Podcasts about ramp

On this special segment of The Full Ratchet, the following Investors are featured: Natalie Dillon of Maveron Larry Cheng of Volition Capital Ben Black of Akkadian Ventures and Powerlaw Corp Each investor highlights a situation where they decided not to invest, why they passed, and how it played out. The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

My guest today is Matthew Smith. Matthew is the founder and CIO of Chronometer Partners, which invests in energy, industrials, materials, power and utilities, and related infrastructure. For the last 18 months he and his team have modeled nearly every natural gas well, pipeline, and processing asset in the United States. He's reached a conclusion most of the market doesn't share.  Starting in 2028, AI data centers and LNG exports will need more gas than the country can produce and deliver. By his math, the US could exhaust its working natural gas storage by 2030. In his words, the upside risk to prices becomes unbounded and convex. We talk about why this was set in motion long before AI arrived, why the US can't just turn off exports, who wins and loses among producers, nuclear, solar, and the hyperscalers, and what he sees as the only long-term solution. Please enjoy my conversation with Matthew Smith. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- In June, Matthew wrote a letter to a small group of confidants laying out the full case behind his natural gas forecast. He has allowed us to publish it. You can read the full letter here. ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like the Best (00:02:02) Episode Intro: Matt Smith (00:03:33) The Conclusion After 18 Months (00:04:56) The Die Was Cast Before AI (00:07:24) Sizing AI's Gas Demand (00:09:33) Why Not Just Stop Exporting? (00:11:38) Is the Gas Even There? (00:13:53) The Timing Problem, Not Supply (00:15:15) Flow Versus Stock (00:19:10) What Slows Gas to Market (00:22:21) If Nothing Changes by 2030 (00:26:11) Could Prices Hit Twenty Dollars? (00:27:00) Gas Producers Poised to Win (00:28:54) Utility-Scale Solar's Windfall (00:30:08) What About Nuclear? (00:32:40) SMRs (00:34:29) The US Consumer Pays (00:36:37) Turbine Makers Building Too Late (00:37:57) Are Hyperscalers Exposed Too? (00:44:25) Kickstarting the Nuclear Build (00:46:20) Put Solar on Every Roof (00:46:52) Implications for the World (00:49:26) No One's Securing Supply (00:52:57) The Challenge for Energy CEOs

Tom deelt een artikel op LinkedIn over degrowth. En de reacties? Geen inhoudelijke discussie, maar een stortvloed aan morele verwerping. Want wie niet gelooft in degrowth, deugt gewoon niet.Maar wat is degrowth eigenlijk? En kloppen de aannames die eronder zitten.Tom de Bruyne en Bas Erlings duiken in de psychologie achter het debat. Want degrowth klinkt redelijk: minder spullen, meer betekenis, respect voor de planeet. Maar zodra je de economische logica erbij haalt, wordt het verhaal een stuk ingewikkelder. En zodra je dat hardop zegt, word je niet gecorrigeerd maar gediscrediteerd.Hoe voer je een goed gesprek met mensen die hun identiteit hebben opgehangen aan een overtuiging? En hoe vind je gezamenlijke grond zonder je eigen standpunt op te geven?See omnystudio.com/listener for privacy information.

Welcome back to the Bar Exam Toolbox podcast! This episode is part of the series in which we demystify the shift from MBE to NextGen multiple-choice questions. Today we're walking through four questions on evidence -- two in classic MBE style and two in the NextGen format. Join us and practice your understanding of hearsay exceptions and character evidence! In this episode, we discuss: Question 1: Hearsay exceptions (MBE) Question 2: Statement not offered for the truth (MBE) Question 3: Statement not offered for the truth (NextGen) Question 4: Character evidence (NextGen) Study tips for multiple-choice questions RAMP study tool Resources: https://barexamtoolbox.com/ramp (https://barexamtoolbox.com/ramp) Podcast Episode 89: Listen and Learn – What Is Hearsay? (https://barexamtoolbox.com/podcast-episode-89-listen-and-learn-what-is-hearsay/) Podcast Episode 101: Listen and Learn – Present Sense Impression vs. State of Mind (https://barexamtoolbox.com/podcast-episode-101-listen-and-learn-present-sense-impression-vs-state-of-mind/) Podcast Episode 114: Listen and Learn – Non-Hearsay (https://barexamtoolbox.com/podcast-episode-114-listen-and-learn-non-hearsay/) Podcast Episode 115: Listen and Learn – Dying Declaration vs. Excited Utterance (https://barexamtoolbox.com/podcast-episode-115-listen-and-learn-dying-declaration-vs-excited-utterance/) Podcast Episode 121: Listen and Learn – Character Evidence (https://barexamtoolbox.com/podcast-episode-121-listen-and-learn-character-evidence/) Podcast Episode 132: Listen and Learn – Hearsay Exceptions: Government and Business Records (https://barexamtoolbox.com/podcast-episode-132-listen-and-learn-hearsay-exceptions-government-and-business-records/) Podcast Episode 138: Listen and Learn – Hearsay Exceptions: Prior Testimony and Past Recollection Recorded (https://barexamtoolbox.com/podcast-episode-138-listen-and-learn-hearsay-exceptions-prior-testimony-and-past-recollection-recorded/) Podcast Episode 143: Listen and Learn – More Hearsay Exceptions (https://barexamtoolbox.com/podcast-episode-143-listen-and-learn-more-hearsay-exceptions/) Podcast Episode 158: Listen and Learn – Multiple Hearsay (https://barexamtoolbox.com/podcast-episode-158-listen-and-learn-multiple-hearsay/) Podcast Episode 214: Listen and Learn – Relevance Issues (Evidence) (https://barexamtoolbox.com/podcast-episode-214-listen-and-learn-relevance-issues-evidence/) Download the Transcript (https://barexamtoolbox.com/episode-354-listen-and-learn-mbe-vs-nextgen-multiple-choice-evidence/) If you enjoy the podcast, we'd love a nice review and/or rating on Apple Podcasts (https://itunes.apple.com/us/podcast/bar-exam-toolbox-podcast-pass-bar-exam-less-stress/id1370651486) or your favorite listening app. And feel free to reach out to us directly. You can always reach us via the contact form on the Bar Exam Toolbox website (https://barexamtoolbox.com/contact-us/). Finally, if you don't want to miss anything, you can sign up for podcast updates (https://barexamtoolbox.com/get-bar-exam-toolbox-podcast-updates/)! Thanks for listening! Alison & Lee

