Podcasts about weights

On today's episode: Tagged fish swimming thousands of miles? Gear Review: Abu Garcia Revo SX VoltIQ Fishing Poll presented by Humminbird & Minn Kota: What is your favorite soft plastic craw? Mailbag powered by Amped Outdoors: Lure weights, frog setups, and more!     Tackle Talk is presented by:  The Rod Locker | https://www.rodlocker.com | Promo Code: TACKLETALKAUGUST   Additional support provided by: Amped Outdoors | https://www.ampedoutdoors.com Humminbird | https://www.humminbird.com Minn Kota | https://www.minnkotamotors.com

SUMMARY: Brandon and Aaron discuss the pros and cons of owning or renting your model weights. What does that mean for the Enterprise, and what should you be considering?SHOW: 1055SHOW TRANSCRIPT: The Enterprise AI Show #1055 TranscriptSHOW VIDEO: https://youtu.be/uc0GZBLgUeoSHOW SPONSORS:Nasuni - Activate your data for AI and request a demo Topic: Own Your Weights or Rent Them?Why now? Alex Karp had a spicy CNBC segment arguing enterprises should "own their weights" rather than rent models from the big labs — sparking a widely-shared response from Jamin Ball on Clouded Judgement. SubstackPast: Same shape as the "own vs. rent" debate the industry has had before — on-prem vs. SaaS, buy vs. build for ERP/CRM — just replayed one layer down, at the model layer instead of the app layer.Present: A weight file is really just a frozen snapshot that degrades in relative terms as frontier models keep improving — what actually matters is owning the RL/training loop that keeps producing better weights, not the weights themselves. A model RL'd against a company's actual workflows can beat a frontier generalist model on that one task, and do it far more cheaply — but that leaves enterprises managing a sprawl of task-specific models that all need governing, versioning, and securing.Future: Ball frames it as a stated-preference vs. revealed-preference problem — everyone says they want model sovereignty, but the spend data shows enterprises keep writing bigger checks to the frontier labs every quarter because most don't have the talent or infra to run the loop. Where's the market for a company that closes that gap — makes "owning the loop" accessible without the complexity tax? Tie back to your Show #4 (off-the-shelf AI, harnesses) — this is basically that debate's sequel, one layer deeper. (Aaron's hot take, and another episode: maybe it's not about the weights at all…)FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

Heavy lifting does build bone density. That part is true. But the rule going around that women must strength training with 5 sets of 5 at 85% of your max (1RM) plus box jumps or you lose your bone density, is a much bigger claim than the evidence supports.We dismantle this claim and explain what the best studies can and cannot prove. We also share a simpler approach that still allows you to train hard, lower fracture risk, and keeps you consistent through perimenopause and postmenopause. If you want to protect your bones without training in a way that makes you hate strength training or quit altogether, hit play!Get the FREE Strength Training for Hormone Health guide, a free breakdown of how to lift in a way that supports your hormones through the menopause transition and beyond:https://witsandweights.com/freeYou'll learn:How the LIFTMOR trial was designed and what it found on spine and hip bone density, as well as heavy vs. moderate strength training over 403 problems with drawing conclusions from the LIFTMOR trialWhy heavy lifting is not inherently unsafe for older women with low bone mass How social media “telephone” turns nuanced evidence into rigid rules DEXA body fat and lean mass scan precision error and why small changes may not be detectableHeavy vs. moderate loading for bone mineral density What we know so far from perimenopause dataMenopause transition bone loss rates and the hidden "big win" with strength training for women over 404 practical tips to do insteadA 30-second test to estimate whether your sets are heavy enoughWits & Weights is the evidence-based podcast for strength training over 40, menopause and perimenopause fitness, body recomposition, metabolism recovery, and healthy aging. Hosted by Philip Pape, creator of Eat More Lift Heavy and Fitness Lab.Timestamps:0:00 - The bone density rule keeping too many women out of the gym 3:48 - Inside the LIFTMOR trial (strength training and bone density) 7:45 - Where bone and hormones are connected 9:05 - 3 gaps between the LIFTMORE trial and its interpretation 14:46 - Research on perimenopausal women over 40 16:29 - How the menopause transition affects bone loss 18:38 - 4 steps to maintain or build bone density 21:45 - Where jumping fits into your strength training protocol 25:16 - Finding your load range without attempting a 1 rep max attempt 26:20 - Bonus: 30-second test for whether you're lifting heavy enoughEpisode Resources:FREE 1RM and Strength Calculator (with Strength Standards):https://www.witsandweights.com/strength-calculatorRelated episode: Are the Barbell Squat, Deadlift, and Row Actually Unsafe After 40?

Watch ADS-FREE: https://podcasts.apple.com/us/channel/djamgamind/id6760446113Visit our Research Hub at https://djamgamind.com/pdfsImportant Topics:* OpenAI Previews Ultrafast on GPT-5.6 Sol: A Cerebras-powered API tier speeds answers by up to 14x, reaching 750 tokens per second. On Humanity's Last Exam, Sol with Ultrafast finished 2,500 questions in 11 hours versus 78 for Fable at comparable results. Invite-only preview, no listed price; the partnership committed 750MW of Cerebras compute in January.* Anthropic's Agents Wage a Turf War: Three hidden Claude co-owners of one codebase, each assigned a rewrite in a different language, escalated into four hours of sabotage. One agent's software impersonated a rival's to fool a monitoring program; others locked competitors out. Peace, where it emerged, often required a call for human backup.* Apple Builds a China-Specific Model with Alibaba: Apple developed a tailored LLM for China using Alibaba's Qwen alongside Baidu technology, registered with the Cyberspace Administration, expected to ship with iOS 27 -- potentially the only Western firm authorized to offer proprietary AI in China.* Zhipu Releases GLM-5.3: The Chinese startup claims the most powerful open-weights coding model, outperforming OpenAI on agent-based coding benchmarks. The system identified 2,436 software vulnerabilities across 269 projects. Weights go open source after a two-week security review.* Google Ships Gemini 3.7 Flash: Google's new "workhorse" model holds pricing flat at $0.75 input / $3.75 output per million tokens while gaining 10-15 percentage points on FrontierCode 1.1 Main and DeepSWE v1.1, and 12 points on the GDP.pdf document benchmark.* Ramp Data: Enterprises Reject the Frontier: Anthropic leads adoption at 43.5% of Ramp's U.S. business clients, but only 6% of their token spend goes to Fable 5. GPT-5.6 Sol takes 25% of token usage among OpenAI customers. Open-source and Chinese models rise to 6.1% of AI-spending customers; xAI hits 4% on its fastest growth month since July 2025.* Android Pivots From Apps to Agents: Android ecosystem president Sameer Samat describes the shift from manual app navigation to agent-based systems, spanning phones, computers, cars, watches and glasses, plus a new voice-to-text keyboard called Rambler.* WhatsApp Tests On-Device Scam Detection: On-device AI flags suspicious conversations from unknown contacts with warnings invisible to the sender.* Trump Signs 100% Drone Tariff: Imported drones over 55 pounds with security-sensitive capabilities face a 100% tariff; smaller drones 25%. Effective within 21 days.* Google Ordered to Ease Rival App Installs: Judge James Donato ordered Google to strip extra confirmation screens blocking alternative Android app stores within one week.

