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We talk a lot about coding and AI and a little less about headlines today. Runner-up: SpaceX is targeting a June/July 2026 IPO at a reported ~$1.75 trillion valuation, which would be the largest public listing in history. The float follows SpaceX's ~$250B all-stock acquisition of xAI in February, folding Starlink, launch, and frontier AI into one entity.Runner-up: Amazon's custom AI chip business — Graviton, Trainium, and Nitro — hit a $20B annual run rate with triple-digit YoY growth. OpenAI committed to about 2 GW of Trainium capacity, Anthropic is scaling to 5 GW, and analysts project a standalone Trainium could become a $50B business.Runner-up: NVIDIA topped a $5.5 trillion market cap and is deploying more than $45B across the AI supply chain, extending its position from chip supplier to investor and customer across the stack.Runner-up: Apple posted record fiscal Q2 2026 revenue of $111.2B, up 17% YoY, with diluted EPS of $2.01. iPhone sales rose 22% and Services climbed about 16% to $26.65B, and the company guided Q3 growth of 14%-17%.Runner-up: AI venture funding shattered records with $297B in Q1 2026, including $35B raised in a single week.If you want a prize, send us a DM:instagram.com/rickerandbontiktok.com/@rickerandbontiktok.com/@rickerandbonyoutube.com/@rickerandbon

Anthropic dropped Opus 4.8 with dynamic workflows for Claude Code and raised $65B at a $965B valuation, overtaking OpenAI. Blue Origin's New Glenn exploded during testing, Amazon killed its AI usage leaderboard, and an AI startup offers free home cleaning for training data. Anthropic launches Opus 4.8, saying it's "more likely to flag uncertainties about its work and less likely to make unsupported claims", at the same price as 4.7 (TechCrunch) Anthropic raised a $65B Series H at a $965B post-money valuation, overtaking OpenAI's $852B valuation, and says its revenue run rate crossed $47B this month (NYT) Blue Origin's New Glenn rocket, which exploded during testing on Thursday, was set to ferry 48 Amazon Leo satellites on Monday; Amazon paid Blue Origin $2.7B (FT) Sources: Amazon has shut down an internal leaderboard that tracked employees' use of AI tools after workers tried to boost their scores with needless tasks (FT) AI startup Shift launches a free home cleaning service in NYC to record first-person video with a camera-equipped cap and use it to train robots (The Verge) Longreads Simon Willison on how coding agents gave Anthropic and OpenAI real product-market fit, burning $1,000+/month in tokens per power user and changing enterprise pricing (Simon Willison) Kirkland & Ellis, the world's highest-grossing law firm, is setting aside $500M to build its own AI platform rather than rely on tools available to its rivals (FT) Learn more about your ad choices. Visit megaphone.fm/adchoices

This is a recap of the top 10 posts on Hacker News on May 28, 2026. This podcast was generated by wondercraft.ai (00:30): Claude Opus 4.8Original post: https://news.ycombinator.com/item?id=48311647&utm_source=wondercraft_ai(01:58): Can we have the day off?Original post: https://news.ycombinator.com/item?id=48302745&utm_source=wondercraft_ai(03:27): Bricks and Minifigs Stole a Man's $200k Lego CollectionOriginal post: https://news.ycombinator.com/item?id=48314136&utm_source=wondercraft_ai(04:56): Disagreement among frontier LLMs on real-world fact-checksOriginal post: https://news.ycombinator.com/item?id=48307887&utm_source=wondercraft_ai(06:25): Show HN: Hallucinate – Massively Multiplayer Online RaveOriginal post: https://news.ycombinator.com/item?id=48304260&utm_source=wondercraft_ai(07:54): Citing 'severe' math deficits, UC faculty demand a return to SAT tests for STEMOriginal post: https://news.ycombinator.com/item?id=48309233&utm_source=wondercraft_ai(09:23): AMD pulls a bait-and-switch on Linux users with Vivado licensing changesOriginal post: https://news.ycombinator.com/item?id=48307231&utm_source=wondercraft_ai(10:52): EU fines Temu €200M for allowing sale of illegal productsOriginal post: https://news.ycombinator.com/item?id=48309302&utm_source=wondercraft_ai(12:21): Anthropic raises $65B in Series H funding at $965B post-money valuationOriginal post: https://news.ycombinator.com/item?id=48313048&utm_source=wondercraft_ai(13:50): Google employee charged with $1M Polymarket insider trading bet on search termOriginal post: https://news.ycombinator.com/item?id=48302822&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Market update for Friday May 28, 2026Check out the Public app for incredible investing tools and to support the show (LINK)Follow us on Instagram (@TheRundownDaily) for bonus content and instant reactions.In today's episode, Zaid covers:Stocks close at record highs as Wall Street bets on an Iran peace dealDell's stock surges after a monster quarter, with AI server sales exploding 757%Anthropic raises $65B and passes OpenAI to become the world's most valuable AI startupMovers: NetApp rips on earnings, Gap gets crushed after cutting guidanceFun Fact: US Treasury to release new $250 bill with Trump's face on it (maybe)

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DOD – Disrupter Disrupters China markets reopening after Lunar New Year Mexico Cartel Wars Refunds requested for the illegal tariffs PLUS we are now on Spotify and Amazon Music/Podcasts! Click HERE for Show Notes and Links DHUnplugged is now streaming live - with listener chat. Click on link on the right sidebar. Love the Show? Then how about a Donation? Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter Warm-Up - The CTP for Caterpillar announced - DOD - Disrupter Disrupters - China markets reopening after Lunar New Year - Mexico Cartel Wars (Jalisco) Markets - Mortgage Rates - looking good! - Tariffs found illegal - that is not stopping anything - Refunds requested for the illegal tariffs - Monday's big drop and AI taking a bite out of stock prices Tariffs - First, who actually knows what is going on. 100% chaos - Supreme court ruled illegal (6-3) - 10% flat across all countries immediately added - Wait a day and make that 15% - FedEx seeks refund for illegal IEEPA tariffs imposed by Trump after the Supreme Court ruled Trump's tariffs exceeded authority - Numerous lawsuits expected for IEEPA tariff refunds - Apple has spent more than $3 billion on tariffs since President Donald Trump enacted his trade policies. What about that? (HOW TO FIGURE OUT WHO GETS THE REFUND) --- Estimate that $175B tariffs have been collected alreay - A group of 22 U.S. Senate Democrats on Monday introduced legislation that would require President Donald Trump's administration to fully refund within 180 days all of the revenue, with interest, collected from tariffs struck down by the U.S. Supreme Court. - The legislation would require the Customs and Border Protection agency, which collects tariffs at U.S. ports of entry, to prioritize small businesses. - The U.S. Customs and Border Protection agency said it will halt collections of tariffs imposed under the International Emergency Economic Powers Act at 12:01 a.m. EST (0501 GMT) on Tuesday Stop The Presses - After years of JCD's rants....... - Apple will soon introduce MacBooks with touch screens - Apple Inc.'s initial touch Macs will have the Dynamic Island at the center top of the display and OLED screen technology. The new MacBook Pro models will have a refreshed, dynamic user interface that can shift between being optimized for touch or point-and-click input. Europe Reacts - "The current situation is not conducive to delivering 'fair, balanced, and mutually beneficial' transatlantic trade and investment, as agreed to by both sides" in the joint statement setting out the terms of last year's trade agreement, the Commission said. "A deal is a deal." - All active discussions are halted on any USA/Europe trade deal The Potential Winners - Brazil and China may be the winners here - Chinese President Xi Jinping has a boost in bargaining power after the US Supreme Court invalidated Donald Trump's broad emergency tariffs, a key point of leverage over China. - The removal of tariff threats will make it harder for Trump to press Xi for larger purchases of certain products and leaves him without a key weapon to strike back if Chinese negotiators make fresh demands. - Xi's team will likely push harder for access to advanced semiconductors, the removal of trade restrictions on Chinese companies, and reduced US support for self-ruled Taiwan, according to Wu Xinbo, director at Fudan University's Center for American Studies. NVDA Earnings - NVIDIA drops its fiscal Q4 2026 (ended Jan 2025) results tomorrow—another make-or-break moment for the AI trade. - The bar is sky-high after years of blowout beats, but whispers of "peak AI" and slowing growth momentum have investors on edge. --- Consensus Expectations : ----Revenue: ~$65.6–$66.1 billion (up ~67–68% YoY from last year's ~$39B; guided $65B ±2% in prior report) ------EPS (adjusted/non-GAAP): ~$1.50–$1.53 (up ~70–72% YoY from $0.89). --------Gross margins: Targeting ~75% non-GAAP (holding strong despite supply chain noise). -----------Key driver: Data Center segment expected to crush ~$58–$60B, fueled by Blackwell ramp and hyperscaler spend. Home Depot Earnings - The home-improvement retailer gained 2.7% after posting fourth-quarter adjusted earnings of $2.72 per share on revenues of $38.20 billion. - That exceeded the per-share earnings of $2.54 on revenues of $38.12 billion expected by analysts polled by LSEG. AMD News - The semiconductor maker rose about 11% after it inked a multiyear deal with Meta to lend up to 6 gigawatts of its graphics processing units to artificial intelligence data centers. - The cost of the deal is unclear, but the companies' agreement includes a a performance-based warrant that could amount to up to 160 million of AMD shares, according to a statement dated Tuesday. - Meta has committed to deploying up to 6 gigawatts (GW) of AMD's Instinct GPUs (high-end graphics processing units optimized for AI workloads) to power its massive AI data centers. - Analysts estimate the GPU portion alone could be worth $60–$100+ billion over 5+ years Mortgage Rates - The average rate on the popular 30-year fixed mortgage fell to 5.99% on Monday, according to Mortgage News Daily, matching its lowest levels since 2022. - Last year at this time the rate was 6.89%. - A buyer putting 20% down on the median priced home, about $400,000 according to the National Association of Realtors, would have a monthly payment of $1,916 for the principal and interest. One year ago, that payment would have been $2,105, a difference of $189. Life Insurance Record - Manulife Financial Corp. sold a $300 million life insurance policy in Singapore, topping what Guinness World Records certified as the most valuable policy ever issued. - The policy surpasses the previous record of $250 million, set by HSBC Life in Hong Kong in 2024. Manulife said in a statement Tuesday that the deal reflects growing demand from ultra-wealthy clients to preserve their assets. - In Singapore over the past 12 months, Manulife has issued 25 individual policies each worth more than $50 million. Bitcoin Rout - Gemini said it was axing as much as a quarter of its staff and exiting the UK, European Union and Australia entirely. - This week, it parted with its chief operating officer, chief financial officer and chief legal officer, all in a single day. - Its stock has fallen more than 80% from a post-listing high last year, collapsing its market value from a peak of almost $4 billion to under $700 million. Over the Greenland - USA sending a "hospital ship" over - Trump's post on the ship came hours after Denmark's Joint Arctic Command said it had evacuated a crew member who required urgent medical treatment from a U.S. submarine in Greenlandic waters, seven nautical miles outside of Greenland's capital, Nuuk. - Greenland said thanks but no thanks So Long! - U.S. investors are pulling money out of their own stock market at the fastest pace in at least 16 years as Big Tech returns fade and better-performing overseas markets look more attractive. - In the last six months, U.S.-domiciled investors have pulled some $75 billion from U.S. equity products, with $52 billion flowing out since the start of 2026 alone, the most in the first eight weeks of the year since at least 2010 AI Disruption - DOD (Disruption of Disrupters) - CrowdStrike -9.8% and other cybersecurity names under heavy pressure again as AI disruption fears build following Anthropic's Claude Code release - - Cybersecurity stocks are under broad pressure today, extending recent weakness following Friday's launch of Claude Code Security by Anthropic. Claude Code Security scans codebases for vulnerabilities and suggests software patches for human review, fueling a narrative that AI platforms may be moving more quickly into parts of the security workflow than investors had previously expected. For cybersecurity, that raises concern around the forward demand outlook and competitive positioning, particularly in areas tied to application security, cloud security, identity workflows, and security operations automation, where AI-native tools could start to narrow perceived differentiation. - The move suggests investors are still sorting through the implications for product overlap, pricing power, and competitive positioning as AI capabilities evolve quickly. - IBM shares dropping toward lows of the session; attributed to news that Claude can automate cobol modernization COBOL (Common Business-Oriented Language) is a high-level, English-like programming language created in 1959 for business, finance, and administrative data processing. It is renowned for its verbosity, readability, and reliability, processing massive amounts of transactions on mainframe systems,, notes NetCom Learning and IBM. Despite being decades old, it remains critical in banking, insurance, and government sectors. - It is estimated that 70-80% of the world's business transactions are processed by COBOL Grok's Prediction about Future of OpenAi/ChatGPT Scenario Likelihood (My Estimate) Key Factors Outcome for OpenAI/ChatGPT Thriving Leader Medium (40%) Sustained breakthroughs, partnerships (e.g., Microsoft), regulatory wins OpenAI as AI giant; ChatGPT as ecosystem hub for agents/robots Evolved Survivor High (50%) Adaptation to agents/hardware; mergers Exists but rebranded; ChatGPT integrated into daily life tools Decline/Acquisition Low (10%) Overcompetition, funding collapse Absorbed or legacy; ChatGPT commoditized or obsolete Quick check on Europe Shares - European company earnings growth is picking up this reporting season against a tentatively improving economic backdrop, but wary investors are demanding more than solid results to justify sky-high valuations. - Companies representing 57% of Europe's market capitalization have reported so far, achieving average earnings growth of 3.9% in the fourth quarter, ahead of estimates for a final result of a contraction of 1.1% --- That is a big differential.... +3.9 vs -1.1 Iran Talks - News over the weekend that Iran will look to discuss a variety of items and potentially get a deal.... energy, mining and aircraft - Best guess: Iran will string us along like Russia is doing and we will say we have some kind of bogus deal. --- There is some talk of US "going in" as we are building military presence. Supposedly there are some saying it could be a multi-week incursion. - What is the plan - Regime change? What is this? - A divided Supreme Court on Tuesday ruled that Americans can't sue the U.S. Postal Service, even when employees deliberately refuse to deliver mail. - By a 5-4 vote, the justices ruled against a Texas landlord, Lebene Konan, who alleges her mail was intentionally withheld for two years. Konan, who is Black, claims racial prejudice played a role in postal employees' actions. - Justice Clarence Thomas, writing for a majority of five conservative justices, said the federal law that generally shields the Postal Service from lawsuits over missing, lost and undelivered mail includes “the intentional nondelivery of mail.” - So can ballots just be thrown in garbage for mail-ins for one party that will throw out another party's?     Love the Show? Then how about a Donation? HE CLOSEST TO THE PIN for CATERPILLAR Winners will be getting great stuff like the new "OFFICIAL" DHUnplugged Shirt!     FED AND CRYPTO LIMERICKS   See this week's stock picks HERE Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter

You can't scale AI on fragmented data. In this episode of Technovation, Peter High speaks with Ogi Redzic, Chief Digital Officer of Caterpillar, about the foundational platform transformation that made rapid AI innovation possible across a $65B industrial enterprise. Ogi shares how retiring legacy systems, consolidating data into the Helios cloud platform, and establishing trusted data pipelines enabled CAT Digital to launch an enterprise AI assistant in just 10 months. Key topics include: Building Helios to process millions of data pipelines daily Turning unplanned downtime into predictive maintenance at scale Scaling $5B in industrial e-commerce Partnering with NVIDIA on edge AI and digital twins Aligning digital teams to measurable business outcomes

In this episode, we sit down with Bob Paulison, Executive Director of the Coastal Bend Industry Association and longtime leader in the Coastal Bend's economic and maritime community.We dive into: • Corpus Christi's rising median income and wage growth • The $65B+ wave of industrial investment that reshaped the region • Bob's Coast Guard journey — from Hawaii to the Coastal Bend • The history of the Coast Guard's presence in South Texas • Maritime commerce, safety, and how our port drives the economy • The future of water, desalination, and why supply reliability matters • How community awareness and advocacy shape our region's successWhether you're a business owner, a community leader, or someone who cares about the Coastal Bend's future, this conversation breaks down where we've been, and what's ahead.

