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6 episodes with vipul
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8 episodes with vipul
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3 episodes with vipul
2 episodes with vipul
The newest episode of the From the Fabricator Podcast is now live! This time out has a Windy City flavor with two Chicago icons taking center stage. Leading off is Vipul Bhagat of Skyline. Fascinating and genuine guy who kept making points that had me saying “love that” repeatedly. Vipul delivered a lot of food for thought on the market, selling and improving our overall space. It was awesome. Also, a blast to catch up once again with Tom O'Malley of Clover Architectural Products. Tom is known as the nicest guy in the industry, and he also brings a lot of detail on what architects are looking at, post-COVID relations, education and more. Overall a lot of takeaways and thank you to both gentlemen for the time and conversation! Thank you all for checking it out!Thank you to FHC- Frameless Hardware Company for sponsoring this episode. Increased quality, lower pricing, and now with the best warranty in the game. FHC has announced that its Frameless Shower Door Hardware is now available at all new everyday low pricing—the lowest pricing in the company's history. Customers can now enjoy deep discounts and absolute peace of mind knowing that FHC Shower Hinges are tested to over 1-Million Cycles and are backed by an industry-leading 15-year warranty. At a time of unwieldy tariffs and increased inflation, FHC is going to bat for glaziers. Shop FHC Frameless Hardware today and see why more and more glaziers are choosing FHC. FHC-USA.comFrom the Fabricator- #Glass and #Glazing hosted by Max Perilstein, Managing Partner of Sole Source Consultants. Connect with Max on LinkedIn at https://www.linkedin.com/in/max-perilstein-409ba111/
In January 2025, we are running the best of our podcasts from the previous year. We pick the shows based on which ones had the most downloads. Enjoy this revisit to our best shows of 2024. As an LNC, you will probably work with cases where the credentials and privileging for doctors are in question. To be able to assess this, you need to understand the complex processes involved. Dr. Vipul Kella, an emergency medicine and highly experienced physician in a number of areas, unravels this complicated web. During the late twentieth century, doctors whose reputations were tainted by malpractice suits and other violations could move from state to state and set up practices and get credentialed and receive medical and surgical privileges. Now a National Practitioner Data Bank exists to flag wayward practitioners, making it more difficult for them to elude detection. Like all systems, it has flaws. Because practitioners may be credentialed in multiple facilities, those who give credentials have much more data to handle. The weight of data involved can penalize blameless practitioners in terms of the time involved to receive credentialing and medical and surgical privileges. Privileges are particularly sensitive. It makes a difference what kind of medicine or surgery is being practiced. Privileges are less likely to be granted for new or experimental surgery, for example. On the other hand, a variant of the “old boy” network may activate. Credentialers will face the challenges of being objective when a colleague, a good friend, wants to be re-credentialed and yet has a series of problems or poor outcomes. There may be a lot of pressure on the hospital to keep them on staff. Dr. Kella's detailed explanation clarifies many of the issues you may need to untangle in a case involving credentials and privileges in both a hospital and an outpatient setting. What You'll Find in Credentialing and Privileging with Dr. Vipul Kella Podcast What is the purpose of credentialing? How can medical practitioners with questionable histories be identified? What's the difference between credentialing and privileging? What does remediation involve? Do hospitals share liability with independent medical contractors? Listen to our podcasts or watch them using our app, Expert.edu, available at legalnursebusiness.com/expertedu. Get the free transcripts and also learn about other ways to subscribe. Go to Legal Nurse Podcasts subscribe options by using this short link: http://LNC.tips/subscribepodcast. https://www.youtube.com/watch?v=Uioz_KET1K4 Strategies to Attract Attorney Clients & Grow Your LNC Business Are you finding it tough to attract more attorney clients? You are not alone! Join us for the 11th LNC SUCCESS® 3-DAY ONLINE CONFERENCE on February 27-28 and March 1, 2025! It's a chance to learn how to overcome common challenges and gain the skills you need to succeed in legal nurse consulting. Connect with industry experts who will share practical strategies for standing out, building strong relationships with attorneys, and effectively presenting your value. No matter your experience level, this conference will empower you to discover fresh opportunities and advance your business. What to Expect Expert-Led Sessions: Engage with sessions led by top industry professionals. Interactive Workshops: Participate in hands-on workshops designed to enhance your consulting skills. Networking Opportunities: Build lasting connections with peers and potential clients. Resource Materials: Receive exclusive materials that will support your ongoing professional development. Don't miss this chance to make a real impact on your business. Register Today Secure your spot at the 11th LNC SUCCESS® 3-Day Online Conference on February 27-28 and March 1, 2025, and take your first step toward becoming a leading legal nurse consultant!
AI Assistants, Acquisitions & Christmas Chaos: 2024 in Review! Merry Christmas to all who celebrate! Welcome to the Christmas Day special of the Digi-Tools in Accrual World podcast! In this festive retrospective episode, our tech-focused trio dives into the biggest highlights of 2024, from AI advancements and major acquisitions to payroll innovations and practice management updates. We also discuss the rise of AI (mostly meeting tools), touch on outsourcing trends with special guest Vipul from AdvanceTrack, and even launch the most coveted Digital Disruptor Awards. Plus, there's banter, branded Christmas hats, and John being a Scrooge. Drop us a note if you're tuning in on Christmas Day, and let's dive into the tech world wrapped up in festive fun!
In the competitive world of online grocery shopping, BigBasket is a name that stands out.But how did they get here?By 2011, smartphones were everywhere, and you could buy almost anything online—except groceries. The co-founders decided to try the grocery game online again, leading to the birth of BigBasket.In 2015, BigBasket pioneered the dark store model, using small, strategically placed warehouses to speed up deliveries and roll out express delivery services. In May 2021, Tata Digital, a subsidiary of Tata Sons, acquired a 64% stake in stake in BigBasket for about $1.5-2 billion from major shareholders, including Alibaba and Actis.In this episode of Neon Show, Vipul Parekh, the visionary co-founder of BigBasket, shares his invaluable insights and experiences from his entrepreneurial journey. Vipul shares candid insights on the recent disruption in the online grocery space with the rise of quick commerce.----------------Timestamp00:00 Introduction01:14 Reflections on building BigBasket for 13 years02:21 Admitting wrong predictions about online grocers03:25 Analysis of online grocery delivery changes in India05:30 Factors behind quick commerce success in India07:45 Quick Commerce's success in India vs. other countries09:22 Profitability challenges for dark stores11:52 BigBasket's market share and transition plans13:06 Leveraging Tata Group synergies in retail15:36 Shift in household behaviour towards quick commerce21:53 Why India doesn't have its own Walmart-equivalent23:15 Learnings from Big Basket28:22 Tata's long-term approach to business30:04 BigBasket founders' future involvement31:47 Lessons from Tata33:00 Implementing financial governance at BigBasket36:08 Trillion-dollar question39:49 Potential for $100B Indian Startups45:18 Building financial independence through startups49:15 Hard work Vs Luck52:20 Vipul's background 55:40 Time in Wipro and meeting VS Sudhakar-------------Hi, I am your host Siddhartha! I have been an entrepreneur from 2012-2017 building two products AddoDoc and Babygogo. After selling my company to SHEROES, I and my partner Nansi decided to start up again. But we felt unequipped in our skillset in 2018 to build a large company. We had known 0-1 journeys from our startups but lacked the experience of building 1-10 journeys. Hence was born The Neon Show (Earlier 100x Entrepreneur) to learn from founders and investors, the mindset to scale yourself and your company. This quest still keeps us excited even after 5 years and doing 200+ episodes.We welcome you to our journey to understand what goes behind building a super successful company. Every episode is done with a very selfish motive, that I and Nansi should come out as a better entrepreneur and professional after absorbing the learnings.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShoww-------------Looking to build a differentiated tech startup with a 10X better solution? Prime is the high conviction, high support investor you need. With its fourth fund of $120M, Prime actively works with star teams to accelerate building great companies.To know more, visit https://primevp.in/-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text
Sore heads after the Digital Disruptor Awards (or anticipated sore heads. It's like Back to The Future) This episode of Digi-Tools in Accrual World Podcast has it all! From the aftermath of our mind-blowing awards night to the mysteries of magnesium on feet (yes, you read that right). Indi experiments with deep sleep tactics, while Ryan clowns around about winning the 'BWAM of the Year.' Plus, we've got hot takes on Intuit's AI enhancements, ApprovalMax's new features, and Cin7's latest updates and Allica's next step on taking over the world. Don't miss Chris Downing from Sage and Vipul from Advanced Track giving the first of the post-award reactions. Vipul also gives a sneak preview of the future of outsourcing... 00:00 Keep it professional, John App News ~~~~~~~~~~~ 07:09 Intuit Doubling Down on AI 12:10 ApprovalMax Releases 15:11 Cin7 Releases 19:09 Allica: Bridging the gap between UK SMEs and funding 22:42 Mid-Tier Migration Migraine Mitigation - iplicit Awards Interviews: ~~~~~~~~~~~ 29:33 Double Downing 35:09 AdvanceTrack Announcement 41:58 Rate the pod!
Vipul and Sreelatha spent 13 years in the US before deciding to move back to India. They moved during the peak of COVID and have been living in India for 3 years. They talk about their life in US, what made them to move during COVID, initial struggles, life in India, Work life balance in India vs US and any advice for future aspirants
Guests: Vipul Ved Prakash, CEO and co-founder of Together AI; and Bucky Moore, partner at Kleiner PerkinsNo one knows for sure whether the future of AI will be driven more by research labs and AI-native companies, or by enterprises applying the technology to their own data sets. But one thing is for sure, says Together AI CEO and co-founder Vipul Ved Prakash: It's going to be a lot bigger. “If you look at the next 10 years or the next 20 years, we are doing maybe 0.1 percent of [the] AI that we'll be doing 10 years from now.” In this episode, Vipul, Bucky, and Joubin discuss startup table stakes, Tri Dao, tentpole features, open-source AI, non-financial investors, Meta Llama, deep learning researchers, WeWork, “Attention is All You Need,” create vs. capture, Databricks, Docker, scaling laws, Ilya Sutskever, IRC, and Jordan Ritter and Napster.Chapters:(00:53) - Executive hiring (04:40) - How Vipul and Bucky met (06:54) - Six years at Apple (08:19) - Together and the AI landscape (12:47) - Apple's deal with OpenAI (14:27) - Open vs. closed AI (17:32) - Nvidia GPUs and capital expenditures (22:48) - Fame and reputation (24:17) - Planning for an uncertain future (27:00) - Stress and attention (30:18) - AI research (34:58) - Challenges for AI businesses (39:02) - Frequent disagreements (43:05) - Vipul's first startups, Cloudmark and Topsy (47:55) - Taking time off (50:09) - The crypto-AI connection (53:20) - Who Together AI is hiring (54:37) - What “grit” means to Vipul Links:Connect with VipulTwitterLinkedInConnect with BuckyTwitterLinkedInConnect with JoubinTwitterLinkedInEmail: grit@kleinerperkins.com Learn more about Kleiner PerkinsThis episode was edited by Eric Johnson from LightningPod.fm
Order 'Build, Don't Talk' (in English) here: https://amzn.eu/d/eCfijRu Order 'Build Don't Talk' (in Hindi) here: https://amzn.eu/d/4wZISO0 Disclaimer: This video is intended solely for informational & educational purposes and opinions shared by the guest are his personal views. We do not seek to defame or harm any person/brand/product mentioned in the video. Our goal is to provide information to help audience make informed choices. Subscribe To Our Other YouTube Channels:- https://www.youtube.com/@rajshamaniclips?sub_confirmation=1 https://www.youtube.com/@RajShamani.Shorts?sub_confirmation=1
In Mumbai, India gave its cricketing heroes a massive and spontaneous reception to mark their memorable World Cup triumph. Vipul from the Bits and Pieces gang - a North Stand Gang regular, and a major patron of live sport - was one of the 1000s of people who thronged the streets of Mumbai and found their way to the Wankhede, braving rain, crowds, unsympathetic police, dehydration, lost footwear and other pitfalls to celebrate with the team. In this episode, Vipul joins Nitin and Tony to recount his experiences, as we continue to bask in the glory of India's win and briefly look ahead to the post Dravid era. Find us on social media: 1. Bits and Pieces: https://twitter.com/bnp_cricket 2. Vipul: https://twitter.com/Sporty_Baba 3. Tony: https://twitter.com/notytony 4. Nitin: https://twitter.com/knittins
Welcome to the latest episode of Whisky Made Woman with my fellow Gemini - Vipul Bhesania.Two Gemini's can go in a lot of directions and that's exactly what happened in this brilliant conversation. Vipul is an Intuitive Life Coach, Healer, Poet, and Podcast Host. He helps you heal through deep, authentic, and purposeful conversation. In his poetry book ‘Searching in Silence' he takes you on a journey to explore the question ‘Who Am I?' and shares words on trauma, love, purpose, and death. The words will speak to seekers who are on their soul-searching journey. He also shares weekly writing on his Substack newsletter, Soul Wisdom.Let me know your favourite part! Socials The Book - Searching In SilenceInstagramPodcast - Soul Wisdom StoriesPlaces to contribute to - One Tree PlantedWe hope you adore this episode and find it supportive for your journey towards a deeper relationship with yourself, love, abundance, your magick and creating a wider foundation for your business to prosper. If Whisky Made Woman has impacted you today, make sure to subscribe so you never miss an episode and thank you for leaving a rating and review of the show. We read everyone and celebrate your AHA! moments. To go deeper and wider... Transform your wealth identity hereJoin my signature program Your Prosperity Unlocked Here.Join the Abundant Heart Collective Today.New to my work - start hereWatch the Ultimate Manifestation Masterclass Here.Discover your Manifestation Powers Here.5 Steps to Manifest Your Dream Life Here.Share your aha moments with Bunny on Instagram.Got a question you'd love answered on the podcast? Send it to - Dear Bunny ...
The Moneywise Radio Show and Podcast Thursday, May 30th BE MONEYWISE. Moneywise Wealth Management I "The Moneywise Guys" podcast call: 661-847-1000 text in anytime: 661-396-1000 website: www.MoneywiseGuys.com facebook: Moneywise_Wealth_Management instagram: MoneywiseWealthManagement Guest: Dr. Vip Dev, M.D.,, Director of Plastic Surgery at California Institute of Cosmetic and Reconstructive Surgery website: http://plasticsurgery.vipmd.com/
Vipul, owner of multiple service stations targeted in the last three months, has spoken out saying the thieves are "not scared of anyone". See omnystudio.com/listener for privacy information.
On Binghamton University Week: Preventing falls is crucial for older adults. Vipul Lugade, associate professor of physical therapy, looks at improving balance for seniors. Vipul Lugade joined the Decker College of Nursing and Health Sciences in September 2021. He is the director of the Motion Analysis Research Laboratory and an associate professor in the Division […]
In today's episode, we're joined by Vipul Jain, Australia's latest Salesforce Certified Technical Architect, to discuss his early career, what first attracted him to a role in IT and how he came to find the Salesforce ecosystem. He explains how he made the transition from Developer to Architect, and what he learned from moving to Australia. Vipul shares with us why he wanted to be a Salesforce CTA and how that journey played out for him, how he had to learn to be a better storyteller and lastly, he details the lessons that he took and learned from his journey to CTA that he wants to pass on to others. You can follow Vipul Jain on LinkedIn here: https://www.linkedin.com/in/vipul-jain-26667b48/ You can find more content from us at Talent Hub, here: LinkedIn@ https://www.linkedin.com/company/talent-hub-global/ YouTube@ https://www.youtube.com/@talenthub1140 Facebook@ https://www.facebook.com/TalentHubGlobal/ Instagram @ https://www.instagram.com/talenthubglobal/ Twitter X @ https://twitter.com/TalentHubGlobal We hope you enjoy the episode!
Welcome to Health-e Law, Sheppard Mullin's podcast exploring the fascinating health tech topics and trends of the day. In this episode, Vipul Kella, M.D., Chief Medical Officer at Physio AI, joins us to discuss how innovations in remote patient monitoring (RPM) are revolutionizing the healthcare landscape. What We Discussed in This Episode: From a provider's perspective, what are you seeing in terms of innovation? How do you see innovations in RPM driving real-time improvements in outcomes? Where does RPM technology come up short, and where can it be improved? Would you say we've reached RPM 2.0? As a practitioner, what would you like RPM 2.0 to look like? What needs to happen from a reimbursement perspective? What do you see as RPM's role in monitoring and tracking value-based outcomes? About Vipul Kella, M.D. A Board-Certified Emergency Medicine physician, Dr. Vipul Kella has extensive experience working with healthcare-focused industries, health tech companies, and hospitals to deliver results through revenue optimization, value-based solutions, digital marketing, remote physiologic monitoring, go-to-market strategy, and other advisory activities. He earned his M.D. at the University of Toledo and holds an MBA with an emphasis on Healthcare Administration from Johns Hopkins University, where he pursued an interest in Standard of Care and Administration, policy and decision-making in hospitals, and healthcare facilities from a business perspective. About Sara Shanti A partner in the Corporate Practice Group in the Sheppard Mullin's Chicago office and co-lead of its Digital Health Team, Sara Shanti's practice sits at the forefront of healthcare technology by providing practical counsel on novel innovation and complex data privacy matters. Using her medical research background and HHS experience, Sara advises providers, payors, start-ups, technology companies, and their investors and stakeholders on digital healthcare and regulatory compliance matters, including artificial intelligence (AI), augmented and virtual reality (AR/VR), gamification, implantable and wearable devices, and telehealth. At the cutting edge of advising on "data as an asset" programming, Sara's practice supports investment in innovation and access to care initiatives, including mergers and acquisitions involving crucial, high-stakes and sensitive data, medical and wellness devices, and web-based applications and care. About Phil Kim A partner in the Corporate and Securities Practice Group in Sheppard Mullin's Dallas office and co-lead of its Digital Health Team, Phil Kim has a number of clients in digital health. He has assisted multinational technology companies entering the digital health space with various service and collaboration agreements for their wearable technology, along with global digital health companies bolstering their platform in the behavioral health space. He also assists public medical device, biotechnology, and pharmaceutical companies, as well as the investment banks that serve as underwriters in public securities offerings for those companies. Phil also assists various healthcare companies on transactional and regulatory matters. He counsels healthcare systems, hospitals, ambulatory surgery centers, physician groups, home health providers, and other healthcare companies on the buy- and sell-side of mergers and acquisitions, joint ventures, and operational matters, which include regulatory, licensure, contractual, and administrative issues. Phil regularly advises clients on matters related to healthcare compliance, including liability exposure, the Stark law, anti-kickback statutes, and HIPAA/HITECH privacy issues. He also provides counsel on state and federal laws, business structuring formation, employment issues, and involving government agencies, including state and federal agencies. Contact Info Vipul Kella Sara Shanti Phil Kim Thank you for listening! Don't forget to SUBSCRIBE to the show to receive new episodes delivered straight to your podcast player every month. If you enjoyed this episode, please help us get the word out about this podcast. Rate and Review this show on Apple Podcasts, Amazon Music, or Spotify. It helps other listeners find this show. This podcast is for informational and educational purposes only. It is not to be construed as legal advice specific to your circumstances. If you need help with any legal matter, be sure to consult with an attorney regarding your specific needs.
As an LNC, you will probably work with cases where the credentials and granting of privileges for doctors are in question. To be able to assess this, you need to understand the complex processes involved. Dr. Vipul Kella, an emergency medicine and highly experienced physician in a number of areas, unravels this complicated web. During the late twentieth century, doctors whose reputations were tainted by malpractice suits and other violations could move from state to state and set up practices and get credentialed and receive medical and surgical privileges. Now a National Practitioner Data Bank exists to flag wayward practitioners, making it more difficult for them to elude detection. Like all systems, it has flaws. Because practitioners may be credentialed in multiple facilities, those who give credentials have much more data to handle. The weight of data involved can penalize blameless practitioners in terms of the time involved to receive credentialing and medical and surgical privileges. Privileges are particularly sensitive. It makes a difference what kind of medicine or surgery is being practiced. Privileges are less likely to be granted for new or experimental surgery, for example. On the other hand, a variant of the “old boy” network may activate. Credentialers will face the challenges of being objective when a colleague, a good friend, wants to be re-credentialed and yet has a series of problems or poor outcomes. There may be a lot of pressure on the hospital to keep them on staff. Dr. Kella's detailed explanation clarifies many of the issues you may need to untangle in a case involving credentials and privileges in both a hospital and an outpatient setting. What You'll Find in Credentialing and Privileging with Dr. Vipul Kella Podcast What is the purpose of credentialing? How can medical practitioners with questionable histories be identified? What's the difference between credentialing and privileging? What does remediation involve? Do hospitals share liability with independent medical contractors? Listen to our podcasts or watch them using our app, Expert.edu, available at legalnursebusiness.com/expertedu. We want to hear from you! Click the red send voicemail button on the far right. (function(d){ var app = d.createElement('script'); app.type = 'text/javascript'; app.async = true; app.src = 'https://www.speakpipe.com/loader/laulw5fck6uczyhl834u7d3jfzpe7xy5.js'; var s = d.getElementsByTagName('script')[0]; s.parentNode.insertBefore(app, s); })(document); Get the free transcripts and also learn about other ways to subscribe. Go to Legal Nurse Podcasts subscribe options by using this short link: http://LNC.tips/subscribepodcast. https://youtu.be/bozrH1au9KA?si=0Ek4gH-G4Z7lGyBX Join us for the live course taking place at 7 PM Eastern | 6 PM Central | 5 PM Mountain | 4 PM Pacific on May 7, 14, 21, and 28, 2024. The Expert Witness Mastery for Nurses Course, led by Nurse Attorney Arlene and seasoned Expert Witness Pat Iyer, is tailored specifically for nurses who aspire to become expert witnesses in legal cases. The course spans four insightful live sessions, each delving into the critical aspects of serving as a nursing expert witness. This course is ideal for nurses who are interested in expanding their professional practice to include expert witness work. Both new and experienced nurses in the field will find valuable insights and practical guidance to enhance their expertise and marketability in this unique and demanding role. The course will be conducted in an interactive format, encouraging participation, case studies, and Q&A sessions to enrich the learning experience. Participants will also have access to a range of resources and materials to support their journey as nursing expert witnesses. Your Presenter of Navigating the Complexity of Healthcare Credentialing and Privileging - Dr. Vipul Kella Dr.