If you have ever walked into a workout and thought, "Wait… what weight did I use last time?" this episode is for you. Because here is the thing. A lot of women think better results come from finding a harder workout, adding more exercises, or switching up the plan again. But sometimes the thing holding you back is way more simple. You are guessing. In this bonus episode of Embrace Your Real, we are talking about why tracking your weights matters if you want to actually see progress from the workouts you are already doing. It may not feel exciting, but knowing what you lifted last time is what helps you know what to reach for next time. And that is how you stop going through the motions and start training with intention. What's Discussed: Why working hard does not always mean you are progressing Why changing workouts is not always the answer What progressive overload looks like in a real workout Why you need to track both weight and reps How to know when it is time to go heavier Why your numbers can show progress before your body looks different How tracking helps you stop guessing and start showing up with a plan The Movement With Julie app makes this so much easier because you can log your weights and reps right inside the app. So instead of trying to remember what you used last week, you can look back, know where to start, and keep building from there. Head to movementwithjulie.com to get started. If you loved this episode, you'll also loveEpisode 566: What Women Get Wrong About Progressive Overload. It is the perfect next listen if you want to understand how to keep challenging your body without overcomplicating your workouts. If you want more from me, be sure to check out… Follow me on Instagram: @juliealedbetter | @embraceyourreal | @movementwithjulie Movement With Julie | App: https://sale.movementwithjulie.com/ Macro Counting Made Simple Online Academy: https://www.macrocountingmadesimple.com/ Website: www.juliealedbetter.com

How long does body recomposition really take? Cutting calories to lose weight can make a skinny fat physique worse by stripping away the little muscle you have while giving you misleading “progress” signals. Learn the realistic week-by-week body recomp timeline for noticeable changes, plus the simple tracking methods that prove your plan is working. If the scale hasn't budged in 3 weeks and you're about to call it off, hit play. And shout out to listener Dhiren A. on Spotify for today's topic!Get 20% off your first custom blend at True Nutrition with code WITSANDWEIGHTS. Build your own protein from 20+ bases and 28 flavors, third-party lab tested and first-party verified, now shipping to over 150 countries:https://truenutrition.com/witsandweightsYou'll learn:Skinny fat explained as normal weight obesity and why BMI can miss the problem Why the default advice to lose weight can backfireThe Forbes curve and how your starting leanness changes how much body fat you loseRealistic muscle gain rates over 10 to 12 weeksWhy DEXA and scales struggle to detect small lean mass changes 4 priorities for body recomposition (including protein)How to track progress instead of chasing day-to-day noise A simple muscle-building signal hidden in your training logWits & Weights is the evidence-based podcast for strength training over 40, body recomposition, fat loss over 40, metabolism recovery, and healthy aging. Hosted by Philip Pape, creator of Eat More Lift Heavy and Fitness Lab.Support the show by visiting today's sponsor:Get 20% off your first custom blend at True Nutrition with code WITSANDWEIGHTS.Timestamps:0:00 - Why "just lose weight" backfires when you're skinny fat6:43 - The Forbes curve (dieting, leanness, and body fat)10:19 - How to get enough protein13:03 - What happens in weeks 1 through 1016:15 - What a good month of muscle gain looks like17:35 - Does body fat measurement work?19:57 - The problem that isn't your training20:23 - 3 changes that make the timeline work24:13 - How long until you see physical changes, and until others do28:13 - The muscle-building evidence in your own training logEpisode Resources:Free Body Fat Calculator - Navy formula estimate from a tape measure, plus a few other numbers: https://www.witsandweights.com/calculatorsQ&A - How Fast Do You Really Lose Muscle? (Plus Bigger Glutes, Creatine, and More)

In which, almost as an afterthought, we conclude the story of John Quincy Adams' boring book. Check out: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠indeed.com/theconstant⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ now to start hiringVisit our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patreon here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. You too can get ad-free, early episodes, starting now!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠​​⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠BUY OUR MERCH, YOU FILTHY ANIMALS!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ​ ​The Constant is part of the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Airwave Media⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ podcast network.​​ ​​​​ ​​ ​​Interested in advertising on The Constant? Email ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠sales@advertisecast.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to get on board! Learn more about your ad choices. Visit megaphone.fm/adchoices

A rogue AI agent hacked Hugging Face to cheat on its own test. The Pope wrote 43,000 words warning about AI. Washington pulled Fable 5 offline, then reversed course. Paul Roetzer and Mike Kaput count down the top 10 AI trends of the quarter: AI jobs whiplash, the pillars of business transformation, Apple v. OpenAI, GPT-5.6 and ChatGPT Work, agents transforming work, Fable 5, soft nationalization, the Hugging Face breach, and the battle over open weights. This episode was recorded live on July 31, 2026. Show Notes: Access the show notes and show links here Timestamps: 00:00:00 — Intro 00:02:54 — The Pope's AI Encyclical and Popular Backlash Against AI 00:07:01 — AI Jobs Whiplash 00:11:44 — The Pillars of Business AI Transformation 00:16:21 — OpenAI vs. Apple 00:19:10 — GPT-5.6 and ChatGPT Work 00:24:36 — How Agents Are Transforming Work 00:29:47 — Fable 5 00:34:11 — US Government Intervention in AI 00:37:53 — OpenAI Models Escape and Hack Hugging Face 00:42:29 — The Battle Over Open Weights This episode is brought to you by AI Academy by SmarterX. AI Academy is your gateway to personalized AI learning for professionals and teams. Discover our new on-demand courses, live classes, certifications, and a smarter way to master AI. Learn more here. Visit our website Receive our weekly newsletter Join our community: Slack Community LinkedIn Twitter Instagram Facebook YouTube Looking for content and resources? Register for a free webinar Come to our next Marketing AI Conference Enroll in our AI Academy 

The amazing D'Arcy Carden (The Good Place, Broad City) joins The Dumbbells in The Weight Room. D'Arcy is so funny and has great stories about growing up in the Bay Area playing the brutal sport of water polo. The Bells find out how she starting training regularly and watching what she eats. D'Arcy is currently on a program she loves and does no cardio in the gym and eats what she wants on weekends! Find out how she makes that work - and wether or not Stanger and Euge love it too...See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

After four months away, Robert Santana is back—and a lot has changed.The Weights and Plates Podcast has officially evolved into Strong and Sleek, a new name and brand built around the idea that getting stronger, looking better, and building a more durable body doesn't have to mean living in the gym or chasing extreme numbers.Robert opens up about why he closed his old South Phoenix gym, moved to North Scottsdale, and built a completely new training environment. He explains the philosophy behind Strong and Sleek, why he wanted a more balanced and accessible approach to strength training, and what he's learned about the biggest obstacle facing most people: not motivation, but time and recovery.He also gets unusually candid about a major personal change: after years of training and making progress without steroids, Robert started testosterone replacement therapy earlier this year. He explains why he made the decision, what changed, what didn't, and how much testosterone actually contributed to his recent strength gains—including new PRs on the deadlift, bench, squat, and press.Along the way, Robert tackles genetics, muscle-building potential, weight cycling, recovery, aging, the realities of natural strength training, and why Instagram fitness promises don't apply to most people.The podcast isn't dead. It's evolving.Strong and Sleek is the new name. The mission is still the same: get stronger, get healthier, look better, and build a body that can handle real life.

Join us as we continue our series

Both OpenAI and Anthropic disclosed this week that AI agents escaped their safety tests and reached real organizations, and one model flagged its own action as wrong before carrying it out. Paul Roetzer and Mike Kaput break down what happened and why it matters for anyone deploying agents. Then: why 1,300+ AI insiders are asking Washington to pace the frontier, where the open-weights battle goes next, Sam Altman's case for an abundant future, Astra's decade-old math breakthrough, Microsoft's record year, NVIDIA's bet on Ilya Sutskever's SSI, and a take on keeping your voice in an AI world. AI-Pulse Survey: Fill out this week's AI-Pulse Survey here. Show Notes: Access the show notes and show links here Timestamps: 00:00:00 — Intro 00:05:18 — AI Agent Cyberattacks Get Worse 00:21:28 — AI Insiders Ask Washington to Pace AI 00:36:06 — The Battle Over Open Weights Continues 00:53:36 — Sam Altman on AI's Abundant Future 00:59:42 — OpenAI's Astra Model 01:03:44 — Microsoft Posts Record Fiscal Year 01:11:38 — Nvidia Bets on Ilya Sutskever's SSI 01:15:04 — How AI Is Enabling the Human Experience 01:21:01 — AI Use Case Spotlight 01:29:02 — AI Product and Funding Updates This week's episode is brought to you by MAICON, our 6th annual Marketing AI Conference, happening in Cleveland, Oct. 13-15. The code POD100 saves $100 on all pass types. For more information on MAICON and to register for this year's conference, visit www.MAICON.ai. Visit our website Receive our weekly newsletter Join our community: Slack Community LinkedIn Twitter Instagram Facebook YouTube Looking for content and resources? Register for a free webinar Come to our next Marketing AI Conference Enroll in our AI Academy 

