Podcasts about Robel

durée : 00:31:40 - Les Nuits de France Culture - par : Albane Penaranda, Mathias Le Gargasson, Antoine Dhulster - Par André Velter - Avec Abdellatif Laâbi (poète, auteur de ("Ecris la vie", La Différence) et Léon Robel (traducteur de Guennadi Aïgui : "Toujours plus loin dans les neiges", Obsidiane) - Lectures Daniel Znyk - Réalisation Patrick Molinier - réalisation : Massimo Bellini, Vincent Abouchar

Send us a textIn this episode, we feature the impressive journey of Tim Robel, a seasoned builder, fabricator and adventurer who has a knack for custom-building rugged vehicles.  Tim's exciting story starts at the age of 17 when he purchased a welder and began lowering and designing custom vehicles. Tim discusses his evolution as a fabricator and his adventures in rock crawling and desert racing. He details the unique builds of his GMC Canyon and his ultimate dream truck, the Oscar Mike, a massive 8 and a half ton military wrecker turned expedition vehicle. Tim also shares valuable insights into his creative process, the challenges and joys of building in a home garage, and his future plans of living the ultimate off-road and adventure lifestyle. If you have every built, dreamt of building, are handy at welding, or want to learn about electric paragliding, then grab your favorite campfire beverage and let's hit the road!Grab a Small Truck Campers Coffee Mug HERE! For only 15$!To Follow Tim's adventures on YouTube, CLICK HERE!For his instagram, CLICK HERE!Support the showSpecial Thanks to our Supporters of the community: Capri Campers- Everything you need- Check there HERE! AFUERA COFFEE- DO GOOD, GO AFUERA! Use code smalltruckcampers for 10% off your next order! Chain Line Designs, best way to deal with camper jacks! Shop all the STC Merch here, and support the channel! For the Small Truck Campers website, click here!To check out our Instagram, click here!For our YouTube Channel, click here!Join our Facebook Group here!

Service made in Germany: Zwischen Frust und Chance

We're back with the latest episode of our series, "Offstage": A sneak peek into the lives, ministries, and passions of our EDM '25 keynote speakers. Robel Chemeda Disasa shares his journey, from a university student to a leader mobilizing others for Christ in Ethiopia. Through his experiences in evangelism, discipleship, and missions, we see the transformative power of a life dedicated to God's global purpose.

The podcast is currently on a break between Season 3 and Season 4. As promised, during this period I'll be uploading previous episode or two, beginning with this edit of what was the Episode 13 of the podcast, and the first of Season 2 – Shiga Kogen Ski Resort: Japan's Largest Ski Resort. In this episode I spoke with two-time Olympian Robel Zemichael about Shiga Kogen Mountain Resort, which lays claim to being both Japan's largest and highest ski resort. Robel is the manager of Shiga International Ski School and based in Shiga Kogen all year round, so there's no better person to speak with and he advocates for Shiga wonderfully.Important to note that this is an edited version of the longer original episode, in which Robel and I went into more detailed discussion about all aspects of the resort, services provide by Shiga International Ski School, accommodation and more. If you haven't already, make sure to listen to the full episode. This edit provides a taste of the longer conversation I had with my guest Robel, but I think it interesting in its own right while it might also prompt you to go back and listen to the longer, original version. I hope you enjoy!Snow Country Stories Japan is a bi-weekly podcast about life and travel in Japan's legendary 'yukiguni'. For more information about the show and your host, visit our website - www.snowcountrystories.com - and make sure to subscribe, rate and review the podcast and follow us on Instagram and Facebook.

Ice is an important facet of Earth's climate system. Since ice affects our climate and sea levels, understanding the way ice sheets develop and change over time helps us better predict the future of our planet. So, what are researchers finding? Dr. Alexander Robel joins the podcast to enlighten us… Dr. Robel is an Assistant Professor in the School of Earth and Atmospheric Sciences at Georgia Tech University, and serves as the head of the Ice and Climate research group. By studying the causes of ice sheet change, Dr. Robel is on a mission to develop conceptual, mathematical, and computational tools to predict future changes.. In this episode, we talk about: What happens when glaciers melt. Where glaciers and ice sheets tend to form, and why they melt.  What “sea ice” is, and where it forms.  How computer models are used in Dr. Robel's research.   Want to learn more about Dr. Robel and his research? Click here now! Take advantage of a 5% discount on Ekster accessories by using the code FINDINGGENIUS. Enhance your style and functionality with premium accessories. Visit bit.ly/3uiVX9R to explore latest collection Episode also available on Apple Podcasts: http://apple.co/30PvU9C

Today we got a #BLACKSTARPOWER episode as Trae connects with the Hosts of The UpNUp Podcast to celebrate their 6th year anniversary! First, she sits with Robel Andmicael to learn more about his journey into podcasting and some of the elements that keep him grounded in the work. Next, Trae chats with Erm Haile to hear how this podcast is keeping folks informed of amazing folks doing great work in our community and how he continues to give his all toward their efforts.

Obwohl es wie aus Eimern schüttet, scheint die Thermoplan Glück zu haben. Robel Getachews Weinroboter-Glas ist immer halbvoll und Sven Schilliger hat erneut das Töggeliturnier gewonnen. Herzlichen Glückwunsch!

Alex ist ein Coach der Technologie nutzt um den Prozess der persönlichen Entwicklung neu zu schreiben. Erfahre mehr über die Rolle von KI in seinem beruflichen Alltag, von der Automatisierung mühsamer Aufgaben bis hin zur Schaffung tiefgreifender Klientenerfahrungen. LinkedIn-Profil von Alex: https://www.linkedin.com/in/alex-robel-980572215/ --- Send in a voice message: https://podcasters.spotify.com/pod/show/nicht-mehr-wegzudenken/message

Du bist im Bereich E-Commerce unterwegs und willst Deine Kundenzufriedenheit steigern, die Logistiklösungen durchdenken oder erweitern, dann höre unbedingt in diese Podcast-Folge hinein. Der Vertriebsleiter Robel Mutlu von BAUR Fulfillment Solutions GmbH gibt tiefe Einblicke in seine Welt aus Customer Services, Logistik Services & Finance Services. Wie Du am besten Kontakt zu Robel Mutlu aufnehmen kannst und wo Du ihn auf einer Messe treffen kannst, erfährst Du in dieser aktuellen Episode. Höre direkt hinein. Hier kannst Du unseren Interviewgast kontaktieren: Tel.: +49 (0)9572 60990 111 Email: robel.mutlu@baur-fs.de https://www.linkedin.com/in/robel-mutlu/ https://www.baur-fs.de/ Damit Du keine weiteren Inhalte von akQuise strategen verpasst: https://www.akquise-strategen.de/ https://www.instagram.com/akquise_str... https://www.facebook.com/profile.php?... https://www.youtube.com/channel/UCrju6C3cIqFn-ACCvjYhthw Damit Du keine weiteren Inhalte von Ihren FREIRAUMschaffern verpasst: https://www.linkedin.com/in/petra-sierks-435033257/ https://www.linkedin.com/company/ihrefreiraumschaffer/ https://www.xing.com/profile/Petra_Sierks/portfolio https://www.instagram.com/petra_sierks/ https://www.instagram.com/ihrefreiraumschaffer/ https://www.facebook.com/petra.sierks/ https://www.facebook.com/IhreFreiraumschaffer https://www.youtube.com/@ihrefreiraumschaffer9475 Hör auch in unsere anderen Episoden rein und erhalte nützliche B2B-Akquise Tipps und spannende Interviews: Spotify: spoti.fi/3P50mpr Apple: apple.co/3J2Fv21 Google: bit.ly/3CdcfSu Du hast Fragen zu einem bestimmten Thema oder suchst Unterstützung bei Deiner B2B-Akquise und willst Dir einen Termin bei Petra Sierks buchen? Schreib uns eine E-Mail an willkommen@akquise-strategen.de oder besuche unsere Webseite www.akquise-strategen.de.

Playlist: Life catch Robel kidena - Life catch Robel kidena about life

Vi går i jultider och terminen börjar lida mot sitt slut. Årets sista poddavsnitt är hämtat live från Äkta i Sundsvall. Denna gång gästas vi av Robel Alemseghed och Lydia Nyström. Båda är ledare för en varsin NG-grupp och delar sina tankar och erfarenheter om att ha en kristen skolgrupp. Vi får även ta del av viktiga tips gällande hur man kan ha med sig Jesus i sin vardag. Låt oss avsluta med flaggan i topp - håll i och håll ut - här kommer ett avsnitt som förhoppningvis kan inspirera dig.

Episode 13 of the podcast – and the first of Season 2 – brings us back to Nagano and Japan's largest ski resort, Shiga Kogen. A host venue in the 1998 Winter Olympic Games, Shiga Kogen Mountain Resort is Japan's largest and highest ski resort and enjoys the longest season of any resort in Nagano or Central Japan. Despite this the resort remains relatively unknown to international visitors…In this episode I speak with Robel Zemichael, manager of Shiga International Ski School. Robel is a two-time Olympian who represented his birth country of Ethiopia at the 2006 Games in Torino and 2010 Games in Vancouver. He is long-term resident of Japan who has called Nagano his home for some time now, and as we wait the beginning of the ski school's ninth season, I was fortunate enough to sit down with Robel for a chat about the ski resort and why he rates it so highly.Make sure to check out the episode page on the Snow Country Stories Japan website for further information, relevant links and a map showing where Shiga Kogen is located. Given the size of Japan's snow country and often unfamiliar place names, understanding where each episode is talking about can be confusing. For that reason, each episode has a page on website including links and maps to help you get your bearings and learn more about the people and places discussed.For more information about the podcast and your host, visit our website: www.snowcountrystories.com. Make sure to subscribe, rate and review the podcast and follow us on Instagram, Twitter and Facebook. 00:00  Episode Introduction 03:30  Robel's Story 08:01  Shiga Kogen Size & Ski Areas 14:11  Recommended Length of Stay 16:08  Altitude & Topography 17:35  Season Length & Best Time to Visit 20:13  Snow Quality & Where to Find the Best Powder 24:32  Shiga International Ski School: Group & Private Lessons 26:41  Shiga International Ski School: Tours & Backcountry31:37  Where to Stay 37:29  Ski & Snowboard Rental 42:33  Episode Close Snow Country Stories Japan is a bi-weekly podcast about life and travel in Japan's legendary 'yukiguni'. For more information about the show and your host, visit our website - www.snowcountrystories.com - and make sure to subscribe, rate and review the podcast and follow us on Instagram, Twitter and Facebook.