Ben Black of Co-founder & MD of Akkadian Ventures and CIO of Powerlaw Corp joins Nick to discuss Opening Late-Stage Venture to Everyone, Beating 10-Year Lockups, Why Logos Don't Equal Alpha, and Building Power Law as a Public VC Fund. In this episode we cover: Details of Power Law's Portfolio and Investment Strategy Challenges and Benefits of Power Law Innovation and Evolution in Venture Capital Balancing Demand and Supply in Power Law Regulatory Compliance and Educational Efforts Future of Power Law and Market Positioning Guest Links: Ben's LinkedIn Ben's X Powerlaw Corp (PWRL)'s LinkedIn Powerlaw Corp (PWRL)'s Website Akkadian Ventures' Website The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter

This episode discusses Joseph Wechsberg's 1966 book, The Merchant Bankers. Rather than recounting the histories of families like the Rothschilds, Barings, Hambros, Warburgs, and Lehman Brothers, I wanted to extract the principles they shared. Merchant banking is fascinating. It's a very distinctive form of entrepreneurship. There is an old-school way of doing business that appeals to me. The merchant bankers' profiled in this book have a combination of: personal honor speed of action clear thinking independent judgment seamless webs of deserved trust discretion and willingness to make unconventional decisions. The founder of every merchant banking dynasty was a merchant before he was merchant banker. Once they discovered financing transactions was more profitable than physically trading goods, their real products became credit, judgment, information, advice, access, and—above all—trust. Their greatest asset was not money. Their greatest asset was their reputation. Made possible by: Ramp: ⁠⁠https://ramp.com⁠⁠  Applovin: ⁠⁠https://www.applovin.com Vanta: ⁠⁠https://vanta.com/founders Add your email here and I will send you my top 10 quotes from every episode.

Founders ✓ Claim : Read the notes at at podcastnotes.org. Don't forget to subscribe for free to our newsletter, the top 10 ideas of the week, every Monday --------- What I learned from reading Zero to One: Notes on Startups, or How to Build the Future by Peter Thiel and Blake Masters (for the 3rd or 4th time) Made possible by: Ramp: ⁠https://ramp.com⁠ Applovin: ⁠https://www.applovin.com/⁠ Vanta: ⁠https://vanta.com/founders

The core structural shift identified is budget reallocation within technology spending, as funds are redirected from legacy software, hardware refreshes, and higher-cost labor toward AI infrastructure, automation, and junior-level hiring. This resource substitution is not additive but redistributive, with spending on AI solutions and related tools coming directly from reductions in traditional IT line items. IBM's $70 billion market valuation loss and delays in large deals signal that even established vendors are affected by this reallocation, with money leaving areas they once dominated. The primary evidence is IBM's issuance of its first profit warning since the early 2000s, attributed to missed large contracts and delayed deals, which triggered a 25% drop in share value, equating to $70 billion in market cap loss. According to Dave Sobel citing Semafor, this reduction was not due to an overall decrease in technology budgets but resulted from enterprise customers reallocating funds toward hardware and AI-related infrastructure. Omnia reported a 3.6% decline in global PC shipments during the second quarter, which was also attributed to rising hardware component costs driven by AI buildouts, causing delays and cancellations in endpoint refresh cycles. Supporting developments include Ramp and Revelio Labs research showing that organizations intensively adopting AI increased headcount by 10% and entry-level hiring by 12% over two years, while CompTIA found IT unemployment fell below 3% even as tech firms cut staff. Futurism cited further labor market reshuffling, with older workers in AI-exposed roles exiting the workforce and younger, cheaper hires being amplified by automation. ConnectWise's rollout of an AI-native platform and KPMG's survey highlighting the importance of leadership accountability in AI projects reinforce that resource allocation is shifting to tools and personnel accountable for AI operation and outcomes. Operationally, this reallocation puts pricing pressure on providers focused on legacy revenue lines such as per-seat licenses, break-fix, and hardware refresh, as these budget categories are shrinking. Evidence from Service Leadership's profitability report shows providers who adopted service desk automation earlier are now earning more per wage dollar, compounding their advantage. The practical implication for MSPs and IT service providers is to identify which client budget categories are “filling” and adjust offerings toward data readiness, AI deployment, and managed accountability, rather than defending legacy categories now facing structural decline. Failure to adapt exposes firms to revenue erosion and intensifies competitive risk from providers aligned with relocated client spend.   00:00 Watch the Money Move  04:37 AI Spend Is Funded by Substitution  07:19 Your Revenue Mix Is the Bet 10:24 Why Do We Care?  Supported by:  Guardz ScalePad 

Signal vs Noise: Jackson Mikalic, Michael Tanguma, Liam Nelson, and Brian Cubellis dig into 96% odds the Fed holds rates flat, a Ramp report showing AI-heavy firms are hiring not firing, a survey where 69% of Americans want OpenAI and Anthropic to give up half their stock, and shocking data on AI companions, with 72% of teens reporting a romantic relationship with AI and marriage rates at a 120-year low.---

What does it take to write the very first check into a company that has almost nothing to show yet, sometimes not even a finished idea?Afore Capital helped invent the pre-seed category. When Gaurav Jain and Anamitra Banerji started the firm ten years ago, "pre-seed" was almost a slight, a label for founders who couldn't raise a proper seed round. They set out to build the world's largest pre-seed fund anyway, closing $47 million on a $40 million target, and every fund since has closed above plan. Afore now runs more than $500 million across four funds, with top-quartile DPI on the first three. The idea has become so mainstream that when Sequoia launched its latest fund, it said, "I guess we're pre-seed investors too."The real substance of the conversation is how Gaurav thinks. He is clear about what matters most in venture, and the order tends to surprise people. Being in the very best companies matters more than anything else, ownership comes after that, and the entry price that so many investors fixate on matters least, because fifty per cent of zero is still zero. He is also convinced that the genuine bottleneck is talent. There is a great deal of money in the world and very few people who can build something truly large, which is why at the earliest stage founders tend to choose their investors as much as investors choose them. You give a founder a million dollars with no collateral, and then you still have to convince them to take it. A pre-seed pitch, he says, is almost entirely storytelling with very little data behind it.If you want to understand how the earliest checks actually get written, and what it really costs to say no, this episode is worth your time.00:00 - Trailer01:00 - From Dehradun to Google to starting Afore02:08 - The Waterloo co-op that talked him out of every job03:18 - Back when "pre-seed" was an insult05:44 - When Sequoia said "I guess we're pre-seed investors too"07:26 - Afore's three products, and the experiments that failed09:01 - Hightouch was a travel company when they invested11:02 - Goldcast: no visa, no money, funded anyway12:07 - The through line is always the team14:44 - The Ramp miss17:24 - "Founders pick us more than we pick them"18:45 - The constraint isn't capital, it's talent22:24 - The Solana miss, when it was still Loom Protocol24:46 - Ramp's Super Bowl ad, the buses, his wife's business25:32 - What he looks for in founders28:50 - Coachability, happy ears, and the Mom Test31:28 - The biggest mistake: falling in love with the idea35:04 - The three things that matter, and "50% of zero is still zero"39:25 - "100% storytelling, 0% data"41:25 - Investing in India, and the fear of being dumb capital44:41 - "Sign the deal before Monday"47:26 - One engineer now does the job of 2051:43 - Raising from LPs, the undiscussed part of VC-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

On this special segment of The Full Ratchet, the following Investors are featured: Jim Tananbaum of Foresite Capital Eric Ries Author of The Lean Startup Grant Demaree of Onebrief We asked guests to tell the most important lesson they've learned in their career. The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

Amerikaanse president Donald Trump verwys gereed na Herbert Hoover, wat die VSA gelei het tydens die Groot Depressie wat in 1929 begin het. Hy maak herhaaldelik melding van die feit dat hy nie Herbert Hoover wil wees nie maar, nou sê 'n Amerikaanse politieke wetenskaplike, professor Robert Pape dat Trump wel soos Hoover onthou sal word – eenvoudig omdat Amerika se reserwes opraak. Hy het met Sky News gepraat.

