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Please Subscribe and Review: Apple Podcasts | RSS Submit your questions for the podcast here News Topic: Bernie Sanders on X El Gato Malo: a crisis of competence Show Notes: The Free Press: TGIF Hellfire Peptides and bioregulators Basis Health and Performance New York Questions:    Constant Sinus Congestion Craig writes: Hello, love the show and all that you do. Our oldest son is 18. We have another son (16) and a daughter (14). The oldest gets colds more often and more seriously than the other two. He seems to have sinus congestion that's nearly constant, needing to blow his nose in the morning and throughout the day. Sometimes a cough as well. He had an undescended testicle at birth and had surgery at age one. Could this contribute to his issues? He had herpetc whitlow and a secondary infection as a toddler and began getting cold sores on his lips a couple times per year since. Does this just set him up to have a weaker immune system than his siblings? His mother also gets cold sores but not always one per year. He eats a pretty typical teenage diet as do the other two, meaning too much sugar and snacks. Are there any foods in particular that may be more likely to contribute? He loves milk and drinks quite a bit. My wife looks in her Holistic Guide to Wellness book which confirms our suspicion to cut out most of that stuff and see the result, but he is a teen who knows it all, so of course he seems uninterested. Being mostly carnivor-ish ourselves it's frustrating. I'm hoping for some ideas or support from someone who isn't his parent to help him understand. Thanks again and keep up the good work! Craig   Peptide Therapy Jordan writes:  Good morning I have been a long term listener of your show. And chocolate salt is my favorite flavor. I am 45 and most likely dealing with hormone issues with the lack of progesterone.   I typically am carnivore with out much dairy.  I exercise at least 5 to 6 days per week.  Variety of crossfit , biking, strength training, hyrox.  However, I am a runner or at least that's what I envision doing.  I have had a long issue with knee and IT band.  On you last show you had mentioned peptide use.  I don't live far from bozeman just down the way in wyoming.  Where do you recommend getting your peptides from I am taking an oral pbc 157 but I am wanting to get the Nest product I can to see if it will help.  Also, I am interested in injections.  Anyways thank you both for all you do, I always look forward to new episodes! -Jordan Next Viral Wave Doug writes: Robb: I am a huge fan. In August, 2021 I got the COVID. My 18 yo son brought it home from his job. I spent a week feeling run down and achey, but still worked and trained normally. At the start of week two, I decided to stay home and try to rest. By the end of week two I was delirious with fever, could not stop coughing, O2 saturation levels were low (I was using a home finger monitor) and I feared death. The ambulance was summoned and I was admitted to the hospital where they treated my symptoms with standard modalities including anti-inflammatories, antibiotics, anti virals, oxygen nasal canula, etc. I did receive Remdesivir. I almost immediately began to feel better and 6 days later was discharged in an emaciated and weakened state with an Rx for home oxygen. My self directed home recovery regimen consisted of breathing exercises with an incentive spirometer, walking, sitting in sun and getting back to lifting as quickly as possible. Even still, I used supplemental oxygen for another 4 weeks until I couldn't deal with the nosebleeds any longer and quit it cold turkey. I was at that time a 51, highly active, well muscled, 15% bodyfat, Floridian with high levels of Vit.D and natural sun exposure, whole food eater, lower carb but not keto, meat/egg heavy diet, non-drinker, non smoker. Lifelong Barbell training for max strength in the big three, plus walks outside for cardio. No medications, no pre-existing conditions. Nurses told me they were shocked I was there with the 'Rona, given my obvious state of vigorous good health. Here we are, 5 years post Corona-19: do we know any damn thing about this disease and why some people got it bad and some people did not? My wife (51) sons (18, 16) and daughter (8) were also sick when I was, but bounced back quickly without issue. Why did I - who was/am 180 degrees from the fat/sick/diseased/aged phenotype - get hit by this so hard? I have excellent lungs and lung capacity, which makes me wonder if I have lung tissue that is more soft, tender, open, porous, etc. that allows for better gas exchange/utilization but, which pre-disposes me to infections like this? I don't know - I am reaching. As we face other pandemics - and they keep 'em coming given how successful C19 was in making political hay - how do I distinguish between hype and reality? Clearly, we can no longer count on our public health officials for a sane, sober and reasonable response and risk assessment. "Science? I am the Science!!!" I am concerned about the next viral wave and my personal health, because I see no reason for why I was vulnerable, but I was. Very. What the hell happened? I have zero regrets about not-having taken the mRNA shots, but I do trust and believe in the value/utility of real vaccines and modern medicine generally, despite the recent C19 tomfoolery. I do feel like I received excellent care when I was hospitalized in 2021 and am grateful for the care I received. I just don't want to ever have to receive it again. Help me, Robb Wolf. Feel free to ramble.   Sponsor: The Healthy Rebellion Radio is sponsored by our electrolyte company, LMNT. It's that time of year again…the days are getting shorter…temps are dropping…and warm beverages reign supreme! LMNT chocolate Medley is BACK! LMNT Chocolate Medley is for hot chocolate lovers everywhere…a hot cup of goodness WITH the electrolytes you need and WITHOUT all the sugar so common in hot winter beverages! The LMNT chocolate medley is a 30 count box containing: 10 sticks of Mint Chocolate, 10 sticks of Chocolate Raspberry, and 10 sticks of Chocolate Chai! As always, LMNT offers no-questions-asked refunds on all orders - so you can try the LMNT Chocolate Medley 100% risk free.  Click here to get your LMNT electrolytes  

