoke was such a brilliant marketing push and why fragrance needs this nowThe worst offenders and what to look forWhat “fragrance” actually is when you see it on a packageSafe swaps that actually workHaving safe and beneficial indoor air quality The air freshener industry is a 7 billion dollar industry. The air filtration industry is less than half of thatPeople are spending double the money to pollute their air than to clean itFragrance = chemicalsThey work by hijacking your ability to smellWhat to know about candles and which to avoid- some can be great!Turning your home into a sanctuary The 80/20 of health: sleep sanctuary, scrub sleeping air, filter water.Resources MentionedJaspr air scrubber - use the link for a discountOrganic beddingHealthy mattress I loveWater filtersHealthy lightingNon-toxic dishwasher and laundry detergentBranch Basics non-toxic cleanerJust Thrive:Just Thrive Health has been one of my longtime favorite brands for gut health and they have an amazing Daily Gut Detox. Your immune system, gut barrier, and digestion get the support they need to stay strong and healthy. You can find this and their probiotics at justthrivehealth.com/wellnessmama or use code wellnessmama for 20% off your order. BioptimizersI love and use so many products from them, but I especially love the magnesium (Magnesium Breakthrough) and digestive enzymes (Masszymes). Visit bioptimizers.com/wellnessmama to get the best deal!

What if everything you've been taught about caring for your dog needs a second look? In this eye-opening conversation, Darin sits down with lifelong animal advocate, senior dog rescue operator, and canine wellness educator Angela Ardolino to explore the hidden forces shaping modern veterinary medicine. After both she and her beloved dog were diagnosed with degenerative diseases, Angela began questioning conventional treatments and discovered the transformative potential of full-spectrum hemp extract, medicinal mushrooms, species-appropriate nutrition, and addressing the root causes of disease. Together they examine the influence of pharmaceutical companies and large pet food manufacturers on veterinary education, the importance of gut health, the endocannabinoid system, functional mushrooms, and Angela's groundbreaking clinical research on reversing canine cognitive dysfunction (dog dementia). Most importantly, they discuss how pet owners can become empowered advocates for the animals they love by making informed, holistic choices. This conversation is a powerful reminder that true healing often begins by supporting the body's innate ability to restore balance. What You'll Learn How Angela reversed her rheumatoid arthritis alongside her dog's degenerative disease Why the endocannabinoid system is critical for both humans and dogs How full-spectrum hemp extract differs from isolated cannabinoids The hidden influence of pharmaceutical companies and pet food manufacturers in veterinary medicine Why gut health is the foundation of your pet's immune system The dangers of synthetic vitamins, fillers, and highly processed pet supplements Why medicinal mushrooms may support cognition, immunity, and longevity Angela's peer-reviewed research on canine cognitive dysfunction (dog dementia) The importance of therapeutic dosing and ingredient quality Why sourcing, extraction, and growing methods matter Practical ways to naturally support your dog's health How becoming an informed pet owner can transform your animal's quality of life Chapters 00:00:00 – Welcome to SuperLife 00:00:33 – Sponsor: TruNiagen 00:02:36 – Introducing Angela Ardolino 00:03:16 – Why conventional veterinary medicine deserves closer scrutiny 00:04:53 – Dogs as mirrors, teachers, and companions 00:06:40 – Angela's rheumatoid arthritis diagnosis 00:08:24 – Her dog's degenerative disease and discovering full-spectrum cannabis 00:11:16 – Learning the endocannabinoid system and shifting to canine wellness 00:13:05 – Inside a senior dog rescue: what Angela kept seeing 00:15:10 – Synthetic supplements, fillers, and hidden ingredients 00:17:37 – Looking beyond symptoms to identify root causes 00:21:21 – Sponsor: Fatty15 00:25:02 – Why kibble and ultra-processed pet foods became the norm 00:26:19 – Pharmaceutical influence on veterinary education 00:27:24 – Drug safety, informed consent, and advocating for your pet 00:31:08 – Building a stronger health foundation for dogs 00:33:30 – Natural plant medicines versus isolated pharmaceuticals 00:36:30 – Practical first steps every pet owner can take today 00:39:00 – Supporting detoxification and the liver naturally 00:42:20 – Functional mushrooms and whole-plant medicine 00:45:01 – Healing degenerative disease with hemp and botanicals 00:46:41 – Preventing disease in aging dogs 00:47:39 – Why sourcing and extraction methods matter 00:50:19 – Medicinal mushrooms, cultivation, and therapeutic potency 00:53:27 – Introducing MycoDog and Angela's formulations 00:54:00 – Understanding canine cognitive dysfunction 00:57:12 – Why dog dementia is often overlooked 00:59:21 – Designing a clinical study using medicinal mushrooms 01:01:21 – The research results: improving canine cognition 01:03:25 – The future of integrative veterinary medicine 01:05:09 – Choosing quality hemp products and final advice for pet owners Thank You to Our Sponsors Truniagen: Go to www.truniagen.com and use code DARINOLIEN20 at checkout for 20% off Fatty15: Get an additional 15% off their 90-day subscription Starter Kit by going to fatty15.com/DARIN and using code DARINOLIEN20 at checkout. Join the SuperLife Community Get Darin's deeper wellness breakdowns — beyond social media restrictions: Weekly voice notes Ingredient deep dives Wellness challenges Energy + consciousness tools Community accountability Extended episodes Join for $7.49/month → https://patreon.com/darinolien Find More from Angela Ardolino Website: angelaardolino.com Instagram: @yournaturaldogpodcast Podcast: Your Natural Dog Youtube: Subscribe to Her Channel!     Find More from Darin Olien: Website: darinolien.com Instagram: @darinolien Book: Fatal Conveniences Platform & Products: superlife.com New Show: Roadmap to Happiness      Key Takeaway "Our dogs depend on us to make informed decisions about their health. When we move beyond symptom management and begin supporting the body's innate intelligence through species-appropriate nutrition, reducing toxic exposures, whole-plant medicine, functional mushrooms, and thoughtful lifestyle choices, we create the conditions for resilience and healing. Becoming your pet's greatest advocate may be one of the most loving acts you can offer."