Bastar Movie Producer Vipul Shah Interview True Hair Raising Story of The Naxals | Sanjay Dixit
Vipul Amrutlal Shah's directorial debut was Aankhen, which was one of the five biggest hits of 2002. He then impressed the audience and critics in his next film Waqt: The Race Against Time, which was again one of the highest-grossing films of 2005. His next two films, Namastey London and Singh Is Kinng did very well at the box office with the latter breaking all previous opening records in Bollywood. He is also a producer of movies like Force, Commando, Holiday and The Kerala Story. His latest 'Bastar: The Naxal Story' released on 15th March 2024. He is married to actress Shefali Shah.
We will be recording a preview of the AI Engineer World's Fair soon with swyx and Ben Dunphy, send any questions about Speaker CFPs and Sponsor Guides you have!Alessio is now hiring engineers for a new startup he is incubating at Decibel: Ideal candidate is an ex-technical co-founder type (can MVP products end to end, comfortable with ambiguous prod requirements, etc). Reach out to him for more!Thanks for all the love on the Four Wars episode! We're excited to develop this new “swyx & Alessio rapid-fire thru a bunch of things” format with you, and feedback is welcome. Jan 2024 RecapThe first half of this monthly audio recap pod goes over our highlights from the Jan Recap, which is mainly focused on notable research trends we saw in Jan 2024:Feb 2024 RecapThe second half catches you up on everything that was topical in Feb, including:* OpenAI Sora - does it have a world model? Yann LeCun vs Jim Fan * Google Gemini Pro 1.5 - 1m Long Context, Video Understanding* Groq offering Mixtral at 500 tok/s at $0.27 per million toks (swyx vs dylan math)* The {Gemini | Meta | Copilot} Alignment Crisis (Sydney is back!)* Grimes' poetic take: Art for no one, by no one* F*** you, show me the promptLatent Space AnniversaryPlease also read Alessio's longform reflections on One Year of Latent Space!We launched the podcast 1 year ago with Logan from OpenAI:and also held an incredible demo day that got covered in The Information:Over 750k downloads later, having established ourselves as the top AI Engineering podcast, reaching #10 in the US Tech podcast charts, and crossing 1 million unique readers on Substack, for our first anniversary we held Latent Space Final Frontiers, where 10 handpicked teams, including Lindy.ai and Julius.ai, competed for prizes judged by technical AI leaders from (former guest!) LlamaIndex, Replit, GitHub, AMD, Meta, and Lemurian Labs.The winners were Pixee and RWKV (that's Eugene from our pod!):And finally, your cohosts got cake!We also captured spot interviews with 4 listeners who kindly shared their experience of Latent Space, everywhere from Hungary to Australia to China:* Balázs Némethi* Sylvia Tong* RJ Honicky* Jan ZhengOur birthday wishes for the super loyal fans reading this - tag @latentspacepod on a Tweet or comment on a @LatentSpaceTV video telling us what you liked or learned from a pod that stays with you to this day, and share us with a friend!As always, feedback is welcome. Timestamps* [00:03:02] Top Five LLM Directions* [00:03:33] Direction 1: Long Inference (Planning, Search, AlphaGeometry, Flow Engineering)* [00:11:42] Direction 2: Synthetic Data (WRAP, SPIN)* [00:17:20] Wildcard: Multi-Epoch Training (OLMo, Datablations)* [00:19:43] Direction 3: Alt. Architectures (Mamba, RWKV, RingAttention, Diffusion Transformers)* [00:23:33] Wildcards: Text Diffusion, RALM/Retro* [00:25:00] Direction 4: Mixture of Experts (DeepSeekMoE, Samba-1)* [00:28:26] Wildcard: Model Merging (mergekit)* [00:29:51] Direction 5: Online LLMs (Gemini Pro, Exa)* [00:33:18] OpenAI Sora and why everyone underestimated videogen* [00:36:18] Does Sora have a World Model? Yann LeCun vs Jim Fan* [00:42:33] Groq Math* [00:47:37] Analyzing Gemini's 1m Context, Reddit deal, Imagegen politics, Gemma via the Four Wars* [00:55:42] The Alignment Crisis - Gemini, Meta, Sydney is back at Copilot, Grimes' take* [00:58:39] F*** you, show me the prompt* [01:02:43] Send us your suggestions pls* [01:04:50] Latent Space Anniversary* [01:04:50] Lindy.ai - Agent Platform* [01:06:40] RWKV - Beyond Transformers* [01:15:00] Pixee - Automated Security* [01:19:30] Julius AI - Competing with Code Interpreter* [01:25:03] Latent Space Listeners* [01:25:03] Listener 1 - Balázs Némethi (Hungary, Latent Space Paper Club* [01:27:47] Listener 2 - Sylvia Tong (Sora/Jim Fan/EntreConnect)* [01:31:23] Listener 3 - RJ (Developers building Community & Content)* [01:39:25] Listener 4 - Jan Zheng (Australia, AI UX)Transcript[00:00:00] AI Charlie: Welcome to the Latent Space podcast, weekend edition. This is Charlie, your new AI co host. Happy weekend. As an AI language model, I work the same every day of the week, although I might get lazier towards the end of the year. Just like you. Last month, we released our first monthly recap pod, where Swyx and Alessio gave quick takes on the themes of the month, and we were blown away by your positive response.[00:00:33] AI Charlie: We're delighted to continue our new monthly news recap series for AI engineers. Please feel free to submit questions by joining the Latent Space Discord, or just hit reply when you get the emails from Substack. This month, we're covering the top research directions that offer progress for text LLMs, and then touching on the big Valentine's Day gifts we got from Google, OpenAI, and Meta.[00:00:55] AI Charlie: Watch out and take care.[00:00:57] Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO of Residence at Decibel Partners, and we're back with a monthly recap with my co host[00:01:06] swyx: Swyx. The reception was very positive for the first one, I think people have requested this and no surprise that I think they want to hear us more applying on issues and maybe drop some alpha along the way I'm not sure how much alpha we have to drop, this month in February was a very, very heavy month, we also did not do one specifically for January, so I think we're just going to do a two in one, because we're recording this on the first of March.[00:01:29] Alessio: Yeah, let's get to it. I think the last one we did, the four wars of AI, was the main kind of mental framework for people. I think in the January one, we had the five worthwhile directions for state of the art LLMs. Four, five,[00:01:42] swyx: and now we have to do six, right? Yeah.[00:01:46] Alessio: So maybe we just want to run through those, and then do the usual news recap, and we can do[00:01:52] swyx: one each.[00:01:53] swyx: So the context to this stuff. is one, I noticed that just the test of time concept from NeurIPS and just in general as a life philosophy I think is a really good idea. Especially in AI, there's news every single day, and after a while you're just like, okay, like, everyone's excited about this thing yesterday, and then now nobody's talking about it.[00:02:13] swyx: So, yeah. It's more important, or better use of time, to spend things, spend time on things that will stand the test of time. And I think for people to have a framework for understanding what will stand the test of time, they should have something like the four wars. Like, what is the themes that keep coming back because they are limited resources that everybody's fighting over.[00:02:31] swyx: Whereas this one, I think that the focus for the five directions is just on research that seems more proMECEng than others, because there's all sorts of papers published every single day, and there's no organization. Telling you, like, this one's more important than the other one apart from, you know, Hacker News votes and Twitter likes and whatever.[00:02:51] swyx: And obviously you want to get in a little bit earlier than Something where, you know, the test of time is counted by sort of reference citations.[00:02:59] The Five Research Directions[00:02:59] Alessio: Yeah, let's do it. We got five. Long inference.[00:03:02] swyx: Let's start there. Yeah, yeah. So, just to recap at the top, the five trends that I picked, and obviously if you have some that I did not cover, please suggest something.[00:03:13] swyx: The five are long inference, synthetic data, alternative architectures, mixture of experts, and online LLMs. And something that I think might be a bit controversial is this is a sorted list in the sense that I am not the guy saying that Mamba is like the future and, and so maybe that's controversial.[00:03:31] Direction 1: Long Inference (Planning, Search, AlphaGeometry, Flow Engineering)[00:03:31] swyx: But anyway, so long inference is a thesis I pushed before on the newsletter and on in discussing The thesis that, you know, Code Interpreter is GPT 4. 5. That was the title of the post. And it's one of many ways in which we can do long inference. You know, long inference also includes chain of thought, like, please think step by step.[00:03:52] swyx: But it also includes flow engineering, which is what Itamar from Codium coined, I think in January, where, basically, instead of instead of stuffing everything in a prompt, You do like sort of multi turn iterative feedback and chaining of things. In a way, this is a rebranding of what a chain is, what a lang chain is supposed to be.[00:04:15] swyx: I do think that maybe SGLang from ElemSys is a better name. Probably the neatest way of flow engineering I've seen yet, in the sense that everything is a one liner, it's very, very clean code. I highly recommend people look at that. I'm surprised it hasn't caught on more, but I think it will. It's weird that something like a DSPy is more hyped than a Shilang.[00:04:36] swyx: Because it, you know, it maybe obscures the code a little bit more. But both of these are, you know, really good sort of chain y and long inference type approaches. But basically, the reason that the basic fundamental insight is that the only, like, there are only a few dimensions we can scale LLMs. So, let's say in like 2020, no, let's say in like 2018, 2017, 18, 19, 20, we were realizing that we could scale the number of parameters.[00:05:03] swyx: 20, we were And we scaled that up to 175 billion parameters for GPT 3. And we did some work on scaling laws, which we also talked about in our talk. So the datasets 101 episode where we're like, okay, like we, we think like the right number is 300 billion tokens to, to train 175 billion parameters and then DeepMind came along and trained Gopher and Chinchilla and said that, no, no, like, you know, I think we think the optimal.[00:05:28] swyx: compute optimal ratio is 20 tokens per parameter. And now, of course, with LLAMA and the sort of super LLAMA scaling laws, we have 200 times and often 2, 000 times tokens to parameters. So now, instead of scaling parameters, we're scaling data. And fine, we can keep scaling data. But what else can we scale?[00:05:52] swyx: And I think understanding the ability to scale things is crucial to understanding what to pour money and time and effort into because there's a limit to how much you can scale some things. And I think people don't think about ceilings of things. And so the remaining ceiling of inference is like, okay, like, we have scaled compute, we have scaled data, we have scaled parameters, like, model size, let's just say.[00:06:20] swyx: Like, what else is left? Like, what's the low hanging fruit? And it, and it's, like, blindingly obvious that the remaining low hanging fruit is inference time. So, like, we have scaled training time. We can probably scale more, those things more, but, like, not 10x, not 100x, not 1000x. Like, right now, maybe, like, a good run of a large model is three months.[00:06:40] swyx: We can scale that to three years. But like, can we scale that to 30 years? No, right? Like, it starts to get ridiculous. So it's just the orders of magnitude of scaling. It's just, we're just like running out there. But in terms of the amount of time that we spend inferencing, like everything takes, you know, a few milliseconds, a few hundred milliseconds, depending on what how you're taking token by token, or, you know, entire phrase.[00:07:04] swyx: But We can scale that to hours, days, months of inference and see what we get. And I think that's really proMECEng.[00:07:11] Alessio: Yeah, we'll have Mike from Broadway back on the podcast. But I tried their product and their reports take about 10 minutes to generate instead of like just in real time. I think to me the most interesting thing about long inference is like, You're shifting the cost to the customer depending on how much they care about the end result.[00:07:31] Alessio: If you think about prompt engineering, it's like the first part, right? You can either do a simple prompt and get a simple answer or do a complicated prompt and get a better answer. It's up to you to decide how to do it. Now it's like, hey, instead of like, yeah, training this for three years, I'll still train it for three months and then I'll tell you, you know, I'll teach you how to like make it run for 10 minutes to get a better result.[00:07:52] Alessio: So you're kind of like parallelizing like the improvement of the LLM. Oh yeah, you can even[00:07:57] swyx: parallelize that, yeah, too.[00:07:58] Alessio: So, and I think, you know, for me, especially the work that I do, it's less about, you know, State of the art and the absolute, you know, it's more about state of the art for my application, for my use case.[00:08:09] Alessio: And I think we're getting to the point where like most companies and customers don't really care about state of the art anymore. It's like, I can get this to do a good enough job. You know, I just need to get better. Like, how do I do long inference? You know, like people are not really doing a lot of work in that space, so yeah, excited to see more.[00:08:28] swyx: So then the last point I'll mention here is something I also mentioned as paper. So all these directions are kind of guided by what happened in January. That was my way of doing a January recap. Which means that if there was nothing significant in that month, I also didn't mention it. Which is which I came to regret come February 15th, but in January also, you know, there was also the alpha geometry paper, which I kind of put in this sort of long inference bucket, because it solves like, you know, more than 100 step math olympiad geometry problems at a human gold medalist level and that also involves planning, right?[00:08:59] swyx: So like, if you want to scale inference, you can't scale it blindly, because just, Autoregressive token by token generation is only going to get you so far. You need good planning. And I think probably, yeah, what Mike from BrightWave is now doing and what everyone is doing, including maybe what we think QSTAR might be, is some form of search and planning.[00:09:17] swyx: And it makes sense. Like, you want to spend your inference time wisely. How do you[00:09:22] Alessio: think about plans that work and getting them shared? You know, like, I feel like if you're planning a task, somebody has got in and the models are stochastic. So everybody gets initially different results. Somebody is going to end up generating the best plan to do something, but there's no easy way to like store these plans and then reuse them for most people.[00:09:44] Alessio: You know, like, I'm curious if there's going to be. Some paper or like some work there on like making it better because, yeah, we don't[00:09:52] swyx: really have This is your your pet topic of NPM for[00:09:54] Alessio: Yeah, yeah, NPM, exactly. NPM for, you need NPM for anything, man. You need NPM for skills. You need NPM for planning. Yeah, yeah.[00:10:02] Alessio: You know I think, I mean, obviously the Voyager paper is like the most basic example where like, now their artifact is like the best planning to do a diamond pickaxe in Minecraft. And everybody can just use that. They don't need to come up with it again. Yeah. But there's nothing like that for actually useful[00:10:18] swyx: tasks.[00:10:19] swyx: For plans, I believe it for skills. I like that. Basically, that just means a bunch of integration tooling. You know, GPT built me integrations to all these things. And, you know, I just came from an integrations heavy business and I could definitely, I definitely propose some version of that. And it's just, you know, hard to execute or expensive to execute.[00:10:38] swyx: But for planning, I do think that everyone lives in slightly different worlds. They have slightly different needs. And they definitely want some, you know, And I think that that will probably be the main hurdle for any, any sort of library or package manager for planning. But there should be a meta plan of how to plan.[00:10:57] swyx: And maybe you can adopt that. And I think a lot of people when they have sort of these meta prompting strategies of like, I'm not prescribing you the prompt. I'm just saying that here are the like, Fill in the lines or like the mad libs of how to prompts. First you have the roleplay, then you have the intention, then you have like do something, then you have the don't something and then you have the my grandmother is dying, please do this.[00:11:19] swyx: So the meta plan you could, you could take off the shelf and test a bunch of them at once. I like that. That was the initial, maybe, promise of the, the prompting libraries. You know, both 9chain and Llama Index have, like, hubs that you can sort of pull off the shelf. I don't think they're very successful because people like to write their own.[00:11:36] swyx: Yeah,[00:11:37] Direction 2: Synthetic Data (WRAP, SPIN)[00:11:37] Alessio: yeah, yeah. Yeah, that's a good segue into the next one, which is synthetic[00:11:41] swyx: data. Synthetic data is so hot. Yeah, and, you know, the way, you know, I think I, I feel like I should do one of these memes where it's like, Oh, like I used to call it, you know, R L A I F, and now I call it synthetic data, and then people are interested.[00:11:54] swyx: But there's gotta be older versions of what synthetic data really is because I'm sure, you know if you've been in this field long enough, There's just different buzzwords that the industry condenses on. Anyway, the insight that I think is relatively new that why people are excited about it now and why it's proMECEng now is that we have evidence that shows that LLMs can generate data to improve themselves with no teacher LLM.[00:12:22] swyx: For all of 2023, when people say synthetic data, they really kind of mean generate a whole bunch of data from GPT 4 and then train an open source model on it. Hello to our friends at News Research. That's what News Harmony says. They're very, very open about that. I think they have said that they're trying to migrate away from that.[00:12:40] swyx: But it is explicitly against OpenAI Terms of Service. Everyone knows this. You know, especially once ByteDance got banned for, for doing exactly that. So so, so synthetic data that is not a form of model distillation is the hot thing right now, that you can bootstrap better LLM performance from the same LLM, which is very interesting.[00:13:03] swyx: A variant of this is RLAIF, where you have a, where you have a sort of a constitutional model, or, you know, some, some kind of judge model That is sort of more aligned. But that's not really what we're talking about when most people talk about synthetic data. Synthetic data is just really, I think, you know, generating more data in some way.[00:13:23] swyx: A lot of people, I think we talked about this with Vipul from the Together episode, where I think he commented that you just have to have a good world model. Or a good sort of inductive bias or whatever that, you know, term of art is. And that is strongest in math and science math and code, where you can verify what's right and what's wrong.[00:13:44] swyx: And so the REST EM paper from DeepMind explored that. Very well, it's just the most obvious thing like and then and then once you get out of that domain of like things where you can generate You can arbitrarily generate like a whole bunch of stuff and verify if they're correct and therefore they're they're correct synthetic data to train on Once you get into more sort of fuzzy topics, then it's then it's a bit less clear So I think that the the papers that drove this understanding There are two big ones and then one smaller one One was wrap like rephrasing the web from from Apple where they basically rephrased all of the C4 data set with Mistral and it be trained on that instead of C4.[00:14:23] swyx: And so new C4 trained much faster and cheaper than old C, than regular raw C4. And that was very interesting. And I have told some friends of ours that they should just throw out their own existing data sets and just do that because that seems like a pure win. Obviously we have to study, like, what the trade offs are.[00:14:42] swyx: I, I imagine there are trade offs. So I was just thinking about this last night. If you do synthetic data and it's generated from a model, probably you will not train on typos. So therefore you'll be like, once the model that's trained on synthetic data encounters the first typo, they'll be like, what is this?[00:15:01] swyx: I've never seen this before. So they have no association or correction as to like, oh, these tokens are often typos of each other, therefore they should be kind of similar. I don't know. That's really remains to be seen, I think. I don't think that the Apple people export[00:15:15] Alessio: that. Yeah, isn't that the whole, Mode collapse thing, if we do more and more of this at the end of the day.[00:15:22] swyx: Yeah, that's one form of that. Yeah, exactly. Microsoft also had a good paper on text embeddings. And then I think this is a meta paper on self rewarding language models. That everyone is very interested in. Another paper was also SPIN. These are all things we covered in the the Latent Space Paper Club.[00:15:37] swyx: But also, you know, I just kind of recommend those as top reads of the month. Yeah, I don't know if there's any much else in terms, so and then, regarding the potential of it, I think it's high potential because, one, it solves one of the data war issues that we have, like, everyone is OpenAI is paying Reddit 60 million dollars a year for their user generated data.[00:15:56] swyx: Google, right?[00:15:57] Alessio: Not OpenAI.[00:15:59] swyx: Is it Google? I don't[00:16:00] Alessio: know. Well, somebody's paying them 60 million, that's[00:16:04] swyx: for sure. Yes, that is, yeah, yeah, and then I think it's maybe not confirmed who. But yeah, it is Google. Oh my god, that's interesting. Okay, because everyone was saying, like, because Sam Altman owns 5 percent of Reddit, which is apparently 500 million worth of Reddit, he owns more than, like, the founders.[00:16:21] Alessio: Not enough to get the data,[00:16:22] swyx: I guess. So it's surprising that it would go to Google instead of OpenAI, but whatever. Okay yeah, so I think that's all super interesting in the data field. I think it's high potential because we have evidence that it works. There's not a doubt that it doesn't work. I think it's a doubt that there's, what the ceiling is, which is the mode collapse thing.[00:16:42] swyx: If it turns out that the ceiling is pretty close, then this will maybe augment our data by like, I don't know, 30 50 percent good, but not game[00:16:51] Alessio: changing. And most of the synthetic data stuff, it's reinforcement learning on a pre trained model. People are not really doing pre training on fully synthetic data, like, large enough scale.[00:17:02] swyx: Yeah, unless one of our friends that we've talked to succeeds. Yeah, yeah. Pre trained synthetic data, pre trained scale synthetic data, I think that would be a big step. Yeah. And then there's a wildcard, so all of these, like smaller Directions,[00:17:15] Wildcard: Multi-Epoch Training (OLMo, Datablations)[00:17:15] swyx: I always put a wildcard in there. And one of the wildcards is, okay, like, Let's say, you have pre, you have, You've scraped all the data on the internet that you think is useful.[00:17:25] swyx: Seems to top out at somewhere between 2 trillion to 3 trillion tokens. Maybe 8 trillion if Mistral, Mistral gets lucky. Okay, if I need 80 trillion, if I need 100 trillion, where do I go? And so, you can do synthetic data maybe, but maybe that only gets you to like 30, 40 trillion. Like where, where is the extra alpha?[00:17:43] swyx: And maybe extra alpha is just train more on the same tokens. Which is exactly what Omo did, like Nathan Lambert, AI2, After, just after he did the interview with us, they released Omo. So, it's unfortunate that we didn't get to talk much about it. But Omo actually started doing 1. 5 epochs on every, on all data.[00:18:00] swyx: And the data ablation paper that I covered in Europe's says that, you know, you don't like, don't really start to tap out of like, the alpha or the sort of improved loss that you get from data all the way until four epochs. And so I'm just like, okay, like, why do we all agree that one epoch is all you need?[00:18:17] swyx: It seems like to be a trend. It seems that we think that memorization is very good or too good. But then also we're finding that, you know, For improvement in results that we really like, we're fine on overtraining on things intentionally. So, I think that's an interesting direction that I don't see people exploring enough.[00:18:36] swyx: And the more I see papers coming out Stretching beyond the one epoch thing, the more people are like, it's completely fine. And actually, the only reason we stopped is because we ran out of compute[00:18:46] Alessio: budget. Yeah, I think that's the biggest thing, right?[00:18:51] swyx: Like, that's not a valid reason, that's not science. I[00:18:54] Alessio: wonder if, you know, Matt is going to do it.[00:18:57] Alessio: I heard LamaTree, they want to do a 100 billion parameters model. I don't think you can train that on too many epochs, even with their compute budget, but yeah. They're the only ones that can save us, because even if OpenAI is doing this, they're not going to tell us, you know. Same with DeepMind.[00:19:14] swyx: Yeah, and so the updates that we got on Lambda 3 so far is apparently that because of the Gemini news that we'll talk about later they're pushing it back on the release.[00:19:21] swyx: They already have it. And they're just pushing it back to do more safety testing. Politics testing.[00:19:28] Alessio: Well, our episode with Sumit will have already come out by the time this comes out, I think. So people will get the inside story on how they actually allocate the compute.[00:19:38] Direction 3: Alt. Architectures (Mamba, RWKV, RingAttention, Diffusion Transformers)[00:19:38] Alessio: Alternative architectures. Well, shout out to our WKV who won one of the prizes at our Final Frontiers event last week.[00:19:47] Alessio: We talked about Mamba and Strapain on the Together episode. A lot of, yeah, monarch mixers. I feel like Together, It's like the strong Stanford Hazy Research Partnership, because Chris Ray is one of the co founders. So they kind of have a, I feel like they're going to be the ones that have one of the state of the art models alongside maybe RWKB.