Podcasts about South Park

In this episode of Quah (Q & A), Sal, Adam & Justin coach four Pump Heads via Zoom. Email live@mindpumpmedia.com if you want to be considered to ask your question on the show. Mind Pump Fit Tip: An active or spiritual practice makes you healthier/happier, and contributes significantly to longevity. (2:33) The famous shell game. (16:30) The ability to create new songs with old artists. (19:31) Celebrating the Godfather of rock n' roll. (21:58) Evidence of social contagion. (23:41) The climate is NOT God. (28:43) Is everything that goes viral staged? (32:07) Kids say the darndest things. (34:28) The genius of adult humor in cartoons/animated films. (37:03) Don't mess with the South Park creators. (42:03) Optimistic or bullish on Disney? (44:21) Updating the audience on Adam's use of Ned post-cannabis. (49:17) New product alert from Caldera Lab. (53:50) Shout out to Dhru Purohit. (1:01:19) #ListenerLive question #1 - Any advice on how to figure out how much I need to eat to continue my muscle-building progress while not increasing my body fat? (1:02:43) #ListenerLive question #2 - What can I do to increase my grip strength? (1:16:11) #ListenerLive question #3 - What is the best way to peak for a 1 rep max? (1:29:49) #ListenerLive question #4 - Any tips or tricks to assure that I am not jeopardizing gains, be it physically, mentally, or in my relationship with my wife due to lack of sleep? (1:51:34) Related Links/Products Mentioned Ask a question to Mind Pump, live! Email: live@mindpumpmedia.com Personal Trainer 3-Day Training – Starting Jan. 15, 2024 Visit NED for an exclusive offer for Mind Pump listeners! Visit Caldera Lab for an exclusive offer for Mind Pump listeners! **Code “mindpump” at checkout for the discount** December Promotion: MAPS Old Time Strength | MAPS OCR 50% off! ** Code DECEMBER50 at checkout ** Watch Escaping Twin Flames | Netflix Official Site The Beatles' final song is now streaming thanks to AI Little Richard documentary United Nations set to call on Americans to reduce meat consumption Staff locked in escape room building by fleeing prankster | SWNS Elon Musk Has a Warning for Disney Why Disney Is Betting $60 Billion on Parks and Cruise Liners No, Snoop Dogg isn't quitting cannabis. Here's why he's 'giving up smoke' Visit Stress Guardian by biOptimizers for an exclusive offer for Mind Pump listeners! Mind Pump #2160: Macro Counting Master Class Mind Pump #1895: Eight Hacks For An Insanely Strong Grip MAPS Strong | MAPS Fitness Products Mind Pump #2150: Why You Should Aim For PR's For Maximum Results 10 Steps to Hit & EXCEED Your PR's With Any Lift In 30 Days (Or Less!) | Mind Pump 1962 Mind Pump #2112: Is 15 Minutes Enough Time For An Effective Workout? MAPS 15 Minutes Mind Pump #1802: Seven Surprising Benefits Of Exercise Mind Pump Podcast – YouTube Mind Pump Free Resources People Mentioned Dhru Purohit (@dhrupurohit) Instagram  

This week we bring you melodic hooks, syrupy vocal harmonies, energetic performances, and "happy"-sounding music! Better said: POWER POP! Rev up your Tesla coils people! Get up and get moving this week as we present something to sing along with, while also being cracked in the ribs by fantastically distorted guitars! What's this InObscuria thing? We're a podcast that exhumes obscure Rock n' Punk n' Metal and puts them in one of 3 categories: the Lost, the Forgotten, or the Should Have Beens. This time around we are bringing you to the buffet to consume all 3. This is the third episode in our series full of great powerfully poppy sonic offerings. Crack a beer and enjoy!Songs this week include:Every Avenue - “Tie Me Down” from Bad Habits (2011)Fuzzbubble - “Big Time Nowhere” from Fuzzbubble (2000)The Dives - “Anticipation” from Everybody's Talkin' - EP (2017)Trip Shakespeare - “Earth, By Revolving” from Are You Shakespearienced? (1989)Big Star - “When My Baby's Beside Me” from #1 Record (1972)Soulcracker - “At Last, For You” from At Last, For You (2001)The Lickerish Quartet - “In The Meantime” from Threesome, Vol. 3 - EP (2022) If you want to hear Robert and Kevin's band from the late 90s – early 00s BIG JACK PNEUMATIC, check it out here: https://bigjackpnuematic.bandcamp.com/Visit us: https://inobscuria.com/https://www.facebook.com/InObscuriahttps://twitter.com/inobscuriahttps://www.instagram.com/inobscuria/Buy cool stuff with our logo on it!: https://www.redbubble.com/people/InObscuria?asc=uCheck out Robert's amazing fire sculptures and metal workings here: http://flamewerx.com/If you'd like to check out Kevin's band THE SWEAR, take a listen on all streaming services or pick up a digital copy of their latest release here: https://theswear.bandcamp.com/

Segment 3, December 2nd, 2023 Twenty years ago a new brand started in the outdoor business.  It started as a business plan that was unveiled at Jesse Brown's.  The business plan was for the brand, Mountain Khakis. The pants, inspired by the  mountain west, where groomed by the south.  In fact, the brand was founded and is still headquartered in Charlotte. Bill Bartee from the Charlotte outdoor store, Jesse Brown's & host of the Carolina Outdoors had a chance to speak with Ronnie Warner from Worth Repping has worked with Mountain Khakis over the past ten years.  He's had a view of the brand and its affect on the industry, which he shares with us on the Carolina Outdoors. Things You'll Learn by Listening: Show Highlights: Warner's first introduction to Mountain Khakis Mountain Khakis helping celebrate the "casual mindset...with a rugged sensibility." The pants featured a gusseted crotch, triple stitch seams, and deep pockets. The trend of dressy to casual in the apparel world The growth of Mountain Khakis into a full collection of men's shirts, pants, and outerwear The Twenty Year Celebration on Thursday, December 7th, 2023, from 2 p.m. to 6 p.m. The Carolina Outdoors is brought to you by Jesse Brown's.  Check out this Charlotte outdoor store located in the Southpark-area. Great Wagon Road distillery released a Legacy Whiskey with Mountain Khakis.  A tasting will be available at Jesse Brown's.

Segment 3, December 2nd, 2023 The cold weather is here & while that means that many like huddling up around a fire place there are still a lot of people that continue to get outdoors.  Hunters, Hikers, & yes, even campers are still being outside during the cold weather months. There are several reasons that cold weather camping is desirable.  We're going to talk about some of those reasons:  Less haze for views, no bugs, no snakes, less sweat & perspiration & also guess what…less crowds. However, there are dangers associated with cold weather camping.  We're going to talk about the ways to be safe & still enjoy going outside. Let's bring him on to see how we can still be outside, enjoy it, and be comfortable and safe. Bill Bartee from the Charlotte outdoor store, Jesse Brown's & host of the Carolina Outdoors had a chance to speak with outfitter, Alex Flowers, from Jesse Brown's.  He's been getting outside---AND has plans to get out more. Things You'll Learn by Listening: Show Highlights: Wear a good base layer to keep you warm and wick moisture. Wear Layers to regulate your warmth with the appropriate association Use warm water in a non-metal water bottle. Eat a Warm Meal Before Bed Put more than one person in a tent Use a cell foam mattress The Carolina Outdoors is brought to you by Jesse Brown's.  Check out this Charlotte outdoor store located in the Southpark-area. Thursday, Dec. 7th, 2—6, Holiday Open House w/ MK Try On Event Wednesday, Dec. 13th, 6—7, Cure Search Cure for Cancer Informational session

We discuss some movies that didn't get standalone episodes!The KillerSouth Park: Joining the PanderverseBottomsStraysPlease Don't Destroy: The Treasure of Foggy MountainThe CreatorThe Little MermaidBUT WAIT THERE'S MORE!Gu discusses his recent Christmas viewings...Muppet Family Christmas, A Charlie Brown Christmas, Sonic Xmas Special, Rugrats: The Santa Experience, Hey Arnold!: Arnold's ChristmasJoin the conversation on Twitter: @MACandGUpodcast

More Elliot Rodger Manifesto full narration: https://www.youtube.com/playlist?list=PLTz_vyR-zjcCWqqxaIyAU85ZEcv0k1FCtElliot Rodger's manifesto My Twisted World is a strange rabbit hole to head down. It mostly consists of a young man talking about how much he cries. Seriously, this guy will throw a tantrum over anything. His manifesto narration obviously has a human narrator, and this true crime incel murderer will make your skin crawl. Take a look inside of Elliot Rodger's head. Terrifying.YouTube: https://www.youtube.com/reddxyTwitch: https://www.twitch.tv/daytondoesDiscord: https://discord.gg/reddxPayPal: https://www.paypal.me/daytondoesPatreon: http://patreon.com/daytondoesTwitter: http://www.twitter.com/daytondoesFacebook: https://www.facebook.com/ReddXD/Teespring: https://teespring.com/stores/reddx

Siguenos en las redes sociales y en Discord https://linktr.ee/eytbiteros Discord: ⁠⁠⁠⁠https://discord.gg/AD6aEfYbnE