Today my guest is John Kim. John is one of the world's top and most prolific fundraisers.  He was chief client officer at General Catalyst, where he helped raise many of the firm's flagship funds. He is now chairman and president of corporate development at Lila Sciences, a company building scientific superintelligence, where he has helped raise several hundred million dollars.  He is also the author of The Tao of Fundraising.  This conversation is really a guide on how to raise money from someone who has done it at the highest level. We talk about why persuasion equals desire minus fear, the difference between belief and trust, the laws of fundraising, and how to build the consensus that moves big pools of capital.  Please enjoy my conversation with John Kim. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:02) Introduction of John Kim (00:02:39) Money Moves at the Speed of Trust (00:05:06) How to Start a Fundraising Campaign (00:08:03) Persuasion Equals Desire Minus Fear (00:12:20) How to Raise a Few Billion Dollars (00:15:58) The Benchmark Story (00:18:36) The Law of Differentiation (00:24:13) Law of Tradeoffs and Law of Pipeline (00:27:52) The Karpman Drama Triangle (00:30:42) Oprah Winfrey (00:33:49) Most Common Fundraising Mistakes (00:38:35) Secretary of State (00:45:40) The Inner Game (00:47:38) The Kindest Thing

All links and images can be found on CISO Series This week's episode is hosted by David Spark, producer of CISO Series, and Andy Ellis, principal of Duha. Joining them is Tim Callahan, CIO/CISO, AFLAC. In this episode: Week one is the wrong time to overreach Nobody has the AI playbook Vulnerability management wasn't built for this clock Stop blaming the human, fix the system A huge thanks to our sponsor, Vanta No, it's not your imagination. Risk and regulations ARE ramping up—and customers now expect proof of security just to do business. That's why Vanta is a game-changer. Vanta automates your compliance process and brings compliance, risk, and customer trust together on one AI-powered platform. So whether you're prepping for a SOC 2 or running an enterprise GRC program, Vanta keeps you secure—and keeps your deals moving. Companies like Ramp and Writer spend 82% less time on audits with Vanta. That's not just faster compliance—it's more time for growth. Get started at Vanta.com/CISO.  

Brock Johnson has posted on Instagram for more than 1,800 consecutive days, and in this episode, he shares the exact AI system he wishes he had from day one. He breaks down his RAMP framework — Research, Assemble, Multiply, Process — and explains how to use Claude, Claude Code, and Claude Cowork to build an Instagram workflow that runs almost entirely on autopilot. The episode covers seven of the biggest content creation challenges: not knowing what to post, writing weak hooks, filming inefficiently, constantly task switching, ignoring carousels, failing to schedule content, and not understanding why posts succeed or fail. For each challenge, Brock demonstrates the automation he recommends, from generating weekly research summaries based on saved posts and favorite accounts, to testing scripts with a "would this make sense to a stranger?" filter, to using Claude as a virtual director that creates optimized shot lists for filming. He also explains how Canva templates combined with Claude Cowork can transform existing Reels into carousels, how Claude Cowork can automatically schedule posts in Metricool based on custom rules, and how his own system keeps more than 150 Instagram posts scheduled up to six months in advance. Toward the end of the episode, Brock shows how Claude Code can build dashboards that analyze content performance and uncover trends creators often miss. One of the biggest takeaways is that no coding experience is required. Instead of writing code, creators simply describe the workflow they want, and Claude Cowork handles the technical implementation. The episode wraps up with a challenge to build one automation using the RAMP framework, along with an invitation to Brock's end-of-July webinar series that teaches how to grow on Instagram in less than 15 minutes a day. Watch On YouTube 

Can AI do more than improve efficiency? In this episode of Future-Proof, we sit down with Brad Gustafson, Head of the Accounting Partner Channel at Ramp, to explore how AI is reshaping the accounting profession and helping firms address long-term talent challenges.Brad shares why AI should be viewed as a capacity multiplier rather than a replacement for accountants, how agentic AI is transforming workflows, and why the firms embracing AI strategically today will be best positioned to grow tomorrow.This conversation offers practical insight into how firm leaders can use AI to strengthen client relationships, increase capacity, and build a more resilient future.Resources:Ramp for Accounting & Finance ProfessionalsBrad Gustafson, Channel Partnership Leader at Ramp, LinkedIn ProfileMACPA Preferred ProvidersMACPA AI Resource Page