This week Chris speaks with private equity executive, entrepreneur, and Colorado native, Andrew Schremp, to discuss his journey across banking, entrepreneurship and investing. He currently serves on the Board of Directors of Colorado PERA providing fiduciary oversight of $65B+ in assets for nearly 700,000 beneficiaries. Formerly, he was the Director at Bow River Capital and prior to that, he was the Founder and CEO of Health Sqyre, Inc., a health tech company acquired by The VGM Group. In this episode, Andrew's shares his unique perspective on the evolution of the Colorado tech ecosystem, valuable insights for navigating the startup scene, plus examples of his biggest lesson on the the importance of building genuine relationshipsListen now on: Amazon Music (Alexa) | Spotify | Apple Podcasts or wherever you get podcasts!Connect with hosts Adam and Chris and the Range VC team on LinkedIn https://www.linkedin.com/company/range-ventures/Check out more about what we're up to at Range.vc See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This week Kyle Samani joins the show to discuss the successful $1.65B raise for Forward Industries. We deep dive into how Forward Industries plan to converge between traditional markets & DeFi, the ultimate vision for the Solana treasury company, the difference between SOL & BTC DATs & why stablecoins are crypto's iPhone moment. Enjoy! Although our guest this week is an Investment Partner of a registered investment adviser, nothing in this podcast should be considered an offer of Multicoin's investment advisory services or should otherwise be confused for investment, tax, legal or other financial advice. -- Follow Kyle: https://x.com/KyleSamani Follow Jason: https://x.com/JasonYanowitz Follow Empire: https://twitter.com/theempirepod -- Katana is a DeFi-first chain built for deep liquidity and real yield, by redirecting chain revenue back to active DeFi users. The 1 billion KAT campaign is live. Bridge and deposit directly into vaults in one simple click and start earning immediately on your ETH, BTC, USDC, and more.  Go to app.katana.network to check it out.  -- Is your treasury losing value to inflation? Learn how to make digital assets like ETH and SOL productive with uncorrelated, protocol-driven staking rewards.  A new report from Liquid Collective and EigenCloud outlines a practical guide for CFOs to integrate institutional-grade staking and restaking.  Read The Productive Treasury Report: https://liquidcollective.io/corporate-treasury-staking/  -- Allora is the new AI standard for DeFi platforms and agents to achieve sharper, faster, and more reliable financial systems than any single language model can offer. Instead of choosing and managing models, users state their goal, and the system delivers the best result.  To learn more about Allora Network, visit https://www.allora.network/ and https://x.com/AlloraNetwork. -- Crypto's premiere institutional conference returns to London in October 2025. Use code 0x100 for £100 off at checkout: https://blockworks.co/event/digital-asset-summit-2025-london -- Blockworks is hiring a Research Data Analyst. If you live in SQL and love making sense of onchain chaos, apply today: https://jobs.ashbyhq.com/Blockworks?utm_source=EQPb2dAAxr -- Subscribe on YouTube: https://bit.ly/3foDS38 Subscribe on Apple: https://apple.co/3SNhUEt Subscribe on Spotify: https://spoti.fi/3NlP1hA Get top market insights and the latest in crypto news. Subscribe to Blockworks Daily Newsletter: https://blockworks.co/newsletter/ Join the 0xResearch Telegram group: https://t.me/+UFFz4z3qyrhhMDYx -- Timestamps: (0:00) Introduction (1:25) The Ultimate Vision For Forward Industries (8:54) Raising $1.65B For Forward Industries (10:52) Partnering With Jump & Galaxy (16:31) Ads (Katana & EigenCloud) (17:28) Integrating DATs Into DeFi (25:22) M&A Strategies (30:00) BTC vs SOL DATs (32:28) Ads (Katana & EigenCloud) (33:36) How Will SOL ETFs Impact Solana DATs? (35:45) What's Next For Forward Industries? (41:55) Lessons Learned From Hyperliquid's USDH (46:36) Allora Ad (47:08) The Internet Capital Markets Thesis (53:58) Stablecoins Are Crypto's iPhone Moment -- Check out Blockworks Research today! Research, data, governance, tokenomics, and models – now, all in one place Blockworks Research: https://www.blockworksresearch.com/ Free Daily Newsletter: https://blockworks.co/newsletter -- Disclaimer: Nothing said on Empire is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only, and any views expressed by anyone on the show are solely our opinions, not financial advice. Santiago, Jason, and our guests may hold positions in the companies, funds, or projects discussed.

This week Kyle Samani joins the show to discuss the successful $1.65B raise for Forward Industries. We deep dive into how Forward Industries plan to converge between traditional markets & DeFi, the ultimate vision for the Solana treasury company, the difference between SOL & BTC DATs & why stablecoins are crypto's iPhone moment. Enjoy! Although our guest this week is an Investment Partner of a registered investment adviser, nothing in this podcast should be considered an offer of Multicoin's investment advisory services or should otherwise be confused for investment, tax, legal or other financial advice.--Follow Kyle: https://x.com/KyleSamaniFollow Jason: https://x.com/JasonYanowitzFollow Empire: https://twitter.com/theempirepod--Join the Empire Telegram: https://t.me/+CaCYvTOB4Eg1OWJhStart your day with crypto news, analysis and data from David Canellis. Subscribe to the Empire newsletter: https://blockworks.co/newsletter/empire?utm_source=podcasts--Welcome to Get Real — Web3's first-ever campaign rewarding you for creating real-world value. Connect your devices to real-world apps on peaq and earn rewards for: Measuring noise pollution Providing compute Mapping the word And more Total prize pool: 5% of $PEAQ's initial supply. Get Real is relaunching soon — follow peaq on X and get ready.--Is your treasury losing value to inflation? Learn how to make digital assets like ETH and SOL productive with uncorrelated, protocol-driven staking rewards. A new report from Liquid Collective and EigenCloud outlines a practical guide for CFOs to integrate institutional-grade staking and restaking. Read The Productive Treasury Report: https://liquidcollective.io/corporate-treasury-staking/--Timestamps: --Disclaimer: Nothing said on Empire is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only, and any views expressed by anyone on the show are solely our opinions, not financial advice. Santiago, Jason, and our guests may hold positions in the companies, funds, or projects discussed.

In this power-packed edition of WOYM, Scott goes live from the heart of the Bakken—Watford City—and dives into everything from the future of North Dakota's energy to fiery city budget talks. He's joined by powerhouse guests like Fargo Mayor Tim Mahoney, Congresswoman Michelle Fischbach, and petroleum engineer Joel Brown. With heartfelt hometown shoutouts, a $10K grocery giveaway, and a deep dive into PEMF therapy, this episode is equal parts grassroots, policy, and good ol' Midwest pride.

As we get to the mid-point of 2025, let's take a look at where the cloud is - what's doing well, what's going through some changes, and what might be in store for the rest of 2025. SHOW: 938SHOW TRANSCRIPT: The Cloudcast #938 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:[FCTR] Try FCTR.io (that's F-C-T-R dot io) free for 60 days. Modern security demands modern solutions. Check out Fctr's Tako AI, the first AI agent for Okta, on their website[VASION] Vasion Print eliminates the need for print servers by enabling secure, cloud-based printing from any device, anywhere. Get a custom demo to see the difference for yourself.SHOW NOTES:Digital Sovereignty concerns, investments and new laws outside the US Growth of regional clouds? Uncertainty in Azure, AWS - employee layoffs, review cycles, focus on non-AI projects/featuresEast Meets West 2025 (keynote presentation) - See slide 27 (GPU share vs Cloud revenue share)Revenues: AWS ($116B run rate); Azure ($107.2B), GCP ($49B), Oracle Cloud ($12B IaaS, $16.8B SaaS), CoreWeave ($1B, since 2022) – OpenAI ($12-13B in 2025)NVIDIA ($130B)Massive Data Center build outs - Stargate ($500B), Meta ($60-65B), Grok/X ($10B)AWS ($100B), Azure ($80B), Saudi Arabia (up to $100B)FEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

Hey folks, this is Alex, finally back home! This week was full of crazy AI news, both model related but also shifts in the AI landscape and big companies, with Zuck going all in on scale & execu-hiring Alex Wang for a crazy $14B dollars. OpenAI meanwhile, maybe received a new shipment of GPUs? Otherwise, it's hard to explain how they have dropped the o3 price by 80%, while also shipping o3-pro (in chat and API). Apple was also featured in today's episode, but more so for the lack of AI news, completely delaying the “very personalized private Siri powered by Apple Intelligence” during WWDC25 this week. We had 2 guests on the show this week, Stefania Druga and Eric Provencher (who builds RepoPrompt). Stefania helped me cover the AI Engineer conference we all went to last week, and shared some cool Science CoPilot stuff she's working on, while Eric is the GOTO guy for O3-pro helped us understand what this model is great for! As always, TL;DR and show notes at the bottom, video for those who prefer watching is attached below, let's dive in! Big Companies LLMs & APIsLet's start with big companies, because the landscape has shifted, new top reasoner models dropped and some huge companies didn't deliver this week! Zuck goes all in on SuperIntelligence - Meta's $14B stake in ScaleAI and Alex WangThis may be the most consequential piece of AI news today. Fresh from the dissapointing results of LLama 4, reports of top researchers leaving the Llama team, many have decided to exclude Meta from the AI race. We have a saying at ThursdAI, don't bet against Zuck! Zuck decided to spend a lot of money (nearly 20% of their reported $65B investment in AI infrastructure) to get a 49% stake in Scale AI and bring Alex Wang it's (now former) CEO to lead the new Superintelligence team at Meta. For folks who are not familiar with Scale, it's a massive company in providing human annotated data services to all the big AI labs, Google, OpenAI, Microsoft, Anthropic.. all of them really. Alex Wang, is the youngest self made billionaire because of it, and now Zuck not only has access to all their expertise, but also to a very impressive AI persona, who could help revive the excitement about Meta's AI efforts, help recruit the best researchers, and lead the way inside Meta. Wang is also an outspoken China hawk who spends as much time in congressional hearings as in Slack, so the geopolitics here are … spicy. Meta just stapled itself to the biggest annotation funnel on Earth, hired away Google's Jack Rae (who was on the pod just last week, shipping for Google!) for brainy model alignment, and started waving seven-to-nine-figure comp packages at every researcher with “Transformer” in their citation list. Whatever disappointment you felt over Llama-4's muted debut, Zuck clearly felt it too—and responded like a founder who still controls every voting share. OpenAI's Game-Changer: o3 Price Slash & o3-pro launches to top the intelligence leaderboards!Meanwhile OpenAI dropping not one, but two mind-blowing updates. First, they've slashed the price of o3—their premium reasoning model—by a staggering 80%. We're talking from $40/$10 per million tokens down to just $8/$2. That's right, folks, it's now in the same league as Claude Sonnet cost-wise, making top-tier intelligence dirt cheap. I remember when a price drop of 80% after a year got us excited; now it's 80% in just four months with zero quality loss. They've confirmed it's the full o3 model—no distillation or quantization here. How are they pulling this off? I'm guessing someone got a shipment of shiny new H200s from Jensen!And just when you thought it couldn't get better, OpenAI rolled out o3-pro, their highest intelligence offering yet. Available for pro and team accounts, and via API (87% cheaper than o1-pro, by the way), this model—or consortium of models—is a beast. It's topping charts on Artificial Analysis, barely edging out Gemini 2.5 as the new king. Benchmarks are insane: 93% on AIME 2024 (state-of-the-art territory), 84% on GPQA Diamond, and nearing a 3000 ELO score on competition coding. Human preference tests show 64-66% of folks prefer o3-pro for clarity and comprehensiveness across tasks like scientific analysis and personal writing.I've been playing with it myself, and the way o3-pro handles long context and tough problems is unreal. As my friend Eric Provencher (creator of RepoPrompt) shared on the show, it's surgical—perfect for big refactors and bug diagnosis in coding. It's got all the tools o3 has—web search, image analysis, memory personalization—and you can run it in background mode via API for async tasks. Sure, it's slower due to deep reasoning (no streaming thought tokens), but the consistency and depth? Worth it. Oh, and funny story—I was prepping a talk for Hamel Hussain's evals course, with a slide saying “don't use large reasoning models if budget's tight.” The day before, this price drop hits, and I'm scrambling to update everything. That's AI pace for ya!Apple WWDC: Where's the Smarter Siri? Oh Apple. Sweet, sweet Apple. Remember all those Bella Ramsey ads promising a personalized Siri that knows everything about you? Well, Craig Federighi opened WWDC by basically saying "Yeah, about that smart Siri... she's not coming. Don't wait up."Instead, we got:* AI that can combine emojis (revolutionary!

Ralph and Lauren pull back the curtain on Meta's latest Advantage+ transformation, powered by the underreported Andromeda algorithm. With Meta silently replacing manual campaign controls with full automation, the hosts dive into what this shift means for media buyers, business owners, and performance marketers. From breaking down Advantage+ Shopping's evolution to analyzing its strategic implications for ad spend and conversion optimization, Ralph and Lauren explain why understanding Meta's AI is now mission-critical. If you're managing Meta ads in 2025, this episode is your wake-up call.Chapters:00:00:00 – Dive Into the Chaos: Welcome to the Professional Traffic Podcast00:01:33 – The Truth About Meta's Advantage+ and the Stealthy Andromeda Rollout00:03:17 – Hot Takes: Our Raw Reactions to Meta's Automated Future00:05:28 – Meta's $65 Billion Bet on AI—What It Really Means for Advertisers00:07:42 – Tariffs, Tension & Traffic: Why It's Time to Diversify Beyond Meta00:11:41 – Beyond the Big Two: Alternative Ad Networks You Shouldn't Ignore00:17:30 – Snapchat Isn't Dead: Surprising Wins and What the Data Shows00:19:23 – TikTok's Rise and the Platforms Quietly Dominating the Feed00:21:00 – Dollars and Downloads: The Platforms Driving Real Economic Growth00:21:09 – Threads is Blowing Up—Here's What That Means for Marketers00:22:03 – Personalized Ads That Don't Suck: Cracking the Relevance Code00:23:06 – Wait, Did We Just Say PornHub? The Untapped Traffic Opportunity00:24:15 – Can Threads Deliver Ad Results? Here's the Smart Way to Find Out00:25:25 – TikTok's Secret Superpower: B2B Lead Gen Done Right00:30:02 – Lead Quality Over Quantity: How to Actually Score Buyers00:31:07 – Mic Drop: Final Thoughts, Must-Know Takeaways, and Show NotesLINKS AND RESOURCES:Meta Andromeda: Supercharging Advantage+ automation with the next-gen personalized ads retrieval engineMeta to spend $65B on AI in 2025, Zuckerberg saysEpisode 693: How Meta Ads Are Changing Forever: Advantage+ & cAPI Subterfuge REVEALED!Episode 691: The MAJOR Meta Advantage+ Changes You Must KnowEpisode 660: 3 Astonishing New Features on TikTok You Didn't Know About…Until NowEpisode 629: (Part 2)How to Use TikTok Ads The Right Way to Grow Your BusinessEpisode 627: How to Use TikTok Ads The Right Way to Grow Your BusinessGet Your nCAC Calculator Now!Tier 11 JobsPerpetual Traffic on

AI and earnings took center stage this week. Perplexity's building a browser, OpenAI wants to be your shopping assistant, and Big Tech dropped their Q1 numbers. We cover: Perplexity builds a browser – CEO Aravind Srinivas explains how Comet aims to work at the operating system level, enabling actions like scraping pages, taking actions on your behalf, and improving ad targeting with granular user data. Hardware partnerships fuel expansion – Perplexity will be pre-installed on new Motorola Razrs and is in talks with Samsung. This move mirrors Google's own bundling tactics… just as Google faces antitrust heat. OpenAI launches shoppable search – You can now shop directly within ChatGPT results, with comparisons, recommendations, and product discovery baked into the interface. Shopify integration is rumored, but no ads—for now. The web is changing – In this new model, websites aren't for browsing. They're for bots to scrape, analyze, and feed insights back to the user in one seamless chat interface. Apple earnings – $95.4B in revenue, beating expectations. Hardware ticked up slightly, but services brought in $26.65B, growing 11.65% YoY. Meta earnings – $42.3B revenue (+16%), driven by ads. Ad impressions rose 5%, pricing jumped 10%. Meta boosted AI capex projections to $64–$72B. Alphabet earnings – $90.2B in revenue (+12%), strong performance in search. YouTube ads slightly missed projections. Tariff pressures are hitting Google Shopping spend. Amazon earnings – $29.3B in ad revenue, right on target, but a gloomy Q2 forecast due to tariffs and consumer uncertainty dragged stock down 4%. Microsoft earnings – $70B revenue (+13%), thanks to resilient software and cloud services. Hardware may take a hit, but they're better positioned than most to handle turbulence. Learn more about your ad choices. Visit megaphone.fm/adchoices

En este episodio, desglosamos los temas más importantes que están marcando el pulso de los mercados: • Mercados atentos al 'Día de la Liberación': Los futuros se mantienen estables mientras los inversores esperan los detalles de los aranceles que Trump anunciará el 2 de abril. Se teme una escalada comercial si las medidas son amplias. También se publican hoy el informe JOLTS (7.69M esperadas) y los indicadores manufactureros de marzo. • Petróleo y oro repuntan: El Brent sube a $75.14 y el WTI a $71.84 tras amenazas de Trump contra Rusia e Irán. El oro alcanza un nuevo récord de $3,148.88 antes de moderarse a $3,132.37, acumulando +20% en 2025. Saxo Bank reporta toma de ganancias en metales y compras sostenidas en energía. • Celsius apuesta por el segmento femenino: $CELH adquiere Alani Nu por $1.65B para atacar el mercado femenino de bebidas energéticas, que se proyecta como el principal motor de crecimiento del sector. Truist elevó la acción a Buy con PT de $45. Las acciones suben +33% YTD y marcan máximos de seis meses. Acompáñanos para entender cómo el panorama arancelario, la demanda por refugios seguros y las nuevas estrategias de mercado están moldeando el rumbo de la economía global.