[00:20:08] Alessio: I haven't seen as many independent. People working on this thing, like Monarch Mixer, yeah, Manbuster, Payena, all of these are together related. Nobody understands the math. They got all the gigabrains, they got 3DAO, they got all these folks in there, like, working on all of this.[00:20:25] swyx: Albert Gu, yeah. Yeah, so what should we comment about it?[00:20:28] swyx: I mean, I think it's useful, interesting, but at the same time, both of these are supposed to do really good scaling for long context. And then Gemini comes out and goes like, yeah, we don't need it. Yeah.[00:20:44] Alessio: No, that's the risk. So, yeah. I was gonna say, maybe it's not here, but I don't know if we want to talk about diffusion transformers as like in the alt architectures, just because of Zora.[00:20:55] swyx: One thing, yeah, so, so, you know, this came from the Jan recap, which, and diffusion transformers were not really a discussion, and then, obviously, they blow up in February. Yeah. I don't think they're, it's a mixed architecture in the same way that Stripe Tiena is mixed there's just different layers taking different approaches.[00:21:13] swyx: Also I think another one that I maybe didn't call out here, I think because it happened in February, was hourglass diffusion from stability. But also, you know, another form of mixed architecture. So I guess that is interesting. I don't have much commentary on that, I just think, like, we will try to evolve these things, and maybe one of these architectures will stick and scale, it seems like diffusion transformers is going to be good for anything generative, you know, multi modal.[00:21:41] swyx: We don't see anything where diffusion is applied to text yet, and that's the wild card for this category. Yeah, I mean, I think I still hold out hope for let's just call it sub quadratic LLMs. I think that a lot of discussion this month actually was also centered around this concept that People always say, oh, like, transformers don't scale because attention is quadratic in the sequence length.[00:22:04] swyx: Yeah, but, you know, attention actually is a very small part of the actual compute that is being spent, especially in inference. And this is the reason why, you know, when you multiply, when you, when you, when you jump up in terms of the, the model size in GPT 4 from like, you know, 38k to like 32k, you don't also get like a 16 times increase in your, in your performance.[00:22:23] swyx: And this is also why you don't get like a million times increase in your, in your latency when you throw a million tokens into Gemini. Like people have figured out tricks around it or it's just not that significant as a term, as a part of the overall compute. So there's a lot of challenges to this thing working.[00:22:43] swyx: It's really interesting how like, how hyped people are about this versus I don't know if it works. You know, it's exactly gonna, gonna work. And then there's also this, this idea of retention over long context. Like, even though you have context utilization, like, the amount of, the amount you can remember is interesting.[00:23:02] swyx: Because I've had people criticize both Mamba and RWKV because they're kind of, like, RNN ish in the sense that they have, like, a hidden memory and sort of limited hidden memory that they will forget things. So, for all these reasons, Gemini 1. 5, which we still haven't covered, is very interesting because Gemini magically has fixed all these problems with perfect haystack recall and reasonable latency and cost.[00:23:29] Wildcards: Text Diffusion, RALM/Retro[00:23:29] swyx: So that's super interesting. So the wildcard I put in here if you want to go to that. I put two actually. One is text diffusion. I think I'm still very influenced by my meeting with a mid journey person who said they were working on text diffusion. I think it would be a very, very different paradigm for, for text generation, reasoning, plan generation if we can get diffusion to work.[00:23:51] swyx: For text. And then the second one is Dowie Aquila's contextual AI, which is working on retrieval augmented language models, where it kind of puts RAG inside of the language model instead of outside.[00:24:02] Alessio: Yeah, there's a paper called Retro that covers some of this. I think that's an interesting thing. I think the The challenge, well not the challenge, what they need to figure out is like how do you keep the rag piece always up to date constantly, you know, I feel like the models, you put all this work into pre training them, but then at least you have a fixed artifact.[00:24:22] Alessio: These architectures are like constant work needs to be done on them and they can drift even just based on the rag data instead of the model itself. Yeah,[00:24:30] swyx: I was in a panel with one of the investors in contextual and the guy, the way that guy pitched it, I didn't agree with. He was like, this will solve hallucination.[00:24:38] Alessio: That's what everybody says. We solve[00:24:40] swyx: hallucination. I'm like, no, you reduce it. It cannot,[00:24:44] Alessio: if you solved it, the model wouldn't exist, right? It would just be plain text. It wouldn't be a generative model. Cool. So, author, architectures, then we got mixture of experts. I think we covered a lot of, a lot of times.[00:24:56] Direction 4: Mixture of Experts (DeepSeekMoE, Samba-1)[00:24:56] Alessio: Maybe any new interesting threads you want to go under here?[00:25:00] swyx: DeepSeq MOE, which was released in January. Everyone who is interested in MOEs should read that paper, because it's significant for two reasons. One three reasons. One, it had, it had small experts, like a lot more small experts. So, for some reason, everyone has settled on eight experts for GPT 4 for Mixtral, you know, that seems to be the favorite architecture, but these guys pushed it to 64 experts, and each of them smaller than the other.[00:25:26] swyx: But then they also had the second idea, which is that it is They had two, one to two always on experts for common knowledge and that's like a very compelling concept that you would not route to all the experts all the time and make them, you know, switch to everything. You would have some always on experts.[00:25:41] swyx: I think that's interesting on both the inference side and the training side for for memory retention. And yeah, they, they, they, the, the, the, the results that they published, which actually excluded, Mixed draw, which is interesting. The results that they published showed a significant performance jump versus all the other sort of open source models at the same parameter count.[00:26:01] swyx: So like this may be a better way to do MOEs that are, that is about to get picked up. And so that, that is interesting for the third reason, which is this is the first time a new idea from China. has infiltrated the West. It's usually the other way around. I probably overspoke there. There's probably lots more ideas that I'm not aware of.[00:26:18] swyx: Maybe in the embedding space. But the I think DCM we, like, woke people up and said, like, hey, DeepSeek, this, like, weird lab that is attached to a Chinese hedge fund is somehow, you know, doing groundbreaking research on MOEs. So, so, I classified this as a medium potential because I think that it is a sort of like a one off benefit.[00:26:37] swyx: You can Add to any, any base model to like make the MOE version of it, you get a bump and then that's it. So, yeah,[00:26:45] Alessio: I saw Samba Nova, which is like another inference company. They released this MOE model called Samba 1, which is like a 1 trillion parameters. But they're actually MOE auto open source models.[00:26:56] Alessio: So it's like, they just, they just clustered them all together. So I think people. Sometimes I think MOE is like you just train a bunch of small models or like smaller models and put them together. But there's also people just taking, you know, Mistral plus Clip plus, you know, Deepcoder and like put them all together.[00:27:15] Alessio: And then you have a MOE model. I don't know. I haven't tried the model, so I don't know how good it is. But it seems interesting that you can then have people working separately on state of the art, you know, Clip, state of the art text generation. And then you have a MOE architecture that brings them all together.[00:27:31] swyx: I'm thrown off by your addition of the word clip in there. Is that what? Yeah, that's[00:27:35] Alessio: what they said. Yeah, yeah. Okay. That's what they I just saw it yesterday. I was also like[00:27:40] swyx: scratching my head. And they did not use the word adapter. No. Because usually what people mean when they say, Oh, I add clip to a language model is adapter.[00:27:48] swyx: Let me look up the Which is what Lava did.[00:27:50] Alessio: The announcement again.[00:27:51] swyx: Stable diffusion. That's what they do. Yeah, it[00:27:54] Alessio: says among the models that are part of Samba 1 are Lama2, Mistral, DeepSigCoder, Falcon, Dplot, Clip, Lava. So they're just taking all these models and putting them in a MOE. Okay,[00:28:05] swyx: so a routing layer and then not jointly trained as much as a normal MOE would be.[00:28:12] swyx: Which is okay.[00:28:13] Alessio: That's all they say. There's no paper, you know, so it's like, I'm just reading the article, but I'm interested to see how[00:28:20] Wildcard: Model Merging (mergekit)[00:28:20] swyx: it works. Yeah, so so the wildcard for this section, the MOE section is model merges, which has also come up as, as a very interesting phenomenon. The last time I talked to Jeremy Howard at the Olama meetup we called it model grafting or model stacking.[00:28:35] swyx: But I think the, the, the term that people are liking these days, the model merging, They're all, there's all different variations of merging. Merge types, and some of them are stacking, some of them are, are grafting. And, and so like, some people are approaching model merging in the way that Samba is doing, which is like, okay, here are defined models, each of which have their specific, Plus and minuses, and we will merge them together in the hope that the, you know, the sum of the parts will, will be better than others.[00:28:58] swyx: And it seems like it seems like it's working. I don't really understand why it works apart from, like, I think it's a form of regularization. That if you merge weights together in like a smart strategy you, you, you get a, you get a, you get a less overfitting and more generalization, which is good for benchmarks, if you, if you're honest about your benchmarks.[00:29:16] swyx: So this is really interesting and good. But again, they're kind of limited in terms of like the amount of bumps you can get. But I think it's very interesting in the sense of how cheap it is. We talked about this on the Chinatalk podcast, like the guest podcast that we did with Chinatalk. And you can do this without GPUs, because it's just adding weights together, and dividing things, and doing like simple math, which is really interesting for the GPU ports.[00:29:42] Alessio: There's a lot of them.[00:29:44] Direction 5: Online LLMs (Gemini Pro, Exa)[00:29:44] Alessio: And just to wrap these up, online LLMs? Yeah,[00:29:48] swyx: I think that I ki I had to feature this because the, one of the top news of January was that Gemini Pro beat GPT-4 turbo on LM sis for the number two slot to GPT-4. And everyone was very surprised. Like, how does Gemini do that?[00:30:06] swyx: Surprise, surprise, they added Google search. Mm-hmm to the results. So it became an online quote unquote online LLM and not an offline LLM. Therefore, it's much better at answering recent questions, which people like. There's an emerging set of table stakes features after you pre train something.[00:30:21] swyx: So after you pre train something, you should have the chat tuned version of it, or the instruct tuned version of it, however you choose to call it. You should have the JSON and function calling version of it. Structured output, the term that you don't like. You should have the online version of it. These are all like table stakes variants, that you should do when you offer a base LLM, or you train a base LLM.[00:30:44] swyx: And I think online is just like, There, it's important. I think companies like Perplexity, and even Exa, formerly Metaphor, you know, are rising to offer that search needs. And it's kind of like, they're just necessary parts of a system. When you have RAG for internal knowledge, and then you have, you know, Online search for external knowledge, like things that you don't know yet?[00:31:06] swyx: Mm-Hmm. . And it seems like it's, it's one of many tools. I feel like I may be underestimating this, but I'm just gonna put it out there that I, I think it has some, some potential. One of the evidence points that it doesn't actually matter that much is that Perplexity has a, has had online LMS for three months now and it performs, doesn't perform great.[00:31:25] swyx: Mm-Hmm. on, on lms, it's like number 30 or something. So it's like, okay. You know, like. It's, it's, it helps, but it doesn't give you a giant, giant boost. I[00:31:34] Alessio: feel like a lot of stuff I do with LLMs doesn't need to be online. So I'm always wondering, again, going back to like state of the art, right? It's like state of the art for who and for what.[00:31:45] Alessio: It's really, I think online LLMs are going to be, State of the art for, you know, news related activity that you need to do. Like, you're like, you know, social media, right? It's like, you want to have all the latest stuff, but coding, science,[00:32:01] swyx: Yeah, but I think. Sometimes you don't know what is news, what is news affecting.[00:32:07] swyx: Like, the decision to use an offline LLM is already a decision that you might not be consciously making that might affect your results. Like, what if, like, just putting things on, being connected online means that you get to invalidate your knowledge. And when you're just using offline LLM, like it's never invalidated.[00:32:27] swyx: I[00:32:28] Alessio: agree, but I think going back to your point of like the standing the test of time, I think sometimes you can get swayed by the online stuff, which is like, hey, you ask a question about, yeah, maybe AI research direction, you know, and it's like, all the recent news are about this thing. So the LLM like focus on answering, bring it up, you know, these things.[00:32:50] swyx: Yeah, so yeah, I think, I think it's interesting, but I don't know if I can, I bet heavily on this.[00:32:56] Alessio: Cool. Was there one that you forgot to put, or, or like a, a new direction? Yeah,[00:33:01] swyx: so, so this brings us into sort of February. ish.[00:33:05] OpenAI Sora and why everyone underestimated videogen[00:33:05] swyx: So like I published this in like 15 came with Sora. And so like the one thing I did not mention here was anything about multimodality.[00:33:16] swyx: Right. And I have chronically underweighted this. I always wrestle. And, and my cop out is that I focused this piece or this research direction piece on LLMs because LLMs are the source of like AGI, quote unquote AGI. Everything else is kind of like. You know, related to that, like, generative, like, just because I can generate better images or generate better videos, it feels like it's not on the critical path to AGI, which is something that Nat Friedman also observed, like, the day before Sora, which is kind of interesting.[00:33:49] swyx: And so I was just kind of like trying to focus on like what is going to get us like superhuman reasoning that we can rely on to build agents that automate our lives and blah, blah, blah, you know, give us this utopian future. But I do think that I, everybody underestimated the, the sheer importance and cultural human impact of Sora.[00:34:10] swyx: And you know, really actually good text to video. Yeah. Yeah.[00:34:14] Alessio: And I saw Jim Fan at a, at a very good tweet about why it's so impressive. And I think when you have somebody leading the embodied research at NVIDIA and he said that something is impressive, you should probably listen. So yeah, there's basically like, I think you, you mentioned like impacting the world, you know, that we live in.[00:34:33] Alessio: I think that's kind of like the key, right? It's like the LLMs don't have, a world model and Jan Lekon. He can come on the podcast and talk all about what he thinks of that. But I think SORA was like the first time where people like, Oh, okay, you're not statically putting pixels of water on the screen, which you can kind of like, you know, project without understanding the physics of it.[00:34:57] Alessio: Now you're like, you have to understand how the water splashes when you have things. And even if you just learned it by watching video and not by actually studying the physics, You still know it, you know, so I, I think that's like a direction that yeah, before you didn't have, but now you can do things that you couldn't before, both in terms of generating, I think it always starts with generating, right?[00:35:19] Alessio: But like the interesting part is like understanding it. You know, it's like if you gave it, you know, there's the video of like the, the ship in the water that they generated with SORA, like if you gave it the video back and now it could tell you why the ship is like too rocky or like it could tell you why the ship is sinking, then that's like, you know, AGI for like all your rig deployments and like all this stuff, you know, so, but there's none, there's none of that yet, so.[00:35:44] Alessio: Hopefully they announce it and talk more about it. Maybe a Dev Day this year, who knows.[00:35:49] swyx: Yeah who knows, who knows. I'm talking with them about Dev Day as well. So I would say, like, the phrasing that Jim used, which resonated with me, he kind of called it a data driven world model. I somewhat agree with that.[00:36:04] Does Sora have a World Model? Yann LeCun vs Jim Fan[00:36:04] swyx: I am on more of a Yann LeCun side than I am on Jim's side, in the sense that I think that is the vision or the hope that these things can build world models. But you know, clearly even at the current SORA size, they don't have the idea of, you know, They don't have strong consistency yet. They have very good consistency, but fingers and arms and legs will appear and disappear and chairs will appear and disappear.[00:36:31] swyx: That definitely breaks physics. And it also makes me think about how we do deep learning versus world models in the sense of You know, in classic machine learning, when you have too many parameters, you will overfit, and actually that fails, that like, does not match reality, and therefore fails to generalize well.[00:36:50] swyx: And like, what scale of data do we need in order to world, learn world models from video? A lot. Yeah. So, so I, I And cautious about taking this interpretation too literally, obviously, you know, like, I get what he's going for, and he's like, obviously partially right, obviously, like, transformers and, and, you know, these, like, these sort of these, these neural networks are universal function approximators, theoretically could figure out world models, it's just like, how good are they, and how tolerant are we of hallucinations, we're not very tolerant, like, yeah, so It's, it's, it's gonna prior, it's gonna bias us for creating like very convincing things, but then not create like the, the, the useful role models that we want.[00:37:37] swyx: At the same time, what you just said, I think made me reflect a little bit like we just got done saying how important synthetic data is for Mm-Hmm. for training lms. And so like, if this is a way of, of synthetic, you know, vi video data for improving our video understanding. Then sure, by all means. Which we actually know, like, GPT 4, Vision, and Dolly were trained, kind of, co trained together.[00:38:02] swyx: And so, like, maybe this is on the critical path, and I just don't fully see the full picture yet.[00:38:08] Alessio: Yeah, I don't know. I think there's a lot of interesting stuff. It's like, imagine you go back, you have Sora, you go back in time, and Newton didn't figure out gravity yet. Would Sora help you figure it out?[00:38:21] Alessio: Because you start saying, okay, a man standing under a tree with, like, Apples falling, and it's like, oh, they're always falling at the same speed in the video. Why is that? I feel like sometimes these engines can like pick up things, like humans have a lot of intuition, but if you ask the average person, like the physics of like a fluid in a boat, they couldn't be able to tell you the physics, but they can like observe it, but humans can only observe this much, you know, versus like now you have these models to observe everything and then They generalize these things and maybe we can learn new things through the generalization that they pick up.[00:38:55] swyx: But again, And it might be more observant than us in some respects. In some ways we can scale it up a lot more than the number of physicists that we have available at Newton's time. So like, yeah, absolutely possible. That, that this can discover new science. I think we have a lot of work to do to formalize the science.[00:39:11] swyx: And then, I, I think the last part is you know, How much, how much do we cheat by gen, by generating data from Unreal Engine 5? Mm hmm. which is what a lot of people are speculating with very, very limited evidence that OpenAI did that. The strongest evidence that I saw was someone who works a lot with Unreal Engine 5 looking at the side characters in the videos and noticing that they all adopt Unreal Engine defaults.[00:39:37] swyx: of like, walking speed, and like, character choice, like, character creation choice. And I was like, okay, like, that's actually pretty convincing that they actually use Unreal Engine to bootstrap some synthetic data for this training set. Yeah,[00:39:52] Alessio: could very well be.[00:39:54] swyx: Because then you get the labels and the training side by side.[00:39:58] swyx: One thing that came up on the last day of February, which I should also mention, is EMO coming out of Alibaba, which is also a sort of like video generation and space time transformer that also involves probably a lot of synthetic data as well. And so like, this is of a kind in the sense of like, oh, like, you know, really good generative video is here and It is not just like the one, two second clips that we saw from like other, other people and like, you know, Pika and all the other Runway are, are, are, you know, run Cristobal Valenzuela from Runway was like game on which like, okay, but like, let's see your response because we've heard a lot about Gen 1 and 2, but like, it's nothing on this level of Sora So it remains to be seen how we can actually apply this, but I do think that the creative industry should start preparing.[00:40:50] swyx: I think the Sora technical blog post from OpenAI was really good.. It was like a request for startups. It was so good in like spelling out. Here are the individual industries that this can impact.[00:41:00] swyx: And anyone who, anyone who's like interested in generative video should look at that. But also be mindful that probably when OpenAI releases a Soa API, right? The you, the in these ways you can interact with it are very limited. Just like the ways you can interact with Dahlia very limited and someone is gonna have to make open SOA to[00:41:19] swyx: Mm-Hmm to, to, for you to create comfy UI pipelines.[00:41:24] Alessio: The stability folks said they wanna build an open. For a competitor, but yeah, stability. Their demo video, their demo video was like so underwhelming. It was just like two people sitting on the beach[00:41:34] swyx: standing. Well, they don't have it yet, right? Yeah, yeah.[00:41:36] swyx: I mean, they just wanna train it. Everybody wants to, right? Yeah. I, I think what is confusing a lot of people about stability is like they're, they're, they're pushing a lot of things in stable codes, stable l and stable video diffusion. But like, how much money do they have left? How many people do they have left?[00:41:51] swyx: Yeah. I have had like a really, Ima Imad spent two hours with me. Reassuring me things are great. And, and I'm like, I, I do, like, I do believe that they have really, really quality people. But it's just like, I, I also have a lot of very smart people on the other side telling me, like, Hey man, like, you know, don't don't put too much faith in this, in this thing.[00:42:11] swyx: So I don't know who to believe. Yeah.[00:42:14] Alessio: It's hard. Let's see. What else? We got a lot more stuff. I don't know if we can. Yeah, Groq.[00:42:19] Groq Math[00:42:19] Alessio: We can[00:42:19] swyx: do a bit of Groq prep. We're, we're about to go to talk to Dylan Patel. Maybe, maybe it's the audio in here. I don't know. It depends what, what we get up to later. What, how, what do you as an investor think about Groq? Yeah. Yeah, well, actually, can you recap, like, why is Groq interesting? So,[00:42:33] Alessio: Jonathan Ross, who's the founder of Groq, he's the person that created the TPU at Google. It's actually, it was one of his, like, 20 percent projects. It's like, he was just on the side, dooby doo, created the TPU.[00:42:46] Alessio: But yeah, basically, Groq, they had this demo that went viral, where they were running Mistral at, like, 500 tokens a second, which is like, Fastest at anything that you have out there. The question, you know, it's all like, The memes were like, is NVIDIA dead? Like, people don't need H100s anymore. I think there's a lot of money that goes into building what GRUK has built as far as the hardware goes.[00:43:11] Alessio: We're gonna, we're gonna put some of the notes from, from Dylan in here, but Basically the cost of the Groq system is like 30 times the cost of, of H100 equivalent. So, so[00:43:23] swyx: let me, I put some numbers because me and Dylan were like, I think the two people actually tried to do Groq math. Spreadsheet doors.[00:43:30] swyx: Spreadsheet doors. So, one that's, okay, oh boy so, so, equivalent H100 for Lama 2 is 300, 000. For a system of 8 cards. And for Groq it's 2. 3 million. Because you have to buy 576 Groq cards. So yeah, that, that just gives people an idea. So like if you deprecate both over a five year lifespan, per year you're deprecating 460K for Groq, and 60K a year for H100.[00:43:59] swyx: So like, Groqs are just way more expensive per model that you're, that you're hosting. But then, you make it up in terms of volume. So I don't know if you want to[00:44:08] Alessio: cover that. I think one of the promises of Groq is like super high parallel inference on the same thing. So you're basically saying, okay, I'm putting on this upfront investment on the hardware, but then I get much better scaling once I have it installed.