What is it actually like to serve aboard a nuclear submarine?Former US Navy sonar operator Aaron Amick joins me to explain the reality of submarine warfare, from the training pipeline and sonar school to real Cold War patrols tracking Russian submarines.We discuss how sonar operators identify ships purely by sound, what happens when another submarine appears unexpectedly beneath the sea, life during months-long deployments without surfacing, submarine tactics, acoustic intelligence, and why experience matters far more than technology.Aaron also shares stories from operational patrols, close encounters with Russian anti-submarine forces, the psychology of life underwater, and some of the strangest things ever detected beneath the ocean.Whether you're interested in submarines, naval warfare or simply how military professionals operate in one of the world's most demanding environments, this is a fascinating look inside the Silent Service.(game videos) Jive Turkey Live: https://www.youtube.com/@jiveturkeylive(lecture series) Sub Brief: https://www.youtube.com/@SubBrief(live streams every Wed. & Fri.) Sonar and Steel: https://www.youtube.com/@sonarandsteel(company Website) https://www.subbrief.com/0:00 Introduction1:50 Welcome to Aaron and episode outline4:20 Inspiration to become a submariner8:17 Sonar A School10:38 Merchant ship vs warship acoustic signatures13:13 Did it come naturally, and what happened to those who washed out?16:07 Understanding the LOFARgram (the “waterfall” display)19:20 The first time detecting another submarine20:45 The submarine tactics playbook and how it is learned28:47 The training cadre32:28 Aaron's YouTube channels35:04 Two years in the Navy before setting foot on a submarine37:13 Life aboard the Los Angeles and Ohio classes: living conditions, accommodation and watch rotations44:20 Can you talk normally inside a submarine?45:32 Deployments vs getting underway, knowing where you're going, and the perks of being a sonarman51:00 The first operational mission during the aftermath of the USS Baton Rouge collision56:35 Experience versus knowledge: the psychological and emotional challenge1:00:25 Six-hour watches: staring at the LOFAR display and staying alert1:04:16 Managing multiple contacts and building situational awareness1:08:30 The evolution of sensor fusion and communicating the tactical picture1:13:38 The last shower before a mission… and living with the smell1:16:08 The longest time spent underwater1:17:30 Medical emergencies at sea1:20:55 The Udaloy encounter1:30:04 Understanding the adversary1:33:00 Maintaining close formation with another submarine1:36:22 Views on Russian and Chinese submarine forces1:42:53 Synthetic aperture radar from space1:45:08 Aaron's journey as a sonarman1:48:04 Underwater UAPs and unexplained contacts1:55:14 The final mission1:58:55 Did the career meet or exceed expectations?2:01:00 Does Modern Naval Warfare bring frustration or fond memories?2:03:05 The SubBrief team2:05:20 The “last last” question: taking content down2:07:40 Wrapping up

Episode 382 Scientists have created a synthetic cell with just 36 genes that can copy DNA and replicate. In an attempt to create a “minimal cell”, a team led by professor Kate Adamala have built “SpudCell” from the ground up, using non-living components. But is it alive - and is it dangerous? Despite being able to carry out some of the tasks of a normal cell, it's not clear that it's capable of evolution - yet. But it is a major breakthrough in the field and could pave the way for the creation of artificial life in the future. Researchers have made their work open source so the next breakthrough can be fasttracked - and hope it will help provide a solution to the climate crisis by replacing the need for petrochemicals. To discuss the news - and its implications for the origin of life - Rowan Hooper and Penny Sarchet are joined by New Scientist reporter Michael Le Page. To read more about these stories, visit https://www.newscientist.com/ Learn more about your ad choices. Visit megaphone.fm/adchoices

The National Final of the Student Enterprise Programme took place recently with 88 students competing for the title of Student Enterprise of the Year. The programme itself is Ireland's largest student entrepreneurship initiative, with over 500,000 students having started businesses since 2003. To find out more I spoke to one of the finalists Seán FitzGerald, a fourth-year student who has created software Fleet Monitor, designed to predict the movements of ships attempting to avoid maritime sanctions across every ocean globally.I spoke Sean about his background, the  Student Enterprise Programme, what Fleet Monitor does, and more.More about Fleet Monitor:A standout in this year's programme, Fleet Monitor uses Automatic Identification System (AIS) data to track the position, heading, speed and size of vessels. The platform then cross checks this data in real time against official EU and Nato sanction lists to identify known or suspected shadow-fleet ships.

Researchers have engineered an artificial cell out of chemicals and biomolecules that, at a basic level, can eat, grow, duplicate its own genetic code, and reproduce itself. The cell, dubbed SpudCell, is aimed at creating a chassis that can be adapted to create biological factories for the chemicals humans rely on for modern life, from fuels to pharmaceuticals. But it also raises the question of what it means for something to be “alive.”  Synthetic biologist Kate Adamala joins Host Ira Flatow to talk about the technological advance, the possibilities for the artificial cell, and a nonprofit organization she hopes will allow the SpudCell to spark an innovation in biotechnology. Guest: Dr. Kate Adamala is a synthetic biologist and an associate professor of  genetics, cell biology, and development at the University of Minnesota. Transcripts for each episode are available within 1-3 days at sciencefriday.com. Subscribe to this podcast. Follow our show on Instagram, TikTok, Facebook, and Bluesky @scifri and sign up for our newsletters. Got a science question that's keeping you up at night? Call us: 877-472-4374 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Global power demand is rising sharply, and geopolitical instability is accelerating the need for secure, affordable, and diversified energy systems. In this episode, host Joe Colletti speaks with Robert Kwan, Head of Global Power, Utilities & Infrastructure Research, and Maurice Choy, Canadian Energy Infrastructure Analyst, to explore how RBC Imagine themes—energy security, affordability, crisis capitalism, synthetic technologies, and shifting superpowers—are reshaping the sector.The conversation highlights how utilities, midstream operators, and infrastructure investors are navigating a world where energy transition, digital demand, and geopolitical conflict collide. As Robert notes, “this current environment is hammering home to a broad population how important energy is to everybody's daily lives”.Key PointsGeopolitical conflict is reinforcing a global “all of the above” approach to energy supply.Rising energy prices are intensifying affordability pressures across households and industries.Hyperscalers and digital platforms are rapidly becoming dominant global electricity consumers.Utilities are investing heavily in resilience to manage climate, cyber, and grid instability risks.Synthetic technologies are improving efficiency and reducing supply‑chain vulnerabilities.Canada is increasingly well‑positioned to expand global energy exports across fuels and electricity.Massive infrastructure investment is required to deliver diversified, secure global energy systems.

Start with the word meme, the way Richard Dawkins meant it in 1976, a piece of culture that copies itself from mind to mind and adapts to whatever medium will carry it. By that measure the Lost Cause ranks among the most successful memes this country has produced. Confederate veterans engineered it after the war, men who had lost the fighting and refused to lose the meaning. Jubal Early and the people around him built a version of the war in which slavery was a sideshow, the South fought for high principle, and the enslaved stayed content until Northern armies disturbed them. That version was false when it was written. It won anyway.