As a native New Orleanian, Mitch Landrieu knows a thing or two about crisis and recovery. He served as the lieutenant governor of Louisiana through Hurricanes Katrina and Rita in 2005 and the compounding effects of subsequent storms including Ike and Gustav. In 2010, he was sworn in as mayor of New Orleans—just one month after the Deepwater Horizon explosion undermined the region's efforts to recover from five years of depopulation and economic decline.  Mayor Landrieu's experience working for the efficient restoration of New Orleans's critical infrastructure later led the Biden Administration to appoint him as an advisor on the national implementation of the 2021 Infrastructure Investment and Jobs Act. Otherwise known as the Bipartisan Infrastructure Law (BIL), this bill has been the largest long-term investment in U.S. infrastructure since the Federal-Aid Highway Act of 1965. It has prioritized and funded an array of essential, future-oriented projects throughout the country.  The aftermath of Hurricane Katrina demonstrated how the increasing scale of environmental disasters will expose vulnerabilities in the nation's aging infrastructure. Local leaders are thus seeking strategies that balance the needs of growth and economic development with the proactive management of current and future risks.  The work that Mayor Landrieu, city staff, and community partners undertook to steer New Orleans's recovery process away from bankruptcy and toward revived communities and a more secure built environment has provided a case study for policymakers and resilience groups around the world.  In part one of this two-part episode, Mayor Landrieu talks with Ten Across founder Duke Reiter about the personal and professional experiences that have influenced his views on equity and resilience and shaped some of the bold positions he's taken in governing. Part two will delve further into his views and outlook on contemporary governance.  We've taken a new approach with this episode, take a listen and let us know what you think by leaving a review on your preferred podcast platform.   Related articles and resources:  “Want to Understand the Future of U.S. Climate Resilience? Look to the Gulf Coast” (Ten Across Conversations podcast, Dec. 2024)  “Sunk Costs, Sunken City: The Story of New Orleans with Richard Campanella” (Ten Across Conversations podcast, June 2023)  “DOGE says it's now saved $65B in federal funds, but that's still impossible to verify” (ABC News, Feb. 26, 2025)  “Veteran crisis hotline may be impacted by federal layoffs” (ABC 15, Feb. 24, 2025) “Angry Over Confederate Flag, Mayor Plans March” (New York Times, March 2000)  “What is in the just-passed House Republican budget bill? What to know” (USA Today, Feb. 26, 2025)

Send us a textThe Department of Government Efficiency (DOGE), helmed by Elon Musk, has been tasked with optimizing federal processes, cutting redundancies, and improving transparency in government operations.Now, large consulting firms are in DOGE's crosshairs. The top 10 largest consulting firms that do business with the U.S. government have been called in to "defend the spend" this Friday. These firms represent over $65B in outstanding consulting contracts.Whether you agree with DOGE's mission or not isn't the point - the U.S. government is the single largest consumer of consulting services in the world, and this steady revenue stream is under threat for some of the world's largest consulting firms.The Wall Street Journal broke this news - read the original article.ChaptersWhat is do federal consultants do? (1:00)What is DOGE? (2:00)What is the current state of play? (3:02)What does this mean for consulting firms? (6:56)What does this mean for job seekers? (8:39)Additional ResourcesExplore the top 10 public sector consulting firms as ranked by Management ConsultedBuild your business acumen through our Black Belt case coaching programUnlock top consulting jobs on the Management Consulted Job BoardConnect With Namaan MianConnect with Namaan Mian on LinkedInConnect With Management Consulted Book a free 15min info call with Katie. Follow Management Consulted on LinkedIn, Instagram, and TikTok for the latest updates and industry insights. Join an upcoming live event - case interviews demos, expert panels, and more. Email our team (team@managementconsulted.com) with any questions or feedback.

The first-ever episode of Good Morning Outdoors is here with Matt Whitermore and Alex Burkett! We're diving into Blackstone's $5.65B acquisition of Safe Harbor Marinas—what does this mean for the future of outdoor recreation and waterfront access? Plus, DOGE is making significant cuts to public lands agencies, and the Outdoor Alliance says it could spell disaster for recreation and conservation. And finally, could we be witnessing Fyre Fest 2.0? We break down the latest controversy and what's really happening in the RV and outdoor travel boom. ---- Good Morning Outdoors is part of the Hospitality.FM Multi-Media Network and is a licensed podcast under Good Morning Hospitality! The hospitality industry is constantly growing, changing, and innovating! This podcast brings you the top news and topics from industry experts across different hospitality fields. Good Morning Hospitality publishes three thirty-minute weekly episodes: every Monday and Wednesday at 7 a.m. PST / 10 a.m. EST and every Tuesday at 8 a.m. CET for our European and UK-focused content. Make sure to tune in during our live show on our LinkedIn page or YouTube every week and join the conversation live! Explore everything Good Morning Hospitality has to offer: • Well & Good Morning Coffee: Enjoy our signature roast—order here! • Retreats: Join us at one of our exclusive retreats—learn more and register your interest here! • Episodes & More: Find all episodes and additional info at GoodMorningHospitality.com Thank you to all of the Hospitality.FM Partners that help make this show possible. If you have any press you want to be covered during the show, email us at goodmorning@hospitality.fm Learn more about your ad choices. Visit megaphone.fm/adchoices

NEWS: Pag-IBIG bares record P55.65B dividend | Feb. 28, 2025Visit our website at https://www.manilatimes.netFollow us:Facebook - https://tmt.ph/facebookInstagram - https://tmt.ph/instagramTwitter - https://tmt.ph/twitterDailyMotion - https://tmt.ph/dailymotionSubscribe to our Digital Edition - https://tmt.ph/digitalSign up to our newsletters: https://tmt.ph/newslettersCheck out our Podcasts:Spotify - https://tmt.ph/spotifyApple Podcasts - https://tmt.ph/applepodcastsAmazon Music - https://tmt.ph/amazonmusicDeezer: https://tmt.ph/deezerStitcher: https://tmt.ph/stitcherTune In: https://tmt.ph/tunein#TheManilaTimes Hosted on Acast. See acast.com/privacy for more information.

Nvidia (NVDA) shares are down 5% from all-time highs, but on track for a positive weekly finish. The company is poised to benefit from increased spending in A.I. infrastructure plans such as "Stargate" announced by President Trump. Meta Platforms (META) shared its plans to spend up to $65B in CapEx to increase its A.I. tech as well. Kevin Hincks looks at example options trades in both big tech names. ======== Schwab Network ======== Empowering every investor and trader, every market day. Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6D Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribe Download the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185 Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7 Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watch Watch on Vizio - https://www.vizio.com/en/watchfreeplus-explore Watch on DistroTV - https://www.distro.tv/live/schwab-network/ Follow us on X – https://twitter.com/schwabnetwork Follow us on Facebook – https://www.facebook.com/schwabnetwork Follow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Ben is Co-Head of GMO's Asset Allocation team and serves as a partner and portfolio manager at the firm. Founded in 1977, GMO manages over $65B (as of 9/30/24), and is renowned for its expertise in multi-asset class portfolios. In this episode, Ben shares his insights on his investment framework, the principles of value investing, and his perspectives on current market conditions.

11/20/24 Hour 3     Vince takes listener calls about Matt Gaetz's nomination and potential confirmation hearing. Opponents of Matt Gaetz are labeling him a sex offender, but the DOJ never pursued the accusations because the accusers lacked credibility.  Vince speaks with Matt Rosendale, Congressman representing Montana's 2nd Congressional District, about Biden bringing us to the verge of WW3 before leaving the WH by authorizing Ukraine to use long range missiles and Biden State Department telling Ukraine they can keep $4.65B in taxpayer money they originally were supposed to pay back.      For more coverage on the issues that matter to you visit www.WMAL.com, download the WMAL app or tune in live on WMAL-FM 105.9 from 3-6pm.    To join the conversation, check us out on social media: @WMAL @VinceCoglianese.      Executive Producer: Corey Inganamort @TheBirdWords See omnystudio.com/listener for privacy information.

Send us a text00:19 | FigureAI (humanoid robots)- AI and humanoid robots drive efficiency and will drive cost of goods/services to $0, drive unlimited GDP- $675m raise $2.6b valuation; OpenAI, Microsoft, Nvidia- 10b humanoid robots by 2040- Musk is projecting 16b to 32b humanoid robots, Telsa Optimus is a humanoid robot- humanoid robots fit easily into a human world to easily replace humans11:49 | Stripe- These companies are so big they're doing share buybacks!- Planning new tender offer to repurchase shares from employees- Entire offer financed with Stripe's own cash, a shift from external funding- Generated $615M in free cash flow in June quarter vs. $500M cash burn in 2022- Valuation at $70B (secondary), up from $50B in 2022 and $65B in last tender- Up to 8,000 employees can sell up to $50,000 of vested shares at $27.51/share- Expanding beyond core payments into billing software; segment on track for $500M annual revenue25:39 | xAI- xAI differentiators are becoming clear; real time data, most accurate answers, Musk effect (i.e. unlimited capital)- AI large language model platform business- Released Grok-2 and Grok-2 Mini beta LLMs on X platform- Enterprise API arriving later this month- Top-four position on LMSYS chatbot leaderboard- Grok-2 Mini: efficient, ideal for speed/resource-critical scenarios- Focus on expanding multimodal understanding- Available to Premium/Premium+ subscribers on X at $8/month- Secondary market valuation: $25.7B (+6.9% vs May 2024 round)

This week saw a range of titles opening wide, with Blink Twice, The Forge, and The Crow each debuting to different unique audiences. We take an in-depth look at each, as well as how opportunities of cross-promotion, subscriptions, and bundling might be being missed. Uncover all the insights into the box office and audience analysis on this week's instalment of Behind the Screens. Topics and times: Year-to-date box office comparison - 0:16 Blink Twice box office overview - 1:00 Blink Twice audience analysis - 1:28 Content warnings in cinema - 3:04 The Forge box office and audience analysis - 7:14 The Crow box office and audience analysis - 10:16 Cross-promotion, subscriptions, and bundling opportunities - 12:22 Box office holdovers - 14:19 Next week - 16:54 Find us at https://www.linkedin.com/company/vista-group-limited/, and follow lifeatvistagroup on Instagram Box Office Overview: Deadpool & Wolverine regained the top spot domestically with an $18M gross, and $20M internationally, bringing the global total to $1.21B. Alien: Romulus grossed $16.2M domestically and $41M internationally with strong holds particularly in China, for a global running total of $225M. Inside Out 2 passed $1B internationally, with a worldwide total now of $1.65B. Blink Twice debuted with $7.3M in the domestic market and $6.7M internationally for a total of $14M. The Forge debuted to $6.6M in the domestic market. And The Crow opened in 8th position at the domestic market, grossing $4.6M.

Noticias Económicas y Financieras Los 14 años de gobierno conservador han terminado en el Reino Unido después de que el Partido Laborista ganara las elecciones generales del país por una abrumadora mayoría, con Keir Starmer como nuevo primer ministro. Ha recibido un importante mandato con 411 escaños de los 650 que tiene el parlamento, consiguiendo 211 escaños en la última votación, según las últimas encuestas a boca de urna. Es un gran cambio para el panorama político británico, que ha lidiado con una década tumultuosa que ha incluido el Brexit, una crisis del coste de la vida tras el COVID y la guerra en Ucrania, así como cuatro primeros ministros conservadores en los últimos cinco años. Las acciones relacionadas con las criptomonedas están bajo presión en las operaciones previas al mercado después de que Bitcoin (BTC-USD) extendiera sus pérdidas por cuarta sesión consecutiva. La criptomoneda más grande ha caído del nivel de $63.000 a alrededor de $54.000 en los últimos días, y la bolsa colapsada Mt. Gox se prepara para distribuir una gran cantidad de BTC a sus acreedores. Un atraco en 2011 se llevó hasta 950K Bitcoins, pero esta semana se realizarán los reembolsos a muchos acreedores, lo que provocó temores de dilución o muchos quieren cobrar sus inversiones recuperadas. El gobierno alemán también acaba de vender miles de bitcoins, que se dice que fueron confiscados en relación con el extinto sitio web de piratería Movie2k. Juntos, más fuertes, podría ser el lema del juego en el negocio del lujo, ya que las tiendas físicas siguen viéndose afectadas por las tendencias del comercio electrónico y la inflación pasa factura a los compradores aspiracionales. Hudson's Bay Co. ha confirmado la compra de Neiman Marcus Group por $2.65B, creando una potencia con $10B en ventas anuales y más de 150 tiendas, incluidas Saks Fifth Avenue, Saks OFF 5th, Neiman Marcus y Bergdorf Goodman. Como se mencionó anteriormente, Amazon $AMZN adquirirá una participación minoritaria en la empresa combinada, que se llamará Saks Global. Salesforce $CRM es otro inversor minoritario y se espera que ayude con la inteligencia artificial. El informe de empleos de hoy, que se publicará a las 8:30 a. m., hora del este de EE. UU., será muy esperado y probablemente será dispar, ya que se espera que el crecimiento continúe, pero a un ritmo más lento que en los meses anteriores. Los economistas estiman que en junio se agregaron 191 mil nóminas no agrícolas, mientras que la tasa de desempleo se mantuvo estable en el 4%. Como la inflación es el principal foco de atención de la Reserva Federal, los datos de ganancias promedio por hora también serán importantes en el próximo informe. Se espera que aumenten un 0.3% intermensual y un 3.9% interanual, frente al 0.4% intermensual y el 4.1% interanual de mayo, y que la tasa de participación de la fuerza laboral aumente ligeramente hasta el 62.6%. El fundador Jeff Bezos vende casi $5B en acciones de Amazon. Kolanovic de JPMorgan dice que dejará el banco después de una serie de malas decisiones. Actas de la Fed: se necesitan datos más favorables para la confianza inflacionaria.

Tanis Jorge is a serial, tech entrepreneur, and a leading advisor to startup founders on entrepreneurship and building successful cofounder partnerships. Over the course of her career in startups, spanning the last 20+ years, Tanis has cofounded, scaled, and successfully exited multiple data-driven businesses.​Her successes culminated with her most recent venture, Trulioo, which she co-founded in 2011 with her long-term business partner, Stephen Ufford. Between 2011 and 2015, Tanis served as Chief Operations Officer of Trulioo, working to lay the groundwork and build the foundation for the trusted, innovative, and disruptive company it has become today. In 2021, Trulioo reached unicorn status (US $1.65B valuation) solidifying its place as the world's leading identity verification company and Jorge's track record for founding successful businesses. Following a record-breaking Series D, she stepped down from the Board of Trulioo to focus on her cofounder advisory work. She remains a minority shareholder of Trulioo.​Tanis cofounded her first start-up, iQuiri in 1999. The company was one of the first to make consumer credit reports available online and was acquired by Experian in 2003. In 2004, she cofounded NCB Data Services, which was again acquired by Experian in 2006. In 2005, Tanis cofounded identity management firm Pharos Global Strategies, which was again acquired four years later.​Today, Tanis is one of the go-to voices and experts on the ‘cofounder relationship', drawing on her experience cofounding and successfully scaling four technology businesses. She is the author of The Cofounder's Handbook (listed in USA Today's Top 10 Business Books To Scale Your Business in 2024) and Founder of The Cofounder's Hub, a platform dedicated to providing entrepreneurs with the necessary tools and resources to establish, nurture, and successfully exit business partnerships. Tanis also advises fast-growing start-ups and leading venture capitalists, focusing her work on how cofounders can function in an open, productive, and symbiotic way to ensure continued and long-term business success.​Tanis sits on the Board of Directors at Ally Global, a non-profit that works to prevent human trafficking and supports survivors through safe homes, education, and work opportunities. Tanis lives in Vancouver, BC with her husband and two boys. When she isn't working she enjoys the “foodie” lifestyle with her husband David Jorge, MasterChef Canada Season 2 winner. She loves water sports and is currently working towards turning her brown belt in kickboxing into black, a lifelong item on her bucket list. Tanis also takes time to mentor students at the private school she founded, Live Learn Launch Academy, which focuses on entrepreneurship, financial literacy, and life skills.