Podden gästas av onkologen Robel Malki där vi tar upp lyssnarnas hälso- och träningsrelaterade frågor, särskilt kopplat till cancer. Exempel på frågor är: Ökar socker risken för cancer? Ökar stimulering av MTOR (signalvägen som bl a leder till muskeluppbyggnad) riskerna för cancer. Hur kan man påverka riskerna att drabbas av cancer? Vi går även in på proteinpulver/kosttillskott och dess hälsopåverkan. För mer från Robel Malki

In this heartfelt podcast episode, Wendy Robel shares her deeply personal journey of loss, grief, and the healing power of faith and love. Wendy opens up about her experience as a widow, the challenges she faced after her husband's passing from stage four liver cancer, and how she found strength and solace in her relationship with God. Through Wendy's candid storytelling, you'll witness her transformation from the depths of despair to embracing joy and laughter again. Join her as she reflects on the highs and lows of her unique path, offering hope and inspiration to those navigating their own grief journeys. Learn more about Wendy and her book here- https://www.wendyrobel.com/ If you have been touched by this podcast, please consider donating to the non-profit we are under, Widow Goals. In addition to this podcast, Widow Goals provides Grief Recovery Classes, Social Media support, resources, and local and soon-to-be national events. ⁠You can give tax-free here; thank you!⁠ Click here to get a free copy of Chapter 1 of our upcoming book, Widowed 2 Soon! Click here to set up a call with Michelle to discuss the Grief Recovery Method and if it could be a good fit for you.  Follow Michelle on TikTok Link to Michelle's Books, Widow Checklist, and More Follow us on Instagram https://www.instagram.com/widowed2soon_/ https://www.instagram.com/widowgoals Join our Facebook Page Subscribe to our YouTube Channel Email us widowed2soon@widowgoals.org Nonprofit Widow Goals  --- Send in a voice message: https://podcasters.spotify.com/pod/show/widowed2soon/message

From February 15, 2023 - Aaron Robel is a CISO with a positive, energetic, and transparent approach that fosters trust and collaboration across the business. He has a consistent track record for developing high-performing security programs and teams that bring business value while maintaining the organization's risk posture. We discuss infosec in the financial services sector, as his current role is CISO for a credit union and I am a former bank CISO and currently work with several financial institutions as a virtual CISO. --- Send in a voice message: https://podcasters.spotify.com/pod/show/virtual-ciso-moment/message

How do glaciers and ice affect our climate system? Dr. Alexander Robel joins the podcast to discuss the intricacies of how and why ice sheets change… Dr. Robel is an Assistant Professor in the School of Earth and Atmospheric Sciences at Georgia Tech University and serves as the head of the Ice and Climate research group. Here, he and his team use math, high-performance computing, and data to better understand changing ice sheets – and their in-depth relationship with the climate system.  Jump in now to discover: The different types of glaciers that exist on our planet.  The compositional difference between oceanic and freshwater glaciers.  How heat is transferred into ice, resulting in melting glaciers.  How computer models can be used to show how glaciers respond to climate change.  Could Dr. Robel's work give us answers to our questions surrounding melting ice and rising sea levels? Join the conversation now to find out! Want to learn more about Dr. Robel and his research? Click here now! Episode also available on Apple Podcasts: https://apple.co/3bO8R6q  

Ice is an important facet of Earth's climate system. Since ice affects our climate and sea levels, understanding the way ice sheets develop and change over time helps us better predict the future of our planet. So, what are researchers finding? Dr. Alexander Robel joins the podcast to enlighten us… Dr. Robel is an Assistant Professor in the School of Earth and Atmospheric Sciences at Georgia Tech University, and serves as the head of the Ice and Climate research group. By studying the causes of ice sheet change, Dr. Robel is on a mission to develop conceptual, mathematical, and computational tools to predict future changes. Offer: Magnesium is integral for 600+ biochemical processes in the human body. The common misconception is that consuming more magnesium will automatically improve health and well-being. The truth is that there are various forms of magnesium, each of which is essential for a variety of physiological processes. Most people are inadequate in all forms of magnesium, while even those considered "healthy" typically only ingest 1 or 2 kinds. Consuming all 7 of magnesium's primary forms is the key to accessing all its health benefits.That's why we packed 7 forms of 450mg of elemental magnesium into each serving of  Wild Mag Complex. One dose a day is all you need. Learn more and grab a bottle today at WildFoods.co. Use code GENIUS for 10% off your order. In this episode, we talk about: What happens when glaciers melt. Where glaciers and ice sheets tend to form, and why they melt.  What “sea ice” is, and where it forms.  How computer models are used in Dr. Robel's research.   Want to learn more about Dr. Robel and his research? Click here now! Episode also available on Apple Podcasts: http://apple.co/30PvU9C