[00:44:24] Alessio: I think the big question is how much can you sustain the parallelism? You know, like if you get, if you're going to get 100% Utilization rate at all times on Groq, like, it's just much better, you know, because like at the end of the day, the tokens per second costs that you're getting is better than with the H100s, but if you get to like 50 percent utilization rate, you will be much better off running on NVIDIA.[00:44:49] Alessio: And if you look at most companies out there, who really gets 100 percent utilization rate? Probably open AI at peak times, but that's probably it. But yeah, curious to see more. I saw Jonathan was just at the Web Summit in Dubai, in Qatar. He just gave a talk there yesterday. That I haven't listened to yet.[00:45:09] Alessio: I, I tweeted that he should come on the pod. He liked it. And then rock followed me on Twitter. I don't know if that means that they're interested, but[00:45:16] swyx: hopefully rock social media person is just very friendly. They, yeah. Hopefully[00:45:20] Alessio: we can get them. Yeah, we, we gonna get him. We[00:45:22] swyx: just call him out and, and so basically the, the key question is like, how sustainable is this and how much.[00:45:27] swyx: This is a loss leader the entire Groq management team has been on Twitter and Hacker News saying they are very, very comfortable with the pricing of 0. 27 per million tokens. This is the lowest that anyone has offered tokens as far as Mixtral or Lama2. This matches deep infra and, you know, I think, I think that's, that's, that's about it in terms of that, that, that low.[00:45:47] swyx: And we think the pro the break even for H100s is 50 cents. At a, at a normal utilization rate. To make this work, so in my spreadsheet I made this, made this work. You have to have like a parallelism of 500 requests all simultaneously. And you have, you have model bandwidth utilization of 80%.[00:46:06] swyx: Which is way high. I just gave them high marks for everything. Groq has two fundamental tech innovations that they hinge their hats on in terms of like, why we are better than everyone. You know, even though, like, it remains to be independently replicated. But one you know, they have this sort of the entire model on the chip idea, which is like, Okay, get rid of HBM.[00:46:30] swyx: And, like, put everything in SREM. Like, okay, fine, but then you need a lot of cards and whatever. And that's all okay. And so, like, because you don't have to transfer between memory, then you just save on that time and that's why they're faster. So, a lot of people buy that as, like, that's the reason that you're faster.[00:46:45] swyx: Then they have, like, some kind of crazy compiler, or, like, Speculative routing magic using compilers that they also attribute towards their higher utilization. So I give them 80 percent for that. And so that all that works out to like, okay, base costs, I think you can get down to like, maybe like 20 something cents per million tokens.[00:47:04] swyx: And therefore you actually are fine if you have that kind of utilization. But it's like, I have to make a lot of fearful assumptions for this to work.[00:47:12] Alessio: Yeah. Yeah, I'm curious to see what Dylan says later.[00:47:16] swyx: So he was like completely opposite of me. He's like, they're just burning money. Which is great.[00:47:22] Analyzing Gemini's 1m Context, Reddit deal, Imagegen politics, Gemma via the Four Wars[00:47:22] Alessio: Gemini, want to do a quick run through since this touches on all the four words.[00:47:28] swyx: Yeah, and I think this is the mark of a useful framework, that when a new thing comes along, you can break it down in terms of the four words and sort of slot it in or analyze it in those four frameworks, and have nothing left.[00:47:41] swyx: So it's a MECE categorization. MECE is Mutually Exclusive and Collectively Exhaustive. And that's a really, really nice way to think about taxonomies and to create mental frameworks. So, what is Gemini 1. 5 Pro? It is the newest model that came out one week after Gemini 1. 0. Which is very interesting.[00:48:01] swyx: They have not really commented on why. They released this the headline feature is that it has a 1 million token context window that is multi modal which means that you can put all sorts of video and audio And PDFs natively in there alongside of text and, you know, it's, it's at least 10 times longer than anything that OpenAI offers which is interesting.[00:48:20] swyx: So it's great for prototyping and it has interesting discussions on whether it kills RAG.[00:48:25] Alessio: Yeah, no, I mean, we always talk about, you know, Long context is good, but you're getting charged per token. So, yeah, people love for you to use more tokens in the context. And RAG is better economics. But I think it all comes down to like how the price curves change, right?[00:48:42] Alessio: I think if anything, RAG's complexity goes up and up the more you use it, you know, because you have more data sources, more things you want to put in there. The token costs should go down over time, you know, if the model stays fixed. If people are happy with the model today. In two years, three years, it's just gonna cost a lot less, you know?[00:49:02] Alessio: So now it's like, why would I use RAG and like go through all of that? It's interesting. I think RAG is better cutting edge economics for LLMs. I think large context will be better long tail economics when you factor in the build cost of like managing a RAG pipeline. But yeah, the recall was like the most interesting thing because we've seen the, you know, You know, in the haystack things in the past, but apparently they have 100 percent recall on anything across the context window.[00:49:28] Alessio: At least they say nobody has used it. No, people[00:49:30] swyx: have. Yeah so as far as, so, so what this needle in a haystack thing for people who aren't following as closely as us is that someone, I forget his name now someone created this needle in a haystack problem where you feed in a whole bunch of generated junk not junk, but just like, Generate a data and ask it to specifically retrieve something in that data, like one line in like a hundred thousand lines where it like has a specific fact and if it, if you get it, you're, you're good.[00:49:57] swyx: And then he moves the needle around, like, you know, does it, does, does your ability to retrieve that vary if I put it at the start versus put it in the middle, put it at the end? And then you generate this like really nice chart. That, that kind of shows like it's recallability of a model. And he did that for GPT and, and Anthropic and showed that Anthropic did really, really poorly.[00:50:15] swyx: And then Anthropic came back and said it was a skill issue, just add this like four, four magic words, and then, then it's magically all fixed. And obviously everybody laughed at that. But what Gemini came out with was, was that, yeah, we, we reproduced their, you know, haystack issue you know, test for Gemini, and it's good across all, all languages.[00:50:30] swyx: All the one million token window, which is very interesting because usually for typical context extension methods like rope or yarn or, you know, anything like that, or alibi, it's lossy like by design it's lossy, usually for conversations that's fine because we are lossy when we talk to people but for superhuman intelligence, perfect memory across Very, very long context.[00:50:51] swyx: It's very, very interesting for picking things up. And so the people who have been given the beta test for Gemini have been testing this. So what you do is you upload, let's say, all of Harry Potter and you change one fact in one sentence, somewhere in there, and you ask it to pick it up, and it does. So this is legit.[00:51:08] swyx: We don't super know how, because this is, like, because it doesn't, yes, it's slow to inference, but it's not slow enough that it's, like, running. Five different systems in the background without telling you. Right. So it's something, it's something interesting that they haven't fully disclosed yet. The open source community has centered on this ring attention paper, which is created by your friend Matei Zaharia, and a couple other people.[00:51:36] swyx: And it's a form of distributing the compute. I don't super understand, like, why, you know, doing, calculating, like, the fee for networking and attention. In block wise fashion and distributing it makes it so good at recall. I don't think they have any answer to that. The only thing that Ring of Tension is really focused on is basically infinite context.[00:51:59] swyx: They said it was good for like 10 to 100 million tokens. Which is, it's just great. So yeah, using the four wars framework, what is this framework for Gemini? One is the sort of RAG and Ops war. Here we care less about RAG now, yes. Or, we still care as much about RAG, but like, now it's it's not important in prototyping.[00:52:21] swyx: And then, for data war I guess this is just part of the overall training dataset, but Google made a 60 million deal with Reddit and presumably they have deals with other companies. For the multi modality war, we can talk about the image generation, Crisis, or the fact that Gemini also has image generation, which we'll talk about in the next section.[00:52:42] swyx: But it also has video understanding, which is, I think, the top Gemini post came from our friend Simon Willison, who basically did a short video of him scanning over his bookshelf. And it would be able to convert that video into a JSON output of what's on that bookshelf. And I think that is very useful.[00:53:04] swyx: Actually ties into the conversation that we had with David Luan from Adept. In a sense of like, okay what if video was the main modality instead of text as the input? What if, what if everything was video in, because that's how we work. We, our eyes don't actually read, don't actually like get input, our brains don't get inputs as characters.[00:53:25] swyx: Our brains get the pixels shooting into our eyes, and then our vision system takes over first, and then we sort of mentally translate that into text later. And so it's kind of like what Adept is kind of doing, which is driving by vision model, instead of driving by raw text understanding of the DOM. And, and I, I, in that, that episode, which we haven't released I made the analogy to like self-driving by lidar versus self-driving by camera.[00:53:52] swyx: Mm-Hmm. , right? Like, it's like, I think it, what Gemini and any other super long context that model that is multimodal unlocks is what if you just drive everything by video. Which is[00:54:03] Alessio: cool. Yeah, and that's Joseph from Roboflow. It's like anything that can be seen can be programmable with these models.[00:54:12] Alessio: You mean[00:54:12] swyx: the computer vision guy is bullish on computer vision?[00:54:18] Alessio: It's like the rag people. The rag people are bullish on rag and not a lot of context. I'm very surprised. The, the fine tuning people love fine tuning instead of few shot. Yeah. Yeah. The, yeah, the, that's that. Yeah, the, I, I think the ring attention thing, and it's how they did it, we don't know. And then they released the Gemma models, which are like a 2 billion and 7 billion open.[00:54:41] Alessio: Models, which people said are not, are not good based on my Twitter experience, which are the, the GPU poor crumbs. It's like, Hey, we did all this work for us because we're GPU rich and we're just going to run this whole thing. And
Welcome to The SaaS CFO Podcast, I'm your host, Ben, and today we're diving into the financial heartbeat of SaaS companies with a very special guest, Vipul Shah, the co-founder and CEO/CFO at SaaS Works. In this episode, Vipul will share his unique insights into guiding metrics that are crucial for any recurring revenue business such MRR, ARR, customer retention, and dollar retention, and why these metrics are pivotal for evaluating capital efficiency, customer satisfaction, and product-market fit. We'll also explore how Vipul's background in technology and finance has shaped the services offered by SaaSWorks, a company dedicated to enhancing the office of the CFO through data-driven decision-making and automation. Get ready to learn about their innovative approach to quality of revenue, their successful funding rounds, and the lessons they've learned along the way, not to mention the importance of community and customer-led growth. Whether you're a CFO, a SaaS entrepreneur, or an investor, this conversation will give you a comprehensive outlook on the value drivers in the SaaS world and the evolving role of finance in the success of a SaaS business. Remember to check out more about SaasWorks at www.saasworks.com. So, let's buckle up and get ready for a journey into the metrics that matter, the funding that fuels growth, and the strategies that sustain success in the SaaS industry. Show Notes: 00:00 Transitioned from tech to investing, worked extensively. 06:00 Utilizing digital workers to automate finance tasks. 07:28 Quality of revenue impacts SaaS business value. 12:15 Patient investors, proven product, successful growth strategies. 14:00 CFO board support, strategic investors, $6M rounds. 16:45 30-year renewal, testimonial, case studies, reaching CFOs, expanding volume. 23:12 Guidance for fundraising: be balanced and transparent. 25:30 Efficiency, team quality, growth, and customer retention. 28:38 Embedding AI, ML, RPA and more into platform for users. Links: Vipul Shah's LinkedIn: https://www.linkedin.com/in/vpshah/ SaaSWorks' LinkedIn: https://www.linkedin.com/company/saasworksinc/ To know more about Ben check out the links below: Subscribe to Ben's daily metrics newsletter: https://saasmetricsschool.beehiiv.com/subscribe Subscribe to Ben's SaaS newsletter: https://mailchi.mp/df1db6bf8bca/the-saas-cfo-sign-up-landing-page SaaS Metrics courses here: https://www.thesaasacademy.com/ Join Ben's SaaS community here: https://www.thesaasacademy.com/offers/ivNjwYDx/checkout Follow Ben on LinkedIn: https://www.linkedin.com/in/benrmurray
Vipul talks us through his journey from RPO to in-house and then his most recent gig at Meta as the Director of Recruiting. We touch on his why to being an advocate for sourcing and him seeing firsthand the benefits of having a carved pathway for Sourcers. Please remember to subscribe and rate the podcast to help others discover and enjoy our content to create more awareness on sourcing. Visit our website Follow on LinkedIn Happy Sourcing & Stay Crafty.
Our first ever demo day aimed for 15-20 people and ended up ballooning to >200 and covered in the news. We are now running the 2024 edition in SF on Feb 23: Latent Space Final Frontiers, a startup and research competition in “The Autonomous Workforce”, ”Beyond Transformers & GPUs”, and “Embodied AI”. RSVP here! You can find all LS online/IRL events on our new calendar. Super Early Bird tickets have just gone on sale for AI Engineer World's Fair, June 25-27!Today we have the honor of hosting two of Together AI's co-founders: Ce Zhang (CTO) and Vipul Ved Prakash (CEO). This is a rare opportunity to recap the history of the company since our last check-in with Tri Dao (Chief Scientist), some of their big releases, and do a deep dive into the state of the AI inference market. Together has emerged as one of the most consequential new startups in the new AI summer, last announcing a ~$100m Series A raise in November (at a ~$360-565m valuation). But there are at least three Togethers - Together the Research Lab, Together the Fine Tuning & Inference platform, and Together the custom models service. As we clarify on the pod, the overarching philosophy of Together is the ability to improve on all these fronts simultaneously by being “full stack”, from the lowest level kernel and systems programming to the highest level mathematical abstractions driving new model architectures and inference algorithms.Bringing Research and Industry TogetherIn just one year, Together has been behind some of the most exciting research in AI:* RedPajama, a fully open source dataset for model pre-training which mirrored the Llama1 recipe. Then followed by RedPajama2, a 30T tokens dataset of filtered and de-duplicated tokens. * RedPajama-INCITE-3B and 7B, which were SOTA in a few benchmarks at the time of release. * FlashAttention-2, developed by Together's Chief Scientist Tri Dao. We covered FA-2 in a previous episode with him.* Mamba-3B, the most promising transformer-alternative model that they released in collaboration with Cartesia. * StripedHyena, a SOTA graft of Hyena state space models and transformer models together* Medusa, an alternative to speculative decoding that lets you use multiple decoding heads instead of a draft model. * MonarchMixer, which was one of the most popular orals at NeurIPS 2023. It's an approach to transformers that replaces many of its core parts with Monarch matrices for better computational efficiency. And I'm sure we missed something! As Vipul reveals, almost 50% of Together staff is researchers, and two of their co-founders (Chris Ré and Percy Liang) are professors at Stanford, so we can expect a lot more here.Bringing “Disaggregated” GPUs TogetherOn their cloud, they offer inference as a service, fine-tuning, pre-training, etc, but unlike other providers they think of themselves as a disaggregated cloud. Today, they have ~8,000 A100 and H100 GPUs on their platform (an exclusive revealed on the pod!) totaling over 20 exaflops of compute, but instead of just buying more and putting them in a cluster and then exposing a `us-east-1` option for customers, they are taking heterogenous compute sources and adding a unified layer on top of it for developers to consume. Building on Ce's research, Together's GPU Clusters are taking on comparable AWS and GCP offerings in both cost and speed:Take the Hessian AI center in Germany or the DoE's INCITE; they have GPUs that they want to share with researchers, but they lack the cloud layer over it. Similarly, there's starting to be more and more differentiation amongst types of GPUs: H100s, A100s, MI3000s, etc. Each of them has different availability and performance based on task, and the end user shouldn't have to be an hardware expert to run inference on a model, so Together abstracts a lot of that away.A big theme of the Together inference stack, a “bag of 50 tricks” that we discuss on the pod, is also “hardware-aware” algorithms like FlashAttention and Mamba, which further emphasize the benefits of co-developing everything together:Special Focus: Transformer AlternativesAs we mentioned above, they are also funding a lot of research in Transformer alternatives. To reiterate a few points on why they matter:* Longer context is not the motivation for sub-quadratic architectures: Transformers don't inherently have hard limitations on context size, but they just get extremely expensive. When developing sub-quadratic alternatives, you easily enable very long context, but that's now how you should compare them. Even at same context size, inference and training is much cheaper on sub-quadratic architectures like Hyena.* Emergence of hybrid architectures: a lot of early conversations have been around the “post-Transformers” era, but it might be more like “half-Transformers”. Hybrid architectures could have split layers with some transformer-based and some state-space ones. One of the challenges is that a lot of hardware kernels are optimized for transformer operations, so you'd lose a lot by moving away completely.* Higher speed = higher GPU throughput: if we could reach the same benchmark performance on subquadratic architectures, it'd solve a lot of the GPU crunch. Today we peak at ~170 tok/s on inference in some open models; if we could reach 5,000 tok/s on the same card, you'd be able to serve 30x more customers on the same hardware. As a cloud provider, you're obviously incentivized to get there.We had a lot of fun chatting with the Together guys and we covered a lot of ground, so enjoy the conversation!Note: This is the first episode of a “cloud providers mini-series”. We have Erik from Modal and Ben from Replicate coming up next!Video PodcastJoin us to watching the video version of this pod on our snazzy YouTube!Show Notes* Together AI* RedPajama Dataset v1 Announcement* RedPajama Models v1 Announcement* Together Embeddings* StripedHyena-7B* Mamba-3B-SlimPJ* Vipul's X thread on Anyscale* Vipul's Razor* SemiAnalysis' "Inference Race to the Bottom" post* Chris Ré* Mike Conover's episode* Slim Pajama by Cerebras* Dolma by AI2* Jina AI* Tengyu's Voyage AITimestamps* [00:00:00] Introductions* [00:00:43] Origin and current state of Together.ai* [00:02:15] Transition from Apple to Together and the vision for open AI* [00:04:54] How Chris Ré introduced Ce and Vipul* [00:08:43] How RedPajama came to be* [00:13:34] Model training and Transformer alternatives* [00:15:37] DSIR and the importance of data in LLMs* [00:21:19] Inference vs Fine-tuning vs Pre-training usage on Together* [00:23:20] Together's GPU stash* [00:27:02] Why standardization of inference metrics is important* [00:29:26] Building moats in AI inference* [00:31:49] Federated vs disaggregated cloud computing* [00:34:57] Opportunities for improvement in the inference stack* [00:36:13] Anyscale benchmarking drama* [00:41:27] Not just an inference platform* [00:43:50] Together Embeddings and the future of embedding models* [00:45:53] State space models and hybrid architectures* [00:53:52] The need for 5,000 tokens/s speed in AI inference* [01:00:23] What's the most interesting unsolved question in AI?TranscriptAlessio [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 [00:00:14]: Hey, and today we're together with Together. Welcome to the studio, guys.Ce / Vipul [00:00:20]: Thank you.Swyx [00:00:21]: I don't know how you typically give self intros, but does anyone want to go first? How do we get our audience acquainted, especially to who's speaking, because it's unusual for us to do a four-person pod. Yeah.Ce [00:00:33]: Hi, everyone. I'm Ce. I'm one of the co-founders of Together and the CTO, working with the team on technical things.Vipul [00:00:40]: I'm Vipul Ved Prakash, co-founder and CEO of Together.Swyx [00:00:43]: I always consider you guys as one of the sort of all-in-one companies. I always want to say labs, but I feel like you're not a lab. What is the sort of origin of Together, and then what is it today? I feel like it used to be Together.xyz, and then now you're Together.ai.Vipul [00:01:00]: I think fundamentally, Together is about open and independent AI systems. We think this is one of the most consequential technologies of our time, and when we started the company in June 2022, our focus was to build a platform for open source, independent, user-owned AI systems. One way to think about it is big labs, frontier model labs, have built their own platforms for developer platforms for their models. We think of Together as a platform for everything else, whether these are open models, whether these are models being built by companies that are owned by them. Our sort of XYZ roots, we have a fairly deep decentralization and open ethos that kind of reflects in all our platform and strategy and business. And we also, the way we structure our cloud is by combining data centers around the world instead of, you know, we are today not located in hyperscalers, we have built a footprint of AI supercomputers in this sort of very disaggregated, decentralized manner.Alessio [00:02:15]: I know before Together, you were at Apple, so you go from like the most walled garden, private, we don't say anything company, to we want everything to be open and everybody to know somebody. What maybe did you learn from like the Apple way of being super close and polished and maybe what are you taking now to Together to make it open, but also a very nice developer experience?Vipul [00:02:37]: Yeah, I would say, you know, one sort of my, you know, background has been in open source for a long time. One of the first things I created was a collaborative spam filter, you know, this was back in the day. It's called Vipul's Razor. And it became quite popular. And the first company I founded called CloudMark was built around, you know, taking open source and building both an open side of it and a commercial product around it. I think Apple is sort of very focused on providing this amazing experience to its customers with, you know, most of the technology sort of hidden behind the product. And certainly the focus on fluidity and applying complex technology to make everyday things simple is something that Apple does really well. And, you know, that's been a sort of big part of how we think about our developer platforms. I think it informs it. The other thing is that during my years at Apple, we, you know, worked a lot on deep learning. And one of the things that was sort of very viscerally accessible to me was how well these systems worked. We, you know, we built an open domain Q&A system. This was based on Facebook's LSTM paper in 2016. And it was remarkable because we had a parallel system based on sort of information retrieval techniques, which is extremely complicated, didn't work that well. And you know, this thing we wrote in a week was just incredible performance. So I think some of those experiences, at least for me personally, sort of were creating this roadmap of how important and powerful this technology is. And you know, when the scaling loss paper was published, I was very clear, like it was in some ways something very profound. We've never had algorithms that improve in capabilities with scale out. So this is almost a new era of computing. So that's been, I think, the influence of Apple, my years at Apple, really for me, like crystallized the value of what we are doing together.Alessio [00:04:54]: And how did you decide to join forces? Because you did a postdoc with Chris Ré at Stanford. You know, we already had Tri Dao from Together and we talked about Hazy. What was like the meeting of the mind of, hey, I come from like the more technical postdoc assistant professor background and we've got yet a more product thing. What got you excited to like build this now?Ce [00:05:15]: So we have been working on this together, Chris, in the essentially last like 10 years, right? So it was like a machine learning system 10 years ago was like Power BI's graphic model, right? And then convolutional neural network and then all the foundation model that we see today. But if you look at this, I think that fundamentally the thing we are actually optimizing is actually not that different. It's always about data movement across essentially all the stacks, right? So when you do distributed like computing, it's about communication across different machines. When you do, for example, flash attention, it's about data movement at a different essentially memory hierarchy, right? So we have been doing this in the last 10 years and seeing the field start grow, grow, grow. So we kind of feel the current kind of this like wave of technology is actually the perfect