Catch us at Modular's ModCon next week with Chris Lattner, and join our community!Due to Bryan's very wide ranging experience in data science and AI across Blue Bottle (!), StitchFix, Weights & Biases, and now Hex Magic, this episode can be considered a two-parter.Notebooks = Chat++We've talked a lot about AI UX (in our meetups, writeups, and guest posts), and today we're excited to dive into a new old player in AI interfaces: notebooks! Depending on your background, you either Don't Like or you Like notebooks — they are the most popular example of Knuth's Literate Programming concept, basically a collection of cells; each cell can execute code, display it, and share its state with all the other cells in a notebook. They can also simply be Markdown cells to add commentary to the analysis. Notebooks have a long history but most recently became popular from iPython evolving into Project Jupyter, and a wave of notebook based startups from Observable to DeepNote and Databricks sprung up for the modern data stack.The first wave of AI applications has been very chat focused (ChatGPT, Character.ai, Perplexity, etc). Chat as a user interface has a few shortcomings, the major one being the inability to edit previous messages. We enjoyed Bryan's takes on why notebooks feel like “Chat++” and how they are building Hex Magic:* Atomic actions vs Stream of consciousness: in a chat interface, you make corrections by adding more messages to a conversation (i.e. “Can you try again by doing X instead?” or “I actually meant XYZ”). The context can easily get messy and confusing for models (and humans!) to follow. Notebooks' cell structure on the other hand allows users to go back to any previous cells and make edits without having to add new ones at the bottom. * “Airlocks” for repeatability: one of the ideas they came up with at Hex is “airlocks”, a collection of cells that depend on each other and keep each other in sync. If you have a task like “Create a summary of my customers' recent purchases”, there are many sub-tasks to be done (look up the data, sum the amounts, write the text, etc). Each sub-task will be in its own cell, and the airlock will keep them all in sync together.* Technical + Non-Technical users: previously you had to use Python / R / Julia to write notebooks code, but with models like GPT-4, natural language is usually enough. Hex is also working on lowering the barrier of entry for non-technical users into notebooks, similar to how Code Interpreter is doing the same in ChatGPT. Obviously notebooks aren't new for developers (OpenAI Cookbooks are a good example), but haven't had much adoption in less technical spheres. Some of the shortcomings of chat UIs + LLMs lowering the barrier of entry to creating code cells might make them a much more popular UX going forward.RAG = RecSys!We also talked about the LLMOps landscape and why it's an “iron mine” rather than a “gold rush”: I'll shamelessly steal [this] from a friend, Adam Azzam from Prefect. He says that [LLMOps] is more of like an iron mine than a gold mine in the sense of there is a lot of work to extract this precious, precious resource. Don't expect to just go down to the stream and do a little panning. There's a lot of work to be done. And frankly, the steps to go from this resource to something valuable is significant.Some of my favorite takeaways:* RAG as RecSys for LLMs: at its core, the goal of a RAG pipeline is finding the most relevant documents based on a task. This isn't very different from traditional recommendation system products that surface things for users. How can we apply old lessons to this new problem? Bryan cites fellow AIE Summit speaker and Latent Space Paper Club host Eugene Yan in decomposing the retrieval problem into retrieval, filtering, and scoring/ranking/ordering:As AI Engineers increasingly find that long context has tradeoffs, they will also have to relearn age old lessons that vector search is NOT all you need and a good systems not models approach is essential to scalable/debuggable RAG. Good thing Bryan has just written the first O'Reilly book about modern RecSys, eh?* Narrowing down evaluation: while “hallucination” is a easy term to throw around, the reality is more nuanced. A lot of times, model errors can be automatically fixed: is this JSON valid? If not, why? Is it just missing a closing brace? These smaller issues can be checked and fixed before returning the response to the user, which is easier than fixing the model.* Fine-tuning isn't all you need: when they first started building Magic, one of the discussions was around fine-tuning a model. In our episode with Jeremy Howard we talked about how fine-tuning leads to loss of capabilities as well. In notebooks, you are often dealing with domain-specific data (i.e. purchases, orders, wardrobe composition, household items, etc); the fact that the model understands that “items” are probably part of an “order” is really helpful. They have found that GPT-4 + 3.5-turbo were everything they needed to ship a great product rather than having to fine-tune on notebooks specifically.Definitely recommend listening to this one if you are interested in getting a better understanding of how to think about AI, data, and how we can use traditional machine learning lessons in large language models. The AI PivotFor more Bryan, don't miss his fireside chat at the AI Engineer Summit:Show Notes* Hex Magic* Bryan's new book: Building Recommendation Systems in Python and JAX* Bryan's whitepaper about MLOps* “Kitbashing in ML”, slides from his talk on building on top of foundation models* “Bayesian Statistics The Fun Way” by Will Kurt* Bryan's Twitter* “Berkeley man determined to walk every street in his city”* People:* Adam Azzam* Graham Neubig* Eugene Yan* Even OldridgeTimestamps* [00:00:00] Bryan's background* [00:02:34] Overview of Hex and the Magic product* [00:05:57] How Magic handles the complex notebook format to integrate cleanly with Hex* [00:08:37] Discussion of whether to build vs buy models - why Hex uses GPT-4 vs fine-tuning* [00:13:06] UX design for Magic with Hex's notebook format (aka “Chat++”)* [00:18:37] Expanding notebooks to less technical users* [00:23:46] The "Memex" as an exciting underexplored area - personal knowledge graph and memory augmentation* [00:27:02] What makes for good LLMops vs MLOps* [00:34:53] Building rigorous evaluators for Magic and best practices* [00:36:52] Different types of metrics for LLM evaluation beyond just end task accuracy* [00:39:19] Evaluation strategy when you don't own the core model that's being evaluated* [00:41:49] All the places you can make improvements outside of retraining the core LLM* [00:45:00] Lightning RoundTranscriptAlessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, Partner and CTO-in-Residence of Decibel Partners, and today I'm joining by Bryan Bischof. [00:00:15]Bryan: Hey, nice to meet you. [00:00:17]Alessio: So Bryan has one of the most thorough and impressive backgrounds we had on the show so far. Lead software engineer at Blue Bottle Coffee, which if you live in San Francisco, you know a lot about. And maybe you'll tell us 30 seconds on what that actually means. You worked as a data scientist at Stitch Fix, which used to be one of the premier data science teams out there. [00:00:38]Bryan: It used to be. Ouch. [00:00:39]Alessio: Well, no, no. Well, you left, you know, so how good can it still be? Then head of data science at Weights and Biases. You're also a professor at Rutgers and you're just wrapping up a new O'Reilly book as well. So a lot, a lot going on. Yeah. [00:00:52]Bryan: And currently head of AI at Hex. [00:00:54]Alessio: Let's do the Blue Bottle thing because I definitely want to hear what's the, what's that like? [00:00:58]Bryan: So I was leading data at Blue Bottle. I was the first data hire. I came in to kind of get the data warehouse in order and then see what we could build on top of it. But ultimately I mostly focused on demand forecasting, a little bit of recsys, a little bit of sort of like website optimization and analytics. But ultimately anything that you could imagine sort of like a retail company needing to do with their data, we had to do. I sort of like led that team, hired a few people, expanded it out. One interesting thing was I was part of the Nestle acquisition. So there was a period of time where we were sort of preparing for that and didn't know, which was a really interesting dynamic. Being acquired is a very not necessarily fun experience for the data team. [00:01:37]Alessio: I build a lot of internal tools for sourcing at the firm and we have a small VCs and data community of like other people doing it. And I feel like if you had a data feed into like the Blue Bottle in South Park, the Blue Bottle at the Hanahaus in Palo Alto, you can get a lot of secondhand information on the state of VC funding. [00:01:54]Bryan: Oh yeah. I feel like the real source of alpha is just bugging a Blue Bottle. [00:01:58]Alessio: Exactly. And what's your latest book about? [00:02:02]Bryan: I just wrapped up a book with a coauthor Hector Yee called Building Production Recommendation Systems. I'll give you the rest of the title because it's fun. It's in Python and JAX. And so for those of you that are like eagerly awaiting the first O'Reilly book that focuses on JAX, here you go. [00:02:17]Alessio: Awesome. And we'll chat about that later on. But let's maybe talk about Hex and Magic before. I've known Hex for a while, I've used it as a notebook provider and you've been working on a lot of amazing AI enabled experiences. So maybe run us through that. [00:02:34]Bryan: So I too, before I sort of like joined Hex, saw it as this like really incredible notebook platform, sort of a great place to do data science workflows, quite complicated, quite ad hoc interactive ones. And before I joined, I thought it was the best place to do data science workflows. And so when I heard about the possibility of building AI tools on top of that platform, that seemed like a huge opportunity. In particular, I lead the product called Magic. Magic is really like a suite of sort of capabilities as opposed to its own independent product. What I mean by that is they are sort of AI enhancements to the existing product. And that's a really important difference from sort of building something totally new that just uses AI. It's really important to us to enhance the already incredible platform with AI capabilities. So these are things like the sort of obvious like co-pilot-esque vibes, but also more interesting and dynamic ways of integrating AI into the product. And ultimately the goal is just to make people even more effective with the platform. [00:03:38]Alessio: How do you think about the evolution of the product and the AI component? You know, even if you think about 10 months ago, some of these models were not really good on very math based tasks. Now they're getting a lot better. I'm guessing a lot of your workloads and use cases is data analysis and whatnot. [00:03:53]Bryan: When I joined, it was pre 4 and it was pre the sort of like new chat API and all that. But when I joined, it was already clear that GPT was pretty good at writing code. And so when I joined, they had already executed on the vision of what if we allowed the user to ask a natural language prompt to an AI and have the AI assist them with writing code. So what that looked like when I first joined was it had some capability of writing SQL and it had some capability of writing Python and it had the ability to explain and describe code that was already written. Those very, what feel like now primitive capabilities, believe it or not, were already quite cool. It's easy to look back and think, oh, it's like kind of like Stone Age in these timelines. But to be clear, when you're building on such an incredible platform, adding a little bit of these capabilities feels really effective. And so almost immediately I started noticing how it affected my own workflow because ultimately as sort of like an engineering lead and a lot of my responsibility is to be doing analytics to make data driven decisions about what products we build. And so I'm actually using Hex quite a bit in the process of like iterating on our product. When I'm using Hex to do that, I'm using Magic all the time. And even in those early days, the amount that it sped me up, that it enabled me to very quickly like execute was really impressive. And so even though the models weren't that good at certain things back then, that capability was not to be underestimated. But to your point, the models have evolved between 3.5 Turbo and 4. We've actually seen quite a big enhancement in the kinds of tasks that we can ask Magic and even more so with things like function calling and understanding a little bit more of the landscape of agent workflows, we've been able to really accelerate. [00:05:57]Alessio: You know, I tried using some of the early models in notebooks and it actually didn't like the IPyNB formatting, kind of like a JSON plus XML plus all these weird things. How have you kind of tackled that? Do you have some magic behind the scenes to make it easier for models? Like, are you still using completely off the shelf models? Do you have some proprietary ones? [00:06:19]Bryan: We are using at the moment in production 3.5 Turbo and GPT-4. I would say for a large number of our applications, GPT-4 is pretty much required. To your question about, does it understand the structure of the notebook? And does it understand all of this somewhat complicated wrappers around the content that you want to show? We do our very best to abstract that away from the model and make sure that the model doesn't have to think about what the cell wrapper code looks like. Or for our Magic charts, it doesn't have to speak the language of Vega. These are things that we put a lot of work in on the engineering side, to the AI engineer profile. This is the AI engineering work to get all of that out of the way so that the model can speak in the languages that it's best at. The model is quite good at SQL. So let's ensure that it's speaking the language of SQL and that we are doing the engineering work to get the output of that model, the generations, into our notebook format. So too for other cell types that we support, including charts, and just in general, understanding the flow of different cells, understanding what a notebook is, all of that is hard work that we've done to ensure that the model doesn't have to learn anything like that. I remember early on, people asked the question, are you going to fine tune a model to understand Hex cells? And almost immediately, my answer was no. No we're not. Using fine-tuned models in 2022, I was already aware that there are some limitations of that approach and frankly, even using GPT-3 and GPT-2 back in the day in Stitch Fix, I had already seen a lot of instances where putting more effort into pre- and post-processing can avoid some of these larger lifts. [00:08:14]Alessio: You mentioned Stitch Fix and GPT-2. How has the balance between build versus buy, so to speak, evolved? So GPT-2 was a model that was not super advanced, so for a lot of use cases it was worth building your own thing. Is with GPT-4 and the likes, is there a reason to still build your own models for a lot of this stuff? Or should most people be fine-tuning? How do you think about that? [00:08:37]Bryan: Sometimes people ask, why are you using GPT-4 and why aren't you going down the avenue of fine-tuning today? I can get into fine-tuning specifically, but I do want to talk a little bit about the good old days of GPT-2. Shout out to Reza. Reza introduced me to GPT-2. I still remember him explaining the difference between general transformers and GPT. I remember one of the tasks that we wanted to solve with transformer-based generative models at Stitch Fix were writing descriptions of clothing. You might think, ooh, that's a multi-modal problem. The answer is, not necessarily. We actually have a lot of features about the clothes that are almost already enough to generate some reasonable text. I remember at that time, that was one of the first applications that we had considered. There was a really great team of NLP scientists at Stitch Fix who worked on a lot of applications like this. I still remember being exposed to the GPT endpoint back in the days of 2. If I'm not mistaken, and feel free to fact check this, I'm pretty sure Stitch Fix was the first OpenAI customer, unlike their true enterprise application. Long story short, I ultimately think that depending on your task, using the most cutting-edge general model has some advantages. If those are advantages that you can reap, then go for it. So at Hex, why GPT-4? Why do we need such a general model for writing code, writing SQL, doing data analysis? Shouldn't a fine-tuned model just on Kaggle notebooks be good enough? I'd argue no. And ultimately, because we don't have one specific sphere of data that we need to write great data analysis workbooks for, we actually want to provide a platform for anyone to do data analysis about their business. To do that, you actually need to entertain an extremely general universe of concepts. So as an example, if you work at Hex and you want to do data analysis, our projects are called Hexes. That's relatively straightforward to teach it. There's a concept of a notebook. These are data science notebooks, and you want to ask analytics questions about notebooks. Maybe if you trained on notebooks, you could answer those questions, but let's come back to Blue Bottle. If I'm at Blue Bottle and I have data science work to do, I have to ask it questions about coffee. I have to ask it questions about pastries, doing demand forecasting. And so very quickly, you can see that just by serving just those two customers, a model purely fine-tuned on like Kaggle competitions may not actually fit the bill. And so the more and more that you want to build a platform that is sufficiently general for your customer base, the more I think that these large general models really pack a lot of additional opportunity in. [00:11:21]Alessio: With a lot of our companies, we talked about stuff that you used to have to extract features for, now you have out of the box. So say you're a travel company, you want to do a query, like show me all the hotels and places that are warm during spring break. It would be just literally like impossible to do before these models, you know? But now the model knows, okay, spring break is like usually these dates and like these locations are usually warm. So you get so much out of it for free. And in terms of Magic integrating into Hex, I think AI UX is one of our favorite topics and how do you actually make that seamless. In traditional code editors, the line of code is like kind of the atomic unit and HEX, you have the code, but then you have the cell also. [00:12:04]Bryan: I think the first time I saw Copilot and really like fell in love with Copilot, I thought finally, fancy auto-complete. And that felt so good. It felt so elegant. It felt so right sized for the task. But as a data scientist, a lot of the work that you do previous to the ML engineering part of the house, you're working in these cells and these cells are atomic. They're expressing one idea. And so ultimately, if you want to make the transition from something like this code, where you've got like a large amount of code and there's a large amount of files and they kind of need to have awareness of one another, and that's a long story and we can talk about that. But in this atomic, somewhat linear flow through the notebook, what