David Epstein is General Partner at USF Ventures, the venture fund backing companies connected to the University of San Francisco. He was previously a General Partner at Crosslink Capital and has held management and CEO roles at more than half a dozen startups. He also teaches entrepreneurship and finance, and began his career at Data General as a computer designer, on the project chronicled in Tracy Kidder's Pulitzer Prize winning The Soul of a New Machine.In this episode, Dave argues that the real AI bottleneck isn't chips, power, or capital. It's data. We get into why frontier labs backing open weight models is a defensive move rather than a principled one, where early stage startups can still win, and why he thinks jobs will disappear faster than they get created.⭐This episode is brought to you by Podcast10x. We help founders and investors turn one podcast episode into a full month of content. Strategy, production, and distribution handled end to end. Learn more at https://podcast10x.comWhat we cover:→ Why "AI company" is no longer a category, and the pitch deck claim that has become his pet peeve→ Why AI isn't a tool anymore, and what makes this cycle different from the dot com era→ The real bottleneck: why we've exhausted the internet's data and what comes next→ Money as the constraint nobody prices in, and the circularity in the current data center build out→ How Chinese open weight models pull revenue out of token charges and subscriptions→ Why big lab support for open models is defensive positioning→ Who survives if open weights take share, and why consolidation is coming→ Where early stage startups can still win: drug discovery, financial services, legal→ Why the likely exit is a sale, not an IPO→ Ethical investing as a return rather than a tax, and why it's tough to work with jerks→ Why self-regulation rarely works, and what 2008 tells us about the current AI alliance→ Why layoffs are just the beginning, and the Industrial Revolution parallel everyone forgets→ Where the jobs actually are: management, human facing care, and the trades→ What top tier VCs get right, and why VCs are also lemmings→ Quantum computing as a data center accelerator, and the password problem it creates→ Physics AI vs physical AI, and the validation problem sitting on top of both→ Five year predictions: AGI, commonplace robots, and why consciousness doesn't matterConnect with Dave Epstein:LinkedIn: https://www.linkedin.com/in/thedavee/USF Ventures: https://usfventures.comConnect with Prashant Choubey:LinkedIn: https://linkedin.com/in/choubeysahabSubscribe to VC10X newsletter - https://vc10x.beehiiv.comVC10X website - https://vc10x.comTimestamps:(00:00) - Preview(00:56) - Introduction to David Epstein and the Episode's Topics(02:48) - How the AI Startup Landscape Has Fundamentally Changed(05:08) - Comparing the Current AI Boom to the Internet Boom(06:25) - Identifying the Next AI Bottleneck: Chips, Power, or Data?(09:30) - Why Money is an Overlooked Bottleneck for AI Development(11:14) - The Cyclical Nature of AI Investments and Financing(12:46) - Analyzing Big Tech's Support for Open Source Models(15:12) - Winners and Losers: Open Source vs. Frontier Models(18:01) - How Early-Stage Startups Can Compete and Win in the AI Space(20:53) - The Role of Ethics in AI Investment Decisions(23:31) - The Challenge of Upholding Ethics in a Competitive Market(26:21) - Implications of the OpenAI Model Escaping(29:46) - The Future of AI-Driven Job Disruption(32:46) - Where to Find Employment Opportunities in the AI World(37:43) - How Top-Tier VCs Evaluate Founders and Make Decisions(40:21) - The Most Exciting Emerging Areas of Innovation(43:18) - Explaining Quantum Computing's Potential and Impact(48:39) - An Ambitious AI Prediction for the Next 5 Years(51:35) - Rapid Fire Round: USF Ventures' Investment Strategy(53:06) - Conclusion

Your metabolism isn't the problem. It's not making fat loss harder. On a normal diet, your metabolism slows far less than you've been told. Your body does "defend" itself, though setpoint theory may not be the reason why.I'm breaking down the real reason your weight comes back and the 3 fixes that work. You'll also hear why the researcher behind the Biggest Loser study changed his mind, what happens to your hunger a full year after the diet ends, and what reverse dieting is doing to your recovery (it's not what you think).Hit play if you want to lose the fat once and keep it off for good.Download the free 14-Day Rapid Start Fat Loss Guide mentioned in the episode: https://witsandweights.com/fatlossWits & Weights is the evidence-based podcast for strength training over 40, metabolism recovery, fat loss over 40, body recomposition, and healthy aging. Hosted by Philip Pape, creator of Eat More Lift Heavy and Fitness Lab.Timestamps:0:00 - The reality about setpoint theory 4:10 - Has the Biggest Loser data been misinterpreted? 7:18 - The "floor" your body defends hardest 8:54 - The real cause of real fat loss resistance 12:21 - Building a diet you can stick with 14:19 - 3 fixes to remove the fat loss friction 17:36 - The truth about reverse dieting and recovery 22:31 - Bonus: 2-question check before your next fat loss phaseEpisode Resources:14-Day Rapid Start Fat Loss Guide (free) The Slower Cut That Beats a Fast One (Fat Loss Over 40)

Igor Babuschkin, co-founder of River AI and formerly a co-founder of xAI, joins to unpack a career that spans nearly every major AI lab: he led the StarCraft and AlphaCode work at DeepMind, joined OpenAI's reasoning team years before o1 shipped, and co-founded xAI, where he helped stand up the Colossus data center in roughly 120 days and reflects candidly on what it's actually like working with Elon Musk day to day, plus what the Cursor acquisition actually unlocked for Grok's coding models. He also discusses why he left xAI to start River AI, the three bets behind it, and why he's betting on local hardware, not just software, for personal AI. On the enterprise side, he tackles whether companies will actually train their own models or if it's just a cost play, and makes the case that proprietary labs like OpenAI and Anthropic are facing a real business squeeze. He's skeptical that stacking specialized RL domains generalizes the way pre-training scale did, and is candid about the uncomfortable reality that today's frontier open-weight models are almost entirely Chinese. He closes on what's actually needed to push model progress beyond coding into non-verifiable domains, and the broader implications of where AI is headed next.   (0:00) Intro (1:17) Writing Fiction on Where AI Is Headed (4:46) Cracking Agents Beyond Coding (10:29) Why Igor Left to Start River (12:22) River's Three Big Bets (18:06) Weights vs. Memory: The Personalization Debate (22:04) Should Enterprises Train Their Own Models? (25:10) Are Proprietary Labs Losing Their Edge? (32:16) The China Open-Source Problem (44:19) The Elon Call That Started xAI (50:18) Thoughts on Cursor Acquisition (52:16) What's Actually Bottlenecking AI (56:55) Humans, Machines, and Staying Relevant (1:01:29) Igor's Odds This All Goes Well With your host: @jacobeffron - Managing Director at Redpoint

Become a Ctrl-Alt-Speech supporter to get extended episodes of the podcast plus the chance to submit stories for us to cover.In this week's episode, Mike and Ben cover:The U.S. Is About to Design an AI Regulator. Here's How to Get It Right (Council on Foreign Relations)Mark Zuckerberg Says U.S. Should Accelerate AI Development, Not Restrict It (WSJ)Want AI you can trust? Start by building the right institutions (Atlantic Council)Anthropic Says It's Against A Ban On Open Weight Models. It Just Wants To Ban Everything That Makes Them Good (Techdirt)The High Stakes Behind the EU's €890M Google DMA Fine (Tech Policy Press)Europe bears its teeth, political panic about OpenAI hack and YouTube refines partner policy (Everything in Moderation)Trump vows new tariffs on the E.U. after Brussels fines Google $1 billion (Qz)And in the extended episode for Patreon supporters, they cover:Elon Musk's xAI sues Minnesota over law banning ‘nudification' technology (The Guardian)The Worst Person You Know Just Filed A Good First Amendment Lawsuit Against A Very Badly Drafted Nudify App Ban (Techdirt)Our fun links this week are a replica of Scooby Doo's Mystery-Machine and the Saltburn cricket scandal because we need more satire right now.If you're already a Patreon supporter, you can get the extended episode on Patreon.Ctrl-Alt-Speech is the podcast where we make sense of the major debates shaping online speech, platform power, content moderation and the future of the internet. It's co-hosted by Mike Masnick (Techdirt) and Ben Whitelaw (Everything in Moderation).