Speaker CFPs and Sponsor Guides are now available for AIE World's Fair — join us on June 25-27 for the biggest AI Engineer conference of 2024!Soumith Chintala needs no introduction in the ML world — his insights are incredibly accessible across Twitter, LinkedIn, podcasts, and conference talks (in this pod we'll assume you'll have caught up on the History of PyTorch pod from last year and cover different topics). He's well known as the creator of PyTorch, but he's more broadly the Engineering Lead on AI Infra, PyTorch, and Generative AI at Meta.Soumith was one of the earliest supporters of Latent Space (and more recently AI News), and we were overjoyed to catch up with him on his latest SF visit for a braindump of the latest AI topics, reactions to some of our past guests, and why Open Source AI is personally so important to him.Life in the GPU-Rich LaneBack in January, Zuck went on Instagram to announce their GPU wealth: by the end of 2024, Meta will have 350k H100s. By adding all their GPU clusters, you'd get to 600k H100-equivalents of compute. At FP16 precision, that's ~1,200,000 PFLOPS. If we used George Hotz's (previous guest!) "Person of Compute" measure, Meta now has 60k humans of compute in their clusters. Occasionally we get glimpses into the GPU-rich life; on a recent ThursdAI chat, swyx prompted PaLM tech lead Yi Tay to write down what he missed most from Google, and he commented that UL2 20B was trained by accidentally leaving the training job running for a month, because hardware failures are so rare in Google.Meta AI's Epic LLM RunBefore Llama broke the internet, Meta released an open source LLM in May 2022, OPT-175B, which was notable for how “open” it was - right down to the logbook! They used only 16 NVIDIA V100 GPUs and Soumith agrees that, with hindsight, it was likely under-trained for its parameter size.In Feb 2023 (pre Latent Space pod), Llama was released, with a 7B version trained on 1T tokens alongside 65B and 33B versions trained on 1.4T tokens. The Llama authors included Guillaume Lample and Timothée Lacroix, who went on to start Mistral.July 2023 was Llama2 time (which we covered!): 3 model sizes, 7B, 13B, and 70B, all trained on 2T tokens. The three models accounted for a grand total of 3,311,616 GPU hours for all pre-training work. CodeLlama followed shortly after, a fine-tune of Llama2 specifically focused on code generation use cases. The family had models in the 7B, 13B, 34B, and 70B size, all trained with 500B extra tokens of code and code-related data, except for 70B which is trained on 1T.All of this on top of other open sourced models like Segment Anything (one of our early hits!), Detectron, Detectron 2, DensePose, and Seamless, and in one year, Meta transformed from a company people made fun of for its “metaverse” investments to one of the key players in the AI landscape and its stock has almost tripled since (about $830B in market value created in the past year).Why Open Source AIThe obvious question is why Meta would spend hundreds of millions on its AI efforts and then release them for free. Zuck has addressed this in public statements:But for Soumith, the motivation is even more personal:“I'm irrationally interested in open source. I think open source has that fundamental way to distribute opportunity in a way that is very powerful. Like, I grew up in India… And knowledge was very centralized, but I saw that evolution of knowledge slowly getting decentralized. And that ended up helping me learn quicker and faster for like zero dollars. And I think that was a strong reason why I ended up where I am. So like that, like the open source side of things, I always push regardless of like what I get paid for, like I think I would do that as a passion project on the side……I think at a fundamental level, the most beneficial value of open source is that you make the distribution to be very wide. It's just available with no friction and people can do transformative things in a way that's very accessible. Maybe it's open source, but it has a commercial license and I'm a student in India. I don't care about the license. I just don't even understand the license. But like the fact that I can use it and do something with it is very transformative to me……Like, okay, I again always go back to like I'm a student in India with no money. What is my accessibility to any of these closed source models? At some scale I have to pay money. That makes it a non-starter and stuff. And there's also the control issue: I strongly believe if you want human aligned AI, you want all humans to give feedback. And you want all humans to have access to that technology in the first place. And I actually have seen, living in New York, whenever I come to Silicon Valley, I see a different cultural bubble.We like the way Soumith put it last year: Closed AI “rate-limits against people's imaginations and needs”!What It Takes For Open Source AI to WinHowever Soumith doesn't think Open Source will simply win by popular demand. There is a tremendous coordination problem with the decentralized nature of the open source AI development right now: nobody is collecting the valuable human feedback in the way that OpenAI or Midjourney are doing.“Open source in general always has a coordination problem. If there's a vertically integrated provider with more resources, they will just be better coordinated than open source. And so now open source has to figure out how to have coordinated benefits. And the reason you want coordinated benefits is because these models are getting better based on human feedback. And if you see with open source models, like if you go to the /r/localllama subreddit, like there's so many variations of models that are being produced from, say, Nous research. I mean, like there's like so many variations built by so many people. And one common theme is they're all using these fine-tuning or human preferences datasets that are very limited and they're not sufficiently diverse. And you look at the other side, say front-ends like Oobabooga or like Hugging Chat or Ollama, they don't really have feedback buttons. All the people using all these front-ends, they probably want to give feedback, but there's no way for them to give feedback… So we're just losing all of this feedback. Maybe open source models are being as used as GPT is at this point in like all kinds of, in a very fragmented way, like in aggregate all the open source models together are probably being used as much as GPT is, maybe close to that. But the amount of feedback that is driving back into the open source ecosystem is like negligible, maybe less than 1% of like the usage. So I think like some, like the blueprint here I think is you'd want someone to create a sinkhole for the feedback… I think if we do that, if that actually happens, I think that probably has a real chance of the open source models having a runaway effect against OpenAI, I think like there's a clear chance we can take at truly winning open source.”If you're working on solving open source coordination, please get in touch!Show Notes* Soumith Chintala Twitter* History of PyTorch episode on Gradient Podcast* The Llama Ecosystem* Apple's MLX* Neural ODEs (Ordinary Differential Equations)* AlphaGo* LMSys arena* Dan Pink's "Drive"* Robotics projects:* Dobb-E* OK Robot* Yann LeCun* Yangqing Jia of Lepton AI* Ed Catmull* George Hotz on Latent Space* Chris Lattner on Latent Space* Guillaume Lample* Yannic Kilcher of OpenAssistant* LMSys* Alex Atallah of OpenRouter* Carlo Sferrazza's 3D tactile research* Alex Wiltschko of Osmo* Tangent by Alex Wiltschko* Lerrel Pinto - RoboticsTimestamps* [00:00:00] Introductions* [00:00:51] Extrinsic vs Intrinsic Success* [00:02:40] Importance of Open Source and Its Impact* [00:03:46] PyTorch vs TinyGrad* [00:08:33] Why PyTorch is the Switzerland of frameworks* [00:10:27] Modular's Mojo + PyTorch?* [00:13:32] PyTorch vs Apple's MLX* [00:16:27] FAIR / PyTorch Alumni* [00:18:50] How can AI inference providers differentiate?* [00:21:41] How to build good benchmarks and learnings from AnyScale's* [00:25:28] Most interesting unexplored ideas* [00:28:18] What people get wrong about synthetic data* [00:35:57] Meta AI's evolution* [00:38:42] How do you allocate 600,000 GPUs?* [00:42:05] Even the GPU Rich are GPU Poor* [00:47:31] Meta's MTIA silicon* [00:50:09] Why we need open source* [00:59:00] Open source's coordination problem for feedback gathering* [01:08:59] Beyond text generation* [01:15:37] Osmo and the Future of Smell Recognition TechnologyTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO in residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:15]: Hey, and today we have in the studio Soumith Chintala, welcome.Soumith [00:00:17]: Thanks for having me.Swyx [00:00:18]: On one of your rare visits from New York where you live. You got your start in computer vision at NYU with Yann LeCun. That was a very fortuitous start. I was actually listening to your interview on the Gradient podcast. So if people want to know more about the history of Soumith, history of PyTorch, they can go to that podcast. We won't spend that much time there, but I just was marveling at your luck, or I don't know if it's your luck or your drive to find AI early and then find the right quality mentor because I guess Yan really sort of introduced you to that world.Soumith [00:00:51]: Yeah, I think you're talking about extrinsic success, right? A lot of people just have drive to do things that they think is fun, and a lot of those things might or might not be extrinsically perceived as good and successful. I think I just happened to like something that is now one of the coolest things in the world or whatever. But if I happen, the first thing I tried to become was a 3D VFX artist, and I was really interested in doing that, but I turned out to be very bad at it. So I ended up not doing that further. But even if I was good at that, whatever, and I ended up going down that path, I probably would have been equally happy. It's just like maybe like the perception of, oh, is this person successful or not might be different. I think like after a baseline, like your happiness is probably more correlated with your intrinsic stuff.Swyx [00:01:44]: Yes. I think Dan Pink has this book on drive that I often refer to about the power of intrinsic motivation versus extrinsic and how long extrinsic lasts. It's not very long at all. But anyway, now you are an investor in Runway, so in a way you're working on VFX. Yes.Soumith [00:02:01]: I mean, in a very convoluted way.Swyx [00:02:03]: It reminds me of Ed Catmull. I don't know if you guys know, but he actually tried to become an animator in his early years and failed or didn't get accepted by Disney and then went and created Pixar and then got bought by Disney and created Toy Story. So you joined Facebook in 2014 and eventually became a creator and maintainer of PyTorch. And there's this long story there you can refer to on the gradient. I think maybe people don't know that you also involved in more sort of hardware and cluster decision affair. And we can dive into more details there because we're all about hardware this month. Yeah. And then finally, I don't know what else, like what else should people know about you on a personal side or professional side?Soumith [00:02:40]: I think open source is definitely a big passion of mine and probably forms a little bit of my identity at this point. I'm irrationally interested in open source. I think open source has that fundamental way to distribute opportunity in a way that is very powerful. Like, I grew up in India. I didn't have internet for a while. In college, actually, I didn't have internet except for GPRS or whatever. And knowledge was very centralized, but I saw that evolution of knowledge slowly getting decentralized. And that ended up helping me learn quicker and faster for zero dollars. And I think that was a strong reason why I ended up where I am. So the open source side of things, I always push regardless of what I get paid for, like I think I would do that as a passion project on the side.Swyx [00:03:35]: Yeah, that's wonderful. Well, we'll talk about the challenges as well that open source has, open models versus closed models. Maybe you want to touch a little bit on PyTorch before we move on to the sort of Meta AI in general.PyTorch vs Tinygrad tradeoffsAlessio [00:03:46]: Yeah, we kind of touched on PyTorch in a lot of episodes. So we had George Hotz from TinyGrad. He called PyTorch a CISC and TinyGrad a RISC. I would love to get your thoughts on PyTorch design direction as far as, I know you talk a lot about kind of having a happy path to start with and then making complexity hidden away but then available to the end user. One of the things that George mentioned is I think you have like 250 primitive operators in PyTorch, I think TinyGrad is four. So how do you think about some of the learnings that maybe he's going to run into that you already had in the past seven, eight years almost of running PyTorch?Soumith [00:04:24]: Yeah, I think there's different models here, but I think it's two different models that people generally start with. Either they go like, I have a grand vision and I'm going to build a giant system that achieves this grand vision and maybe one is super feature complete or whatever. Or other people say they will get incrementally ambitious, right? And they say, oh, we'll start with something simple and then we'll slowly layer out complexity in a way that optimally applies Huffman coding or whatever. Like where the density of users are and what they're using, I would want to keep it in the easy, happy path and where the more niche advanced use cases, I'll still want people to try them, but they need to take additional frictional steps. George, I think just like we started with PyTorch, George started with the incrementally ambitious thing. I remember TinyGrad used to be, like we would be limited to a thousand lines of code and I think now it's at 5,000. So I think there is no real magic to which why PyTorch has the kind of complexity. I think it's probably partly necessitated and partly because we built with the technology available under us at that time, PyTorch is like 190,000 lines of code or something at this point. I think if you had to rewrite it, we would probably think about ways to rewrite it in a vastly simplified way for sure. But a lot of that complexity comes from the fact that in a very simple, explainable way, you have memory hierarchies. You have CPU has three levels of caches and then you have DRAM and SSD and then you have network. Similarly, GPU has several levels of memory and then you have different levels of network hierarchies, NVLink plus InfiniBand or Rocky or something like that, right? And the way the flops are available on your hardware, they are available in a certain way and your computation is in a certain way and you have to retrofit your computation onto both the memory hierarchy and like the flops available. When you're doing this, it is actually a fairly hard mathematical problem to do this setup, like you find the optimal thing. And finding the optimal thing is, what is optimal depends on the input variables themselves. So like, okay, what is the shape of your input tensors and what is the operation you're trying to do and various things like that. Finding that optimal configuration and writing it down in code is not the same for every input configuration you have. Like for example, just as the shape of the tensors change, let's say you have three input tensors into a Sparstar product or something like that. The shape of each of these input tensors will vastly change how you do this optimally placing this operation onto the hardware in a way that will get you maximal throughput. So a lot of our complexity comes from writing out hundreds of configurations for each single PyTorch operator and templatizing these things and symbolically generating the final CUDA code or CPU code. There's no way to avoid it because mathematically we haven't found symbolic ways to do this that also keep compile time near zero. You can write a very simple framework, but then you also should be willing to eat the long compile time. So if searching for that optimal performance at runtime, but that's the trade off. There's no, like, I don't think unless we have great breakthroughs George's vision is achievable, he should be thinking about a narrower problem such as I'm only going to make this for work for self-driving car connets or I'm only going to make this work for LLM transformers of the llama style. Like if you start narrowing the problem down, you can make a vastly simpler framework. But if you don't, if you need the generality to power all of the AI research that is happening and keep zero compile time and in all these other factors, I think it's not easy to avoid the complexity.Pytorch vs MojoAlessio [00:08:33]: That's interesting. And we kind of touched on this with Chris Lattner when he was on the podcast. If you think about frameworks, they have the model target. They have the hardware target. They have different things to think about. He mentioned when he was at Google, TensorFlow trying to be optimized to make TPUs go brr, you know, and go as fast. I think George is trying to make especially AMD stack be better than ROCm. How come PyTorch has been such as Switzerland versus just making Meta hardware go brr?Soumith [00:09:00]: First, Meta is not in the business of selling hardware. Meta is not in the business of cloud compute. The way Meta thinks about funding PyTorch is we're funding it because it's net good for Meta to fund PyTorch because PyTorch has become a standard and a big open source project. And generally it gives us a timeline edge. It gives us leverage and all that within our own work. So why is PyTorch more of a Switzerland rather than being opinionated? I think the way we think about it is not in terms of Switzerland or not. We actually the way we articulate it to all hardware vendors and software vendors and all who come to us being we want to build a backend in core for PyTorch and ship it by default is we just only look at our user side of things. Like if users are using a particular piece of hardware, then we want to support it. We very much don't want to king make the hardware side of things. So as the MacBooks have GPUs and as that stuff started getting increasingly interesting, we pushed Apple to push some engineers and work on the NPS support and we spend significant time from Meta funded engineers on that as well because a lot of people are using the Apple GPUs and there's demand. So we kind of mostly look at it from the demand side. We never look at it from like oh which hardware should we start taking opinions on.Swyx [00:10:27]: Is there a future in which, because Mojo or Modular Mojo is kind of a superset of Python, is there a future in which PyTorch might use Mojo features optionally?Soumith [00:10:36]: I think it depends on how well integrated it is into the Python ecosystem. So if Mojo is like a pip install and it's readily available and users feel like they can use Mojo so smoothly within their workflows in a way that just is low friction, we would definitely look into that. Like in the same way PyTorch now depends on Triton, OpenAI Triton, and we never had a conversation that was like huh, that's like a dependency. Should we just build a Triton of our own or should we use Triton? It almost doesn't, like those conversations don't really come up for us. The conversations are more well does Triton have 10,000 dependencies and is it hard to install? We almost don't look at these things from a strategic leverage point of view. We look at these things from a user experience point of view, like is it easy to install? Is it smoothly integrated and does it give enough benefits for us to start depending on it? If so, yeah, we should consider it. That's how we think about it.Swyx [00:11:37]: You're inclusive by default as long as it meets the minimum bar of, yeah, but like maybe I phrased it wrongly. Maybe it's more like what problems would you look to solve that you have right now?Soumith [00:11:48]: I think it depends on what problems Mojo will be useful at.Swyx [00:11:52]: Mainly a performance pitch, some amount of cross compiling pitch.Soumith [00:11:56]: Yeah, I think the performance pitch for Mojo was like, we're going to be performant even if you have a lot of custom stuff, you're going to write arbitrary custom things and we will be performant. And that value proposition is not clear to us from the PyTorch side to consider it for PyTorch. So PyTorch, it's actually not 250 operators, it's like a thousand operators. PyTorch exposes about a thousand operators and people kind of write their ideas in the thousand operators of PyTorch. Mojo is like, well, maybe it's okay to completely sidestep those thousand operators of PyTorch and just write it in a more natural form. Just write raw Python, write for loops or whatever, right? So from the consideration of how do we intersect PyTorch with Mojo, I can see one use case where you have custom stuff for some parts of your program, but mostly it's PyTorch. And so we can probably figure out how to make it easier for say Torch.compile to smoothly also consume Mojo subgraphs and like, you know, the interoperability being actually usable, that I think is valuable. But Mojo as a fundamental front end would be replacing PyTorch, not augmenting PyTorch. So in that sense, I don't see a synergy in more deeply integrating Mojo.Pytorch vs MLXSwyx [00:13:21]: So call out to Mojo whenever they have written something in Mojo and there's some performance related thing going on. And then since you mentioned Apple, what should people think of PyTorch versus MLX?Soumith [00:13:32]: I mean, MLX is early and I know the folks well, Ani used to work at FAIR and I used to chat with him all the time. He used to be based out of New York as well. The way I think about MLX is that MLX is specialized for Apple right now. It has a happy path because it's defined its product in a narrow way. At some point MLX either says we will only be supporting Apple and we will just focus on enabling, you know, there's a framework if you use your MacBook, but once you like go server side or whatever, that's not my problem and I don't care. For MLS, it enters like the server side set of things as well. Like one of these two things will happen, right? If the first thing will happen, like MLX's overall addressable market will be small, but it probably do well within that addressable market. If it enters the second phase, they're going to run into all the same complexities that we have to deal with. They will not have any magic wand and they will have more complex work to do. They probably wouldn't be able to move as fast.Swyx [00:14:44]: Like having to deal with distributed compute?Soumith [00:14:48]: Distributed, NVIDIA and AMD GPUs, like just like having a generalization of the concept of a backend, how they treat compilation with plus overheads. Right now they're deeply assumed like the whole NPS graph thing. So they need to think about all these additional things if they end up expanding onto the server side and they'll probably build something like PyTorch as well, right? Like eventually that's where it will land. And I think there they will kind of fail on the lack of differentiation. Like it wouldn't be obvious to people why they would want to use it.Swyx [00:15:24]: I mean, there are some cloud companies offering M1 and M2 chips on servers. I feel like it might be interesting for Apple to pursue that market, but it's not their core strength.Soumith [00:15:33]: Yeah. If Apple can figure out their interconnect story, maybe, like then it can become a thing.Swyx [00:15:40]: Honestly, that's more interesting than the cars. Yes.Soumith [00:15:43]: I think the moat that NVIDIA has right now, I feel is that they have the interconnect that no one else has, like AMD GPUs are pretty good. I'm sure there's various silicon that is not bad at all, but the interconnect, like NVLink is uniquely awesome. I'm sure the other hardware providers are working on it, but-Swyx [00:16:04]: I feel like when you say it's uniquely awesome, you have some appreciation of it that the rest of us don't. I mean, the rest of us just like, you know, we hear marketing lines, but what do you mean when you say NVIDIA is very good at networking? Obviously they made the acquisition maybe like 15 years ago.Soumith [00:16:15]: Just the bandwidth it offers and the latency it offers. I mean, TPUs also have a good interconnect, but you can't buy them. So you have to go to Google to use it.PyTorch MafiaAlessio [00:16:27]: Who are some of the other FAIR PyTorch alumni that are building cool companies? I know you have Fireworks AI, Lightning AI, Lepton, and Yangqing, you knew since college when he was building Coffee?Soumith [00:16:40]: Yeah, so Yangqing and I used to be framework rivals, PyTorch, I mean, we were all a very small close-knit community back then. Caffe, Torch, Theano, Chainer, Keras, various frameworks. I mean, it used to be more like 20 frameworks. I can't remember all the names. CCV by Liu Liu, who is also based out of SF. And I would actually like, you know, one of the ways it was interesting is you went into the framework guts and saw if someone wrote their own convolution kernel or they were just copying someone else's. There were four or five convolution kernels that were unique and interesting. There was one from this guy out of Russia, I forgot the name, but I remembered who was awesome enough to have written their own kernel. And at some point there, I built out these benchmarks called ConNet benchmarks. They're just benchmarking all the convolution kernels that are available at that time. It hilariously became big enough that at that time AI was getting important, but not important enough that industrial strength players came in to do these kinds of benchmarking and standardization. Like we have MLPerf today. So a lot of the startups were using ConNet benchmarks in their pitch decks as like, oh, you know, on ConNet benchmarks, this is how we fare, so you should fund us. I remember Nirvana actually was at the top of the pack because Scott Gray wrote amazingly fast convolution kernels at that time. Very interesting, but separate times. But to answer your question, Alessio, I think mainly Lepton, Fireworks are the two most obvious ones, but I'm sure the fingerprints are a lot wider. They're just people who worked within the PyTorch Cafe2 cohort of things and now end up at various other places.Swyx [00:18:50]: I think as a, both as an investor and a people looking to build on top of their services, it's a uncomfortable slash like, I don't know what I don't know pitch. Because I've met Yang Tsing and I've met Lin Chao. Yeah, I've met these folks and they're like, you know, we are deep in the PyTorch ecosystem and we serve billions of inferences a day or whatever at Facebook and now we can do it for you. And I'm like, okay, that's great. Like, what should I be wary of or cautious of when these things happen? Because I'm like, obviously this experience is extremely powerful and valuable. I just don't know what I don't know. Like, what should people know about like these sort of new inference as a service companies?Soumith [00:19:32]: I think at that point you would be investing in them for their expertise of one kind. So if they've been at a large company, but they've been doing amazing work, you would be thinking about it as what these people bring to the table is that they're really good at like GPU programming or understanding the complexity of serving models once it hits a certain scale. You know, various expertise like from the infra and AI and GPUs point of view. What you would obviously want to figure out is whether their understanding of the external markets is clear, whether they know and understand how to think about running a business, understanding how to be disciplined about making money or, you know, various things like that.Swyx [00:20:23]: Maybe I'll put it like, actually I will de-emphasize the investing bit and just more as a potential customer. Oh, okay. Like, it's more okay, you know, you have PyTorch gods, of course. Like, what else should I know?Soumith [00:20:37]: I mean, I would not care about who's building something. If I'm trying to be a customer, I would care about whether...Swyx [00:20:44]: Benchmarks.Soumith [00:20:44]: Yeah, I use it and it's usability and reliability and speed, right?Swyx [00:20:51]: Quality as well.Soumith [00:20:51]: Yeah, if someone from some random unknown place came to me and say, user stuff is great. Like, and I have the bandwidth, I probably will give it a shot. And if it turns out to be great, like I'll just use it.Benchmark dramaSwyx [00:21:07]: Okay, great. And then maybe one more thing about benchmarks, since we already brought it up and you brought up Confident Benchmarks. There was some recent drama around AnyScale. AnyScale released their own benchmarks and obviously they look great on their own benchmarks, but maybe didn't give the other... I feel there are two lines of criticism. One, which is they didn't test some apples for apples on the kind of endpoints that the other providers, that they are competitors with, on their benchmarks and that is due diligence baseline. And then the second would be more just optimizing for the right thing. You had some commentary on it. I'll just kind of let you riff.Soumith [00:21:41]: Yeah, I mean, in summary, basically my criticism of that was AnyScale built these benchmarks for end users to just understand what they should pick, right? And that's a very good thing to do. I think what they didn't do a good job of is give that end user a full understanding of what they should pick. Like they just gave them a very narrow slice of understanding. I think they just gave them latency numbers and that's not sufficient, right? You need to understand your total cost of ownership at some reasonable scale. Not oh, one API call is one cent, but a thousand API calls are 10 cents. Like people can misprice to cheat on those benchmarks. So you want to understand, okay, like how much is it going to cost me if I actually subscribe to you and do like a million API calls a month or something? And then you want to understand the latency and reliability, not just from one call you made, but an aggregate of calls you've made over several various times of the day and times of the week. And the nature of the workloads, is it just some generic single paragraph that you're sending that is cashable? Or is it like testing of real world workload? I think that kind of rigor, like in presenting that benchmark wasn't there. It was a much more narrow sliver of what should have been a good benchmark. That was my main criticism. And I'm pretty sure if before they released it, they showed it to their other stakeholders who would be caring about this benchmark because they are present in it, they would have easily just pointed out these gaps. And I think they didn't do that and they just released it. So I think those were the two main criticisms. I think they were fair and Robert took it well.Swyx [00:23:40]: And he took it very well. And we'll have him on at some point and we'll discuss it. But I think it's important for, I think the market being maturing enough that people start caring and competing on these kinds of things means that we need to establish what best practice is because otherwise everyone's going to play dirty.Soumith [00:23:55]: Yeah, absolutely. My view of the LLM inference market in general is that it's the laundromat model. Like the margins are going to drive down towards the bare minimum. It's going to be all kinds of arbitrage between how much you can get the hardware for and then how much you sell the API and how much latency your customers are willing to let go. You need to figure out how to squeeze your margins. Like what is your unique thing here? Like I think Together and Fireworks and all these people are trying to build some faster CUDA kernels and faster, you know, hardware kernels in general. But those modes only last for a month or two. These ideas quickly propagate.Swyx [00:24:38]: Even if they're not published?Soumith [00:24:39]: Even if they're not published, the idea space is small. So even if they're not published, the discovery rate is going to be pretty high. It's not like we're talking about a combinatorial thing that is really large. You're talking about Llama style LLM models. And we're going to beat those to death on a few different hardware SKUs, right? Like it's not even we have a huge diversity of hardware you're going to aim to run it on. Now when you have such a narrow problem and you have a lot of people working on it, the rate at which these ideas are going to get figured out is going to be pretty rapid.Swyx [00:25:15]: Is it a standard bag of tricks? Like the standard one that I know of is, you know, fusing operators and-Soumith [00:25:22]: Yeah, it's the standard bag of tricks on figuring out how to improve your memory bandwidth and all that, yeah.Alessio [00:25:28]: Any ideas instead of things that are not being beaten to death that people should be paying more attention to?Novel PyTorch ApplicationsSwyx [00:25:34]: One thing I was like, you know, you have a thousand operators, right? Like what's the most interesting usage of PyTorch that you're seeing maybe outside of this little bubble?Soumith [00:25:41]: So PyTorch, it's very interesting and scary at the same time, but basically it's used in a lot of exotic ways, like from the ML angle, what kind of models are being built? And you get all the way from state-based models and all of these things to stuff nth order differentiable models, like neural ODEs and stuff like that. I think there's one set of interestingness factor from the ML side of things. And then there's the other set of interesting factor from the applications point of view. It's used in Mars Rover simulations, to drug discovery, to Tesla cars. And there's a huge diversity of applications in which it is used. So in terms of the most interesting application side of things, I think I'm scared at how many interesting things that are also very critical and really important it is used in. I think the scariest was when I went to visit CERN at some point and they said they were using PyTorch and they were using GANs at the same time for particle physics research. And I was scared more about the fact that they were using GANs than they were using PyTorch, because at that time I was a researcher focusing on GANs. But the diversity is probably the most interesting. How many different things it is being used in. I think that's the most interesting to me from the applications perspective. From the models perspective, I think I've seen a lot of them. Like the really interesting ones to me are where we're starting to combine search and symbolic stuff with differentiable models, like the whole AlphaGo style models is one example. And then I think we're attempting to do it for LLMs as well, with various reward models and search. I mean, I don't think PyTorch is being used in this, but the whole alpha geometry thing was interesting because again, it's an example of combining the symbolic models with the gradient based ones. But there are stuff like alpha geometry that PyTorch is used at, especially when you intersect biology and chemistry with ML. In those areas, you want stronger guarantees on the output. So yeah, maybe from the ML side, those things to me are very interesting right now.Swyx [00:28:03]: Yeah. People are very excited about the alpha geometry thing. And it's kind of like, for me, it's theoretical. It's great. You can solve some Olympia questions. I'm not sure how to make that bridge over into the real world applications, but I'm sure people smarter than me will figure it out.Synthetic Data vs Symbolic ModelsSoumith [00:28:18]: Let me give you an example of it. You know how the whole thing about synthetic data will be the next rage in LLMs is a thing?Swyx [00:28:27]: Already is a rage.Soumith [00:28:28]: Which I think is fairly misplaced in how people perceive it. People think synthetic data is some kind of magic wand that you wave and it's going to be amazing. Synthetic data is useful in neural networks right now because we as humans have figured out a bunch of symbolic models of the world or made up certain symbolic models because of human innate biases. So we've figured out how to ground particle physics in a 30 parameter model. And it's just very hard to compute as in it takes a lot of flops to compute, but it only has 30 parameters or so. I mean, I'm not a physics expert, but it's a very low rank model. We built mathematics as a field that basically is very low rank. Language, a deep understanding of language, like the whole syntactic parse trees and just understanding how language can be broken down and into a formal symbolism is something that we figured out. So we basically as humans have accumulated all this knowledge on these subjects, either synthetic, we created those subjects in our heads, or we grounded some real world phenomenon into a set of symbols. But we haven't figured out how to teach neural networks symbolic world models directly. The only way we have to teach them is generating a bunch of inputs and outputs and gradient dissenting over them. So in areas where we have the symbolic models and we need to teach all the knowledge we have that is better encoded in the symbolic models, what we're doing is we're generating a bunch of synthetic data, a bunch of input output pairs, and then giving that to the neural network and asking it to learn the same thing that we already have a better low rank model of in gradient descent in a much more over-parameterized way. Outside of this, like where we don't have good symbolic models, like synthetic data obviously doesn't make any sense. So synthetic data is not a magic wand where it'll work in all cases in every case or whatever. It's just where we as humans already have good symbolic models off. We need to impart that knowledge to neural networks and we figured out the synthetic data is a vehicle to impart this knowledge to. So, but people, because maybe they don't know enough about synthetic data as a notion, but they hear, you know, the next wave of data revolution is synthetic data. They think it's some kind of magic where we just create a bunch of random data somehow. They don't think about how, and then they think that's just a revolution. And I think that's maybe a gap in understanding most people have in this hype cycle.Swyx [00:31:23]: Yeah, well, it's a relatively new concept, so. Oh, there's two more that I'll put in front of you and then you can see what you respond. One is, you know, I have this joke that it's, you know, it's only synthetic data if it's from the Mistral region of France, otherwise it's just a sparkling distillation, which is what news research is doing. Like they're distilling GPT-4 by creating synthetic data from GPT-4, creating mock textbooks inspired by Phi 2 and then fine tuning open source models like Llama. And so I don't know, I mean, I think that's, should we call that synthetic data? Should we call it something else? I don't know.Soumith [00:31:57]: Yeah, I mean, the outputs of LLMs, are they synthetic data? They probably are, but I think it depends on the goal you have. If your goal is you're creating synthetic data with the goal of trying to distill GPT-4's superiority into another model, I guess you can call it synthetic data, but it also feels like disingenuous because your goal is I need to copy the behavior of GPT-4 and-Swyx [00:32:25]: It's also not just behavior, but data set. So I've often thought of this as data set washing. Like you need one model at the top of the chain, you know, unnamed French company that has that, you know, makes a model that has all the data in it that we don't know where it's from, but it's open source, hey, and then we distill from that and it's great. To be fair, they also use larger models as judges for preference ranking, right? So that is, I think, a very, very accepted use of synthetic.Soumith [00:32:53]: Correct. I think it's a very interesting time where we don't really have good social models of what is acceptable depending on how many bits of information you use from someone else, right? It's like, okay, you use one bit. Is that okay? Yeah, let's accept it to be okay. Okay, what about if you use 20 bits? Is that okay? I don't know. What if you use 200 bits? I don't think we as society have ever been in this conundrum where we have to be like, where is the boundary of copyright or where is the boundary of socially accepted understanding of copying someone else? We haven't been tested this mathematically before,Swyx [00:33:38]: in my opinion. Whether it's transformative use. Yes. So yeah, I think this New York Times opening eye case is gonna go to the Supreme Court and we'll have to decide it because I think we never had to deal with it before. And then finally, for synthetic data, the thing that I'm personally exploring is solving this great stark paradigm difference between rag and fine tuning, where you can kind of create synthetic data off of your retrieved documents and then fine tune on that. That's kind of synthetic. All you need is variation or diversity of samples for you to fine tune on. And then you can fine tune new knowledge into your model. I don't know if you've seen that as a direction for synthetic data.Soumith [00:34:13]: I think you're basically trying to, what you're doing is you're saying, well, language, I know how to parametrize language to an extent. And I need to teach my model variations of this input data so that it's resilient or invariant to language uses of that data.Swyx [00:34:32]: Yeah, it doesn't overfit on the wrong source documents.Soumith [00:34:33]: So I think that's 100% synthetic. You understand, the key is you create variations of your documents and you know how to do that because you have a symbolic model or like some implicit symbolic model of language.Swyx [00:34:48]: Okay.Alessio [00:34:49]: Do you think the issue with symbolic models is just the architecture of the language models that we're building? I think maybe the thing that people grasp is the inability of transformers to deal with numbers because of the tokenizer. Is it a fundamental issue there too? And do you see alternative architectures that will be better with symbolic understanding?Soumith [00:35:09]: I am not sure if it's a fundamental issue or not. I think we just don't understand transformers enough. I don't even mean transformers as an architecture. I mean the use of transformers today, like combining the tokenizer and transformers and the dynamics of training, when you show math heavy questions versus not. I don't have a good calibration of whether I know the answer or not. I, you know, there's common criticisms that are, you know, transformers will just fail at X. But then when you scale them up to sufficient scale, they actually don't fail at that X. I think there's this entire subfield where they're trying to figure out these answers called like the science of deep learning or something. So we'll get to know more. I don't know the answer.Meta AI and Llama 2/3Swyx [00:35:57]: Got it. Let's touch a little bit on just Meta AI and you know, stuff that's going on there. Maybe, I don't know how deeply you're personally involved in it, but you're our first guest with Meta AI, which is really fantastic. And Llama 1 was, you know, you are such a believer in open source. Llama 1 was more or less the real breakthrough in open source AI. The most interesting thing for us covering on this, in this podcast was the death of Chinchilla, as people say. Any interesting insights there around the scaling models for open source models or smaller models or whatever that design decision was when you guys were doing it?Soumith [00:36:31]: So Llama 1 was Guillaume Lample and team. There was OPT before, which I think I'm also very proud of because we bridged the gap in understanding of how complex it is to train these models to the world. Like until then, no one really in gory detail published.Swyx [00:36:50]: The logs.Soumith [00:36:51]: Yeah. Like, why is it complex? And everyone says, oh, it's complex. But no one really talked about why it's complex. I think OPT was cool.Swyx [00:37:02]: I met Susan and she's very, very outspoken. Yeah.Soumith [00:37:05]: We probably, I think, didn't train it for long enough, right? That's kind of obvious in retrospect.Swyx [00:37:12]: For a 175B. Yeah. You trained it according to Chinchilla at the time or?Soumith [00:37:17]: I can't remember the details, but I think it's a commonly held belief at this point that if we trained OPT longer, it would actually end up being better. Llama 1, I think, was Guillaume Lample and team Guillaume is fantastic and went on to build Mistral. I wasn't too involved in that side of things. So I don't know what you're asking me, which is how did they think about scaling loss and all of that? Llama 2, I was more closely involved in. I helped them a reasonable amount with their infrastructure needs and stuff. And Llama 2, I think, was more like, let's get to the evolution. At that point, we kind of understood what we were missing from the industry's understanding of LLMs. And we needed more data and we needed more to train the models for longer. And we made, I think, a few tweaks to the architecture and we scaled up more. And that was Llama 2. I think Llama 2, you can think of it as after Guillaume left, the team kind of rebuilt their muscle around Llama 2. And Hugo, I think, who's the first author is fantastic. And I think he did play a reasonable big role in Llama 1 as well.Soumith [00:38:35]: And he overlaps between Llama 1 and 2. So in Llama 3, obviously, hopefully, it'll be awesome.Alessio [00:38:42]: Just one question on Llama 2, and then we'll try and fish Llama 3 spoilers out of you. In the Llama 2 paper, the loss curves of the 34 and 70B parameter, they still seem kind of steep. Like they could go lower. How, from an infrastructure level, how do you allocate resources? Could they have just gone longer or were you just, hey, this is all the GPUs that we can burn and let's just move on to Llama 3 and then make that one better?Soumith [00:39:07]: Instead of answering specifically about that Llama 2 situation or whatever, I'll tell you how we think about things. Generally, we're, I mean, Mark really is some numbers, right?Swyx [00:39:20]: So let's cite those things again. All I remember is like 600K GPUs.Soumith [00:39:24]: That is by the end of this year and 600K H100 equivalents. With 250K H100s, including all of our other GPU or accelerator stuff, it would be 600-and-something-K aggregate capacity.Swyx [00:39:38]: That's a lot of GPUs.Soumith [00:39:39]: We'll talk about that separately. But the way we think about it is we have a train of models, right? Llama 1, 2, 3, 4. And we have a bunch of GPUs. I don't think we're short of GPUs. Like-Swyx [00:39:54]: Yeah, no, I wouldn't say so. Yeah, so it's all a matter of time.Soumith [00:39:56]: I think time is the biggest bottleneck. It's like, when do you stop training the previous one and when do you start training the next one? And how do you make those decisions? The data, do you have net new data, better clean data for the next one in a way that it's not worth really focusing on the previous one? It's just a standard iterative product. You're like, when is the iPhone 1? When do you start working on iPhone 2? Where is the iPhone? And so on, right? So mostly the considerations are time and generation, rather than GPUs, in my opinion.Alessio [00:40:31]: So one of the things with the scaling loss, like Chinchilla is optimal to balance training and inference costs. I think at Meta's scale, you would rather pay a lot more maybe at training and then save on inference. How do you think about that from infrastructure perspective? I think in your tweet, you say you can try and guess on like how we're using these GPUs. Can you just give people a bit of understanding? It's like, because I've already seen a lot of VCs say, Llama 3 has been trained on 600,000 GPUs and that's obviously not true, I'm sure. How do you allocate between the research, FAIR and the Llama training, the inference on Instagram suggestions that get me to scroll, like AI-generated stickers on WhatsApp and all of that?Soumith [00:41:11]: Yeah, we haven't talked about any of this publicly, but as a broad stroke, it's like how we would allocate resources of any other kinds at any company. You run a VC portfolio, how do you allocate your investments between different companies or whatever? You kind of make various trade-offs and you kind of decide, should I invest in this project or this other project, or how much should I invest in this project? It's very much a zero sum of trade-offs. And it also comes into play, how are your clusters configured, like overall, what you can fit of what size and what cluster and so on. So broadly, there's no magic sauce here. I mean, I think the details would add more spice, but also wouldn't add more understanding. It's just gonna be like, oh, okay, I mean, this looks like they just think about this as I would normally do.Alessio [00:42:05]: So even the GPU rich run through the same struggles of having to decide where to allocate things.Soumith [00:42:11]: Yeah, I mean, at some point I forgot who said it, but you kind of fit your models to the amount of compute you have. If you don't have enough compute, you figure out how to make do with smaller models. But no one as of today, I think would feel like they have enough compute. I don't think I've heard any company within the AI space be like, oh yeah, like we feel like we have sufficient compute and we couldn't have done better. So that conversation, I don't think I've heard from any of my friends at other companies.EleutherSwyx [00:42:47]: Stella from Eleuther sometimes says that because she has a lot of donated compute. She's trying to put it to interesting uses, but for some reason she's decided to stop making large models.Soumith [00:42:57]: I mean, that's a cool, high conviction opinion that might pay out.Swyx [00:43:01]: Why?Soumith [00:43:02]: I mean, she's taking a path that most people don't care to take about in this climate and she probably will have very differentiated ideas. I mean, think about the correlation of ideas in AI right now. It's so bad, right? So everyone's fighting for the same pie. In some weird sense, that's partly why I don't really directly work on LLMs. I used to do image models and stuff and I actually stopped doing GANs because GANs were getting so hot that I didn't have any calibration of whether my work would be useful or not because, oh yeah, someone else did the same thing you did. It's like, there's so much to do, I don't understand why I need to fight for the same pie. So I think Stella's decision is very smart.Making BetsAlessio [00:43:53]: And how do you reconcile that with how we started the discussion about intrinsic versus extrinsic kind of like accomplishment or success? How should people think about that especially when they're doing a PhD or early in their career? I think in Europe, I walked through a lot of the posters and whatnot, there seems to be mode collapse in a way in the research, a lot of people working on the same things. Is it worth for a PhD to not take a bet on something that is maybe not as interesting just because of funding and visibility and whatnot? Or yeah, what suggestions would you give?Soumith [00:44:28]: I think there's a baseline level of compatibility you need to have with the field. Basically, you need to figure out if you will get paid enough to eat, right? Like whatever reasonable normal lifestyle you want to have as a baseline. So you at least have to pick a problem within the neighborhood of fundable. Like you wouldn't wanna be doing something so obscure that people are like, I don't know, like you can work on it.Swyx [00:44:59]: Would a limit on fundability, I'm just observing something like three months of compute, right? That's the top line, that's the like max that you can spend on any one project.Soumith [00:45:09]: But like, I think that's very ill specified, like how much compute, right? I think that the notion of fundability is broader. It's more like, hey, are these family of models within the acceptable set of, you're not crazy or something, right? Even something like neural or DS, which is a very boundary pushing thing or states-based models or whatever. Like all of these things I think are still in fundable territory. When you're talking about, I'm gonna do one of the neuromorphic models and then apply image classification to them or something, then it becomes a bit questionable. Again, it depends on your motivation. Maybe if you're a neuroscientist, it actually is feasible. But if you're an AI engineer, like the audience of these podcasts, then it's more questionable. The way I think about it is, you need to figure out how you can be in the baseline level of fundability just so that you can just live. And then after that, really focus on intrinsic motivation and depends on your strengths, like how you can play to your strengths and your interests at the same time. Like I try to look at a bunch of ideas that are interesting to me, but also try to play to my strengths. I'm not gonna go work on theoretical ML. I'm interested in it, but when I want to work on something like that, I try to partner with someone who is actually a good theoretical ML person and see if I actually have any value to provide. And if they think I do, then I come in. So I think you'd want to find that intersection of ideas you like, and that also play to your strengths. And I'd go from there. Everything else, like actually finding extrinsic success and all of that, I think is the way I think about it is like somewhat immaterial. When you're talking about building ecosystems and stuff, slightly different considerations come into play, but that's a different conversation.Swyx [00:47:06]: We're gonna pivot a little bit to just talking about open source AI. But one more thing I wanted to establish for Meta is this 600K number, just kind of rounding out the discussion, that's for all Meta. So including your own inference needs, right? It's not just about training.Soumith [00:47:19]: It's gonna be the number in our data centers for all of Meta, yeah.Swyx [00:47:23]: Yeah, so there's a decent amount of workload serving Facebook and Instagram and whatever. And then is there interest in like your own hardware?MTIASoumith [00:47:31]: We already talked about our own hardware. It's called MTIA. Our own silicon, I think we've even showed the standard photograph of you holding the chip that doesn't work. Like as in the chip that you basically just get like-Swyx [00:47:51]: As a test, right?Soumith [00:47:52]: Yeah, a test chip or whatever. So we are working on our silicon and we'll probably talk more about it when the time is right, but-Swyx [00:48:00]: Like what gaps do you have that the market doesn't offer?Soumith [00:48:04]: Okay, I mean, this is easy to answer. So basically, remember how I told you about there's this memory hierarchy and like sweet spots and all of that? Fundamentally, when you build a hardware, you make it general enough that a wide set of customers and a wide set of workloads can use it effectively while trying to get the maximum level of performance they can. The more specialized you make the chip, the more hardware efficient it's going to be, the more power efficient it's gonna be, the more easier it's going to be to find the software, like the kernel's right to just map that one or two workloads to that hardware and so on. So it's pretty well understood across the industry that if you have a sufficiently large volume, enough workload, you can specialize it and get some efficiency gains, like power gains and so on. So the way you can think about everyone building, every large company building silicon, I think a bunch of the other large companies are building their own silicon as well, is they, each large company has a sufficient enough set of verticalized workloads that can be specialized that have a pattern to them that say a more generic accelerator like an NVIDIA or an AMD GPU does not exploit. So there is some level of power efficiency that you're leaving on the table by not exploiting that. And you have sufficient scale and you have sufficient forecasted stability that those workloads will exist in the same form, that it's worth spending the time to build out a chip to exploit that sweet spot. Like obviously something like this is only useful if you hit a certain scale and that your forecasted prediction of those kind of workloads being in the same kind of specializable exploitable way is true. So yeah, that's why we're building our own chips.Swyx [00:50:08]: Awesome.Open Source AIAlessio [00:50:09]: Yeah, I know we've been talking a lot on a lot of different topics and going back to open source, you had a very good tweet. You said that a single company's closed source effort rate limits against people's imaginations and needs. How do you think about all the impact that some of the Meta AI work in open source has been doing and maybe directions of the whole open source AI space?Soumith [00:50:32]: Yeah, in general, I think first, I think it's worth talking about this in terms of open and not just open source, because like with the whole notion of model weights, no one even knows what source means for these things. But just for the discussion, when I say open source, you can assume it's just I'm talking about open. And then there's the whole notion of licensing and all that, commercial, non-commercial, commercial with clauses and all that. I think at a fundamental level, the most benefited value of open source is that you make the distribution to be very wide. It's just available with no friction and people can do transformative things in a way that's very accessible. Maybe it's open source, but it has a commercial license and I'm a student in India. I don't care about the license. I just don't even understand the license. But like the fact that I can use it and do something with it is very transformative to me. Like I got this thing in a very accessible way. And then it's various degrees, right? And then if it's open source, but it's actually a commercial license, then a lot of companies are gonna benefit from gaining value that they didn't previously have, that they maybe had to pay a closed source company for it. So open source is just a very interesting tool that you can use in various ways. So there's, again, two kinds of open source. One is some large company doing a lot of work and then open sourcing it. And that kind of effort is not really feasible by say a band of volunteers doing it the same way. So there's both a capital and operational expenditure that the large company just decided to ignore and give it away to the world for some benefits of some kind. They're not as tangible as direct revenue. So in that part, Meta has been doing incredibly good things. They fund a huge amount of the PyTorch development. They've open sourced Llama and those family of models and several other fairly transformative projects. FICE is one, Segment Anything, Detectron, Detectron 2. Dense Pose. I mean, it's-Swyx [00:52:52]: Seamless. Yeah, seamless.Soumith [00:52:53]: Like it's just the list is so long that we're not gonna cover. So I think Meta comes into that category where we spend a lot of CapEx and OpEx and we have a high talent density of great AI people and we open our stuff. And the thesis for that, I remember when FAIR was started, the common thing was like, wait, why would Meta wanna start a open AI lab? Like what exactly is a benefit from a commercial perspective? And for then the thesis was very simple. It was AI is currently rate limiting Meta's ability to do things. Our ability to build various product integrations, moderation, various other factors. Like AI was the limiting factor and we just wanted AI to advance more and we didn't care if the IP of the AI was uniquely in our possession or not. However the field advances, that accelerates Meta's ability to build a better product. So we just built an open AI lab and we said, if this helps accelerate the progress of AI, that's strictly great for us. But very easy, rational, right? Still the same to a large extent with the Llama stuff. And it's the same values, but the argument, it's a bit more nuanced. And then there's a second kind of open source, which is, oh, we built this project, nights and weekends and we're very smart people and we open sourced it and then we built a community around it. This is the Linux kernel and various software projects like that. So I think about open source, like both of these things being beneficial and both of these things being different. They're different and beneficial in their own ways. The second one is really useful when there's an active arbitrage to be done. If someone's not really looking at a particular space because it's not commercially viable or whatever, like a band of volunteers can just coordinate online and do something and then make that happen. And that's great.Open Source LLMsI wanna cover a little bit about open source LLMs maybe. So open source LLMs have been very interesting because I think we were trending towards an increase in open source in AI from 2010 all the way to 2017 or something. Like where more and more pressure within the community was to open source their stuff so that their methods and stuff get adopted. And then the LLMs revolution kind of took the opposite effect OpenAI stopped open sourcing their stuff and DeepMind kind of didn't, like all the other cloud and all these other providers, they didn't open source their stuff. And it was not good in the sense that first science done in isolation probably will just form its own bubble where people believe their own b******t or whatever. So there's that problem. And then there was the other problem which was the accessibility part. Like, okay, I again always go back to I'm a student in India with no money. What is my accessibility to any of these closers models? At some scale I have to pay money. That makes it a non-starter and stuff. And there's also the control thing. I strongly believe if you want human aligned stuff, you want all humans to give feedback. And you want all humans to have access to that technology in the first place. And I actually have seen, living in New York, whenever I come to Silicon Valley, I see a different cultural bubble. Like all the friends I hang out with talk about some random thing like Dyson Spheres or whatever, that's a thing. And most of the world doesn't know or care about any of this stuff. It's definitely a bubble and bubbles can form very easily. And when you make a lot of decisions because you're in a bubble, they're probably not globally optimal decisions. So I think open source, the distribution of open source powers a certain kind of non-falsifiability that I think is very important. I think on the open source models, like it's going great in the fact that LoRa I think came out of the necessity of open source models needing to be fine-tunable in some way. Yeah, and I think DPO also came out of the academic open source side of things. So do any of the closed source labs, did any of them already have LoRa or DPO internally? Maybe, but that does not advance humanity in any way. It advances some companies probability of doing the winner takes all that I talked about earlier in the podcast.Open Source and TrustI don't know, it just feels fundamentally good. Like when people try to, you know, people are like, well, what are the ways in which it is not okay? I find most of these arguments, and this might be a little controversial, but I find a lot of arguments based on whether closed source models are safer or open source models are safer very much related to what kind of culture they grew up in, what kind of society they grew up in. If they grew up in a society that they trusted, then I think they take the closed source argument. And if they grew up in a society that they couldn't trust, where the norm was that you didn't trust your government, obviously it's corrupt or whatever, then I think the open source argument is what they take. I think there's a deep connection to like people's innate biases from their childhood and their trust in society and governmental aspects that push them towards one opinion or the other. And I'm definitely in the camp of open source is definitely going to actually have better outcomes for society. Closed source to me just means that centralization of power, which, you know, is really hard to trust. So I think it's going well