you ultimately want to do is you want to reason with the agent at the level of these individual thoughts, these atomic ideas. Usually it's good practice in say Jupyter notebook to not let your cells get too big. If your cell doesn't fit on one page, that's like kind of a code smell, like why is it so damn big? What are you doing in this cell? That also lends some hints as to what the UI should feel like. I want to ask questions about this one atomic thing. So you ask the agent, take this data frame and strip out this prefix from all the strings in this column. That's an atomic task. It's probably about two lines of pandas. I can write it, but it's actually very natural to ask magic to do that for me. And what I promise you is that it is faster to ask magic to do that for me. At this point, that kind of code, I never write. And so then you ask the next question, which is what should the UI be to do chains, to do multiple cells that work together? Because ultimately a notebook is a chain of cells and actually it's a first class citizen for Hex. So we have a DAG and the DAG is the execution DAG for the individual cells. This is one of the reasons that Hex is reactive and kind of dynamic in that way. And so the very next question is, what is the sort of like AI UI for these collections of cells? And back in June and July, we thought really hard about what does it feel like to ask magic a question and get a short chain of cells back that execute on that task. And so we've thought a lot about sort of like how that breaks down into individual atomic units and how those are tied together. We introduced something which is kind of an internal name, but it's called the airlock. And the airlock is exactly a sequence of cells that refer to one another, understand one another, use things that are happening in other cells. And it gives you a chance to sort of preview what magic has generated for you. Then you can accept or reject as an entire group. And that's one of the reasons we call it an airlock, because at any time you can sort of eject the airlock and see it in the space. But to come back to your question about how the AI UX fits into this notebook, ultimately a notebook is very conversational in its structure. I've got a series of thoughts that I'm going to express as a series of cells. And sometimes if I'm a kind data scientist, I'll put some text in between them too, explaining what on earth I'm doing. And that feels, in my opinion, and I think this is quite shared amongst exons, that feels like a really nice refinement of the chat UI. I've been saying for several months now, like, please stop building chat UIs. There is some irony because I think what the notebook allows is like chat plus plus. [00:15:36]Alessio: Yeah, I think the first wave of everything was like chat with X. So it was like chat with your data, chat with your documents and all of this. But people want to code, you know, at the end of the day. And I think that goes into the end user. I think most people that use notebooks are software engineer, data scientists. I think the cool things about these models is like people that are not traditionally technical can do a lot of very advanced things. And that's why people like code interpreter and chat GBT. How do you think about the evolution of that persona? Do you see a lot of non-technical people also now coming to Hex to like collaborate with like their technical folks? [00:16:13]Bryan: Yeah, I would say there might even be more enthusiasm than we're prepared for. We're obviously like very excited to bring what we call the like low floor user into this world and give more people the opportunity to self-serve on their data. We wanted to start by focusing on users who are already familiar with Hex and really make magic fantastic for them. One of the sort of like internal, I would say almost North Stars is our team's charter is to make Hex feel more magical. That is true for all of our users, but that's easiest to do on users that are already able to use Hex in a great way. What we're hearing from some customers in particular is sort of like, I'm excited for some of my less technical stakeholders to get in there and start asking questions. And so that raises a lot of really deep questions. If you immediately enable self-service for data, which is almost like a joke over the last like maybe like eight years, if you immediately enabled self-service, what challenges does that bring with it? What risks does that bring with it? And so it has given us the opportunity to think about things like governance and to think about things like alignment with the data team and making sure that the data team has clear visibility into what the self-service looks like. Having been leading a data team, trying to provide answers for stakeholders and hearing that they really want to self-serve, a question that we often found ourselves asking is, what is the easiest way that we can keep them on the rails? What is the easiest way that we can set up the data warehouse and set up our tools such that they can ask and answer their own questions without coming away with like false answers? Because that is such a priority for data teams, it becomes an important focus of my team, which is, okay, magic may be an enabler. And if it is, what do we also have to respect? We recently introduced the data manager and the data manager is an auxiliary sort of like tool on the Hex platform to allow people to write more like relevant metadata about their data warehouse to make sure that magic has access to the best information. And there are some things coming to kind of even further that story around governance and understanding. [00:18:37]Alessio: You know, you mentioned self-serve data. And when I was like a joke, you know, the whole rush to the modern data stack was something to behold. Do you think AI is like in a similar space where it's like a bit of a gold rush? [00:18:51]Bryan: I have like sort of two comments here. One I'll shamelessly steal from a friend, Adam Azzam from Prefect. He says that this is more of like an iron mine than a gold mine in the sense of there is a lot of work to extract this precious, precious resource. And that's the first one is I think, don't expect to just go down to the stream and do a little panning. There's a lot of work to be done. And frankly, the steps to go from this like gold to, or this resource to something valuable is significant. I think people have gotten a little carried away with the old maxim of like, don't go pan for gold, sell pickaxes and shovels. It's a much stronger business model. At this point, I feel like I look around and I see more pickaxe salesmen and shovel salesmen than I do prospectors. And that scares me a little bit. Metagame where people are starting to think about how they can build tools for people building tools for AI. And that starts to give me a little bit of like pause in terms of like, how confident are we that we can even extract this resource into something valuable? I got a text message from a VC earlier today, and I won't name the VC or the fund, but the question was, what are some medium or large size companies that have integrated AI into their platform in a way that you're really impressed by? And I looked at the text message for a few minutes and I was finding myself thinking and thinking, and I responded, maybe only co-pilot. It's been a couple hours now, and I don't think I've thought of another one. And I think that's where I reflect again on this, like iron versus gold. If it was really gold, I feel like I'd be more blown away by other AI integrations. And I'm not yet. [00:20:40]Alessio: I feel like all the people finding gold are the ones building things that traditionally we didn't focus on. So like mid-journey. I've talked to a company yesterday, which I'm not going to name, but they do agents for some use case, let's call it. They are 11 months old. They're making like 8 million a month in revenue, but in a space that you wouldn't even think about selling to. If you were like a shovel builder, you wouldn't even go sell to those people. And Swix talks about this a bunch, about like actually trying to go application first for some things. Let's actually see what people want to use and what works. What do you think are the most maybe underexplored areas in AI? Is there anything that you wish people were actually trying to shovel? [00:21:23]Bryan: I've been saying for a couple of months now, if I had unlimited resources and I was just sort of like truly like, you know, on my own building whatever I wanted, I think the thing that I'd be most excited about is building sort of like the personal Memex. The Memex is something that I've wanted since I was a kid. And are you familiar with the Memex? It's the memory extender. And it's this idea that sort of like human memory is quite weak. And so if we can extend that, then that's a big opportunity. So I think one of the things that I've always found to be one of the limiting cases here is access. How do you access that data? Even if you did build that data like out, how would you quickly access it? And one of the things I think there's a constellation of technologies that have come together in the last couple of years that now make this quite feasible. Like information retrieval has really improved and we have a lot more simple systems for getting started with information retrieval to natural language is ultimately the interface that you'd really like these systems to work on, both in terms of sort of like structuring the data and preparing the data, but also on the retrieval side. So what keys off the query for retrieval, probably ultimately natural language. And third, if you really want to go into like the purely futuristic aspect of this, it is latent voice to text. And that is also something that has quite recently become possible. I did talk to a company recently called gather, which seems to have some cool ideas in this direction, but I haven't seen yet what I, what I really want, which is I want something that is sort of like every time I listen to a podcast or I watch a movie or I read a book, it sort of like has a great vector index built on top of all that information that's contained within. And then when I'm having my next conversation and I can't quite remember the name of this person who did this amazing thing, for example, if we're talking about the Memex, it'd be really nice to have Vannevar Bush like pop up on my, you know, on my Memex display, because I always forget Vannevar Bush's name. This is one time that I didn't, but I often do. This is something that I think is only recently enabled and maybe we're still five years out before it can be good, but I think it's one of the most exciting projects that has become possible in the last three years that I think generally wasn't possible before. [00:23:46]Alessio: Would you wear one of those AI pendants that record everything? [00:23:50]Bryan: I think I'm just going to do it because I just like support the idea. I'm also admittedly someone who, when Google Glass first came out, thought that seems awesome. I know that there's like a lot of like challenges about the privacy aspect of it, but it is something that I did feel was like a disappointment to lose some of that technology. Fun fact, one of the early Google Glass developers was this MIT computer scientist who basically built the first wearable computer while he was at MIT. And he like took notes about all of his conversations in real time on his wearable and then he would have real time access to them. Ended up being kind of a scandal because he wanted to use a computer during his defense and they like tried to prevent him from doing it. So pretty interesting story. [00:24:35]Alessio: I don't know but the future is going to be weird. I can tell you that much. Talking about pickaxes, what do you think about the pickaxes that people built before? Like all the whole MLOps space, which has its own like startup graveyard in there. How are those products evolving? You know, you were at Wits and Biases before, which is now doing a big AI push as well. [00:24:57]Bryan: If you really want to like sort of like rub my face in it, you can go look at my white paper on MLOps from 2022. It's interesting. I don't think there's many things in that that I would these days think are like wrong or even sort of like naive. But what I would say is there are both a lot of analogies between MLOps and LLMops, but there are also a lot of like key differences. So like leading an engineering team at the moment, I think a lot more about good engineering practices than I do about good ML practices. That being said, it's been very convenient to be able to see around corners in a few of the like ML places. One of the first things I did at Hex was work on evals. This was in February. I hadn't yet been overwhelmed by people talking about evals until about May. And the reason that I was able to be a couple of months early on that is because I've been building evals for ML systems for years. I don't know how else to build an ML system other than start with the evals. I teach my students at Rutgers like objective framing is one of the most important steps in starting a new data science project. If you can't clearly state what your objective function is and you can't clearly state how that relates to the problem framing, you've got no hope. And I think that is a very shared reality with LLM applications. Coming back to one thing you mentioned from earlier about sort of like the applications of these LLMs. To that end, I think what pickaxes I think are still very valuable is understanding systems that are inherently less predictable, that are inherently sort of experimental. On my engineering team, we have an experimentalist. So one of the AI engineers, his focus is experiments. That's something that you wouldn't normally expect to see on an engineering team. But it's important on an AI engineering team to have one person whose entire focus is just experimenting, trying, okay, this is a hypothesis that we have about how the model will behave. Or this is a hypothesis we have about how we can improve the model's performance on this. And then going in, running experiments, augmenting our evals to test it, et cetera. What I really respect are pickaxes that recognize the hybrid nature of the sort of engineering tasks. They are ultimately engineering tasks with a flavor of ML. And so when systems respect that, I tend to have a very high opinion. One thing that I was very, very aligned with Weights and Biases on is sort of composability. These systems like ML systems need to be extremely composable to make them much more iterative. If you don't build these systems in composable ways, then your integration hell is just magnified. When you're trying to iterate as fast as people need to be iterating these days, I think integration hell is a tax not worth paying. [00:27:51]Alessio: Let's talk about some of the LLM native pickaxes, so to speak. So RAG is one. One thing is doing RAG on text data. One thing is doing RAG on tabular data. We're releasing tomorrow our episode with Kube, the semantic layer company. Curious to hear your thoughts on it. How are you doing RAG, pros, cons? [00:28:11]Bryan: It became pretty obvious to me almost immediately that RAG was going to be important. Because ultimately, you never expect your model to have access to all of the things necessary to respond to a user's request. So as an example, Magic users would like to write SQL that's relevant to their business. And it's important then to have the right data objects that they need to query. We can't expect any LLM to understand our user's data warehouse topology. So what we can expect is that we can build a RAG system that is data warehouse aware, data topology aware, and use that to provide really great information to the model. If you ask the model, how are my customers trending over time? And you ask it to write SQL to do that. What is it going to do? Well, ultimately, it's going to hallucinate the structure of that data warehouse that it needs to write a general query. Most likely what it's going to do is it's going to look in its sort of memory of Stack Overflow responses to customer queries, and it's going to say, oh, it's probably a customer stable and we're in the age of DBT, so it might be even called, you know, dim customers or something like that. And what's interesting is, and I encourage you to try, chatGBT will do an okay job of like hallucinating up some tables. It might even hallucinate up some columns. But what it won't do is it won't understand the joins in that data warehouse that it needs, and it won't understand the data caveats or the sort of where clauses that need to be there. And so how do you get it to understand those things? Well, this is textbook RAG. This is the exact kind of thing that you expect RAG to be good at augmenting. But I think where people who have done a lot of thinking about RAG for the document case, they think of it as chunking and sort of like the MapReduce and the sort of like these approaches. But I think people haven't followed this train of thought quite far enough yet. Jerry Liu was on the show and he talked a little bit about thinking of this as like information retrieval. And I would push that even further. And I would say that ultimately RAG is just RecSys for LLM. As I kind of already mentioned, I'm a little bit recommendation systems heavy. And so from the beginning, RAG has always felt like RecSys to me. It has always felt like you're building a recommendation system. And what are you trying to recommend? The best possible resources for the LLM to execute on a task. And so most of my approach to RAG and the way that we've improved magic via retrieval is by building a recommendation system. [00:30:49]Alessio: It's funny, as you mentioned that you spent three years writing the book, the O'Reilly book. Things must have changed as you wrote the book. I don't want to bring out any nightmares from there, but what are the tips for people who want to stay on top of this stuff? Do you have any other favorite newsletters, like Twitter accounts that you follow, communities you spend time in? [00:31:10]Bryan: I am sort of an aggressive reader of technical books. I think I'm almost never disappointed by time that I've invested in reading technical manuscripts. I find that most people write O'Reilly or similar books because they've sort of got this itch that they need to scratch, which is that I have some ideas, I have some understanding that we're hard won, I need to tell other people. And there's something that, from my experience, correlates between that itch and sort of like useful information. As an example, one of the people on my team, his name is Will Kurt, he wrote a book sort of Bayesian statistics the fun way. I knew some Bayesian statistics, but I read his book anyway. And the reason was because I was like, if someone feels motivated to write a book called Bayesian statistics the fun way, they've got something to say about Bayesian statistics. I learned so much from that book. That book is like technically like targeted at someone with less knowledge and experience than me. And boy, did it humble me about my understanding of Bayesian statistics. And so I think this is a very boring answer, but ultimately like I read a lot of books and I think that they're a really valuable way to learn these things. I also regrettably still read a lot of Twitter. There is plenty of noise in that signal, but ultimately it is still usually like one of the first directions to get sort of an