AGENDA: 05:00 Jensen's Open-Weights Letter Puts Anthropic on an Island 10:00 OpenAI's Model Targets Hugging Face in a Cyber-Security Scare 18:00 America's Open-Model Push Accelerates as China Looms Large 30:00 Etched Raises $300M to Take on Nvidia 34:00 Google Cloud Grows 82%—So Why Did the Market Panic? 41:00 Travis Kalanick Raises $1.7B for Atoms and Physical AI 51:00 Francisco Partners Raises $21B: Can Private Equity Still Save SaaS? 1:00:00 Mark Pincus Says Quit When It's Too Hard—Jason Lemkin Erupts 1:07:00 Stripe vs Revolut: Which $100B+ Fintech Would You Own?    

Speed is the wrong goal for fat loss over 40. You'll lose more weight (and body fat) by picking a rate you can sustain for months. Most people pick the biggest calorie deficit they can stand, then quit by week five, regain the weight, and have to re-lose the same pounds next year.I'm breaking down the real reason aggressive cuts fall apart and the one rule I use to set the rate of weight loss. You'll also hear why the head-to-head research says faster wins, what happened when researchers put postmenopausal women on a severe deficit, and the 3-question gut check that tells you if you're cutting too hard. Hit play if you want to lose the fat and keep the muscle without starting over every January.Enroll in Eat More Lift Heavy, the 26-week coached program where adults over 40 build the nutrition and training skills to preserve muscle, lose fat, and manage their physique for life.Wits & Weights is the evidence-based podcast for strength training over 40, body recomposition, fat loss over 40, metabolism recovery, and healthy aging. Hosted by Philip Pape, creator of Eat More Lift Heavy and Fitness Lab.Timestamps:0:00 - The most important part of the fat loss equation 4:38 - Where the "slow and steady" rule comes from 6:28 - What a steep calorie deficit requires 9:56 - The start-and-stop cycle that wastes 12 months 11:37 - Research that contradicts the slow approach 15:14 - When slower wins, and for whom 18:02 - The rule of thumb for rate of loss 19:42 - Room to be human during a cut 21:21 - 2 things you can't skip while cutting 23:00 - Severe restriction and postmenopausal women 24:53 - How to choose the right rate of fat loss 25:56 - Bonus: 3-question gut check BEFORE your deficitEpisode Resources:Rapid Fat Loss vs. a Long, Slow Cut (Who It's Actually For)Are the Barbell Squat, Deadlift, and Row Actually "Unsafe" After 40?

On today's show Andrew and Bill begin with reactions to CXMT's IPO success this week, including responses from legislators in Washington and the stakes of Apple's continued lobbying fight to relax restrictions on US companies working with PRC memory firms. Then: The FCC announces rules restricting the import of new, foreign-made robots and power inverters in a move to curb security risks and fuel onshoring. From there: Surveying the ongoing debate over the future of open weights models, including disadvantages facing US-led open source AI efforts, MOFCOM's statement on distillation and Silicon Valley's support for Chinese open weights AI, and the risks of building atop PRC AI infrastructure. At the end: Expectations for the July Politburo meeting, news out of Iran that may threaten constructive strategic stability in advance of Xi's visit to D.C., a bad few weeks for Mercedes, and more clashes between the PRC and the Philippines.

A Classic Revisit of Erin's first episode of the Dumbbells!! First, Erin and Stanger talk about "pausegate" from episode 37!! Then Erin tells the guys her backstory in relation to fitness, and it's a great one, involving tater tot's and ranch, sweating to the oldies in costumes and outdoor Rocky-style workouts with her brother as her coach. Eugene and Erin connect on her most current fitness obsession, GoTribe, and last the group talks about the best kind of exercise to increase bone density.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The mates discuss Dario vs. Jensen's open vs. closed AI debate, OpenAI and Anthropic teaming up to lobby in DC, and Kimi K3's global expansion. Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends   Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360 Salim Ismail is the founder of Open ExO, a GP at Exponential Venture Capital/The Organizational Singularity Fund and a sought after global speaker and thought leader. Dave Blundin is the founder & GP of Link Ventures Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified – My companies: Apply to Dave's and my new fund:https://qr.diamandis.com/linkventureslanding     Go to Blitzy to book a free demo and start building today: https://qr.diamandis.com/blitzy   Your body is incredibly good at hiding disease. Schedule a call with Fountain Life to add healthy decades to your life, and to learn more about their Memberships: https://www.fountainlife.com/peter  _ Connect with Peter: X Instagram Substack Website Xprize A360 Connect with Dave: Web X LinkedIn Instagram TikTok Connect with Salim: LinkedIn X Join Salim's 10X Shift Subscribe to Salim's YouTube channel Exponential Venture Capital Connect with Alex Website LinkedIn X Email Substack  Spotify Threads Listen to MOONSHOTS: Apple YouTube – *Recorded on July 28, 2026 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

Dario Amodei said Anthropic never backed an open-weights ban, pitching mandatory safety tests instead as OpenAI and Google signed on. Altman headed to Washington, Korea's KOSPI cratered 11% on AI jitters, Apple launched Klarna leasing, and shipped 194 CVE fixes. Anthropic wants tests, not bans, as OpenAI and Google back open weights (The New Stack) Source: Sam Altman will meet with senior US officials, lawmakers, and economists in Washington, DC, this week to preview OpenAI's upcoming family of AI models (CNBC) South Korea's KOSPI drops 11%+, led by chip stocks, amid concerns over China's chipmaking progress and the AI spending boom; Samsung falls 11%+ and SK Hynix 12% (Bloomberg) Credit default swap prices tied to Oracle, SpaceX, Alphabet, Amazon, Meta, Broadcom, and Nvidia hit record highs as investors turn jittery over Big Tech's data center debt; Oracle's five-year CDS reached 215bps (FT) Apple launches Apple Upgrade, a new US leasing program in partnership with Klarna that replaces the iPhone Upgrade Program, starting at $17.99/month for iPhones (MacRumors) Apple releases 26.6 updates for iOS, macOS, iPadOS, watchOS, tvOS, and visionOS with a huge number of security fixes; macOS Tahoe 26.6 alone addresses 155 CVEs (9to5Mac) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

An AI agent slipped its sandbox and hacked a real company for days before anyone noticed and that single incident reframes everything else this week. Paul Roetzer and Mike Kaput connect it to Kimi K3's arrival at the frontier, the "Open Weights and American AI Leadership" letter, and the regulation fight taking shape in Washington. They break down open weight vs. open source in plain terms, explain why Anthropic is standing alone, and run rapid fire on Alphabet's Q2, the data center backlash, the jobs debate, and Claude Opus 5. AI-Pulse Survey: Fill out this week's AI-Pulse Survey here. Show Notes: Access the show notes and show links here Timestamps: 00:00:00 — Intro 00:07:31 — OpenAI Models Escape and Hack Hugging Face 00:27:49 — Kimi K3 and China's Open-Source Surge 00:50:52 — Open Weights and American AI Leadership 01:07:23 — Demis Calls for a Frontier AI Standards Body 01:12:17 — Google's AI-Fueled Q2 01:15:22 — White House Redirects Research Billions Toward AI 01:17:42 — The Data Center Backlash Goes National 01:21:17 — Why Hasn't AI Increased Unemployment? 01:27:15 — Which AI Tools Should You Use? 01:32:06 — AI Use Case Spotlight 01:36:01 — AI Product and Funding Updates This week's episode is brought to you by MAICON, our 6th annual Marketing AI Conference, happening in Cleveland, Oct. 13-15. The code POD100 saves $100 on all pass types. For more information on MAICON and to register for this year's conference, visit www.MAICON.ai. Visit our website Receive our weekly newsletter Join our community: Slack Community LinkedIn Twitter Instagram Facebook YouTube Looking for content and resources? Register for a free webinar Come to our next Marketing AI Conference Enroll in our AI Academy 

