Podcasts about Shreyas

It is a Wednesday, and Shreyas is back to talk Jayhawk Athletics.

Today's guest is Shreyas Becker, Head of AI & Data Products, Manufacturing & Supply at Sanofi. Shreyas joins Emerj Managing Editor Matthew DeMello to discuss the practical application of AI in life sciences, focusing on tools that are already improving supply chain resilience and manufacturing efficiency. He explains how AI helps navigate geopolitical disruptions, optimize production workflows, and ensure the reliable delivery of critical medications. The conversation also covers the evolution of AI systems developed during the pandemic and their role in shaping future innovations. For pharma and life sciences professionals, Shreyas provides valuable insights into where AI is delivering impact today and the continuing importance of human expertise in these processes. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the ‘AI in Business' podcast! This episode is sponsored by Arkestro. Learn more about Arkestro's upcoming Advisory Council event here. Find out more about sponsored content and how to engage with the Emerj audience at emerj.com/ad1.

Brad Haddin joins Adam Peacock live from India ahead of the Punjab Kings' qualifier final against RCB tonight. Hadds talks us through the Australian movement ahead of the WTC final, Josh Inglis's comparison to Jos Buttler, Mitch Marsh's superb form, and the talented young openers currently with Punjab. Plus, Hadds shares why he thinks Rishabh Pant will be the best keeper-batsman India has ever seen, Tim David's efficient work rate in the IPL, Harry Brook's first challenge as England's white-ball leader, and some of his favourite stories from cricket camp and his best memory of the great Steve Harmison! Send your cricket club cap to Producer Joel at the following address: Joel Harrison 50 Goulburn St, Sydney, NSW, 2000 Follow on Apple, Spotify and the LiSTNR app Watch on YouTube Drop us a message on Instagram and TikTok! See omnystudio.com/listener for privacy information.

‘Transformation is not optional in this programme. Especially because of the Strategic Leadership Development course.' – Shreyas Kapse (GEMBA '25) When asked about key take-aways of the Global Experiential Learning modules, Shreyas shared: ‘The GELS's are truly the experiential part of the learning. It is the most pragmatic kind of learning that you can expect.' Listen to the full episode to learn more about his journey, his experiences and some examples of the Global Experiential Learning trips.

Adam Peacock joins Brad Haddin live from Kolkata, India to give us an update on the 2025 IPL season thus far, Hadds talks about the trends in the competition he has seen, the poor fielding, some of his favourite stories to come from the IPL, KL Rahul's class, what's happening with the Sunrisers and how the Australians have fit in with the local and other international players at the Kings, Hadds shares his thoughts on Gill, Iyer and Jansen and the class they bring to the game. Plus, in a special edition of Ask Hadds, the Willow Talk followers have reached out with a wide range for Bradley Haddin, including "Which player would he bring from the IPL to Shield cricket in Australia." Send your cricket club cap to Producer Joel at the following address: Joel Harrison 50 Goulburn St, Sydney, NSW, 2000 Follow on Apple, Spotify and the LiSTNR app Watch on YouTube Drop us a message on Instagram and TikTok! Donate to Glen Waverly Hawks: https://www.gofundme.com/f/glen-waverley-hawks-cricket-club-fire-recoverySee omnystudio.com/listener for privacy information.

Featuring in this episode are: Aditya Joshi (Team Sports Katta) https://x.com/aditya1387 Amol Karhadkar (Deputy Editor, The Hindu) https://x.com/karhacter श्रेयस अय्यर हा IPL २०२४ चा विजेता संघ KKR चा कर्णधार होता. पण KKR ने विजेत्या संघाच्या कर्णधाराला रिटेन केलं नाही आणि पंजाबने विक्रमी बोली लावून अय्यरला ऑक्शनमध्ये आपल्याकडे खेचून आणला. पंजाब विरूद्ध कोलकाता सामन्याची त्यामुळेच उत्सुकता होती. अय्यरने त्याच्या आधीच्या संघाला धडा शिकवला का? करूण नायर हा गेल्या आठवड्यातील ट्रेंडिग विषय, बुमराहला त्याने मारलेले चौके आणि धक्के हे लक्षात राहणारे आहेत. धोनीने मिळवलेलं मॅन ऑफ द मॅच हे तुक्का होतं का आवाक्यात असलेलं लक्ष्य त्यांना सोपं गेलं? अशा अनेक विषयांवर द हिंदूचे अमोल क-हाडकर आणि स्पोर्ट्स कट्टाच्या आदित्य जोशी यांनी वीकली कट्टामध्ये गप्पा मारल्या आहेत Follow us on: YouTube: https://www.youtube.com/@SportsKattaMarathi Instagram: https://www.instagram.com/sportskattamarathi Facebook: https://www.facebook.com/SportsKattaMarathi Twitter: https://twitter.com/Sports_Katta Email : barachkaahi@gmail.com

In this episode of the show, Raj and Stu look back at two midweek games: Punjab Kings vs Lucknow Super Giants and Royal Challengers Bengaluru vs Gujarat Titans. We start by discussing the most recent game, which saw Gujarat spoil Raj's return to our IPL coverage with a dominant win in Bengaluru. Mohammed Siraj knocked the top off the RCB batting lineup and despite a recovery through the middle and a messy fielding performance from GT, it turned out that 169 wasn't close to enough. Sai Sudharsan set the platform for GT in the powerplay, before Jos Buttler's 73 guided them home. Next we move to Punjab's impressive win against LSG. There's talk about Shreyas Iyer's excellent run of form, PBKS's Indian core doing the business, LSG's absences, Nicholas Pooran's ball-striking and whether it's time to start worrying about Rishabh Pant's lack of runs. To finish, there's a quick look ahead to the upcoming SRH v KKR clash - a 2024 final rematch that now shapes as an important fixture for different reasons in 2025. We'll be back in your feed on Monday with our weekly IPL Buy/Sell/Hold show. If you're enjoying our content then please take the time to give us a like, follow, share or subscribe on all our channels (@toporderpod on Twitter & Facebook, and @thetoporderpodcast on Instagram & YouTube) and a (5-Star!) review at your favourite podcast provider, or tell a friend to download. It really helps others find the show and is the best thing you can do to support us. You can also find all our written content, including our Hall of Fame series, at our website. You can also dip back into our guest episodes - including conversations with Mike Hesson, Shane Bond and Mike Hussey, current players such as Matt Henry, Sophie Devine and Ish Sodhi, coaches Gary Stead, Jeetan Patel and Luke Wright, as well as Barry Richards, Frankie Mackay, Bharat Sundaresan and many more fascinating people from all across the cricketing world. And if you'd like to reach out to us with feedback, questions or guest suggestions, get in touch at thetoporderpodcast@gmail.com. Thanks for listening. 0:00 Intro 0:20 GT v RCB - Siraj, Buttler & Sudharsan shine in big GT win 14:30 PBKS v LSG - Shreyas Iyer is on fire 33:50 SRH v KKR - Important game ahead Learn more about your ad choices. Visit podcastchoices.com/adchoices

Send us a textEver wondered how heat pump systems differ between the UK and the US? In this episode, Nathan chats with Shreyas Sudhakar, a leading heat pump installer and educator from California, to explore the contrasting approaches. Nathan, who has fond memories of his time in America, and his apprenticeship on a US airbase in the UK, emphasises the strong connection he feels to the country. He and Shreyas engage in a fun discussion, highlighting the strengths of both systems and gently suggesting areas where US installations might benefit from UK insights. They both strongly believe that sharing knowledge and best practices is key to maximising heat pump efficiency and sustainability. Join us for a conversation that celebrates innovation and the potential for transatlantic collaboration!This season is sponsored by Primary Pro a professional outdoor pipe insulation system used by top engineers in the UKSupport the showLearn more about heat pump heating by followingNathan on Linkedin, Twitter and BlueSky

The team is back and we're previewing the Champions Trophy!Items under discussion:0:00 - Welcome1:41 - Ritvik has sources2:14 - CT Winner Stats2:58 - Ritvik's source revealed4:45 - Ritvik reminiscing about tri-series4:45 - Vijay humble-bragging6:30 - Democracy in Bangladesh7:49 - India without Jassi Bhai9:07 - Gautam Gambhir Probation period12:18 - Bazball is evil14:57 - Ritvik - NZ to win CT16:34 - Democracy in Bangladesh17:24 - PK as bad luck charm18:54 - India's bowling selection20:08 - Shreyas and the short ball21:41 - Champions Trophy Pakistan v Dubai24:13 - Australia's chances

Why fix something that isn't broken? India seem to be overcomplicating their combination in the ODI series against England, which is a worrying sign ahead of the Champions Trophy. There appears to be a real problem of plenty for coach Gautam Gambhir and captain Rohit Sharma.Why on earth did they leave out Shreyas Iyer for the series opener in Nagpur? An injury to Virat Kohli meant Shreyas got an opportunity after a late phone call from the skipper. The star batter revealed he was not in the XI as late as the eve of the match. India had initially decided to play both Yashasvi Jaiswal and Shubman Gill in the XI at the expense of Shreyas.However, after Shreyas let his bat do the talking in the first ODI, India dropped Yashasvi for the second match.Similarly, India picked four spinners in their squad for the Champions Trophy and later added Varun Chakravarthy. Why are we mixing formats again? Why not Kuldeep Yadav instead of Varun?The team combination issue isn't limited to India. Injuries have forced the likes of Pakistan and Australia to rethink their line-ups.With just over a week to go before the Champions Trophy, things are starting to heat up.In the latest episode of the Sledging Room podcast, we discuss the selection muddle and the build-up to the Champions Trophy.Tune In!Produced by Garvit SrivastavaSound mixed by Rohan Bharti

Today's guests are two senior executives from Sanofi's manufacturing division, Head of Data & AI for Manufacturing & Supply Chain Kartik Pant and Head of AI & Data Products, Manufacturing & Supply Shreyas Becker, respectively. Together they return to the podcast to cover the challenges and opportunities in adopting data infrastructure and AI solutions in life sciences manufacturing. Throughout the episode they emphasize the need for a unified data architecture, starting small, and articulating a problem-driven approach to technology adoption. Kartik and Shreyas also discuss the importance of understanding specific use cases and leveraging data to identify areas for optimizing processes. If you're interested in unlocking our AI best practice guides, frameworks for AI ROI, and specific resources for AI consultants, visit emerj.com/p1.  

China's budget-friendly AI model, DeepSeek, is making waves, sparking debates on its impact on global AI competition. On the health front, we break down the Guillain-Barré Syndrome outbreak in Pune and what it means for public hygiene. Music lovers, Sonu Nigam has called out the Padma Awards for ignoring legends like Kishore Kumar, Alka Yagnik, and Shreya Ghoshal—was he right? Plus, we analyze his Instagram video that’s got everyone talking. Over in cricket, Travis Head's explosive 57 off 40 balls against Sri Lanka is making headlines, and we bring you live updates from the match. Tech enthusiasts, is JioCoin India's next big cryptocurrency or just another reward token? We decode its implications. Tune in for unfiltered takes, hilarious takes, and no-BS insights with Shreyas Manohar and Govind Menon on this edition of Cock & Bull. Don't forget to like, share, and subscribe! #RepublicDay2025 #Memes #DeepSeek #SonuNigam #Cricket #JioCoin #CyrusSays #CockAndBull #ShreyasManohar #GovindMenonSee omnystudio.com/listener for privacy information.