instinct for what's valuable. The other comment that I want to make is we are in this age of sort of like archive is becoming more of like an ad platform. I think that's a little challenging right now to kind of use it the way that I used to use it, which is for like higher signal. I've chatted a lot with a CMU professor, Graham Neubig, and he's been doing LLM evaluation and LLM enhancements for about five years and know that I didn't misspeak. And I think talking to him has provided me a lot of like directionality for more believable sources. Trying to cut through the hype. I know that there's a lot of other things that I could mention in terms of like just channels, but ultimately right now I think there's almost an abundance of channels and I'm a little bit more keen on high signal. [00:33:18]Alessio: The other side of it is like, I see so many people say, Oh, I just wrote a paper on X and it's like an article. And I'm like, an article is not a paper, but it's just funny how I know we were kind of chatting before about terms being reinvented and like people that are not from this space kind of getting into AI engineering now. [00:33:36]Bryan: I also don't want to be gatekeepy. Actually I used to say a lot to people, don't be shy about putting your ideas down on paper. I think it's okay to just like kind of go for it. And I, I myself have something on archive that is like comically naive. It's intentionally naive. Right now I'm less concerned by more naive approaches to things than I am by the purely like advertising approach to sort of writing these short notes and articles. I think blogging still has a good place. And I remember getting feedback during my PhD thesis that like my thesis sounded more like a long blog post. And I now feel like that curmudgeonly professor who's also like, yeah, maybe just keep this to the blogs. That's funny.Alessio: Uh, yeah, I think one of the things that Swyx said when he was opening the AI engineer summit a couple of weeks ago was like, look, most people here don't know much about the space because it's so new and like being open and welcoming. I think it's one of the goals. And that's why we try and keep every episode at a level that it's like, you know, the experts can understand and learn something, but also the novices can kind of like follow along. You mentioned evals before. I think that's one of the hottest topics obviously out there right now. What are evals? How do we know if they work? Yeah. What are some of the fun learnings from building them into X? [00:34:53]Bryan: I said something at the AI engineer summit that I think a few people have already called out, which is like, if you can't get your evals to be sort of like objective, then you're not trying hard enough. I stand by that statement. I'm not going to, I'm not going to walk it back. I know that that doesn't feel super good because people, people want to think that like their unique snowflake of a problem is too nuanced. But I think this is actually one area where, you know, in this dichotomy of like, who can do AI engineering? And the answer is kind of everybody. Software engineering can become AI engineering and ML engineering can become AI engineering. One thing that I think the more data science minded folk have an advantage here is we've gotten more practice in taking very vague notions and trying to put a like objective function around that. And so ultimately I would just encourage everybody who wants to build evals, just work incredibly hard on codifying what is good and bad in terms of these objective metrics. As far as like how you go about turning those into evals, I think it's kind of like sweat equity. Unfortunately, I told the CEO of gantry several months ago, I think it's been like six months now that I was sort of like looking at every single internal Hex request to magic by hand with my eyes and sort of like thinking, how can I turn this into an eval? Is there a way that I can take this real request during this dog foodie, not very developed stage? How can I make that into an evaluation? That was a lot of sweat equity that I put in a lot of like boring evenings, but I do think ultimately it gave me a lot of understanding for the way that the model was misbehaving. Another thing is how can you start to understand these misbehaviors as like auxiliary evaluation metrics? So there's not just one evaluation that you want to do for every request. It's easy to say like, did this work? Did this not work? Did the response satisfy the task? But there's a lot of other metrics that you can pull off these questions. And so like, let me give you an example. If it writes SQL that doesn't reference a table in the database that it's supposed to be querying against, we would think of that as a hallucination. You could separately consider, is it a hallucination as a valuable metric? You could separately consider, does it get the right answer? The right answer is this sort of like all in one shot, like evaluation that I think people jump to. But these intermediary steps are really important. I remember hearing that GitHub had thousands of lines of post-processing code around Copilot to make sure that their responses were sort of correct or in the right place. And that kind of sort of defensive programming against bad responses is the kind of thing that you can build by looking at many different types of evaluation metrics. Because you can say like, oh, you know, the Copilot completion here is mostly right, but it doesn't close the brace. Well, that's the thing you can check for. Or, oh, this completion is quite good, but it defines a variable that was like already defined in the file. Like that's going to have a problem. That's an evaluation that you could check separately. And so this is where I think it's easy to convince yourself that all that matters is does it get the right answer? But the more that you think about production use cases of these things, the more you find a lot of this kind of stuff. One simple example is like sometimes the model names the output of a cell, a variable that's already in scope. Okay. Like we can just detect that and like we can just fix that. And this is the kind of thing that like evaluations over time and as you build these evaluations over time, you really can expand the robustness in which you trust these models. And for a company like Hex, who we need to put this stuff in GA, we can't just sort of like get to demo stage or even like private beta stage. We really hunting GA on all of these capabilities. Did it get the right answer on some cases is not good enough. [00:38:57]Alessio: I think the follow up question to that is in your past roles, you own the model that you're evaluating against. Here you don't actually have control into how the model evolves. How do you think about the model will just need to improve or we'll use another model versus like we can build kind of like engineering post-processing on top of it. How do you make the choice? [00:39:19]Bryan: So I want to say two things here. One like Jerry Liu talked a little bit about in his episode, he talked a little bit about sort of like you don't always want to retrain the weights to serve certain use cases. Rag is another tool that you can use to kind of like soft tune. I think that's right. And I want to go back to my favorite analogy here, which is like recommendation systems. When you build a recommendation system, you build the objective function. You think about like what kind of recs you want to provide, what kind of features you're allowed to use, et cetera, et cetera. But there's always another step. There's this really wonderful collection of blog posts from Eugene Yon and then ultimately like even Oldridge kind of like iterated on that for the Merlin project where there's this multi-stage recommender. And the multi-stage recommender says the first step is to do great retrieval. Once you've done great retrieval, you then need to do great ranking. Once you've done great ranking, you need to then do a good job serving. And so what's the analogy here? Rag is retrieval. You can build different embedding models to encode different features in your latent space to ensure that your ranking model has the best opportunity. Now you might say, oh, well, my ranking model is something that I've got a lot of capability to adjust. I've got full access to my ranking model. I'm going to retrain it. And that's great. And you should. And over time you will. But there's one more step and that's downstream and that's the serving. Serving often sounds like I just show the s**t to the user, but ultimately serving is things like, did I provide diverse recommendations? Going back to Stitch Fix days, I can't just recommend them five shirts of the same silhouette and cut. I need to serve them a diversity of recommendations. Have I respected their requirements? They clicked on something that got them to this place. Is the recommendations relevant to that query? Are there any hard rules? Do we maybe not have this in stock? These are all things that you put downstream. And so much like the recommendations use case, there's a lot of knobs to pull outside of retraining the model. And even in recommendation systems, when do you retrain your model for ranking? Not nearly as much as you do other s**t. And even this like embedding model, you might fiddle with more often than the true ranking model. And so I think the only piece of the puzzle that you don't have access to in the LLM case is that sort of like middle step. That's okay. We've got plenty of other work to do. So right now I feel pretty enabled. [00:41:56]Alessio: That's great. You obviously wrote a book on RecSys. What are some of the key concepts that maybe people that don't have a data science background, ML background should keep in mind as they work in this area? [00:42:07]Bryan: It's easy to first think these models are stochastic. They're unpredictable. Oh, well, what are we going to do? I think of this almost like gaseous type question of like, if you've got this entropy, where can you put the entropy? Where can you let it be entropic and where can you constrain it? And so what I want to say here is think about the cases where you need it to be really tightly constrained. So why are people so excited about function calling? Because function calling feels like a way to constrict it. Where can you let it be more gaseous? Well, maybe in the way that it talks about what it wants to do. Maybe for planning, if you're building agents and you want to do sort of something chain of thoughty. Well, that's a place where the entropy can happily live. When you're building applications of these models, I think it's really important as part of the problem framing to be super clear upfront. These are the things that can be entropic. These are the things that cannot be. These are the things that need to be super rigid and really, really aligned to a particular schema. We've had a lot of success in making specific the parts that need to be precise and tightly schemified, and that has really paid dividends. And so other analogies from data science that I think are very valuable is there's the sort of like human in the loop analogy, which has been around for quite a while. And I have gone on record a couple of times saying that like, I don't really love human in the loop. One of the things that I think we can learn from human in the loop is that the user is the best judge of what is good. And the user is pretty motivated to sort of like interact and give you kind of like additional nudges in the direction that you want. I think what I'd like to flip though, is instead of human in the loop, I'd like it to be AI in the loop. I'd rather center the user. I'd rather keep the user as the like core item at the center of this universe. And the AI is a tool. By switching that analogy a little bit, what it allows you to do is think about where are the places in which the user can reach for this as a tool, execute some task with this tool, and then go back to doing their workflow. It still gets this back and forth between things that computers are good at and things that humans are good at, which has been valuable in the human loop paradigm. But it allows us to be a little bit more, I would say, like the designers talk about like user-centered. And I think that's really powerful for AI applications. And it's one of the things that I've been trying really hard with Magic to make that feel like the workflow as the AI is right there. It's right where you're doing your work. It's ready for you anytime you need it. But ultimately you're in charge at all times and your workflow is what we care the most about. [00:44:56]Alessio: Awesome. Let's jump into lightning round. What's something that is not on your LinkedIn that you're passionate about or, you know, what's something you would give a TED talk on that is not work related? [00:45:05]Bryan: So I walk a lot. [00:45:07]Bryan: I have walked every road in Berkeley. And I mean like every part of every road even, not just like the binary question of, have you been on this road? I have this little app that I use called Wanderer, which just lets me like kind of keep track of everywhere I've been. And so I'm like a little bit obsessed. My wife would say a lot a bit obsessed with like what I call new roads. I'm actually more motivated by trails even than roads, but like I'm a maximalist. So kind of like everything and anything. Yeah. Believe it or not, I was even like in the like local Berkeley paper just talking about walking every road. So yeah, that's something that I'm like surprisingly passionate about. [00:45:45]Alessio: Is there a most underrated road in Berkeley? [00:45:49]Bryan: What I would say is like underrated is Kensington. So Kensington is like a little town just a teeny bit north of Berkeley, but still in the Berkeley hills. And Kensington is so quirky and beautiful. And it's a really like, you know, don't sleep on Kensington. That being said, one of my original motivations for doing all this walking was people always tell me like, Berkeley's so quirky. And I was like, how quirky is Berkeley? Turn it out. It's quite, quite quirky. It's also hard to say quirky and Berkeley in the same sentence I've learned as of now. [00:46:20]Alessio: That's a, that's a good podcast warmup for our next guests. All right. The actual lightning ground. So we usually have three questions, acceleration, exploration, then a takeaway acceleration. What's, what's something that's already here today that you thought would take much longer to arrive in AI and machine learning? [00:46:39]Bryan: So I invited the CEO of Hugging Face to my seminar when I worked at Stitch Fix and his talk at the time, honestly, like really annoyed me. The talk was titled like something to the effect of like LLMs are going to be the like technology advancement of the next decade. It's on YouTube. You can find it. I don't remember exactly the title, but regardless, it was something like LLMs for the next decade. And I was like, okay, they're like one modality of model, like whatever. His talk was fine. Like, I don't think it was like particularly amazing or particularly poor, but what I will say is damn, he was right. Like I, I don't think I quite was on board during that talk where I was like, ah, maybe, you know, like there's a lot of other modalities that are like moving pretty quick. I thought things like RL were going to be the like real like breakout success. And there's a little pun with Atari and breakout there, but yeah, like I, man, I was sleeping on LLMs and I feel a little embarrassed. I, yeah. [00:47:44]Alessio: Yeah. No, I mean, that's a good point. It's like sometimes the, we just had Jeremy Howard on the podcast and he was saying when he was talking about fine tuning, everybody thought it was dumb, you know, and then later people realize, and there's something to be said about messaging, especially like in technical audiences where there's kind of like the metagame, you know, which is like, oh, these are like the cool ideas people are exploring. I don't know where I want to align myself yet, you know, or whatnot. So it's cool exploration. So it's kind of like the opposite of that. You mentioned RL, right? That's something that was kind of like up and up and up. And then now it's people are like, oh, I don't know. Are there any other areas if you weren't working on, on magic that you want to go work on? [00:48:25]Bryan: Well, I did mention that, like, I think this like Memex product is just like incredibly exciting to me. And I think it's really opportunistic. I think it's very, very feasible, but I would maybe even extend that a little bit, which is I don't see enough people getting really enthusiastic about hardware with advanced AI built in. You're hearing whispering of it here and there, put on the whisper, but like you're starting to see people putting whisper into pieces of hardware and making that really powerful. I joked with, I can't think of her name. Oh, Sasha, who I know is a friend of the pod. Like I joked with Sasha that I wanted to make the big mouth Billy Bass as a babble fish, because at this point it's pretty easy to connect that up to whisper and talk to it in one language and have it talk in the other language. And I was like, this is the kind of s**t I want people building is like silly integrations between hardware and these new capabilities. And as much as I'm starting to hear whisperings here and there, it's not enough. I think I want to see more people going down this track because I think ultimately like these things need to be in our like physical space. And even though the margins are good on software, I want to see more like integration into my daily life. Awesome. [00:49:47]Alessio: And then, yeah, a takeaway, what's one message idea you want everyone to remember and think about? [00:49:54]Bryan: Even though earlier I was talking about sort of like, maybe like not reinventing things and being respectful of the sort of like ML and data science, like ideas. I do want to say that I think everybody should be experimenting with these tools as much as they possibly can. I've heard a lot of professors, frankly, express concern about their students using GPT to do their homework. And I took a completely opposite approach, which is in the first 15 minutes of the first class of my semester this year, I brought up GPT on screen and we talked about what GPT was good at. And we talked about like how the students can sort of like use it. I showed them an example of it doing data analysis work quite well. And then I showed them an example of it doing quite poorly. I think however much you're integrating with these tools or interacting with these tools, and this audience is probably going to be pretty high on that distribution. I would really encourage you to sort of like push this into the other people in your life. My wife is very technical. She's a product manager and she's using chat GPT almost every day for communication or for understanding concepts that are like outside of her sphere of excellence. And recently my mom and my sister have been sort of like onboarded onto the chat GPT train. And so ultimately I just, I think that like it is our duty to help other people see like how much of a paradigm shift this is. We should really be preparing people for what life is going to be like when these are everywhere. [00:51:25]Alessio: Awesome. Thank you so much for coming on, Bryan. This was fun. [00:51:29]Bryan: Yeah. Thanks for having me. And use Hex magic. [00:51:31] Get full access to Latent Space at www.latent.space/subscribe