2023 is the year of Multimodal AI, and Latent Space is going multimodal too! * This podcast comes with a video demo at the 1hr mark and it's a good excuse to launch our YouTube - please subscribe! * We are also holding two events in San Francisco — the first AI | UX meetup next week (already full; we'll send a recap here on the newsletter) and Latent Space Liftoff Day on May 4th (signup here; but get in touch if you have a high profile launch you'd like to make). * We also joined the Chroma/OpenAI ChatGPT Plugins Hackathon last week where we won the Turing and Replit awards and met some of you in person!This post featured on Hacker News.Out of the five senses of the human body, I'd put sight at the very top. But weirdly when it comes to AI, Computer Vision has felt left out of the recent wave compared to image generation, text reasoning, and even audio transcription. We got our first taste of it with the OCR capabilities demo in the GPT-4 Developer Livestream, but to date GPT-4's vision capability has not yet been released. Meta AI leapfrogged OpenAI and everyone else by fully open sourcing their Segment Anything Model (SAM) last week, complete with paper, model, weights, data (6x more images and 400x more masks than OpenImages), and a very slick demo website. This is a marked change to their previous LLaMA release, which was not commercially licensed. The response has been ecstatic:SAM was the talk of the town at the ChatGPT Plugins Hackathon and I was fortunate enough to book Joseph Nelson who was frantically integrating SAM into Roboflow this past weekend. As a passionate instructor, hacker, and founder, Joseph is possibly the single best person in the world to bring the rest of us up to speed on the state of Computer Vision and the implications of SAM. I was already a fan of him from his previous pod with (hopefully future guest) Beyang Liu of Sourcegraph, so this served as a personal catchup as well. Enjoy! and let us know what other news/models/guests you'd like to have us discuss! - swyxRecorded in-person at the beautiful StudioPod studios in San Francisco.Full transcript is below the fold.Show Notes* Joseph's links: Twitter, Linkedin, Personal* Sourcegraph Podcast and Game Theory Story* Represently* Roboflow at Pioneer and YCombinator* Udacity Self Driving Car dataset story* Computer Vision Annotation Formats* SAM recap - top things to know for those living in a cave* https://segment-anything.com/* https://segment-anything.com/demo* https://arxiv.org/pdf/2304.02643.pdf * https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/* https://blog.roboflow.com/segment-anything-breakdown/* https://ai.facebook.com/datasets/segment-anything/* Ask Roboflow https://ask.roboflow.ai/* GPT-4 Multimodal https://blog.roboflow.com/gpt-4-impact-speculation/Cut for time:* WSJ mention* Des Moines Register story* All In Pod: timestamped mention* In Forbes: underrepresented investors in Series A* Roboflow greatest hits* https://blog.roboflow.com/mountain-dew-contest-computer-vision/* https://blog.roboflow.com/self-driving-car-dataset-missing-pedestrians/* https://blog.roboflow.com/nerualhash-collision/ and Apple CSAM issue * https://www.rf100.org/Timestamps* [00:00:19] Introducing Joseph* [00:02:28] Why Iowa* [00:05:52] Origin of Roboflow* [00:16:12] Why Computer Vision* [00:17:50] Computer Vision Use Cases* [00:26:15] The Economics of Annotation/Segmentation* [00:32:17] Computer Vision Annotation Formats* [00:36:41] Intro to Computer Vision & Segmentation* [00:39:08] YOLO* [00:44:44] World Knowledge of Foundation Models* [00:46:21] Segment Anything Model* [00:51:29] SAM: Zero Shot Transfer* [00:51:53] SAM: Promptability* [00:53:24] SAM: Model Assisted Labeling* [00:56:03] SAM doesn't have labels* [00:59:23] Labeling on the Browser* [01:00:28] Roboflow + SAM Video Demo * [01:07:27] Future Predictions* [01:08:04] GPT4 Multimodality* [01:09:27] Remaining Hard Problems* [01:13:57] Ask Roboflow (2019)* [01:15:26] How to keep up in AITranscripts[00:00:00] Hello everyone. It is me swyx and I'm here with Joseph Nelson. Hey, welcome to the studio. It's nice. Thanks so much having me. We, uh, have a professional setup in here.[00:00:19] Introducing Joseph[00:00:19] Joseph, you and I have known each other online for a little bit. I first heard about you on the Source Graph podcast with bian and I highly, highly recommend that there's a really good game theory story that is the best YC application story I've ever heard and I won't tease further cuz they should go listen to that.[00:00:36] What do you think? It's a good story. It's a good story. It's a good story. So you got your Bachelor of Economics from George Washington, by the way. Fun fact. I'm also an econ major as well. You are very politically active, I guess you, you did a lot of, um, interning in political offices and you were responding to, um, the, the, the sheer amount of load that the Congress people have in terms of the, the support.[00:01:00] So you built, representing, which is Zendesk for Congress. And, uh, I liked in your source guide podcast how you talked about how being more responsive to, to constituents is always a good thing no matter what side of the aisle you're on. You also had a sideline as a data science instructor at General Assembly.[00:01:18] As a consultant in your own consultancy, and you also did a bunch of hackathon stuff with Magic Sudoku, which is your transition from N L P into computer vision. And apparently at TechCrunch Disrupt, disrupt in 2019, you tried to add chess and that was your whole villain origin story for, Hey, computer vision's too hard.[00:01:36] That's full, the platform to do that. Uh, and now you're co-founder c e o of RoboFlow. So that's your bio. Um, what's not in there that[00:01:43] people should know about you? One key thing that people realize within maybe five minutes of meeting me, uh, I'm from Iowa. Yes. And it's like a funnily novel thing. I mean, you know, growing up in Iowa, it's like everyone you know is from Iowa.[00:01:56] But then when I left to go to school, there was not that many Iowans at gw and people were like, oh, like you're, you're Iowa Joe. Like, you know, how'd you find out about this school out here? I was like, oh, well the Pony Express was running that day, so I was able to send. So I really like to lean into it.[00:02:11] And so you kind of become a default ambassador for places that. People don't meet a lot of other people from, so I've kind of taken that upon myself to just make it be a, a part of my identity. So, you know, my handle everywhere Joseph of Iowa, like I I, you can probably find my social security number just from knowing that that's my handle.[00:02:25] Cuz I put it plastered everywhere. So that's, that's probably like one thing.[00:02:28] Why Iowa[00:02:28] What's your best pitch for Iowa? Like why is[00:02:30] Iowa awesome? The people Iowa's filled with people that genuinely care. You know, if you're waiting a long line, someone's gonna strike up a conversation, kinda ask how you were Devrel and it's just like a really genuine place.[00:02:40] It was a wonderful place to grow up too at the time, you know, I thought it was like, uh, yeah, I was kind of embarrassed and then be from there. And then I actually kinda looking back it's like, wow, you know, there's good schools, smart people friendly. The, uh, high school that I went to actually Ben Silverman, the CEO and, or I guess former CEO and co-founder of Pinterest and I have the same teachers in high school at different.[00:03:01] The co-founder, or excuse me, the creator of crispr, the gene editing technique, Dr. Jennifer. Doudna. Oh, so that's the patent debate. There's Doudna. Oh, and then there's Fang Zang. Uh, okay. Yeah. Yeah. So Dr. Fang Zang, who I think ultimately won the patent war, uh, but is also from the same high school.[00:03:18] Well, she won the patent, but Jennifer won the[00:03:20] prize.[00:03:21] I think that's probably, I think that's probably, I, I mean I looked into it a little closely. I think it was something like she won the patent for CRISPR first existing and then Feng got it for, uh, first use on humans, which I guess for commercial reasons is the, perhaps more, more interesting one. But I dunno, biolife Sciences, is that my area of expertise?[00:03:38] Yep. Knowing people that came from Iowa that do cool things, certainly is. Yes. So I'll claim it. Um, but yeah, I, I, we, um, at Roble actually, we're, we're bringing the full team to Iowa for the very first time this last week of, of April. And, well, folks from like Scotland all over, that's your company[00:03:54] retreat.[00:03:54] The Iowa,[00:03:55] yeah. Nice. Well, so we do two a year. You know, we've done Miami, we've done. Some of the smaller teams have done like Nashville or Austin or these sorts of places, but we said, you know, let's bring it back to kinda the origin and the roots. Uh, and we'll, we'll bring the full team to, to Des Moines, Iowa.[00:04:13] So, yeah, like I was mentioning, folks from California to Scotland and many places in between are all gonna descend upon Des Moines for a week of, uh, learning and working. So maybe you can check in with those folks. If, what do they, what do they decide and interpret about what's cool. Our state. Well, one thing, are you actually headquartered in Des Moines on paper?[00:04:30] Yes. Yeah.[00:04:30] Isn't that amazing? That's like everyone's Delaware and you're like,[00:04:33] so doing research. Well, we're, we're incorporated in Delaware. Okay. We we're Delaware Sea like, uh, most companies, but our headquarters Yeah. Is in Des Moines. And part of that's a few things. One, it's like, you know, there's this nice Iowa pride.[00:04:43] And second is, uh, Brad and I both grew up in Brad Mc, co-founder and I grew up in, in Des Moines. And we met each other in the year 2000. We looked it up for the, the YC app. So, you know, I think, I guess more of my life I've known Brad than not, uh, which is kind of crazy. Wow. And during yc, we did it during 2020, so it was like the height of Covid.[00:05:01] And so we actually got a house in Des Moines and lived, worked outta there. I mean, more credit to. So I moved back. I was living in DC at the time, I moved back to to Des Moines. Brad was living in Des Moines, but he moved out of a house with his. To move into what we called our hacker house. And then we had one, uh, member of the team as well, Jacob Sorowitz, who moved from Minneapolis down to Des Moines for the summer.[00:05:21] And frankly, uh, code was a great time to, to build a YC company cuz there wasn't much else to do. I mean, it's kinda like wash your groceries and code. It's sort of the, that was the routine[00:05:30] and you can use, uh, computer vision to help with your groceries as well.[00:05:33] That's exactly right. Tell me what to make.[00:05:35] What's in my fridge? What should I cook? Oh, we'll, we'll, we'll cover[00:05:37] that for with the G P T four, uh, stuff. Exactly. Okay. So you have been featured with in a lot of press events. Uh, but maybe we'll just cover the origin story a little bit in a little bit more detail. So we'll, we'll cover robo flow and then we'll cover, we'll go into segment anything.[00:05:52] Origin of Roboflow[00:05:52] But, uh, I think it's important for people to understand. Robo just because it gives people context for what you're about to show us at the end of the podcast. So Magic Sudoku tc, uh, techers Disrupt, and then you go, you join Pioneer, which is Dan Gross's, um, YC before yc.[00:06:07] Yeah. That's how I think about it.[00:06:08] Yeah, that's a good way. That's a good description of it. Yeah. So I mean, robo flow kind of starts as you mentioned with this magic Sudoku thing. So you mentioned one of my prior business was a company called Represent, and you nailed it. I mean, US Congress gets 80 million messages a year. We built tools that auto sorted them.[00:06:23] They didn't use any intelligent auto sorting. And this is somewhat a solved problem in natural language processing of doing topic modeling or grouping together similar sentiment and things like this. And as you mentioned, I'd like, I worked in DC for a bit and been exposed to some of these problems and when I was like, oh, you know, with programming you can build solutions.[00:06:40] And I think the US Congress is, you know, the US kind of United States is a support center, if you will, and the United States is sports center runs on pretty old software, so mm-hmm. We, um, we built a product for that. It was actually at the time when I was working on representing. Brad, his prior business, um, is a social games company called Hatchlings.[00:07:00] Uh, he phoned me in, in 2017, apple had released augmented reality kit AR kit. And Brad and I are both kind of serial hackers, like I like to go to hackathons, don't really understand new technology until he build something with them type folks. And when AR Kit came out, Brad decided he wanted to build a game with it that would solve Sudoku puzzles.[00:07:19] And the idea of the game would be you take your phone, you hover hold it over top of a Sudoku puzzle, it recognizes the state of the board where it is, and then it fills it all in just right before your eyes. And he phoned me and I was like, Brad, this sounds awesome and sounds like you kinda got it figured out.[00:07:34] What, what's, uh, what, what do you think I can do here? It's like, well, the machine learning piece of this is the part that I'm most uncertain about. Uh, doing the digit recognition and, um, filling in some of those results. I was like, well, I mean digit recognition's like the hell of world of, of computer vision.[00:07:48] That's Yeah, yeah, MNIST, right. So I was like, that that part should be the, the easy part. I was like, ah, I'm, he's like, I'm not so super sure, but. You know, the other parts, the mobile ar game mechanics, I've got pretty well figured out. I was like, I, I think you're wrong. I think you're thinking about the hard part is the easy part.[00:08:02] And he is like, no, you're wrong. The hard part is the easy part. And so long story short, we built this thing and released Magic Sudoku and it kind of caught the Internet's attention of what you could do with augmented reality and, and with computer vision. It, you know, made it to the front ofer and some subreddits it run Product Hunt Air app of the year.[00:08:20] And it was really a, a flash in the pan type app, right? Like we were both running separate companies at the time and mostly wanted to toy around with, with new technology. And, um, kind of a fun fact about Magic Sudoku winning product Hunt Air app of the year. That was the same year that I think the model three came out.[00:08:34] And so Elon Musk won a Golden Kitty who we joked that we share an award with, with Elon Musk. Um, the thinking there was that this is gonna set off a, a revolution of if two random engineers can put together something that makes something, makes a game programmable and at interactive, then surely lots of other engineers will.[00:08:53] Do similar of adding programmable layers on top of real world objects around us. Earlier we were joking about objects in your fridge, you know, and automatically generating recipes and these sorts of things. And like I said, that was 2017. Roboflow was actually co-found, or I guess like incorporated in, in 2019.[00:09:09] So we put this out there, nothing really happened. We went back to our day jobs of, of running our respective businesses, I sold Represently and then as you mentioned, kind of did like consulting stuff to figure out the next sort of thing to, to work on, to get exposed to various problems. Brad appointed a new CEO at his prior business and we got together that summer of 2019.[00:09:27] We said, Hey, you know, maybe we should return to that idea that caught a lot of people's attention and shows what's possible. And you know what, what kind of gives, like the future is here. And we have no one's done anything since. No one's done anything. So why is, why are there not these, these apps proliferated everywhere.[00:09:42] Yeah. And so we said, you know, what we'll do is, um, to add this software layer to the real world. Will build, um, kinda like a super app where if you pointed it at anything, it will recognize it and then you can interact with it. We'll release a developer platform and allow people to make their own interfaces, interactivity for whatever object they're looking at.