Featuring Haroldo Andrade, North American Commercial and Logistics Director for Unimetals Episode Summary In this episode, Mike sits down with Haroldo Andrade of Unimetals for a timely conversation about synthetic graphite, sustainability, and the critical materials needed to power heavy industry, battery production, AI data centers, and the energy transition. Haroldo explains why synthetic graphite is essential to modern manufacturing and infrastructure, how circular carbon practices can reduce environmental impact, and why the United States faces a major domestic supply gap as demand grows. The conversation also explores the tension between decarbonization goals and manufacturing costs, regional differences in sustainability strategy, workforce development, and Unimetals' global expansion. Key Topics Discussed Why synthetic graphite is a critical material for steelmaking, battery anodes, and energy storage The projected U.S. synthetic graphite supply deficit and why domestic production matters How China's dominance in graphite processing affects global supply chains The role of circular carbon and petroleum coke feedstock in reducing environmental impact Balancing decarbonization goals with the realities of manufacturing cost and competitiveness How higher-quality carbon products can improve furnace efficiency, reduce energy use, and lower emissions The growing energy demands of AI data centers and their connection to energy infrastructure Regional differences in sustainability approaches across North America and Latin America University partnerships, workforce development, and the need for new technical talent Unimetals' international expansion through acquisitions in Germany and the United States About the Guest Haroldo Andrade is the North American Commercial and Logistics Director for Unimetals. In his role, he works at the intersection of industrial supply chains, premium carbon products, and strategic growth across North America and global markets. Why It Matters Synthetic graphite is becoming increasingly important as industries modernize, electrification expands, and demand rises for batteries, renewable energy storage, steel, and advanced infrastructure. Haroldo highlights why reliable domestic supply, cleaner production practices, and international collaboration will be essential as companies navigate energy transition, industrial growth, and economic volatility. Listener Takeaway This episode offers a practical look at how critical materials, circular carbon, and resilient supply chains shape the future of heavy industry and clean energy. Listeners will come away with a clearer understanding of why synthetic graphite is more than a niche industrial input—it is a strategic material tied to manufacturing, energy security, and global competitiveness. Become a Green Insider Be sure to subscribe to The Green Insider, powered by ERENEWABLE, wherever you get your podcasts—and don't forget to leave us a five‑star rating! To learn more about our guests or to inquire about sponsorship opportunities, please contact ERENEWABLE and The Green Insider Podcast. #SyntheticGraphite #CriticalMaterials #EnergyTransition #CircularCarbon #SupplyChain #BatteryMaterials #IndustrialSustainability #ManufacturingInnovation The post Synthetic Graphite, Circular Carbon, and the Future of Critical Materials appeared first on eRENEWABLE.

Rajiv Parikh talks with Dr. Christophe Morin about why AI can be deceptively convincing, how to use custom GPT guardrails to make it safer, and what synthetic consciousness might actually mean. We also explore neurospirituality and neuroplasticity as practical tools for founders who want to scale without letting stress, anxiety, or savior patterns run their lives.• AI as a persuasive mirror that can hallucinate and mislead• Synthetic consciousness versus consciousness claims• Custom GPTs as curated knowledge containers with guardrails• Synthetic personas for market research and messaging tests• COVID as a catalyst for depression and a deeper pivot into wellness• Stress, anxiety, depression, addiction, and trauma as nervous system signals• 26 balance-restoring practices including nature, prayer, and meditation• Psychedelics as an emerging neuroplasticity conversation• Savior complex leadership patterns and how neuroplasticity rewires them• Why talk-only approaches can be slow and inaccessible for many people• Flow state as hypofrontality and trusted performance under pressureAI is getting better at sounding right, and that's exactly what makes it risky. We sit down with Dr. Christophe Morin, bestselling author of "The Persuasion Code", "Neuromarketing AI", and "Open", to unpack why generative AI can be deceptively persuasive, how hallucinations turn into “trusted” advice, and why using chatbots for emotional support can create a dangerous feedback loop for vulnerable people.From there, we get concrete. Christophe explains how custom GPTs and curated LLM setups can act like safer containers for vetted, proprietary knowledge, instead of a grab bag of blended theories and fabricated citations. We also explore how AI can help marketing teams through synthetic personas and pattern detection, not to emotionally “sell” the machine, but to test and sharpen messaging meant for real humans whose decisions start in the primal, emotional brain.The conversation turns personal and practical for founders and CEOs: stress is not always bad, but toxic stress spreads fast and can hijack leadership. Christophe shares how neuroplasticity enables real rewiring of long-held beliefs, why savior-complex leadership can quietly fuel anxiety and addiction patterns, and how neurospiritual practices like meditation, nature exposure, prayer, and emerging psychedelic research fit into a grounded self-healing framework. We close with flow state science, plus a fun Spark Tank detour into feline behavior that somehow reinforces the point: nervous systems need real release valves.Subscribe for more conversations at the intersection of AI, persuasion, and human performance, and if this one helps you, share it with a founder friend and leave a review.Christope Morin: https://www.linkedin.com/in/christophemorinphd/Dr. Christophe Morin is the bestselling author of multiple books, including The Persuasion Code, Neuromarketing AI, and OPEN: A Neurospiritual Exploration of the Self-Healing Power of Your Brain.  Christophe is the co-architect of the NeuroMap® model of persuasion and the creator of the OPEN™ framework, and the CEO and Co-Founder of SalesBrain, the world's first neuromarketing agency, and more recently the Founder of the Institute for Soul.  Christophe holds an MBA from Bowling Green State University and a Ph.D. in Media Psychology from Fielding Graduate University, where he serves as adjunct faculty alongside his role as an AI lecturer at Johns Hopkins University. Website: https://www.position2.com/podcast/Rajiv Parikh: https://www.linkedin.com/in/rajivparikh/Email us with any feedback for the show: sparkofages.podcast@position2.com

What if the biggest stories shaping our reality never actually happened the way we were told? In this mind-bending episode of Nephilim Death Squad, David Lee Corbo (The Raven) and TopLobsta welcome HV from Human Vibration back to the show for a deep dive into reality creation, AI-generated narratives, media psyops, Epstein, JonBenét Ramsey, trauma-based programming, conspiracy culture, and the energetic consequences of modern information warfare.HV shares her evolving perspective on how media events, conspiracy narratives, and digital technologies may be influencing human consciousness on a massive scale. From AI-generated content and synthetic realities to the emotional energy harvested through fear-driven news cycles, this conversation challenges listeners to reconsider the nature of truth itself.Topics discussed include:Human Vibration theoryAI-generated reality and synthetic mediaEpstein and manufactured narrativesJonBenét Ramsey and media constructionConspiracy culture and trauma loopsInformation warfare and psychological operationsEnergy harvesting and "loosh"Consciousness, reality, and perceptionThe future of AI and human identityIf you enjoy conversations about conspiracy, spirituality, consciousness, AI, and the hidden forces shaping our world, this episode is for you.