The goal of schools pitting their students against their parents is no longer a secret operation. The televisions show, South Park, hits it on the nose again when it comes to the culture. And the United Nations has decided the best way to deal with starvation in Africa and around the world is to starve the United States. Let's talk about it.

Episode #182 Lawrence Gowan is the keyboardist in Styx, he's a vocalist, songwriter, vinyl collector and fellow purple haired person. During the Styx tour Lawrence/Larry/Gowan caught up with Mistress Carrie to talk their latest album Crash Of The Crown, hair dye, juke boxes, The Beatles, South Park, Scotland, Abbey Road studio, rockstars with Veteran parents, hockey, the Styx Vegas residency, songwriting, NASA, Boston, and so much more! Episode Notes Check out the custom playlist for Episode #182 here Find Lawrence Gowan online: Instagram Facebook Find Styx Online: Website Facebook Instagram X Youtube Find Mistress Carrie online: Official Website The Mistress Carrie Backstage Pass on Patreon X Facebook Instagram Threads YouTube Cameo Pantheon Podcast Network Find The Mistress Carrie Podcast online: Instagram Threads Learn more about your ad choices. Visit megaphone.fm/adchoices

Semana llena de información de videojuegos y ya nos vamos preparando para cerrar este 2023. Estos fueron algunos de los temas más importantes tratados en el programa. Se anuncia Cyberpunk 2077: Ultimate Edition. Por fin tenemos gameplay de South Park: Snow Day! Nuevos juegos a Nintendo 64 de Nintendo Switch. Conozcan más de Sonic Dream Team, Conozcan el juego Arcadian Atlas. The Last of Us Part II Remastered en camino. Baldur's Gate III tendrá edición física. Hideki Kamiya interesado en regresar a Okami y Viewtiful Joe. La reseña de esta semana es Super Mario RPG.

On the audio version of the latest episode of CvsP we discuss Doctor Who: The Star Beast, Doctor Who Retrospective, South Park: Joining the Panderverse, the collapse of Disney and the MCU, and MORE Episode timings: 00:00 - Intro 02:00 - The Collapse of Disney and the MCU and South Park: Joining the Panderverse - Discussion 20:45 - Doctor Who - The Starbeast (2023) - Review 28:48 - Paul's Classic Doctor WHO rewatch - Review 56:25 - Doctor Who Am I? - Review Subscribe (and review us) at Apple Podcasts Check out Mike's other show  The Rewatch Project Check out Mike's new video series covering 80's action TV shows Rolling Thunder Feedback appreciated at chinstrokervspunter@gmail.com and hang with us on facebook Video version of the podcast available on the Chin Stroker VS Punter YouTube Channel

Soaring so high above the world! Never thought we could be so free! Boy was this episode a blast, or should we say, a "Marklar"?Regardless, not only do we get a hilarous Star Wars parody, intergalactic travel, as well as the classic Parker/Stone takedown of organised religion, but little Starvin Marvin gets to save his people!Support the show for EARLY & AD-FREE access to every show we produce, as well as 100 hours of exclusive content! Join the FFD family today at patreon.com/fourfingerdiscountCHECK OUT OUR OTHER PODCASTS:Four Finger Discount - spreaker.com/show/four-finger-discount-simpsons-podcastToon'd In! with Jim Cummings - spreaker.com/show/toond-in-with-jim-cummingsGoin' Down To South Park - spreaker.com/show/goin-down-to-south-parkSpeaKing Of The Hill - spreaker.com/show/speaking-of-the-hill-a-king-of-the-hill-Talking Seinfeld - spreaker.com/show/talking-seinfeldThe One About Friends - spreaker.com/show/the-one-about-friends-podcastThe Office Talk - spreaker.com/sThis show is part of the Spreaker Prime Network, if you are interested in advertising on this podcast, contact us at https://www.spreaker.com/show/5828978/advertisement

Salty & Petty Ep #43: South Park - Joining The Panderverse Welcome back to Salty & Petty!  For this episode Phil and Lilith review the newest South Park special on Paramount Plus, South Park: Joining The Panderverse. PLUS: discussion on the current state of the streaming apps and random chat.   Tune in today and don't forget to review the show on Apple Podcasts, Spotify, YouTube, and anywhere else you can!     Salty & Petty's Links  → Twitter https://twitter.com/SaltyPettyPod → Instagram https://www.instagram.com/clsidekicks → Facebook https://www.facebook.com/SaltyPettyPod → YouTube https://www.youtube.com/c/CapesandLunatics   ==================  

Romans 5:12–21  We turn in Romans to the apostle Paul's examination of the story of Adam, which is a story of death, and the story of Jesus, which is a story of life.