(A note before we get into it: this article is educational, not medical advice. Talk to your doctor or a registered dietitian before making major changes to your diet — especially if you have diabetes or pre-diabetes, take blood-sugar medication, are pregnant or nursing, or are managing any chronic condition. Nothing here is intended to diagnose, treat, cure, or prevent any disease.)Research shows well-built plant-based diets usually lower blood sugar. If yours moved the other way, one of six silent glucose traps is almost always the reason — and every one of them is fixable.TLDR* The research is actually on vegetarianism's side. Large reviews of clinical trials show vegetarian and vegan diets tend to lower A1C, not raise it.* So if your numbers went up, something in how the diet was built is the real cause — not the fact that it's plant-based.* Six usual suspects: too little protein, “naked” carbs with no brakes, shrinking muscle mass, too much fermentable fiber too fast, high stress/cortisol, and late-night eating.* Each one is a fixable habit, not a reason to abandon vegetarianism.Wait — Doesn't “Plant-Based” Mean Healthier Blood Sugar?Usually, yes. When researchers actually put vegetarian diets to the test in clinical trials, the pattern is consistent: people following them tend to see their A1C go down, not up. A meta-analysis pooling multiple randomized trials found vegetarian diets were linked to a meaningful drop in A1C, and a separate meta-analysis of nine trials found a similar reduction — enough that it would be considered clinically significant by the FDA's own bar for new diabetes drugs.So if you switched to vegetarian eating and your last lab draw came back worse, I want to be straight with you: that's not the expected outcome, and it's not “just what happens” when you cut out meat. Something specific is working against you. The good news is that it's almost always one of a handful of well-understood mechanisms — and once you know which one, it's a quick fix, not a diet overhaul.Think of it like this: a car engine doesn't run worse because you switched to a different brand of gas. It runs worse because something specific — a clogged filter, bad timing, low oil — is getting in the way. Same idea here. Let's go through the six most common “clogs.”THRIVE 120 - TriplePlayDoc is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.1. The Protein Drop You Didn't NoticeThis is the single most common one. When people cut out meat, they usually don't replace that protein gram-for-gram with plant protein — they replace it with more carbs, because carbs are what fill the plate: extra rice, extra bread, extra pasta.Here's why that matters for blood sugar specifically. Protein eaten alongside carbohydrate isn't just “extra food” — it changes how your body handles the carbs. A meta-analysis of controlled feeding trials found that adding protein to a carb-containing meal meaningfully lowers the post-meal glucose spike, largely by boosting the insulin response that clears sugar out of the bloodstream faster. Pull protein out of the meal and carbs hit your blood essentially unguarded.Think of protein as the brakes on a sugar rollercoaster. Take the brakes off, and the same carbs you were eating before now send your blood sugar higher and faster than they used to.2. Naked Carbs, No BrakesThis is the same problem from a different angle: oatmeal for breakfast, a rice bowl for lunch, a smoothie for a snack. All reasonable foods — but if none of them are paired with enough protein or fat, and you're not about to go move that sugar with a workout, it hits your bloodstream as a fast, unbuffered spike. Your body answers with a bigger insulin surge, and doing that meal after meal, day after day, is exactly the pattern linked to declining insulin sensitivity over time.3. The Muscle FactorThis one surprises people: your muscles are the biggest “storage tank” for the sugar you eat. After a meal, skeletal muscle soaks up roughly 70–80% of the glucose that comes out of your bloodstream, using it to refill glycogen stores. Researchers who study this consider muscle glucose uptake the single biggest lever on whole-body insulin sensitivity.So when protein intake drops and resistance training isn't part of the picture, muscle mass tends to shrink over time — and that storage tank gets smaller. Less tank space means the same meal now leaves more sugar circulating in your blood for longer, which shows up as a slow, creeping rise in A1C.Muscle is basically your body's biggest gas tank for sugar. Shrink the tank, and the same fill-up overflows.4. High Fiber, High StressFiber itself is not the villain here — in fact, the right kind of fiber is one of the best tools for blood sugar control. Viscous, soluble fibers (found in oats, beans, and psyllium) form a gel in your gut that physically slows down how fast sugar gets absorbed, and trials show this type of fiber measurably lowers both A1C and fasting blood sugar.The issue is a different kind of fiber problem: volume and speed. Vegetarian diets are often naturally high in fermentable fiber — beans, lentils, certain grains — and when your gut bacteria break that fiber down, the fermentation process produces gas as a byproduct. Ramp up fiber intake quickly, especially the fast-fermenting kinds, and you can outpace your gut's ability to comfortably process it, leading to bloating and discomfort. That physical stress on your body, especially combined with everyday life stress, triggers cortisol release — and cortisol has a direct, well-documented job of raising blood glucose by signaling your liver to make more sugar and by making your cells less responsive to insulin.Think of fiber fermentation like a construction project in your gut. The building itself (your microbiome) benefits — but a project moving too fast kicks up a lot of dust (gas and bloating) along the way.5. Timing and Circadian RhythmsThe lifestyle shift that often comes with going vegetarian — more grazing, more small meals, more snacking — can quietly push more of your eating into the evening. That timing matters more than most people realize. Multiple studies using identical meals given at different times of day have found that the exact same meal produces a bigger blood sugar spike at night than it does in the morning, because your insulin sensitivity and beta-cell function both naturally dip in the evening. One study even found a late meal after 8pm was independently linked to worse A1C.Your body runs on a work shift for carbs. The day shift (morning, early afternoon) handles them efficiently. The night shift is running on a skeleton crew — the same carb load left unprocessed for longer, working against you while you sleep.6. Cortisol: The Common ThreadYou'll notice cortisol keeps coming up — that's not a coincidence. Cortisol's actual job during stress is to make more glucose available fast, by triggering your liver to produce it and by directly blunting how well your cells respond to insulin. Whether the stressor is emotional (a hard week at work) or physical (a gut that's working overtime to ferment more fiber than it's used to, or a body that's lost muscle mass and is struggling to regulate blood sugar as efficiently), the hormonal response is the same: more sugar released, less efficiently cleared.Who's Most Likely to Notice This?Not everyone who goes vegetarian sees their A1C move. The people most likely to notice these effects are those who already have some combination of insulin resistance, chronic stress, poor sleep, low muscle mass, or a sensitive gut. For them, a vegetarian diet doesn't cause the problem — it tends to reveal a metabolic bottleneck that was already there, quietly, underneath a different diet.This isn't a case against vegetarian eating. The clinical research is genuinely favorable toward it. It's a case for building it correctly — with enough protein, the right kind and pace of fiber, resistance training to protect muscle, and carbs eaten earlier in the day, paired with something that slows them down.Six Bottlenecks at a GlanceThe Bottom LineThe question was never really “is vegetarianism good or bad for blood sugar?” — the research says it's generally good when it's built well. The real question is: what's your body's current metabolic bottleneck? Is it protein? Muscle? Fiber pacing? Timing? Stress? Most people are dealing with one or two of these more than the others, and once you know which, the fix is usually a small, specific adjustment — not throwing out the diet.The goal isn't just to tweak what's on your plate. It's to find the actual thing standing between you and the results you were expecting when you made this change in the first place.References* Vegetarian and Vegan Dietary Patterns to Treat Adult Type 2 Diabetes: A Systematic Review and Meta-Analysis of RCTs — ScienceDirect: https://www.sciencedirect.com/science/article/pii/S2161831324001285* Vegetarian diets and glycemic control in diabetes: a systematic review and meta-analysis — PubMed: https://pubmed.ncbi.nlm.nih.gov/25414824/* The Effect of Adding Protein to a Carbohydrate Meal on Postprandial Glucose and Insulin Responses: A Systematic Review and Meta-Analysis — ScienceDirect: https://www.sciencedirect.com/science/article/pii/S0022316624003924* The Role of Skeletal Muscle Glycogen Breakdown for Regulation of Insulin Sensitivity by Exercise — Frontiers in Physiology: https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2011.00112/full* Glucose Uptake by Skeletal Muscle within the Contexts of Type 2 Diabetes and Exercise — MDPI/Nutrients: https://www.mdpi.com/2072-6643/14/3/647* Effect of viscous soluble dietary fiber on glucose and lipid metabolism in patients with T2DM: systematic review and meta-analysis — PMC: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500602/* Dietary fiber in irritable bowel syndrome (fermentation and gas production) — PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC5548066/* Fibre, fermentation, FODMAPs and flatulence — Quadram Institute: https://quadram.ac.uk/blogs/fibre-fermentation-fodmaps-and-flatulence/* Physiology, Cortisol — StatPearls, NCBI Bookshelf: https://www.ncbi.nlm.nih.gov/books/NBK538239/* Endogenous circadian system and circadian misalignment impact glucose tolerance via separate mechanisms in humans — PNAS: https://www.pnas.org/doi/10.1073/pnas.1418955112* Impact of circadian disruption on glucose metabolism: implications for type 2 diabetes — Diabetologia/PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC7002226/* Chronotype, Chrononutrition and Glucose Tolerance Among Prediabetic Individuals (late-dinner/A1C link): https://cdn.clinicaltrials.gov/large-docs/64/NCT05163964/Prot_SAP_ICF_004.pdfTHRIVE 120 - TriplePlayDoc is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit tripleplaydoc.substack.com/subscribe