When the wind is just right, on a small beach in Titusville, Florida, horseshoe crabs crawl out of the water and onto the beach to lay their eggs. Jim and Colin joined up with two marine biologists—Bob Sluka who works with A Rocha, a Christian conservation organization and Margaret Miller, a coral biologist who works with SECORE International—and three A Rocha interns to survey the horseshoe crabs. That experience began an exploration into paying attention to many of the creatures that surround us, extending hospitality, and learning from the creatures, even from the ocean itself, about how we might better worship the creator of it all.  Theme song and credits music by Breakmaster Cylinder. Other music in this episode by Vesper Tapes, Klimenko Music, Evergreen, High Street Music, Magentize Music, & Sirius Music, courtesy of Shutterstock, Inc. Links and Resources: Learn about A Rocha Do your own nurdle hunt Atlantic Article about Synthetic alternatives to LAL Radiolab Episode about Horseshoe Crabs Listen to our most recent episode about A Rocha work in Oahu, Hawaii

Stewart Alsop hosts a conversation with Oliver Polzin, a founding team member of Meow Wolf and naturalist, exploring the intersection of creativity, conservation, and architecture. Oliver discusses his current postgraduate work at SCI-Arc in Los Angeles studying synthetic landscapes through an architectural lens, his deep fascination with Pleistocene megafauna and the La Brea Tar Pits, and his vision for creating a "biophilic culture" that reframes humanity's relationship with other species and ecosystems. The discussion ranges from Oliver's early work building mud caves at Meow Wolf to his current explorations of AI-assisted design tools, 3D printing with recycled materials, holistic grazing management systems for the Great Plains, and the ancient Amazonian practice of creating terra preta soil—all part of his broader investigation into how we can design interventions for climate and conservation issues while maintaining what makes us fundamentally human.Timestamps00:00 Stewart introduces Oliver Polzin from Meow Wolf's founding team and discusses how his yoga teaching there inspired the podcast's exploration of creativity and stress relationships.05:00 Oliver describes his architecture graduate program studying climate and conservation through synthetic landscapes, contrasting dark green naturalist ecology with bright green capitalist environmentalism.10:00 Discussion of conservation ethics and AI's potential for monitoring environmental systems, with Oliver explaining his journey from painting to experimental mud construction at early Meow Wolf.15:00 Stewart shares his robotics learning journey with ESP32s in Buenos Aires while Oliver questions humanoid robot design, suggesting functional form factors matter more than human resemblance.20:00 Oliver explores cardboard as material obsession and explains treasure hunt mechanics in Meow Wolf exhibits, creating dopamine-driven discovery experiences through layered storytelling.25:00 Stewart describes creating treasure hunts for Spanish learners in Buenos Aires parks while Oliver validates experiential art's growing importance in an increasingly digital culture.30:00 Conversation shifts to three-d printing flexible filaments for architectural models and Oliver's megafauna book project about La Brea Tar Pits Pleistocene fossils.35:00 Oliver connects Earth consciousness to Pale Blue Dot perspective, arguing humans face developmental threshold understanding planetary responsibility after 300,000 years as anatomically modern species.40:00 Deep dive into end-Pleistocene extinction events and megafauna loss, discussing two-ton capybaras and how predator relationships shaped human psychology and anxiety responses.45:00 Oliver presents speculative Great Plains biopreserve concept with de-extinct megafauna, contrasting holistic rotational grazing with destructive monoculture agriculture systems.50:00 Discussion concludes with Amazonian dark earth technology and indigenous landscape management, emphasizing need for biophilic culture embracing deep time ecological perspective.Key Insights1. Oliver Polzin is part of the founding team of Meow Wolf and is currently studying at SCI-Arc in Downtown LA in a postgraduate program called Synthetic Landscapes, which examines global scale climate and conservation issues through an architectural lens. Architecture exists between art and science, and he believes architectural thinking offers a valuable framework for designing interventions for climate and conservation challenges. This program represents a significant evolution from his earlier work at Meow Wolf, where he created immersive experiential art installations using materials like adobe and cardboard.2. There is an important distinction in ecological thought between what Paul Kingsnorth calls dark green and light green approaches to environmentalism. The dark green strain represents the older naturalist movement from the early twentieth century, focusing on biological systems, ecosystems, and endangered species. Light green emerged in the 1970s after the Earth Day movement and centers on clean energy, solar panels, and wind power as a way to maintain our current lifestyle. Oliver argues that the bright green approach represents a capitalist overlay that has captured the conservation movement, whereas true conservation requires focusing on actual biological systems rather than just technological solutions.3. The experiential art form that Meow Wolf pioneered still has enormous untapped potential, particularly as society becomes increasingly digital. Oliver believes there will be a huge wave of experiential desire in this decade as people crave human connection and real-world excitement. The treasure hunt and scavenger hunt format represents a compelling form of real-life RPG that creates meaningful human interactions. This type of experience design, which Meow Wolf developed through installations like the House of Eternal Return, plays with human dopamine systems by compelling people to open doors, explore spaces, and follow narrative threads through physical environments.4. The architectural model or dollhouse concept represents a crucial rhetorical tool that Oliver is learning to apply to climate and conservation work. Architects have long created physical models to show stakeholders what a building will be like, and this practice of showing a story in compelling ways for different types of brains is essential for getting traction on projects. While architectural models used to be made from foam core, paper, and balsa wood, they are now largely created through 3D printing, which allows for incredibly complex forms and interlocking structures that would have been impossible to construct manually.5. Oliver is obsessed with megafauna and the end Pleistocene extinction event that occurred roughly twelve thousand years ago. For three hundred thousand years, anatomically modern humans existed alongside massive beasts like short faced bears and American lions, and we were the smaller creatures in the ecosystem. The extinction of over one hundred genera of animals over ninety nine pounds, combined with sea level rise of nearly four hundred feet, fundamentally changed human existence and led to the development of agriculture and civilization. Much of our current psychological development, including anxiety responses, is still based on this time period when we lived among these massive animals.6. The current food system in the Great Plains is fundamentally broken compared to the historical managed food system maintained by Plains tribes, who sustained thirty to sixty million bison through 1800. Oliver explored a speculative project about turning the Great Plains into a massive biopreserve of de-extinct megafauna, contrasting the natural system of rotational grazing where predators keep herds moving with the current monoculture crop agriculture that requires external inputs like fertilizer, pesticides, and herbicides. The natural system builds soil and increases fecundity, while industrial agriculture degrades soil, creates toxic runoff, and produces genetically modified crops that feed animals in toxic concentrated feeding operations.7. The fundamental challenge facing humanity now is creating what Oliver calls a biophilic or ecophilic culture that is loving of other species and our home planet. This requires both psychological shifts and changes in how we design systems at all scales. The Amazon provides a powerful example of this, as recent LiDAR mapping has revealed that what appeared to be pristine wilderness was actually a vast tended garden created by indigenous civilizations who developed technologies like Amazonian dark earth through burning middens with various additives. These cultures understood how to be embedded in a web with other species while playing an important orchestrating role, offering a model for how humans might relate to other forms of life in our current era.