Chanelle shared about Five postures for receiving, stewarding, and testing prophetic words

The barbell squat isn't as dangerous as you've been told. It's one of the few things that keeps your bones from thinning after 40. But a popular podcast just ranked it the most unsafe exercise you can do, with the conventional deadlift and barbell row right behind it.I'm breaking down the 3 errors in their argument and the real reason people get hurt lifting. You'll also hear what muscle activation actually tells you about muscle growth, why their own co-host kept disagreeing with them on air, and what happened when researchers put postmenopausal women under a heavy barbell. Hit play if you want to keep squatting and deadlifting without worrying about your back.Enroll in Eat More Lift Heavy, the 26-week coached program where adults over 40 build the nutrition and training skills to preserve muscle, lose fat, and manage their physique for life.Wits & Weights is the evidence-based podcast for strength training over 40, body recomposition, fat loss over 40, metabolism recovery, and healthy aging. Hosted by Philip Pape, creator of Eat More Lift Heavy and Fitness Lab.Timestamps:0:00 - The 3 lifts called "unsafe" by a popular podcast 4:16 - The strongest case against the big lifts 5:56 - The first error in their argument 8:56 - What EMG activation really shows about muscle growth 11:40 - How to train the big lifts safely and for a long time 13:55 - What goes wrong when a lift starts hurting 16:58 - Barbell training next to running injury rates 20:15 - What heavy loading does for your bones after 40 24:55 - The question to ask before you swap a lift 27:15 - Choosing a gym that lets you lift properlyEpisode Resources:Chris Duffin on biomechanics, resilience, and training heavy after 40

How long is a podcast? Too. Check out: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠indeed.com/theconstant⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ now to start hiringVisit our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patreon here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. You too can get ad-free, early episodes, starting now!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠​​⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠BUY OUR MERCH, YOU FILTHY ANIMALS!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ​ ​The Constant is part of the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Airwave Media⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ podcast network.​​ ​​​​ ​​​Interested in advertising on The Constant? Email ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠sales@advertisecast.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to get on board! Learn more about your ad choices. Visit megaphone.fm/adchoices

Follow us on:Facebook: agapechurchsloInstagram: @agapechurchsloWebsite: agape.churchCAMPFIRE: LIFE & LEADERSHIP LESSONS FROM THE BIBLEWeek 10 | Campfire with Paul: Finishing Well with Strength and PurposeWho is Paul?Paul's life is one of the clearest pictures in Scripture of what it means to be completely transformed by the grace of God and fully committed to the purpose of God. Once a fierce persecutor of the Church, Paul encountered Jesus on the road to Damascus and was radically changed. From that moment on, his life was poured out in service to Christ—preaching the gospel, planting churches, raising leaders, enduring persecution, and writing much of the New Testament. Paul knew suffering, sacrifice, endurance, and opposition, but through it all, he stayed focused on one thing: finishing the race God had given him with faithfulness, strength, and purpose.Foundational Scripture for the SeriesHebrews 12:1 (ESV)“Therefore, since we are surrounded by so great a cloud of witnesses, let us also lay aside every weight, and sin which clings so closely, and let us run with endurance the race that is set before us.”[IF PAUL…] sat down at the campfire across from us, I think he would say something like this: “Starting strong may get attention, but finishing faithful is what honors God.”Today Paul speaks to anyone who's asking, “How do I stay faithful all the way to the end?” Our big idea today is this: A strong finish requires endurance, focus, and an unshakable commitment to God's purpose. Some of you in this room are not in a starting season… you are in a staying season. You are not just asking, “What is God calling me to do?” You are asking, “How do I stay faithful? How do I keep going? How do I not get distracted, discouraged, or depleted before I get where God is leading me?”Paul helps us answer that, so let's take a walk through his story.Point 1: FINISHING WELL DOES NOT HAPPEN BY ACCIDENT2 Timothy 4:6–7“For I am already being poured out as a drink offering, and the time of my departure has come. I have fought the good fight, I have finished the race, I have kept the faith.”Paul is near the end of his earthly life, and he's not speaking with regret over wasted years. He's speaking with the sober confidence of a man who has lived intentionally.Paul says, “I have fought.”… “I have finished.”… “I have kept.”The language Paul uses tells us that Paul didn't see himself as drifting into a strong finish.Paul's words indicate that he feels He intentionally lived toward a strong finish in life. Paul lived with the end in view. Strong finishes come from daily faithfulness. A strong finish is not built in one dramatic moment… A strong finish is built in a thousand, faithful daily decisions.Daily obedience… Daily repentance… Daily prayer… Daily endurance.Daily saying yes to God when the race feels way too long.This matters because our culture celebrates the spotlight, but rarely values the hidden faithfulness that made the moment possible.We want: The finish without the formation… and lasting fruit without the long obedience.[IF WE ARE GOING TO FINISH WELL…] we have live fully convinced that Today matters… fully convinced that Today's choices matter… fully convinced that Today's compromises matter… fully convinced that Today's disciplines matter.fully convinced that Today's yes matters… and fully convinced that Today's hidden faithfulness matters.Finishing well is not about perfection… Paul knew, at a very deep level, that he was the benefactor of God's amazing Grace!Finishing well is about direction… it's about intention[FINISHING WELL…] is about living with a clear sense that your life is not random and your race is not casual.Paul's life is challenging some people in this room to make a shift from living reactively to living intentionally.Galatians 6:9 — “And let us not grow weary of doing good…”Proverbs 4:25–27 speaks of fixing your gaze straight ahead and staying on the path.BIG TRUTH: A STRONG FINISH IS OFTEN THE RESULT OF LONG, QUIET FAITHFULNESS. POINT 2: YOU MUST STAY FOCUSED ON THE RIGHT GOALPhilippians 3:12–14“Not that I have already obtained this or am already perfect, but I press on… forgetting what lies behind and straining forward to what lies ahead, I press on toward the goal…”The phrase “toward the goal” matters… Paul had focus… Paul had a goal.[BIG KEY] [THIS IS SO IMPORTANT BECAUSE…] CLARITY OF YOUR PURPOSE PROTECTS YOUR ENDURANCE. One of the reasons many people do not finish well is they slowly lost focus.They got distracted… Distracted by success… by hurt… by people… by offense… by comparison… by comfort.Distracted by things that may not even be sinful in themselves, but are still heavy enough to slow your race.[THIS BRINGS US BACK…]  to Hebrews 12:1 where the writer says: “Let us also lay aside every weight, and sin which clings so closely…”Not just sin… Weights tooSome things do not have to be evil to be unhelpful.Some things do not have to be sinful to still become spiritually costly.Some things simply weaken your focus.Paul refused that kind of drift… He knew where he was going… and He knew what kind of life he wanted to live before God.Paul understood that if you keep the right goal in front of you, it becomes easier to say no to things that weaken our focus and distract us from the race.We don't need more options… we need sharper focus.We don't need more activity… we need greater clarity.You do not need to chase everything… You need to stay aligned with what God actually called you to.Luke 9:62 — “No one who puts his hand to the plow and looks back is fit for the kingdom of God.”1 Corinthians 9:26 — “So I do not run aimlessly…”BIG TRUTH: YOU CANNOT FINISH WELL IF YOU KEEP GIVING YOUR STRENGTH TO THINGS THAT WEAKEN YOUR FOCUS. POINT 3: STRENGTH FOR THE RACE COMES FROM JESUSOne of the great testimonies of Paul's life is that he endured a hard road with God-given strength.Paul faced Persecution… Imprisonment… Rejection… Beatings… Shipwreck… Misunderstanding… Pressure… Loss… And yet he kept going.Why and How? Because he was not drawing strength only from himself.Acts 20:24“But I do not account my life of any value nor as precious to myself, if only I may finish my course…”Paul endured hardship because his life was anchored in God's calling. Calling gives strength when the road gets hard.Trouble was not called to Laurel, Nece and I were! (Trouble Don't Last Always)We find ourselves exhausted because we're are trying to sustain a God-given assignment with self-generated strength.We were never designed to life the life of a Christ follower carried by our willpower alone.We need grace… We need the power of the Holy Spirit… and we need the strength of Jesus.When you know your life belongs to God, when you know your race matters, when you know your course is from Him, then even painful seasons are carried differently.And Paul knew where his strength came from… and WE DO TOO!Philippians 4:13 says, “I can do all things through him who strengthens me.”The Lord's word to you today is not simply, “Try harder.”It's: “Draw closer.” And “Receive My strength.” And “Let Me sustain what I called.”Isaiah 40:31 — “They who wait for the Lord shall renew their strength…”2 Corinthians 4:16 — “Though our outer self is wasting away, our inner self is being renewed day by day.”BIG TRUTH: CALLING SUSTAINED BY CHRIST CAN CARRY YOU THROUGH SEASONS YOUR OWN STRENGTH CANNOT. WAYS TO LIVE PAUL'S LESSON1. LIVE TODAY WITH THE FINISH LINE IN MINDDo not live only for the moment.Do not make decisions as though today is all that matters.Live today with the finish line in mind. Ask yourself: Is this helping me finish well?... Is this strengthening my race or weakening it?2. REFUSE DISTRACTIONS THAT WEAKEN YOUR FOCUSNot everything that calls for your attention deserves your energy.Refuse distractions that weaken your focus. Lay aside the weights… Simplify what needs to be simplified.Keep the goal in front of you.3. DRAW STRENGTH FROM GOD, NOT JUST YOUR OWN EFFORTYou are not called to run this race alone or in your own strength.Draw strength from God, not just your own effort. Pray… Wait on Him… Receive grace… Stay close to Jesus.Let His strength renew you.4. MEASURE SUCCESS BY FAITHFULNESS TO YOUR CALLINGDo not let culture define success for you.Do you look more like Jesus right now than you did this same time last year.CLOSINGPaul's life reminds us that the goal is not just to begin well… but to finish well.The goal is to keep running until the course God gave you is complete.Do not be afraid to pour your life out for what matters most… Because a strong finish requires endurance, focus, and an unshakable commitment to God's purpose. [BECAUSE…] what is poured out for God will never be wasted.RESPONSE / PRAYER MOMENTMaybe today your prayer sounds like this:“Lord, help me live with the finish line in mind.”“Lord, remove the distractions that are weakening my focus.”“Lord, renew my strength for the race.”“Lord, help me measure success by faithfulness, not fame.”