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Applications close Monday for the NYC AI Engineer Summit focusing on AI Leadership and Agent Engineering! If you applied, invites should be rolling out shortly.The search landscape is experiencing a fundamental shift. Google built a >$2T company with the “10 blue links” experience, driven by PageRank as the core innovation for ranking. This was a big improvement from the previous directory-based experiences of AltaVista and Yahoo. Almost 4 decades later, Google is now stuck in this links-based experience, especially from a business model perspective. This legacy architecture creates fundamental constraints:* Must return results in ~400 milliseconds* Required to maintain comprehensive web coverage* Tied to keyword-based matching algorithms* Cost structures optimized for traditional indexingAs we move from the era of links to the era of answers, the way search works is changing. You're not showing a user links, but the goal is to provide context to an LLM. This means moving from keyword based search to more semantic understanding of the content:The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share... but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways.All of this is now powered by a $5M cluster with 144 H200s:This architectural choice enables entirely new search capabilities:* Comprehensive result sets instead of approximations* Deep semantic understanding of queries* Ability to process complex, natural language requestsAs search becomes more complex, time to results becomes a variable:People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned... But what if searches can take like a minute or 10 minutes or a whole day, what can you then do?Unlike traditional search engines' fixed-cost indexing, Exa employs a hybrid approach:* Front-loaded compute for indexing and embeddings* Variable inference costs based on query complexity* Mix of owned infrastructure ($5M H200 cluster) and cloud resourcesExa sees a lot of competition from products like Perplexity and ChatGPT Search which layer AI on top of traditional search backends, but Exa is betting that true innovation requires rethinking search from the ground up. For example, the recently launched Websets, a way to turn searches into structured output in grid format, allowing you to create lists and databases out of web pages. The company raised a $17M Series A to build towards this mission, so keep an eye out for them in 2025. Chapters* 00:00:00 Introductions* 00:01:12 ExaAI's initial pitch and concept* 00:02:33 Will's background at SpaceX and Zoox* 00:03:45 Evolution of ExaAI (formerly Metaphor Systems)* 00:05:38 Exa's link prediction technology* 00:09:20 Meaning of the name "Exa"* 00:10:36 ExaAI's new product launch and capabilities* 00:13:33 Compute budgets and variable compute products* 00:14:43 Websets as a B2B offering* 00:19:28 How do you build a search engine?* 00:22:43 What is Neural PageRank?* 00:27:58 Exa use cases * 00:35:00 Auto-prompting* 00:38:42 Building agentic search* 00:44:19 Is o1 on the path to AGI?* 00:49:59 Company culture and nap pods* 00:54:52 Economics of AI search and the future of search technologyFull YouTube TranscriptPlease like and subscribe!Show Notes* ExaAI* Web Search Product* Websets* Series A Announcement* Exa Nap Pods* Perplexity AI* Character.AITranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:10]: Hey, and today we're in the studio with my good friend and former landlord, Will Bryk. Roommate. How you doing? Will, you're now CEO co-founder of ExaAI, used to be Metaphor Systems. What's your background, your story?Will [00:00:30]: Yeah, sure. So, yeah, I'm CEO of Exa. I've been doing it for three years. I guess I've always been interested in search, whether I knew it or not. Like, since I was a kid, I've always been interested in, like, high-quality information. And, like, you know, even in high school, wanted to improve the way we get information from news. And then in college, built a mini search engine. And then with Exa, like, you know, it's kind of like fulfilling the dream of actually being able to solve all the information needs I wanted as a kid. Yeah, I guess. I would say my entire life has kind of been rotating around this problem, which is pretty cool. Yeah.Swyx [00:00:50]: What'd you enter YC with?Will [00:00:53]: We entered YC with, uh, we are better than Google. Like, Google 2.0.Swyx [00:01:12]: What makes you say that? Like, that's so audacious to come out of the box with.Will [00:01:16]: Yeah, okay, so you have to remember the time. This was summer 2021. And, uh, GPT-3 had come out. Like, here was this magical thing that you could talk to, you could enter a whole paragraph, and it understands what you mean, understands the subtlety of your language. And then there was Google. Uh, which felt like it hadn't changed in a decade, uh, because it really hadn't. And it, like, you would give it a simple query, like, I don't know, uh, shirts without stripes, and it would give you a bunch of results for the shirts with stripes. And so, like, Google could barely understand you, and GBD3 could. And the theory was, what if you could make a search engine that actually understood you? What if you could apply the insights from LLMs to a search engine? And it's really been the same idea ever since. And we're actually a lot closer now, uh, to doing that. Yeah.Alessio [00:01:55]: Did you have any trouble making people believe? Obviously, there's the same element. I mean, YC overlap, was YC pretty AI forward, even 2021, or?Will [00:02:03]: It's nothing like it is today. But, um, uh, there were a few AI companies, but, uh, we were definitely, like, bold. And I think people, VCs generally like boldness, and we definitely had some AI background, and we had a working demo. So there was evidence that we could build something that was going to work. But yeah, I think, like, the fundamentals were there. I think people at the time were talking about how, you know, Google was failing in a lot of ways. And so there was a bit of conversation about it, but AI was not a big, big thing at the time. Yeah. Yeah.Alessio [00:02:33]: Before we jump into Exa, any fun background stories? I know you interned at SpaceX, any Elon, uh, stories? I know you were at Zoox as well, you know, kind of like robotics at Harvard. Any stuff that you saw early that you thought was going to get solved that maybe it's not solved today?Will [00:02:48]: Oh yeah. I mean, lots of things like that. Like, uh, I never really learned how to drive because I believed Elon that self-driving cars would happen. It did happen. And I take them every night to get home. But it took like 10 more years than I thought. Do you still not know how to drive? I know how to drive now. I learned it like two years ago. That would have been great to like, just, you know, Yeah, yeah, yeah. You know? Um, I was obsessed with Elon. Yeah. I mean, I worked at SpaceX because I really just wanted to work at one of his companies. And I remember they had a rule, like interns cannot touch Elon. And, um, that rule actually influenced my actions.Swyx [00:03:18]: Is it, can Elon touch interns? Ooh, like physically?Will [00:03:22]: Or like talk? Physically, physically, yeah, yeah, yeah, yeah. Okay, interesting. He's changed a lot, but, um, I mean, his companies are amazing. Um,Swyx [00:03:28]: What if you beat him at Diablo 2, Diablo 4, you know, like, Ah, maybe.Alessio [00:03:34]: I want to jump into, I know there's a lot of backstory used to be called metaphor system. So, um, and it, you've always been kind of like a prominent company, maybe at least RAI circles in the NSF.Swyx [00:03:45]: I'm actually curious how Metaphor got its initial aura. You launched with like, very little. We launched very little. Like there was, there was this like big splash image of like, this is Aurora or something. Yeah. Right. And then I was like, okay, what this thing, like the vibes are good, but I don't know what it is. And I think, I think it was much more sort of maybe consumer facing than what you are today. Would you say that's true?Will [00:04:06]: No, it's always been about building a better search algorithm, like search, like, just like the vision has always been perfect search. And if you do that, uh, we will figure out the downstream use cases later. It started on this fundamental belief that you could have perfect search over the web and we could talk about what that means. And like the initial thing we released was really just like our first search engine, like trying to get it out there. Kind of like, you know, an open source. So when OpenAI released, uh, ChachBt, like they didn't, I don't know how, how much of a game plan they had. They kind of just wanted to get something out there.Swyx [00:04:33]: Spooky research preview.Will [00:04:34]: Yeah, exactly. And it kind of morphed from a research company to a product company at that point. And I think similarly for us, like we were research, we started as a research endeavor with a, you know, clear eyes that like, if we succeed, it will be a massive business to make out of it. And that's kind of basically what happened. I think there are actually a lot of parallels to, of w between Exa and OpenAI. I often say we're the OpenAI of search. Um, because. Because we're a research company, we're a research startup that does like fundamental research into, uh, making like AGI for search in a, in a way. Uh, and then we have all these like, uh, business products that come out of that.Swyx [00:05:08]: Interesting. I want to ask a little bit more about Metaforesight and then we can go full Exa. When I first met you, which was really funny, cause like literally I stayed in your house in a very historic, uh, Hayes, Hayes Valley place. You said you were building sort of like link prediction foundation model, and I think there's still a lot of foundation model work. I mean, within Exa today, but what does that even mean? I cannot be the only person confused by that because like there's a limited vocabulary or tokens you're telling me, like the tokens are the links or, you know, like it's not, it's not clear. Yeah.Will [00:05:38]: Uh, what we meant by link prediction is that you are literally predicting, like given some texts, you're predicting the links that follow. Yes. That refers to like, it's how we describe the training procedure, which is that we find links on the web. Uh, we take the text surrounding the link. And then we predict. Which link follows you, like, uh, you know, similar to transformers where, uh, you're trying to predict the next token here, you're trying to predict the next link. And so you kind of like hide the link from the transformer. So if someone writes, you know, imagine some article where someone says, Hey, check out this really cool aerospace startup. And they, they say spacex.com afterwards, uh, we hide the spacex.com and ask the model, like what link came next. And by doing that many, many times, you know, billions of times, you could actually build a search engine out of that because then, uh, at query time at search time. Uh, you type in, uh, a query that's like really cool aerospace startup and the model will then try to predict what are the most likely links. So there's a lot of analogs to transformers, but like to actually make this work, it does require like a different architecture than, but it's transformer inspired. Yeah.Alessio [00:06:41]: What's the design decision between doing that versus extracting the link and the description and then embedding the description and then using, um, yeah. What do you need to predict the URL versus like just describing, because you're kind of do a similar thing in a way. Right. It's kind of like based on this description, it was like the closest link for it. So one thing is like predicting the link. The other approach is like I extract the link and the description, and then based on the query, I searched the closest description to it more. Yeah.Will [00:07:09]: That, that, by the way, that is, that is the link refers here to a document. It's not, I think one confusing thing is it's not, you're not actually predicting the URL, the URL itself that would require like the, the system to have memorized URLs. You're actually like getting the actual document, a more accurate name could be document prediction. I see. This was the initial like base model that Exo was trained on, but we've moved beyond that similar to like how, you know, uh, to train a really good like language model, you might start with this like self-supervised objective of predicting the next token and then, uh, just from random stuff on the web. But then you, you want to, uh, add a bunch of like synthetic data and like supervised fine tuning, um, stuff like that to make it really like controllable and robust. Yeah.Alessio [00:07:48]: Yeah. We just have flow from Lindy and, uh, their Lindy started to like hallucinate recrolling YouTube links instead of like, uh, something. Yeah. Support guide. So. Oh, interesting. Yeah.Swyx [00:07:57]: So round about January, you announced your series A and renamed to Exo. I didn't like the name at the, at the initial, but it's grown on me. I liked metaphor, but apparently people can spell metaphor. What would you say are the major components of Exo today? Right? Like, I feel like it used to be very model heavy. Then at the AI engineer conference, Shreyas gave a really good talk on the vector database that you guys have. What are the other major moving parts of Exo? Okay.Will [00:08:23]: So Exo overall is a search engine. Yeah. We're trying to make it like a perfect search engine. And to do that, you have to build lots of, and we're doing it from scratch, right? So to do that, you have to build lots of different. The crawler. Yeah. You have to crawl a bunch of the web. First of all, you have to find the URLs to crawl. Uh, it's connected to the crawler, but yeah, you find URLs, you crawl those URLs. Then you have to process them with some, you know, it could be an embedding model. It could be something more complex, but you need to take, you know, or like, you know, in the past it was like a keyword inverted index. Like you would process all these documents you gather into some processed index, and then you have to serve that. Uh, you had high throughput at low latency. And so that, and that's like the vector database. And so it's like the crawling system, the AI processing system, and then the serving system. Those are all like, you know, teams of like hundreds, maybe thousands of people at Google. Um, but for us, it's like one or two people each typically, but yeah.Alessio [00:09:13]: Can you explain the meaning of, uh, Exo, just the story 10 to the 16th, uh, 18, 18.Will [00:09:20]: Yeah, yeah, yeah, sure. So. Exo means 10 to the 18th, which is in stark contrast to. To Google, which is 10 to the hundredth. Uh, we actually have these like awesome shirts that are like 10th to 18th is greater than 10th to the hundredth. Yeah, it's great. And it's great because it's provocative. It's like every engineer in Silicon Valley is like, what? No, it's not true. Um, like, yeah. And, uh, and then you, you ask them, okay, what does it actually mean? And like the creative ones will, will recognize it. But yeah, I mean, 10 to the 18th is better than 10 to the hundredth when it comes to search, because with search, you want like the actual list of, of things that match what you're asking for. You don't want like the whole web. You want to basically with search filter, the, like everything that humanity has ever created to exactly what you want. And so the idea is like smaller is better there. You want like the best 10th to the 18th and not the 10th to the hundredth. I'm like, one way to say this is like, you know how Google often says at the top, uh, like, you know, 30 million results found. And it's like crazy. Cause you're looking for like the first startups in San Francisco that work on hardware or something. And like, they're not 30 million results like that. What you want is like 325 results found. And those are all the results. That's what you really want with search. And that's, that's our vision. It's like, it just gives you. Perfectly what you asked for.Swyx [00:10:24]: We're recording this ahead of your launch. Uh, we haven't released, we haven't figured out the, the, the name of the launch yet, but what is the product that you're launching? I guess now that we're coinciding this podcast with. Yeah.Will [00:10:36]: So we've basically developed the next version of Exa, which is the ability to get a near perfect list of results of whatever you want. And what that means is you can make a complex query now to Exa, for example, startups working on hardware in SF, and then just get a huge list of all the things that match. And, you know, our goal is if there are 325 startups that match that we find you all of them. And this is just like, there's just like a new experience that's never existed before. It's really like, I don't know how you would go about that right now with current tools and you can apply this same type of like technology to anything. Like, let's say you want, uh, you want to find all the blog posts that talk about Alessio's podcast, um, that have come out in the past year. That is 30 million results. Yeah. Right.Will [00:11:24]: But that, I mean, that would, I'm sure that would be extremely useful to you guys. And like, I don't really know how you would get that full comprehensive list.Swyx [00:11:29]: I just like, how do you, well, there's so many questions with regards to how do you know it's complete, right? Cause you're saying there's only 30 million, 325, whatever. And then how do you do the semantic understanding that it might take, right? So working in hardware, like I might not use the words hardware. I might use the words robotics. I might use the words wearables. I might use like whatever. Yes. So yeah, just tell us more. Yeah. Yeah. Sure. Sure.Will [00:11:53]: So one aspect of this, it's a little subjective. So like certainly providing, you know, at some point we'll provide parameters to the user to like, you know, some sort of threshold to like, uh, gauge like, okay, like this is a cutoff. Like, this is actually not what I mean, because sometimes it's subjective and there needs to be a feedback loop. Like, oh, like it might give you like a few examples and you say, yeah, exactly. And so like, you're, you're kind of like creating a classifier on the fly, but like, that's ultimately how you solve the problem. So the subject, there's a subjectivity problem and then there's a comprehensiveness problem. Those are two different problems. So. Yeah. So you have the comprehensiveness problem. What you basically have to do is you have to put more compute into the query, into the search until you get the full comprehensiveness. Yeah. And I think there's an interesting point here, which is that not all queries are made equal. Some queries just like this blog post one might require scanning, like scavenging, like throughout the whole web in a way that just, just simply requires more compute. You know, at some point there's some amount of compute where you will just be comprehensive. You could imagine, for example, running GPT-4 over the internet. You could imagine running GPT-4 over the entire web and saying like, is this a blog post about Alessio's podcast, like, is this a blog post about Alessio's podcast? And then that would work, right? It would take, you know, a year, maybe cost like a million dollars, but, or many more, but, um, it would work. Uh, the point is that like, given sufficient compute, you can solve the query. And so it's really a question of like, how comprehensive do you want it given your compute budget? I think it's very similar to O1, by the way. And one way of thinking about what we built is like O1 for search, uh, because O1 is all about like, you know, some, some, some questions require more compute than others, and we'll put as much compute into the question as we need to solve it. So similarly with our search, we will put as much compute into the query in order to get comprehensiveness. Yeah.Swyx [00:13:33]: Does that mean you have like some kind of compute budget that I can specify? Yes. Yes. Okay. And like, what are the upper and lower bounds?Will [00:13:42]: Yeah, there's something we're still figuring out. I think like, like everyone is a new paradigm of like variable compute products. Yeah. How do you specify the amount of compute? Like what happens when you. Run out? Do you just like, ah, do you, can you like keep going with it? Like, do you just put in more credits to get more, um, for some, like this can get complex at like the really large compute queries. And like, one thing we do is we give you a preview of what you're going to get, and then you could then spin up like a much larger job, uh, to get like way more results. But yes, there is some compute limit, um, at, at least right now. Yeah. People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned, uh, to have search that takes 500 milliseconds. But like search engines like Google, right. No matter how complex your query to Google, it will take like, you know, roughly 400 milliseconds. But what if searches can take like a minute or 10 minutes or a whole day, what can you then do? And you can do very powerful things. Um, you know, you can imagine, you know, writing a search, going and get a cup of coffee, coming back and you have a perfect list. Like