1. Yaz - Nobody's Diary2. Men At Work - It's A Mistake3. Adam Ant - Desperate But Not Serious4. U2 - With Or Without You5. Cure - Lovecats6. Duran Duran - Union Of The Snake7. Depeche Mode - But Not Tonight8. Flock Of Seagulls - Nightmares9. Howard Jones - New Song10. Pet Shop Boys - West End Girls11. Berlin - The Metro12. INXS - The One Thing13. Level 42 - Something About You14. Oingo Boingo - On The Outside15. Spandau Ballet - Lifeline (12inch Version)16. Thompson Twins - In The Name Of Love17. Human League - Love Action (I Believe In Love)18. Smiths - Panic19. Joy Division - Love Will Tear Us Apartwww.djroyski.comwww.patreon.com/royskiwww.mixcloud.com/djroyskiwww.facebook.com/djroyskiwww.twitter.com/djroyski

1. Yaz - Nobody's Diary2. Men At Work - It's A Mistake3. Adam Ant - Desperate But Not Serious4. U2 - With Or Without You5. Cure - Lovecats6. Duran Duran - Union Of The Snake7. Depeche Mode - But Not Tonight8. Flock Of Seagulls - Nightmares9. Howard Jones - New Song10. Pet Shop Boys - West End Girls11. Berlin - The Metro12. INXS - The One Thing13. Level 42 - Something About You14. Oingo Boingo - On The Outside15. Spandau Ballet - Lifeline (12inch Version)16. Thompson Twins - In The Name Of Love17. Human League - Love Action (I Believe In Love)18. Smiths - Panic19. Joy Division - Love Will Tear Us Apartwww.djroyski.comwww.patreon.com/royskiwww.mixcloud.com/djroyskiwww.facebook.com/djroyskiwww.twitter.com/djroyski

AI have done many pictures of me from my pictures I had in my camera. I have paid for those AI pictures.

Send us a text182 Bonus Episode (Thoughts): 2025 Predictions - Part 12025 is here and just like last year, in this episode I talk with my sister-in-law about our predictions and what we are feeling for 2025. Next episode will be 2025-02-02  and a brand new meditation! If you like this podcast please share it with a friend! Share the Light. Spread the Light. Shine!LDS AND MEDIUM PODCAST BACKGROUND:I was born into the Church of Jesus Christ of Latter-Day Saints, or LDS for short. All my life I felt awkward and out of place, not because of my religion, but because of my spiritual gifts. It was hard to combine the idea of what first seemed like two separate worlds. So my whole life I have tried. I have learnt more about my gifts, and stayed faithful to my faith. But then I heard people left the church because it seems impossible to combine the two. So I am here, to try and mend the rift. To show that it is possible to be both LDS and to have, and use, our spiritual gifts. I am not alone on this journey, but my sister, who just is waking up to her spiritual gifts is by my side and she will also share her story.Jesus Christ, our older brother, was a healer. He asked us to love one another. So let´s follow in his footsteps. Please join me on this journey, let´s make a community of strong spiritual LDS, or whatever religion you belong to, and all work together to make this world a better place.If you like what I do, then you can find me here:https://linktr.ee/ldsandmediumIf you have words of support or stories that you anonymously want me to share please write me at ldsandmedium@gmail.com. I will try and answer all your emails BUT I am very busy with my life and I hope you can have some patience with me.If you would like access to the Podcast before it is released, all the bonuses and extra trainings and the meditation prayers as an mp3 you can support me on Patreon: https://www.patreon.com/ldsandmedium or send a Donation through Paypal. It will also be available as an archive that you can subscribe to on https://payhip.com/LDSandMediumDISCLAIMER: This Podcast is not official LDS doctrine, nor is it in any way financially supported by the LDS church. All the content is either our own personal thoughts and reflections or stories from our lives or the lives of others. Any quotes included will come from the Bible, The Book of Mormon or other scriptures, Church publications, hymns, General Conference or spiritual sites.Support the show

1. Yaz - Nobody's Diary2. Men At Work - It's A Mistake3. Adam Ant - Desperate But Not Serious4. U2 - With Or Without You5. Cure - Lovecats6. Duran Duran - Union Of The Snake7. Depeche Mode - But Not Tonight8. Flock Of Seagulls - Nightmares9. Howard Jones - New Song10. Pet Shop Boys - West End Girls11. Berlin - The Metro12. INXS - The One Thing13. Level 42 - Something About You14. Oingo Boingo - On The Outside15. Spandau Ballet - Lifeline (12inch Version)16. Thompson Twins - In The Name Of Love17. Human League - Love Action (I Believe In Love)18. Smiths - Panic19. Joy Division - Love Will Tear Us Apartwww.djroyski.comwww.patreon.com/royskiwww.mixcloud.com/djroyskiwww.facebook.com/djroyskiwww.twitter.com/djroyski