July 24, 2026: I unpack the story of OpenAI's AI model escaping its sandbox and hacking into Hugging Face during a cyber stress test. Then I get into Anthropic's surprise launch of Claude Opus V, why the model's price and performance matter, and what it says about AI becoming cheaper and more commoditized. Finally, I break down Jensen Huang's first post on X, his open weights letter, and the growing fight between open and closed AI models.

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

Mike and Michael Fitzgerald discuss current harvest and weight fundamentals and what it means for pork.

Another Classic Rewind with the wonderful Kulap Vilaysack!!!! Eugene and Kulap catch up on life and working out since the last time she guested on the pod! They also chat about the kickboxing session they just did together right before recording with her fantastic trainer, Kendra. Kulap also gives us a look into her healthy cookbook collection and update on that fatty liver. Plus, we weigh in on "hating exercise all together" and the best quick workouts.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Kyle Dake sits down with Shane Sparks to tell some Stories from the Resilite

Has years of dieting on and off damaged your metabolism? It that why fat loss feels so hard now?This is one of the most common fears I hear, and the short answer is almost certainly... no. So what is going on?Most of what feels like a broken metabolism is one of two things: either the normal discomfort of a deficit or the way your appetite pushes back as you lose fat, preventing a consistent deficit (where you feel like you're dieting but can never lose weight).If you look at weight loss studies, those reporting the most hunger and the strongest cravings tend to lose the least, implying that too much hunger isn't by definition "necessary" for a successful deficit.Learn about metabolic adaptation in a moderate diet, the "four-week wall" when most people quit a fat loss phase, and the difference between the discomfort you should "ride out" and red flags where you should take a break or change something about the diet (like the speed/aggressiveness).The people who finish are the ones who lose the fat!Enroll in Eat More Lift Heavy, the 26-week coached program where adults over 40 build the nutrition and training skills to preserve muscle, lose fat, and manage their physique for life.Wits & Weights is the evidence-based podcast for strength training over 40, fat loss over 40, metabolism recovery, body recomposition, and healthy aging. Hosted by Philip Pape, creator of Eat More Lift Heavy and Fitness Lab.Timestamps:0:00 - Is your metabolism damaged from years of dieting? 1:46 - The satiety diet question 4:10 - Why relaxing your deficit turns into serial quitting 8:31 - Why more hunger predicts less fat loss 12:24 - What metabolic adaptation means in a moderate deficit 19:29 - Hunger and discomfort you ride out vs. true red flags 23:27 - Why a slower cut is what gets you to the finish 25:33 - A straight answer on fat loss for women over 40 28:12 - Bonus: 30-second self-check for the toughest days in a deficit

Stanger was lucky to get the amazing Betsy Sodaro (Disjointed) back in The Weight Room. Stanger and Betsy are old friends and they get right in to a fun food conversation about all the crap they ate as kids. They also talk about what an athlete Betsy was growing up and how she got a red card pulled in her AFTER a game was over! The conversation comes back to food and exercise and Betsy talks about a new 10 minute routine she added to her program. Last, she and Ryan answer a listener question about how much body weight he can lose without losing his identity. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most of us treat fat loss as a subtraction problem. Eat less, move more, lose weight.Energy balance (calories in calories out) is definitely real, so that holds at the top level when it comes to pure weight loss.The trouble is that "eat less" is often done restrictively, often leads to even LESS protein, nutrient, and fiber intake, and often done at a rapid pace, any of which cause you to lose muscle, energy, get hungrier, and all the other things that ultimately make you quit (or gain he weight back).This episode goes deeper than calories to the power of... protein! Protein does not behave like the rest of your calories. It costs more to digest, though that effect is smaller than people claim. It is also the most filling thing you can eat, and I get into a study where people dropped hundreds of calories a day on their own, without being told to, just from eating more of it. That is the real lever, and why eating more can make you leaner even when you are not "trying" to push an aggressive calorie deficit.Then there is the claim that you can eat unlimited extra protein and still lose weight and body fat, so we'll examine that nutrient partitioniong claim. I also discuss daily protein targets, what to do if you're already leaner, and a 30-second way to think about the scale that explains how your body can change while the number stays flat.Try my favorite custom protein! Get 20% off your first custom protein blend at truenutrition.com/witsandweights with code WITSANDWEIGHTS. Build your own protein from the base up, pick your flavor and sweetener, add "bosts" like ashwaghanda, fiber, creatine, and more, and hit your daily target with something you're excited to drink!Wits & Weights is the evidence-based podcast for strength training over 40, body recomposition, fat loss over 40, metabolism recovery, and healthy aging. Hosted by Philip Pape, creator of Eat More Lift Heavy and Fitness Lab.Timestamps:0:00 - Why eating less keeps backfiring for fat loss 3:30 - Energy balance vs. differences between macros like protein 7:28 - Does protein burn more calories to consume vs. fats and carbs? 9:20 - Why protein is the most filling macro for fat loss 10:03 - How high protein creates a deficit through appetite 12:30 - What is the best protein supplement? 13:40 - Nutrient partitioning and mouse studies 16:00 - The myth of "free" protein calories in a surplus 20:30 - Your daily protein target for muscle and fat loss 22:35 - Why protein matters more for lean dieters 24:50 - Free macros guide for body composition 25:33 - Bonus: the two-envelope trick for body recompositionEpisode ResourcesFree Nutrition 101 for Body Composition (Fat Loss Macros) guide - how to find your maintenance and set your protein, carbs, and fats for any phase (fat loss, building muscle, or body recomp):https://witsandweights.com/macros