In this Marketing Over Coffee: Learn why an AI gets taken offline, how agentic features change SaaS, getting better business travel food, and more!! Direct Link to File Fable gets pulled? Claude to Haiku to Sonnet to Opus Is Fable that much better by eating a lot more tokens? New York’s Synthetic Performer Disclosure Law and California SB-942 […] The post Now with More Synthetic Performers and Less Fable! appeared first on Marketing Over Coffee Marketing Podcast.

Synthetic kratom's dangerous rise, fixing college sports, and faith-based medicine in a changing New York. Plus, Mollie Hemingway on Justice Samuel Alito's character, a world-record pizza maker, a Father's Day commentary from Seth Troutt, and the Thursday morning news.Support The World and Everything in It today at wng.org/donateAdditional support comes from Harbinger Tours, supporting Israel through luxury tours, with a November departure led by Marshall and Jessica Pennell. HarbingerTours.net

How should brands disclose AI-generated people in advertising—and what happens when the rules aren't clear? In this episode, we unpack New York's new “synthetic performer” law, which requires advertisers to conspicuously disclose when ads feature AI-generated or algorithmically created human-like performers. We explore the many unanswered questions surrounding the law, including whether it applies to background characters, partial performers, and other common creative elements, as well as the challenges advertisers face in determining what qualifies as a sufficiently clear disclosure. As states continue to push AI transparency requirements into the advertising space, companies using AI-generated content should be paying close attention to how these new rules could reshape marketing compliance and creative strategy. Hosted by Simone Roach. Based on a blog post by Gonzalo E. Mon.

Send us Fan MailIn this episode of Embedded Insiders, Dr. Kavyashree Keremane, a postdoctoral researcher in materials science and engineering, and Dr. Bed Poudel, a research professor at Penn State University, join the podcast to share their work exploring something that sounds almost futuristic, but is quickly becoming very real: using synthetic DNA as a medium for data storage.Watch the video interview here: https://youtu.be/_GupJZZypn4Read the story here: https://embeddedcomputing.com/technology/storage/how-synthetic-dna-for-data-storage-could-help-the-memory-crisisNext, Rich and John Grady, the CEO of Ayla Networks, discuss the challenges of supporting IoT across various regions. For more information, visit embeddedcomputing.com

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Damon Haley Co‑founder of Glow and Flow Beauty, discussing his transition from entertainment and sports marketing into the beauty-supply industry, his mission to elevate service for Black and Brown communities, and the franchising model he is rolling out nationwide. Hosted by Rushion McDonald on Money Making Conversations Masterclass, the conversation highlights Haley’s business philosophy, community-driven approach, and long-term vision to create ownership opportunities through franchising.

https://youtu.be/TNwDOMFY9O4 (FULL)In this powerful full episode, we sit down with Chelsea Kent of Solutions Pet Products, LLC and Hero's Pets in Colorado to talk about one of the biggest issues in the pet food world: synthetic vitamins and what they may be doing to our dogs and cats.At Pure Pawsitivity™, we are proudly synthetic-free because we believe pets deserve nutrition from real, whole food ingredients, not isolated synthetic additives, fillers, or questionable sourcing. And synthetic vitamins are only one piece of the much bigger conversation around what is really happening in the commercial pet food industry.Chelsea brings so much knowledge to this episode, from working within AAFCO regulations to understanding how companies can formulate complete and balanced foods using 100% whole food ingredients. We also talk about HTMA hair testing through ParsleyPet.com, which can help identify mineral imbalances, deficiencies, and potential toxic metal exposure.We trust Solutions Pet Products deeply, and after this conversation, we are even more grateful to carry and support this incredible company.Please follow and subscribe. It's free, and it helps us so much as we continue getting this information out to pets and the people who love them.Learn more:Solutions Pet ProductsHero's Pets in ColoradoParsleyPet.com for HTMA hair testingPurePawsitivity.comAvailable on YouTube, Spotify, Apple Podcasts, and all major podcast platforms.@followers