that's okay for a lot of use cases. Yeah.Alessio [00:14:43]: Yeah. I mean, the use case closest to me is venture capital, right? So, uh, no, I mean, eight years ago, I built one of the first like data driven sourcing platforms. So we were. You look at GitHub, Twitter, Product Hunt, all these things, look at interesting things, evaluate them. If you think about some jobs that people have, it's like literally just make a list. If you're like an analyst at a venture firm, your job is to make a list of interesting companies. And then you reach out to them. How do you think about being infrastructure versus like a product you could say, Hey, this is like a product to find companies. This is a product to find things versus like offering more as a blank canvas that people can build on top of. Oh, right. Right.Will [00:15:20]: Uh, we are. We are a search infrastructure company. So we want people to build, uh, on top of us, uh, build amazing products on top of us. But with this one, we try to build something that makes it really easy for users to just log in, put a few, you know, put some credits in and just get like amazing results right away and not have to wait to build some API integration. So we're kind of doing both. Uh, we, we want, we want people to integrate this into all their applications at the same time. We want to just make it really easy to use very similar again to open AI. Like they'll have, they have an API, but they also have. Like a ChatGPT interface so that you could, it's really easy to use, but you could also build it in your applications. Yeah.Alessio [00:15:56]: I'm still trying to wrap my head around a lot of the implications. So, so many businesses run on like information arbitrage, you know, like I know this thing that you don't, especially in investment and financial services. So yeah, now all of a sudden you have these tools for like, oh, actually everybody can get the same information at the same time, the same quality level as an API call. You know, it just kind of changes a lot of things. Yeah.Will [00:16:19]: I think, I think what we're grappling with here. What, what you're just thinking about is like, what is the world like if knowledge is kind of solved, if like any knowledge request you want is just like right there on your computer, it's kind of different from when intelligence is solved. There's like a good, I've written before about like a different super intelligence, super knowledge. Yeah. Like I think that the, the distinction between intelligence and knowledge is actually a pretty good one. They're definitely connected and related in all sorts of ways, but there is a distinction. You could have a world and we are going to have this world where you have like GP five level systems and beyond that could like answer any complex request. Um, unless it requires some. Like, if you say like, uh, you know, give me a list of all the PhDs in New York city who, I don't know, have thought about search before. And even though this, this super intelligence is going to be like, I can't find it on Google, right. Which is kind of crazy. Like we're literally going to have like super intelligences that are using Google. And so if Google can't find them information, there's nothing they could do. They can't find it. So, but if you also have a super knowledge system where it's like, you know, I'm calling this term super knowledge where you just get whatever knowledge you want, then you can pair with a super intelligence system. And then the super intelligence can, we'll never. Be blocked by lack of knowledge.Alessio [00:17:23]: Yeah. You told me this, uh, when we had lunch, I forget how it came out, but we were talking about AGI and whatnot. And you were like, even AGI is going to need search. Yeah.Swyx [00:17:32]: Yeah. Right. Yeah. Um, so we're actually referencing a blog post that you wrote super intelligence and super knowledge. Uh, so I would refer people to that. And this is actually a discussion we've had on the podcast a couple of times. Um, there's so much of model weights that are just memorizing facts. Some of the, some of those might be outdated. Some of them are incomplete or not. Yeah. So like you just need search. So I do wonder, like, is there a maximum language model size that will be the intelligence layer and then the rest is just search, right? Like maybe we should just always use search. And then that sort of workhorse model is just like, and it like, like, like one B or three B parameter model that just drives everything. Yes.Will [00:18:13]: I believe this is a much more optimal system to have a smaller LM. That's really just like an intelligence module. And it makes a call to a search. Tool that's way more efficient because if, okay, I mean the, the opposite of that would be like the LM is so big that can memorize the whole web. That would be like way, but you know, it's not practical at all. I don't, it's not possible to train that at least right now. And Carpathy has actually written about this, how like he could, he could see models moving more and more towards like intelligence modules using various tools. Yeah.Swyx [00:18:39]: So for listeners, that's the, that was him on the no priors podcast. And for us, we talked about this and the, on the Shin Yu and Harrison chase podcasts. I'm doing search in my head. I told you 30 million results. I forgot about our neural link integration. Self-hosted exit.Will [00:18:54]: Yeah. Yeah. No, I do see that that is a much more, much more efficient world. Yeah. I mean, you could also have GB four level systems calling search, but it's just because of the cost of inference. It's just better to have a very efficient search tool and a very efficient LM and they're built for different things. Yeah.Swyx [00:19:09]: I'm just kind of curious. Like it is still something so audacious that I don't want to elide, which is you're, you're, you're building a search engine. Where do you start? How do you, like, are there any reference papers or implementation? That would really influence your thinking, anything like that? Because I don't even know where to start apart from just crawl a bunch of s**t, but there's gotta be more insight than that.Will [00:19:28]: I mean, yeah, there's more insight, but I'm always surprised by like, if you have a group of people who are really focused on solving a problem, um, with the tools today, like there's some in, in software, like there are all sorts of creative solutions that just haven't been thought of before, particularly in the information retrieval field. Yeah. I think a lot of the techniques are just very old, frankly. Like I know how Google and Bing work and. They're just not using new methods. There are all sorts of reasons for that. Like one, like Google has to be comprehensive over the web. So they're, and they have to return in 400 milliseconds. And those two things combined means they are kind of limit and it can't cost too much. They're kind of limited in, uh, what kinds of algorithms they could even deploy at scale. So they end up using like a limited keyword based algorithm. Also like Google was built in a time where like in, you know, in 1998, where we didn't have LMS, we didn't have embeddings. And so they never thought to build those things. And so now they have this like gigantic system that is built on old technology. Yeah. And so a lot of the information retrieval field we found just like thinks in terms of that framework. Yeah. Whereas we came in as like newcomers just thinking like, okay, there here's GB three. It's magical. Obviously we're going to build search that is using that technology. And we never even thought about using keywords really ever. Uh, like we were neural all the way we're building an end to end neural search engine. And just that whole framing just makes us ask different questions, like pursue different lines of work. And there's just a lot of low hanging fruit because no one else is thinking about it. We're just on the frontier of neural search. We just are, um, for, for at web scale, um, because there's just not a lot of people thinking that way about it.Swyx [00:20:57]: Yeah. Maybe let's spell this out since, uh, we're already on this topic, elephants in the room are Perplexity and SearchGPT. That's the, I think that it's all, it's no longer called SearchGPT. I think they call it ChatGPT Search. How would you contrast your approaches to them based on what we know of how they work and yeah, just any, anything in that, in that area? Yeah.Will [00:21:15]: So these systems, there are a few of them now, uh, they basically rely on like traditional search engines like Google or Bing, and then they combine them with like LLMs at the end to, you know, output some power graphics, uh, answering your question. So they like search GPT perplexity. I think they have their own crawlers. No. So there's this important distinction between like having your own search system and like having your own cache of the web. Like for example, so you could create, you could crawl a bunch of the web. Imagine you crawl a hundred billion URLs, and then you create a key value store of like mapping from URL to the document that is technically called an index, but it's not a search algorithm. So then to actually like, when you make a query to search GPT, for example, what is it actually doing it? Let's say it's, it's, it could, it's using the Bing API, uh, getting a list of results and then it could go, it has this cache of like all the contents of those results and then could like bring in the cache, like the index cache, but it's not actually like, it's not like they've built a search engine from scratch over, you know, hundreds of billions of pages. It's like, does that distinction clear? It's like, yeah, you could have like a mapping from URL to documents, but then rely on traditional search engines to actually get the list of results because it's a very hard problem to take. It's not hard. It's not hard to use DynamoDB and, and, and map URLs to documents. It's a very hard problem to take a hundred billion or more documents and given a query, like instantly get the list of results that match. That's a much harder problem that very few entities on, in, on the planet have done. Like there's Google, there's Bing, uh, you know, there's Yandex, but you know, there are not that many companies that are, that are crazy enough to actually build their search engine from scratch when you could just use traditional search APIs.Alessio [00:22:43]: So Google had PageRank as like the big thing. Is there a LLM equivalent or like any. Stuff that you're working on that you want to highlight?Will [00:22:51]: The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share. And so if everyone is sharing some Paul Graham essay about fundraising, then like our model is more likely to predict it. So like inherent in our training objective is this, uh, a sense of like high canonicity and like high quality, but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways. That someone refers that Paul Graham, I say, while also learning how important that Paul Graham essay is. Um, so it's like, it's like PageRank on steroids kind of thing. Yeah.Alessio [00:23:26]: I think to me, that's the most interesting thing about search today, like with Google and whatnot, it's like, it's mostly like domain authority. So like if you get back playing, like if you search any AI term, you get this like SEO slop websites with like a bunch of things in them. So this is interesting, but then how do you think about more timeless maybe content? So if you think about, yeah. You know, maybe the founder mode essay, right. It gets shared by like a lot of people, but then you might have a lot of other essays that are also good, but they just don't really get a lot of traction. Even though maybe the people that share them are high quality. How do you kind of solve that thing when you don't have the people authority, so to speak of who's sharing, whether or not they're worth kind of like bumping up? Yeah.Will [00:24:10]: I mean, you do have a lot of control over the training data, so you could like make sure that the training data contains like high quality sources so that, okay. Like if you, if you're. Training data, I mean, it's very similar to like language, language model training. Like if you train on like a bunch of crap, your prediction will be crap. Our model will match the training distribution is trained on. And so we could like, there are lots of ways to tweak the training data to refer to high quality content that we want. Yeah. I would say also this, like this slop that is returned by, by traditional search engines, like Google and Bing, you have the slop is then, uh, transferred into the, these LLMs in like a search GBT or, you know, our other systems like that. Like if slop comes in, slop will go out. And so, yeah, that's another answer to how we're different is like, we're not like traditional search engines. We want to give like the highest quality results and like have full control over whatever you want. If you don't want slop, you get that. And then if you put an LM on top of that, which our customers do, then you just get higher quality results or high quality output.Alessio [00:25:06]: And I use Excel search very often and it's very good. Especially.Swyx [00:25:09]: Wave uses it too.Alessio [00:25:10]: Yeah. Yeah. Yeah. Yeah. Yeah. Like the slop is everywhere, especially when it comes to AI, when it comes to investment. When it comes to all of these things for like, it's valuable to be at the top. And this problem is only going to get worse because. Yeah, no, it's totally. What else is in the toolkit? So you have search API, you have ExaSearch, kind of like the web version. Now you have the list builder. I think you also have web scraping. Maybe just touch on that. Like, I guess maybe people, they want to search and then they want to scrape. Right. So is that kind of the use case that people have? Yeah.Will [00:25:41]: A lot of our customers, they don't just want, because they're building AI applications on top of Exa, they don't just want a list of URLs. They actually want. Like the full content, like cleans, parsed. Markdown. Markdown, maybe chunked, whatever they want, we'll give it to them. And so that's been like huge for customers. Just like getting the URLs and instantly getting the content for each URL is like, and you can do this for 10 or 100 or 1,000 URLs, wherever you want. That's very powerful.Swyx [00:26:05]: Yeah. I think this is the first thing I asked you for when I tried using Exa.Will [00:26:09]: Funny story is like when I built the first version of Exa, it's like, we just happened to store the content. Yes. Like the first 1,024 tokens. Because I just kind of like kept it because I thought of, you know, I don't know why. Really for debugging purposes. And so then when people started asking for content, it was actually pretty easy to serve it. But then, and then we did that, like Exa took off. So the computer's content was so useful. So that was kind of cool.Swyx [00:26:30]: It is. I would say there are other players like Gina, I think is in this space. Firecrawl is in this space. There's a bunch of scraper companies. And obviously scraper is just one part of your stack, but you might as well offer it since you already do it.Will [00:26:43]: Yeah, it makes sense. It's just easy to have an all-in-one solution. And like. We are, you know, building the best scraper in the world. So scraping is a hard problem and it's easy to get like, you know, a good scraper. It's very hard to get a great scraper and it's super hard to get a perfect scraper. So like, and, and scraping really matters to people. Do you have a perfect scraper? Not yet. Okay.Swyx [00:27:05]: The web is increasingly closing to the bots and the scrapers, Twitter, Reddit, Quora, Stack Overflow. I don't know what else. How are you dealing with that? How are you navigating those things? Like, you know. You know, opening your eyes, like just paying them money.Will [00:27:19]: Yeah, no, I mean, I think it definitely makes it harder for search engines. One response is just that there's so much value in the long tail of sites that are open. Okay. Um, and just like, even just searching over those well gets you most of the value. But I mean, there, there is definitely a lot of content that is increasingly not unavailable. And so you could get through that through data partnerships. The bigger we get as a company, the more, the easier it is to just like, uh, make partnerships. But I, I mean, I do see the world as like the future where the. The data, the, the data producers, the content creators will make partnerships with the entities that find that data.Alessio [00:27:53]: Any other fun use case that maybe people are not thinking about? Yeah.Will [00:27:58]: Oh, I mean, uh, there are so many customers. Yeah. What are people doing on AXA? Well, I think dating is a really interesting, uh, application of search that is completely underserved because there's a lot of profiles on the web and a lot of people who want to find love and that I'll use it. They give me. Like, you know, age boundaries, you know, education level location. Yeah. I mean, you want to, what, what do you want to do with data? You want to find like a partner who matches this education level, who like, you know, maybe has written about these types of topics before. Like if you could get a list of all the people like that, like, I think you will unblock a lot of people. I mean, there, I mean, I think this is a very Silicon Valley view of dating for sure. And I'm, I'm well aware of that, but it's just an interesting application of like, you know, I would love to meet like an intellectual partner, um, who like shares a lot of ideas. Yeah. Like if you could do that through better search and yeah.Swyx [00:28:48]: But what is it with Jeff? Jeff has already set me up with a few people. So like Jeff, I think it's my personal exit.Will [00:28:55]: my mom's actually a matchmaker and has got a lot of married. Yeah. No kidding. Yeah. Yeah. Search is built into the book. It's in your jeans. Yeah. Yeah.Swyx [00:29:02]: Yeah. Other than dating, like I know you're having quite some success in colleges. I would just love to map out some more use cases so that our listeners can just use those examples to think about use cases for XR, right? Because it's such a general technology that it's hard to. Uh, really pin down, like, what should I use it for and what kind of products can I build with it?Will [00:29:20]: Yeah, sure. So, I mean, there are so many applications of XR and we have, you know, many, many companies using us for very diverse range of use cases, but I'll just highlight some interesting ones. Like one customer, a big customer is using us to, um, basically build like a, a writing assistant for students who want to write, uh, research papers. And basically like XR will search for, uh, like a list of research papers related to what the student is writing. And then this product has. Has like an LLM that like summarizes the papers to basically it's like a next word prediction, but in, uh, you know, prompted by like, you know, 20 research papers that X has returned. It's like literally just doing their homework for them. Yeah. Yeah. the key point is like, it's, it's, uh, you know, it's, it's, you know, research is, is a really hard thing to do and you need like high quality content as input.Swyx [00:30:08]: Oh, so we've had illicit on the podcast. I think it's pretty similar. Uh, they, they do focus pretty much on just, just research papers and, and that research. Basically, I think dating, uh, research, like I just wanted to like spell out more things, like just the big verticals.Will [00:30:23]: Yeah, yeah, no, I mean, there, there are so many use cases. So finance we talked about, yeah. I mean, one big vertical is just finding a list of companies, uh, so it's useful for VCs, like you said, who want to find like a list of competitors to a specific company they're investigating or just a list of companies in some field. Like, uh, there was one VC that told me that him and his team, like we're using XR for like eight hours straight. Like, like that. For many days on end, just like, like, uh, doing like lots of different queries of different types, like, oh, like all the companies in AI for law or, uh, all the companies for AI for, uh, construction and just like getting lists of things because you just can't find this information with, with traditional search engines. And then, you know, finding companies is also useful for, for selling. If you want to find, you know, like if we want to find a list of, uh, writing assistants to sell to, then we can just, we just use XR ourselves to find that is actually how we found a lot of our customers. Ooh, you can find your own customers using XR. Oh my God. I, in the spirit of. Uh, using XR to bolster XR, like recruiting is really helpful. It is really great use case of XR, um, because we can just get like a list of, you know, people who thought about search and just get like a long list and then, you know, reach out to those people.Swyx [00:31:29]: When you say thought about, are you, are you thinking LinkedIn, Twitter, or are you thinking just blogs?Will [00:31:33]: Or they've written, I mean, it's pretty general. So in that case, like ideally XR would return like the, the really blogs written by people who have just. So if I don't blog, I don't show up to XR, right? Like I have to