Vandaag aan de microfoon Bob Brekelmans, Brand Director, en Kristof Baeten, Chief Sales Officer van State of Art, een iconisch modemerk dat vakmanschap, stijl en innovatie moeiteloos combineert. In deze aflevering duiken we in de 37-jarige reis van State of Art, van hun oorsprong in een breifabriek in de Achterhoek tot het uitbouwen van een wereldwijd bekend merk dat mannen van kop tot teen kleedt.We bespreken hoe het merk zichzelf opnieuw uitvond, de balans vond tussen heritage en actualiteit, en een volledig vernieuwd winkelconcept lanceerde dat het DNA van State of Art ademt. Natuurlijk komen ook de klassieke Porsches aan bod – een knipoog naar hun rijke geschiedenis en een bron van inspiratie.Laat je inspireren door het verhaal van een merk dat met passie, vakmanschap en een dosis eigenzinnigheid relevant blijft in een continu veranderende markt. Een must-listen voor retailers, merkbouwers en ondernemers die zichzelf willen uitdagen!In deze podcast gaan we in gesprek met eigenzinnige retailerondernemers die zich met hun unieke visie weten onderscheiden in hun branche. Dat doen we om onze brede community van retailers en retailondernemers, #teamretail, te inspireren positief naar de toekomst te kijken. De podcast wordt gehost door Tim Gielen, oprichter van design studio Wave of Engagement die de winkel, showroom & hospitalityconcepten van morgen bedenken en ontwerpen.Heb je plannen, ideeën die je wil uitwerken contacteer ons dan op tim@waveofengagement.com Sponsor: https://solutions.dobit.com/

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

Join Richard Hutchinson and Andy Preston for the latest edition of Fantha Tracks Radio's Collecting Tracks. On this episode, Andy and Richard take a look at going back to school as a UK Star Wars fan. The rulers, rubbers, sharpeners and pencil cases of the 70's and 80's - there's plenty to discuss on the 13th Wave of Collecting Tracks. Remember to tune in to Good Morning Tatooine, LIVE Sunday and Thursday evenings at 9.00pm UK, 4.00pm Eastern and 1.00pm Pacific on Facebook, YouTube and Twitch and check out our Fantha Tracks Radio Friday Night Rotation every Friday at 7.00pm UK for new episodes of The Fantha From Down Under, Planet Leia, Desert Planet Discs, Start Your Engines, Collecting Tracks, Canon Fodder and special episodes of Making Tracks, and every Tuesday at 7.00pm UK time for your weekly episode of Making Tracks. You can contact any of our shows and send in your listeners questions by emailing radio@fanthatracks.com or comment on our social media feeds: https://www.youtube.com/@FanthaTracksTV/ https://links.fanthatracks.com/ www.instagram.com/fanthatracks www.facebook.com/FanthaTracks www.twitter.com/FanthaTracks www.pinterest.co.uk/fanthatracks/ www.fanthatracks.tumblr.com/ www.tiktok.com/@fanthatracks www.twitch.tv/fanthatracks www.threads.net/@FanthaTracks

Catch up with or listen again to Going Indieground broadcast on Mad Wasp Radio week commencing 7 January 2025. On this show you can hear San Carol – GoldenwingsThis Poison! – Driving Skills (Peel Session)Frank Tovey – Sam HallFauns – Shake Your HairBackwards Charm – Positive LinesResonars – Little Grey ManU2 -Stay (Faraway, So Close)March … Continue reading →

Es beginnt mit Philosophie und harten Drogen ... Herzlich willkommen zu einer ganz besonderen Episode eures Lieblings-Musikpodcasts! Lars und Markus werfen einen schwelgerischen, sehr persönlichen Blick zurück auf das musikalische Jahr 2024.

In this episode, we hear a passionate plea on behalf of a friend, as portrayed in Sangam Literary work, Kalithogai 121, penned by Nallanthuvanaar. The verse is situated in the 'Neythal' or 'Coastal Landscape' and depicts the state of a maiden with elements of nature.

The Upshift Guys are back to kick off 2025 and talk motorcycles and so much more. Did someone say Gold Wing, a possibility on this episode. Brian tells a story on himself. Do you give the motorcycle wave or just keep on rollin? A shovel head update a FXR update and a tribute to the founding fathers of The Motor Company Harley-Davidson. Thank you for supporting the Upshift Guys! Go check out Suspension Technologies USA and tell them we sent you. Happy New Year! The Upshift Guys. Brian & Brian

Today we talk to Spencer Macnaughton, founder and editor-in-chief, of the new Uncloseted Media online publication which features objective journalism at its finest.  The new publication is committed to providing the public with objective, nonpartisan, rigorous, LGBTQ-focused journalism that examines America's anti-LGBTQ ecosystem and elevates the voices of everyday American heroes. While doing deep-dive journalism that uncovers the people, the money, and the power behind America's anti-LGBTQ ecosystem and – at the same time – highlighting the voices of the people fighting back against these systems of ignorance and injustice, this is a publication for the LGBTQ community breaking out of the queer echo chamber to bring critically important stories to Americans in every county in all 50 states.  Uncloseted Media approaches its sources with a healthy combination of compassion and skepticism. They define balance not along partisan lines, but rather through a survey of methodologically sound, evidence-based research. Rather than providing opinions on important LGBTQ issues, they present facts through receipts, timelines, screenshots, and proof. They are on a mission to hold the people, the power, and the money behind America's anti-LGBTQ world accountable simply by digging in and presenting the public with the truth. Spencer has written and produced for 60 Minutes, The Wall Street Journal, The New York Times, Vice, Vox, Time, NBC News, The Guardian and Rolling Stone. he has won the Grace Award, and has been nominated for an Emmy. He teaches LGBTQ Journalism at New York University.  