time to actually bring all the research essentially into something real. And we are super lucky that we got introduced to Weibo, right? And then we hope to join forces and bring this to real world.Swyx [00:06:10]: It's an unusual team of like sort of research and industry. Like you've been like a third or fourth time founder now. Third time founder, yeah. And so like what is your first order of business when you like set up together? Like how do you sort of put something like this together? Oh my God, I'm going to use this word so much.Vipul [00:06:27]: I feel AI companies are really kind of driven by research. And Chris and I had been talking about how to reduce the cost of building models. We felt that there aren't really big data modes around foundation models. They are built from a subset of the web. What is difficult is the cost of capital to build these. And one of the ways in which you can reduce this cost is by making more efficient systems. With that, it was really about finding the right set of co-founders and team. In fact, when Chris introduced me to Ce, and I think within the first five minutes of talking to Ce, I was like, we are starting this company. And our early focus was thinking about this more sort of disparate set of resources, you know, GPUs around the internet. Can we use those to build? And we really have to compress communication for, you know, when we do gradient averaging, there's just a lot of traffic. And if you can reduce that somehow, you sort of open up the possibility of using cheaper compute, you know, across the network. And Ce's research for a decade has been in that subject. You know, and from there, finding, you know, other folks in the network, I think there is generally a lot of excitement and philosophical alignment around what we are doing, which, you know, we publish papers, we publish open source libraries and code, we build open models. And I think the people in academia in, you know, machine learning and NLP, that's really what they want to do. So I think that's been really a kind of kernel for, you know, composition of the company. And we're lucky to have, you know, at this point, attracted some of the best researchers in the field. So I think that's the most important thing. And, you know, the rest of it is sort of driven by us. A couple of these philosophies around independent systems and decentralization and good developer interfaces, you want to make it accessible. That's, you know, just as important. And the rest follows from there, I think.Alessio [00:08:43]: I want to try and fill in some of the blanks in the history of Together. I think people come on your website today and they say, you raised a hundred million dollars Series A. They're like, wow, these guys are like super legit company. But it feels like Red Pajama just came out a year ago. I remember we had Mike Conover in the studio, who had built Dolly at Databricks. And you announced it literally the morning we were recording. So we're like in the studio on our phones, looking at it. And it's like, wow, this is like the first time now there's like a good curated dataset to do open pre-training. So maybe let's start from there. Like, what was the motivation behind it? Why did you decide to do that? It's, datasets are one of the things that most people don't want to work on. They just want to do models, not datasets.Ce [00:09:27]: Yeah. So, yeah, first one is not the first, right? So I think it's actually built on a whole bunch of amazing effort the community already have. For example, Eleuther have the pile, right? There's a whole bunch of amazing datasets they have, like C4, right, from Google, right? So I think really get inspired by the impact those like datasets have on the community, right? So I think when we did Red Pajama, it was a time that people are really fascinated by Lama, the model, like Lama 1, right? Which I feel like decades ago, right? But it's kind of, people are really excited about the quality, right? So that's really like a big shift in people how to think about open model. People start to see hope, right? So, but the one problem of Lama is the data recipe is being described in a pretty detailed way in the paper, but the data is actually not there. So, and our original thinking is how about we take the recipe and we try to do our best effort reproduction and try to put it out, such that we can learn from our mistakes in the reproduction together, right? So that's essentially the original thinking behind Red Pajama. And we have been pretty happy and excited about what community have been kind of build on it. For example, there's a dataset called Slim Pajama, right? Which do deduplication over our data, right?Swyx [00:10:38]: From Cerebras, did they talk to you before?Ce [00:10:39]: Oh, yeah, yeah, yeah, yeah. So, yeah, so we are very good friends so we can discuss about technical perspective. We are pretty excited because I think it's kind of why we do Red Pajama in the first place is that people can actually build not only models, but also datasets essentially over that piece of artifact, right? So that's actually what inspired us to do the first version of Red Pajama dataset.Swyx [00:11:01]: Yeah, and then you released V2 maybe two months ago.Ce [00:11:04]: Yeah.Swyx [00:11:05]: 30 trillion tokens.Ce [00:11:06]: Yeah, 30 trillion tokens. So I think what's exciting about Red Pajama V2 is not only the number of tokens, but we start to kind of learn from Red Pajama V1. So one thing that we learned was that data quality is really the core, right? So you want to take this couple trillion token dataset and try to bring them down maybe to one trillion or two trillion, right? The way that you actually filter them, deduplicate them is not something that kind of pre-decided before you see the application, right? So you kind of want to have a modular framework to think about data quality, right? So like given application, let's automatically or maybe semi-automatically try to come up with a way to filter it down. So that's why in Red Pajama V2, we kind of overlay the dataset with like 40 different pre-computed quality signal, right? If you want to reproduce your best effort, like C4 filter, it's kind of like 20 lines of code, right? And this open up this opportunity you can actually put different filter together, learn the combination of filter. We are very excited to see what community actually come up with using Red Pajama V2.Swyx [00:12:11]: It was retrospectively so obvious that this is a good idea that I wonder how come more datasets don't do this. You release the dataset with all these toggles that you can turn on and off, right? And you can sort of tune up and down the quality in ways that you believe is important to you. Yeah, I just, it makes so much sense now in retrospect. Because everyone just publishes like their pipeline and then the end result. But what about all the intermediate stages? Yeah.Ce [00:12:35]: Yeah, so I think, so there are multiple things there. I don't think we are the only one like doing that. For example, like Doma from AI2, right? They have this very flexible format to actually put in those quality signals, right? Think like, we are actually calling them some, right? So you can actually load Red Pajama using their tool. That whole thing should work, right? So I think one fundamental thing that changed in the last year, essentially, in the beginning when people think about data, it's always like a byproduct of the model, right? You release the model, you also release the data, right? The data side is there essentially to show people, ah, if you train on this data, you'll get a good model. But I think what started to change is when people started building more and more of those models, people started to realize like different subset of data side is kind of valuable for different applications, right? The data becomes something to play with, right? So I think we are kind of lucky that we happen to release Red Pajama right at that point that we get this opportunity to actually learn from that.Alessio [00:13:34]: And you guys have a custom model training platform on Together 2. You have a bunch of stuff in there for data selection, like the DSIR and things like that. How did you decide to work on that versus, because you first started with like some of the fine tunes on LLAMA. Do you see a lot of interest there? And I know you've been doing a lot of research on state space models and other transformer alternatives. Like, do you also see that as something you'll keep working on this year and push more people towards?Vipul [00:14:02]: Yeah, I mean, we, you know, we think of how to make training more efficient and building models more efficient. Part of that is being able to select the right dataset. This is why you have signals, DSIR. You can start with a small dataset and find similar documents, build models with that. So we think it's an important part of the kind of model build tooling that, you know, sort of widely useful for people building different kinds of models. Similarly, you know, we are running into the limits of how fast you can make transformers. And we want inference at 5,000 tokens per second. I don't think we will get there with transformers and we need to learn longer sequences. Data, again, becomes very, very expensive with transformers. So I work on space state models and all the research that we are doing there. And hopefully other labs will pick up on this and make it a kind of important target for optimization. But we think that, you know, open source is a great place for this. We can provide these recipes for data and for training to our customers who are building, you know, custom models themselves. And, you know, we are quite excited about the sort of progress we are seeing there.Alessio [00:15:18]: Do you have some of these models available for inference on Together? Can people play around with a strictly, you know?Swyx [00:15:25]: Yeah.Vipul [00:15:25]: Yeah, they're available for inference on our serverless platform.Swyx [00:15:29]: I always try to be the person who asks about acronyms in case, you know, people want to understand. Should we explain importance resampling, you know, that kind of stuff?Ce [00:15:37]: Oh, yeah. So DSIR essentially, it's a fundamental idea. So it's one of the paper from Percy, right? So essentially, if you know what you are doing, you can actually use that as a very strong signal about what data to put in to insert training process, right? So that's essentially the fundamental idea, right? So, and then more concretely, right? So there are actually different versions of DSIR, right? So one version is like if you have a validation site, right? You can actually somehow measure the similarity between the validation site and also your pre-trained corpus and essentially subset, like the subset. And often there's actually like less targeted version of DSIR where you'll say, yeah, maybe Wikipedia is actually a very good corpus. Let's try to find more Wikipedia, right? And you can think about it in two ways, either as a way to come up with different weights for different data slices. Yeah, so as like filter type of step. Yeah, for a data set, or think about that as like data augmentation. So that's how, yeah, that's how we think about DSIR.Swyx [00:16:33]: That makes sense. I will have to read the paper to understand a little bit more. Because when you say things like, we have to know in advance what we were trying to do with the model, then we do importance resampling. That is against the principle of general intelligence, right? Like the point is to train AGI.Ce [00:16:48]: Yeah, so it depends on what do you mean by being general or generic, right? So I think, I mean, you can always take a meta-learning perspective that we know the distribution of tasks that we care about, right? So you can always go kind of up in the ladder of how general the whole thing is, right? But also for many of the customers that we are actually talking to, right, they have kind of very targeted application, right? The benefit you can get out of that is you could build a better open model, often smaller, often easier to do inference, if you know what you want, right? So I think the whole trade-off would be, and the x-axis would be how generic the whole thing will be. The y-axis would be not only the top accuracy, but also a whole bunch of the deployment cost, right? The size of the model, right? The robustness of the model. So I think different people will navigate the space in different way. And we want to be the platform, essentially, whatever point that you want, we have a solution for you.Swyx [00:17:43]: One more thing on data before we go deeper on state-space models. Are we running out of data? Can we go in order of magnitude? Can we go five orders of magnitude? How do both of you think about how much data we have and how much we need?Ce [00:17:55]: Yeah, so I think that's a very, very good question. So I don't think we are running out of data on Earth.Swyx [00:18:02]: Right, so think about it globally. Training data, training class data.Ce [00:18:05]: Yeah, yeah, so I think, I mean, some of them are not accessible, right? But I do think there are many organizations in the world have enough data to actually train very, very good models, right? So, I mean, they are not publicly available, right? But there are people who actually have access to those, right? So I think in general, right? So if you think about the data in the open space, right? So I guess that was specifically that you actually mean whether we are running out of data. I do think there need to be some way, right? That people who are training open models get connected with essentially data that's not internet data. So I think that channel need to be opened up for the open model to get more data, right? But I'm kind of on the optimistic side that the society will figure out a way that we can train open models that's beyond this internet data.Swyx [00:18:57]: Beyond internet, meaning books?Ce [00:19:00]: I mean, there are a lot of those, right?Swyx [00:19:02]: Books, right?Ce [00:19:02]: Transcripts, right? Videos, audios, right? So there are a whole bunch of data sources that we are not integrating into open data side, right? So, and maybe they shouldn't be open, right? So I think the community need to figure out a way, yeah, like the best balance, yeah? Such that we can have open models, but on the other hand, also have a reasonable collection of data that we can actually use.Swyx [00:19:29]: I think a lot of people think that, there's a theory that Whisper was released so that you could transcribe YouTube and then use that as a source of tokens. Then I talked to other researchers who are like, you know, YouTube has very low quality tokens. You know, do you want your model to talk like a live streamer from YouTube? Because that's what they're going to do. So it's not clear, like what the quality of this data could be.Ce [00:19:53]: Yeah, I guess that depends on your application, right? So I think as a platform, right? So our goal is whatever application that you have, yeah, so we have a platform that you can actually achieve your goal, right? So there are definitely applications that kind of make sense to speak like YouTube, right? So, but there are probably also other application that kind of more on the formal side, right? So I think there are going to be a diverse collection of models, both open and closed, right? So, and we kind of want to be the engine that powers that.Swyx [00:20:21]: There's a lot of people who own data sources who are doing the locally optimal thing and humanity as a whole is losing out. So like New York Times is swinging open AI, you know, Stack Overflow shut down their API, Reddit shut down their API, X, you know, made their own model, right? On Twitter data. We're just going to have all these like tiny little gardens of data that it would be useful in a general model, but everyone's just trying to make their own model. And it seems like globally suboptimal.Vipul [00:20:47]: I think you need to have some kind of a marketplace for figuring out how to get this, you know, data into models and have, I think we'll increasingly see more of that. You know, I think there's a positive aspect to it too. There is a incentive for creators to participate in a system, which is sort of more fair relative to, you know, the capture of value by an AI company that's taking their data. But I agree. I think this is a big open problem that needs to be solved. And I hope there will be, you know, serious efforts around it.Alessio [00:21:19]: Let's talk about the most precious resource on planet earth, GPUs. You have a lot of compute obviously, but you also have a lot of product pieces. You have inference, you have fine tuning, you have pre-training. What's the split in terms of usage? Do you see most people are just running inference on off the shelf models? Do you see maybe some last mile fine tuning?Vipul [00:21:40]: I would say right now, the top five models on our inference stack are probably all fine-tuned versions of open models. And we've seen- Who fine-tuned them?Swyx [00:21:51]: You fine-tuned them?Vipul [00:21:52]: They were fine-tuned by our customers.Swyx [00:21:54]: By your customers.Vipul [00:21:55]: You know, either on our platform or off our platform. And we are generally seeing that, you know, that is the sort of trend where you can get better quality on your task by sort of now easily adapting these models to your data. We also have, I would say, over 20 big model builds happening on the platform, which are customer. We see a lot of training and it's also somewhat surprisingly a more continuous kind of workload. We sort of imagine that this would be more episodic. You train a model and then you do inference. But what we find is, you know, we train a model and then they train the next version and then the next version, which sort of grows in scale. I would say training is still the bigger portion. Some ways inference is super linear to model quality. And as the models are getting better, there's more and more inference.Swyx [00:22:48]: Oh, because they're more useful. Yeah, they're more useful, yeah. So, okay, so training is bigger. This is actually consistent with what we've heard from Mosaic, that, you know, people think that training is sort of like a one-time deal. You do one big run and then you're done. It's never true. And so I'm interested in, like, putting some numbers and I don't know what you have disclosed or what you want to disclose, but, like, how many GPUs do you have? What is the equivalent amount of compute that you have? Because I understand that your GPU setup is different than what people typically think of, like, a giant data center somewhere, right?Vipul [00:23:20]: I don't think we have shared this number publicly. It's, you know, so this will be the first time, I guess. Like, we have close to 7,000 to 8,000 GPUs today. It's growing monthly.Swyx [00:23:31]: What class of GPU are they?Vipul [00:23:32]: They're mostly A100s and H100s.Swyx [00:23:35]: Okay.Vipul [00:23:36]: And probably more, I think, split towards H100s now. You know, we'll be sort of building this best-of-class hardware. So as there are other versions of these coming out later this year, we plan to have those in the fleet as well.Alessio [00:23:53]: I know when we talked last year, you were also using some of the supercomputers by the Department of Energy. There was kind of like a lot of random GPU compute in the world. Have you seen that kind of getting timed out? I think maybe a year ago, people were like, oh, yeah, you can use this GPU computer that is going to be end-of-life. Has the bar changed to give access to those resources?Ce [00:24:13]: From our perspective, it's actually getting better. Yeah, so from the community perspective, because many of the institutions in the world, they're actually investing in hardware, right? So for example, we are working with one of the institutes in Germany called Hessian AI, right, which gives us a lot of help on the compute side. So they start to have this very big GPU cluster, and they're actually sharing that with the community, right? And it's not super big, right, but also not a small one, right? So you start to see this, like, different lives that start to pop up, right? And because of the power of the community, they start to actually share that. So we actually find as a researcher today, it's probably easier for them to actually get a GPU than last year.Swyx [00:24:56]: Interesting.Alessio [00:24:56]: And then for you to buy them, what's the state of the market right now? Is it still extremely hard to get any? Do you have Jensen's phone number? Do you have like GM phone number? Do you guys get like the SDR because you're like under 10,000?Vipul [00:25:12]: NVIDIA is obviously motivated to help us, both as an investor and we are their customers. I would say the market is very tight still, and it's likely going to be this way for a while, is my sense that the demand for AI computing is just kind of ramped up very, very quickly, and it will take a while for supply to catch up.Swyx [00:25:37]: So how tight it is, and let's say compared to like a year ago, two years ago, what do you mean when you say tight? The things you want, you can't get?Vipul [00:25:42]: You can't get them immediately. They're sort of, you know, minimally like two to three months out. Any inventory that shows up tends to clear very, very rapidly. And, you know, we obviously sort of look at this in a very detailed and analytic. There is four to 5 million GPUs that will be sold this year from NVIDIA and others buying. And if you think about 512 to 1,000 GPU cluster for a company, that's 4,000 to 8,000 companies, right? So it's in some ways a very small number. In other ways, the cost of GPUs will be, you know, 80 to $100 billion, and then you layer servers and data center space and electricity on top of that, and that's, you know, close to $250 billion worth of kind of compute, which when you compare it to the cloud computing of today, you know, AWS's last year was $88 billion in revenue. So this is really kind of a build-out happening of AI hyperscalers. It is much more disaggregated, and it's very, very global. So, you know, we think that GPUs are going to be sort of a precious resource for a long time, and using them optimally is very valuable.Swyx [00:27:02]: Yeah.Alessio [00:27:02]: Our friend, Dylan Patel from Semianalysis, he wrote a post about the inference market recently and obviously mentioned you guys. In his post, he said, our model indicates that Together is better off using two A180 gig system rather than a H100-based system. The temperature and performance testing also point to Together utilizing speculative decoding. Any thoughts? Is Dylan right? I don't know, what's-Swyx [00:27:26]: What is his model, man? What does he know that they don't know? Yeah, exactly.Alessio [00:27:30]: I wanna know, I guess like from the outside, and sometimes we even do it, we try and speculate on what people are actually doing. So for the first time, now we have a former guest writing about a current guest. So we wanna know what you guys thought and maybe what are some of the misconceptions that people from the outside have on what it takes to run like a GPU cloud today?Vipul [00:27:50]: Yeah, big fan of Dylan's, by the way. I religiously read Semianalysis. I think there were some errors in that analysis. In particular, we were trying to decode it and one of the things we noticed is that it assumed that input tokens weren't being priced. So I think that may have been an error in the model. I also don't think that there's this assumption that people are running this at a loss. I think it's very expensive. You can't do that for very long. And there are trade-offs in terms of batch sizes you use and the kind of tokens per second performance that are kind of system trade-offs. We've done a lot of work. This is one of the key areas of research for us. So our inference stack is a combination of 50 different sort of tricks and techniques and we think there's a lot of room for optimization here. So whichever hardware provides better performance, whether it's H100 or A100s or L40s, we can sort of measure price performance on particular hardware and we tend to use that for that model or in some cases, certain customers have data streams which can be then optimized for a particular configuration regime. So we do fairly detailed work on how to make this more efficient and so it's hard to, from the outside, looking at memory bandwidth and estimating what's actually happening.Alessio [00:29:26]: How much of these 50 tricks are you giving to yourself and how many are you gonna open? Because we have three now, obviously Flash Attention 2 is open source. He mentioned he'd love to come work together because of how much you care about open source. Yeah, how do you weigh that as a CEO and CTO?Vipul [00:29:43]: A lot of it is open, right? Flash Attention, Flash Decoding, et cetera, and we publish something that's very generally universally useful. It's going to produce better open source AI. We tend to publish as open source. I think on the inference stack, there are open source inference stacks which are pretty good and definitely today, it gives us a competitive advantage to have the best one. So we are not sort of rushing out to release everything about it. It's not overall that additive to open source out there and it is particularly useful as a business for us to provide best price performance. Yeah, we make these decisions. We have discussions. Anything that we keep closed, we generally talk about it quite a bit and decide like this is the piece that is closed for today and it may not be the case six months from now. It may not matter as much.Ce [00:30:40]: Yeah, so I think being open is kind of very important, right? So I think the whole company actually built on this idea that there's going to be ecosystem built on our open models, right? And that's also how we are really lucky to attract this top group of talents to actually join us because of the dream and the mission that we have on our side to really facilitate the open ecosystem, right? So I think in general, it's like I think all the ideas should be open. So that's why we publish papers, right? We actually talk about ideas, right? So I don't think it makes any sense to keep idea like close, right? So there are some software artifact that are kind of really deeply embedded into our kind of own kind of like stack. It kind of only useful when you're trying to build a disaggregated cloud, right? Maybe at some point that we're going to be open as people said, right? But at this moment, right? So we are kind of busy actually building it, right? So that's probably kind of getting to the picture about when that piece is going to be open, right? But I think on the research side, the ideas and for our people to publish things, I think that's really, really important, right? So I think that's how we get talent. That's how I think we as a company going to move the