[00:10:04] And we decided to start with board games because one, we had a little bit of history there with, with Sudoku two, there's social by default. So if one person, you know finds it, then they'd probably share it among their friend. Group three. There's actually relatively few barriers to entry aside from like, you know, using someone else's brand name in your, your marketing materials.[00:10:19] Yeah. But other than that, there's no real, uh, inhibitors to getting things going and, and four, it's, it's just fun. It would be something that'd be bring us enjoyment to work on. So we spent that summer making, uh, boggle the four by four word game provable, where, you know, unlike Magic Sudoku, which to be clear, totally ruins the game, uh, you, you have to solve Sudoku puzzle.[00:10:40] You don't need to do anything else. But with Boggle, if you and I are playing, we might not find all of the words that adjacent letter tiles. Unveil. So if we have a, an AI tell us, Hey, here's like the best combination of letters that make high scoring words. And so we, we made boggle and released it and that, and that did okay.[00:10:56] I mean maybe the most interesting story was there's a English as a second language program in, in Canada that picked it up and used it as a part of their curriculum to like build vocabulary, which I thought was kind of inspiring. Example, and what happens just when you put things on the internet and then.[00:11:09] We wanted to build one for chess. So this is where you mentioned we went to 2019. TechCrunch Disrupt TechCrunch. Disrupt holds a Hackathon. And this is actually, you know, when Brad and I say we really became co-founders, because we fly out to San Francisco, we rent a hotel room in the Tenderloin. We, uh, we, we, uh, have one room and there's like one, there's room for one bed, and then we're like, oh, you said there was a cot, you know, on the, on the listing.[00:11:32] So they like give us a little, a little cot, the end of the cot, like bled and over into like the bathroom. So like there I am sleeping on the cot with like my head in the bathroom and the Tenderloin, you know, fortunately we're at a hackathon glamorous. Yeah. There wasn't, there wasn't a ton of sleep to be had.[00:11:46] There is, you know, we're, we're just like making and, and shipping these, these sorts of many[00:11:50] people with this hack. So I've never been to one of these things, but[00:11:52] they're huge. Right? Yeah. The Disrupt Hackathon, um, I don't, I don't know numbers, but few hundreds, you know, classically had been a place where it launched a lot of famous Yeah.[00:12:01] Sort of flare. Yeah. And I think it's, you know, kind of slowed down as a place for true company generation. But for us, Brad and I, who likes just doing hackathons, being, making things in compressed time skills, it seemed like a, a fun thing to do. And like I said, we'd been working on things, but it was only there that like, you're, you're stuck in a maybe not so great glamorous situation together and you're just there to make a, a program and you wanna make it be the best and compete against others.[00:12:26] And so we add support to the app that we were called was called Board Boss. We couldn't call it anything with Boggle cause of IP rights were called. So we called it Board Boss and it supported Boggle and then we were gonna support chess, which, you know, has no IP rights around it. Uh, it's an open game.[00:12:39] And we did so in 48 hours, we built an app that, or added fit capability to. Point your phone at a chess board. It understands the state of the chess board and converts it to um, a known notation. Then it passes that to stock fish, the open source chess engine for making move recommendations and it makes move recommendations to, to players.[00:13:00] So you could either play against like an ammunition to AI or improve your own game. We learn that one of the key ways users like to use this was just to record their games. Cuz it's almost like reviewing game film of what you should have done differently. Game. Yeah, yeah, exactly. And I guess the highlight of, uh, of chess Boss was, you know, we get to the first round of judging, we get to the second round of judging.[00:13:16] And during the second round of judging, that's when like, TechCrunch kind of brings around like some like celebs and stuff. They'll come by. Evan Spiegel drops by Ooh. Oh, and he uh, he comes up to our, our, our booth and um, he's like, oh, so what does, what does this all do? And you know, he takes an interest in it cuz the underpinnings of, of AR interacting with the.[00:13:33] And, uh, he is kinda like, you know, I could use this to like cheat on chess with my friends. And we're like, well, you know, that wasn't exactly the, the thesis of why we made it, but glad that, uh, at least you think it's kind of neat. Um, wait, but he already started Snapchat by then? Oh, yeah. Oh yeah. This, this is 2019, I think.[00:13:49] Oh, okay, okay. Yeah, he was kind of just checking out things that were new and, and judging didn't end up winning any, um, awards within Disrupt, but I think what we won was actually. Maybe more important maybe like the, the quote, like the co-founders medal along the way. Yep. The friends we made along the way there we go to, to play to the meme.[00:14:06] I would've preferred to win, to be clear. Yes. You played a win. So you did win, uh,[00:14:11] $15,000 from some Des Moines, uh, con[00:14:14] contest. Yeah. Yeah. The, uh, that was nice. Yeah. Slightly after that we did, we did win. Um, some, some grants and some other things for some of the work that we've been doing. John Papa John supporting the, uh, the local tech scene.[00:14:24] Yeah. Well, so there's not the one you're thinking of. Okay. Uh, there's a guy whose name is Papa John, like that's his, that's his, that's his last name. His first name is John. So it's not the Papa John's you're thinking of that has some problematic undertones. It's like this guy who's totally different. I feel bad for him.[00:14:38] His press must just be like, oh, uh, all over the place. But yeah, he's this figure in the Iowa entrepreneurial scene who, um, he actually was like doing SPACs before they were cool and these sorts of things, but yeah, he funds like grants that encourage entrepreneurship in the state. And since we'd done YC and in the state, we were eligible for some of the awards that they were providing.[00:14:56] But yeah, it was disrupt that we realized, you know, um, the tools that we made, you know, it took us better part of a summer to add Boggle support and it took us 48 hours to add chest support. So adding the ability for programmable interfaces for any object, we built a lot of those internal tools and our apps were kind of doing like the very famous shark fin where like it picks up really fast, then it kind of like slowly peters off.[00:15:20] Mm-hmm. And so we're like, okay, if we're getting these like shark fin graphs, we gotta try something different. Um, there's something different. I remember like the week before Thanksgiving 2019 sitting down and we wrote this Readme for, actually it's still the Readme at the base repo of Robo Flow today has spent relatively unedited of the manifesto.[00:15:36] Like, we're gonna build tools that enable people to make the world programmable. And there's like six phases and, you know, there's still, uh, many, many, many phases to go into what we wrote even at that time to, to present. But it's largely been, um, right in line with what we thought we would, we would do, which is give engineers the tools to add software to real world objects, which is largely predicated on computer vision. So finding the right images, getting the right sorts of video frames, maybe annotating them, uh, finding the right sort of models to use to do this, monitoring the performance, all these sorts of things. And that from, I mean, we released that in early 2020, and it's kind of, that's what's really started to click.[00:16:12] Why Computer Vision[00:16:12] Awesome. I think we should just kind[00:16:13] of[00:16:14] go right into where you are today and like the, the products that you offer, just just to give people an overview and then we can go into the, the SAM stuff. So what is the clear, concise elevator pitch? I think you mentioned a bunch of things like make the world programmable so you don't ha like computer vision is a means to an end.[00:16:30] Like there's, there's something beyond that. Yeah.[00:16:32] I mean, the, the big picture mission for the business and the company and what we're working on is, is making the world programmable, making it read and write and interactive, kind of more entertaining, more e. More fun and computer vision is the technology by which we can achieve that pretty quickly.[00:16:48] So like the one liner for the, the product in, in the company is providing engineers with the tools for data and models to build programmable interfaces. Um, and that can be workflows, that could be the, uh, data processing, it could be the actual model training. But yeah, Rob helps you use production ready computer vision workflows fast.[00:17:10] And I like that.[00:17:11] In part of your other pitch that I've heard, uh, is that you basically scale from the very smallest scales to the very largest scales, right? Like the sort of microbiology use case all the way to[00:17:20] astronomy. Yeah. Yeah. The, the joke that I like to make is like anything, um, underneath a microscope and, and through a telescope and everything in between needs to, needs to be seen.[00:17:27] I mean, we have people that run models in outer space, uh, underwater remote places under supervision and, and known places. The crazy thing is that like, All parts of, of not just the world, but the universe need to be observed and understood and acted upon. So vision is gonna be, I dunno, I feel like we're in the very, very, very beginnings of all the ways we're gonna see it.[00:17:50] Computer Vision Use Cases[00:17:50] Awesome. Let's go into a lo a few like top use cases, cuz I think that really helps to like highlight the big names that you've, big logos that you've already got. I've got Walmart and Cardinal Health, but I don't, I don't know if you wanna pull out any other names, like, just to illustrate, because the reason by the way, the reason I think that a lot of developers don't get into computer vision is because they think they don't need it.[00:18:11] Um, or they think like, oh, like when I do robotics, I'll do it. But I think if, if you see like the breadth of use cases, then you get a little bit more inspiration as to like, oh, I can use[00:18:19] CVS lfa. Yeah. It's kind of like, um, you know, by giving, by making it be so straightforward to use vision, it becomes almost like a given that it's a set of features that you could power on top of it.[00:18:32] And like you mentioned, there's, yeah, there's Fortune One there over half the Fortune 100. I've used the, the tools that Robel provides just as much as 250,000 developers. And so over a quarter million engineers finding and developing and creating various apps, and I mean, those apps are, are, are far and wide.[00:18:49] Just as you mentioned. I mean everything from say, like, one I like to talk about was like sushi detection of like finding the like right sorts of fish and ingredients that are in a given piece of, of sushi that you're looking at to say like roof estimation of like finding. If there's like, uh, hail damage on, on a given roof, of course, self-driving cars and understanding the scenes around us is sort of the, you know, very early computer vision everywhere.[00:19:13] Use case hardhat detection, like finding out if like a given workplace is, is, is safe, uh, disseminate, have the right p p p on or p p e on, are there the right distance from various machines? A huge place that vision has been used is environmental monitoring. Uh, what's the count of species? Can we verify that the environment's not changing in unexpected ways or like river banks are become, uh, becoming recessed in ways that we anticipate from satellite imagery, plant phenotyping.[00:19:37] I mean, people have used these apps for like understanding their plants and identifying them. And that dataset that's actually largely open, which is what's given a proliferation to the iNaturalist, is, is that whole, uh, hub of, of products. Lots of, um, people that do manufacturing. So, like Rivian for example, is a Rubal customer, and you know, they're trying to scale from 1000 cars to 25,000 cars to a hundred thousand cars in very short order.[00:20:00] And that relies on having the. Ability to visually ensure that every part that they're making is produced correctly and right in time. Medical use cases. You know, there's actually, this morning I was emailing with a user who's accelerating early cancer detection through breaking apart various parts of cells and doing counts of those cells.[00:20:23] And actually a lot of wet lab work that folks that are doing their PhDs or have done their PhDs are deeply familiar with that is often required to do very manually of, of counting, uh, micro plasms or, or things like this. There's. All sorts of, um, like traffic counting and smart cities use cases of understanding curb utilization to which sort of vehicles are, are present.[00:20:44] Uh, ooh. That can be[00:20:46] really good for city planning actually.[00:20:47] Yeah. I mean, one of our customers does exactly this. They, they measure and do they call it like smart curb utilization, where uhhuh, they wanna basically make a curb be almost like a dynamic space where like during these amounts of time, it's zoned for this during these amounts of times.[00:20:59] It's zoned for this based on the flows and e ebbs and flows of traffic throughout the day. So yeah, I mean the, the, the truth is that like, you're right, it's like a developer might be like, oh, how would I use vision? And then all of a sudden it's like, oh man, all these things are at my fingertips. Like I can just, everything you can see.[00:21:13] Yeah. Right. I can just, I can just add functionality for my app to understand and ingest the way, like, and usually the way that someone gets like almost nerd sniped into this is like, they have like a home automation project, so it's like send Yeah. Give us a few. Yeah. So send me a text when, um, a package shows up so I can like prevent package theft so I can like go down and grab it right away or.[00:21:29] We had a, uh, this one's pretty, pretty niche, but it's pretty funny. There was this guy who, during the pandemic wa, wanted to make sure his cat had like the proper, uh, workout. And so I've shared the story where he basically decided that. He'd make a cat workout machine with computer vision, you might be alone.[00:21:43] You're like, what does that look like? Well, what he decided was he would take a robotic arm strap, a laser pointer to it, and then train a machine to recognize his cat and his cat only, and point the laser pointer consistently 10 feet away from the cat. There's actually a video of you if you type an YouTube cat laser turret, you'll find Dave's video.[00:22:01] Uh, and hopefully Dave's cat has, has lost the weight that it needs to, cuz that's just the, that's an intense workout I have to say. But yeah, so like, that's like a, um, you know, these, uh, home automation projects are pretty common places for people to get into smart bird feeders. I've seen people that like are, are logging and understanding what sort of birds are, uh, in their background.[00:22:18] There's a member of our team that was working on actually this as, as a whole company and has open sourced a lot of the data for doing bird species identification. And now there's, I think there's even a company that's, uh, founded to create like a smart bird feeder, like captures photos and tells you which ones you've attracted to your yard.