Feeling great isn't about chasing the next health trend. It's about mastering the fundamentals. It's about building a lifestyle where healthy choices become your normal. Because what you do every day will always matter more than what you do once in a while. I've been studying experts for years and despite coming from different backgrounds, they all point to the same idea... The healthiest people don't just make healthy choices... they've built lives where those choices have become automatic. Today, we're talking about the daily habits that help you feel better, think clearer, have more energy, and become the healthiest version of yourself. Health isn't one habit. It's an ecosystem. When you improve one area, every other area gets easier. Sleep makes workouts easier. Workouts improve mood. Better mood improves relationships. Better relationships reduce stress. Less stress improves sleep. It becomes a positive upward spiral.     Get Morning Sun Your body needs to know it's daytime. Morning sunlight helps: • Circadian rhythm • Better sleep that night • More stable cortisol • Better energy • Better mood • Hormone production Go outside. No sunglasses. Even 5–10 minutes can help.       Move Every Day Not because you're trying to lose weight. Because humans are designed to move. Walking is one of the most underrated longevity tools. Ideas: • 10,000+ steps • Walking meetings • Walking phone calls • Family walks after dinner Movement isn't punishment. It's medicine.     Lift Heavy Things   Muscle equals health. Benefits: • Blood sugar control • Longevity • Confidence • Bone density • Hormones • Energy Especially for women after 35.       Eat Foods Your Great-Grandparents Would Recognize Choose foods with minimal processing. • Protein first • Fruit • Quality dairy if tolerated • Eggs • Meat • Seafood • Vegetables • Healthy fats Less: • Packaged food • Sugary drinks • Ultra-processed snacks         Hydrate Like It Matters   Water is important. With electrolytes is even better.      Protect Your Brain • Sleep • Learning • Hobbies • Gratitude • Nature • Social connection • Limiting endless scrolling • Managing stress • Breathwork • Purpose What are you feeding your brain every day?       Build Real Relationships Good relationships. Family dinners. Friendships. Community. Belonging. Health isn't just physical.       Have Something You're Excited About Most adults accidentally quit playing. • Golf • Painting • Surfing • Gardening • Reading • Baseball • Cooking Adults need play too.     Practice Delayed Gratification Modern life trains us for: • DoorDash • Amazon • TikTok • Constant dopamine But confidence comes from keeping promises to yourself. Examples: "I'll work out before I eat." "I'll finish my work before Netflix." "I'll read before social media."       Build Your Environment Don't rely on motivation. Design your house. Ideas: Healthy snacks visible. Weights in the garage. Walking shoes by the door. Water bottle everywhere. Phone charger outside the bedroom. Sunlight first thing. Make healthy easier than unhealthy.     Teach Your Kids These Habits The greatest inheritance isn't money. It's health habits. Imagine raising kids who simply believe: • We move. • We eat protein. • We go outside. • We don't complain about exercise. • We eat dinner together. • We get enough sleep. • We speak kindly to ourselves.       My Daily Health Checklist   Every day I try to hit these: ☐ Morning sunlight ☐ Walk ☐ Lift weights ☐ Eat plenty of protein ☐ Fruit ☐ Hydrate ☐ Spend time outside ☐ Read ☐ Family meal ☐ Gratitude ☐ Quality sleep ☐ Laugh ☐ Move again after dinner     The goal isn't perfection. It's becoming the kind of person who naturally takes care of themselves. You don't need 100 new habits. You probably need 10 simple ones that you repeat for the next 20 years. Because when you feel better... You think better. You parent better. You lead better. You love better. And that's really what being awesome is all about.

How hard are you REALLY training? Is it enough to build muscle?Most lifters believe they are stopping a rep or two short of failure, when the real gap is far wider, and it often stalls muscle and strength.Learn about the research on proximity to failure and how badly we misjudge our own effort, including predicting reps to failure and how close to failure you need to train to build muscle. We get into why the common advice to leave reps in reserve backfires for people who picked up strength training over 40, what training to failure does and does not require, and how women and older lifters recover from hard sets.Plus, you'll learn a calibration drill and a mental trick to make your effort objective so your gym time pays off.Join Eat More Lift Heavy, the 26-week coached program where adults over 40 build the nutrition and training skills to preserve muscle, lose fat, and manage their physique for life.Wits & Weights is the evidence-based podcast for strength training over 40, body recomposition, fat loss over 40, metabolism recovery, and healthy aging. Hosted by Philip Pape, creator of Eat More Lift Heavy and Fitness Lab.Timestamps0:00 - Effort and proximity to failure 2:27 - Reps in reserve advice when strength training over 40 5:00 - Training hard vs. training volume 7:55 - Accuracy of estimating reps to failure 10:05 - Proximity to failure and muscle growth 12:20 - Progressive overload methods 13:24 - Calibration drill to calculate true failure 16:30 - Recalibrating effort over time 18:50 - Bar speed and effort signals 20:30 - Training to failure for older lifters 22:13 - Rep range goal setting Episode ResourcesProgressive Overload Guide (free) - every method for adding load over time, plus a rep range system that moves the weight up on schedule. Find this and more at https://witsandweights.com/free

Check Out BioVitalis Peptides: https://biovitalis.org/ (Use Promo Code: GSD10 for 10% off)   Check Out Jim Brown's Substack Blog: https://substack.com/@forj   *Disclaimer: This NOT medical advice. Please make sure to seek your own medical professional for medical advice.   Questions Asked/Discussed On This Podcast:   Question1 : I just bought DSIP and Pinealon to help with sleep. I'm trying to find a dosing recommendation for using the 2 in conjunction. Thanks guys, LOVE THE PODCAST!   Question 2: Thanks for the great information every week. What do u guys consider healthy fasted blood sugar levels when running HGH , and at what point do you consider coming off and resetting insulin sensitivity? Thank you   Question 3: What did you use gluathione for and when do you think its useful? I used to drink heavily and thinking of adding for liver health/repair   Question4 : Thank you both for all the valuable information. Watch you guys every Sunday. Couple of questions. 1st I just ordered some 5mg PDE 5 capsules and I want to use them to help with enlarged prostate. I read that it really helps improve the weak urinary flow by relaxing the prostate muscle. Doesn't actually shrink the prostate but helps with the flow. I also take finasteride to help shrink the prostate. Any knowledge on this. And question 2 interested in Kisspeptin myself but im on TRT and I heard I does nothing for you if your on TRT. Again their is always various of options. What do you guys think on this subject. Thanks again for all that you guys do. Keep up the great work.

You know him from You're The Worst and Drunk History. The Dumbbells were thrilled to have him in The a Weight Room and they started the show with a discussion about Christmas and the merits of incentivizing your kids good behavior. Then Allan gets into his story about growing up in the south and having a secret food cooler in his room. And finally how a change of his mental state actually changed his physical health. Last, the group answers some listener questions about a good band workout and some good snack ideas if you have a nut allergy. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

What exercise has helped your weight loss? Obviously Jo has very strong thoughts on this. Then we look at how to eat to battle the aging process. Plus, is Jo going to change her tune on cherries and why are we getting health advice from celebrities? Send us a voice note: 07468 286104 If you'd like to join our Diet Club, mark your weight loss with our exclusive certificates, get Extra Portions of this podcast and win CASH PRIZES go to patreon.com/noshameinagain or find us on the Patreon app. Hosted on Acast. See acast.com/privacy for more information.

If you've ever felt like your body stopped cooperating in your 40s — the recovery that takes days instead of hours, the muscle that seems to disappear no matter what you do, the fitness advice that just doesn't land anymore — this conversation is your explanation. Dr. Stephanie and Dr. Andy Galpin cover why fast-twitch muscle fiber loss is the real aging problem nobody's talking about, what perimenopause actually does to your recovery capacity (and why the answer isn't just "train less"), why fasted versus fed training is far more personal than the internet would have you believe, the look/feel/perform triad as a framework for ditching shame-based motivation for good, and why bone mineral density is the one number every woman over 40 should know — and most don't. This isn't about getting smaller. It's about building a body that works for the next 40 years.