Today we talk to Spencer Macnaughton, founder and editor-in-chief, of the new Uncloseted Media online publication which features objective journalism at its finest.  The new publication is committed to providing the public with objective, nonpartisan, rigorous, LGBTQ-focused journalism that examines America's anti-LGBTQ ecosystem and elevates the voices of everyday American heroes. While doing deep-dive journalism that uncovers the people, the money, and the power behind America's anti-LGBTQ ecosystem and – at the same time – highlighting the voices of the people fighting back against these systems of ignorance and injustice, this is a publication for the LGBTQ community breaking out of the queer echo chamber to bring critically important stories to Americans in every county in all 50 states.  Uncloseted Media approaches its sources with a healthy combination of compassion and skepticism. They define balance not along partisan lines, but rather through a survey of methodologically sound, evidence-based research. Rather than providing opinions on important LGBTQ issues, they present facts through receipts, timelines, screenshots, and proof. They are on a mission to hold the people, the power, and the money behind America's anti-LGBTQ world accountable simply by digging in and presenting the public with the truth. Spencer has written and produced for 60 Minutes, The Wall Street Journal, The New York Times, Vice, Vox, Time, NBC News, The Guardian and Rolling Stone. he has won the Grace Award, and has been nominated for an Emmy. He teaches LGBTQ Journalism at New York University.  

Wave From Cruise Ship Overturns Boat is the lead story on Wednesday Travel and Cruise Industry Podcast, January 8, 2025 with Chillie Falls. From the capsized vessel, 2 adults and 1 dog were rescued. Also today, Holland America to Expand Denali; NCLH Appoints Jason Montague As Chief Luxury Officer; Proposed Offer of Senior Notes; Sailing With The Temptations; Non-alcoholic bar menu; Huge Cell Bill; Regal Princess Cancellation; Sandra's Cruise Tours; Epic Drops Corfu; AIDA ship Skips Southampton; Propulsion Issue For Another Carnival Ship; NCL Amplifies Asia; and Lots more, live today at 11 AM EST. #wednesdaytravelandcruiseindustrynews #podcast #cruisenews #travelnews #cruise #travel #chilliescruises #chilliefalls #whill_us Thanks for visiting my channel. NYTimes The Daily, the flagship NYT podcast with a massive audience. "Vacationing In The Time Of Covid" https://nyti.ms/3QuRwOS To access the Travel and Cruise Industry News Podcast; https://cms.megaphone.fm/channel/trav... or go to https://accessadventure.net/ To subscribe: http://bit.ly/chi-fal I appreciate super chats or any other donation to support my channel. For your convenience, please visit: https://paypal.me/chillie9264?locale.... Chillie's Cruise Schedule: https://www.accessadventure.net/chillies-trip-calendar/ For your mobility needs, contact me, Whill.inc/US, at (844) 699-4455 use SRN 11137 or call Scootaround at 1.888.441.7575. Use SRN 11137. YouTube: https://www.youtube.com/ChilliesCruises Facebook: https://www.facebook.com/chillie.falls X: https://x.com/ChillieFalls Learn more about your ad choices. Visit megaphone.fm/adchoices

Hello! Still on our journey through. the Bible. Today's reflection is based on: Jeremiah 39-40, Judith 10-11, and Proverbs 17:9-12. As always, I encourage you to spend a few minutes reading and meditating on them, and then join me on the show right after!

Have you ever noticed how a single morning can spiral into either your best or worst day? Picture this: You wake up, stub your toe, spill your coffee, and suddenly - your entire day seems to cascade into a series of unfortunate events. Your car gets a flat tire, your meeting goes poorly, and your refrigerator mysteriously stops working. We've all experienced these days where everything seems to go wrong, one thing after another. But what if I told you this isn't just random bad luck? What if these events are actually connected by an invisible wave of energy that you unknowingly set in motion the moment you woke up? This isn't just a theory - it's a principle that's been understood by masters of reality creation for generations. Your morning energy creates a wave that ripples through your entire day, attracting similar experiences and circumstances. If negative energy can create a cascade of unwanted events, then imagine what's possible when you consciously create a wave of fortune, a surge of positive energy that attracts synchronicities, opportunities, and wonderful experiences throughout your day. I'm going to show you how to harness this powerful force. You're about to  ride your own wave of fortune - a practice so potent it can transforms every day into gateways of extraordinary possibilities.  