What I learned from reading Zero to One: Notes on Startups, or How to Build the Future by Peter Thiel and Blake Masters (for the 3rd or 4th time) Made possible by:  Ramp: ⁠https://ramp.com⁠  Applovin: ⁠https://www.applovin.com/⁠  Vanta: ⁠https://vanta.com/founders

Interpol's fraud sweep goes global China flags Claude Code Old GitHub accounts, new tricks Get the show notes here: https://cisoseries.com/cybersecurity-news-interpols-global-fraud-sweep-chinas-claude-code-flag-old-github-account-tricks/ Thanks to our episode sponsor, Vanta Your team just added its 67th AI tool. And unfortunately, also your 67th security blind spot. The good news: The Vanta Agent works like a GRC engineer in the background, finding every app your team uses, scoring the risk, and drafting fixes for you. Vanta is the platform used by over sixteen thousand fast-moving companies like Ramp, Cursor, and Harvey who are shaping the future with AI, AND staying ahead of AI risk. Get started at vanta.com/headlines. 

Link to the episode This week's Department of Know is hosted by Rich Stroffolino, with guests Davi Ottenheimer, principal, Flying Penguin, and Chris Ray, field CTO, GigaOm. Missed the live show? Check it out on YouTube. The Department of Know is live every Friday at 4:00 p.m. ET. Join us each week by registering for the open discussion at CISOSeries.com. Huge thanks to our sponsor, Vanta Your team just added its 67th AI tool. And unfortunately, also your 67th security blind spot. The good news: The Vanta Agent works like a GRC engineer in the background, finding every app your team uses, scoring the risk, and drafting fixes for you. Vanta is the platform used by over sixteen thousand fast-moving companies like Ramp, Cursor, and Harvey who are shaping the future with AI, AND staying ahead of AI risk. Get started at vanta.com/headlines. 

On this special segment of The Full Ratchet, the following Investors are featured: David Ulevitch of Andreessen Horowitz Eric Ries author of "The Lean Startup" Larry Cheng of Volition Capital We asked guests to share the best question they've ever been asked by an allocator. The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

Mexico's first cyber test gets tested Snoops break into Roundcube mailservers Cash App owner pays up over lax security Get the show notes here: https://cisoseries.com/cybersecurity-news-mexicos-big-cyber-test-roundcube-mailserver-snooped-on-cash-app-found-lax/ Thanks to our episode sponsor, Vanta Your team just added its 67th AI tool. And unfortunately, also your 67th security blind spot. The good news: The Vanta Agent works like a GRC engineer in the background, finding every app your team uses, scoring the risk, and drafting fixes for you. Vanta is the platform used by over sixteen thousand fast-moving companies like Ramp, Cursor, and Harvey who are shaping the future with AI, AND staying ahead of AI risk. Get started at vanta.com/headlines. 

The ramp from southbound I-5 to southbound I-205 at Exit 7 in Vancouver closes Friday, July 10 at 10 p.m. through Saturday at 8 p.m. for pavement repairs and striping. All southbound I-205 lanes between the I-5/I-205 split and Exit 36 at Northeast 134th Street will also close. https://www.clarkcountytoday.com/news/southbound-i-5-to-i-205-connection-closes-for-paving-work-in-vancouver-july-10-11/ #I5 #I205 #Vancouver #WSDOTTraffic #ClarkCounty #RoadClosure #Washington #TrafficAlert

Free GTM Prompt Pack for Perplexity Computer: https://clickhubspot.com/gbvs Ep. 434 Can one RevOps leader and a team of AI agents really replace a 10-15 person operations team?  Kieran and guest Nate Follen (Head of strategy and Ops for Perplexity) dive into the real-world impact of Perplexity on modern go-to-market and RevOps workflows. Learn more on automating tedious CRM audits, building thoughtful event workflows with AI-powered model councils, and creating a scalable skills library for proactive, agent-driven teams. Mentions Nate Follen https://www.linkedin.com/in/follen Perplexity https://www.perplexity.ai/ Ramp https://ramp.com/ Ironclad https://ironcladapp.com/ Apollo https://www.apollo.io/ Nvidia https://www.nvidia.com/en-us/ Gemini https://gemini.google.com/app Claude https://claude.ai/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

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

The UK's Cyber Pledge and Cyber Shield Millions exposed in Japanese telco attack China looking to curb overseas model access Get the show notes here: Thanks to our episode sponsor, Vanta Your team just added its 67th AI tool. And unfortunately, also your 67th security blind spot. The good news: The Vanta Agent works like a GRC engineer in the background, finding every app your team uses, scoring the risk, and drafting fixes for you. Vanta is the platform used by over sixteen thousand fast-moving companies like Ramp, Cursor, and Harvey who are shaping the future with AI, AND staying ahead of AI risk. Get started at vanta.com/headlines. 