[00:22:32] I met that. Do, you know, get around the, uh, car sharing company that heard it? Them never used them. They did a SPAC last year and they had raised at like, They're unicorn. They raised at like 1.2 billion, I think in the, the prior round and inspected a similar price. I met the CTO of, of Getaround because he was, uh, using Rob Flow to hack into his Tesla cameras to identify other vehicles that are like often nearby him.[00:22:56] So he's basically building his own custom license plate recognition, and he just wanted like, keep, like, keep tabs of like, when he drives by his friends or when he sees like regular sorts of folks. And so he was doing like automated license plate recognition by tapping into his, uh, camera feeds. And by the way, Elliot's like one of the like OG hackers, he was, I think one of the very first people to like, um, she break iPhones and, and these sorts of things.[00:23:14] Mm-hmm. So yeah, the project that I want, uh, that I'm gonna work on right now for my new place in San Francisco is. There's two doors. There's like a gate and then the other door. And sometimes we like forget to close, close the gate. So like, basically if it sees that the gate is open, it'll like send us all a text or something like this to make sure that the gate is, is closed at the front of our house.[00:23:32] That's[00:23:32] really cool. And I'll, I'll call out one thing that readers and listeners can, uh, read out on, on your history. One of your most popular initial, um, viral blog post was about, um, autonomous vehicle data sets and how, uh, the one that Udacity was using was missing like one third of humans. And, uh, it's not, it's pretty problematic for cars to miss humans.[00:23:53] Yeah, yeah, actually, so yeah, the Udacity self-driving car data set, which look to their credit, it was just meant to be used for, for academic use. Um, and like as a part of courses on, on Udacity, right? Yeah. But the, the team that released it, kind of hastily labeled and let it go out there to just start to use and train some models.[00:24:11] I think that likely some, some, uh, maybe commercial use cases maybe may have come and, and used, uh, the dataset, who's to say? But Brad and I discovered this dataset. And when we were working on dataset improvement tools at Rob Flow, we ran through our tools and identified some like pretty, as you mentioned, key issues.[00:24:26] Like for example, a lot of strollers weren't labeled and I hope our self-driving cars do those, these sorts of things. And so we relabeled the whole dataset by hand. I have this very fond memory is February, 2020. Brad and I are in Taiwan. So like Covid is actually just, just getting going. And the reason we were there is we were like, Hey, we can work on this from anywhere for a little bit.[00:24:44] And so we spent like a, uh, let's go closer to Covid. Well, you know, I like to say we uh, we got early indicators of, uh, how bad it was gonna be. I bought a bunch of like N 90 fives before going o I remember going to the, the like buying a bunch of N 95 s and getting this craziest look like this like crazy tin hat guy.[00:25:04] Wow. What is he doing? And then here's how you knew. I, I also got got by how bad it was gonna be. I left all of them in Taiwan cuz it's like, oh, you all need these. We'll be fine over in the us. And then come to find out, of course that Taiwan was a lot better in terms of, um, I think, yeah. Safety. But anyway, we were in Taiwan because we had planned this trip and you know, at the time we weren't super sure about the, uh, covid, these sorts of things.[00:25:22] We always canceled it. We didn't, but I have this, this very specific time. Brad and I were riding on the train from Clay back to Taipei. It's like a four hour ride. And you mentioned Pioneer earlier, we were competing in Pioneer, which is almost like a gamified to-do list. Mm-hmm. Every week you say what you're gonna do and then other people evaluate.[00:25:37] Did you actually do the things you said you were going to do? One of the things we said we were gonna do was like this, I think re-release of this data set. And so it's like late, we'd had a whole week, like, you know, weekend behind us and, uh, we're on this train and it was very unpleasant situation, but we relabeled this, this data set, and one sitting got it submitted before like the Sunday, Sunday countdown clock starts voting for, for.[00:25:57] And, um, once that data got out back out there, just as you mentioned, it kind of picked up and Venture beat, um, noticed and wrote some stories about it. And we really rereleased of course, the data set that we did our best job of labeling. And now if anyone's listening, they can probably go out and like find some errors that we surely still have and maybe call us out and, you know, put us, put us on blast.[00:26:15] The Economics of Annotation (Segmentation)[00:26:15] But,[00:26:16] um, well, well the reason I like this story is because it, it draws attention to the idea that annotation is difficult and basically anyone looking to use computer vision in their business who may not have an off-the-shelf data set is going to have to get involved in annotation. And I don't know what it costs.[00:26:34] And that's probably one of the biggest hurdles for me to estimate how big a task this is. Right? So my question at a higher level is tell the customers, how do you tell customers to estimate the economics of annotation? Like how many images do, do we need? How much, how long is it gonna take? That, that kinda stuff.[00:26:50] How much money and then what are the nuances to doing it well, right? Like, cuz obviously Udacity had a poor quality job, you guys had proved it, and there's errors every everywhere. Like where do[00:26:59] these things go wrong? The really good news about annotation in general is that like annotation of course is a means to an end to have a model be able to recognize a thing.[00:27:08] Increasingly there's models that are coming out that can recognize things zero shot without any annotation, which we're gonna talk about. Yeah. Which, we'll, we'll talk more about that in a moment. But in general, the good news is that like the trend is that annotation is gonna become decreasingly a blocker to starting to use computer vision in meaningful ways.[00:27:24] Now that said, just as you mentioned, there's a lot of places where you still need to do. Annotation. I mean, even with these zero shot models, they might have of blind spots, or maybe you're a business, as you mentioned, that you know, it's proprietary data. Like only Rivian knows what a rivian is supposed to look like, right?[00:27:39] Uh, at the time of, at the time of it being produced, like underneath the hood and, and all these sorts of things. And so, yeah, that's gonna necessarily require annotation. So your question of how long is it gonna take, how do you estimate these sorts of things, it really comes down to the complexity of the problem that you're solving and the amount of variance in the scene.[00:27:57] So let's give some contextual examples. If you're trying to recognize, we'll say a scratch on one specific part and you have very strong lighting. You might need fewer images because you control the lighting, you know the exact part and maybe you're lucky in the scratch. Happens more often than not in similar parts or similar, uh, portions of the given part.[00:28:17] So in that context, you, you, the function of variance, the variance is, is, is lower. So the number of images you need is also lower to start getting up to work. Now the orders of magnitude we're talking about is that like you can have an initial like working model from like 30 to 50 images. Yeah. In this context, which is shockingly low.[00:28:32] Like I feel like there's kind of an open secret in computer vision now, the general heuristic that often. Users, is that like, you know, maybe 200 images per class is when you start to have a model that you can rely[00:28:45] on? Rely meaning like 90, 99, 90, 90%, um,[00:28:50] uh, like what's 85 plus 85? Okay. Um, that's good. Again, these are very, very finger in the wind estimates cuz the variance we're talking about.[00:28:59] But the real question is like, at what point, like the framing is not like at what point do it get to 99, right? The framing is at what point can I use this thing to be better than the alternative, which is humans, which maybe humans or maybe like this problem wasn't possible at all. And so usually the question isn't like, how do I get to 99?[00:29:15] A hundred percent? It's how do I ensure that like the value I am able to get from putting this thing in production is greater than the alternative? In fact, even if you have a model that's less accurate than humans, there might be some circumstances where you can tolerate, uh, a greater amount of inaccuracy.[00:29:32] And if you look at the accuracy relative to the cost, Using a model is extremely cheap. Using a human for the same sort of task can be very expensive. Now, in terms of the actual accuracy of of what you get, there's probably some point at which the cost, but relative accuracy exceeds of a model, exceeds the high cost and hopefully high accuracy of, of a human comparable, like for example, there's like cameras that will track soccer balls or track events happening during sporting matches.[00:30:02] And you can go through and you know, we actually have users that work in sports analytics. You can go through and have a human. Hours and hours of footage. Cuz not just watching their team, they're watching every other team, they're watching scouting teams, they're watching junior teams, they're watching competitors.[00:30:15] And you could have them like, you know, track and follow every single time the ball goes within blank region of the field or every time blank player goes into, uh, this portion of the field. And you could have, you know, exact, like a hundred percent accuracy if that person, maybe, maybe not a hundred, a human may be like 95, 90 7% accuracy of every single time the ball is in this region or this player is on the field.[00:30:36] Truthfully, maybe if you're scouting analytics, you actually don't need 97% accuracy of knowing that that player is on the field. And in fact, if you can just have a model run at a 1000th, a 10000th of the cost and goes through and finds all the times that Messi was present on the field mm-hmm. That the ball was in this region of the.[00:30:54] Then even if that model is slightly less accurate, the cost is just so orders of magnitude different. And the stakes like the stakes of this problem, of knowing like the total number of minutes that Messi played will say are such that we have a higher air tolerance, that it's a no-brainer to start to use Yeah, a computer vision model in this context.[00:31:12] So not every problem requires equivalent or greater human performance. Even when it does, you'd be surprised at how fast models get there. And in the times when you, uh, really look at a problem, the question is, how much accuracy do I need to start to get value from this? This thing, like the package example is a great one, right?[00:31:27] Like I could in theory set up a camera that's constantly watching in front of my porch and I could watch the camera whenever I have a package and then go down. But of course, I'm not gonna do that. I value my time to do other sorts of things instead. And so like there, there's this net new capability of, oh, great, I can have an always on thing that tells me when a package shows up, even if you know the, the thing that's gonna text me.[00:31:46] When a package shows up, let's say a flat pack shows up instead of a box and it doesn't know what a flat pack likes, looks like initially. Doesn't matter. It doesn't matter because I didn't have this capability at all before. And I think that's the true case where a lot of computer vision problems exist is like it.[00:32:00] It's like you didn't even have this capability, this superpower before at all, let alone assigning a given human to do the task. And that's where we see like this explosion of, of value.[00:32:10] Awesome. Awesome. That was a really good overview. I want to leave time for the others, but I, I really want to dive into a couple more things with regards to Robo Flow.[00:32:17] Computer Vision Annotation Formats[00:32:17] So one is, apparently your original pitch for Robo Flow was with regards to conversion tools for computer vision data sets. And I'm sure as, as a result of your job, you have a lot of rants. I've been digging for rants basically on like the best or the worst annotation formats. What do we know? Cause most of us, oh my gosh, we only know, like, you know, I like,[00:32:38] okay, so when we talk about computer vision annotation formats, what we're talking about is if you have an image and you, you picture a boing box around my face on that image.[00:32:46] Yeah. How do you describe where that Monty box is? X, Y, Z X Y coordinates. Okay. X, y coordinates. How, what do you mean from the top lefts.[00:32:52] Okay. You, you, you, you take X and Y and then, and then the. The length and, and the width of the, the[00:32:58] box. Okay. So you got like a top left coordinate and like the bottom right coordinate or like the, the center of the bottom.[00:33:02] Yeah. Yeah. Top, left, bottom right. Yeah. That's one type of format. Okay. But then, um, I come along and I'm like, you know what? I want to do a different format where I wanna just put the center of the box, right. And give the length and width. Right. And by the way, we didn't even talk about what X and Y we're talking about.[00:33:14] Is X a pixel count? Is a relative pixel count? Is it an absolute pixel count? So the point is, the number of ways to describe where a box lives in a freaking image is endless, uh, seemingly and. Everyone decided to kind of create their own different ways of describing the coordinates and positions of where in this context of bounding Box is present.[00:33:39] Uh, so there's some formats, for example, that like use re, so for the x and y, like Y is, uh, like the left, most part of the image is zero. And the right most part of the image is one. So the, the coordinate is like anywhere from zero to one. So 0.6 is, you know, 60% of your way right up the image to describe the coordinate.[00:33:53] I guess that was, that was X instead of Y. But the point is there, of the zero to one is the way that we determined where that was in the position, or we're gonna do an absolute pixel position anyway. We got sick, we got sick of all these different annotation formats. So why do you even have to convert between formats?[00:34:07] Is is another part of this, this story. So different training frameworks, like if you're using TensorFlow, you need like TF Records. If you're using PyTorch, it's probably gonna be, well it depends on like what model you're using, but someone might use Coco JSON with PyTorch. Someone else might use like a, just a YAML file and a text file.[00:34:21] And to describe the cor it's point is everyone that creates a model. Or creates a dataset rather, has created different ways of describing where and how a bounding box is present in the image. And we got sick of all these different formats and doing these in writing all these different converter scripts.[00:34:39] And so we made a tool that just converts from one script, one type of format to another. And the, the key thing is that like if you get that converter script wrong, your model doesn't not work. It just fails silently. Yeah. Because the bounding boxes are now all in the wrong places. And so you need a way to visualize and be sure that your converter script, blah, blah blah.[00:34:54] So that was the very first tool we released of robo. It was just a converter script, you know, like these, like these PDF to word converters that you find. It was basically that for computer vision, like dead simple, really annoying thing. And we put it out there and people found some, some value in, in that.[00:35:08] And you know, to this day that's still like a surprisingly painful[00:35:11] problem. Um, yeah, so you and I met at the Dall-E Hackathon at OpenAI, and we were, I was trying to implement this like face masking thing, and I immediately ran into that problem because, um, you know, the, the parameters that Dall-E expected were different from the one that I got from my face, uh, facial detection thing.