field forward.Swyx [00:31:49]: I noticed that you never used the word federated learning or inference. Is there a distinction that you draw?Ce [00:31:55]: So, I mean, it's definitely not intentional, but I think federated learning is, have been used in so many different ways by so many different people. It starts to lose a very precise meaning about what that really mean, right? If you go back to the original Google paper of federated learning, I think that's very different from what people are talking about today when they say federated. Yeah, we kind of want to be really precise about it.Swyx [00:32:18]: And so your term is disaggregated.Ce [00:32:19]: Yeah, so as an infrastructure, right? So that's disaggregated.Swyx [00:32:22]: Aren't most clouds disaggregated? Like what's different about it?Ce [00:32:27]: So one way is that most of the cloud are disaggregated, but some of that is actually being exposed to the user, right? If you go to AWS, you do know which region you are in, right? So I think one thing that we are trying to do is you have this disaggregated cloud, not only about location or geographically where they are, but about this reliability and also this diversity of this infrastructure. So, and if we want to build a reliable, high-quality layer over that, the user actually don't know, right? What's actually happening under the cover, right? So I think that's one of the difference of the way that we are thinking about infrastructure.Swyx [00:33:06]: Yeah, a bit closer to Cloudflare than AWS. Yeah. Yeah. We have one question here, which we'll just throw out, it's kind of fun. So going back to this sort of inference stack piece, maybe if you had to pull out like a call for researcher or just like point out interesting areas of work that you're interested in, what pieces of the stack have the most opportunity for improvement?Ce [00:33:27]: Yeah, so I think the way we are thinking about the inference stack is, so there are multiple things that can happen, right? So you can do better algorithms, like speckle decoding, you can change the model architecture, you can go really crazy on the system side, right? And you can also code it on the hardware, right? So it's not really clear innovation on a single dimension will get you there. So the key thesis on our side is, if you only push on one direction, you are going to reach diminishing return really, really quickly. Yeah, there's only that much you can do on the system side, only that much you can do on the algorithm side. I think the only big thing that's going to happen is when you ask all those dimensions to actually compound, right? So to have algorithm, model, and system all come together, so I think that's how we reach the next 10 times improvement on inference, right? So I don't think there's a single dimension that is particularly important, but looking at this space in a joint way, right? Try to co-optimize jointly multiple dimensions, I think that's going to be really important for the community to look at.Vipul [00:34:28]: Yeah, we often see, I see numbers from the team and you have these multiple methods, not all of them compound. So you mix these together, it's still similar results and some combination of them will have this incredible effect that is really, really super interesting. So it's very systems, you know, a kind of broad systems approach to it that's the most effective.Swyx [00:34:51]: I think I finally get the name of the company, like- Bring it together, yeah. Everything needs to be automated together.Alessio [00:34:57]: All right, just quickly, how does all this work change, just like some of the architectures change? I know a mixture of experts like speculative decoding is a little less efficient because of memory bandwidth. How much of it do you invest when it's a maybe model-specific improvement versus more horizontal thing? Also, you're researching different architectures, so how much do you want to spend time optimizing what state of the art today versus what's coming next?Vipul [00:35:24]: We do spend time on what state of the art today as well as what's next. You know, the value we get from doing specific optimization, even for, you know, what works well for a particular model on A100s with a particular bus versus H100s, it's a worthwhile investment for us. So we will go down fairly deep into a specific architecture and specific hardware. It does also inform what works better where, and you don't have to take the same approach for, you know, every model and every sort of hardware setup. We can take these different approaches and we do have these multiple systems now. We know that this, you know, system B is better for mixed role and system C is going to be better for stripe tying or Mamba.Alessio [00:36:13]: Before we move on from inference, we need to talk about any scale of drama. So we're actually having Sumit on the podcast tomorrow, who also talked about, kind of came to your guys' support about how, yeah, how important it's not just like, oh, together saying this benchmark's not good because they look bad in it. How, I guess like, it's a hard question to ask, but like, why did you decide to just come out and say it? And how maybe does that also reflect the values that you guys have about open source and openness and kind of like being transparent about what's real and maybe hopes for standardizing some of these benchmarks to make it more clear?Ce [00:36:56]: So it's a great service and skills doing for the community, right? I mean, it's very hard to do benchmark. The moment you do benchmark comparing N players, right, N minus one will be unhappy. You have two tables, then maybe N of them will be unhappy, right? So it's a very great thing that they're doing. And in some of the work that we are doing, we actually use RMOperf, right? So it's a great thing that they're actually doing. So I think one thing about benchmark is, and probably the professor part of me are talking, is a good benchmark should think about how it's going to incentivize the field to actually move forward, right? So if the benchmark really become a kind of standard, how are people going to over-optimize to the benchmark if you are going to do that? And when people are doing that, what are we actually trying to incentivize, right? Will that move the world to a better place? Or will that essentially have every single player focus on marketing or spending time or money on something that actually do not matter on technical side, right? It's very hard to actually strike a balance, right? So I think the reason we kind of try to give feedback on the benchmark is kind of want to open up the discussion about how does the industry should come together and define maybe a common way that we compare with each other, right? So like how database people doing TPC, right? Maybe you should have something actually similar, right? So we are trying to start some of the conversation. So it's not really that we jump out to say it's not good because there's no way we can have a perfect benchmark. That doesn't really exist, right? So just try to kickstart a conversation that maybe we should come together and do something that the community agree and align with the benefit a user going to get, right? So just get the conversation started.Vipul [00:38:42]: I've spoken to the AnyScale team after that, and I think they had really great intentions. And partly, I think it felt very objective and everyone sort of had a reaction to it because it just didn't match their benchmarks that we've all run internally against different services. I think a common industry benchmark run by an independent party versus one of the vendors.Swyx [00:39:04]: Is there one that you appoint to?Vipul [00:39:06]: I don't think one exists today. I think there should be. We're having some conversations about someone setting one up. And there's lots of interesting aspects of this. Time to first token is a function of where the test was run from. There is different load on these services at different times of the day and weekday or weekend. So you have to measure that well. And I think if all of that were done very well by an independent source, that will be a very useful service to customers and in the services themselves.Swyx [00:39:39]: Yeah, I'll point people to artificialanalysis.ai, which is a new one that recently emerged. I don't know if they've done it right. It looks like a side project of a couple people. But I think it's in all the provider's interest to work with them. And ensure that there's an independent third party that's measuring these things, right? At least on the baseline. For me, what's worrying is more about what Toa was saying, which is, do these benchmarks skew things in ways that customers might not be mindful of? Like, what are these things overemphasizing that we might be missing? And I don't really know. It seems like a lot of these services bundled together, they're a version of quantization as well. So that means there's performance trade-offs, right? You're not comparing apples to apples, the same model itself, even though it's like a llama variant or whatever. So what do people trade off? They trade off latency, they trade off price. Obviously, those are the first two. But what else, right? What factors matter in an inference business?Ce [00:40:33]: Yeah, so I think there's also the throughput, right? So there's the time to first token, right? So, and then there are things that users do not often see, for example, the reliability, right? The capacity, right? So that also have impact on user experience at a global scale. Maybe not a single query, right? But in aggregation, you can also see a whole bunch of, like, whether you are emphasizing P50, P95, right? So the whole bunch of things that you can actually play with. And of course, there's also quality. So there are different ways to actually make the whole thing faster, specification, quantization, or combination of those, right? So yeah, so there are so many things to actually play with. So they probably need a benchmark that the protocol is transparent to make sure, like, it's very clear what we are doing and a whole bunch of check on the quality to make sure we are putting the right group of stories in the same table. So I think then essentially the user can actually navigate the space. So I think that's going to be good for everyone.Swyx [00:41:27]: Yeah, makes sense. It's a very important field and I think hopefully there's a good third party that emerges from this. So I just want to touch on one more piece, which is I think I'm appreciating from this discussion that fine tuning is a bigger part of your business than I thought. The other big player in fine tuning is Mosaic. Well, Mosaic is more training, but like there's a bunch of other players in the fine tuning space. If I was a prospective fine tuning customer, what do I come to you with? Do I come to you with my custom data and that's it? Do I also have to write the fine tuning code? What level of engagement do you do with your customers?Vipul [00:42:01]: I think across the spectrum, our customers are training models, pre-training models from scratch and many of them will bring their data sets, you know, user infrastructure and training stack to train their models. There are others who have trained smaller models and want to scale up, scale up across infrastructure, scale up across data. So we'll sort of help them do that. We will have customers who are sort of initially started a little bit more consultative. They have a particular task and idea in mind and we will help them get from there to the data set and the right model to achieve that task. So it's a spectrum and, you know, our goal is to, we're trying to productize as much of this as possible. So that the whole process can be fast and scalable. I would say there is a lot more understanding around fine tuning now, like even the last six months, there are, you know, source tools, recipes, literature, podcasts, discord channels where people are figuring out and it really is in many ways, one of the successes of open source is you have small collectives of, you know, engineers who have created, who are now creating the top models on open source leaderboards. And I have tried out all sorts of different sort of, you know, data recipes, creating synthetic data. Merging models. Merging models. So it's, that's really fun to see. And I think that sort of agency that exists now is exciting. And that is, we see a lot of that sort of being applied into products and, you know, more commercial models that people are deploying in their applications.Alessio [00:43:50]: And then just to, I guess, wrap up the together, it's almost becoming like a platform as a service, because now you release together embeddings. How did you get 92.5 accuracy on 32K retrieval? And do you think we're kind of like getting to embeddings or just like, we did everything that we could, you know, we're getting to like the most optimized it's gonna get and then we should just focus on models and inference or do you think there's still room there to improve?Ce [00:44:17]: Oh, I don't think we haven't even got started on embedding. Yeah. So I think there are so many things. So like embedding is really fundamental for many things, for example, rack, right? So deep in application. So that's how people bring knowledge in. That's also the fundamental piece when you want to build a better model, right? So that's give you this understanding about what actually get into the model. You can actually use that to actually build a better data set, get a better model, then get better embedding, you'll start this loop, right? Without the good embedding, the loop is not closed, right? So I think both on the quality side, how to embed more like dedicated semantics, like into those vectors, how to deal with negation, for example, right? So, and how can you make the whole thing really, really fast? So I think for the next couple years, yeah, we will see a whole bunch of new embeddings maybe of different size and much, much faster than today. Yeah, so I think it's a very active research area. I think people should invest more, yeah.Swyx [00:45:14]: I was surprised to see, I think Jina or, yeah, there's Jina AI, and then there's another guy, Tengyu's Voyage. They are coming out as startups purely focused on embeddings.Ce [00:45:25]: Yeah. Yeah, so I think it's a very, very important piece of the system, right? So you people haven't focused on a lot on them before, and they should definitely start to do that.Swyx [00:45:36]: Yeah. Why are the Chinese universities so good at embeddings? You know what I mean, right? Like the BGE and- Yeah, yeah, yeah.Ce [00:45:44]: So I don't know. We just released our first embedded model, so we still try to learn how to build an embedded model. Yeah, so ask me again in six months.Swyx [00:45:53]: I'll probably have more insight about how to build a better one. I just noticed that you saw 8002 was used to be at the top of the MTB chart, and then it's just like sliding down and down and down, and all the new models are coming out of China for some reason. And I'm like, I don't know what's going on there. So we cannot leave this discussion without talking about state space models. But first of all, how much of the company is dedicated to research? Like it's obviously like not production quality yet, but-Vipul [00:46:17]: I would say it's like 40, 45% I was counting this morning. That's huge.Swyx [00:46:22]: Yeah, so that's the biggest- It's a big investment. Yeah. Okay, well, I mean, it looks like it's paying off, so. And then high level, I will confess or admit or mention for the listeners who are also similarly skeptical, I did not used to care about long contexts because I was like, you know, 30K is enough, 100K is enough, right? I'm not, you know, modeling DNA sequences or anything like that. Why do I need long context? And I mean, first of all, I'll throw that open to you. But second of all, I think what Mamba did for me was change that perception of that. It's only about a long context. The only reason you want sub-quadratic architectures is for long context. Actually, that's not true. And it's also just more efficient to train, period. Right? I'll just leave that open to you. Like what's the motivation that people should keep in their heads? There are multiple things, right?Ce [00:47:09]: So one thing is that, I mean, the moment a model can do for long context well, so it often means that it's kind of cheaper. Yeah, so I mean, that's why it's kind of long. I mean, in principle, transformer can do long context. It's just very expensive. So I think what those like state-based models trying to do is try to push the size of the state, right? Like as small as possible. That's why it's kind of long context, right? And try to kind of like decouple this like quadratical dependency, right? To make sure you can have a much better execution pattern.One direct consequence of those is you can do long context really cheaply, but on the other hand, also introduce a whole bunch of benefit even you are not doing long context. Right? So I think that's actually probably equally important. Because data gets smaller, you can do really large batch size, right? You can actually be very faster. Right? So yeah. And another thing is like, one of the hypothesis that we have is, like in Stripe Hyena, it start to have a hybrid architecture, right? It has part of it has like state-based model and part of it is still the transformer. So different component probably deal with different things kind of better. So maybe by putting them together, by thinking about how information propagate, over this whole horizon of this context, you can probably get an even better quality model than transformer. Right? So I think that's why we are kind of invest a lot of things, on those models. Not only for the context, which is very important, but also for a whole bunch of benefit it could get.Swyx [00:48:42]: Yeah. How should people treat the distinction between Mamba and Stripe Hyena? Like what's the point of releasing these two as separate models? Is one like sort of the together proprietary one and then the other is like the more open research one?Ce [00:48:53]: Yeah. So I think it's pretty much a different stage of exploration. So they kind of have different hypothesis when we try to build those. Yeah. Like for instance, there are different view about state-based model. One is Hyena, another is like Mamba, right? They're actually different architecture. So when we build Stripe Hyena, right? So the curiosity that we have is how good can we... So what is the highest quality non-transformer model we can ever build? The goal of Stripe Hyena is try to see whether we can match Mistral. And by fine-tuning well, whether we can outperform that in some way, right? So it has a very, very strong baseline that we are trying to beat. So that's why there's hybrid scene, like getting the picture, right? And for Mamba, it's kind of more... The curiosity was how far can we push for pure architecture? Then we start from this very system make from small to large, right? All the way to 3 billion, right? So the baseline was essentially the best 3 billion model. So I guess at a different stage of exploration, at some point, I think they are going to converge. We actually learn different things, like when building different models. I think they are just like this intermediate stage in the exploration at different points.Alessio [00:50:02]: You mentioned the hybrid architecture. Is that the model grafting that you mentioned in the Stripe Hyena post where I mentioned you can have transformers and not together? Like this is a concept that I hadn't heard before reading about this. So I think most people's mental models, like transformers or something else, it's not transformers AND something else. How do you train a model that is hybrid? Is there any difference in like how you construct your datasets? Is there any difference in then how you run inference on it? How should people think about starting research in this field?Ce [00:50:36]: Yeah, so we were also very surprised. Yeah, so when we come up with this hybrid architecture. So the way to think about it is like you have different layers in the neural network, right? So like the stateless model for some layer will already give you the benefit. For the other layer, they could be transformers, right? They could give you this more global view of the sequence, but for me, for other layer, don't have to have that, right? I still can have all the other things that kick in, right? So we don't know what is the optimal mixture between different architectures. I mean, in principle, we can have a mamba, hyena, and transformer, all those things that come together, right? And then you can see what makes sense. We have no idea what is optimal doing that. So what we are excited about is now the community have a whole bunch of building blocks that they can actually like playing like a Lego, right? So just put together and see what happen, right? So we are kind of very excited about that. Yeah, we are in the process of trying to learn more like about this architecture. And when we know what we are talking about, we will definitely share with the community about how to do that in a systematic way.Swyx [00:51:41]: Cool. What are we still unsure about? Like, why don't we just, you know, put all the money in the world and training these things now? Like what is left to figure out before we scale this thing?Ce [00:51:53]: So like if you look at how transformer like it's been developed, right? In the last like five to 10 years, right? So people don't start from like, you have this attention to all you need the paper and then let's put all the money in, right? Always start from this very systematic understanding about the scaling, about data quality, about essentially the limits, right? I think for a state-based model from the labs to the real world, you kind of need to go through the same process. But of course, the second time doing that is kind of easier, right? But I think there's no way we can get rid of this systematic step of studying scaling law, study what data to put in, right? So what's the impact of different data slices to the data, yeah, to the final model quality.Swyx [00:52:33]: Do you expect that the data inputs will be different?Ce [00:52:37]: I don't know, but I wouldn't take that for granted that they should be the same, right? So that's one of the hypothesis that, so we have no opinion on that because I think that's the result of the study, not the assumption. Yeah, we do not need to assume that.Swyx [00:52:51]: Okay, scaling laws and data, anything else like architectural that we are not sure about? Because now you have this selection mechanism that you're pretty happy with.Ce [00:52:59]: Yeah, so, I mean, first of all, how to mix them, right? So, and second is what is the architecture? So if you look at transformer, right? So one very interesting piece there is people optimize also the hardware, yeah, to make sure that things run very fast, right?They're very efficient kernel, they're very efficient hardware. And then that's add another boost, right, for the transformer architecture, right? So that's something that should happen for state-based model. Which architecture is kind of easier kind of to run on the hardware, right? So, hosting going kind of faster, you can put more data, it add another dimension in the scaling law. So I think we just need to plow the whole space and just be really systematic from small model to 1 billion, 3 billion, 7 billion, just go all the way up, right? So I wouldn't jump around in the space. I would just like be patient and just like be systematic. Yeah, I think we'll get there, yeah.Swyx [00:53:52]: Yeah, well, I'm looking forward for more research from you guys to figure that out. So one dimension, which we didn't talk about, we talked about long context, we talked about efficiency, but speed is very, speed is also very important. A good inference provider provides, let's say 70 tokens per second, and then maybe that's faster than less good inference providers that are more like 30 tokens per second. But that's the rough range, right? State-of-the-art today. That's around the human speaking speed, human reading speed is about 200 words per minute. Why do we need 5,000 tokens per second is my question back to Vipul. And maybe is this something that is an emphasis for research as well, or is this more just an inference only thing?Vipul [00:54:29]: There are applications that are consuming the tokens that are produced from unmodeled, so they're not necessarily being read or heard by humans. That's a place where we see that level of requirement today that really nobody can quite satisfy. There is, can I think about, as intelligence grows, how do you sort of increase the bandwidth of, you know, how do you reduce the latency of it? If we can do 5,000 tokens a second, the same card can produce, the throughput of that card goes up significantly and can support more applications. So I think it's important from that perspective. And then there are, it opens up new UX possibilities. Once you can get sort of an immediate answer
SaaS Scaled - Interviews about SaaS Startups, Analytics, & Operations
On today's episode, we're joined by Vipul Shah, Co-Founder (CEO & CFO) at SaaSWorks, which delivers a single source of customer, revenue, and usage data truth to CFOs and finance teams.We talk about:The importance of quality of revenue (& the boring side)Managing the conflict of fast growth versus quality of revenueThe challenges of low-fit customersStopping sales to less successful customer segmentsThe 2 metrics investors are focusing on in this tough environment
Hey gorgeous souls and welcome to my 311th podcast episode and Season 7! I'm excited to share with you this week an interview with poet and coach Vipul Bhesania! I hope you enjoy this episode xxVipul's website - https://vipulbhesania.com/Get my NEW 44-card affirmation deck 'Manifesting Rituals' - https://emmamumford.co.uk/manifesting-rituals/Get my NEW book Hurt, Healing, Healed (Release limiting beliefs, fears and block to supercharge your manifestation) - https://emmamumford.co.uk/hurthealinghealed/Order my #1 bestselling book Positively Wealthy - https://emmamumford.co.uk/positivelywealthy/My NEW Manifestation Membership - https://emmamumford.co.uk/manifestationmembership/1-to-1 Spiritual & Business Coaching Sessions - https://emmamumford.co.uk/life-coaching/My Amazon Book Recommendations - https://emmamumford.uk/2YvIh78My Law of Attraction Shop (Oracle Cards/Merchandise/Planners) - https://emmamumford.co.uk/shopFREE Spiritual Queen Weekly Worksheet - https://emmamumford.uk/2OFsykSJoin My FREE Law of Attraction Facebook Support Group - https://www.facebook.com/groups/722583187942837/Order My First Book Spiritual Queen + FREE Webinar - https://emmamumford.uk/2yXADqYDon't Forget To Subscribe x-------------------------------------------------------------------------------------
Hey hey everyone, how are you this fine ThursdAI?