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My guest today is Jeremy Giffon.  Jeremy has been on the show before as one of our most popular guests, and this conversation is every bit as enjoyable as the first. Over the last 18 months, Jeremy has had hundreds of conversations with founders and with the capital behind their companies. I don't know many investors with such a high rep count in the most interesting corners of private markets, so I asked him what he has learned. We talk about what those lessons mean for founders and investors, why everyone has become subservient to the poster class, the hidden intellectual history behind Silicon Valley and much more. Please enjoy my conversation with my friend, Jeremy Giffon. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:02) Jeremy Giffon (00:02:34) Lessons from 18 Months of Founder Conversations (00:07:01) The Billion-Dollar PDF (00:08:13) The Unifeed & Rise of the Timeline (00:17:02) Power Law & Breakout Content (00:18:48) AI Algorithms Driving Content (00:20:38) Timeline-Native White House (00:21:09) Traits of Great Posters (00:25:27) Peak Guy & the Billionaire Priest Class (00:32:13) Billionaires Now Defer to Posters (00:34:52) Freedom vs. Relevance (00:38:53) AI & White-Collar Job Displacement (00:40:53) Stewarding Your Gifts as Moral Duty (00:43:18) Next Wave of Finance: Equity-First Firms (00:53:26) East Coast vs. West Coast Finance (00:55:34) Beating the Market Is Not That Hard (01:00:40) SPV Feudalism & Allocation (01:02:10) Egregious SPV Fee Structures (01:04:50) Simplicity vs. Complexity in Investing (01:07:15) Hiring: Attracting Differentiated Talent (01:11:00) Silicon Valley's Hidden Intellectual Traditions

Click here to receive today's free gift on the Radio Page:  For Better or Worse – Disability has a way of trying even the best of marriages. The cumulative effects of daily routines that never vary, social isolation, financial pressures, unmet expectations, and a life that is vastly different from most couples can wear on the spirits of the strongest husband and wife. Married to Joni Eareckson Tada for more than 37 years, Ken Tada shares that disability does not have to be the defining word in your marriage. Instead disability is an invitation for you and your spouse to depend on Jesus in your weakness and grow closer to each other than you ever thought possible. --------Thank you for listening! Your support of Joni and Friends helps make this show possible. Joni and Friends envisions a world where every person with a disability finds hope, dignity, and their place in the body of Christ. Become part of the global movement today at www.joniandfriends.org. Find more encouragement on Instagram, TikTok, Facebook, and YouTube.

Suspected China-Nexus hackers use fake Indian tax filing utility to deploy DcRAT Prompt injection attacks trick AI Agents into making crypto payments France to stop certifying products without quantum-safe encryption Get the show notes here: https://cisoseries.com/cybersecurity-news-india-tax-rat-prompt-injection-crypto-scam-france-pushes-quantum-safe/ Thanks to our episode sponsor, Vanta Your team just added its 67th AI tool. And unfortunately, also your 67th security blind spot. The good news: The Vanta Agent works like a GRC engineer in the background, finding every app your team uses, scoring the risk, and drafting fixes for you. Vanta is the platform used by over sixteen thousand fast-moving companies like Ramp, Cursor, and Harvey who are shaping the future with AI, AND staying ahead of AI risk. Get started at vanta.com/headlines. 

Chez Ramp, l'intelligence artificielle est pensée comme un co-équipier intégré aux processus métiers, capable de transformer en profondeur le travail des entreprises. Entre agents, automatisation et nouveaux systèmes d'exploitation du travail, l'IA devient une infrastructure centrale.

Welcome back to the Bar Exam Toolbox podcast! This episode is part of the series in which we demystify the shift from MBE to NextGen multiple-choice questions. Today Lee walks through four questions on constitutional law -- two in classic MBE style and two in the NextGen format. Practice applying the right level of scrutiny, the right test, and the right doctrine with questions on free speech, equal protection, and procedural due process. In this episode, we discuss: Question 1: First Amendment (MBE) Question 2: Equal Protection Clause (MBE) Question 3: Equal Protection Clause (NextGen) Question 4: Procedural due process (NextGen) Study tips for multiple-choice questions RAMP study tool Resources: https://barexamtoolbox.com/ramp (https://barexamtoolbox.com/ramp) Mathews v. Eldridge (https://supreme.justia.com/cases/federal/us/424/319/?__cf_chl_f_tk=.74Z9udXxZoFyahgsTM1pK81SZNmqVJisR00kAFlKgw-1782814803-1.0.1.1-iLPq65lW8SPU8r7xgE2zIbhxEOfzNroBfyhDeaySJNY) Podcast Episode 117: Listen and Learn – Due Process and Equal Protection (Con Law) (https://barexamtoolbox.com/podcast-episode-117-listen-and-learn-due-process-and-equal-protection-con-law/) Podcast Episode 123: Listen and Learn – First Amendment (Content-Neutral Restrictions) (https://barexamtoolbox.com/podcast-episode-123-listen-and-learn-first-amendment-content-neutral-restrictions/) Download the Transcript (https://barexamtoolbox.com/episode-353-listen-and-learn-mbe-vs-nextgen-multiple-choice-constitutional-law/) If you enjoy the podcast, we'd love a nice review and/or rating on Apple Podcasts (https://itunes.apple.com/us/podcast/bar-exam-toolbox-podcast-pass-bar-exam-less-stress/id1370651486) or your favorite listening app. And feel free to reach out to us directly. You can always reach us via the contact form on the Bar Exam Toolbox website (https://barexamtoolbox.com/contact-us/). Finally, if you don't want to miss anything, you can sign up for podcast updates (https://barexamtoolbox.com/get-bar-exam-toolbox-podcast-updates/)! Thanks for listening! Alison & Lee

Larry Cheng of Volition Capital joins Nick to discuss Is SpaceX Over or Undervalued, Why Consensus Kills, How Chewy Beat Amazon, and the GameStop Saga from a Board Member. In this episode we cover: E-commerce and AI-Driven Era GameStop's Transformative Moves SpaceX's Market Cap and Future Value AI and Software Industry Impact of AI on Jobs and Companies Volition Capital's Investment Thesis Investor Mindset and Risk Management Board Management and Advice for Founders Guest Links: Larry's LinkedIn Larry's X Volition's LinkedIn Volition's Website The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

JadePuffer ransomware used AI agent to automate entire attack AdaptHealth suffers cyberattack UK's National Cyber Action Plan launch delayed by political leadership crisis Get the show notes here: https://cisoseries.com/cybersecurity-news-first-ai-ransomware-adapthealth-suffers-cyberattack-uk-cyber-plan-delayed/ Thanks to our episode sponsor, Vanta Your team just added its 67th AI tool. And unfortunately, also your 67th security blind spot. The good news: The Vanta Agent works like a GRC engineer in the background, finding every app your team uses, scoring the risk, and drafting fixes for you. Vanta is the platform used by over sixteen thousand fast-moving companies like Ramp, Cursor, and Harvey who are shaping the future with AI, AND staying ahead of AI risk. Get started at vanta.com/headlines. 