[00:35:28] One day it'll go away, but that day is not today. Uh, the worst format that we work with is, is. The mart form, it just makes no sense. And it's like, I think, I think it's a one off annotation format that this university in China started to use to describe where annotations exist in a book mart. I, I don't know, I dunno why that So best[00:35:45] would be TF record or some something similar.[00:35:48] Yeah, I think like, here's your chance to like tell everybody to use one one standard and like, let's, let's, can[00:35:53] I just tell them to use, we have a package that does this for you. I'm just gonna tell you to use the row full package that converts them all, uh, for you. So you don't have to think about this. I mean, Coco JSON is pretty good.[00:36:04] It's like one of the larger industry norms and you know, it's in JS O compared to like V xml, which is an XML format and Coco json is pretty descriptive, but you know, it has, has its own sort of drawbacks and flaws and has random like, attribute, I dunno. Um, yeah, I think the best way to handle this problem is to not have to think about it, which is what we did.[00:36:21] We just created a, uh, library that, that converts and uses things. Uh, for us. We've double checked the heck out of it. There's been hundreds of thousands of people that have used the library and battle tested all these different formats to find those silent errors. So I feel pretty good about no longer having to have a favorite format and instead just rely on.[00:36:38] Dot load in the format that I need. Great[00:36:41] Intro to Computer Vision Segmentation[00:36:41] service to the community. Yeah. Let's go into segmentation because is at the top of everyone's minds, but before we get into segment, anything, I feel like we need a little bit of context on the state-of-the-art prior to Sam, which seems to be YOLO and uh, you are the leading expert as far as I know.[00:36:56] Yeah.[00:36:57] Computer vision, there's various task types. There's classification problems where we just like assign tags to images, like, you know, maybe safe work, not safe work, sort of tagging sort of stuff. Or we have object detection, which are the boing boxes that you see and all the formats I was mentioning in ranting about there's instant segmentation, which is the polygon shapes and produces really, really good looking demos.[00:37:19] So a lot of people like instant segmentation.[00:37:21] This would be like counting pills when you point 'em out on the, on the table. Yeah. So, or[00:37:25] soccer players on the field. So interestingly, um, counting you could do with bounding boxes. Okay. Cause you could just say, you know, a box around a person. Well, I could count, you know, 12 players on the field.[00:37:35] Masks are most useful. Polygons are most useful if you need very precise area measurements. So you have an aerial photo of a home and you want to know, and the home's not a perfect box, and you want to know the rough square footage of that home. Well, if you know the distance between like the drone and, and the ground.[00:37:53] And you have the precise polygon shape of the home, then you can calculate how big that home is from aerial photos. And then insurers can, you know, provide say accurate estimates and that's maybe why this is useful. So polygons and, and instant segmentation are, are those types of tasks? There's a key point detection task and key point is, you know, if you've seen those demos of like all the joints on like a hand kind of, kind of outlined, there's visual question answering tasks, visual q and a.[00:38:21] And that's like, you know, some of the stuff that multi-modality is absolutely crushing for, you know, here's an image, tell me what food is in this image. And then you can pass that and you can make a recipe out of it. But like, um, yeah, the visual question in answering task type is where multi-modality is gonna have and is already having an enormous impact.[00:38:40] So that's not a comprehensive survey, very problem type, but it's enough to, to go into why SAM is significant. So these various task types, you know, which model to use for which given circumstance. Most things is highly dependent on what you're ultimately aiming to do. Like if you need to run a model on the edge, you're gonna need a smaller model, cuz it is gonna run on edge, compute and process in, in, in real time.[00:39:01] If you're gonna run a model on the cloud, then of course you, uh, generally have more compute at your disposal Considerations like this now, uh,[00:39:08] YOLO[00:39:08] just to pause. Yeah. Do you have to explain YOLO first before you go to Sam, or[00:39:11] Yeah, yeah, sure. So, yeah. Yeah, we should. So object detection world. So for a while I talked about various different task types and you can kinda think about a slide scale of like classification, then obvious detection.[00:39:20] And on the right, at most point you have like segmentation tasks. Object detection. The bounding boxes is especially useful for a wide, like it's, it's surprisingly versatile. Whereas like classification is kind of brittle. Like you only have a tag for the whole image. Well, that doesn't, you can't count things with tags.[00:39:35] And on the other hand, like the mask side of things, like drawing masks is painstaking. And so like labeling is just a bit more difficult. Plus like the processing to produce masks requires more compute. And so usually a lot of folks kind of landed for a long time on obvious detection being a really happy medium of affording you with rich capabilities because you can do things like count, track, measure.[00:39:56] In some CAGR context with bounding boxes, you can see how many things are present. You can actually get a sense of how fast something's moving by tracking the object or bounding box across multiple frames and comparing the timestamp of where it was across those frames. So obviously detection is a very common task type that solves lots of things that you want do with a given model.[00:40:15] In obviously detection. There's been various model frameworks over time. So kind of really early on there's like R-CNN uh, then there's faster rc n n and these sorts of family models, which are based on like resnet kind of architectures. And then a big thing happens, and that is single shot detectors. So faster, rc n n despite its name is, is very slow cuz it takes two passes on the image.[00:40:37] Uh, the first pass is, it finds par pixels in the image that are most interesting to, uh, create a bounding box candidate out of. And then it passes that to a, a classifier that then does classification of the bounding box of interest. Right. Yeah. You can see, you can see why that would be slow. Yeah. Cause you have to do two passes.[00:40:53] You know, kind of actually led by, uh, like mobile net was I think the first large, uh, single shot detector. And as its name implies, it was meant to be run on edge devices and mobile devices and Google released mobile net. So it's a popular implementation that you find in TensorFlow. And what single shot detectors did is they said, Hey, instead of looking at the image twice, what if we just kind of have a, a backbone that finds candidate bounding boxes?[00:41:19] And then we, we set loss functions for objectness. We set loss function. That's a real thing. We set loss functions for objectness, like how much obj, how object do this part of the images. We send a loss function for classification, and then we run the image through the model on a single pass. And that saves lots of compute time and you know, it's not necessarily as accurate, but if you have lesser compute, it can be extremely useful.[00:41:42] And then the advances in both modeling techniques in compute and data quality, single shot detectors, SSDs has become, uh, really, really popular. One of the biggest SSDs that has become really popular is the YOLO family models, as you described. And so YOLO stands for you only look once. Yeah, right, of course.[00:42:02] Uh, Drake's, uh, other album, um, so Joseph Redman introduces YOLO at the University of Washington. And Joseph Redman is, uh, kind of a, a fun guy. So for listeners, for an Easter egg, I'm gonna tell you to Google Joseph Redman resume, and you'll find, you'll find My Little Pony. That's all I'll say. And so he introduces the very first YOLO architecture, which is a single shot detector, and he also does it in a framework called Darknet, which is like this, this own framework that compiles the Cs, frankly, kind of tough to work with, but allows you to benefit from the speedups that advance when you operate in a low level language like.[00:42:36] And then he releases, well, what colloquially is known as YOLO V two, but a paper's called YOLO 9,000 cuz Joseph Redmond thought it'd be funny to have something over 9,000. So get a sense for, yeah, some fun. And then he releases, uh, YOLO V three and YOLO V three is kind of like where things really start to click because it goes from being an SSD that's very limited to competitive and, and, and superior to actually mobile That and some of these other single shot detectors, which is awesome because you have this sort of solo, I mean, him and and his advisor, Ali, at University of Washington have these, uh, models that are becoming really, really powerful and capable and competitive with these large research organizations.[00:43:09] Joseph Edmond leaves Computer Vision Research, but there had been Alexia ab, one of the maintainers of Darknet released Yola VI four. And another, uh, researcher, Glenn Yer, uh, jocker had been working on YOLO V three, but in a PyTorch implementation, cuz remember YOLO is in a dark implementation. And so then, you know, YOLO V three and then Glenn continues to make additional improvements to YOLO V three and pretty soon his improvements on Yolov theory, he's like, oh, this is kind of its own things.[00:43:36] Then he releases YOLO V five[00:43:38] with some naming[00:43:39] controversy that we don't have Big naming controversy. The, the too long didn't read on the naming controversy is because Glen was not originally involved with Darknet. How is he allowed to use the YOLO moniker? Roe got in a lot of trouble cuz we wrote a bunch of content about YOLO V five and people were like, ah, why are you naming it that we're not?[00:43:55] Um, but you know,[00:43:56] cool. But anyway, so state-of-the-art goes to v8. Is what I gather.[00:44:00] Yeah, yeah. So yeah. Yeah. You're, you're just like, okay, I got V five. I'll skip to the end. Uh, unless, unless there's something, I mean, I don't want, well, so I mean, there's some interesting things. Um, in the yolo, there's like, there's like a bunch of YOLO variants.[00:44:10] So YOLOs become this, like this, this catchall for various single shot, yeah. For various single shot, basically like runs on the edge, it's quick detection framework. And so there's, um, like YOLO R, there's YOLO S, which is a transformer based, uh, yolo, yet look like you only look at one sequence is what s stands were.[00:44:27] Um, the pp yo, which, uh, is PAT Paddle implementation, which is by, which Chinese Google is, is their implementation of, of TensorFlow, if you will. So basically YOLO has like all these variants. And now, um, yo vii, which is Glen has been working on, is now I think kind of like, uh, one of the choice models to use for single shot detection.[00:44:44] World Knowledge of Foundation Models[00:44:44] Well, I think a lot of those models, you know, Asking the first principal's question, like let's say you wanna find like a bus detector. Do you need to like go find a bunch of photos of buses or maybe like a chair detector? Do you need to go find a bunch of photos of chairs? It's like, oh no. You know, actually those images are present not only in the cocoa data set, but those are objects that exist like kind of broadly on the internet.[00:45:02] And so computer visions kind of been like us included, have been like really pushing for and encouraging models that already possess a lot of context about the world. And so, you know, if GB T's idea and i's idea OpenAI was okay, models can only understand things that are in their corpus. What if we just make their corpus the size of everything on the internet?[00:45:20] The same thing that happened in imagery, what's happening now? And that's kinda what Sam represents, which is kind of a new evolution of, earlier on we were talking about the cost of annotation and I said, well, good news. Annotations then become decreasingly necessary to start to get to value. Now you gotta think about it more, kind of like, you'll probably need to do some annotation because you might want to find a custom object, or Sam might not be perfect, but what's about to happen is a big opportunity where you want the benefits of a yolo, right?[00:45:47] Where it can run really fast, it can run on the edge, it's very cheap. But you want the knowledge of a large foundation model that already knows everything about buses and knows everything about shoes, knows everything about real, if the name is true, anything segment, anything model. And so there's gonna be this novel opportunity to take what these large models know, and I guess it's kind of like a form of distilling, like distill them down into smaller architectures that you can use in versatile ways to run in real time to run on the edge.[00:46:13] And that's now happening. And what we're seeing in actually kind of like pulling that, that future forward with, with, with Robo Flow.[00:46:21] Segment Anything Model[00:46:21] So we could talk a bit about, um, about SAM and what it represents maybe into, in relation to like these, these YOLO models. So Sam is Facebook segment Everything Model. It came out last week, um, the first week of April.[00:46:34] It has 24,000 GitHub stars at the time of, of this recording within its first week. And why, what does it do? Segment? Everything is a zero shot segmentation model. And as we're describing, creating masks is a very arduous task. Creating masks of objects that are not already represented means you have to go label a bunch of masks and then train a model and then hope that it finds those masks in new images.[00:47:00] And the promise of Segment anything is that in fact you just pass at any image and it finds all of the masks of relevant things that you might be curious about finding in a given image. And it works remarkably. Segment anything in credit to Facebook and the fair Facebook research team, they not only released the model permissive license to move things forward, they released the full data set, all 11 million images and 1.1 billion segmentation masks and three model sizes.[00:47:29] The largest ones like 2.5 gigabytes, which is not enormous. Medium ones like 1.2 and the smallest one is like 400, 3 75 megabytes. And for context,[00:47:38] for, for people listening, that's six times more than the previous alternative, which, which is apparently open images, uh, in terms of number images, and then 400 times more masks than open[00:47:47] images as well.[00:47:48] Exactly, yeah. So huge, huge order magnitude gain in terms of dataset accessibility plus like the model and how it works. And so the question becomes, okay, so like segment. What, what do I do with this? Like, what does it allow me to do? And it didn't Rob float well. Yeah, you should. Yeah. Um, it's already there.[00:48:04] You um, that part's done. Uh, but the thing that you can do with segment anything is you can almost, like, I almost think about like this, kinda like this model arbitrage where you can basically like distill down a giant model. So let's say like, like let's return to the package example. Okay. The package problem of, I wanna get a text when a package appears on my front porch before segment anything.[00:48:25] The way that I would go solve this problem is I would go collect some images of packages on my porch and I would label them, uh, with bounding boxes or maybe masks in that part. As you mentioned, it can be a long process and I would train a model. And that model it actually probably worked pretty well cause it's purpose-built.[00:48:44] The camera position, my porch, the packages I'm receiving. But that's gonna take some time, like everything that I just mentioned the