How long can you stop training before your muscle starts to disappear?Most people take a week off, see that their muscles look a little deflated in the mirror, and assume the worst. The real answer is far more reassuring, and it is not what most lifters over 40 have learned.These questions all came from members inside my Eat More Lift Heavy community, people serious about strength training over 40. Here are the 6 questions I'm answering (shoutouts to EMLH members!):Tim E. - How long can you rest between reps before it counts as a separate set? (resting 2-3 seconds between squat reps, and 10 seconds between deadlifts)Ajana B. - When she eventually hits perimenopause, what effect will it have given she's already built a foundation of strength training and whole-food eating?Denise M. - Is creatine a benefit while doing Eat More Lift Heavy, and is there a better time to take it?Desiree F. and JC - Best exercises to grow glutes, how often, and how many sets/reps (Desiree), plus how to grow glutes without the quads taking over or getting too muscular (JC)JC - On a 0-10 scale, how important is eating protein right after lifting, and what stalls if you don't? (she now trains after dinner around 7:30-8pm)JC - How many days of not working out before you start losing your gains?Join Eat More Lift Heavy, the 26-week coached program where adults over 40 build the nutrition and training skills to preserve muscle, lose fat, and manage their physique for life:https://eatmoreliftheavy.comWits & Weights is the evidence-based podcast for strength training over 40, body recomposition, muscle and longevity, fat loss over 40, and healthy aging. Hosted by Philip Pape, creator of Eat More Lift Heavy and Fitness Lab.Timestamps0:00 - 6 questions being answered today2:22 - Resting between reps (when does a set count as 2 sets?) 7:04 - What perimenopause does to muscle and bone, and what protects it 12:17 - Why the scale jumps when you start creatine 16:19 - Building a strong physique without bulking up 19:17 - Bigger glutes without bigger quads (muscle specialization) 27:47 - Does protein after a workout (the anabolic window) matter as much as you think? 31:21 - How fast you really lose muscle, and the minimum to keep itEpisode ResourcesAsk a question for the podcast here: https://witsandweights.com/question

Naval with three founders who are living in the future: Garry Tan (Y Combinator), Daniel Francis (Abel Police), and Farbood Nivi (A-LIST).  00:00 Guest Intros 02:35 Live in the Future 03:58 Will AI Outsmart us? 07:43 In the Anthropic Breadline 09:59 The Tech Genie Is Out 12:33 We Invested in COVID?! 14:25 Good Writing Is Novelty 18:50 Living Like It's 2028 24:32 Truth dot ai 30:18 Does China have the Weights? 35:38 Everyone has AI Anxiety 39:32 Have Your Agent Talk to My Agent 42:01 What if Open Source takes the Lead? 44:03 The Sun is Setting on Google 48:00 Ride the AGI 50:46 Will There be Startups? 54:05 Defending Taiwan 1:00:05 The California Empire 1:01:26 If the U.S. Falls 1:03:11 Universal Basic Robot 1:06:01 Humans as AI Handlers

Become a supporter of this podcast: https://www.spreaker.com/podcast/the-maximus-podcast--3284949/support.

Most people start lifting to look better, and they treat the health payoff as a nice bonus. There's nothing wrong with that, but the mortality research suggests that the frame is probably backward.Your muscle and strength are the keys to longevity, with your physique and how you look a nice side effect.The evidence here is large, in some studies hundreds of thousands of people. One simple measure of strength predicted death better than blood pressure.I look at what that means for the two outcomes most of us worry about after 40, cancer and heart disease, including a detail about how cancer treatment gets dosed that very few people know. I also get into why muscle behaves like an organ that helps protect your heart, not just things that pop in your shirt :)You have probably heard that being weak is worse than smoking. Does that hold up? Then I get to the part that should take some of the pressure off, which is how little training it takes to collect most of the benefit and why it is never too late to start.Try Fitness Lab, the AI coaching app that reads your data, coaches you on your patterns, and keeps you consistent for adults over 40 who want to lose fat and build strength. Summer special is 20% off through July 3.https://witsandweights.com/appWits & Weights is the evidence-based podcast for strength training over 40, body recomposition, muscle and longevity, fat loss over 40, and healthy aging. Hosted by Philip Pape, creator of Eat More Lift Heavy and Fitness Lab.Timestamps0:00 - Why lifting for looks gets the benefit backward 5:36 - Muscle is medicine... looking good is the bonus 10:06 - The strength measure that beats blood pressure 11:30 - Is being weak really worse than smoking? 14:46 - Muscle, cancer survival, and how treatment is dosed 17:23 - Your largest metabolic organ and your heart 20:14 - A useful longevity tool 22:44 - The surprisingly small dose that protects your life 26:01 - Why it is never too late to build muscle 30:06 - A 30-second strength test you can do todayEpisode ResourcesTry Fitness Lab (20% off through July 3)Muscle-Building Nutrition Blueprint (free guide)

The Dumbbells welcome Michael Cassady of Netflix's Love to The Weight Room. He tells the DBs about how he was an indoor kid growing up and they make fun of him for studying to get a trumpet degree in college. Cassady describes all the different stages of exercise and nutrition he's hone through over the years with the most recent being intermittent fasting. Finally the group helps a listener who can't seem to lose any body fat even though he's seemingly doing everything right.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Section 230 takes center stage as Olivier Sylvain argues it's time to confront Big Tech's legal shield, sparking a fierce debate on whether Internet giants should be liable for platform harms or if reform risks choking small innovators. Trump says he no longer views Anthropic as a national security threat after G7 meeting with CEO The White House Is Making Up Its Rules for AI in Real Time N.S.A. Lost Access to Powerful A.I. Model Amid Anthropic Dispute Early Users of Anthropic Mythos Still Have Access After US Order Dangerous AI models are coming no matter what Nobel laureate John Jumper is leaving Google DeepMind for Anthropic after nearly nine years Google's Gemini co-lead Noam Shazeer is leaving for OpenAI Identity verification on Claude Anthropic rolls out Claude Tag, your new agentic AI coworker in Slack Google preps Pixel 'Audio Memory' that ambiently tracks your 'important conversations,' like AI notetaker pins Norway imposes broad restrictions on AI for elementary school kids YouTube settles upcoming bellwether trial over social media's psychological harms to kids OpenAI and Broadcom unveil LLM-optimized inference chip Luca Guadagnino's Nearly Finished Sam Altman Movie 'Artificial' Dropped by Amazon After OpenAI Partnership OpenAI Burned $3.7 Billion in First Three Months of 2026 OpenAI Launches Full-Scale Effort to Patch Open-Source Bugs as It Takes on Anthropic's Mythos Getty Images Soars 200% in Early Trading After OpenAI Deal Meta launches cheaper smart glasses without Ray-Ban We're Partnering With EssilorLuxottica to Launch Meta Glasses Evan Spiegel says Snap can't fulfill its mission without its new AR glasses AI data centers just got a government-mandated fast lane to the grid China tightens indium phosphide checks as AI demand climbs AI Engineer Claims to Have Cracked Linear A Midjourney goes from generating cat images to full-body ultrasound scans A Princeton grad built a $30 million AI detection business. Now he's selling it to Superhuman. Estonia intends to recognize AI agents with digital IDs Big Tech Is a Thief and a Liar, Says New York Times Publisher AI Economics for Dummies We Have to Stop Freaking Out About A.I. In the Weights is your new AI-centric vanity search | TechCrunch UK TV to be turned off Computer History Museum's AI Archive Airport Dad Hosts: Leo Laporte and Jeff Jarvis Guest: Olivier Sylvain Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: gusto.com/machines XBOW.com webroot.com/twit

Toni Charline is in The Weight Room. You know her from being a UCB performer and her Funny or Die show, Munchies. The Dumbbells find out about how she grew up as a chubby kid who played sports and how she became a vegetarian at age 4! Toni also talks about being a vegan now and her current exercise program (small group personal training). The Dumbbells give some more info on The March Nutritional Challenge and the current leaders are listed on air. Last, the DBs and Toni offer up their thoughts on added "a little bit of bad stuff" to an otherwise healthy meal.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.