Microsoft's layoffs have sent the FTC into a tizzy, adding fuel to the ongoing fire. Also: It's earnings season, with EA, Microsoft, Capcom, Nintendo, Square Enix, Paradox, Take-Two, Sega, and Meta reporting. We also cover the latest in the layoff storm that continues to drown the industry and Disney's $1.5B investment in Epic. This is episode is sponsored by Magic Mind, a supplement shot that includes ingredients to lower your stress, reduce fatigue, and boost your immune system. You can get 20% off your order of Magic Mind at https://www.magicmind.com using the code VirtualEconomy20.  You can support Virtual Economy's growth via our Ko-Fi and also purchase Virtual Economy merchandise! TIME STAMPS [00:01:14] - EA Earnings [00:12:44] - Microsoft Earnings [00:22:59] - Capcom Earnings [00:28:23] - Nintendo Earnings [00:31:09] - Square Enix Earnings [00:37:45] - Paradox Earnings [00:42:38] - Take-Two Earnings [00:49:17] - SegaSammy Earnings [00:53:09] - Meta Earnings (Brief) [00:55:57] - Investment Interlude [01:12:13] - Quick Hits [01:19:36] - Labor Report SOURCES Electronic Arts Reports Strong Q3 FY24 Results | EA Microsoft Earnings Release FY24 Q2 | Microsoft Capcom On Track to Achieve Full-Year Guidance | Capcom Consolidated Results for the Nine Months Ended December 31, 2022 and 2023 | Nintendo Consolidated Financial Results for the Nine-Month Period Ended December 31, 2023 | Square Enix Year-end report January – December 2023 | Paradox Take-Two Interactive Software, Inc. Reports Results for Fiscal Third Quarter 2024 | Take-Two SegaSammy Q3 Results Presentation | SegaSammy Meta Reality Labs loses $4.65B in Q4 | Meta Tencent CEO feels its game business "achieved nothing" during 2023 | Game Developer Embracer lays off staff at Star Trek: Infinite dev Nimble Giant | Game Developer Wayfinder dev Airship Syndicate lays off 12 employees | Game Developer Square Enix absorbs Tokyo RPG Factory | Gematsu Hidden Path Layoffs | Jeff Pobst on LinkedIn Roughly half of Devolver's Artificer studio laid off | Games Industry Devolver Digital CEO Douglas Morin steps down | Games Industry Layoffs at Netease, MiHoYo, Tencent | Core Esports The FTC is Going After Microsoft Over Layoffs | Tom Warren (@tomwarren) on Twitter …And Microsoft has Responded | Tom Warren (@tomwarren) on Twitter INVESTMENT INTERLUDE Nordcurrent snaps up Cinemaware library | Games Industry Disney and Epic Games to Create Expansive and Open Games and Entertainment Universe Connected to Fortnite | Disney Nitro Games receives further €3.5 million to continue Warframe mobile development | Pocket Gamer Six Nations-backer CVC in talks to buy Runescape-maker for £900m | Sky News FromSoftware owner Kadokawa acquires Octopath Traveler studio | Eurogamer