blog. well, I mean, you could show up. That's like an incentive for people to blog.Swyx [00:31:47]: Well, if you've written about, uh, search in on Twitter and we, we do, we do index a bunch of tweets and then we, we should be able to service that. Yeah. Um, I mean, this is something I tell people, like you have to make yourself discoverable to the web, uh, you know, it's called learning in public, but like, it's even more imperative now because otherwise you don't exist at all.Will [00:32:07]: Yeah, no, no, this is a huge, uh, thing, which is like search engines completely influence. They have downstream effects. They influence the internet itself. They influence what people. Choose to create. And so Google, because they're a keyword based search engine, people like kind of like keyword stuff. Yeah. They're, they're, they're incentivized to create things that just match a lot of keywords, which is not very high quality. Uh, whereas XR is a search algorithm that, uh, optimizes for like high quality and actually like matching what you mean. And so people are incentivized to create content that is high quality, that like the create content that they know will be found by the right person. So like, you know, if I am a search researcher and I want to be found. By XR, I should blog about search and all the things I'm building because, because now we have a search engine like XR that's powerful enough to find them. And so the search engine will influence like the downstream internet in all sorts of amazing ways. Yeah. Uh, whatever the search engine optimizes for is what the internet looks like. Yeah.Swyx [00:33:01]: Are you familiar with the term? McLuhanism? No, it's not. Uh, it's this concept that, uh, like first we shape tools and then the tools shape us. Okay. Yeah. Uh, so there's like this reflexive connection between the things we search for and the things that get searched. Yes. So like once you change the tool. The tool that searches the, the, the things that get searched also change. Yes.Will [00:33:18]: I mean, there was a clear example of that with 30 years of Google. Yeah, exactly. Google has basically trained us to think of search and Google has Google is search like in people's heads. Right. It's one, uh, hard part about XR is like, uh, ripping people away from that notion of search and expanding their sense of what search could be. Because like when people think search, they think like a few keywords, or at least they used to, they think of a few keywords and that's it. They don't think to make these like really complex paragraph long requests for information and get a perfect list. ChatGPT was an interesting like thing that expanded people's understanding of search because you start using ChatGPT for a few hours and you go back to Google and you like paste in your code and Google just doesn't work and you're like, oh, wait, it, Google doesn't do work that way. So like ChatGPT expanded our understanding of what search can be. And I think XR is, uh, is part of that. We want to expand people's notion, like, Hey, you could actually get whatever you want. Yeah.Alessio [00:34:06]: I search on XR right now, people writing about learning in public. I was like, is it gonna come out with Alessio? Am I, am I there? You're not because. Bro. It's. So, no, it's, it's so about, because it thinks about learning, like in public, like public schools and like focuses more on that. You know, it's like how, when there are like these highly overlapping things, like this is like a good result based on the query, you know, but like, how do I get to Alessio? Right. So if you're like in these subcultures, I don't think this would work in Google well either, you know, but I, I don't know if you have any learnings.Swyx [00:34:40]: No, I'm the first result on Google.Alessio [00:34:42]: People writing about learning. In public, you're not first result anymore, I guess.Swyx [00:34:48]: Just type learning public in Google.Alessio [00:34:49]: Well, yeah, yeah, yeah, yeah. But this is also like, this is in Google, it doesn't work either. That's what I'm saying. It's like how, when you have like a movement.Will [00:34:56]: There's confusion about the, like what you mean, like your intention is a little, uh. Yeah.Alessio [00:35:00]: It's like, yeah, I'm using, I'm using a term that like I didn't invent, but I'm kind of taking over, but like, they're just so much about that term already that it's hard to overcome. If that makes sense, because public schools is like, well, it's, it's hard to overcome.Will [00:35:14]: Public schools, you know, so there's the right solution to this, which is to specify more clearly what you mean. And I'm not expecting you to do that, but so the, the right interface to search is actually an LLM.Swyx [00:35:25]: Like you should be talking to an LLM about what you want and the LLM translates its knowledge of you or knowledge of what people usually mean into a query that excellent uses, which you have called auto prompts, right?Will [00:35:35]: Or, yeah, but it's like a very light version of that. And really it's just basically the right answer is it's the wrong interface and like very soon interface to search and really to everything will be LLM. And the LLM just has a full knowledge of you, right? So we're kind of building for that world. We're skating to where the puck is going to be. And so since we're moving to a world where like LLMs are interfaced to everything, you should build a search engine that can handle complex LLM queries, queries that come from LLMs. Because you're probably too lazy, I'm too lazy too, to write like a whole paragraph explaining, okay, this is what I mean by this word. But an LLM is not lazy. And so like the LLM will spit out like a paragraph or more explaining exactly what it wants. You need a search engine that can handle that. Traditional search engines like Google or Bing, they're actually... Designed for humans typing keywords. If you give a paragraph to Google or Bing, they just completely fail. And so Exa can handle paragraphs and we want to be able to handle it more and more until it's like perfect.Alessio [00:36:24]: What about opinions? Do you have lists? When you think about the list product, do you think about just finding entries? Do you think about ranking entries? I'll give you a dumb example. So on Lindy, I've been building the spot that every week gives me like the top fantasy football waiver pickups. But every website is like different opinions. I'm like, you should pick up. These five players, these five players. When you're making lists, do you want to be kind of like also ranking and like telling people what's best? Or like, are you mostly focused on just surfacing information?Will [00:36:56]: There's a really good distinction between filtering to like things that match your query and then ranking based on like what is like your preferences. And ranking is like filtering is objective. It's like, does this document match what you asked for? Whereas ranking is more subjective. It's like, what is the best? Well, it depends what you mean by best, right? So first, first table stakes is let's get the filtering into a perfect place where you actually like every document matches what you asked for. No surgeon can do that today. And then ranking, you know, there are all sorts of interesting ways to do that where like you've maybe for, you know, have the user like specify more clearly what they mean by best. You could do it. And if the user doesn't specify, you do your best, you do your best based on what people typically mean by best. But ideally, like the user can specify, oh, when I mean best, I actually mean ranked by the, you know, the number of people who visited that site. Let's say is, is one example ranking or, oh, what I mean by best, let's say you're listing companies. What I mean by best is like the ones that have, uh, you know, have the most employees or something like that. Like there are all sorts of ways to rank a list of results that are not captured by something as subjective as best. Yeah. Yeah.Alessio [00:38:00]: I mean, it's like, who are the best NBA players in the history? It's like everybody has their own. Right.Will [00:38:06]: Right. But I mean, the, the, the search engine should definitely like, even if you don't specify it, it should do as good of a job as possible. Yeah. Yeah. No, no, totally. Yeah. Yeah. Yeah. Yeah. It's a new topic to people because we're not used to a search engine that can handle like a very complex ranking system. Like you think to type in best basketball players and not something more specific because you know, that's the only thing Google could handle. But if Google could handle like, oh, basketball players ranked by like number of shots scored on average per game, then you would do that. But you know, they can't do that. So.Swyx [00:38:32]: Yeah. That's fascinating. So you haven't used the word agents, but you're kind of building a search agent. Do you believe that that is agentic in feature? Do you think that term is distracting?Will [00:38:42]: I think it's a good term. I do think everything will eventually become agentic. And so then the term will lose power, but yes, like what we're building is agentic it in a sense that it takes actions. It decides when to go deeper into something, it has a loop, right? It feels different from traditional search, which is like an algorithm, not an agent. Ours is a combination of an algorithm and an agent.Swyx [00:39:05]: I think my reflection from seeing this in the coding space where there's basically sort of classic. Framework for thinking about this stuff is the self-driving levels of autonomy, right? Level one to five, typically the level five ones all failed because there's full autonomy and we're not, we're not there yet. And people like control. People like to be in the loop. So the, the, the level ones was co-pilot first and now it's like cursor and whatever. So I feel like if it's too agentic, it's too magical, like, like a, like a one shot, I stick a, stick a paragraph into the text box and then it spits it back to me. It might feel like I'm too disconnected from the process and I don't trust it. As opposed to something where I'm more intimately involved with the research product. I see. So like, uh, wait, so the earlier versions are, so if trying to stick to the example of the basketball thing, like best basketball player, but instead of best, you, you actually get to customize it with like, whatever the metric is that you, you guys care about. Yeah. I'm still not a basketballer, but, uh, but, but, you know, like, like B people like to be in my, my thesis is that agents level five agents failed because people like to. To kind of have drive assist rather than full self-driving.Will [00:40:15]: I mean, a lot of this has to do with how good agents are. Like at some point, if agents for coding are better than humans at all tests and then humans block, yeah, we're not there yet.Swyx [00:40:25]: So like in a world where we're not there yet, what you're pitching us is like, you're, you're kind of saying you're going all the way there. Like I kind of, I think all one is also very full, full self-driving. You don't get to see the plan. You don't get to affect the plan yet. You just fire off a query and then it goes away for a couple of minutes and comes back. Right. Which is effectively what you're saying you're going to do too. And you think there's.Will [00:40:42]: There's a, there's an in-between. I saw. Okay. So in building this product, we're exploring new interfaces because what does it mean to kick off a search that goes and takes 10 minutes? Like, is that a good interface? Because what if the search is actually wrong or it's not exactly, exactly specified to what you mean, which is why you get previews. Yeah. You get previews. So it is iterative, but ultimately once you've specified exactly what you mean, then you kind of do just want to kick off a batch job. Right. So perhaps what you're getting at is like, uh, there's this barrier with agents where you have to like explain the full context of what you mean, and a lot of failure modes happen when you have, when you don't. Yeah. There's failure modes from the agent, just not being smart enough. And then there's failure modes from the agent, not understanding exactly what you mean. And there's a lot of context that is shared between humans that is like lost between like humans and, and this like new creature.Alessio [00:41:32]: Yeah. Yeah. Because people don't know what's going on. I mean, to me, the best example of like system prompts is like, why are you writing? You're a helpful assistant. Like. Of course you should be an awful, but people don't yet know, like, can I assume that, you know, that, you know, it's like, why did the, and now people write, oh, you're a very smart software engineer, but like, you never made, you never make mistakes. Like, were you going to try and make mistakes before? So I think people don't yet have an understanding, like with, with driving people know what good driving is. It's like, don't crash, stay within kind of like a certain speed range. It's like, follow the directions. It's like, I don't really have to explain all of those things. I hope. But with. AI and like models and like search, people are like, okay, what do you actually know? What are like your assumptions about how search, how you're going to do search? And like, can I trust it? You know, can I influence it? So I think that's kind of the, the middle ground, like before you go ahead and like do all the search, it's like, can I see how you're doing it? And then maybe help show your work kind of like, yeah, steer you. Yeah. Yeah.Will [00:42:32]: No, I mean, yeah. Sure. Saying, even if you've crafted a great system prompt, you want to be part of the process itself. Uh, because the system prompt doesn't, it doesn't capture everything. Right. So yeah. A system prompt is like, you get to choose the person you work with. It's like, oh, like I want, I want a software engineer who thinks this way about code. But then even once you've chosen that person, you can't just give them a high level command and they go do it perfectly. You have to be part of that process. So yeah, I agree.Swyx [00:42:58]: Just a side note for my system, my favorite system, prompt programming anecdote now is the Apple intelligence system prompt that someone, someone's a prompt injected it and seen it. And like the Apple. Intelligence has the words, like, please don't, don't hallucinate. And it's like, of course we don't want you to hallucinate. Right. Like, so it's exactly that, that what you're talking about, like we should train this behavior into the model, but somehow we still feel the need to inject into the prompt. And I still don't even think that we are very scientific about it. Like it, I think it's almost like cargo culting. Like we have this like magical, like turn around three times, throw salt over your shoulder before you do something. And like, it worked the last time. So let's just do it the same time now. And like, we do, there's no science to this.Will [00:43:35]: I do think a lot of these problems might be ironed out in future versions. Right. So, and like, they might, they might hide the details from you. So it's like, they actually, all of them have a system prompt. That's like, you are a helpful assistant. You don't actually have to include it, even though it might actually be the way they've implemented in the backend. It should be done in RLE AF.Swyx [00:43:52]: Okay. Uh, one question I was just kind of curious about this episode is I'm going to try to frame this in terms of this, the general AI search wars, you know, you're, you're one player in that, um, there's perplexity, chat, GPT, search, and Google, but there's also like the B2B side, uh, we had. Drew Houston from Dropbox on, and he's competing with Glean, who've, uh, we've also had DD from, from Glean on, is there an appetite for Exa for my company's documents?Will [00:44:19]: There is appetite, but I think we have to be disciplined, focused, disciplined. I mean, we're already taking on like perfect web search, which is a lot. Um, but I mean, ultimately we want to build a perfect search engine, which definitely for a lot of queries involves your, your personal information, your company's information. And so, yeah, I mean, the grandest vision of Exa is perfect search really over everything, every domain, you know, we're going to have an Exa satellite, uh, because, because satellites can gather information that, uh, is not available publicly. Uh, gotcha. Yeah.Alessio [00:44:51]: Can we talk about AGI? We never, we never talk about AGI, but you had, uh, this whole tweet about, oh, one being the biggest kind of like AI step function towards it. Why does it feel so important to you? I know there's kind of like always criticism and saying, Hey, it's not the smartest son is better. It's like, blah, blah, blah. What? You choose C. So you say, this is what Ilias see or Sam see what they will see.Will [00:45:13]: I've just, I've just, you know, been connecting the dots. I mean, this was the key thing that a bunch of labs were working on, which is like, can you create a reward signal? Can you teach yourself based on a reward signal? Whether you're, if you're trying to learn coding or math, if you could have one model say, uh, be a grading system that says like you have successfully solved this programming assessment and then one model, like be the generative system. That's like, here are a bunch of programming assessments. You could train on that. It's basically whenever you could create a reward signal for some task, you could just generate a bunch of tasks for yourself. See that like, oh, on two of these thousand, you did well. And then you just train on that data. It's basically like, I mean, creating your own data for yourself and like, you know, all the labs working on that opening, I built the most impressive product doing that. And it's just very, it's very easy now to see how that could like scale to just solving, like, like solving programming or solving mathematics, which sounds crazy, but everything about our world right now is crazy.Alessio [00:46:07]: Um, and so I think if you remove that whole, like, oh, that's impossible, and you just think really clearly about like, what's now possible with like what, what they've done with O1, it's easy to see how that scales. How do you think about older GPT models then? Should people still work on them? You know, if like, obviously they just had the new Haiku, like, is it even worth spending time, like making these models better versus just, you know, Sam talked about O2 at that day. So obviously they're, they're spending a lot of time in it, but then you have maybe. The GPU poor, which are still working on making Lama good. Uh, and then you have the follower labs that do not have an O1 like model out yet. Yeah.Will [00:46:47]: This kind of gets into like, uh, what will the ecosystem of, of models be like in the future? And is there room is, is everything just gonna be O1 like models? I think, well, I mean, there's definitely a question of like inference speed and if certain things like O1 takes a long time, because that's the thing. Well, I mean, O1 is, is two things. It's like one it's it's use it's bootstrapping itself. It's teaching itself. And so the base model is smarter. But then it also has this like inference time compute where it could like spend like many minutes or many hours thinking. And so even the base model, which is also fast, it doesn't have to take minutes. It could take is, is better, smarter. I believe all models will be trained with this paradigm. Like you'll want to train on the best data, but there will be many different size models from different, very many different like companies, I believe. Yeah. Because like, I don't, yeah, I mean, it's hard, hard to predict, but I don't think opening eye is going to dominate like every possible LLM for every possible. Use case. I think for a lot of things, like you just want the fastest model and that might not involve O1 methods at all.Swyx [00:47:42]: I would say if you were to take the exit being O1 for search, literally, you really need to prioritize search trajectories, like almost maybe paying a bunch of grad students to go research things. And then you kind of track what they search and what the sequence of searching is, because it seems like that is the gold mine here, like the chain of thought or the thinking trajectory. Yeah.Will [00:48:05]: When it comes to search, I've always been skeptical. I've always been skeptical of human labeled data. Okay. Yeah, please. We tried something at our company at Exa recently where me and a bunch of engineers on the team like labeled a bunch of queries and it was really hard. Like, you know, you have all these niche queries and you're looking at a bunch of results and you're trying to identify which is matched to query. It's talking about, you know, the intricacies of like some biological experiment or something. I have no idea. Like, I don't know what matches and what, what labelers like me tend to do is just match by keyword. I'm like, oh, I don't know. Oh, like this document matches a bunch of keywords, so it must be good. But then you're actually completely missing the meaning of the document. Whereas an LLM like GB4 is really good at labeling. And so I actually think like you just we get by, which we are right now doing using like LLM