In this episode Shad and Paul interview Robel Chemeda Disasa. Robel has been serving as the General Secretary of the Evangelical Students Union of Ethiopia (EvaSUE) since 2016. His role is leading the organization as CEO to achieve its vision and mission. His role includes to set strategic directions to the organization, to mobilize resource for the ministry, to create and maintain partnerships, and to set a model for the staff of EvaSUE. EvaSUE is currently serving more than 50,000 students. EvaSUE is one of the two largest movement in the IFES family.  Show Notes: EvaSUE Horn of Africa Mission Engagers (HO-ME)

On this episode of Rewind With Besa, Besa Gordon is joined by the hosts of the UpNUp Podcast Robel Andmicael and Erm Haile. They talk about all things podcasting, from how they started their podcast 5 years ago to advice on promoting, prepping for interviews, picking recurring segments, and more for your podcast. If you've wanted to learn more about how to get into podcasting, this is one episode you'll want to watch all the way to the end and take notes.

Helping Truckers Save Money on Fuel With Robel Haile Fuel prices are going crazy and as an owner operator it can be hard to gauge costs and those costs directly reflect on your bottom line. Today we talk with Robel Haile of Portland Fuel about their fuel program that allows fuel pricing to be a fixed cost for owner operators and fleets. Find out how to manage your fuel costs and improve the cash in your pocket. You can learn more about Portland Fuels at www.portland-fuel.ca  This episode is sponsored by RIMS Transport who is looking for owner operators and drivers to  work cross border operations out of Hamilton Ontario. You can learn more about the opportunities at www.rimstransport.com   DriverCheck is a leader in drug and alcohol, cognitive, and workplace testing helping employers have a safe workplace for their staff. Learn how DriverCheck can help you be safe at www.drivercheck.ca   DriverVerified is an online platform that allows you to keep a profile that can be found by potential employers, sent directly to insurance professionals, or updated with documents that help you stand out from other applicants. You can learn more about DriverVerified at www.driververified.com  Set up your free profile today.    About the Show LISTEN TO THE PODCAST- The show is available at www.theleadpedalpodcast.com  , ITunes, Stitcher, Spotify, Tunein, iHeartradio, SoundCloud, and other popular podcast platforms. Thanks for listening JOIN THE LEAD PEDAL PODCAST FAN CLUB  www.TheLeadPedalPodcastFanClub.com  LISTEN TO LEAD PEDAL RADIO at www.LeadPedalRadio.com  The Lead Pedal Podcast for Truck Drivers talks all things trucking for people in the transportation industry helping them improve their business and careers. Interviews with industry professionals and truck drivers, trucking information, and other features on the industry are meant to be helpful for truck drivers and those in transportation. The Lead Pedal Podcast for Truck Drivers has main episodes released every Monday, Wednesday, and Friday with bonus material on other days. You can learn more about the host and show on our website and make sure to SUBSCRIBE to the show on your favourite podcast platform. www.theleadpedalpodcast.com  What does The Lead Pedal Podcast mean? The Lead (pronounced - Led) stands for acceleration or fast-track of your career or business. It is a play on words and we certainly are not here promoting speeding in the industry. We are hoping this information will help you become a professional driver faster than if you didn't know about many of these topics. Are you enjoying the show? If so we would appreciate you leaving us a rating and review on iTunes or on your favourite podcast platform. www.theleadpedalpodcast.com  Join The Lead Pedal Podcast Fan Club where are loyal fans get first chance at specials, discounts on merchandise and much more.The club is free to join and you can learn more at www.theleadpedalpodcastfanclub.com   