L'IA coûte-t-elle plus qu'elle ne rapporte ? • WhatsApp prépare la fin du numéro de téléphone obligatoire • Google déploie finalement sa recherche IA en France • Une alternative française à Google Maps • Robots humanoïdes émotionnels à vendre • Sony prépare la fin des jeux vidéo en version physique. • Ramp montre comment l'IA peut transformer les entreprises. • Check Point alerte sur les nouveaux risques liés aux agents IA. • Data4 défend le rôle stratégique des data centers européens.⭐️ Découvrez Frogans, l'innovation française qui réinvente le Web

On this special segment of The Full Ratchet, the following Investors are featured: Ethan Austin of Outside VC Willy Schlacks of EquipmentShare Grant Demaree of Onebrief We asked guests for the most important piece of advice that they'd share with folks early in their venture career. The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

New data from Ramp, Revelio Labs, Box, and the Center for AI Safety complicates the AI jobs narrative: AI is automating more real work, but the companies using it most aggressively are also growing headcount faster. In the headlines: OpenAI reportedly floats giving the US government a stake in the company, Meta explores selling AI compute, and Fable 5 returns to mixed but intense reactions.Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠kpmg.com/us/Sophisticated⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Hyperagent - Hire a fleet of always-on agents. New users get $1,000 in inference. ⁠hyperagent.com/aidailybrief⁠Rackspace Technology- One accountable partner to build, operate and run your full enterprise AI stack ⁠https://www.rackspace.com/⁠Section - Section turns AI investment into workforce transformation and ROI - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.sectionai.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Scrunch - The AI customer experience platform - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://scrunch.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

Join Cher's Hope Notes newsletter and start your free Solution-Focused  3-day training here: https://www.thefocusedmindset.com/leadwithhopechallenge  Cher shares her personal journey of alignment and attitude, and shares what she learned on the road to her recognition as a finalist for the 2027 California School Counselor of the Year, emphasizing the importance of deliberate attitude and alignment in professional and personal life. In her preparation to apply for RAMP, the power of alignment became more important than ever! Hear how this all comes together in this episode.  TEDx: How to Lead with Hope: Solution Focused Conversation Navigation https://youtu.be/Am3ZoF53BS0?si=ZaflEtnhsdjgJ2oN Instagram: Cher Kretz The Focused Mindset Podcast https://www.instagram.com/cherkretz_thefocusedmindset/ TikTok: @Cher Kretz The Focused Mindset https://www.tiktok.com/@cherkretz?lang=en Tip Jar:  Your generous support helps me create more free resources and keep this podcast going strong. Thank you. https://thefocusedmindset.ck.page/products/tips-4-cher  Key topics Authenticity and its impact on relationships The importance of alignment in personal and professional life How to deliberately choose and practice a positive attitude The significance of authenticity in achieving recognition Strategies for maintaining alignment in challenging situations  

My guests today are Gavin Uberti and Rob Wachen, the founders of Etched.  A few years ago, when they set out to build a better AI chip than the largest companies in the world, almost everyone I called told me it could not be done. They have since done it, taping out a working chip on their first attempt and becoming the first hardware company founded after ChatGPT to do so. They already have more than a billion dollars of customer demand for their first product, and have raised eight hundred million dollars to build it.  Etched builds chips and systems designed to run AI models faster and at lower cost. They started the company in 2023, and that product is a complete rack for inference, the chip along with the boards, the power delivery, the interconnects, and the manufacturing to produce it all. We talk about the technical bets behind their architecture, how they hired industry legends and paired them with elite 22 year-olds, and why they believe inference will become one of the largest markets in the world. I think you will find the story of what they have built hard to forget. Please enjoy my conversation with Gavin and Rob. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:07) Gavin Uberti and Rob Wachen (00:03:54) Two 21-Year-Olds Taking on NVIDIA (00:07:52) The Two Technical Bets Behind Their Architecture (00:14:15) Why Inference Becomes the Biggest Market (00:20:23) Rob and Gavin's Origins Stories (00:28:38) How They Recruit Industry Legends (00:36:30) Moving a Dozen Engineers to Bangalore for Six Months (00:38:01) Speed Wins (00:43:58) Getting More Concurrency Out of Every Megawatt (00:52:44) Vertical Integration (00:57:43) Hardest Obstacles to Overcome (01:01:09) Raising The Largest AI Chip Series A Ever (01:06:29) TSMC (01:13:20) Designing Gen 2 for Gigawatt-Scale Production (01:16:42) Why Machines Don't Think Like People (01:20:03) A Year of Compute Compressed Into a Month (01:23:44) The Trillion-Dollar Data Center (01:26:19) The Kindest Thing

What I learned from reading Honda: The Man and His Machines by Sol Sanders. Made possible by: Ramp: ⁠https://ramp.com⁠  Applovin: ⁠https://www.applovin.com/⁠  Vanta: ⁠https://vanta.com/founders

Founders ✓ Claim : Read the notes at at podcastnotes.org. Don't forget to subscribe for free to our newsletter, the top 10 ideas of the week, every Monday --------- What I learned from reading Pulitzer: A Life in Politics, Print, and Power by James McGrath Morris. Made possible by: Ramp: ⁠https://ramp.com⁠ Applovin: ⁠https://www.applovin.com/⁠ Vanta: ⁠https://vanta.com/founders

On this special segment of The Full Ratchet, the following Investors are featured: Eric Byunn of Centana Growth David Ulevitch of Andreessen Horowitz Jake Saper of Emergence Capital We asked guests to tell the most important lesson they've learned in their career. The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

Save 20% on your first online Lucy order at lucy.co/youbetcha with promo code youbetchaWe kick things off by chatting about the viral firefighter video—check it out on our main page! Then, we dive into the ultimate debate: what is the difference between cabin boat owners and regular boat owners? We also discuss the high-stress nightmare that is launching a boat at a public access ramp.In segment two, we give you the best tips and tricks on how to be a good golf cart mate. Finally, we wrap up the episode with a doomsday bunker draft and a fun fact from Tyler.

My guest today is Vlad Barbalat, the Chief Investment Officer of Liberty Mutual Investments, the $120 billion investment platform that sits within one of the largest insurance companies in the world.  Vlad grew up in Soviet Moldova, came to America in 1990, and built a career that eventually led him to one of the most distinctive capital allocator seats anywhere in finance.  Today we talk about how the mutual insurance structure creates a unique investment platform, what Liberty looks for in a new deal or partner, and what it means to build a career and a life in a country that gave you opportunities you never would have had anywhere else.  Please enjoy my conversation with Vlad Barbalat. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Timestamps: (00:00:00) Welcome to Invest Like The Best (00:00:53) Vlad Barbalat (00:01:28) The Most Interesting Seat in the Market (00:05:53) Breaking Down the $120B (00:10:41) How the Portfolio Is Constructed (00:11:00) The House View (00:13:49) What Liberty Looks for in a GP (00:16:32) Why Not Just Buy Bonds (00:18:30) Benefits of the Mutual Structure (00:23:40) The Luxury of the American Citizen Through Immigrant Eyes (00:30:26) How Immigration Shaped His Worldview (00:32:45) Direct Deals vs. GP Allocations (00:35:23) Branded Capital (00:39:07) Geopolitics & Investing (00:43:48) AI's Impact on Investing (00:46:22) The Valuation Debate (00:50:47) Public vs. Private Markets (00:53:53) Lessons from Goldman (00:54:41) Why Excellence Matters (00:57:30) Managing Permanent Capital (01:03:54) The Kindest Thing 