KU has a matchup tonight. We get some 'Hawk talk with our insider, Shreyas Laddha.

As India's economic landscape evolves amid population growth, crafting regulations that foster progress is critical. But how can India leverage its advanced digital infrastructure and young workforce to drive innovation and create sustainable job opportunities? What regulatory reforms could help create an environment that encourages start-up growth and technological entrepreneurship? And how do proposed reforms like the draft Digital Competition Bill shape up? In this conversation, Shane Tews is joined by Shruti Rajagopalan and Shreyas Narla. Shruti is a senior research fellow at the Mercatus Center and a fellow at the Classical Liberal Institute at the New York University School of Law. She leads the India Political Economy program and Emergent Ventures India at Mercatus. Shreyas is a research scholar with the India Political Economy program at Mercatus. Join us as they explore the challenges regulatory frameworks pose, the importance of digital infrastructure, and the need for reforms to foster innovation and growth in India's economy.

Join Shreyas Manohar, Ayushi Amin, and Cyrus for a laugh-out-loud Cock & Bull episode as they tackle the explosive Adani bribe controversy, the excitement of the India vs. Australia Test Series, and Jaguar's bold new rebrand. They also dive into hot topics like the COP29 climate finance drama, Zomato's surprising job offer policy, and a bizarre robot heist in Shanghai. Packed with humor, sharp insights, and witty banter, this episode delivers a perfect mix of trending news and comedy. Don't miss the fun—watch now and stay entertained!See omnystudio.com/listener for privacy information.

The Asiatic caracal is India's rarest wild cat. In fact, with the population estimated to be less than 100, the caracal is on the verge of becoming the second extinct species of cat in India after the recently reintroduced cheetah.In Episode 7, I explore the ecology and conservation of this fascinating cat with Shreyas Vijay, the founder of the Caracal Conservation and Research Project (CCRP). We discuss his research on mapping the population and distribution of caracals in India. Shreyas talks about the threats of human-caracal conflict and the illegal pet trade to caracals across its range in Asia and Africa. Tune in to learn more about the future of caracals in India.The Think Wildlife Podcast is also available on iTunes, Spotify and YouTube. Please do consider upgrading to an optional paid subscription on Substack. 30% of the revenue from this episode will be donated to the CCRP!Meet the HostAnish Banerjee: https://x.com/anishwildlifeThink Wildlife Foundation: https://thinkwildlifefoundation.com/Meet the GuestsShreyas Vijay: https://shreyasvijay11.wixsite.com/indiaRecommended Wildlife Conservation BooksWildlife Conservation in India by HS Pabla: https://amzn.to/3Ypx9ZhIndian Mammals: A Field Guide by Vivek Menon: https://amzn.to/4fhMiCLAt the Feet of Living Things by Aparajita Datta: https://amzn.to/3BZmtsN Get full access to The Think Wildlife Podcast at anishbanerjee.substack.com/subscribe

I'm joined by product leader turned founder Shreyas Doshi to discuss the founder's journey and when starting a company might be the right move for you. We challenge common startup wisdom and explore why traditional career advice often misses the mark for aspiring founders.Key topics:Which PMs are secretly great founder materialRed flags: when founding isn't for youThe right time to start your companyKey skills that set you up for successThriving in ambiguityDebunking the "venture scale or bust" mindsetThe counterintuitive way to avoid burnoutMuch moreReferenced:4 questions Shreyas wished he'd asked himself sooner: https://www.youtube.com/watch?v=atS060bNpE0&t=698sAvoiding burnout for high achievers: https://www.youtube.com/watch?v=5Iwymgai-ZMCrafting a compelling career story: https://www.youtube.com/watch?v=0Reh9wTUIYc&t=928sImproving your product sense: https://maven.com/shreyas-doshi/product-senseIs my next job at a startup or big tech?: https://www.youtube.com/watch?v=H59CRHQ6va0&t=2053sManaging your PM career in 2025 and beyond: https://maven.com/shreyas-doshi/product-management-career?utm_source=lennyNikhyl's career advice highlights at Lenny's Summit: https://www.youtube.com/watch?v=rujK7HvD6es&t=124sStage of Company: https://www.youtube.com/watch?v=H59CRHQ6va0&t=2053sWhere to find Shreyas:Twitter/XLinkedInYouTubeWhere to find Nikhyl:Twitter/XLinkedInFind The Skip:WebsiteSubstackYouTubeSpotifyApple PodcastsTikTokDon't forget to subscribe to The Skip to hear me coach you through timely career lessons. If you're interested in joining me on a future call, send me a note on LinkedIn, Threads, or Twitter. You can also email me at nikhyl@skip.communityTimestamps(00:00) Teaser: Why average PMs can make great founders(01:17) Introduction(02:34) The essential first step before founding(06:21) Successful founder traits(10:48) Managing at scale vs managing uncertainty(18:25) Thriving in ambiguity(21:29) Red flags: when founding isn't for you(23:34) The surprising link between average PMs and founding success(25:51) Building better product sense(29:35) The right time to start your company(37:03) Beyond venture scale: rethinking startup success(44:26) A guide to avoiding burnout(49:23) The real truth about big tech working hours(52:27) Why taking a "demotion" might be a good move(58:45) Learn more: Shreyas' Product Sense course(63:15) Key takeaways(65:22) Get in touch with Nikhyl

Today on Send It In, Shreyas Laddha joined the show to talk Kansas Basketball and Football, PJ Glasser then hits the following topics: College Football Week 12 Picks, NFL Week 11 Slate Preview, SEC College Basketball Futures! Lucy Burdge joins the show with BetQL 5-Star Plays! PJ then wraps up the show with his BEST BETS! To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

KSHMR is back with a fresh #DharmaRadio featuring his new remix of Kratex & Shreyas' "Taambdi Chaamdi" as well as new music from The MVI, Diego Miranda & Mëlbëc, Martin Garrix & Sem Vox, Maddix & Sander van Doorn, Argy, GENESI, David Guetta and many more!  