- Introduction to Jessica, a proactive AI personal assistant that interacts through Telegram.- Jessica's integration with Google Calendar: Capable of creating event invites.- Jessica's local Mac command execution: Discussing Jessica's ability to execute bash commands with explicit permissions.- Jessica's long-term memory capabilities: Insight into how Jessica stores and retains information.- Understand how Jessica comprehends message context and provides appropriate responses.- Discussion on G-Asset: A basic, task-based chatbot.- The process of deploying G-Asset using the ProactiveJS repository.- Guidance on creating .env files and gathering necessary credentials for bot deployment.- Future upgrades on Jessica's functionalities: From enhancing Google Calendar Integration and User Interface to gaining access to applications like Gmail and ToDo.- Proposed integration with smart speaker technology using a device like Mycroft AI and executing commands through voice recognition.## References:- Join us at our [Discord](https://discord.gg/T38WpgkHGQ) for further discussion.- [Radio-T Podcast](https://www.radio-t.com)- Pop culture mention: South Park's special episode on Ponderverse.- Mention of Open AI Day.## Additional Notes:- A Call-to-action for listeners to experiment with Jessica the AI bot.- Announcement: A video guide on effectively deploying Jessica coming soon.- The show host extends gratitude to listeners and wishes a Happy Thanksgiving. Please note that Jessica is still under development and its functionality may change for future updates.

In this episode of Quah (Q & A), Sal, Adam & Justin coach four Pump Heads via Zoom. Mind Pump Fit Tip: FIX your gut health and chronic inflammation may be solved. (1:49) Spilling ALL the tea on Gary Brecka. (10:36) A pill that affects your decision-making. (21:28) The tallest slide in the world! (27:00) When training with David Goggins does NOT make sense. (30:43) Houses today are NOT the same size they used to be. (33:49) The best South Park episode ever! (43:11) A technique to dramatically improve your creative thinking space. (44:19) People like gummies. (47:46) The origins of occlusion training. (49:53) Shout out to the Mind Pump Personal Trainer 3-Day Training. (52:57) #ListenerLive question #1 - In your opinion, does bulking on low testosterone make sense or since my body will find it harder to build muscle most of the excess calories will be stored as fat? (55:17) #ListenerLive question #2 - Is the number of calories I'm taking in to gain weight but still lose weight normal? (1:06:32) #ListenerLive question #3 - Regarding your programming, I have noticed a trend I can't make sense of while running through the MAPS programs. During the heavy weight/low rep phases, I seem to peak during week 2 of the phase (for example Phase 1 Week 2 of MAPS Anabolic). I will go into week 3 of the Phase expecting to be able to add weight but I can't even hit the previous week's weight. Is this to be expected? (1:15:45) #ListenerLive question #4 – Any advice on what program I should run next? Currently, my goals are to look the best I can on my 39th birthday and for our family's spring break trip to the Dominican Republic. (1:33:44) Related Links/Products Mentioned BLACK FRIDAY SPECIAL: ALL MAPS Fitness Products & Bundles 60% off!  **Promo code BLACKFRIDAY at checkout** (Code expires Sunday Nov. 26th) Ask a question to Mind Pump, live! Email: live@mindpumpmedia.com Visit Seed for an exclusive offer for Mind Pump listeners! **Promo code MINDPUMP at checkout for 30% off your first month's supply of Seed's DS-01® Daily Synbiotic** Visit Organifi for the exclusive offer for Mind Pump listeners! **Promo code MINDPUMP at checkout** Sports are changing at the youth level Gary Brecka #2060 - Gary Brecka - The Joe Rogan Experience A pain reliever that alters perceptions of risk | ScienceDaily ARCELORMITTAL ORBIT Walmart is Selling Tiny Homes for $10,000 and You Can Be an Owner With a One Time Payment South Park: Joining the Panderverse (TV Movie 2023) - IMDb Upcoming South Park episode mocks Disney and Snow White actor Rachel Zegler in the most brutal of ways Edison and Dali's "creative nap" trick seems to actually work Blood flow restriction training - Wikipedia Occlusion Training Guide | MAPS Fitness Products   Occlusion Training Tutorial- How to Increase Muscle Size Using Blood Flow Restriction Personal Trainer 3-Day Training – Starting Jan. 15, 2024 Visit biOptimizers for an exclusive offer for Mind Pump listeners! **Promo code MINDPUMP10 at checkout** Mind Pump #2187: Why Building Muscle Is More Important Than Losing Fat With Dr. Gabrielle Lyon Mind Pump #1925: How To Build A Great Physique In 15 Minutes A Day Mind Pump #2080: Get Jacked With Bands! Mind Pump #952: Chad Wesley Smith Of Juggernaut Training Systems Mind Pump Podcast – YouTube Mind Pump Free Resources People Mentioned Dr. Stephen Cabral (@stephencabral) Instagram Gary Brecka (@garybrecka) Instagram Joe Rogan (@joerogan) Instagram  Layne Norton, Ph.D. (@biolayne) Instagram Andrew Huberman, Ph.D. (@hubermanlab) Instagram Tony Ferguson (@tonyfergusonxt) Instagram David Goggins (@davidgoggins) Instagram Rich Roll (@richroll) Instagram Dr. Gabrielle Lyon (@drgabriellelyon) Instagram Chad Wesley Smith (@chadwesleysmith) Instagram  

Giga Bytes Podcast #275: Hoy hablamos de el nuevo puesto de Filoni, Superman Legacy, PS Portal, COD, mas despidos, South Park regresa al gaming y mucho más!!! Filoni CCO de Star Wars, habla de su rol y el futuro de Baylan Superman Legacy recibe fecha, noticias del elenco PS Portal hands on COD Black OPS Gulf War? DLSS en próximo Switch no lo que se esperaba? Embracer despide 900, 505 Games un 30% de su fuerza laboral The Last of Us Part II Remastered anunciado Primer vistazo al juego de South Park: Snow Day! Circana: PS5 9% adelante de PS4, Xbox Series 11% atrás de Xbox One Steam Deck OLED disponible ya, Steam Deck 2 en 2-3 años Sigueme y Suscribete: Facebook.com/elgiga Youtube.com/elgiga947 Instagram.com/elgiga947                                                                                                                                            Twitch.tv/elgiga947 Twitter.com/elgiga947 Giga Bytes Podcast   #monsterenergypr @monsterenergy @Stephreyesmarketing @caribbeanxsports @eriberto213 #gigabytespodcast #PS5Slim #PS5 #GrandTheftAuto6 #GTA6 #Mariowonder #Spiderman2 #BaldursGate3 #AlanWake2 #Zelda #TOTK #RE4 #ResidentEvil4 #marvel #spiderman #spiderman2 #Xbox #XboxSeriesX #XboxSeriesS #FF7Rebirth

More Elliot Rodger Manifesto full narration: https://www.youtube.com/playlist?list=PLTz_vyR-zjcCWqqxaIyAU85ZEcv0k1FCtElliot Rodger's manifesto My Twisted World is a strange rabbit hole to head down. It mostly consists of a young man talking about how much he cries. Seriously, this guy will throw a tantrum over anything. His manifesto narration obviously has a human narrator, and this true crime incel murderer will make your skin crawl. Take a look inside of Elliot Rodger's head. Terrifying.YouTube: https://www.youtube.com/reddxyTwitch: https://www.twitch.tv/daytondoesDiscord: https://discord.gg/reddxPayPal: https://www.paypal.me/daytondoesPatreon: http://patreon.com/daytondoesTwitter: http://www.twitter.com/daytondoesFacebook: https://www.facebook.com/ReddXD/Teespring: https://teespring.com/stores/reddx

We're cracking open the Pandora's box of AI and tech, exploring the stormy departure of Sam Altman from OpenAI and his potential move to Microsoft. Our guest, Robert Plotkin, co-founder of Blue ShiftIP, casts a new light on the fears around AI replacing authors, artists, and inventors. Rather than replace, he argues, AI will enhance skills and intensify competition. Feel the pressure rise as we venture into the need for individuals to adapt and upskill in a world increasingly dominated by AI.Disney, Comcast, and Apple are shaking up the social media landscape, leaving platform X due to anti-Semitic content. We're also dissecting Elon Musk's divisive tweets and shedding light on the satirical commentary of the latest South Park episode.  Stick around as we switch gears to discuss the success of Nintendo's Wii and its impact on the gaming market.Last but not least, trust your palates to our resident whiskey aficionado, Marc, as he shares his experience with Elijah Craig's 18-year bourbon. Discover how this particular whiskey measures up in quality and whether its high price point merits a place in your liquor cabinet. So raise your glass, and welcome to TechTime Radio with your Host Nathan Mumm.Episode 180: Starts at 1:29This week on TechTime with Nathan Mumm®, the show is packed with exciting topics to keep you on the edge of your seat. We'll be discussing the Sam Altman Saga with a complete timeline and the impact this has on AI for the future, Elon Musk's X, and AI that can detect individual geese. We'll also explore why a company using AI hits our technology fail of the week. Then, Guest Robert Plotkin from BLueShiftIP, who recently authored an article for Inc. Magazine titled "Patent Law Isn't Ready for AI," will join the show to help us bust some AI misconceptions. We have a special "Myth Busters Thanksgiving Episode," So sit back, raise a glass, and welcome to a ThankFul TechTimeRadio. Tune into our live show on TechTimeRadio.com with Nathan Mumm, the show that makes you go "Hmmm" Technology news of the week for November 19th – 25th, 2023 --- [Now on Today's Show]: Starts at 3:07--- [Top Stories in Technology]: Starts at 5:18A Deep dive into Sam Altman Saga as Microsoft might have just pulled the most significant coup in Technology. - https://tinyurl.com/mszev9rwDisney, Comcast and Apple join advertiser exodus from Elon Musk's X over antisemitism - https://tinyurl.com/bdfyx7pu--- [Pick of the Day - Whiskey Tasting Reveal]: Starts at 26:36Elijah Craig 18 Year - Single Barrel | 90 Proof| $160 MSRP--- [Myth-Busters Thanksgiving Special]: Starts at 29:45Robert Plotkin, one of the co-founders of BlueShiftIP, He is going to be helping us with our Myth-Busters Thanksgiving Special--- [This Week in Technology]: Starts at 43:20November 19, 2006 Nintendo Releases Wii--- [Marc's Whiskey Mumble]: Starts at 45:32Marc Gregoire's review of this week's whiskey--- [Technology Fail of the Week]: Starts at 49:40This week's “Technology Fail” comes to us from UnitedHealth as it uses an AI model with 90% error rate to deny care.--- [Mike's Mesmerizing Moment brought to us by StoriCoffee®]: Starts at 52:07Question: Does peer pressure cause us to by phones? --- [Pick of the Day Whiskey Review]: Starts at 54:46Elijah Craig 18 Year - Single Barrel | 90 Proof| $160 MSRPMike: Thumbs UpNathan: Thumbs Up

En este episodio, Hermes y Dr. Malo conversan sobre el más reciente palo mediático recibido por Disney, el especial South Park: Joining the Panderverse (2023). Gracias por escuchar y recuerden que lo esperamos en www.patreon.com/hermeselsabio

A first-person shooter video game based on the first few seasons of South Park, mostly taking inspiration from the episodes "Starvin' Marvin", "An Elephant Makes Love to a Pig", and "Cartman Gets an Anal Probe". The game is powered by the Turok 2 game engine and was released in 1998 by Acclaim for the PC, Nintendo 64, and Sony PlayStation. Hosted on Acast. See acast.com/privacy for more information.