With the start of the new year, do you feel discouraged when you look at your sales figures for the year?  I understand how easily this can happen when you consistently think you are starting the year out with zero sales and you have a huge mountain to climb to get your sales to where you want them to be.   In today's podcast episode, I'm letting you in on a little secret to avoid that discouragement altogether.  It's a new way to think about your sales.  It will create a positive mindset that will have you bringing in additional sales before you know it.  My goal with this podcast episode is to help you feel more at ease with your sales figures when starting out the year and help you be more productive while moving forward this year.  After all, if you are like most business owners, your goal is to continuously increase your sales numbers, inspire more customers with the products and services you provide, and fulfill your goals.  Whether you are starting a business or side hustle, you're a self-employed individual, a solopreneur, entrepreneur, mompreneur, freelancer, small business owner, a remote, virtual, online, or in-house bookkeeper, or a virtual assistant or VA; I want you to incorporate this simple yet resourceful way to think about and monitor your sales not only at the beginning of the year but throughout the year as well.  These tips are essential whether you are using a computerized software system like QuickBooks, Xero, Wave, or FreshBooks for your business finances; or doing your bookkeeping manually with an Excel spreadsheet or even a Google Document… Schedule your Complimentary Stress Audit And Clarity Session, where we'll work together to create a clear and focused plan and overcome the obstacles that stand in your way so that you can move forward and immediately start enjoying your life with less stress, increased productivity, and more time to spend doing what you love with the people you care about: https://www.financialadventure.com/work-with-me Accountants, CPAs, Bookkeepers, Tax Preparers & Financial Professionals, sign up here to get updates on upcoming opportunities & grab the Audit Of Your Well-Being & Balance Guide here: https://www.financialadventure.com/accountant Ready to set up your business?  I have a program to help you get your business set up so that you can start making money.  Sign up for this program here: https://www.financialadventure.com/start Are you ready to try coaching?  Schedule an Introductory Coaching Session today.  You'll have the opportunity to see how you like coaching with an Introductory Coaching Session: https://www.financialadventure.com/intro Join us in the Mastering Your Small Business Finances PROFIT LAB if you are ready to take control of your business finances and create the profitable business you are striving for.  Are you ready to generate revenues and increase the profit in your business: https://www.financialadventure.com/profit If You Are Ready To Choose, Start Or Grow Your Side Hustle, Get Your Free Checklist And Assessment Here: https://www.financialadventure.com/sidehustle Grab Your FREE guide:  5 Essential Strategies For Stress-Free Bookkeeping: https://www.financialadventure.com/5essentials Your FREE Online Virtual Bookkeeping Business Starter Guide & Success Path Is Waiting For You: https://www.financialadventure.com/starterguide Join Our Facebook Community:  https://www.facebook.com/groups/womenbusinessownersultimatediybookkeepingboutique The Strategic Bookkeeping Academy, including Bookkeeping Basics, is open for registration!  You can learn more and sign up here: https://www.financialadventure.com/sba Looking for a payroll solution for your business?  You can get an exclusive 15% discount on your payroll services when you sign up here: https://www.financialadventure.com/adp QuickBooks Online - Save 30% Your First 6 Months: https://www.financialadventure.com/quickbooks Sign up for a virtual coffee chat to see if starting a Bookkeeping Business is right for you: https://www.financialadventure.com/discovery Show Notes:  https://www.financialadventure.com This podcast is sponsored by Financial Adventure, LLC ~ visit https://www.financialadventure.com for additional information and free resources.

D Forreal on Conscious Wave host Aphotik on Sub FM 26th November 2024 - https://www.sub.fm

Conscious Wave Christmas Eve host Aphotik on Sub FM 24th December 2024 - https://www.sub.fm

Adobe (ADBE) got a downgrade from Deutsche Bank as the latest hit to its near-30% dip over the last year. The analyst says Adobe needs to show how Firefly's A.I. features affected the company's financials. Caroline Woods highlights how this note is the latest of an analyst trend and weighs the question: did Adobe miss the A.I. wave? ======== Schwab Network ======== Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribe Download the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185 Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7 Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watch Watch on Vizio - https://www.vizio.com/en/watchfreeplus-explore Watch on DistroTV - https://www.distro.tv/live/schwab-network/ Follow us on X – / schwabnetwork Follow us on Facebook – / schwabnetwork Follow us on LinkedIn - / schwab-network About Schwab Network - https://schwabnetwork.com/about

More than a decade ago, a wave of research pointing to the inefficacy of remedial education was followed by a massive investment in rethinking how we prepare students who need extra support to access college. So why, after all that, does remedial ed still play such a big role on college campuses today? To help us tackle that question, we're joined by Anne Kim, FutureEd Senior Fellow and author of a recent report on the remedial education reform movement. She discusses the history of remedial education in America and what it will take to move reform forward. Then, Jeff fields some provocative questions from Michael about who should go to college and how we can align incentives so no one profits off of unprepared students. This episode is made with support from the Gates Foundation.Links We ShareIncomplete: The Unfinished Revolution in College Remedial Education by Anne Kim, FutureEdStudent Success 2.0, Future U. The New Student Ready College, Future UChapters0:00 - Intro02:49 - Remedial Education in Context07:26 - Calls for Change11:29 - A Wave of Reform14:53 - Progress Stalls16:59 - Recommendations for a Reform Rebound19:33 - Responding to Criticisms of Remedial Ed24:58 - A New System with More Options31:00 - Correcting a Broken Business Model Connect with Michael Horn:Sign Up for the The Future of Education NewsletterWebsiteLinkedInX (Twitter)ThreadsConnect with Jeff Selingo:Sign Up for the Next NewsletterWebsiteX (Twitter)ThreadsLinkedInConnect with Future U:TwitterYouTubeThreadsInstagramFacebookLinkedInSubmit a question and if we answer it on air we'll send you Future U. swag!Sign up for Future U. emails to get special updates and behind-the-scenes content.