KSHMR is back with a fresh #DharmaRadio featuring his new remix of Kratex & Shreyas' "Taambdi Chaamdi" as well as new music from The MVI, Diego Miranda & Mëlbëc, Martin Garrix & Sem Vox, Maddix & Sander van Doorn, Argy, GENESI, David Guetta and many more!

Shreyas Doshi is a former product leader at Stripe, Twitter, Google, and Yahoo. He's now a full-time advisor and coach to founders and executives. Shreyas is known for his incredibly insightful writing on products, which has garnered him a passionate following in the PM and startup community. Last week, we sat down together at the very first Lenny and Friends Summit in San Francisco for a special live episode. We covered:• Why product leaders often feel overwhelmed with work, and how to combat it• The importance of developing good taste, and how to do it• How to reduce frustration in your product leadership role• The critical skill of truly listening as a leader• Common pitfalls in annual planning and decision-making• Lots of laughs—To learn more from Shreyas, check out these courses:• Improving Your Product Sense: https://bit.ly/product-sense• Managing Your PM Career: https://bit.ly/pm-career-course—Brought to you by:• WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs• Paragon—Ship every SaaS integration your customers want• Vanta—Automate compliance. Simplify security—Find the transcript at: https://www.lennysnewsletter.com/p/shreyas-doshi-live—Where to find Shreyas Doshi:• X: https://x.com/shreyas• LinkedIn: https://www.linkedin.com/in/shreyasdoshi/• Threads: https://www.threads.net/@shreyas.threads• Linktree: https://linktr.ee/shreyasdoshi• YouTube: https://www.youtube.com/@ShreyasDoshiVideos—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction(05:35) Question one: Why am I so busy?(10:08) Annual planning as an example(16:48) Tactical tips for staying less busy(25:20) Question two: Do I actually have good taste?(38:09) Question three: Why does my job feel so frustrating?(43:29) Question four: Am I really listening?(44:35) Closing remarks—Referenced:• Shreyas Doshi on pre-mortems, the LNO framework, the three levels of product work, why most execution problems are strategy problems, and ROI vs. opportunity cost thinking: https://www.lennysnewsletter.com/p/episode-3-shreyas-doshi• LNO framework: https://twitter.com/shreyas/status/1492345184171945984• Time management techniques that actually work: https://www.lennysnewsletter.com/p/time-management-techniques-that-actually• Part 2: Time management techniques that actually work: https://www.lennysnewsletter.com/p/part-2-time-management-techniques• Eisenhower quote: https://www.brainyquote.com/quotes/dwight_d_eisenhower_164720• Stripe Connect: https://stripe.com/connect• Jeff Bezos explains one-way door decisions and two-way door decisions: https://www.youtube.com/watch?v=rxsdOQa_QkM• Spotify Is America's Most Loved Workplace: https://www.newsweek.com/2021/10/29/spotify-americas-most-loved-workplace-1639982.html• Shreyas on “thinking is cheap”: https://www.linkedin.com/posts/shreyasdoshi_thinking-is-very-cheap-doing-is-very-expensive-activity-7225237421813116929-Qzi3/• Good Product Strategy, Bad Product Strategy from Shreyas: https://x.com/shreyas/status/1244810075908128768• Shreyas on annual planning and metrics:https://x.com/shreyas/status/1302423854095036421https://x.com/shreyas/status/1304628719374544896• Jensen Huang on LinkedIn: https://www.linkedin.com/in/jenhsunhuang/• Patrick Mahomes's website: https://www.adidas.com/us/patrick_mahomes• Virat Kohli: https://en.wikipedia.org/wiki/Virat_Kohli• Reversible and Irreversible Decisions: https://fs.blog/reversible-irreversible-decisions/• Fail fast: https://en.wikipedia.org/wiki/Fail_fast• 3 levels of product work: https://twitter.com/shreyas/status/1370248637842812936• Shakespeare quote: https://nosweatshakespeare.com/quotes/famous/to-thine-own-self-be-true/• Rick Rubin: Legendary Music Producer | Lex Fridman Podcast #275: https://Dwww.youtube.com/watch?v=H_szemxPcTI• Blake Burge on Rick Ruben: https://x.com/blakeaburge/status/1794470295828341222• Rick Rubin on X: https://x.com/RickRubin• Dee Hock on X: https://x.com/deewhock• Dee Hock quote on listening: https://x.com/shreyas/status/1351279398423465984• Peter Drucker: https://en.wikipedia.org/wiki/Peter_Drucker• Peter Drucker quotes on listening: https://www.azquotes.com/author/4147-Peter_Drucker/tag/listening• Lenny's first podcast recording: https://www.youtube.com/watch?v=YP_QghPLG-8—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

With the October 31 deadline for IPL Player Retention fast approaching, it's time to take stock of whether Mumbai Indians can retain all its stars and the big names that could be released. Amol Karhadkar, The Hindu's sports journalist, joins Team Sports Katta's Aditya Joshi and reveals a new rule that could be the game-changer. Watch our retention picks in Weekly Katta and list your preferred retention for your favourite, if not all, IPL teams  आयपीएल प्लेअर रिटेन्शनसाठी ३१ ऑक्टोबरची अंतिम मुदत जवळ येत असताना, मुंबई इंडियन्स आपले सर्व स्टार्स स्वतःकडे ठेवू शकतात की नाही आणि कोणते मोठे खेळाडू लिलावात जाणं पसंत करतात, याचा आढावा घेण्याची वेळ आली आहे. अमोल कऱ्हाडकर, 'द हिंदू'चे क्रीडा पत्रकार, 'स्पोर्ट्स कट्टा'च्या दित्य जोशीशी चर्चा करताना एका नव्या नियमाबद्दल सांगत आहेत, ज्याने सगळा खेळच पालटू शकतो. 'वीकली कट्टा' मध्ये आमची निवड पहा आणि तुमच्या रिटेंशनची यादी कमेंट बॉक्समध्ये लिहा

Melissa sits down with Shreyas Laddha of the KC Star to talk about the upcoming game between TCU and Kansas. Jamie chats with D1 Baseball's Kendall Rogers about college baseball's unique position in the NIL and scholarship world, and what to look forward to about TCU baseball in 2025. Visit www.flyingtclub.com to learn more about the Flying T Club, TCU's NIL Collective.

In this episode of "The Heart of Yoga " Rosalind kicks off the Yogic Arts Series with a deep and enlightening conversation with artist and Yogini Melissa Forbes. They explore the intersection of art and spirituality through the study of Yantra, numerology, and Jyotish (Vedic astrology). Melissa shares her personal journey into sacred geometry and how these ancient traditions have shaped her practice, teaching, and artwork. Through this conversation, listeners are invited into the rich, intricate world of sacred Yogic arts and the deeper meaning behind these practices.     They discuss… The profound relationship between Yantra, numerology, and the energies of the planets, exploring how specific shapes, colors, and numbers influence one's life and spiritual journey. Melissa's background in sacred geometry and her teachings on how constructing Yantras can help align an individual's energy with cosmic forces. The influence of Melissa's teacher, Harish Johari, and the tradition of integrating chanting, numerology, and sacred geometry into the spiritual practice. Insights into the powerful energy of certain colors and planets, such as working with green and emerald to harness the healing energy of Mercury. The importance of natural movement and geometry in understanding the true essence of Yoga beyond its physical form.     Favorite Phrases: "The study of the macrocosm via the microcosm. Tantra offers a holistic approach to the universe through the lens of the individual." "The energy of the Yantras is all about alignment—through sound, color, and shape, we can tap into the deeper forces of the cosmos." "Working with a spiral in sacred geometry mirrors nature's own patterns; it's a theme that's as universal as the DNA in our bodies or the form of a fern unfurling."     Resources Mentioned: Books by Harish Johari: Numerology, Tantra, Ayurveda: https://www.simonandschuster.com/authors/Harish-Johari/410046710 https://melissaforbes.art/retreats You are invited to join Melissa on her Sacred Geometry luxury retreat in India, January 2025. Melissa leads an annual retreat at Shreyas, member of Relais & Chateaux, focusing on Sacred Geometry. An excellent opportunity to explore the inner dimensions and rekindle our creative energies, and direct it towards spiritual unfoldment. Yoga, Ayurveda and Art in a refined beautiful environment. Connect with Melissa on IG @melissaforbes8   Timestamps: [00:00:00] Introduction to the episode and guest, Melissa Forbes [00:01:00] Melissa shares her initial journey into Yoga and sacred geometry [00:03:00] Discussion on numerology and the relationship mandala [00:06:00] Exploring the deeper meaning of Yantra and the connection with planets [00:20:00] Transition into Jyotish and how numerology ties into personal energies [00:36:00] Retreat discussions and how participants interact with the Yogic arts [00:50:00] Personal anecdotes about Yuji and Melissa's experiences in sacred spaces [00:59:00] Conclusion and final reflections on art, spirituality, and cosmic energy

Discover how to learn Sanskrit in the easiest way with Uday Shreyas, a passionate advocate of this ancient language. In this insightful interview, Uday shares his journey of mastering Sanskrit and offers practical tips for beginners. Whether you're just starting or looking to deepen your knowledge, this video covers the best resources, learning techniques, and mindset needed to make Sanskrit learning accessible and enjoyable for everyone.

The anticipation was finally over as the BCCI released the Indian squad for the first Test against Bangladesh on a lazy and uneventful Sunday night on September 8. As expected, the regulars were all picked in the 16-member squad, which welcomed Rishabh Pant back into all-whites.While Pant had a great return in the Duleep Trophy, could the BCCI have given him more game time before making the call to have him back in the squad? Dhruv Jurel did a fine job behind the stumps during the England series and has shown he is a serious contender for the Australia squad.The big name missing from the squad list was Shreyas Iyer, who is back into the good books of BCCI after all the misunderstanding during the England series. However, the Indian batter seems out of the Test picture as KL Rahul has been brought back into the mix. Rahul is tipped to replace Sarfaraz Khan in the lineup for the 1st Test as the competition for the middle-order spot is heating up. Shreyas may need to take his game to the next level in the domestics, especially given the emergence of talents like Musheer Khan.Yash Dayal was one of the heartwarming stories from the squad announcement. He has completed a redemption arc for the ages. Dayal's inclusion comes as India looks to find their next consistent left-arm pacer to create a big talent pool.Regarding the lineup, the big toss-up will be between Kuldeep Yadav and Axar Patel. Will India go for more strength in their batting lineup, or will Kuldeep's proficiency give him the edge?Akshay Ramesh, Saurabh Kumar, and Alan Jose John dissect the big questions ahead of the first Test between India and Bangladesh.Tune in!Produced by Anna PriyadarshiniSound mix by Nitin Rawat

In this episode, I'm joined by product and leadership expert Shreyas Doshi to dissect Founder Mode, why it's appealing, and how to activate it. We dive deep into Paul Graham's recent essay which struck a chord in the tech community and resonated strongly with many founders and leaders.We also discuss:Why founder mode is a mindset, not a titleWhether founder mode is good or badBalancing detail and delegationThe importance of product sense and good judgmentMastering founder mode as a product leaderOperational insights from Meta and StripeReferenced:Crafting a compelling career story: https://www.youtube.com/watch?v=0Reh9wTUIYc&t=928sPaul Graham's "Founder Mode": https://paulgraham.com/foundermode.htmlShreyas' Tweet on founder mode': https://x.com/shreyas/status/1830767630917214318Six superpowers of product managers: https://www.youtube.com/watch?v=OnsSwHP3d-cShreyas' previous appearance on the podcast: https://www.youtube.com/watch?v=y_TnXtnY3QM&t=23sThe 3 types of product leaders: https://x.com/shreyas/status/1375491623308550144Where to find Shreyas:Twitter/XLinkedInYouTubeWhere to find Nikhyl:Twitter/XLinkedInFind The Skip:WebsiteSubstackYouTubeSpotifyApple PodcastsTikTokDon't forget to subscribe to The Skip to hear me coach you through timely career lessons. If you're interested in joining me on a future call, send me a note on LinkedIn, Threads, or Twitter. You can also email me at nikhyl@skip.communityTimestamps(00:00) Teaser: Unpacking founder mode(02:47) What Shreyas has been up to(04:04) Shreyas' take on founder mode(08:59) Why judgment and product sense is critical(13:04) Don't confuse founder mode with micromanagement(20:57) The key question for founders about ownership(23:01) Not every company needs a CPO or Head of Product(28:07) When product people should prioritize detail(31:32) What everyone gets wrong about Steve Jobs(34:22) Shreyas' observation about John and Patrick Collison(38:17) Nikhyl's observation about Mark Zuckerberg(40:05) Founder vs manager mode(43:40) Should leaders avoid manager mode?(48:17) Chase impact, not optics(57:00) The 3 types of product leaders(60:46) Aligning your environment, opinion, and superpower(63:10) Shreyas' non-consensus view on building product(67:52) Mastering founder mode as a PM(72:58) Getting in touch with Shreyas(74:49) How to find Nikhyl

FREE DOWNLOAD - https://www.mediafire.com/file/q17osjxzffkztt5/Taambdi+Chaamdi+-+(Vicky+Noise+Faktory)+-+Kratex,+Shreyas.wav.zip/file

Today's guests include two senior executives from Sanofi's manufacturing division, Head of Data & AI for Manufacturing & Supply Chain Kartik Pant and Head of AI & Data Products, Manufacturing & Supply Shreyas Becker, respectively. The pair join us on today's podcast to identify disconnects between marketing and supply chain workflows that commonly lead to issues such as stockouts and fulfillment delays. Later, they share insights on building what they describe as an ‘end-to-end control tower capability' for data tools to solve these and similar logistics challenges in life sciences spaces. If you're interested in unlocking our AI best practice guides, frameworks for AI ROI, and specific resources for AI consultants, visit emerj.com/p1.