Pat stops by to give a mastodonic review of his favorite South Park episode. You won't want to miss this one, it's HUGE! --- Support this podcast: https://podcasters.spotify.com/pod/show/the-spirit-of-south-park/support

In this episode of Behind Massive Screens, Petter and Dóri meet Julia Stegemann, Lead Knowledge Manager in the Snowdrop team at Massive Entertainment and Ola Holmdahl, Snowdrop Operations Director, to find out more about how their job works, through a discussion that touches on Academia, table-top games, late medieval love songs and more! The Snowdrop engine, with its flexible and empowering tools, has helped develop a number of Ubisoft titles such as The Division, Mario + Rabbids, The Settlers, South Park and more upcoming games from Massive and Ubisoft. But how did Snowdrop come to be? Why was it developed in the first place, and how do you set up the users, the game developers, to easily find the information and guidelines they need to do their craft?

“In the episode, people from a poverty-stricken future year of 3045 travel back in time to find work, via a recently discovered time portal. When the boys try to earn some extra money, the time-traveling immigrants are willing to do the same work for next to nothing, causing the boys to lose their jobs. This affects the town's economy and the employment of the original occupants. "Goobacks" serves as a satire of illegal immigration, and mocks both sides of the debate concerning it.The episode is widely remembered as the origin of the catchphrase "They took our jobs!". ” - Factually Exclaims wikipedia.org “I remain a huge South Park fan, even if its politics leave me leery and its celebrity hatred seems excessive.” - rottentomatoes.com "Boycotting this movie might give it more of an audience, but the rational thing to do is to stay away from any theater playing it. Regrettably, curious children will see it and be corrupted. The future of our society looks very dim after thinking what those children will do and how they will behave after this powerful entertainment virus corrupts their hearts and minds."  - Proselytizes movieguide.org “So, If I Purchase These Trix, There'll Be No Trouble?”  - letterboxd.com Get inspired by our  Top Ten time travel movie lists Check out @time_pop_pod on Instagram, Twitter, & TikTok Please Like, Subscribe, and tell a friend about Time Pop. Send questions and comments and movie recommendations to timepoppod@gmail.com Find more great podcasts at What Sounds Awesome from We Mixed It  Comedy Spirituality - All the Answers Fitness Nutrition    -   Truth Not Trends The Wheel of Time  - Thank the Light Awesome Women  -   Be Brave Fitness Nutrition    -    That Fitness Couple

Join us for this edition of Off The Record. We take a look at the following films: South Park Joining the Panderverse (2023), Enemy Mine (1985), Behind Her Eyes (2021 TV Series), The Homesman (2014), Dragon Inn (1967), The Watcher (2022), and a special mention of the film When Evil Lurks (2023). Be sure to listen to the very end, as there is a special outtake for the audience.Notable Actors include: Trey Parker and Matt Stone, Dennis Quaid, Tom Bateman, Tommy Lee Jones, Lingfeng Shih, Naomi Watts, and Ezequiel Rodriguez.

Our Patreon podcasts are FINALLY available on Spotify! You can browse the entire catalog by searching for 'Remember The Game? Industries' on Spotify now! Are you on social media? Of course you are. So follow us!  Twitter: @MemberTheGame Instagram: @MemberTheGame Twitch.tv/MemberTheGame Youtube.com/RememberTheGame And if you want access to hundreds of bonus (ad-free) podcasts, along with multiple new shows EVERY WEEK, consider showing us some love over at Patreon. Subscriptions start at just $3/month, and 5% of our patreon income every month will be donated to our 24 hour Extra-Life charity stream at the end of the year! Patreon.com/RememberTheGame And show Andre some love: X, Mastodon, Blue Sky & Hive @thatcanadaguy TikTok, Instagram & Threads @thatcanadadude Facbook facebook.com/AndreAndMelball Youtube.com/@andreandmelballwrestlingtalk Twitch.tv/ourlocalestablishment youtube.com/@OURLOCALESTABLISHMENT youtube.com/@backbreakervideo We waited way too long to cover this one, but it's time to talk farts, Canada, crab people, and truthful sticks with South Park: The Stick of Truth. This might just be the funniest game ever made. And it's not only hilarious, but it's a serviceable RPG, too! I don't think there's enough meat on the bone here to satisfy diehard JRPG fans that don't know anything about South Park, but if you have even a passing interest in the show, I think The Stick of Truth is a must-play. It's like a season of the show was turned into a video game, and it's brilliant. My buddy Andre has been asking to come on the show to talk about The Stick of Truth for years, and the time has finally come. Call upon Paladin Butters, recruit Jimmy the Bard, and keep your eyes peeled for ManBearPig. I'm so fucking stoked for this episode! And before we fart on each other, I deliver another edition of the Infamous Intro! This week, we talk about the PS Plus price increase. Did Xbox fuck up by banning third-party controllers? And what is Thanksgiving like in Canada? Plus we play another round of 'Play One, Remake One, Erase One', too! This one features 3 South Park duds: Chef's Luv Shack, South Park Rally, and South Park (N64/PS1)! Learn more about your ad choices. Visit megaphone.fm/adchoices

On the 105th episode of the SKIDS PODCAST;We discuss the new developments in the death of Adam Johnson in Hockey; The new Will Smith controversy that has made the news; Pooping and the Three Sea Shells; Westley Snipes; Pee Wee Herman; The dangerous surge of Main Character Syndrome that has gripped the western world; The crazy success of James Cameron's career, and much more!!Opening Video -Dumpster fire Brighton Fire 04-18-13https://www.youtube.com/watch?v=8n3ZzWKXaU4Velvet Alley Designs -https://velvet-alley.com/Coffee Brand Coffee -https://coffeebrandcoffee.com/Use the coupon code: gps1 to receive 5% off your purchase.  You will be supporting an independent, growing company, as well as our show in the process!!#skids #skidspodcast #garbagepailskids #gps #podcast #comedy #discussion #adamjohnson #hockey #willsmith #brotherbilal #jadapinkettsmith #duanemartin #scandal #controversy #google #southpark #baseketball #cannibalthemusical #orgasmo #trueblood #hbo #peeweeherman #paulreubens #westleysnipes #threeseashells #demolitionman #maincharactersyndrome #80s #horror #80shorror #movies #jamescameron #terminator #terminator2 #aliens #titanic #avatar #theabyss #isreal #palastine #computers #harveyscomedyclub #heliumcomedyclub #bobsaget #georgecarlin #thearistocrats #howiemandel #bladerunner #bladerunner2047 #normmacdonald #judgejudy 

Before he launched The Daily Show, Chappelle's Show and South Park, and before her oversaw MTV, VH1 and Comedy Central simultaneously, Doug Herzog was MTV's first News Director. This week, the Broadcasting & Cable Hall Of Famer shares stories about almost turning down the offer from MTV co-founders, John Sykes and Bob Pittman; branding ten-to-the-hour-every-hour news briefs; hiring Kurt Loder; launching the Week in Rock; and generally transforming the fledgling network's news operation from VJ-hosted rip and reads to prime time, enterprise journalism. Along the way, Doug recalls bum-rushing Lionel Ritchie at Live Aid, office visits from the then-teenage Beastie Boys, and an unforgettable photo op with Bruce Springsteen.

Come listeners, let us take this podcast home and place it high up on the mantel...Seriously though, another awesome episode that not only pokes fun at the idea of home-schooling, features a near-perfect depiction of the jungle that is the schoolyard, but it also has a monkey jacking it!Oh and trust us, if Guy had his own way, this entire episode would've been recorded in Papa Cotswolds' voice.Support the show for EARLY & AD-FREE access to every show we produce, as well as 100 hours of exclusive content! Join the FFD family today at patreon.com/fourfingerdiscountCHECK OUT OUR OTHER PODCASTS:Four Finger Discount - spreaker.com/show/four-finger-discount-simpsons-podcastToon'd In! with Jim Cummings - spreaker.com/show/toond-in-with-jim-cummingsGoin' Down To South Park - spreaker.com/show/goin-down-to-south-parkSpeaKing Of The Hill - spreaker.com/show/speaking-of-the-hill-a-king-of-the-hill-Talking Seinfeld - spreaker.com/show/talking-seinfeldThe One About Friends - spreaker.com/show/the-one-about-friends-podcastThe Office Talk - spreaker.com/show/the-office-talk-podcastThis show is part of the Spreaker Prime Network, if you are interested in advertising on this podcast, contact us at https://www.spreaker.com/show/5828978/advertisement

More Elliot Rodger Manifesto full narration: https://www.youtube.com/playlist?list=PLTz_vyR-zjcCWqqxaIyAU85ZEcv0k1FCtElliot Rodger's manifesto My Twisted World is a strange rabbit hole to head down. It mostly consists of a young man talking about how much he cries. Seriously, this guy will throw a tantrum over anything. His manifesto narration obviously has a human narrator, and this true crime incel murderer will make your skin crawl. Take a look inside of Elliot Rodger's head. Terrifying.YouTube: https://www.youtube.com/reddxyTwitch: https://www.twitch.tv/daytondoesDiscord: https://discord.gg/reddxPayPal: https://www.paypal.me/daytondoesPatreon: http://patreon.com/daytondoesTwitter: http://www.twitter.com/daytondoesFacebook: https://www.facebook.com/ReddXD/Teespring: https://teespring.com/stores/reddx

In this episode CHUD and Lanni talk about the new South Park movie and how men are slowly losing the ability to do useful things for themselves. You can't eat your diploma. The World as it is Today is now also on Spotify! Listen to CHUD's new podcast, Are We Content?, on Apple or Google or grab the RSS feed HERE. You can donate to help us cover the cost of our podcasts! Venmo: @greenerpostures Paypal: Paypal.me/greenerpostures To check out Azure Standard CLICK HERE To sign up for Greener Postures workshop replays click here CLICK HERE Merch (teespring store): Support us by BUYING COOL STUFF GET IN TOUCH: EMAIL: greenerpostures@pm.me  WEBSITE:GreenerPostures.com RSS FEED: https://feed.podbean.com/Theworldasitistoday/feed.xml Our Linktree: YouTube, Instagram, Twitter, Teespring CLICK HERE Lanni on YouTube: youtube.com/@preservingtoday  

Nov 17-23: Two Oldboys go head to head, Atari's last chance, Anthony Hopkins represses himself, Kathy Ireland is an alien, Sam and Max hit the road, Snoop Dogg finds the perfect drink, Halle Berry is insane, South Park gives a history lesson, Matthew McConaughey drops the weight, Dr. Who hits 50, and an entire network devoted to food? All that and more as we look 30, 20, and 10 years ago!