In light of the serious allegations against Sean "Diddy" Combs, including charges of sex trafficking and racketeering, several celebrities are reportedly distancing themselves from him to protect their reputations. Notably, figures such as Leonardo DiCaprio, Ashton Kutcher, and Justin Bieber, who were previously associated with Combs' events, are now making efforts to minimize their connections. Additionally, high-profile individuals like LeBron James, Patrick Mahomes, Kim Kardashian, Janet Jackson, and Naomi Campbell have reportedly unfollowed Combs on social media or removed posts featuring him.Further complicating the situation, Ray J has claimed that certain celebrities are paying alleged victims to keep their names out of the scandal, suggesting a "catch and kill" strategy to suppress damaging information. He mentioned that some celebrities have approached him, fearing their associations with Combs might be exposed.  (commercial at 8:43)to contact me:bobbycapucci@protonmail.comsource:Ray J claims celebrities are paying off Sean 'Diddy' Combs' alleged victims to 'keep' their names clearSean 'Diddy' Combs' A-list friends are 'scared to death' of testifying in court - Mirror OnlineBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-epstein-chronicles--5003294/support.

Noah Mintz was only in knee-deep water when he was almost paralyzed by a rogue wave, rising suddenly out of the ocean at twice the size of the waves around it. We learn more about these deadly walls of water in the documentary The Wave.