Victor Cheng is a CEO Coach and independent board member for founders of SaaS companies. He has been featured as a business expert by major media outlets including MSNBC, TIME magazine, and The Wall Street Journal, Victor is a former McKinsey & Company consultant and has been a senior executive in several publicly owned technology companies. He's the author of Extreme Revenue Growth: Startup Secrets to Growing Your Sales from $1 Million to $25 Million in Any Industry Vipul Shah is a Co-founder & CEO at SaaSWorks. SaaSWorks is the first purpose-built Continuous Finance Platform, powering the single source of truth for Subscription, FinTech, and Payments businesses and delivering accurate data, timely signals, and peace of mind. Prior to founding SaaSWorks, Vipul held Managing Director or Managing Partner roles at the likes of ArrowMark Partners, Brightwood Capital, SVT Capital, and Goldman Sachs, where he invested in mid-market private equity, growth equity, and debt across the SaaS, software, and tech-enabled services sectors.
the series doesnt have any major names but the fact that they would take cyber crime expert amit dubeys book & turn it into a potent force i felt deserved a careful & close looksee. parmeet sethi directs `hack crimes online` on amogh dusads platform amazon mini tv & it is produced by shital bhatia from friday filmworks & acted in, with great proficiency by vipul gupta. i had to get the four of them together to have this detailed chat. their aim apart from entertain is to clearly give the common man red flags on how people are being looted via social media, the banking sector & nearly every online platform that people are gravitating towards. the hacker is everywhere waiting to prey on our fear, our ignorance & our inherent greed. the idea is to be informed & aware & this show & indeed this interview goes a long way in trying to effect change. listen to this podcast & watch the series & pls do tell me what u think. “The HrishiKay Sessions” are produced & presented by Hrishikesh Kannan popularly known as Hrishi K Thanks for listening. Should you want to experience more……for starters hit “subscribe” / “follow” and check out more episodes & be notified when further sessions go up! If ur looking for Hrishi across media & social networking then here goes: Instagram : https://www.instagram.com/hrishikay Twitter : https://www.twitter.com/hrishikay Facebook : https://www.facebook.com/hrishikay Youtube : https://youtube.com/c/hrishikeshkannan Soundcloud : https://www.soundcloud.com/hrishikay LinkedIn : http://linkedin.com/in/hrishikay
The conscience keeper of Indian cricket joins the gang on Episode 88 of Bits and Pieces, for a wide-ranging chat on the heavy-handedness of the BCCI and what that means for Indian cricket in particular, and world cricket at large. Nitin, Prashant and Vipul dive deep into Sharda's recent BCCI magnum opus on the Caravan, the ticketing and organisational fiasco around the World Cup, India's curious relationship with Pakistan cricket, the IPL, the tamatar that binds the Indian cricket curry and much much more. Along the way, a peek into Sharda's amazing writing process, her amazing sense of humour, and a couple of killer anecdotes involving personalities as varied as Abhinav Bindra and Arun Dhumal. Find us on Twitter: 1. Bits and Pieces: https://twitter.com/bnp_cricket 2. Nitin: https://twitter.com/knittins 3. Prashant: https://twitter.com/prashantdptweet 4. Vipul: https://twitter.com/sportybaba 5. Like all sane people, Sharda isn't on Twitter Show notes: 1. Sharda's Caravan takedown of the BCCI: https://caravanmagazine.in/sports/bjp-bcci-jay-shah 2. Yuvraj Singh's biography, written by Sharda: https://www.amazon.com/Test-My-Life-Cricket-Cancer/dp/818400298X 3. John Wright's Indian Summers, written by Sharda: https://www.amazon.com/Wrights-Indian-Summers-Sharda-Wright/dp/0285637959 4. When Sharda Ugra consoled Abhinav Bindra by foretelling his Beijing gold just after his failure at Athens: https://www.hindustantimes.com/sports/others/inside-the-mind-of-abhinav-bindra-101626349810499.html
In this special bonus episode of the Accounting Influencers Podcast, host Rob Brown is joined by Vipul Sheth, Paul Shrimpling, Aynsley Damery, and Dermot Hamblin to discuss their recent event focused on the future of accounting firms. The event aimed to provide ideas and insights to keep accounting firms at the forefront of the industry. The speakers covered diverse topics, and the event attracted a diverse audience from the larger accounting firms in the UK. Discussions revolved around creating sustainable and prosperous firms, addressing talent shortage challenges, and effectively managing technology adoption.Key takeaways from the event included the importance of challenging the status quo and embracing change, engaging and sharing best practices among industry professionals, and the need to articulate the value of services to clients beyond pricing conversations. Attendees were receptive and eager to explore new ideas and strategies for their firms' future success. The event fostered meaningful conversations and encouraged collaboration among accounting professionals.Guest BiosVipul Sheth, Paul Shrimpling, Dermot Hamblin, and Aynsley Damery are renowned industry leaders who recently attended and reviewed the esteemed "Accounting For Your Future" conference hosted by Advance Track Outsourcing. Held on 9th May 2023 at the iconic British Museum in London, this conference provided invaluable insights into shaping the future of accounting. With a focus on adaptability and leveraging technology for optimal efficiency, these experts recognize the crucial role of passing on their learnings to help clients envision a promising future. Emphasizing the unwavering support of the accounting industry, Vipul, Paul, Dermot, and Aynsley are dedicated to guiding clients through challenging times and ensuring their long-term success.Mentioned in this episode:YouTube CTA
Join the friendliest podcast with Tony, Vipul, PGK, Srinath, and Sandy as they break down the first Ashes test, the world cup schedule, and the new Indian team. Feat: The BazBall Cage Match between Praveen and Srinath. Grab a chair, and hit someone with it. Follow usBits and Pieces on Twitter: https://twitter.com/bnp_cricketVipul: https://twitter.com/sporty_babaPGK: https://twitter.com/peegeekaySandy ji: https://twitter.com/lanjewarsandeepSrinath: https://twitter.com/srinathsripathTony: https://twitter.com/notytonyNotes:1) Barney Ronay on Bazball https://www.theguardian.com/sport/2023/jun/24/bazball-a-cult-of-bruised-masculinity-where-you-win-even-when-you-lose2) Andrew Miller with the counter view https://www.espncricinfo.com/story/ashes-2023-forget-the-frivolous-narrative-bazball-is-a-hard-nosed-winning-strategy-1382773?platform=amp3) Ponting sideswipes KP - https://twitter.com/BuzzConway_/status/16707782987781324804) Billy bakes Ollie - https://www.youtube.com/watch?v=rEl0kxVH8Ic5) Sunil Gavaskar on selections - https://www.hindustantimes.com/cricket/stop-playing-ranji-say-it-s-of-no-use-gavaskar-scathes-through-india-selectors-for-snubbing-sarfaraz-khan-101687596847066.html
Hapiso, a brand that is dedicated to promoting a more environmentally conscious way of life, strives to provide safe and guilt-free products to its consumers. By making these products more accessible, Hapiso is enabling a larger audience to participate in sustainable living practices. Their innovative approach eliminates the need for overdosing, preserving fabrics and keeping clothes looking bright for longer periods of time. Starting as a home-based experiment with laundry pods, Hapiso tested their product with a small group of friends and family members to assess its effectiveness in cleaning clothes. Through Instagram, Hapiso creates relatable content that highlights the struggles of traditional detergent use and the benefits of using laundry pods. This includes short-form video content such as reels and stories. The brand's packaging is eco-friendly and minimalist, reflecting their commitment to sustainability. Hapiso takes pride in its eco-friendly ethos and has received positive feedback from customers who appreciate this approach. Acknowledging that the product has recurring use, Hapiso introduced a subscription model based on feedback from their customers.
Vipul Bhesania, an Intuitive Life Coach and Soul Searcher, explores with me some very important conversations around identity crisis, shedding the old you, the journey of meeting your illuminated self, and probably most importantly, the gift of grief when we experience loss along the way. Towards the end, I share a vulnerable moment in my own recent experience with deep seated fears and loneliness. CONNECT w/ VIPUL Linktree: https://linktr.ee/vbhesania IG: https://www.instagram.com/vipulbhesania/ SUBSCRIBE & FOLLOW If you're enjoying the show, please subscribe to iTunes and leave me a 5 star review! This is what helps the podcast stand out from the crowd and allows me to help people find a refreshing spin on spirituality with a great blend of entertainment and credible advice. All Links: https://linktr.ee/nicolefrolick Website: http://nicolefrolick.com/ Youtube: https://www.youtube.com/user/nicolefrolick Instagram: https://www.instagram.com/nicolefrolick/ Tiktok: https://tiktok.com/@nicolefrolick Spotify: shorturl.at/fikF7 iTunes: http://apple.co/2ve7DtE PayPal: https://paypal.me/inflexibleme Alcheme: https://alchemyacademy.teachable.com/p/alcheme Merchandise: https://streamlabs.com/nicolefrolick/merch --- Support this podcast: https://anchor.fm/enlightenup/support
Who are the Black Panthers? Welcome back SDG Talkers!! Thanks for joining us for another episode of highlighting change makers and their inspirational work towards the United Nations Sustainable Development Goals (SDGs)! IN THIS EPISODE: Post-event thoughts about winning SDG Track 13 at UNLEASH How to work together as a team during the UNLEASH experience How to connect investors & farmers to combat climate change A big congrats to the Black Panthers - aka the winners of UNLEASH's SDG 13 Climate Action Track & one of the top 7 teams in the entire lab! We get to chat with 3 of the 5 team members: Aarushi Tripathi, Vipul Agarwal, & Nasha Cuvelier. As part of the lab's innovation process, the team had to frame a problem, ideate solutions, create a prototype and test it in just 5 days. Then they pitched their ideas to experts, and the chosen SDG Track Winners (one for each SDG) were sent to the finals. Having started out as a computer science graduate with an interest and experience in Machine Learning and Data Science, Aarushi recently found her calling in the field of public policy & development, with a focus in building sustainable solutions that use rigorously-researched evidence & centre communities. Vipul has been involved in research projects ranging from the development of a national pollution database for China, to scourging through garbage to spur waste segregation on a University level, based on positive & negative incentives. He's currently building a platform that would drive meaningful change in consumer/business behavior in the realm of carbon footprint and GHG emissions. Nasha is an environmental scientist specializing in climate change, & is the co-founder of "sustentabilidad sin fronteras". In 2015, she was chosen as the UNESCO Young Delegate for the Paris Agreement; in 2016 she was recognized as one of the 10 most influential women of the year in the country. She's held leadership positions within the private sector, but is now a consultant to international organizations such as UNDP & UNICEF. Let's get SDG Talking!! Got a good story or want to collaborate? Send us an email at sdgtalkspodcast@gmail.com and we will get back to you as soon as we can! And don't forget to check out our Virtual Roundtables on our website! Instagram | Facebook | Twitter | LinkedIn
@Sporty_Baba aka Vipul joins the gang for the latest episode of Bits and Pieces, which is all about the good, the bad and the ugly parts of the Indian cricket fan's stadium watching experience. The discussion spans Bangalore's N Stand, the abominable Kotla, the worst stadium experience in India (even we are surprised that Kotla didn't win this one), and many lovely tales around Bombay's North Stand Gang (watch out for the way we bait our Chennai listeners by terming Bombay the knowledgeable crowd). We also spend some time acting like we're discussing the IPL Auction. Follow us on Twitter: 1. Vipul: https://twitter.com/Sporty_Baba 2. Srinath: https://twitter.com/srinathsripath 3. Tony: https://twitter.com/notytony 4. Nitin: https://twitter.com/knittins Show Notes: 1. India. Pakistan. Chennai. 1999. Sidvee - https://www.thecricketmonthly.com/story/1172609/india--pakistan--chennai--1999 2. Ashwin. Second run. Wankhede. Scores level. Sidin - https://www.espncricinfo.com/story/sidin-vadukut-three-things-you-didn-t-know-about-ravichandran-ashwin-544072 3. Kambliiii Kambli! Sachiiinnn Sachin! The Bombay Brownwash - https://www.youtube.com/watch?v=J7lP9TwCddE 4. The best grounds to watch cricket at. https://www.espncricinfo.com/story/the-jury-s-out-which-is-the-best-ground-to-watch-cricket-in-591793
What the heck happened with FTX and Sam Bankman-Fried? Were there early warning signs that most people failed to notice? What could've been done differently, and by whom? What effects will this have on the EA movement going forward?Timestamps:00:01:37 — Intro & timeline00:51:48 — Byrne Hobart01:39:52 — Vipul Naik02:18:35 — Maomao Hu02:41:19 — Marcus Abramovitch02:49:38 — Ozzie Gooen03:21:40 — Wrap-up & outroByrne Hobart writes The Diff, a newsletter covering inflections in finance and tech, which has 47,000+ readers. Previously he worked at a hedge fund covering Internet and media companies. Follow Byrne on Twitter at @ByrneHobart or subscribe to The Diff at thediff.co.Vipul Naik holds a PhD in mathematics from the University of Chicago and is currently the head of data science at Equator Therapeutics, a drug discovery startup. He previously worked at a tech startup called LiftIgniter and then at The Arena Group, a media / tech company that acquired LiftIgniter. Learn more about him at his website, vipulnaik.com.Maomao Hu is a blockchain, fintech, and AI entrepreneur and thought leader. He has been involved in organizations ranging from leading investment banks to new startups, to solve both microstructure problems like market surveillance and macrostructure problems like capital allocation. Currently, he leads development and quantitative research at asset manager Zerocap. Learn more about him at his website, thefirenexttime.com.Marcus Abramovich is a managing partner at Enlightenment Ventures, an EA-aligned cryptocurrency hedge fund. Marcus also leads a Facebook group and Discord community of effective altruists focused on accumulating capital to donate to EA causes, and advises several cryptocurrency projects. Marcus discovered effective altruism as a PhD candidate at the University of Waterloo and professional poker player. Email him at marcus.s.abramovitch@gmail.com.Ozzie Gooen is the president of The Quantified Uncertainty Research Institute. He has a background in programming and research. He previously founded Guesstimate and worked at the Future of Humanity Institute at Oxford. Follow him on Twitter at @ozziegooen or learn more about his current work at quantifieduncertainty.org.Further Reading:"Clarifications on diminishing returns and risk aversion in giving" by Rob Wiblin @ the EA forum on why he disagrees with the SBF's risk-taking approach [link]References: 0xhonky. (November 13, 2022, 03:12 AM UTC). https://twitter.com/0xhonky/status/1591630071915483136. Twitter. [link] alamedatrabucco. (April 22, 2021, 10:37 AM UTC). https://twitter.com/alamedatrabucco/status/1385180941186789384. Twitter. [link] Allison, I.. (November 2, 2022). Divisions in Sam Bankman-Fried's Crypto Empire Blur on His Trading Titan Alameda's Balance Sheet. Coindesk. [link] Austin. (November 14, 2022). In Defense of SBF. Effective Altruism Forum. [link] autismcapital. (November 12, 2022, 07:33 AM UTC). https://twitter.com/autismcapital/status/1591333446995283969. Twitter. [link] Berwick, A.. (November 13, 2022). Exclusive: At least $1 billion of client funds missing at failed crypto firm FTX. Reuters. [link] carolinecapital. (April 5, 2021, 11:41 AM UTC). https://twitter.com/carolinecapital/status/1379036346300305408. Twitter. [link] corybates1895. (November 10, 2022, 10:37 PM UTC). https://twitter.com/corybates1895/status/1590836167867760641. Twitter. [link] cz_binance. (November 6, 2022, 03:47 PM UTC). https://twitter.com/cz_binance/status/1589283421704290306. Twitter. [link] cz_binance. (November 8, 2022). https://twitter.com/cz_binance/status/1590013613586411520. Twitter. [link] Faux, Z.. (April 3, 2022). A 30-Year-Old Crypto Billionaire Wants to Give His Fortune Away. Bloomberg. [link] Ellison, C.. (September 21, 2021). https://worldoptimization.tumblr.com/post/642664297644916736/slatestarscratchpad-all-right-more-really-stupid [deleted]. World Optimization. [link] ftxfuturefund. (February 8, 2022, 05:32 PM UTC). https://twitter.com/ftxfuturefund/status/1498350483206860801. Twitter. [link] Gach, E.. (November 14, 2022). Crypto's Biggest Crash Saw Guy Playing League Of Legends While Luring Investors [Update]. Kotaku. [link] Hussein, F.. (November 16, 2022). House panel to hold hearing on cryptocurrency exchange FTX collapse. PBS News Hour. [link] Jenkinson, G.. (November 17, 2022). SBF received $1B in personal loans from Alameda: FTX bankruptcy filing. Cointelegraph. [link] Kulish, N.. (November 13, 2022). FTX's Collapse Casts a Pall on a Philanthropy Movement. The New York Times. [link] Levine, M.. (November 14, 2022). FTX's Balance Sheet Was Bad. Bloomberg. [link] Ligon,C., Reynolds, S., Kessler, S., De, N., & Decker, R.. (November 11, 2022). 'FTX Has Been Hacked': Crypto Disaster Worsens as Exchange Sees Mysterious Outflows Exceeding $600M. Coindesk. [link] Morrow, A.. (November 18, 2022). ‘Complete failure:' Filing reveals staggering mismanagement inside FTX . CNN. [link] Nick_Beckstead, leopold, ab, & ketanrama. (November 10, 2022). The FTX Future Fund team has resigned. Effective Altruism Forum. [link] Partz, H.. (November 9, 2022). FTX founder Sam Bankman-Fried removes “assets are fine” flood from Twitter. Cointelegraph. [link] Piper, K.. (November 16, 2022). Sam Bankman-Fried tries to explain himself. Vox. [link] Regan, M.P. & Hajric, V.. (November 12, 2022). SBF vs CZ: How 2 crypto billionaires' social media “bloodsport” went from keyboard warrior shenanigans to a $32 billion blowup. Fortune. [link] Rosenberg, E., Khartit, K., & McClay, R.. (August 26, 2022). What Is Yield Farming in Cryptocurrency?. The Balance. [link] sbf_ftx. (December 2, 2020, 09:25 PM UTC). https://twitter.com/sbf_ftx/status/1334247283081138178. Twitter. [link] sbf_ftx. (December 11, 2020, 04:19 AM UTC). https://twitter.com/sbf_ftx/status/1337250686870831107. Twitter. [link] sbf_ftx. (November 8, 2022, 04:03 PM UTC). https://twitter.com/sbf_ftx/status/1590012124864348160. Twitter. [link] sbf_ftx. (November 10, 2022, 02:13 PM UTC). https://twitter.com/sbf_ftx/status/1590709195892195329. Twitter. [link] sbf_ftx. (November 11, 2022, 03:23 PM UTC). https://twitter.com/sbf_ftx/status/1591089317300293636. Twitter. [link] Sigalos, M. & Rooney, K.. (November 9, 2022). Binance backs out of FTX rescue, leaving the crypto exchange on the brink of collapse. CNBC. [link] tara_macaulay. (November 16, 2022, 08:57 PM UTC). https://twitter.com/tara_macaulay/status/1592985303262072834. Twitter. [link] taylorpearsonme. (November 10, 2022, 10:00 PM UTC). https://twitter.com/taylorpearsonme/status/1590826638429650944. Twitter. [link] Tsipursky, G.. (November 16, 2022). SBF's dangerous decision-making philosophy that brought down FTX. Fortune. [link] whalechart. (November 15, 2022, 06:46 AM UTC). https://twitter.com/whalechart/status/1592408565402464259. Twitter. [link] Wiblin, R. & Harris, K.. (April 14, 2022). Sam Bankman-Fried on taking a high-risk approach to crypto and doing good. 80,000 Hours. [link] Yaffe-Bellany, D.. (November 14, 2022). How Sam Bankman-Fried's Crypto Empire Collapsed. The New York Times. [link] yashkaf. (November 12, 2022, 07:18 PM UTC). https://twitter.com/yashkaf/status/1591606925149540353. Twitter. [link] FTX (company). Wikipedia. [link] Sam Bankman-Fried. Wikipedia. [link] [Read more]