Dave's been the Director of Sales Development for EMEA at Snowflake for the last year. He shares his lessons from managing and coaching five SDR managers and 50 SDRs at a unicorn company valued over $65B. *Subscribe to SDR Hire Insights newsletter: https://sdrhire.com/#newsletter In this episode, Srba and Dave discuss: - how Dave went from a BDR at Palo Alto Networks in 2013 to leading a global SDR team - what's it like leading a 50 people SDR team - how to build a successful SDR team from scratch - what are the winning traits of A players - what's the difference between a manager and a leader Hope you share and enjoy! - Connect with David on LinkedIn: https://www.linkedin.com/in/daveewilkins/ - Connect with Srba on LinkedIn: https://www.linkedin.com/in/srbamarkovic - Connect with Stefan on LinkedIn: https://www.linkedin.com/in/stefan-conic/ --------------------------------------- Resources and creators mentioned: - David's Ebook: Closing the Gap: Turbocharge Your SDR and Sales Team Alignment: https://payhip.com/b/qDTGY - Davis's newsletter: https://www.linkedin.com/newsletters/mastering-sales-development-7135961833558958080/ - Andy Laws: https://www.linkedin.com/in/andy-laws/ --------------------------------------- SDR Hire helps build remote SDR and sales teams. Experienced, trained, near to native, ready to be hired. Listen on YouTube, Apple, Spotify, Google podcast.