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Dr. Melanye “Dr. Mac.” Maclin joins Rushion McDonald to discuss the serious health risks associated with hair relaxers, permanent dyes, and synthetic braids—particularly among Black women. Drawing from over 25 years of research and patient experience, she explains how chemicals used in these products absorb through the scalp, disrupt hormones, and significantly increase the risks of breast cancer, ovarian cancer, uterine cancer, early puberty, fibroids, and infertility. The conversation also highlights systemic resistance from the beauty industry, government agencies, and even consumers themselves—primarily due to financial incentives and lack of awareness. Dr. Mac advocates for safer hair practices, increased education, and protective measures to reduce exposure. She also discusses her pioneering internal hair‑health supplements, Bella Nutri, for women (2004) and men (2008), and how she helped introduce the U.S. market to nutritional hair support long before it was mainstream. Purpose of the Interview The purpose of the interview is to: 1. Educate listeners about the hidden health dangers …of chemical hair treatments including relaxers, permanent dyes, and synthetic hair containing benzene. 2. Advocate for informed hair‑care decisions Dr. Mac wants women—especially Black women—to understand how beauty practices impact long‑term health. 3. Encourage the beauty industry to adopt safety protocols Such as scalp protection, warning labels, and honest communication about risks. 4. Highlight Dr. Mac’s work and products Including her Bella Nutri supplements and educational platforms (Ask Dr. Mac). 5. Empower parents to protect children By avoiding chemical treatments on young girls whose bodies are especially vulnerable. Key Takeaways 1. Chemical relaxers and permanent hair dyes are strongly linked to increased cancer risks. Permanent dyes raise the risk of breast, uterine, and ovarian cancer. Black women exhibit a 45% increased risk of breast cancer when using permanent dyes. Combining dyes with relaxers significantly compounds the danger. 2. The danger comes from chemical absorption into the scalp. Relaxer chemicals include sodium, calcium, guanine, and lithium hydroxide. These chemicals burn through the scalp, entering the bloodstream and disrupting hormones, leading to early puberty, fibroids, infertility, and cancer. 3. Synthetic braiding hair contains benzene—a carcinogen. Benzene exposure affects both the stylist and the client. Risks include lung cancer and leukemia. 4. The beauty industry resists change because of profit. Salons rarely display warnings because “it affects business.” The relaxer–damage→hair‑loss→extensions cycle creates a lucrative revenue loop. 5. Children are especially vulnerable to chemical exposure. Relaxers on children under 10 can cause: early puberty fibroids infertility early hysterectomies increased cancer risk Dr. Mac advises never relaxing a child’s hair, but if done, the product must stay on no more than 5–10 minutes with complete scalp protection. 6. Scalp protection is essential for anyone still using relaxers. Use petroleum jelly over the entire scalp, not just the hairline. This reduces chemical absorption during both application and rinsing. 7. Dr. Mac pioneered the U.S. hair‑supplement industry. Developed Bella Nutri after research with a Finnish company (Scalp). Initially dismissed as a “witch doctor,” but now the hair‑supplement market is mainstream. 8. She refuses to participate in relaxer‑related lawsuits. Because she has warned people for 20+ years, she cannot ethically testify for those who ignored repeated warnings. Notable Quotes On the impact of chemicals: “The chemicals burn through the scalp… getting into the main bloodstream and causing hormone disruption.” On the increased cancer risk: “African‑Americans have a more than 45% increased risk when we use permanent hair dyes.” On synthetic braids: “As long as that synthetic hair is on her head, she is breathing in benzene.” On industry pushback: “People are about the green‑eyed devil called money.” On relaxing children’s hair: “Hopefully a mother doesn’t take her child to get a relaxer.” “Hair chemicals can lead to early puberty, fibroids, infertility, even hysterectomies before age 40.” On the vicious cycle of damage and profit: “It’s a 360‑degree money‑making cycle.” On caring more than her patients: “I feel like I’m caring more about someone’s health than they are caring about their own.” On pioneering supplements: “Hair and skin are internal organs—they manifest externally.” #SHMS #STRAW #BESTSupport the show: https://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Dr. Melanye “Dr. Mac.” Maclin joins Rushion McDonald to discuss the serious health risks associated with hair relaxers, permanent dyes, and synthetic braids—particularly among Black women. Drawing from over 25 years of research and patient experience, she explains how chemicals used in these products absorb through the scalp, disrupt hormones, and significantly increase the risks of breast cancer, ovarian cancer, uterine cancer, early puberty, fibroids, and infertility. The conversation also highlights systemic resistance from the beauty industry, government agencies, and even consumers themselves—primarily due to financial incentives and lack of awareness. Dr. Mac advocates for safer hair practices, increased education, and protective measures to reduce exposure. She also discusses her pioneering internal hair‑health supplements, Bella Nutri, for women (2004) and men (2008), and how she helped introduce the U.S. market to nutritional hair support long before it was mainstream. Purpose of the Interview The purpose of the interview is to: 1. Educate listeners about the hidden health dangers …of chemical hair treatments including relaxers, permanent dyes, and synthetic hair containing benzene. 2. Advocate for informed hair‑care decisions Dr. Mac wants women—especially Black women—to understand how beauty practices impact long‑term health. 3. Encourage the beauty industry to adopt safety protocols Such as scalp protection, warning labels, and honest communication about risks. 4. Highlight Dr. Mac’s work and products Including her Bella Nutri supplements and educational platforms (Ask Dr. Mac). 5. Empower parents to protect children By avoiding chemical treatments on young girls whose bodies are especially vulnerable. Key Takeaways 1. Chemical relaxers and permanent hair dyes are strongly linked to increased cancer risks. Permanent dyes raise the risk of breast, uterine, and ovarian cancer. Black women exhibit a 45% increased risk of breast cancer when using permanent dyes. Combining dyes with relaxers significantly compounds the danger. 2. The danger comes from chemical absorption into the scalp. Relaxer chemicals include sodium, calcium, guanine, and lithium hydroxide. These chemicals burn through the scalp, entering the bloodstream and disrupting hormones, leading to early puberty, fibroids, infertility, and cancer. 3. Synthetic braiding hair contains benzene—a carcinogen. Benzene exposure affects both the stylist and the client. Risks include lung cancer and leukemia. 4. The beauty industry resists change because of profit. Salons rarely display warnings because “it affects business.” The relaxer–damage→hair‑loss→extensions cycle creates a lucrative revenue loop. 5. Children are especially vulnerable to chemical exposure. Relaxers on children under 10 can cause: early puberty fibroids infertility early hysterectomies increased cancer risk Dr. Mac advises never relaxing a child’s hair, but if done, the product must stay on no more than 5–10 minutes with complete scalp protection. 6. Scalp protection is essential for anyone still using relaxers. Use petroleum jelly over the entire scalp, not just the hairline. This reduces chemical absorption during both application and rinsing. 7. Dr. Mac pioneered the U.S. hair‑supplement industry. Developed Bella Nutri after research with a Finnish company (Scalp). Initially dismissed as a “witch doctor,” but now the hair‑supplement market is mainstream. 8. She refuses to participate in relaxer‑related lawsuits. Because she has warned people for 20+ years, she cannot ethically testify for those who ignored repeated warnings. Notable Quotes On the impact of chemicals: “The chemicals burn through the scalp… getting into the main bloodstream and causing hormone disruption.” On the increased cancer risk: “African‑Americans have a more than 45% increased risk when we use permanent hair dyes.” On synthetic braids: “As long as that synthetic hair is on her head, she is breathing in benzene.” On industry pushback: “People are about the green‑eyed devil called money.” On relaxing children’s hair: “Hopefully a mother doesn’t take her child to get a relaxer.” “Hair chemicals can lead to early puberty, fibroids, infertility, even hysterectomies before age 40.” On the vicious cycle of damage and profit: “It’s a 360‑degree money‑making cycle.” On caring more than her patients: “I feel like I’m caring more about someone’s health than they are caring about their own.” On pioneering supplements: “Hair and skin are internal organs—they manifest externally.” #SHMS #STRAW #BESTSee omnystudio.com/listener for privacy information.