Today on Send It In, Kate Constable talks with Shreyas Laddha about Kansas then she breaks down Kansas & Rams Season Previews on the Gridiron Glass. Matt joins the show to give out BetQL 5-Star Plays, and then Kate wraps up the show with his BEST BETS. To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

The Bulletproof Dental Podcast Episode 361   HOSTS: Dr. Peter Boulden, Dr. Craig Spodak GUEST: Shreyas Parab   DESCRIPTION In this conversation, Peter and Craig interview Shreyas Parab, an AI specialist, about the applications of AI in dentistry. They discuss the potential for AI to increase efficiency in various areas, such as revenue cycle management, chart notes, and phone calls. They also explore the idea of using AI to interact with insurance companies. They explore the benefits of AI receptionists, automated insurance verification, and claims processing. They also discuss the concept of a decentralized DSO and the use of AI in improving operational efficiency. The conversation highlights the importance of leveraging technology to enhance the patient experience and alleviate the burden on dental teams. Overall, the conversation emphasizes the positive impact of AI in dentistry and the potential for growth and innovation in the field. TAKEAWAYS AI can be used to increase efficiency in various areas of dentistry, such as revenue cycle management and chart notes. Using AI for phone calls, especially for interactions with insurance companies, can save time and reduce frustration for dental practices. AI is not meant to replace humans, but rather to augment their abilities and allow them to focus on tasks that require a human touch. AI can be used to automate various tasks in dentistry, such as receptionist duties, insurance verification, and claims processing. A decentralized DSO model, supported by AI technology, can level the playing field for solo dental practices. The rising tide of AI in dentistry can benefit the entire industry by sharing knowledge and improving processes. The potential of AI in dentistry is vast, and it offers opportunities for growth and innovation. CHAPTERS 00:00 - Introduction and Background 03:11 - The Energy and Mindset of the Bulletproof Community 06:02 - Understanding Large Language Models (LLMs) 09:28 - Applications of AI in Dentistry: Revenue Cycle Management 13:43 - Automating Phone Calls and Interactions with Insurance Companies 20:45 - Addressing Bottlenecks in Dental Practices with AI 25:20 - The Future of AI in Dentistry: Outbound Calls and Insurance Interactions 29:19 - The Benefits of AI in Dental Operations and Patient Care 34:58 - Automating Receptionist Duties 37:45 - The Rise of Decentralized DSOs 42:23 - AI and the Future of Dentistry 48:49 - Selling to a DSO and Decentralization 56:37 - Conclusion and Looking Ahead REFERENCES Bulletproof Mastermind Shreyas Parab  

Welcome to Ideas of India, where we examine academic ideas that can propel India forward. My name is Shruti Rajagopalan, and I am a senior research fellow at the Mercatus Center at George Mason University. Today my guests are Shreyas Narla and Kadambari Shah, who are my colleagues at the Mercatus Center and research scholars working with me on the 1991 Project.  We spoke about the kinds of policy change we would like to see in the coalition government led by Modi's in his third term. We talked about the research Shreyas, Kadambari and I have been working on in the areas of competition policy, regulating India's digital marketplace, labor law reforms, scaling India's manufacturing, streamlining GST, and much more.  Recorded July 1st, 2024. Read a full transcript enhanced with helpful links. Connect with Ideas of India Follow us on X Follow Shruti on X Follow Shreyas on X Follow Kadambari on X Click here for the latest Ideas of India episodes sent straight to your inbox. Timestamps (00:00:00) - Intro (00:01:31) - Past Budgets Announcements and Upcoming Budget (00:09:38) - Restarting reforms (00:22:56) - The Tinkering of Government (00:27:08) - Regulation of Big Tech Companies (00:51:35) - India's Labor Regulations (01:10:33) - Solutions to India's Regulatory Environment (01:20:27) - Outro

(00:00-25:46) – Query & Company opens on a Wednesday with Jimmy Cook, James Boyd, and Eddie Garrison by discussing the Indianapolis Colts with James primary job being a beat reporter for the horseshoe. The focus on their conversation surrounds listing some of their most irreplaceable Colts players heading into the 2024 season. They get into a debate if the Colts would be better with this season Joe Flacco compared to Gardner Minshew's 2023 season. (25:46-37:53) – Kansas City Star writer Shreyas Laddha joins Query & Company with Jimmy Cook and James Boyd to provide a little bit on context as to who the Indiana Pacers are getting in Johnny Furphy, shares what kind of professional basketball player Furphy can be, expresses how impressed he has been with what he has seen from Flory Bidunga so far, and reveals how the Jayhawks football team looks at Chris Ballard's son, Cole, as a backup quarterback. (37:53-44:21) – Hour one concludes with James and Jimmy discussing the Caitlin Clark effect with the Indiana Fever playing in front of a sold-out crowd at noon on a Wednesday in July. (44:21-1:13:14) – Hour number two with Jimmy Cook, James Boyd, and Eddie Garrison sharing their top three most irreplaceable Colts for the upcoming season that they gave in the opening segment of the show. They finish their top five by listing their final two along with explaining why. (1:13:14-1:32:14) – College football writer, Chris Kamrani, from The Athletic joins Jimmy Cook and James Boyd on Query & Company to explain the significance of EA Sports bringing back the College Football video game, states that this could be a fascinating season for college quarterbacks because there really isn't a consensus top quarterback right now, shares his thoughts on the expanded College Football Playoff, and reveals what he expects to see from Notre Dame and Indiana this upcoming season. (1:32:14-1:35:47) – Jimmy, James, and Eddie close out the second hour of the show by discussing their upcoming The Fan Back 9 Golf event! (1:35:47-2:01:21) – The final hour of the show with Jimmy Cook, James Boyd, and producer Eddie Garrison by discussing some of the Indianapolis Colts players that have the most to prove this upcoming season. Additionally, they debate if the rookie of the year award is a discussion between Caitlin Clark and Angel Reese with Reese extending her double-double streak this afternoon. (2:01:22-2:18:03) – Ben Golliver from the Washington Post joins Query & Company with Jimmy Cook and James Boyd from Las Vegas to share what he has seen from Team USA so far in the practices ahead of tonight's tune up game against Canada. Additionally, he shares his expectations for Tyrese Haliburton, predicts what the starting lineup could look like tonight, agrees that this is probably the last Olympics for LeBron James, Kevin Durant, and Steph Curry, and why LeBron decided to join this Olympic team considering his age and trying to manage his workload. (2:18:03-2:23:05) – Today's show ends with Jimmy sharing his JCook Plays of the Day! Plus, Jimmy, James, and Eddie rounding out their list of five Colts players with the most to prove!Support the show: https://1075thefan.com/query-and-company/See omnystudio.com/listener for privacy information.

Welcome to Cyrus Says! Kripya subscribe to the channel: https://www.youtube.com/c/CyrusSays Listen to the full audio episodes at: Spotify: https://spoti.fi/3AbBLqXApple Podcasts: https://apple.co/3BV5uWpGoogle Podcasts: https://bit.ly/3JMY7T2 Email your AMA questions to us at whatcyrussays@gmail.com Don't forget to follow Cyrus Says' official Instagram handle at @whatcyrussays[https://www.instagram.com/whatcyrussays/] Punit Pania - https://www.instagram.com/punitpania/Shreyas Manohar - https://www.instagram.com/shreyas_manohar/ Connect with Cyrus on socials: Instagram: https://www.instagram.com/cyrus_broacha/Twitter: https://twitter.com/Broacha_Cyrus Aur like, share, comment karna na bhule! #comedypodcast #livepodcastSee omnystudio.com/listener for privacy information.

This is the last episode of the #designersdigest series where we have Daniel Burka and co-host Shreyas Satish. We talk about blurring lines between product and design, the importance of being a generalist in design, and the role of product managers in the design process. This series is created by Audiogyan in partnership with @godrejdesignlab Designer's Digest series is about Design as a profession, its daily grind, the secrets to climbing the design career ladder, and what edge we'll need to thrive in the captivating world of design. Daniel is the director of product and design at the not-for-profit Resolve to Save Lives, where he spends most of his time on the open-source project, Simple. Simple is used by thousands of hospitals in India, Bangladesh, and Ethiopia to manage over 2 million patients with hypertension and diabetes. He is on the board of Laboratoria, a not-for-profit based in Peru helping Latin American women build successful careers in tech. In 2021, Daniel also started the open-source Health icons project to provide free icons to healthcare projects around the world. He is also a member of Adobe's Design Circle, which grants scholarships to a diverse group of designers each year. Previously at Google Ventures as a Design Partner, Co-founder of Milk.inc and SiverOrange, and more…   Questions At RTSL, You're a Director of both Product and Design. How do you distinguish between the two verticals daily, especially concerning concerns and metrics? Who is a Product manager and who is a designer according to you? Who according to you is supposed to focus on defining the right problem and then crafting the perfect solution? How blurred are these lines? What are the primary differences if I may ask? Seems like a designer can become a PM. Can it be the other way around? This is in the context of a few hard skills. A PM is torn between a thousand things from business to analytics and many other things. How can designers venture into this role? Also, can you steelman the case for a designer to become a PM? In a lot of companies, tech and design functions are both product reports, while in many these are separate verticals. In your experience what works best and when? One criticism of product managers, by folks like Marty Cagan, is that product managers often function as project managers. What in your view should a product manager focus on bringing to the table?* Designers in their romantic vision want to solve problems for all users. While Product folks go after those getting the dollars. Can you give any example from your experience where you have balanced it elegantly? What did it take? One death is a tragedy while a thousand deaths are statistics. How do you see this in the world of Product managers obsessed with data over real emotions? This is specifically for your work in healthcare. Some companies Like Airbnb have evolved their org structures to have Product Marketing Managers and Apple of course has Program Managers who report to a Product Director. Do you have a framework to think about organizational design with product teams, of course, knowing that different organizations have designed differently based on what they are focused on? What do you consider the key responsibilities of a product designer? Again, from tiny startups to large MNCs* You work on Simple, which is of course, primarily focused on creating impact. Can you talk a little bit about what it's like designing for social impact compared to increasing market share or profitability? In a digital landscape, how can we ensure our products create real value and positive impact beyond just solving problems? What is the future of Product Managers and Designers in the AI world? What does the career ladder look like? What skills do we acquire for the future of WWW? Reference links https://audiogyan.com/?type=wrtd-series https://audiogyan.com/2021/10/06/shreyas-satish/ https://twitter.com/shreyas_satish https://www.ownpath.com/about https://www.linkedin.com/in/shreyassatish/?originalSubdomain=in https://designup.school/teacher/daniel-burka/ https://library.gv.com/defense-against-the-dark-arts-of-design-a114e5f048bb https://iconscout.com/contributors/healthicons https://medium.com/@dburka https://x.com/dburka?lang=en https://www.instagram.com/dburka/ https://www.linkedin.com/in/dburka/?original_referer=https%3A%2F%2Fwww.google.com%2F&originalSubdomain=uk https://danielburka.com/ https://en.wikipedia.org/wiki/Daniel_Burka

This is the second episode of a 10 Part series, "Designer's Digest” with Hardik Pandya, Sr. VP of Design at Unacademy Group. This series is about Design as a profession, it's daily grind, the secrets to climbing the design career ladder and what edge we'll need to thrive in the captivating world of design. I have a co-host with me, Shreyas Satish. He is the founder of ownpath.com, ownpath is a platform for designers to upskill, find community, and unlock exciting opportunities, and also helps companies grow their design teams. I had Shreyas as a guest in episode 218 when I did a series “Where are the designers” talking to 12 top influential Design leaders from India. Hello Shreyas, welcome back on Audiogyan and also a welcome as my co-host And for today's episode which is also my domain of designing Digital products, we have a perfect guest and a common friend, Hardik Pandya. He is a Design leader with an innate love for building products with good design. Currently He is a Senior Vice President, Design of The Unacademy Group. Previously a Design Lead at Google Search, G Suite and Google Cloud, Ola and more.   Questions How did you get into Design? You are a lateral entrant? What were early days like? Can you walk us through your journey towards being a lead designer? Were there things that came fairly naturally, like taking ownership and initiative, and were things you had to deliberately learn? In No Career Conversations in Isolation, you write “The way to get to the work you want to be doing in the future is earning the trust of your manager. Are there any stories or examples you can share where earning that trust unlocked the opportunity you were looking for? Now that you are heading teams, how does your typical day look like? Do you happen to work hands-on still? From where and how do you hire? Do you look for talent laterally? How do you spot talent? Junior / nerdy / geeky / high end colleges / pedigree? Is hiring a gamble? What are some traits you look for when you're hiring a senior designer? How do you actually tell if they possess those traits? What are some common mistakes you see designers make with portfolios? Who have been your best hires and why? Which background did they come from? A lot of hiring conversations tend to be very backward looking i.e the work they've done, the situations they've been in and so on. But, I believe the real alpha, especially from a company's point of view is being able to gauge what they can do in the future. What's your take on this and how do you try to identify potential in designers? What skills do you expect from designers for the future in the world of AI? Reference links https://twitter.com/hvpandya https://www.linkedin.com/in/hardikpandya/?originalSubdomain=in https://medium.com/@hvpandya https://hardik.substack.com/ https://www.ownpath.com/ https://hvpandya.com/ https://www.instagram.com/godrejdesignlab/ https://www.godrejdesignlab.com/ https://www.youtube.com/playlist?list=PLrrt1Y8BkAyph0bmVRVsRF1UTgsf1Lxo9    