* Hunter Biden: I am responsible for my mistakes. * House GOP Starts Contempt Proceedings Against Hunter Biden - Forbes. * December White House Survey, Paid for by the NRSC, Not authorized by any candidate or candidate's committee - NRSC.org * Who should be the Republican Presidential nominee in 2024? * How important is it for a Republican Presidential nominee to advocate for a SECURE southern border? * How important is it for a Republican Presidential nominee to be TOUGH on crime? * How Important is it for a Republican Presidential nominee to UPHOLD conservative values? * Foreign investors are expected to pull $65B in capital out of China in 2024 as rising tensions and worry over the country's economy plague future outlooks - The Institute of International Finance (IIF). * Treasury Secretary Janet Yellen gave a speech on Thursday to celebrate the 50th anniversary of the US -China Business Council, emphasizing that the two countries should not economically "decouple," - Politico.

This week, we have a bonus mini episode where Jacquelyn talks with TechCrunch+ editor-in-chief Alex Wilhelm to dive back into the Sam Bankman-Fried trial and what has transpired in its second week. Major witnesses who were involved in the downfall of FTX and its sister company Alameda testified like Gary Wang, CTO and cofounder of FTX, and Caroline Ellison, CEO of Alameda. The two of them plead guilty to a number of charges and could face maximum sentences of 50 to 110 years, respectively. It's also worth noting Wang and Ellison testified as part of a cooperation agreement for pleading guilty. Jacquelyn and Alex talk about key points from the trial, anecdotes that you can't read on a transcript and what she anticipates from both the prosecutors and defense going forward. Want more? Here's the latest on the SBF trial: Former Alameda CEO Caroline Ellison explains how FTX hid losses, sandbagged lendersAlameda Research allegedly paid Chinese officials around $150M to regain $1B worth of exchange accountsAlameda Research's ex-CEO Caroline Ellison testifies, claims SBF directed her to commit crimesAlameda had a $65B line of credit and ‘unlimited withdrawals'As SBF's trial heads into its second week, here's what we know so farChain Reaction comes out every Thursday at 12:00 p.m. ET, so be sure to subscribe to us on Apple Podcasts, Spotify or your favorite pod platform to keep up with the action.

This week, we have a bonus mini episode where Jacquelyn talks with TechCrunch+ editor-in-chief Alex Wilhelm to dive back into the Sam Bankman-Fried trial and what has transpired in its second week. Major witnesses who were involved in the downfall of FTX and its sister company Alameda testified like Gary Wang, CTO and cofounder of FTX, and Caroline Ellison, CEO of Alameda. The two of them plead guilty to a number of charges and could face maximum sentences of 50 to 110 years, respectively. It's also worth noting Wang and Ellison testified as part of a cooperation agreement for pleading guilty. Jacquelyn and Alex talk about key points from the trial, anecdotes that you can't read on a transcript and what she anticipates from both the prosecutors and defense going forward. Want more? Here's the latest on the SBF trial: Former Alameda CEO Caroline Ellison explains how FTX hid losses, sandbagged lendersAlameda Research allegedly paid Chinese officials around $150M to regain $1B worth of exchange accountsAlameda Research's ex-CEO Caroline Ellison testifies, claims SBF directed her to commit crimesAlameda had a $65B line of credit and ‘unlimited withdrawals'As SBF's trial heads into its second week, here's what we know so farChain Reaction comes out every Thursday at 12:00 p.m. ET, so be sure to subscribe to us on Apple Podcasts, Spotify or your favorite pod platform to keep up with the action.

*) Türkiye destroys terror targets in Syria Türkiye has hit the terrorist group YPG/PKK positions as part of its anti-terror operations in northern Syria, destroying dozens of targets and "neutralising" many terrorists. 30 targets, including an oil well and a storage facility, caves, bunkers, shelters and warehouses used by terrorists were destroyed, Turkish Defence Ministry said in a statement. The Turkish anti-terror operation came after PKK/YPG attacked the Interior Ministry in the Turkish capital Ankara last Sunday. *) Bangladesh gets first uranium shipment from Russia for nuclear power plant Bangladesh has received the first Russian shipment of uranium fuel for its $12.65B debut nuclear power plant, making it the 33rd country in the world to produce nuclear power. The South Asian country is building the first of two nuclear power plants in collaboration with Russian state-owned atomic company Rosatom. Ninety percent of the project is financed through a Russian loan repayable within 28 years with a 10-year grace period. *) Biden to extend US border wall with Mexico using Trump-era funds US President Joe Biden has defended his plans to extend the border wall with Mexico, saying he didn't think such barriers worked, but he was bound by laws introduced under former president Donald Trump. Biden, who is polling neck-and-neck with rival Trump ahead of a likely 2024 rematch, insisted on Thursday his predecessor had tied his hands on the wall-building. "They have to use the money for what it was appropriated for. I can't stop that," he told reporters in the Oval Office. The US president also stressed that the border wall is ineffective. *) Drone attack on Syrian military ceremony kills 80, wounds 240 An attack on a Syrian crowded military graduation ceremony has killed 80 and wounded 240 others. Civilians, including children, and military personnel were among the dead. Syria's regime said in an earlier statement that drones laden with explosives targeted the ceremony. No group has claimed responsibility for the attack. *) Eastern Canada breaks autumn heat records Eastern Canada has shattered heat records this week with temperatures close to 30 degrees Celsius, worrying experts and everyday people struggling to cope with extreme weather made worse by the climate crisis. In the last three days, heat records were broken in Quebec and adjacent provinces. On Wednesday, the mercury reached 29.3 degrees Celsius in Montreal, surpassing the record of 26.7 degrees set in 2005.

Salesforce's Q2 FY24 total revenue came in at $8.6B, beating analysts expected $8.53B, and represented 11% growth year-over-year. Salesforce raised their full year FY24 revenue guidance from $34.7B to $34.8B, which would represent 11% growth year-over-year and beat the expected guidance of $34.65B. They stated recent price increases were accounted for in these numbers, but the increases did not have a significant influence on what they are now expecting for revenue. Elongated sales cycles, additional deal approval layers and deal compression were also factored into Salesforce's revenue guidance. It was made abundantly clear that Salesforce is going to be heavily pushing Data Cloud and AI solutions, like AI CRM, onto their customer base. Both are seen as significant revenue and cloud expansion drivers for Benioff's modern version of Salesforce: “AI + Data + CRM + Trust.” In this podcast, our Salesforce Practice Leader, Adam Mansfield, discusses what customers should expect from Salesforce leading up to their year-end and beyond. He also shares how customers should approach Salesforce and how best to leverage Salesforce's clear goals during their upcoming negotiations. Host: Adam Mansfield: https://bit.ly/3rPGp8r SalesForce Commercial Advisory Services: https://bit.ly/2V78ADX

We are now launching our dedicated new YouTube and Twitter! Any help in amplifying our podcast would be greatly appreciated, and of course, tell your friends! Notable followon discussions collected on Twitter, Reddit, Reddit, Reddit, HN, and HN. Please don't obsess too much over the GPT4 discussion as it is mostly rumor; we spent much more time on tinybox/tinygrad on which George is the foremost authority!We are excited to share the world's first interview with George Hotz on the tiny corp!If you don't know George, he was the first person to unlock the iPhone, jailbreak the PS3, went on to start Comma.ai, and briefly “interned” at the Elon Musk-run Twitter. Tinycorp is the company behind the deep learning framework tinygrad, as well as the recently announced tinybox, a new $15,000 “luxury AI computer” aimed at local model training and inference, aka your “personal compute cluster”:* 738 FP16 TFLOPS* 144 GB GPU RAM* 5.76 TB/s RAM bandwidth* 30 GB/s model load bandwidth (big llama loads in around 4 seconds)* AMD EPYC CPU* 1600W (one 120V outlet)* Runs 65B FP16 LLaMA out of the box (using tinygrad, subject to software development risks)(In the episode, we also talked about the future of the tinybox as the intelligence center of every home that will help run models, at-home robots, and more. Make sure to check the timestamps

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: MetaAI: less is less for alignment., published by Cleo Nardo on June 13, 2023 on LessWrong. Summary In May 2023, MetaAI submitted a paper to arxiv called LIMA: Less Is More for Alignment. It's a pretty bad paper and (in my opinion) straightforwardly misleading. Let's get into it. The Superficial Alignment Hypothesis The authors present an interesting hypothesis about LLMs We define the Superficial Alignment Hypothesis: A model's knowledge and capabilities are learnt almost entirely during pretraining, while alignment teaches it which subdistribution of formats should be used when interacting with users. If this hypothesis is correct, and alignment is largely about learning style, then a corollary of the Superficial Alignment Hypothesis is that one could sufficiently tune a pretrained language model with a rather small set of examples. We hypothesize that alignment can be a simple process where the model learns the style or format for interacting with users, to expose the knowledge and capabilities that were already acquired during pretraining. (1) This hypothesis would have profound implications for AI x-risk It suggests that we could build a safe competent oracle by pretraining an LLM on the entire internet corpus, and then finetuning the LLM on a curated dataset of safe competent responses. It suggests that we could build an alignment researcher by pretraining an LLM on the entire internet corpus, and then finetuning the LLM on a curated dataset of alignment research. (2) Moreover, as by Ulisse Mini writes in their review of the LIMA paper, Along with TinyStories and QLoRA I'm becoming increasingly convinced that data quality is all you need, definitely seems to be the case for finetuning, and may be the case for base-model training as well. Better scaling laws through higher-quality corpus? Also for who haven't updated, it seems very likely that GPT-4 equivalents will be essentially free to self-host and tune within a year. Plan for this! (3) Finally, the hypothesis would've supported many of the intuitions in the Simulators sequence by Janus, and I share these intuitions. So I was pretty excited to read the paper! Unfortunately, the LIMA results were unimpressive upon inspection. MetaAI's experiment The authors finetune MetaAI's 65B parameter LLaMa language model on 1000 curated prompts and responses (mostly from StackExchange, wikiHow, and Reddit), and then compare it to five other LLMs (Alpaca 65B, DaVinci003, Bard, Claude, GPT4). Method: To compare LIMA to other models, we generate a single response for each test prompt. We then ask crowd workers to compare LIMA outputs to each of the baselines and label which one they prefer. We repeat this experiment, replacing human crowd workers with GPT-4, finding similar agreement levels. Results: In a controlled human study, responses from LIMA are either equivalent or strictly preferred to GPT-4 in 43% of cases; this statistic is as high as 58% when compared to Bard and 65% versus DaVinci003, which was trained with human feedback. Conclusion: The fact that simple fine-tuning over so few examples is enough to compete with the state of the art strongly supports the Superficial Alignment Hypothesis, as it demonstrates the power of pretraining and its relative importance over large-scale instruction tuning and reinforcement learning approaches. Problems with their experiment (1) Human evaluators To compare two chatbots A and B, you could ask humans whether they prefer A's response to B's response across 300 test prompts. But this is pretty bad proxy, because here's what users actually care about: What's the chatbots' accuracy on benchmark tests, e.g. BigBench, MMLU? Can the chatbot pass a law exam, or a medical exam? Can the chatbot write Python code that actually matches the specification? Can the chatbot perform worthwhi...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: MetaAI: less is less for alignment., published by Cleo Nardo on June 13, 2023 on The AI Alignment Forum. Summary In May 2023, MetaAI submitted a paper to arxiv called LIMA: Less Is More for Alignment. It's a pretty bad paper and (in my opinion) straightforwardly misleading. Let's get into it. The Superficial Alignment Hypothesis The authors present an interesting hypothesis about LLMs We define the Superficial Alignment Hypothesis: A model's knowledge and capabilities are learnt almost entirely during pretraining, while alignment teaches it which subdistribution of formats should be used when interacting with users. If this hypothesis is correct, and alignment is largely about learning style, then a corollary of the Superficial Alignment Hypothesis is that one could sufficiently tune a pretrained language model with a rather small set of examples. We hypothesize that alignment can be a simple process where the model learns the style or format for interacting with users, to expose the knowledge and capabilities that were already acquired during pretraining. (1) This hypothesis would have profound implications for AI x-risk It suggests that we could build a safe competent oracle by pretraining an LLM on the entire internet corpus, and then finetuning the LLM on a curated dataset of safe competent responses. It suggests that we could build an alignment researcher by pretraining an LLM on the entire internet corpus, and then finetuning the LLM on a curated dataset of alignment research. (2) Moreover, as by Ulisse Mini writes in their review of the LIMA paper, Along with TinyStories and QLoRA I'm becoming increasingly convinced that data quality is all you need, definitely seems to be the case for finetuning, and may be the case for base-model training as well. Better scaling laws through higher-quality corpus? Also for who haven't updated, it seems very likely that GPT-4 equivalents will be essentially free to self-host and tune within a year. Plan for this! (3) Finally, the hypothesis would've supported many of the intuitions in the Simulators sequence by Janus, and I share these intuitions. So I was pretty excited to read the paper! Unfortunately, the LIMA results were unimpressive upon inspection. MetaAI's experiment The authors finetune MetaAI's 65B parameter LLaMa language model on 1000 curated prompts and responses (mostly from StackExchange, wikiHow, and Reddit), and then compare it to five other LLMs (Alpaca 65B, DaVinci003, Bard, Claude, GPT4). Method: To compare LIMA to other models, we generate a single response for each test prompt. We then ask crowd workers to compare LIMA outputs to each of the baselines and label which one they prefer. We repeat this experiment, replacing human crowd workers with GPT-4, finding similar agreement levels. Results: In a controlled human study, responses from LIMA are either equivalent or strictly preferred to GPT-4 in 43% of cases; this statistic is as high as 58% when compared to Bard and 65% versus DaVinci003, which was trained with human feedback. Conclusion: The fact that simple fine-tuning over so few examples is enough to compete with the state of the art strongly supports the Superficial Alignment Hypothesis, as it demonstrates the power of pretraining and its relative importance over large-scale instruction tuning and reinforcement learning approaches. Problems with their experiment (1) Human evaluators To compare two chatbots A and B, you could ask humans whether they prefer A's response to B's response across 300 test prompts. But this is pretty bad proxy, because here's what users actually care about: What's the chatbots' accuracy on benchmark tests, e.g. BigBench, MMLU? Can the chatbot pass a law exam, or a medical exam? Can the chatbot write Python code that actually matches the specification? Can the chatbot per...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: LIMA: Less Is More for Alignment, published by Ulisse Mini on May 30, 2023 on The AI Alignment Forum. Abstract Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences. We measure the relative importance of these two stages by training LIMA, a 65B parameter LLaMa language model fine-tuned with the standard supervised loss on only 1,000 carefully curated prompts and responses, without any reinforcement learning or human preference modeling. LIMA demonstrates remarkably strong performance, learning to follow specific response formats from only a handful of examples in the training data, including complex queries that range from planning trip itineraries to speculating about alternate history. Moreover, the model tends to generalize well to unseen tasks that did not appear in the training data. In a controlled human study, responses from LIMA are either equivalent or strictly preferred to GPT-4 in 43% of cases; this statistic is as high as 58% when compared to Bard and 65% versus DaVinci003, which was trained with human feedback. Taken together, these results strongly suggest that almost all knowledge in large language models is learned during pretraining, and only limited instruction tuning data is necessary to teach models to produce high quality output. Implications Data Quality & Capabilities Along with TinyStories and QLoRA I'm becoming increasingly convinced that data quality is all you need, definitely seems to be the case for finetuning, and may be the case for base-model training as well. Better scaling laws through higher-quality corpus? Also for who haven't updated, it seems very likely that GPT-4 equivalents will be essentially free to self-host and tune within a year. Plan for this! Perplexity != Quality When fine-tuning LIMA, we observe that perplexity on held-out Stack Exchange data (2,000 examples) negatively correlates with the model's ability to produce quality responses. To quantify this manual observation, we evaluate model generations using ChatGPT, following the methodology described in Section 5. Figure 9 shows that as perplexity rises with more training steps – which is typically a negative sign that the model is overfitting – so does the quality of generations increase. Lacking an intrinsic evaluation method, we thus resort to manual checkpoint selection using a small 50-example validation set. Because of this, the authors manually select checkpoints between the 5th and 10th epochs (out of 15) using the held-out 50-example development set. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