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Dr. Melanye “Dr. Mac.” Maclin joins Rushion McDonald to discuss the serious health risks associated with hair relaxers, permanent dyes, and synthetic braids—particularly among Black women. Drawing from over 25 years of research and patient experience, she explains how chemicals used in these products absorb through the scalp, disrupt hormones, and significantly increase the risks of breast cancer, ovarian cancer, uterine cancer, early puberty, fibroids, and infertility. The conversation also highlights systemic resistance from the beauty industry, government agencies, and even consumers themselves—primarily due to financial incentives and lack of awareness. Dr. Mac advocates for safer hair practices, increased education, and protective measures to reduce exposure. She also discusses her pioneering internal hair‑health supplements, Bella Nutri, for women (2004) and men (2008), and how she helped introduce the U.S. market to nutritional hair support long before it was mainstream. Purpose of the Interview The purpose of the interview is to: 1. Educate listeners about the hidden health dangers …of chemical hair treatments including relaxers, permanent dyes, and synthetic hair containing benzene. 2. Advocate for informed hair‑care decisions Dr. Mac wants women—especially Black women—to understand how beauty practices impact long‑term health. 3. Encourage the beauty industry to adopt safety protocols Such as scalp protection, warning labels, and honest communication about risks. 4. Highlight Dr. Mac’s work and products Including her Bella Nutri supplements and educational platforms (Ask Dr. Mac). 5. Empower parents to protect children By avoiding chemical treatments on young girls whose bodies are especially vulnerable. Key Takeaways 1. Chemical relaxers and permanent hair dyes are strongly linked to increased cancer risks. Permanent dyes raise the risk of breast, uterine, and ovarian cancer. Black women exhibit a 45% increased risk of breast cancer when using permanent dyes. Combining dyes with relaxers significantly compounds the danger. 2. The danger comes from chemical absorption into the scalp. Relaxer chemicals include sodium, calcium, guanine, and lithium hydroxide. These chemicals burn through the scalp, entering the bloodstream and disrupting hormones, leading to early puberty, fibroids, infertility, and cancer. 3. Synthetic braiding hair contains benzene—a carcinogen. Benzene exposure affects both the stylist and the client. Risks include lung cancer and leukemia. 4. The beauty industry resists change because of profit. Salons rarely display warnings because “it affects business.” The relaxer–damage→hair‑loss→extensions cycle creates a lucrative revenue loop. 5. Children are especially vulnerable to chemical exposure. Relaxers on children under 10 can cause: early puberty fibroids infertility early hysterectomies increased cancer risk Dr. Mac advises never relaxing a child’s hair, but if done, the product must stay on no more than 5–10 minutes with complete scalp protection. 6. Scalp protection is essential for anyone still using relaxers. Use petroleum jelly over the entire scalp, not just the hairline. This reduces chemical absorption during both application and rinsing. 7. Dr. Mac pioneered the U.S. hair‑supplement industry. Developed Bella Nutri after research with a Finnish company (Scalp). Initially dismissed as a “witch doctor,” but now the hair‑supplement market is mainstream. 8. She refuses to participate in relaxer‑related lawsuits. Because she has warned people for 20+ years, she cannot ethically testify for those who ignored repeated warnings. Notable Quotes On the impact of chemicals: “The chemicals burn through the scalp… getting into the main bloodstream and causing hormone disruption.” On the increased cancer risk: “African‑Americans have a more than 45% increased risk when we use permanent hair dyes.” On synthetic braids: “As long as that synthetic hair is on her head, she is breathing in benzene.” On industry pushback: “People are about the green‑eyed devil called money.” On relaxing children’s hair: “Hopefully a mother doesn’t take her child to get a relaxer.” “Hair chemicals can lead to early puberty, fibroids, infertility, even hysterectomies before age 40.” On the vicious cycle of damage and profit: “It’s a 360‑degree money‑making cycle.” On caring more than her patients: “I feel like I’m caring more about someone’s health than they are caring about their own.” On pioneering supplements: “Hair and skin are internal organs—they manifest externally.” #SHMS #STRAW #BESTSteve Harvey Morning Show Online: http://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.

On Food Talk with Dani Nierenberg, Dani speaks with Kristin Coates, Co-Founder and CEO of Regenerative California. They talk about creating a regenerative farm in a region dominated by conventional agriculture, pathways to build a more hopeful food future, and how the organization's model can be spread to other counties and beyond. Plus, demand for raw milk continues despite health risks, a new briefing affirms that African countries already have proven alternatives to synthetic pesticides—now they need to scale, the presence of New World Screwworm is confirmed in the United States, a marine biologist works with fishers to protect endangered species, and more. While you're listening, subscribe, rate, and review the show; it would mean the world to us to have your feedback. You can listen to "Food Talk with Dani Nierenberg" wherever you consume your podcasts.

Ben Chance of Early Warning Services, the bank‑owned company that provides fraud detection services and operates the Zelle network, joins host Paul Benda on this edition of the ABA Fraudcast to talk about not just the very recent history of fraudsters targeting banks and consumers, but how fraud fighters are learning to fight effectively. ABA offers resources to help banks prevent, identify, measure and report fraud, and to serve and protect consumers. Follow the ABA Fraudcast at the ABA Banking Journal, Apple Podcasts, Spotify or other podcast apps.  Host of the ABA Fraudcast is Paul Benda, EVP, risk, fraud and cybersecurity at American Bankers Association.

95% of senior marketing leaders are already using or planning to use synthetic data within 12 months. So why are so many marketers still on the fence?In this episode, Elena, Angela, and Rob talk with Peter Weinberg, co-founder of Evidenza and former head of research at LinkedIn's B2B Institute. They discuss where to start with synthetic audiences, how to assess accuracy, and why brand building still matters as AI changes how people search and decide.Topics covered:•    [00:00] Introductions and what synthetic research actually is•    [03:00] Why 95% of marketing leaders plan to use synthetic data within 12 months•    [05:00] Synthetic research really replaces ignorance, not traditional surveys•    [09:00] How to evaluate accuracy in synthetic research tools•    [10:30] Where marketers should start: find the white spaces first•    [16:00] Why AI can be creative and what 'temperature' means for marketers•    [24:00] Why brand still matters in an AI-driven search world•    [28:00] How Evidenza applied Ehrenberg-Bass principles to build their own brand•    [34:00] Why more real-time data can lead to worse decisionsTo learn more, visit marketingarchitects.com/podcast or subscribe to our newsletter at marketingarchitects.com/newsletter.Resources:2025 Qualtrics Article: https://www.qualtrics.com/articles/strategy-research/synthetic-research-breakthrough/Peter's LinkedIn: https://www.linkedin.com/in/weinbergpeter/Get more research-backed marketing strategies by subscribing to The Marketing Architects on Apple Podcasts, Spotify, or wherever you listen to podcasts.