Today's guest is Shreyas Becker, Head of AI & Data Products, Manufacturing & Supply at Sanofi. Shreyas joins Emerj Senior Editor Matthew DeMello on today's podcast to talk about alleviating pain points for supply chain leaders in life sciences spaces. From building systems that deliver “real world evidence” to the subject matter experts and managers who need it to the pros and cons of sharing sensorial data with big tech data storage platforms like Amazon Web Services – Shreyas helps the executive podcast audience understand how problems are both viewed and solved through the lens of data, from no matter where they arise. If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

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Shreyas has worked as a product management leader at Stripe, Twitter, Google, and Yahoo. He has been a startup advisor to several companies including Airtable and Chainlink and has angel invested in many companies over the last decade. He is currently teaching a course through which he has coached more than 2,000 product managers. Follow Shreyas on X @shreyas and check out his course, "Managing your PM Career in 2024 and beyond": https://maven.com/shreyas-doshi/product-management-career [0:16] - Shreyas' transition from PM leader to advisor/coach/teacher [5:19] - Trading stories on health habits, experiences with back pain [11:32] - Shreyas' philosophy on and approach to time management [17:04] - Why Shreyas decided to create an online course and how he built the #1 course on Maven through a product-first approach [30:04] - The benefits of writing online, considering when to start [43:44] - The curse of brilliance [54:22] - The illusion of luck [1:00:13] - The power of a stack rank [1:12:40] - Using mental simulations to make complex decisions For more episodes, go to ⁠⁠podofjake.com⁠⁠. Previous guests include ⁠⁠Mark Cuban⁠⁠, ⁠⁠Vitalik Buterin⁠⁠, ⁠⁠Brian Armstrong⁠⁠, ⁠⁠Balaji Srinivasan⁠⁠, ⁠⁠Keith⁠⁠ ⁠⁠Rabois⁠⁠, ⁠⁠Ali Spagnola⁠⁠, ⁠⁠Anthony Pompliano⁠⁠, ⁠⁠Raoul Pal⁠⁠, ⁠⁠Julia Galef⁠⁠, ⁠⁠Jack Butcher⁠⁠, ⁠⁠Tim Draper⁠⁠, and over 100 others alike. Learn from founders and CEOs of companies like ⁠⁠OpenAI⁠⁠, ⁠⁠Coinbase⁠⁠, ⁠⁠Solana⁠⁠, ⁠⁠Polygon⁠⁠, ⁠⁠AngelList⁠⁠⁠, ⁠⁠Oura⁠⁠⁠, and ⁠⁠Replit⁠⁠, and investors from ⁠⁠Founders⁠⁠ ⁠⁠Fund⁠⁠, ⁠⁠a16z⁠⁠, ⁠⁠Union Square Ventures⁠⁠, and many more. I appreciate your support and hope you enjoy. Thanks to ⁠⁠⁠Chase Devens⁠⁠⁠ for the show notes and ⁠⁠⁠Yiction⁠⁠⁠ for the music. Lastly, I love hearing from fans of the pod. Feel free to email me any time at ⁠⁠jake@blogofjake.com⁠⁠. Thank you!

Welcome to Cyrus Says, Cock & Bull!Become a member of Club Cyrus SaysIn today's episode, Cyrus is joined by Aakash and Shreyas! Cyrus discusses the need for a new intro tune for the show.During the episode, Shreyas is late once again, and Aakash shares the story of his attempt to quit smoking. Topics discussed include Dawood Ibrahim, the mastermind behind the 1993 Mumbai blasts, reportedly 'poisoned' in Pakistan, and the Maharashtra government giving the go-ahead for building a new city called ‘Third Mumbai'.Tune in for this and much more!Subscribe to the Cyrus Says YouTube Channel for full video episodes!Follow Aakash on Instagram at @kuchbhimehtaFollow Shreyas on Instagram at @shreyas_manoharListen to Cyrus Says across Audio PlatformsApple Podcasts | Spotify | Google Podcasts | Gaana | Amazon Music | Jio SaavnEmail your AMA questions to us at whatcyrussays@gmail.comDon't forget to follow Cyrus Says' official Instagram handle at @whatcyrussaysConnect with Cyrus on socials:Instagram | TwitterAnd don't forget to rate us!-x-x-xDisclaimer: The views, opinions, and statements expressed in the episodes of the shows hosted on the IVM Podcasts network are solely those of the individual participants, hosts, and guests, and do not necessarily reflect the official policy or position of IVM Podcasts or its management. IVM Podcasts does not endorse or assume responsibility for any content, claims, or representations made by the participants during the shows. This includes, but is not limited to, the accuracy, completeness, or reliability of any information provided. Any reliance you place on such information is strictly at your own risk. IVM Podcasts is not liable for any direct, indirect, consequential, or incidental damages arising out of or in connection with the use or dissemination of the content featured in the shows. Listener discretion is advised.See omnystudio.com/listener for privacy information.

This Week in Startups is brought to you by… Vanta. Compliance and security shouldn't be a deal-breaker for startups to win new business. Vanta makes it easy for companies to get a SOC 2 report fast. TWiST listeners can get $1,000 off for a limited time at http://www.vanta.com/twist .Tech Domains has a new program called startups.tech, where you can get your startup featured on This Week in Startups. Go to http://www.startups.tech/jason to find out how! OpenPhone. Create business phone numbers for you and your team that work through an app on your smartphone or desktop. TWiST listeners can get an extra 20% off any plan for your first 6 months at https://openphone.com/twist * Today's show: Jason and Kelly Schricker introduce the Founder University pitch competition (1:11), then we hear from Abe at GolfGolf (7:16), Mircea from Cicada (15:25), Spencer of dotflo (27:16), and Shreyas from Clinic Assist (34:54). Finally, Jason awards a $25K investment (34:54), and much more! * Timestamps: (0:00) Jason kicks off the show (1:11) Jason and Kelly Schricker introduce the Founder University pitch competition (7:16) Abe pitches GolfGolf (13:31) Vanta - Get $1000 off your SOC 2 at http://www.vanta.com/twist (15:25) Mircea pitches Cicada (22:14) .Tech Domains - Apply to get your startup featured on This Week in Startups at http://www.startups.tech/jason (27:16) Spencer pitches dotflo (32:41) OpenPhone - Get 20% off your first six months at https://openphone.com/twist (34:54) Shreyas pitches Clinic Assist (43:52) Jason awards a $25K investment * Check out the pitch competitors here: GolfGolf - golfgolf.tech Cicada - cicadamusic.net Dotflo - dotflo.co Clinic Assist - clinicassist.ai * Thanks to our partners: (13:31) Vanta - Get $1000 off your SOC 2 at http://www.vanta.com/twist (22:14) .Tech Domains - Apply to get your startup featured on This Week in Startups at http://www.startups.tech/jason (32:41) OpenPhone - Get 20% off your first six months at https://openphone.com/twist • Follow Jason: X: https://twitter.com/jason Instagram: https://www.instagram.com/jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis Great 2023 interviews: Steve Huffman, Brian Chesky, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland Check out Jason's suite of newsletters: https://substack.com/@calacanis Follow TWiST: Substack: https://twistartups.substack.com Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin * Subscribe to the Founder University Podcast: https://www.founder.university/podcast

Welcome to Cyrus Says, Cock & Bull!Become a member of Club Cyrus SaysIn today's episode, Cyrus is joined by Shaad & Sheryas. Today, Cyrus starts the show with a writer's pun.During the episode, Shaad & Shreyas delve into the ongoing Open AI saga, providing their unique perspectives. Additionally, we get insights into some secretive gigs being carried out by the comedians of this age, our panelists. Topics discussed include the case registered against Shiv Sena UBT leader Aaditya Thackeray and the Air Force scrambling Rafales after a 'UFO' sighting near Imphal airport.Tune in for this and much more!Subscribe to the Cyrus Says YouTube Channel for full video episodes!Follow Shaad on Instagram at @shaadshafiFollow Shreyas on Instagram at @shreyas_manoharListen to Cyrus Says across Audio PlatformsApple Podcasts | Spotify | Google Podcasts | Gaana | Amazon Music | Jio SaavnEmail your AMA questions to us at whatcyrussays@gmail.comDon't forget to follow Cyrus Says' official Instagram handle at @whatcyrussaysConnect with Cyrus on socials:Instagram | TwitterAnd don't forget to rate us!-x-x-xDisclaimer: The views, opinions, and statements expressed in the episodes of the shows hosted on the IVM Podcasts network are solely those of the individual participants, hosts, and guests, and do not necessarily reflect the official policy or position of IVM Podcasts or its management. IVM Podcasts does not endorse or assume responsibility for any content, claims, or representations made by the participants during the shows. This includes, but is not limited to, the accuracy, completeness, or reliability of any information provided. Any reliance you place on such information is strictly at your own risk. IVM Podcasts is not liable for any direct, indirect, consequential, or incidental damages arising out of or in connection with the use or dissemination of the content featured in the shows. Listener discretion is advised.See omnystudio.com/listener for privacy information.

We talk the first semi final of #cwc23 as India beat New Zealand to enter the World Cup final. We'll talk tons by Kohli (record breaking 50th) and Shreyas Iyer and Mohammad Shami's 7 for 57.

Welcome to Cyrus Says, Cock & Bull!Become a member of Club Cyrus SaysIn today's episode, Cyrus is joined by Shreyas & Punit! Today, Cyrus kicks off the show by announcing Shreyas' funky pants as a gift for one lucky viewer of the show.During the show, Shreyas makes a long-awaited appearance and jokingly engages in a mock fight with one of our live audience members, while Punit hosts the show, maintaining complete political correctness (just kidding). Topics discussed include Narayana Murthy's suggestion of a 70-hour work week, Mumbai's iconic ‘Premier Padmini' taxis going off the road, and the Kerala serial blasts.Tune in for this and much more!Subscribe to the Cyrus Says YouTube Channel for full video episodes!Follow Shreyas on Instagram at @shreyas_manoharFollow Punit on Instagram at @punitpaniaListen to Cyrus Says across Audio PlatformsApple Podcasts | Spotify | Google Podcasts | Gaana | Amazon Music | Jio SaavnEmail your AMA questions to us at whatcyrussays@gmail.comDon't forget to follow Cyrus Says' official Instagram handle at @whatcyrussaysConnect with Cyrus on socials:Instagram | TwitterAnd don't forget to rate us!-x-x-xDisclaimer: The views, opinions, and statements expressed in the episodes of the shows hosted on the IVM Podcasts network are solely those of the individual participants, hosts, and guests, and do not necessarily reflect the official policy or position of IVM Podcasts or its management. IVM Podcasts does not endorse or assume responsibility for any content, claims, or representations made by the participants during the shows. This includes, but is not limited to, the accuracy, completeness, or reliability of any information provided. Any reliance you place on such information is strictly at your own risk. IVM Podcasts is not liable for any direct, indirect, consequential, or incidental damages arising out of or in connection with the use or dissemination of the content featured in the shows. Listener discretion is advised.See omnystudio.com/listener for privacy information.

Calling on more than two decades of experience working with some of the biggest companies in tech, Shreyas Doshi joins The Knowledge Project for a deep dive into the connection between building a solid team and building a better product. He also discusses the three levels of product work, the origins of conflict on your team, the difference between measurement and evaluation, the benefits and drawbacks of a writing culture, decision-making, growing your competence, and the agency/talent matrix. Doshi is best known as the leader of some of the most successful products from Stripe, where he was one of the company's first product managers. He also led and grew several products at Twitter, Google, and Yahoo. He currently advises fast-growing startups on strategy, scaling, and product management. Doshi is also a frequent angel investor and has privately coached product managers from Amazon, Meta, Salesforce, Uber, and LinkedIn. -- Want even more? Members get early access, hand-edited transcripts, member-only episodes, and so much more. Learn more here: https://fs.blog/membership/ Every Sunday our Brain Food newsletter shares timeless insights and ideas that you can use at work and home. Add it to your inbox: https://fs.blog/newsletter/ Follow Shane on Twitter at: https://twitter.com/ShaneAParrish Our Sponsors: MetaLab: Helping the world's top companies design, build, and ship amazing products and services. https://www.metalab.com Aeropress: Press your perfect cup, every time. https://aeropress.com