Improviser/showrunner Brian Robel is a late bloomer when it comes to love of D&D, particularly CRITICAL ROLE. Expect BD(&D)E throughout. Episode Links: Episode Links: Honeymoon Improv Joe's Patreon Mr. Owl's Website

durée : 00:27:24 - Les Nuits de France Culture - par : Albane Penaranda - De fin 1977 à début 1978, une série d'émissions est consacrée au linguiste Roman Jakobson. Dans le dernier volet 14/14 intitulé "La guirlande des poètes", ses amis Jean-Pierre Faye, Jean-Claude Milner, Léon Robel, Tzvetan Todorov lui rendent un hommage ponctué de nombreuses lectures de poésies. - invités : Roman Jakobson Linguiste; Jean-Pierre Faye écrivain et philosophe; Jean-Claude Milner linguiste et philosophe; Tzvetan Todorov Critique, historien et philosophe

Meditation on Romans 10:1-10 (and the Robel's Fiftieth Anniversary!!!).  Fr. Anthony describes how the commitment to show up every day transforms us into saints.  First, it keeps us together and in the game; and second, it allows our relationships with others to work with our attitude of continual repentance to teach us how to do better every day.  Enjoy the show! 

Today's guest is on a big mission to change the academic culture from negativity and chronic stress to one where people express their unique brilliance and live a joyful, happy life. She grew up in Berlin, Germany, and completed a Ph.D. in Neuroscience in Munich before relocating to the US to pursue her passion for […] The post OTS 283: Success Path – Stefanie Robel appeared first on Robbie Samuels.

Today's guest is on a big mission to change the academic culture from negativity and chronic stress to one where people express their unique brilliance and live a joyful, happy life. She grew up in Berlin, Germany, and completed a Ph.D. in Neuroscience in Munich before relocating to the US to pursue her passion for brain research. She is an Associate Professor at an internationally renowned research university and academic medical center in the US. She is also the founder and head coach at Team Leadership In Research, a business that delivers leadership and management training programs for academic research teams. She became a personal development and leadership nerd during her postdoctoral training, initially to increase her competitiveness for faculty appointments. She discovered she could use these strategies to create a happier, balanced life, AND increase her chances of success. That led her to train as a life coach to help other academics to achieve similar results. Please join me in welcoming Dr. Stefanie Robel. In this episode we discuss: her thoughts on leadership: “Leadership means having a vision and taking responsibility to get there. Leadership starts with leading yourself.” when she discovered her leadership capabilities during riding lessons in her teens. the importance of communication in entrepreneurial relationships. how her parents influenced her studies in the sciences. how she found her people - entrepreneurs and business owners - after moving to the US. her finding out that the overwhelm she felt was completely within her control to change once she learned to change her mindset. the joy she gets from empowering others. how she learned to sell, which helped her build her confidence and her business. the mastermind that helped her build relationships outside of academia and in entrepreneurship. how she turned contacts into thriving relationships and connections. why she loves having and is always on the lookout for new mentors. her personal policy of helping someone before she needs to ask for help. how she nurtures and sustains the outer levels of her network. Listen, subscribe and read show notes at www.OnTheSchmooze.com.

If you are interested in contacting Maggie for some life coaching or relationship coaching, follow the links below to get in contact with her!Instagram: @thesanguinecoachFacebook: Maggie Robel - Dating/Relationship CoachWebsite: www.sanguinelifecoaching.com 

In this episode, I interview Dr. Stefanie Robel, a Neuroscientist, and CEO of her own firm, Team Leadership in Research. In our discussion, Dr. Robel points out the vital role influence is playing in her research and how she coaches her team members to higher levels of success and job satisfaction through her own influence. For more success resources or to contact Lacy, visit our website at https://realwomenrealsuccess.com, or email Lacy directly at lacy@lacyschoen.com To assess your level of influence at work, take the quiz, How Influential Are You at Work? here: https://mailchi.mp/d8d0c100d3c1/how-influential-are-you-at-work

Robel talks about how you can approach your partner about how to talk about money and how you can avoid one of the biggest financial risk of your life; Divorce. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

Robel talks how you can invest in yourself and how you can pivot to be one of the best in your sector as automation comes in the next years. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

Robel talks all things crypto what it means for Bitcoin to be legal tender in El Salvador and how you can manage your portfolio to benefit from this new opportunity. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

Robel talks about the change coming to Social Security and how you can focus your future for it as well as ESG's and the future of green investments. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

Robel details what the rich are buying and how you can mimic their portfolio for success. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

Robel talks about everything equity and how you can build your wealth while you are working. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

Robel talks about a recent Bloomberg article and how Millennials can pivot to enhancing their finances. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

Robel talks state of play in the Real Estate market and cities/states he thinks will do well long term. He also discuss news on ProPublica's controversial report. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

Robel talks inflation and how you can set yourself up for success. Additionally, he mentions alternative investments like private companies and crypto bets. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

Robel discusses how you can put yourself in a better situation than your peers and build your future success. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

Robel shares his thoughts on the new capital gains tax hike and how you can shelter your assets to avoid new tax increases. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

Robel discusses the importance of Blockchain, NFT's, and crypto how it will change the world, and how you can get ahead in the space. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

Robel recaps the year 2020 and his takeaways to come in 2021 and onward. He discusses the trends he sees moving forward with Tech, AI, and blockchain technology and tells you how you can position yourself ahead of the curve. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

Robel talks about the key steps to financial literacy and the issue of debt. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

In Episode 916, Sean and Robel (@robeltussin) chat about some of the most exciting prospects at the top of the 2021 NBA Draft as Raptors fans grow more fixated on the team's lottery odds than its playoff hopes. Sean and Robel start out by discussing what makes this year's draft so exciting, and where the Raptors could most stand to receive an infusion of talent on the floor. Next, they talk about Robel's clear top three in the draft: Cade Cunningham, Evan Mobley and Jalen Suggs, and what each of them would bring to the Raptors if they get lucky on lottery night. Lastly, they discuss some of the guys who could still be around if the Raptors fail to move up in the lottery, including Jonathan Kuminga, Jalen Green and the Tennessee duo of Jaden Springer and Keon Johnson. All that, plus another Tankathon Sim of the Day.Support Us By Supporting Our Sponsors!Built BarBuilt Bar is a protein bar that tastes like a candy bar. Go to builtbar.com and use promo code “LOCKED15” and you'll get 15% off your next order.BetOnline AGThere is only 1 place that has you covered and 1 place we trust. Betonline.ag! Sign up today for a free account at betonline.ag and use that promocode: LOCKEDON for your 50% welcome bonus.Rock AutoAmazing selection. Reliably low prices. All the parts your car will ever need. Visit RockAuto.com and tell them Locked On sent you. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Robel discusses how to spend your stimulus check and where to put it. In his baller segment Robel talks about the innovative company of Lemonade. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

Robel talks about the key to investing in real estate and what he uses right now as well as diversification is the key to help you build wealth. Baller segment includes the excitement around blockchain technology. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/robel-kelkile/support

In Episode 812, Sean and Robel (@robeltussin, Sub me in Coach on YouTube) continue to dive deep into the options for the Raptors at 29. They kick things off by discussing each of their absolute favourite Raptors targets at 29 -- for Sean, it's Michigan State big man Xavier Tillman, while Robel is super high on Arkansas guard Isaiah Joe. Next, they take a look at a pair of point guards currently being mocked to the Raptors at 29 in Tillman's Spartans teammate Cassius Winston, and French guard Theo Maledon. Lastly, they take a look at who might be worth prioritizing with the 59th pick, and run through some names that could be around on the undrafted free agent market once the draft is complete.Support Us By Supporting Our Sponsors! Rock AutoAmazing selection. Reliably low prices. All the parts your car will ever need. Visit RockAuto.com and tell them Locked On sent you.Built BarBuilt Bar is a protein bar that tastes like a candy bar. Go to builtbar.com and use promo code “LOCKEDON,” and you'll get 20% off your next order.BuiltGoVisit BuiltGO.com and use promo code “LOCKED,” and you'll get 20% off your next order. Learn more about your ad choices. Visit podcastchoices.com/adchoices

In Episode 811, Sean chats with NBA Draft expert Robel (@robeltussin) about the NBA's reported plan to return by Christmas, Raps assistant GM Dan Tolzman's comments on the flat nature of this years' draft, and a pair of prospects worked out by the Raptors over the weekend: Malachi Flynn (San Diego State) and Devon Dotson (Kansas). They start out by taking a look at the NBA's target start date of December 22nd, and try to determine whether the expedited return may affect the Raptors negatively, positively or not at all. Then they dive into the comments of Tolzman, who on Friday said the team was looking at roughly 50 different prospects to potentially take with one of their two picks in the draft. Lastly, after the Raptors worked out Flynn and Dotson over the weekend, Robel discusses both prospects and why they do or don't fit with the Raptors. Check back for more draft talk with Robel tomorrow!Support Us By Supporting Our Sponsors! Rock AutoAmazing selection. Reliably low prices. All the parts your car will ever need. Visit RockAuto.com and tell them Locked On sent you.Built BarBuilt Bar is a protein bar that tastes like a candy bar. Go to builtbar.com and use promo code “LOCKEDON,” and you'll get 20% off your next order.BuiltGoVisit BuiltGO.com and use promo code “LOCKED,” and you'll get 20% off your next order. Learn more about your ad choices. Visit podcastchoices.com/adchoices

at a time when we as a human family are facing covid challenges, it's easy to see that others are also struggling. some are easily seen and it's often easy to help. others yet, are less seen, less heard...we want to ease their suffering and yet, we're not sure exactly how to make that happen. meet my artist friend melissa robel. melissa is not only lifting the world through her art, but she's also the brains behind pads4refugees, a non-profit helping women and girls in refugee camps. hear about melissa's unique education journey of art and law school(s!)...as well as her time in a yazidi refugee camp in greece. learn how melissa has used these experiences and connections to ease the burdens and challenges of the women and girls in the camps and find out how you can also help. LINKS:pads4refugeesabout the yazidia little moremelissa's instagrammelissa's etsy shopLEAVE ME FEEDBACK: - what should i talk about next?  let me know in the comments below. - did you enjoy this episode?  if so, leave me a review!CONNECT WITH ME:katrinaberg.cominstagram @katrina.bergemail: k@katrinaberg.comjoin my SWEETlist

In Episode 735, Sean and NBA Draft expert Robel (Sub Me in Coach on Youtube) chat about the 2020 NBA Draft and which players might pique the Raptors' interest in the late stages of the first round. They first start out by discussing the overall strength of this year's draft class, which looks quite weak up at the top, but might be rich in solid rotation players outside of the top five or ten. Then they dive into some of the players that might be there when the Raptors are on the clock, including Michigan State big man Xavier Tillman, Argentine guard Leandro Bolmaro, injury prone Gonzaga big man Killian Tillie, and DePaul's hyper-athletic big man Paul Reed. Lastly they close out the show by talking about a collection of interesting guards, including Grant Riller (Charleston), Devon Dotson (Kansas) and Tre Jones (Duke), as well as smooth scoring Arizona big man Zeke Nnaji, who is rumoured to have already met with the Raptors.Be sure to subscribe to Robel's excellent YouTube page, Sub Me in Coach: youtube.com/submeincoach !Rock AutoAmazing selection. Reliably low prices. All the parts your car will ever need. Visit RockAuto.com and tell them Locked On sent you.  Learn more about your ad choices. Visit podcastchoices.com/adchoices