Synthetic biology promises to revolutionize manufacturing, growing materials in fermentation tanks instead of relying on traditional industrial or agricultural sources. But can it scale? Zero100 Chief Content Officer Matt Davis sits down with Mathias Cousin, Managing Director at Monitor Deloitte and a SynBio veteran, to find out. Mathias reveals why manufacturing economics, not science, determines who survives, how AI is compressing design cycles from years to months, and why the biggest breakthroughs may come from replacing cows, not crude oil. With real-world examples, he shows what supply chain leaders need to understand as biological manufacturing moves from the lab to industrial reality.

Wayne McCroy is a truth seeker, independent researcher, an author of several books & host of Alchemical Tech Revolution Podcast.Wayne's links:https://alchemicaltechrevolution.com

The American consumer is being misread. Surveys say people are panicking, but their behavior tells a completely different story. On this episode of Christopher Lochhead: Follow Your Different, we take a page out of The Pirate Street Journal, as Christopher Lochhead, Eddie Yoon, and Bri Clark broke down three forces reshaping the economy through a category design lens. From historic lows in consumer confidence to AI-generated buyers to an entire generation betting on prediction markets, the picture is not one of collapse. It is one of reinvention. You're listening to Christopher Lochhead: Follow Your Different. We are the real dialogue podcast for people with a different mind. So get your mind in a different place, and hey ho, let's go.   Record Low Consumer Sentiment Is a Category Creation Engine The University of Michigan Consumer Sentiment Index dropped to 44.8 in May, the lowest reading ever recorded, following what was already a record low in April. Yet unemployment is near zero, GDP is growing, and the stock market keeps hitting new highs. The numbers do not add up because the survey is measuring something different than economic health. It is measuring the death of an old life script. The linear path of college, marriage, house, promotion, and retirement no longer delivers the meaning it once promised. People are not curling up in a ball. They are buying fewer cars, skipping packaged foods, and trading stuff for experiences. When an old script breaks, people are forced to find meaning on their own terms, and that search is historically the most powerful category creation engine the economy has ever seen.   The Synthetic Customer Will Scale Mediocrity If You Let It Research shows that AI-generated synthetic customers can replicate roughly 90 percent of real conjoint study outcomes, including which features drive choice and early price sensitivity. Companies like Target and US Bank are already testing products on synthetic audiences before launch. The technology is genuinely exciting and could transform how businesses plan, build, and compete. The danger is that most companies will point their synthetic customer tools at the fat part of the bell curve, optimizing for the average buyer and calling it an insight. Eddie Yoon has spent decades proving that the super consumer, roughly 8 to 10 percent of any customer base, can drive up to 90 percent of gross margins. Synthetic customers are only as powerful as the data they are trained on. Train them on average, and you simulate mediocrity at scale. The unlock is running synthetic studies on super consumers first, then non-consumers, and finding where those two extremes could meet. That intersection is where new categories are born. Proprietary data sets and purpose-built AI applications will separate the companies that discover the next wave from the ones that simply made the status quo slightly cheaper to produce.   Gen Z Is Not Irrational, They Are Responding to Real Data Roughly 32 percent of Gen Z investors have played prediction markets, a similar share are in crypto, and about 69 percent of Polymarket accounts have lost money since 2022. On the surface this looks like recklessness. In context, it makes complete sense. This generation grew up through 9/11, the 2008 financial crisis, and Covid, all before they could legally drink. Every institution that promised safety failed at least once during their formative years. The Nasdaq 100 returned roughly 21 percent annually over the last decade. The S&P returned 13 to 14 percent. Sitting still in an index fund would have made them wealthy. But when certainty has detonated repeatedly, patience does not feel safe, it feels naive. The speculation is not stupidity. It is a rational response to a world where the old guarantees proved hollow. The prescription from Eddie Yoon is to hold all three investment buckets at once: a boring cash safety net covering 3 to 18 months of expenses, smart index-based investments with consistent long-term returns, and a smaller speculative position built on genuine expertise and category-level knowledge. Speculation itself is not the enemy. Speculating without a superpower, without real edge, is where the damage gets done. To hear more from the Pirate Street Journal, download and listen to this episode. You can also read more Pirate Street Journal entries in the Category Pirates newsletter.   We hope you enjoyed this episode of Christopher Lochhead: Follow Your Different™! Christopher loves hearing from his listeners. Feel free to email him, connect on Facebook, X (formerly Twitter), LinkedIn, and subscribe on Apple Podcast / Spotify!

In this episode, hosts Annika Brockschmidt and Roland Meyer sit down with media scholar Roland Meyer to dissect the unsettling intersection of generative AI, right-wing extremism, and the emerging aesthetics of digital fascism. Meyer, a professor at the University of Zurich, breaks down why AI-generated visual culture is uniquely suited for far-right propaganda. Rather than acting as a neutral mirror of reality, these algorithms are structurally nostalgic—relying on past training data to build idealized, hyper-masculine mythologies, clean ethnic landscapes, and weaponized "slopaganda." From American frontier myths to European Islamophobic imagery, Meyer explains how mass-produced, engagement-optimized AI content is being actively weaponized to construct collective, racist fantasy worlds at an unprecedented scale. The conversation pushes past simple deepfakes to examine the darker political economy and Silicon Valley ideologies underlying the modern tech ecosystem. Meyer and the hosts unpack the rise of "slop"—voted Merriam-Webster's word of the year—and how algorithmic distribution networks reward dehumanizing depictions of marginalized groups while turning tech leaders into gladiatorial icons. By exploring how figures like Elon Musk, Peter Thiel's Alex Karp, and Charlie Kirk use these tools, the episode exposes a shared tech-authoritarian vision. This ideology frames AI not as a tool for public good, but as an unregulated, masculine force engineered for white, Western dominance, proving that the struggle over generative AI is fundamentally a battle over who controls the future of reality. Subscribe for $3.65: ⁠https://axismundi.supercast.com/⁠ Subscribe to our free newsletter: ⁠https://swaj.substack.com/⁠ Order American Caesar by Brad Onishi: ⁠https://static.macmillan.com/static/essentials/american-caesar-9781250427922/⁠ Donate to SWAJ: https://axismundi.supercast.com/donations/new Learn more about your ad choices. Visit megaphone.fm/adchoices

Google dropped like 197 new AI features this week.