Live from the LinkedIn Lounge at Cannes Lions, we break down why traditional advertising is broken and what's actually working today. Spending millions on a single, polished TV commercial doesn't work anymore—it just gets lost in the noise. We sit down with creator Anthpo, Ramp's Kendall Hope Tucker, and Adobe's Lara Balazs to look at how real brands are catching people's attention by being entertaining and acting like creators themselves. What we cover: The Power of Stunts: Why Ramp puts Kevin from The Office in a glass box, and how they pull it off without a corporate committee ruining the idea. The New Brand Deal: Why creators are moving past basic shoutouts and actually helping big companies build their entire strategy. Moving Fast with AI: How Adobe uses new tools to handle the boring parts of making videos and images so teams can focus on the big ideas. The End of Google Search: Why people are looking for things inside AI chatbots instead of search engines, and what that means for brands. Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome back to the Bar Exam Toolbox podcast! This episode is part of the series in which we demystify the shift from MBE to NextGen multiple-choice questions. Today Lee walks through four questions on civil procedure -- two in classic MBE style and two in the NextGen format. Wondering how to keep straight two doctrines that sound alike: personal jurisdiction and subject matter jurisdiction? Find out in this episode!  In this episode, we discuss: Question 1: Personal jurisdiction (MBE) Question 2: Subject matter jurisdiction (MBE) Question 3: Subject matter jurisdiction (NextGen) Question 4: Issue-spotting (NextGen) Study tips for multiple-choice questions RAMP study tool Resources: https://barexamtoolbox.com/ramp (https://barexamtoolbox.com/ramp) Podcast Episode 92: Listen and Learn – Subject Matter Jurisdiction (https://barexamtoolbox.com/podcast-episode-92-listen-and-learn-subject-matter-jurisdiction/) Podcast Episode 169: Listen and Learn – Personal Jurisdiction (Civ Pro) (https://barexamtoolbox.com/podcast-episode-169-listen-and-learn-personal-jurisdiction-civ-pro/) Podcast Episode 148: Listen and Learn – Claim and Issue Preclusion (Civil Procedure) (https://barexamtoolbox.com/podcast-episode-148-listen-and-learn-claim-and-issue-preclusion-civil-procedure/) Download the Transcript (https://barexamtoolbox.com/episode-352-listen-and-learn-mbe-vs-nextgen-multiple-choice-civil-procedure/) If you enjoy the podcast, we'd love a nice review and/or rating on Apple Podcasts (https://itunes.apple.com/us/podcast/bar-exam-toolbox-podcast-pass-bar-exam-less-stress/id1370651486) or your favorite listening app. And feel free to reach out to us directly. You can always reach us via the contact form on the Bar Exam Toolbox website (https://barexamtoolbox.com/contact-us/). Finally, if you don't want to miss anything, you can sign up for podcast updates (https://barexamtoolbox.com/get-bar-exam-toolbox-podcast-updates/)! Thanks for listening! Alison & Lee

Grant Demaree of Onebrief joins Nick to discuss The Future of Military Planning: Defense Tech Beyond the Bubble, AI Software as Combat Power, and the Rise of AI Wargaming. In this episode we cover: West Point and Army Background's Impact on Founding One Brief Challenges and Insights in Military Planning Co-Founder Relationships and Founder Mode Future of One Brief and Defense Tech Defense Tech and American Hegemony Military Staff of the Future Guest Links: Grant's LinkedIn Grant's X Onebrief's LinkedIn Onebrief's Website The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

What I learned from reading Pulitzer: A Life in Politics, Print, and Power by James McGrath Morris. Made possible by: Ramp: ⁠https://ramp.com⁠ Applovin: ⁠https://www.applovin.com/⁠ Vanta: ⁠https://vanta.com/founders

Have you ever been so ready to fix something that you forgot to ask if fixing was even what was needed? In this final episode of the six-part series with therapist and communication coach Jason VanRuler, Candace and Jason shift from how we disconnect to how we reconnect. What does the on-ramp actually look like? It starts with willingness, with curiosity, and with giving the people we love room to grow at their own speed. Listener questions take center stage here: A wife who has been worn down by a husband who seems chronically disappointed. A person who absorbs everyone's moods and can't shake them. A newly married woman asking what no one told her going in. Jason's answers are practical and honest, and Candace adds what she has actually lived, including the moment she realized that praying together changed everything in her own marriage. This one brings the series home with tools you can use today, and a reminder that the relationships worth having are worth the long game. Go to candace.com to join the email list, get the Healthy Connection Guide, and ask your own questions. You can also grab Jason's book and access more resources from the series. Life is like a rollercoaster, but it's better when we go through it together. Connect with Candace and Jason  Candace on Instagram @candacecbure  Follow the Podcast on Instagram @candacecameronburepodcast  Follow the Podcast on TikTok @ccbpodcast Jason on IG: https://www.instagram.com/jason.vanruler/ Jason on Youtube https://www.youtube.com/c/Jasonvrcounselor Website: https://www.jasonvr.com/ Sponsors For This Episode PHD – Visit myphdweightloss.com and call #864-644-1900 and mention Candace.  IFCJ ifcj.com  NOCD - Book a free call by visiting nocd.com GCU gcu.edu 316 Financial https://bank316.com/candace-cameron-bure Learn more about your ad choices. Visit megaphone.fm/adchoices

My guest today is Kareem Amin, co-founder and CEO of Clay. Clay has become one of the fastest-growing software companies of the last few years, valued at over four billion dollars. It helps companies find their best customers and reach them at scale. But this conversation is about a lot more than Clay. Kareem is one of the most original thinkers I know.  We talk about the statues he keeps at the center of how he runs Clay — truth, justice, and courage — and what those words demand of him in practice. We talk about risk, ambition, and what he learned about both on a ten-day silent meditation retreat.  I've had a lot of conversations with Kareem over the years. This is one I'll remember. Please enjoy this unique conversation with Kareem Amin. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:29) Kareem Amin (00:03:07) Clay's Origin (00:10:50) Truth, Courage and Justice (00:16:09) Adulation (00:18:28) Risk, Courage & Self-Respect (00:21:14) Jony Ive & Steve Jobs (00:21:42) Role of Introspection (00:23:08) Lack to Wholeness (00:27:27) The Day Five Insight (00:29:57) Running a Startup Unusually (00:34:41) Learning from Magicians (00:36:27) Music's Role in Your Life (00:39:38) Making People Feel Something New (00:41:20) Vision in Company Building (00:44:29) Wealth & What It's Taught You (00:47:40) All Problems Are Communication Problems (00:52:14) Death Doula & Scaling (00:55:06) The Kindest Thing