Preparing For Tuesday's Cunnilingus Class.By Quinn_McMullen. Listen to the Podcast at Steamy Stories.Story RecapI'm Dan, a senior majoring in mechanical engineering. I needed two general education requirements to complete my degree, at a university in the Chicago area. It's a cold January, on campus. I was able to enroll in an English literature class and Dr. Martin's Sociology 369 Human Sexuality course, for the second semester.On the first day of class, we learned about male and female orgasms. Dr. Martin allowed students to submit a standard analysis and reading notes or to provide an alternative assignment. The first alternative assignment was to masturbate either alone or with an observer and report on our experience.Hannah, a coed was sitting in class next to me. I asked to be her partner. While we were trying to get comfortable with one another, we ended up having sex.DinnerAs I sat with Hannah at dinner, I had so many questions I wanted to ask her, but she started off.She looked me in the eye, "So you're what? Twenty two? Twenty three?"Twenty two.""I'm twenty one. So, I have to say that I am not normally that aggressive. I don't know what came over me.""I thought it was wonderful."Hannah nodded, "I'm sure you did, but I'm not sure what happened. I had every intention in the world of sitting there with you, reading until dinner. And then." She averted her gaze, then looked back into my eyes, "Then I just wanted you. I can't explain it. I've never started anything with a guy before. It has always been the guy coming on to me.""I really didn't mind. It was definitely a gift. Guys often leave a gal frustrated because they can't work up the nerve to initiate things. Especially when the gal is as gorgeous as you.”Hannah reached out and touched my hand, "Ah, thanks! Dan, it's okay. I guess I'm just trying to understand my actions. It was so out of character for me.""Maybe you're comfortable with me. I hope, anyway?"She laughed, "Undoubtedly. You're a really nice guy." She released my hand, "No, more than that. I know we just met, but I feel like you care about me as a person. I just hope that by being the initiator, I haven't made you not like me.""Hannah, I would never think that. You are self-confident, that's all. You're very respectful and sensitive. You weren't pushy at all.""See, that's the thing. I've never really been all that self-confident. Certainly not when it comes to sex.""How much sex have you,” I stopped myself. “I'm sorry. That's not polite for me to ask.”"No, I'm okay with talking to you about it. There was my date for the senior prom. Spring semester freshman year I had a boyfriend for a couple of weeks. Probably had sex two or three times with him. Sophomore year I had sex a couple of more times. Once over the summer. Once with Jeff last semester. Altogether, I've probably had sex less than ten times in my entire life. Not counting Sterling.”“Sterling?” I was puzzled.“Sterling is my chrome vibrator.” Hannah explained. “Until today, only Sterling could reliably bring me to orgasms. Today was the first time someone not named Sterling, gave me an orgasm. How about you?""I wouldn't say that I'm promiscuous, but I've had sex a lot more than that. It was always in a relationship. Let's see, I've been intimate with five girlfriends. This was the first time I had sex with someone on the first day I met them.""Same here. I hope you didn't mind.""Do I in any way look like I didn't have the time of my life? Not at all. It was amazing. Maybe it was a desire to try out some of the things we learned in class."Hannah smiled, "Well you tried out something you learned. Did you rub my g spot?""Yeah. It was all wrinkly." I added. “Nothing like a good hands-on lab project.”"I was already cumming and then that sent me into orbit. Holy shit." Hannah exclaimed."As an observer, that was pretty cool. I obviously ‘pressed your button', right?” I gloated."Observer? Dan, you did that to me. You were my lover. Come to think of it, you propped me up so you could screw me like that dildo in the film.""I did." I proudly admitted."That was quite creative." She marveled."Thanks." I grinned.We ate in silence for several minutes. It wasn't the least bit awkward though.Hannah stopped eating, "Normally I would try to fill silence. I don't feel like I have to. It's like we're two old married people that are completely comfortable with each other."I stopped eating, "You're right. I don't know very much about you, but it's like I've known you a long time."She nodded and continued eating. Another minute of silence passed.I reached out and touched her hand, "Do you believe in fate?""I haven't given it much thought." She wondered where this was going."I mean, what are the odds that I would have to sit next to you, you would ask me to be your partner, and we would hit it off so well?""Look it, Dan. I'm not a religious person. I'm sure there is a perfectly good probability that that would happen."I nodded, "Okay, but the odds are not that high.""That two random college students sitting next to one another would become friends, partners, and lovers? It think they are very high. That those two people would be you and me, that's just the universe rolling the dice." She squelched my sentimentality.She was right, but the thought was disappointing. I pulled my hand back.Hannah grabbed my hand, "Dan, I feel like I just burst a bubble on you.""Yeah. Maybe. A little. I had the thought that maybe someone was looking out for us and;""If it makes you feel any better, “ she interrupted; “I could very well be wrong. I agree that it is very cool that you sat down next to me. Whatever force in the universe made that happen, I'm very grateful. Done eating?""Yeah."The LibraryTo avoid temptation, we agreed to study together in the library. About nine we headed to Hannah's room. The cold and wind had eased a bit, making it easier to talk.Once outside, Hannah took my hand, "How should we decide who goes first?""I don't have a problem going first."Hannah laughed, "I'm kind of excited. I've never seen a guy get himself off.""I guess that's a reason Dr. Miller said we could have an observer."I held the door for her when we got to her dorm. Inside her room I took off my jacket.Hannah stopped to watch me, "This feels very clinical again. Could I undress you?""Sure."Hannah came over to me and put her hands behind my neck, "Maybe some mood-setting would help too." She went on tip-toe and kissed me, "Yes. That's what we need." She turned on her stereo. In a couple seconds, Simple Minds was playing ‘Don't you Forget About Me'.She pulled me to her bed, sat down, and pulled me down with her, "Much better."We lay side-by-side, exploring each other's mouth. I finally pulled her on top of me so I could hold her ass cheeks. She seems to love having her ass caressed.After several minutes of necking I spoke into her mouth, "I think I'm ready.""I feel something." She said with a horny wink. Her mix tape was now playing Rick Astley, singing ‘Never Gunna Give You Up'. I'm guessing this was Hannah's mood music for when she pleasured herself. But that was her business. I just decided to enjoy the moment.Hannah rolled off me and I stood up. I pulled my shirt off and dropped my jeans, standing there in my partially tented boxers."Ready?"She smiled and I let my boxers fall to my ankles, flipping them across the room with a flick of my foot. I slowly stroked my tumescent cock.Hannah stood up, "I think I promised to let you see me naked to help get you aroused.""You did." I recalled."Why don't you sit on the bed and let me give you something to get aroused about."I climbed up to the headboard and placed a pillow behind my back. Hannah began a little bump and grind strip tease. I pulled on my cock to firm it up. By now her mix tape was playing Tears for Fears, singing ‘Everybody Wants To Rule the World' with its sultry beat.She turned away from me and pulled her shirt off. She swiveled her jean-clad hips, then kicked off her shoes. Hannah loved to dance and it's clear she was very good at it. Somehow her socks went flying. She stepped up onto the bed and stood above me, one leg on either side.She placed her hands on the wall to balance herself and then placed her foot on my cock, "Let me rub that for you."I wouldn't call it rubbing, but she did move her foot up and down on my shaft. It wasn't exactly effective and soon she was back standing on the floor. She turned away and dropped her jeans to the floor. Hannah flipped them aside with her foot, then climbed back up on the bed. Standing above me in her bra and panties was more arousing. She stepped forward so that her crotch was inches from my face.I leaned forward, pressed my nose into her crotch, and took a deep breath, "God, you smell good." Her pheromones made a beeline to my cock.Hannah stepped back on the floor and removed her bra and panties, "Sorry, I'm not very creative with a strip tease.""I appreciate the creativity" I encouraged her."Thanks. How about if I just stand here in all my nakedness?""That works."I was rock hard now and it was easy to stroke myself. I was thinking I needed some lube when Hannah crawled up on the bed and got very close to my cock.She smiled, "I'm observing."I laughed.Hannah ran her hand down my thigh, "Perhaps you should position yourself so that I can see your asshole. I need to know if it contracts when you cum.""Oh. Okay."I slouched down at her headboard. I placed my feet so that my legs were spread  out and knees bent; and she had a good view of my anus. She was kneeling in front of me. I licked my stroke hand to provide some lubrication. Now her stereo was playing A-Ha. The cut was ‘Take On Me'.Hannah said, "I can provide some saliva." She leaned forward dribbled some spit on the head of my cock.I smiled, "That's helpful.""But probably not enough." She grabbed my stroke hand and placed her mouth on my cock, sucking away."I think that defeats the purpose of the assignment."She came off for a moment, "I don't care."Hannah soon had my entire shaft wet with saliva. She stroked me with her hands while sucking and churning the head in her mouth. The stereo filled the room with ‘Shout' from Tears For Fears. When I groaned my appreciation, I felt her finger at my ass. I think she had her juices on her finger because she slipped right in. She found my prostate."Oh god, Hannah. Hannah. Cumming. I'm cumming." The stereo rumbled; “Shout, shout, let it all out”.Everything sped up, her mouth, her fingers rubbing in my ass. I grabbed my knees and pulled my legs toward me. The point of no return approached and my world narrowed to my cock and ass. My body started quaking uncontrollably. Hannah's eyes were locked in on mine as she swallowed everything I shot. I threw my head back as my orgasm continued. All I could feel was her fingers rubbing my prostate and her mouth on my cock, churning, sucking, licking. I tried to say something, but a croaking sound came out.