Romans 5:1–11 - Our series, Pure Gospel, continues as we turn to Paul's words in Romans 5 that tell us what our position of righteousness in God's eyes brings us: peace, rejoicing, endurance, hope, and more.

Kyle Kamas joined me this week to talk about his journey into detailing. Like many others it all started with wanting to take care of his personal vehicle. Living close to Rupes in Colorado Kyle took every advantage to attend training classes at the Rupes Training Facility. This eventually led to Jason Rose offering him a position at Rupes to help with trainings. After a little over a year Kyle left Rupes to pay more attention to his own business and work on growing that. --- Support this podcast: https://podcasters.spotify.com/pod/show/detailsolutionspodcast/support

No media Allowed

Welcome to a brand new episode of Shark's Pond: A South Park Podcast. Join Bill as this week he reviews the season twenty-two episode "The Scoots". Topics discussed include Mr. Mackey hating scoots, Kenny wants a normal Halloween, how much candy the town needs to survive Halloween, a quick look into Fortnite and much more.Theme song courtesy of Joseph McDade https://josephmcdade.com/ Follow the show on Twitter https://twitter.com/sharkspond97 Join the shows Facebook group https://www.facebook.com/groups/sharkspond/

Segment 2, November 18th, 2023 Thanksgiving brings so many friends & families together each year.  It is a time of celebration.  Famed North Carolina chef, Jim Noble, returns to the Carolina Outdoors to talk about the heritage & traditions of Thanksgiving but also the mission of feeding the spiritual soul of many with Noble & companies other mission. Listeners to the Carolina Outdoors always like a good meal. Bill Bartee from the Charlotte outdoor store, Jesse Brown's & host of the Carolina Outdoors had a chance to speak with Chef Jim Noble. Things You'll Learn by Listening: Show Highlights: Jim Noble's preparation of Chicken Gizzards will make you like them The foods of Thanksgiving can evolve within different friends & families.  Foodies are necessarily a qualifiable title Food unites everyone whether business, faith, family, friends Many Traditions are individual & personal Restaurants to feed people & Ministry to feed a communities need More Insights from the Outdoor Guys:  Restaurants under Noble are Roosters, The Kings Kitchen, Copain Bakery, Noble Smoke, Bossy Beulah's, Field Pea Catering The Pursuits portion include the Charlotte Mecklenburg Dream Center, The King's Kitchen, & Le Premier Miracle Wine   Noble uses the outdoors to refresh.  Upland bird hunting & Fly Fishing are two of his favorites. The Carolina Outdoors is brought to you by Jesse Brown's.  Check out this Charlotte outdoor store located in the Southpark-area.

Segment 3, November 18th, 2023 Studies show that being around trees lowers stress, improves focus, & invites calming feelings. Also, people that take breaks in a green environment have more task consistency & make less errors that those that take breaks looking at concrete. Listeners to the Carolina Outdoors may be bird watchers, deer hunters, hikers, or just like a tree for shade on a sunny day. Bill Bartee from the Charlotte outdoor store, Jesse Brown's & host of the Carolina Outdoors had a chance to speak with the Executive Director of Trees Charlotte, Jane Singleton Myers.  She took  over the local non-profit that advocates for trees. Things You'll Learn by Listening: Show Highlights: Jane is known as the "Tree Lady" to many.  A credit to her step-daughter when she became the Trees Charlotte leader Charlotte has been called the "City of Trees" Her outlook on the recent report listing a tree canopy decline in Charlotte the past four years (2018-2022) from 47.8% to 47.3% How Trees help limit sick days, ADHD, Asthma, & traffic speeds The seasons for a tree & what happens each season What's a Tree Master with Trees Charlotte?  A Tree Ambassador? Although it is not managed directly by Trees Charlotte, the Treasure Tree program helps promote special trees in the area. Education about tree maintenance, health, & importance is something that Trees Charlotte is expandingMore Insights from the Outdoor Guys:  Tree Plantings through Trees Charlotte are a great way to team build for corporate groups, clubs, families, & more.   The Carolina Outdoors is brought to you by Jesse Brown's.  Check out this Charlotte outdoor store located in the Southpark-area.        

All podcast links:https://linktr.ee/aguyinhisroomNew a guy in his room #184!This time I'm solo and talking about "Dimensional Jumping", multiverses, the new Panderverse South Park, and seeing Tim Dillon at Carnegie Hall! Sike and Lubscribe now!Topics:Guest cancelled!It's just me on this podcast,I'm my own dom and sub,Lorena Bobbitt,Chopping peens off is common now,Live streaming infinity window,Parallel universes,Dimension jumping reddit,Getting into new age beliefs,Philosophical talk,Dimension jumping reddit posts,Numerology,Synchronicities,Intuition and gut feelings,South park panderverse,I saw Tim Dillon live at Carnegie Hall,The left constantly exaggerating,The boy who cried N**i,#aguyinhisroom #podcast #timdillon #carnegiehall #southpark #panderverse #dimensionaljumping #dimensionjump #multiverse #paralleluniverse #newage #synchronicity #thesecret #timdillonpodcast

This week on The Enemies List, Rick is joined by comedian, filmmaker, and activist, Toby Morton. Toby is also known for his legendary voice role as Scott Tenorman in "South Park." Together, they discuss his journey from animation to the sharp edges of political parody, crafting websites that humorously skewer prominent figures in today's tumultuous political landscape. Their conversation, charged with insights and revelations, offers a glimpse into the transformative power of satire in shaping public discourse. Timestamps: [00:02:22] Reacting to parody messaging [00:08:52] Moms for Liberty [00:16:40] The Presidential race Follow Resolute Square: Instagram Twitter TikTok Find out more at Resolute Square Learn more about your ad choices. Visit megaphone.fm/adchoices

Douglas Murray is a journalist, author and associate editor of The Spectator. As the turmoil of global events dominates the media, it can feel as though the world is spiralling into chaos. If we can't agree on what's happening, how can we make sense of the world? What's the solution in a post-truth world? Expect to learn how Victoria's Secret betrayed the body positivity movement, why people are struggling to agree on what's true anymore, how the “Gays for Gaza” movement will get on, whether we are past peak wokeness, why there is such a huge increase in conspiratorial thinking, what the most recent South Park episode has to say about our culture and much more... Sponsors: Get a 20% discount on your first order from Maui Nui Venison by going to https://www.mauinuivenison.com/modernwisdom (use code MODERNWISDOM) Get the Whoop 4.0 for free and get your first month for free at https://join.whoop.com/modernwisdom (discount automatically applied) Get a Free Sample Pack of all LMNT Flavours with your first box at https://www.drinklmnt.com/modernwisdom (automatically applied at checkout) Extra Stuff: Get my free Reading List of 100 books to read before you die → https://chriswillx.com/books/ Buy my productivity energy drink Neutonic: https://neutonic.com/modernwisdom - Get in touch. Instagram: https://www.instagram.com/chriswillx Twitter: https://www.twitter.com/chriswillx YouTube: https://www.youtube.com/modernwisdompodcast Email: https://chriswillx.com/contact/ Learn more about your ad choices. Visit megaphone.fm/adchoices

This Week in Startups is brought to you by… Northwest Registered Agent. When starting your business, it's important to use a service that will actually help you. Northwest Registered Agent is that service. They'll form your company fast, give you the documents you need to open a business bank account, and even provide you with mail scanning and a business address to keep your personal privacy intact. Visit http://northwestregisteredagent.com/twist to get a 60% discount on your next LLC. Lemon.io. Get access to Lemon Hire, a platform with more than 80,000 pre-vetted engineers that you can interview within 48 hours. Get $2000 off your first hire at http://lemon.io/hire today! .Tech Domains has a new program called startups.tech, where you can get your startup featured on This Week in Startups. Go to startups.tech/jason to find out how! * Today's show: TechCrunch's Alex Wilhelm joins Jason to break down the latest earnings reports from Uber (4:04), Lyft (24:28), and Nextdoor (44:15). Then, the two dive into WeWork's failed business model (1:01:44), the viability of various careers as AI advances (38:33), and much more! * Time stamps: (0:00) Tech Crunch's Alex Wilhelm joins Jason (4:04) Uber's earning report (7:04) Pirate cut-throat mentality analogy and the money-ball system (10:11) NetSuite - Download your free KPI Checklist at http://www.netsuite.com/twist (11:12) Did the press get Uber's Earnings report right? (17:39) Uber and the J-Curve (20:29) Miro - Sign up for a free account at https://www.miro.com/startups (22:17) Uber, China, and DiDi (23:14) ZIRP environment playbook to own a market (24:28) Lyft gets a participation trophy (25:22) Comparing Lyft to Uber's Revenue (Quarterly) (27:53) Labor availability (31:05) Arising Ventures - head to http://www.arisingventures.com/TWIST to learn more and connect with the team (32:15) Immigration policy and a record-low unemployment (37:37) The underground economy (38:33) AI effects on unemployment (39:12) Let's build new cities! (44:15) NextDoor earnings (55:34) A case for hiring remotely (59:13) Combining remote work with the power of AI (1:01:44) Clip from TWiST E969 with Alex about WeWork (1:04:22) Earnings “supplements” (1:07:35) When Alex discovered business as a youth (1:14:18) A South Park clip! * Check out TWiST E969: https://www.youtube.com/watch?v=aM1DDVq3_vs Follow Alex: https://twitter.com/alex * Read LAUNCH Fund 4 Deal Memo: https://www.launch.co/four Apply for Funding: https://www.launch.co/apply Buy ANGEL: https://www.angelthebook.com Great 2023 interviews: Steve Huffman, Brian Chesky, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland Check out Jason's suite of newsletters: https://substack.com/@calacanis * Follow Jason: Twitter: https://twitter.com/jason Instagram: https://www.instagram.com/jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis * Follow TWiST: Substack: https://twistartups.substack.com Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin

Kinsey Schofield from the fantastic Royal Family-Themed YouTube Podcast @TheEdgeofRoyalty joined me on a livestream about, well, the British Royal Family. This time we discussed Harry's therapist, Gabor Mate - and what he said after the session they had. The business of Harry and Meghan Markle has been a cause of problems with Spotify, Netflix, Harry's book Spare's ghostwriter and anyone who has worked with them - and now Family Guy have done a South Park in mocking the ex royal pair. They disbanded their Archetypes company and stopped the podcast, and are now trying a new kind of charm offensive…but will it work? #princeharry #meghanmarkle #royals Kinsey's links: https://www.youtube.com/channel/UCATUu9uInw97gu66-YGN3aw https://todifordaily.com Andrew Gold links: http://YouTube.com/andrewgold1 http://instagram.com/andrewgold_ok http://twitter.com/andrewgold_ok https://andrewgold.locals.com Learn more about your ad choices. Visit megaphone.fm/adchoices

Is 'The Fall of the House of Usher' about Usher? Dan discusses his inability to spend more money on another streaming service to watch South Park's "Panderverse" before the crew goes to the Bucket of Death. Then, Peter Rosenberg joins us to discuss his new podcast "Over the Top" with Michelle Beadle, working with Don La Greca, doing what he loves, taking the high road, Kendrick Lamar, and more. Plus, we have a segment of 'Pablo Torre Finds Out' with Pablo, Domonique Foxworth, and Wyatt Cenac examining God, Magic Mushrooms, Ninja Turtles, and more. Learn more about your ad choices. Visit megaphone.fm/adchoices

Join Lara Trump, Brianna Lyman, & Libby Emmons as they discuss the White House insurrection, Male athletes injuring female athletes in sports, South Park, airplane etiquette, AND MUCH MORE! #TheRightView