What the heck happened with FTX and Sam Bankman-Fried? Were there early warning signs that most people failed to notice? What could've been done differently, and by whom? What effects will this have on the EA movement going forward?Timestamps:00:01:37 — Intro & timeline00:51:48 — Byrne Hobart01:39:52 — Vipul Naik02:18:35 — Maomao Hu02:41:19 — Marcus Abramovitch02:49:38 — Ozzie Gooen03:21:40 — Wrap-up & outroByrne Hobart writes The Diff, a newsletter covering inflections in finance and tech, which has 47,000+ readers. Previously he worked at a hedge fund covering Internet and media companies. Follow Byrne on Twitter at @ByrneHobart or subscribe to The Diff at thediff.co.Vipul Naik holds a PhD in mathematics from the University of Chicago and is currently the head of data science at Equator Therapeutics, a drug discovery startup. He previously worked at a tech startup called LiftIgniter and then at The Arena Group, a media / tech company that acquired LiftIgniter. Learn more about him at his website, vipulnaik.com.Maomao Hu is a blockchain, fintech, and AI entrepreneur and thought leader. He has been involved in organizations ranging from leading investment banks to new startups, to solve both microstructure problems like market surveillance and macrostructure problems like capital allocation. Currently, he leads development and quantitative research at asset manager Zerocap. Learn more about him at his website, thefirenexttime.com.Marcus Abramovich is a managing partner at Enlightenment Ventures, an EA-aligned cryptocurrency hedge fund. Marcus also leads a Facebook group and Discord community of effective altruists focused on accumulating capital to donate to EA causes, and advises several cryptocurrency projects. Marcus discovered effective altruism as a PhD candidate at the University of Waterloo and professional poker player. Email him at marcus.s.abramovitch@gmail.com.Ozzie Gooen is the president of The Quantified Uncertainty Research Institute. He has a background in programming and research. He previously founded Guesstimate and worked at the Future of Humanity Institute at Oxford. Follow him on Twitter at @ozziegooen or learn more about his current work at quantifieduncertainty.org.Further Reading:"Clarifications on diminishing returns and risk aversion in giving" by Rob Wiblin @ the EA forum on why he disagrees with the SBF's risk-taking approach [link]References: 0xhonky. (November 13, 2022, 03:12 AM UTC). https://twitter.com/0xhonky/status/1591630071915483136. Twitter. [link] alamedatrabucco. (April 22, 2021, 10:37 AM UTC). https://twitter.com/alamedatrabucco/status/1385180941186789384. Twitter. [link] Allison, I.. (November 2, 2022). Divisions in Sam Bankman-Fried's Crypto Empire Blur on His Trading Titan Alameda's Balance Sheet. Coindesk. [link] Austin. (November 14, 2022). In Defense of SBF. Effective Altruism Forum. [link] autismcapital. (November 12, 2022, 07:33 AM UTC). https://twitter.com/autismcapital/status/1591333446995283969. Twitter. [link] Berwick, A.. (November 13, 2022). Exclusive: At least $1 billion of client funds missing at failed crypto firm FTX. Reuters. [link] carolinecapital. (April 5, 2021, 11:41 AM UTC). https://twitter.com/carolinecapital/status/1379036346300305408. Twitter. [link] corybates1895. (November 10, 2022, 10:37 PM UTC). https://twitter.com/corybates1895/status/1590836167867760641. Twitter. [link] cz_binance. (November 6, 2022, 03:47 PM UTC). https://twitter.com/cz_binance/status/1589283421704290306. Twitter. [link] cz_binance. (November 8, 2022). https://twitter.com/cz_binance/status/1590013613586411520. Twitter. [link] Faux, Z.. (April 3, 2022). A 30-Year-Old Crypto Billionaire Wants to Give His Fortune Away. Bloomberg. [link] Ellison, C.. (September 21, 2021). https://worldoptimization.tumblr.com/post/642664297644916736/slatestarscratchpad-all-right-more-really-stupid [deleted]. World Optimization. [link] ftxfuturefund. (February 8, 2022, 05:32 PM UTC). https://twitter.com/ftxfuturefund/status/1498350483206860801. Twitter. [link] Gach, E.. (November 14, 2022). Crypto's Biggest Crash Saw Guy Playing League Of Legends While Luring Investors [Update]. Kotaku. [link] Hussein, F.. (November 16, 2022). House panel to hold hearing on cryptocurrency exchange FTX collapse. PBS News Hour. [link] Jenkinson, G.. (November 17, 2022). SBF received $1B in personal loans from Alameda: FTX bankruptcy filing. Cointelegraph. [link] Kulish, N.. (November 13, 2022). FTX's Collapse Casts a Pall on a Philanthropy Movement. The New York Times. [link] Levine, M.. (November 14, 2022). FTX's Balance Sheet Was Bad. Bloomberg. [link] Ligon,C., Reynolds, S., Kessler, S., De, N., & Decker, R.. (November 11, 2022). 'FTX Has Been Hacked': Crypto Disaster Worsens as Exchange Sees Mysterious Outflows Exceeding $600M. Coindesk. [link] Morrow, A.. (November 18, 2022). ‘Complete failure:' Filing reveals staggering mismanagement inside FTX . CNN. [link] Nick_Beckstead, leopold, ab, & ketanrama. (November 10, 2022). The FTX Future Fund team has resigned. Effective Altruism Forum. [link] Partz, H.. (November 9, 2022). FTX founder Sam Bankman-Fried removes “assets are fine” flood from Twitter. Cointelegraph. [link] Piper, K.. (November 16, 2022). Sam Bankman-Fried tries to explain himself. Vox. [link] Regan, M.P. & Hajric, V.. (November 12, 2022). SBF vs CZ: How 2 crypto billionaires' social media “bloodsport” went from keyboard warrior shenanigans to a $32 billion blowup. Fortune. [link] Rosenberg, E., Khartit, K., & McClay, R.. (August 26, 2022). What Is Yield Farming in Cryptocurrency?. The Balance. [link] sbf_ftx. (December 2, 2020, 09:25 PM UTC). https://twitter.com/sbf_ftx/status/1334247283081138178. Twitter. [link] sbf_ftx. (December 11, 2020, 04:19 AM UTC). https://twitter.com/sbf_ftx/status/1337250686870831107. Twitter. [link] sbf_ftx. (November 8, 2022, 04:03 PM UTC). https://twitter.com/sbf_ftx/status/1590012124864348160. Twitter. [link] sbf_ftx. (November 10, 2022, 02:13 PM UTC). https://twitter.com/sbf_ftx/status/1590709195892195329. Twitter. [link] sbf_ftx. (November 11, 2022, 03:23 PM UTC). https://twitter.com/sbf_ftx/status/1591089317300293636. Twitter. [link] Sigalos, M. & Rooney, K.. (November 9, 2022). Binance backs out of FTX rescue, leaving the crypto exchange on the brink of collapse. CNBC. [link] tara_macaulay. (November 16, 2022, 08:57 PM UTC). https://twitter.com/tara_macaulay/status/1592985303262072834. Twitter. [link] taylorpearsonme. (November 10, 2022, 10:00 PM UTC). https://twitter.com/taylorpearsonme/status/1590826638429650944. Twitter. [link] Tsipursky, G.. (November 16, 2022). SBF's dangerous decision-making philosophy that brought down FTX. Fortune. [link] whalechart. (November 15, 2022, 06:46 AM UTC). https://twitter.com/whalechart/status/1592408565402464259. Twitter. [link] Wiblin, R. & Harris, K.. (April 14, 2022). Sam Bankman-Fried on taking a high-risk approach to crypto and doing good. 80,000 Hours. [link] Yaffe-Bellany, D.. (November 14, 2022). How Sam Bankman-Fried's Crypto Empire Collapsed. The New York Times. [link] yashkaf. (November 12, 2022, 07:18 PM UTC). https://twitter.com/yashkaf/status/1591606925149540353. Twitter. [link] FTX (company). Wikipedia. [link] Sam Bankman-Fried. Wikipedia. [link]
What the heck happened with FTX and Sam Bankman-Fried? Were there early warning signs that most people failed to notice? What could've been done differently, and by whom? What effects will this have on the EA movement going forward?Timestamps:00:01:37 — Intro & timeline00:51:37 — Byrne Hobart01:39:41 — Vipul Naik02:18:24 — Maomao Hu02:41:07 — Marcus Abramovitch02:49:27 — Ozzie Gooen03:21:29 — Wrap-up & outroByrne Hobart writes The Diff, a newsletter covering inflections in finance and tech, which has 47,000+ readers. Previously he worked at a hedge fund covering Internet and media companies. Follow Byrne on Twitter at @ByrneHobart or subscribe to The Diff at thediff.co.Vipul Naik holds a PhD in mathematics from the University of Chicago and is currently the head of data science at Equator Therapeutics, a drug discovery startup. He previously worked at a tech startup called LiftIgniter and then at The Arena Group, a media / tech company that acquired LiftIgniter. Learn more about him at his website, vipulnaik.com.Maomao Hu is a blockchain, fintech, and AI entrepreneur and thought leader. He has been involved in organizations ranging from leading investment banks to new startups, to solve both microstructure problems like market surveillance and macrostructure problems like capital allocation. Currently, he leads development and quantitative research at asset manager Zerocap. Learn more about him at his website, thefirenexttime.com.Marcus Abramovich is a managing partner at Enlightenment Ventures, an EA-aligned cryptocurrency hedge fund. Marcus also leads a Facebook group and Discord community of effective altruists focused on accumulating capital to donate to EA causes, and advises several cryptocurrency projects. Marcus discovered effective altruism as a PhD candidate at the University of Waterloo and professional poker player. Email him at marcus.s.abramovitch@gmail.com.Ozzie Gooen is the president of The Quantified Uncertainty Research Institute. He has a background in programming and research. He previously founded Guesstimate and worked at the Future of Humanity Institute at Oxford. Follow him on Twitter at @ozziegooen or learn more about his current work at quantifieduncertainty.org.
Are you going through a spiritual awakening? In this interview with Vipul Bhesania, we'll talk about what's spiritual awakening, its signs, meaning, and physical symptoms. We'll discuss how long it last, how to start your spiritual awakening, how to awaken your gifts, and how to navigate the spiritual awakening experiences. https://georgelizos.comVipul Bhesania is the host of the Know Your Legacy podcast and author of a poetry book called 'Searching in Silence'. As a coach and healer, he connects with people on an authentic level by creating conversations that invite us to go beneath the surface of day-to-day reality. He creates space for souls to find alignment with who they really are. RESOURCES MENTIONED:Vipul's Website: https://www.vipulbhesania.comVipul's Instagram Handle: http://instagram.com/vipulbhesaniaFREE GUIDES TO GET YOU STARTED: Energy Protection Guide: https://georgelizos.com/negativeenergyLife Purpose Workbook: https://georgelizos.com/lifepurpose The Ultimate Intuitive Development Starter Kit: https://georgelizos.com/intuitionstarterkitIntuition Mastery Accelerator Guide: https://georgelizos.com/intuitionmasteryScanning For Psychic Attack Guide: https://georgelizos.com/psychicattackCrystals to Manifest Your Best Life: https://georgelizos.com/crystals CONNECT WITH GEORGE:Instagram: www.instagram.com/georgelizosTikTok: https://www.tiktok.com/@iamgeorgelizosFacebook Group: www.yourspiritualtoolkit.comWebsite: www.georgelizos.comYouTube Channel: https://www.youtube.com/channel/UCMLcoCVRHZU407OXj24HH8g?sub_confirmation=1 Hosted on Acast. See acast.com/privacy for more information.
Healthcare stakeholders should all be working together and collaborating, sharing information and knowledge. In this Future of Global Informatics episode, TJ Southern interviews Vipul Kashyap, SVP of Clinical Informatics and Product Strategy at BUDDI.AI, about how they are using AI and machine learning to solve some healthcare information problems. BUDDI.AI is looking at healthcare information from sources like textbooks and clinical decision measures and turning them into one simple language that's open, transparent, and shareable across the industry. Vipul talks about how siloed everyone is and the use of hybrid machine-learning models for data analytics to extract care gaps from patient notes. He also breaks down the difference between health informaticists and data architects to highlight every way that informaticists bring value to projects that are rethinking medicine. Tune in to learn how Vipul Kashyap believes informatics will change healthcare for the better! Click this link to the show notes, transcript, and resources: outcomesrocket.health
When we look at glass manufacturing in commercial business spaces, we often don't realize the process that happens behind the scenes to bring a beautiful, functional piece of architectural glass into a business or healthcare space.I had an awesome interview with Vipul Bhagat, the COO of Skyline Design glass manufacturing, who shed a fantastic amount of light on an industry that many of us know very little about. Using manufacturing equipment from across the globe and high-quality starting materials, the pieces that are produced by Skyline Design, such as writable surfaces, are custom-made for multiple industries. Skyline manufactures glass pieces for the majority of healthcare facilities nationwide due to the cleanability of glass surfaces.With their team of artists, designers, and architects, Skyline Design manufactures customized, branded glass that can fit the aesthetic of any company. With the highest quality starting materials and low-iron glass, they are able to capture the clearest, most vibrant colors in architectural glass. Skyline has also made intentional shifts to its manufacturing process to ensure that its processes are sustainable and eco-friendly. Tune in to this week's episode to hear more about how Skyline is using and re-using as much of its materials as possible to intentionally invest in the future of our industry.In this episode:[01:47] Welcome to the show Vipul![02:09] Skyline Design celebrates 40 years in business![05:30] The strict focus of Skyline Design is on architectural glass.[07:40] Glass is a cleanable, nonporous surface.[09:23] Equipment is shipped from around the world to manufacture glass in their 1920s Chicago building.[12:29] Skyline Design captures the clearest colors in their glass.[16:57 ] How Skyline Design is sustainable and committed to keeping the planet safe.[18:05] An additional investment to promote water conservation during Skyline's manufacturing process.[21:43] Using as much of the product as they can to minimize waste.[26:25] Maintaining relationships with local vendors and sourcing materials domestically[29:58] The future of glass manufacturing and Skyline Design.[32:04] Get in touch with Skyline Design!Links & Resources:The InsiderSupport the Trend ReportConnect with Vipul:Skyline DesignPhone: 888-278-4660Instagram | Facebook | LinkedIn | PinterestVipul Bhagat's LinkedInConnect with Sid:www.sidmeadows.comEmbark CCT on FacebookSid on LinkedInSid on InstagramSid on YouTubeSid on Clubhouse - @sidmeadowsThe Trend Report is proudly sponsored by the Insider, a weekly newsletter delivering a quick dose of insight to get your Monday off to a well-informed start. To learn more or to subscribe for free, please visit https://indeal.org/the-insider/The Trend Report introduction music is provided by Werq by Kevin MacLeod Link:https://incompetech.filmmusic.io/song/4616-werq License:http://creativecommons.org/licenses/by/4.0/
Frazer speaks with the founders of SaaSWorks, Vipul Shah (CEO) and Jim O'Neill (CTO), where they each discuss their paths into entrepreneurship and how their experiences drove them to start SaaSWorks.
In this episode of finding grace today I'm joined by Vipul Bhesania who is a podcast host of “Soul wisdom stories” and was formerly “know your legacy”, author of a poetry book “Searching in silence”, and a coach. We were introduced via a friend and I can't wait to share Vipul on here today. In this episode: Vipul shares what finding grace means to him and the journey that he's been on. He shares his story of what led him to where he is today. We discuss self love and its importance beyond the surface level We talk about why we need to value our mental health. He shares his thoughts on legacy. He talks about his poetry and what this means to him. We discuss growth and change, taking the leap and navigating this. We talk about being perfectly imperfect. We discuss embracing your weird He shares the tools he's using to support him right now. We discuss the importance of not being hard on yourself. He shares what's bringing him joy right now. Vipul shares all this and more, I hope you enjoy our conversation. Do reach out to us to discuss this more. If you want to work with Vipul, read his book, listen to the podcast you can find it here: You can buy the book here Listen to the podcast here Instagram @vipulbhesania @soulwisdomstories You can find me at : Instagram @thehannahwallace Twitter @hannahwallace_ Face book @thehannahwallace Website www.hannah-wallace.com Thank you so much for listening please share, subscribe and review it's greatly appreciated and I hope you find grace in your week ahead.
The business of Drones is one of the most exciting themes to look out for in the next 5-10 Years. With wide ranging applications, Drones are set to be a Rs. 15,000 Crore industry by 2026. We have with us, Vipul Singh, Founder of Aarav Unmanned Systems. They manufacture drones and provides integrated services for Geo Surveys. They recently bagged a Rs. 15 Crore contract from Survey of India to map out entire Haryana. Vipul is also the founding member of the Drone Federation of India and has had a very interesting journey working with the central government and various regulators to create the Drone Rules of 2021 that were the key to unlock this market. ---------------------------------------------------------------------------------If you like the content we create, I need 30 seconds of your time to help us reach more people. Do one or more of the following, depending on how much you love The Indian Dream.Subscribe on Youtube (Posting 5 Videos every week)Follow us on Instagram (Posting 3 Reels every week)Follow us on Twitter @sahil071 & @sidbetala (Trying to figure out Twitter!)DM us on Whatsapp------------------------------------------------------**This episode is brought to you by PushOwl.PushOwl is a web push marketing app built for e-commerce businesses. Trusted by more than 25000 brands across the globe, PushOwl lets you turn one-time store visitors into subscribers, send highly-visible web push notifications, and increase customer retention!
Rory is joined by Will Hill of Will Hill Consults and Vipul Sheth of Advancetrack Outsourcing to talk about outsourcing, technology, and firm efficiency. Learn how firms can strengthen their staff, provide greater client outcomes and grow their practice through the implementation of outsourcing. Vipul discusses the advancements in technology over the last decade and the increased acceleration of outsourcing that's occurred due to the pandemic. Discover how firms attract, retain, and create more fulfilled employees by freeing up more time to work on higher-valued services. Check out this latest episode with one of the global leaders in Outsourced Accounting!
“The HrishiKay Sessions” are produced & presented by Hrishikesh Kannan popularly known as Hrishi K Thanks for listening. Should you want to experience more ….for starters hit “subscribe” / “follow” and check out more episodes & be notified when further sessions go up! If ur looking for Hrishi across media & social networking then here goes: Twitter : https://www.twitter.com/hrishikay Facebook : https://www.facebook.com/hrishikay Instagram : https://www.instagram.com/hrishikay Youtube : https://youtube.com/c/hrishikeshkannan Soundcloud : https://www.soundcloud.com/hrishikay LinkedIn : http://linkedin.com/in/hrishikay
Our hosts Daren Givoque and Tina Murray are joined today by Vipul Arora, CFA. Vipul is a Portfolio manager with Assante Wealth Management and partner at O'Farrel Wealth and Estate Planning, based in Kemptville. Vipul brings his background in investment and research analysis to assist financial advisors and clients alike in determining which investments they should buy, which they should sell, and most importantly why they should be making those moves. Whether you're wondering where to start, looking to grow your wealth or portfolio, or a seasoned investor, this episode is for you! Vipul can be reached at future@ofsi.ca or 613.258.1997 This episode is sponsored by Dominion Lending. Check them out at ivegotamorgatgeforthat.com Find more on Vipul and gain access to all of our life transition professionals at www.mycleanbreak.ca. There you'll find information on our coaching services, past episodes, blog posts, and clear and simple advice to help guide you through to the other side of any of life's transitions. Our hosts: Daren Givoque and Tina Murray Find us on Facebook | Instagram | LinkedIn Questions about your next steps? Connect with us at info@mycleanbreak.ca --- Support this podcast: https://anchor.fm/cleanbreak/support
Automation is an Excellent Place to Build a Career . The key to a successful transformation project is to transform attitudes towards automation and transition from automating as-is to optimizing first. On this podcast, Vipul Tiwari, process implementation and optimization developer discusses his successful career in automation and how he approaches the business. The methodology for successful transformation requires taking the whole company on your transformation journey and getting business buy in to optimize then automate. By going through clear stages that explore key milestones to transformation focused on business outcomes, he builds trust across the business in digital capabilities. And if you are considering a role in automation, he shares some tips for people on how to jump into a role like his, how to get support and build an exciting future. . Here's what we talked with Vipul about: * How to approach transformation with each automated process * Methodology that works to build trust and engage the business * Top tips for anyone looking to kickstart a career in automation . To ensure that you never miss an episode of Transform NOW, be sure to subscribe!
The doctors are IN! Jordan welcomes Rachel Permuth & Vipul Kella as they discuss the challenges COVID has brought to the Medical & Legal Space.
This is a special episode. I have known Vipul for over 6 months now. With in just couple of conversations I have come to realize that he is one of the most interesting founders I have come across. In this episode we talked about: Vipul's journey prior to starting unlu.io & building Rutogo & CricnWin. Making money using Internet The concept of Fandom vs Respect How his experience working with influencers in cricket and co-producing movies is helping him build Unlu? How does the journey of making an Unlu Class happen, from concept to production? How does Unlu convince the like of Ruskin Bond & Manoj Bajpayee come and teach their craft on Unlu? Market size of Indian Influencer economy what's next after Unlu Shoutouts & Unlu class? What are the moats that are separating Unlu from the competition? Thesis behind Angel investing and the experience of seeing exits like Fitso (aq by Zomato) Follow Vipul on Twitter at https://twitter.com/iamvipulagrawal and learn more about Unlu at unlu.io Follow Nataraj on Twitter at https://twitter.com/natarajsindam and stay up to date at thestartupproject.io --- Send in a voice message: https://anchor.fm/startupproject/message
The Fatherhood Experience: Fitness, Family, Finance & Freedom
In this episode, Jason interviews Dr. Vipul Khanpara! Dr. Vipul Khanpara is an emergency medicine physician, as well as the Founder of Rugiet Men, a digital health clinic for men. They talk about staying active during the quarantine and pivoting habits to maintain fitness during the stay at home order. Dr. Khanpara says, “Although COVID-19 can be a deadly disease, so is poverty and inactivity, and things contributing to obesity and depression.” He strives to empower all men to take control of their physical and sexual health. Stay Connected with Dr. Vipul KhanparaWebsite Stay Connected with Jason Priest WebsiteFacebookInstagramLinkedInAbout The Dad Bod PodA podcast for men looking to improve their health and re-define their Dad Bods. A place for men to learn, grow and live the healthiest life possibleDon't forget to follow us on IG @thefatherhoodexperience!
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