It's 2006 on DrunkFriend and topics range from new console releases (PS3 and Wii) to soccer headbutts to YouTube being bought by Google. There's lots to talk about, so listen while Alex and Trav bring sexy back!SportsColts beat Bears, Peyton finally wins, Prince halftime showFlorida wins nattys in both football and basketball, Colt Brennan throws for 58 TDsGeorge Mason makes Final FourItaly wins World Cup, Zidane headbuttDomination: Florida college sports, Tiger Woods, Roger Federer, Jimmie JohnsonHeat beat Mavs, Kobe scores 81Cardinals beat Tigers, Japan wins first ever World Baseball ClassicCarolina beats EdmontonGamingPS3 is releasedBlu-ray Resistance: Fall of ManRidge Racer 7Final Fantasy 12 (PS2)Wii is releasedLoZ Twilight PrincessWii SportsTrauma Center: Second OpinionXbox 360Gears of WarDead RisingSaints RowSonic 06Nintendo DSNew Super Mario BrosMetroid Prime HuntersCooking MamaPSP released in 2005GTA Vice City StoriesMetal Gear Acid 2Me & My KatamariPCElder Scrolls 4: OblivionHalf-Life 2: Episode OneBattlefield 2142SongsWhat was everywhereYou're Beautiful - James BluntCrazy - Gnarls BarkleySexyBack - Justin TimberlakeDani California - RHCPWhat we likedSteady, as she goes - RaconteursWolf Like Me - TV on the RadioAlbumsTV on the Radio, “Return to Cookie Mountain”The Hold Steady, “Boys and Girls in America”Mastodon, “Blood Mountain”Tool - 10,000 DaysMoviesThe DepartedLittle Miss SunshineThe PrestigeCasino Royale300IdiocracyTalladega NightsNacho LibreNotable eventsGoogle Buys YouTube for $1.65B in stockPluto downgraded to dwarf planetSteve Irwin killedMost popular TV shows Released in 2006IT CrowdDeath Note30 RockDexterInternetMyspace was most popular social network stillNumaNuma video was very popularEvolution of Dance video was most watchedShared your pics via FlickRSupport the show Find more of our work on the Polymedia Network Find Travis on Twitter Find Alex on Twitter Send us an email drunkfriendpodcast@gmail.com

This week's Misfit Entrepreneur is Tanis Jorge.  Tanis is a serial, tech entrepreneur and a leading advisor to startup founders on entrepreneurship and building successful cofounder partnerships.  During her career has spanning the last 20+ years, she has cofounded, scaled, and successfully exited multiple data-driven businesses. This culminated with her most recent venture, Trulioo, which she co-founded in 2011 with my long-term business partner.  In 2021, Trulioo reached unicorn status with an over $1.65B valuation solidifying its place as the world's leading identity verification company and Tanis's track record for founding successful businesses. Trulioo's success also made her one of only three Canadian female “Unicorn” founders. Tanis has a wealth of knowledge in starting and building businesses, but I'm not if you caught something with what I just shared that has a been a constant for her and a big part of her success – all of her biggest and most successful ventures have been with another great co-founder and it has been their partnership that made it go.  In fact, Tanis recently wrote the best-selling book, the Cofounder's Handbook detailing how she does this and that is exactly what I want to speak with her about today as one of the best way to succeed is by having a great partnership in your business.  I know this from experience.   www.TheCoFoundersHub.com Tanis met her co-founder in High School starting in grade 8.  They were locker buddies and their time in high school and getting to know each other laid the groundwork for their future success.  Right after High School, he partner approached her to be the first to do online credit reports in Canada.  It was their first business and gave them a ton of learning experience.  They found their roles and ultimately exited after 3 years.  They then did that 2 more times over the next 10 years.  After that, Tanis took a break to focus on family and her partner went to Silicon Valley to pitch an idea they had had previous.  The idea was well received and she was pulled back in.  A year and half into it, they raised their first seed round and it that helped them to get to a valuation over $1.5 billion. Talk to us about the magic of co-founding partnerships.  Why and how can they work so well? They are either the greatest asset or greatest liability in your business. 65% of business fail because of issues between the founders. Every business with a partnership will either make or break on that partnership. When Tanis talked to people with bad partnerships, she noticed it came down to values that did not align.  A great partnership that is aligned can take businesses to levels not possible with just a single founder. Elements of great partnerships? Shared values Intentionality.  Great partners are focused on their partnership. A partnership is not “set it and forget it.” They help hold each other up on the rollercoaster of building a business. Did you ever have times where you got sick of each other in your partnership?  How did you get things back on track and keep the partnership strong? Your mentality going into the partnership is important.  In the end, you are all striving for the same goal. Honest mistakes happen – very seldom are they malicious. Realize that you are in it together.  Don't get in your own way when you don't have to. It is like a marriage, but it is not.  Marriage is focusing on keeping each other happy.  In business, the business is the focus.  The founders need to focus there and make that the priority. Ask what is the best for the business…. At the 20 min mark, Tanis talks about the dynamics of partnerships… There is a customization to a good partnership.  Look at yourself first.  Look at your model and determine what you need in a partner.  Tanis developed a self-assessment for this. What is the best way to find a business partner? The first and most important thing is to do a self-assessment.  You can do this at https://thecofoundershub.com/ It is worth the time and money to invest in the process of figuring things out. Your network is a great place to start and ask, “Who do you know?” There are Meet-ups in different markets that give opportunities as well. You also want to think through all the major points of the partnership ahead of time and have the conversations in discovery sessions with potential partners. You will also want to look at the legal documents needed to create a great business partnership. What have your learned on how to build and scale companies? Tanis loves the early stage of a business. Build your business like you will own it forever, but have the long-term vision to build it to be sold. Play chess.  Every month, take time to whiteboard and stay steps ahead.  Game plan and plan intensively the moves in your business. What have you learned about how to sell a business? It is stressful and like having a second job. It always take longer. It always costs more than you think. You need to prepare ahead of time and know it will not be easy. You must plan for it and ensure you are ready.  Know your number.  You will not get as much as you want, but you can get more than what you would settle for. ​ What is something you wish you have known earlier in your entrepreneur career? Tanis was very young when she started and green to the business world. It took a couple of businesses to realize that regardless of age, she knew her business the best and to have confidence in herself. Don't be afraid to take charge and step out in your business.   Best Quote:  Co-founder partnerships can be the greatest asset or greatest liability in your business.   Tanis's Misfit 3: Choose the key people in your life carefully and invest in them. Question everything!  Always look at both sides.  Be curious. Enjoy the journey.   Show Sponsors: Benchmark Email (Free account): www.MisfitEntrepreneur.com/Benchmark 5 Minute Journal: www.MisfitEntrepreneur.com/Journal

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Meta open sources LMs competitive with Chinchilla, PaLM, and code-davinci-002 (Paper), published by Lawrence Chan on February 24, 2023 on The AI Alignment Forum. As the title says, Meta trained 4 foundational models with 7B, 13B, 33B, and 65B parameters respectively, and is open sourcing them for research. You can get their code on their Github repo: but you need to fill in a Google form to get the weights. On downstream benchmarks, the models do comparably well with Chinchilla and PaLM and only a bit worse than Flan-PaLM-540B and code-davinci-002/text-davinci-002. (The authors don't evaluate on those models, but you can look at their performance from other work such as Stanford's HELM or Chung, Hou, Longpre et al's "Scaling Instruction-Finetuned Language Models". Abstract: We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla70B and PaLM-540B. We release all our models to the research community. Twitter thread from authors: Eliezer guesses that the model won't be impressive in practice: I blindly guess, could be wrong, that this model will turn out sufficiently unimpressive in practice that nobody uses it for much. Basically based on a guess that more than benchmarks matter, and Meta has no people competent to do the tricky stuff needed to stay on current edge. It's not necessarily open source as you think of it -- you need to fill in a Google form, and then they might give it to you: In order to download the checkpoints and tokenizer, fill this google form The license is intended only for non-commercial, research work: Meta grants you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty free and limited license under Meta's copyright interests to reproduce, distribute, and create derivative works of the Software solely for your non-commercial research purposes. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Healthcare Ecosystem–Times They Are A-Changin' with Fahad Rahman, CEO, Lumi Health! Fahad Rahman, CEO, Lumi Health, joins Maureen Shaffer, CEO, Mingletoe, to chat about the rapidly changing healthcare ecosystem for patients, providers, facilities, and especially for medtech startups. Fahad also shared his thoughts on how to think through referral patterns changes, hospital at home, and health equity.

Constellation Brands (STZ) has divested part of its mainstream and premium wine portfolio to The Wine Group. Adam Lampe discusses this, as well as the takeaways from STZ's earnings. He talks about how STZ's revenue came in at $2.65B versus an estimated $2.50B. He then evaluates STZ investment in Canopy Growth (CGC). Finally, he goes over the long-term outlook for STZ. Tune in to find out ore about the stock market today.

This is a special episode pulled from the Just Go Grind vault! Dr. Iman Abuzeid is the Co-founder and CEO of Incredible Health, a career marketplace whose custom matching technology offers hospitals the fastest, most effective way to hire qualified permanent nursing staff. By 2024, the United States is poised to experience its biggest employment crisis to date, a shortage of one million nurses, putting patient care in jeopardy and hospitals at risk of significant financial loss. The COVID-19 pandemic has accelerated the nursing shortage, highlighting the need for qualified nurses across the country. Incredible Health is on a mission to reinvent the hospital and healthcare staffing landscape, helping to solve the nursing shortage. As a former medical doctor whose immediate family includes three surgeons, Iman understands the importance of helping healthcare professionals find and do their best work. Incredible Health is based in San Francisco, backed by top tier venture capital firm Andreessen Horowitz, and is used by hundreds of leading hospitals across the country, including Cedars-Sinai Medical Center, Stanford Healthcare, Baylor Scott & White and many more. Dr. Abuzeid holds an MBA from The Wharton School of the University of Pennsylvania. In August 2022, Iman led Incredible Health to $1.65B unicorn status with an $80M Series B raise. This makes Incredible Health the highest valued tech-enabled career marketplace in healthcare. This new funding comes on the heels of several major milestones for Incredible Health. In 2021, revenue grew 500% as their marketplace grew exponentially. More than 10,000 US nurses now join Incredible Health's platform every single week, and they've expanded from partnering with 200 hospitals to 600 hospitals. Their marketplace technology has reduced the average time to hire permanent nurses to only 14 days – from an industry standard of 82 days – while saving each hospital location at least $2M annually in travel nurse, overtime, and HR costs. Topics Covered by Iman Abuzeid in this Episode How Incredible Health got started Being 1 million nurses short in the U.S. by 2024 How Iman figured out what the initial version of Incredible Health would be Creating a solution that's 10x better than what's currently available How Iman met her co-founder, Rome, and the conversation around going into business together How Iman thought through the equity split early on Raising a few hundred thousand dollars from friends and family to get Incredible Health off the ground Raising a $2.4M Seed Round in 2-3 months after going through the NFX accelerator Raising a $15M Series A in 4-5 weeks led by Jeff Jordan at Andreessen Horowitz Iman's advice for fundraising and creating a process and how her own skills evolved How Iman brought on the first hospitals through cold-calling The MVP of Incredible Health and why you have to fight the urge to ship perfection How the team has grown for Incredible Health The values of Incredible Health and how it's part of everything they do How Incredible Health uses customer insights to develop their platform, operations, and marketing Why Incredible Health offers free continuing education for every nurse in the country How Iman thinks through the products and tools they're going to provide to their customers The sales process of getting hospitals on their platform The 3 biggest value adds of the investors Iman has for Incredible Health Questions that Iman asked of CEOs for reference checks on investors How Iman takes care of her own mental health and why she has a therapist How getting an MBA from Wharton impacted Iman's journey as an entrepreneur Books and blogs that Iman recommends How Iman recharges away from work and why she doesn't work on Saturdays Iman's experience as a minority woman founder Diversity debt Links from the Episode Rome Portlock NFX Jeff Jordan Andreessen Horowitz Obvious Ventures signal.nfx.com James Joaquin The Hard Thing About Hard Things Who (A book on hiring) Above the Crowd by Bill Gurley Stephanie Lampkin Blendoor Listen to all episodes of the Just Go Grind Podcast: https://www.justgogrind.com Follow Justin Gordon on Twitter: https://twitter.com/justingordon212

Only 4 in 10 employees use all of their Paid Time Off each year, leaving over 750 million days unused annually in the US. Unused PTO either sits on the balance sheet of companies as a liability, or is forfeited by employees - representing a staggering $65B in lost compensation each year.PTO Exchange is out to fit this problem. Through their platform organizations are able to offer their employees easy ways to convert unused PTO into retirement plans, charitable contributions, wellness programs and more.In this conversation, we chat with the founder of PTO Exchange, Rob Whalen. We discuss the massive impact this problem has on both employees and companies, how the landscape has shifted as a result of COVID, what the future of employee benefits looks like and much more.If you enjoyed the episode, please leave a review! https://kite.link/disruptors-ptoexchangePODCAST INFO:Podcast website: https://manifold.group/podcastApple Podcasts: https://apple.co/3EbkMEkSpotify: https://spoti.fi/3nqwNiDRSS: https://bit.ly/3ntcFw1Full episodes playlist: https://youtube.com/playlist?list=PLdnqR-lZH65HqCM09dwh6otuU7TArkq8JClips playlist: https://youtube.com/playlist?list=PLdnqR-lZH65HrrlaeQZnf1sicFkprjpqkSOCIAL:- Sean Johnson on Twitter: https://twitter.com/intentionally- Sean Johnson on LinkedIn: https://linkedin.com/in/seanjohnson- Sean Johnson on Instagram: https://www.instagram.com/intentionally_- Manifold on Twitter: https://twitter.com/manifold_group- Manifold on LinkedIn: https://www.linkedin.com/m/company/manifold-group- Manifold on Facebook: https://www.facebook.com/themanifoldgroup- Manifold on Instagram: https://www.instagram.com/manifold_group/- Manifold on Medium: https://medium.com/build-better-products

Tim Cook-led Apple (AAPL) earned $1.20 per share on $82.96B in revenue. That's a 2% increase year-over-year. Revenue attributed to the iPhone came in $40.65B. Services revenue rose to $19.6B. Daniel Rubino and Kyle Clark discuss factors driving Apple's success including sales of the iPad, Mac, Wearables and Accessories which came in at $7.22B, $7.38B, and $8.08B, respectively.

Today's word of the day is ‘66' as in 66 massage therapists as in a report came out yesterday that Deshaun Watson used 66 massage therapists in a 17-month window while with the Houston Texans. There have been 24 civil suits against Watson. More details allege that the Texans may have helped set up some of these meetings. The NFL better start doing real work now to figure out what is right. Deshaun Watson's future in the NFL should very much be in doubt (although it should have been before). (22:55) Review: Unknown. (27:35) Joe Maddon was fired as the manager of the Angels. They had lost 12-straight games. Mike Trout couldn't get a hit. Shohei Ohtani couldn't hit. They went from first to 10 games back in the AL West. (35:10) It looks like another manager could be on the hot hot hot seat. Don Mattingly. The Miami Marlins held a 90-minute team meeting before its 12-2 win over the Nationals. The team is underachieving. The team has some issues in the clubhouse. And they cannot blame me anymore! (43:55) NPPOD. (46:00) The Denver Broncos are being sold to Walmart for $4.65B. That's a nice chunk of change.  Learn more about your ad choices. Visit megaphone.fm/adchoices