The first episode of 2025. Dave the Wave has a new adventure selling time shares. Will Blaiser buy into it? What does Mr. Deep Voice think about all of this?

In light of the serious allegations against Sean "Diddy" Combs, including charges of sex trafficking and racketeering, several celebrities are reportedly distancing themselves from him to protect their reputations. Notably, figures such as Leonardo DiCaprio, Ashton Kutcher, and Justin Bieber, who were previously associated with Combs' events, are now making efforts to minimize their connections. Additionally, high-profile individuals like LeBron James, Patrick Mahomes, Kim Kardashian, Janet Jackson, and Naomi Campbell have reportedly unfollowed Combs on social media or removed posts featuring him.Further complicating the situation, Ray J has claimed that certain celebrities are paying alleged victims to keep their names out of the scandal, suggesting a "catch and kill" strategy to suppress damaging information. He mentioned that some celebrities have approached him, fearing their associations with Combs might be exposed.  (commercial at 8:43)to contact me:bobbycapucci@protonmail.comsource:Ray J claims celebrities are paying off Sean 'Diddy' Combs' alleged victims to 'keep' their names clearSean 'Diddy' Combs' A-list friends are 'scared to death' of testifying in court - Mirror Online

Noxide on Conscious Wave on Sub FM 19th March 2024 - https://www.sub.fm

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#PACIFICWATCH: #VEGASREPORT: Tragedy at Trump Hotel. and a 108 foot wave at Santa Cruz  @JCBLISS 1948 Las Vegas

Sonal Sachdev Patel, author of Little Wave & Other Stories and Gita: Battle of the Worlds, shares her mission to teach emotional resilience, spirituality, and life's profound lessons to children through storytelling. As CEO of GMSP Foundation, she bridges humanity and philanthropy, creating transformative change across communities. 00:34- About Sonal Sachdev Patel Sonal is the Chief Executive Officer of the GMSP Foundation.  She is the author of a book titled 'Little Wave & Other Stories' and 'Gita - The Battle of the Worlds'.

MASSIVE WAVE OF TERRORIST ATTACKS ARE COMMENCING! DOUG & DAVE INTEL REPORT

For the second revisit episode of our Holiday break, we're returning to one of the most inspiring conversations of last year, with Cemal Ezel, Founder of the social enterprise Change Please and Old Spike Roastery.Cemal's insight into balancing social impact with commercial success is thought-provoking and powerful. In this extended interview, he shares how Change Please uses a unique business model to tackle homelessness – one cup of coffee at a time.Cemal also talks about his recent collaboration with Nespresso and reveals exciting plans for global expansion, showcasing how purpose-driven businesses can create real, meaningful change.

In this edition of In Depth, Audacy's Liz Hernandez from 94.7 The Wave in Los Angeles spoke with American Foundation for Suicide Prevention expert Vic Armstrong about mental health and suicide prevention in the Hispanic community.

Catch up with or listen again to Going Indieground broadcast on Mad Wasp Radio week commencing 31 December 2024. On this show you can hear: Primal Scream – LoadedSoup Dragons – Mother Universe808 State – Pacific StateDeee-lite – Groove Is In The HeartBow Wow Wow – Go Wild In The CountryBloc Party – BanquetMGMT – … Continue reading →

- Terrorist Attacks and Initial News Segment (0:10) - Introduction to New Music Videos (1:04) - Special Report on AI and Decentralization (2:00) - Analysis of Terrorist Attacks and CIA Involvement (4:11) - Interview with Former CIA Officer Sarah Adams (9:00) - Psychological Impact and Preparedness (22:23) - Transition to Positive Topics and AI Technology (28:33) - Music Video: Where the Money Go, Joe (39:48) - Special Report on Decentralized AI and Human Empowerment (1:08:42) - Final Thoughts and Future Plans (1:23:24) - Music Video "Where the Money Go" (1:23:42) - Discussion on the Song's Creation (1:28:57) - AI as a Creative Tool (1:33:01) - AI's Impact on Education and Creativity (1:38:30) - Addressing AI Skepticism and Misconceptions (1:41:41) - The Role of AI in Society (1:47:39) - Brighton Universe and AI-Generated Content (1:47:56) - Nutritional Products and Health Ranger Store (2:01:25) - Final Thoughts on AI and Positive Change (2:08:38) For more updates, visit: http://www.brighteon.com/channel/hrreport NaturalNews videos would not be possible without you, as always we remain passionately dedicated to our mission of educating people all over the world on the subject of natural healing remedies and personal liberty (food freedom, medical freedom, the freedom of speech, etc.). Together, we're helping create a better world, with more honest food labeling, reduced chemical contamination, the avoidance of toxic heavy metals and vastly increased scientific transparency. ▶️ Every dollar you spend at the Health Ranger Store goes toward helping us achieve important science and content goals for humanity: https://www.healthrangerstore.com/ ▶️ Sign Up For Our Newsletter: https://www.naturalnews.com/Readerregistration.html ▶️ Brighteon: https://www.brighteon.com/channels/hrreport ▶️ Join Our Social Network: https://brighteon.social/@HealthRanger ▶️ Check In Stock Products at: https://PrepWithMike.com