Podcast appearances and mentions of David Wright

In this special episode of Health Matters, host Courtney Allison visits Citi Field, home of the New York Mets, to speak with two guests: legendary Mets third baseman David Wright and Dr. Tony Puliafico, a psychologist with NewYork-Presbyterian and Columbia. Together, they discuss the importance of approaching challenges and failures with a growth mindset—in professional sports, at home, at work, at school, and beyond. Through the latest clinical research and stories from David's time with the Mets, they explore healthy habits for approaching failure, connecting to a supportive community, and building resilience for the long term. ___Anthony Puliafico, Ph.D. is a psychologist with the Center for Youth Mental Health at NewYork-Presbyterian. He is also an associate professor of clinical psychology in the Division of Child and Adolescent Psychiatry at Columbia University and serves as Director of the Columbia University Clinic for Anxiety and Related Disorders (CUCARD) -Westchester, an outpatient clinic that specializes in the treatment of anxiety disorders, obsessive-compulsive disorder (OCD) and related disorders in children, adolescents and adults. Dr. Puliafico specializes in the assessment and cognitive-behavioral treatment of anxiety, mood and externalizing disorders. His clinical work and research have focused on the treatment of pediatric OCD, school refusal, and adapting treatments for young children with anxiety.David Wright was a third baseman and captain for the New York Mets from 2004 to 2018. A seven-time All-Star, two-time Gold Glove Award winner, two-time Silver Slugger Award winner, and a member of the 30–30 club, Wright was recently inducted into the Mets Hall of Fame and had his number 5 retired by the team. ___Health Matters is your weekly dose of health and wellness information, from the leading experts. Join host Courtney Allison to get news you can use in your own life. New episodes drop each Wednesday.If you are looking for practical health tips and trustworthy information from world-class doctors and medical experts you will enjoy listening to Health Matters. Health Matters was created to share stories of science, care, and wellness that are happening every day at NewYork-Presbyterian, one of the nation's most comprehensive, integrated academic healthcare systems. In keeping with NewYork-Presbyterian's long legacy of medical breakthroughs and innovation, Health Matters features the latest news, insights, and health tips from our trusted experts; inspiring first-hand accounts from patients and caregivers; and updates on the latest research and innovations in patient care, all in collaboration with our renowned medical schools, Columbia and Weill Cornell Medicine. To learn more visit: https://healthmatters.nyp.org Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

On this episode of CA Media Podcast, I had the honor and privelege to interview Rev. David Wright who is the Pastor of New Grace Tabernacle Christian Center Church of God in Christ in Brooklyn, NY. He is the son of Legendary Gospel Singer Rev. Timothy Wright & Evangelist. Betty Wright. We dive into his upbringing, his musical ministry, call to ministry and his current role in gospel music. We also did a rapid fire questions in which you will enjoy. So come and enjoy this amazing interview with Rev. Wright.You can follow Rev. David Wright:IG: Instagram.com/pastordw3Facebook: Facebook.com/david.wright.12023New Grace Tabernacle Christian Center COGIC:   / newgracetabernaclechristiancenter  You can listen to the podcast on the following platforms:Apple Podcast: https://podcasts.apple.com/us/podcast...SPOTIFY: https://open.spotify.com/show/0T1qlQv...You can follow the podcast atFacebook: ⁠facebook.com/CAMediaPodcast⁠Instagram: ⁠Instagram.com/CAMediaPodcast⁠Blue Sky:⁠https://bsky.app/profile/camediapodca...X: ⁠https://x.com/CAMediaPodcast⁠IF you want to be on the podcast you can email the podcast at ⁠camediapodcast@gmail.com⁠ or book on linktree at linktr.ee/CAMediaPodcast and click in the booking link.Visionary Minds Public Relations and Media is a founding supporting sponsor of the CAMedia PodcastMake sure you get your Publicity, Digital Marketing, Writing, Media Consulting Services at visionarymindsny@gmail.com where Tammy Reese is the owner.

Tune in to a selection of originals, throwbacks, and remixes from the 90s on, featuring uptempo Hip-Hop, Latin, R&B, Dancehall, and everything in between. Watch on YouTube: https://youtu.be/qU4iaR4OnHM ---------- Follow David Wright ◊ https://soundcloud.com/david-jamaal-wright ◊ https://www.instagram.com/davidjwright_/ ---------- Follow MSYH.FM » http://MSYH.FM » http://x.com/MSYHFM » http://instagram.com/MSYH.FM » http://facebook.com/MSYH.FM » http://patreon.com/MSYHFM ---------- Follow Make Sure You Have Fun™ ∞ http://MakeSureYouHaveFun.com ∞ http://x.com/MakeSureYouHave ∞ http://instagram.com/MakeSureYouHaveFun ∞ http://facebook.com/MakeSureYouHaveFun ∞ http://youtube.com/@makesureyouhavefun ∞ http://twitch.tv/@MakeSureYouHaveFun

Living and Working in Spain with David Wright   The Ultimate Expat Guide   Welcome to Living and Working in Spain with David Wright   The leading radio show and podcast for anyone dreaming of a new life abroad.   If you're planning to move to Spain and need trustworthy advice on residency, visas, employment, or lifestyle tips – this is your go-to resource.   David Wright shares:✅ Practical guidance✅ Expert interviews✅ Insider knowledge  …to help make your move to Spain stress-free and successful. 

Join Lois Houston and Nikita Abraham as they chat with Yunus Mohammed, a Principal Instructor at Oracle University, about the key stages of AI model development. From gathering and preparing data to selecting, training, and deploying models, learn how each phase impacts AI's real-world effectiveness. The discussion also highlights why monitoring AI performance and addressing evolving challenges are critical for long-term success.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last episode, we spoke about generative AI and gen AI agents. Today, we're going to look at the key stages in a typical AI workflow. We'll also discuss how data quality, feedback loops, and business goals influence AI success. With us today is Yunus Mohammed, a Principal Instructor at Oracle University.  01:00 Lois: Hi Yunus! We're excited to have you here! Can you walk us through the various steps in developing and deploying an AI model?  Yunus: The first point is the collect data. We gather relevant data, either historical or real time. Like customer transactions, support tickets, survey feedbacks, or sensor logs. A travel company, for example, can collect past booking data to predict future demand. So, data is the most crucial and the important component for building your AI models. But it's not just the data. You need to prepare the data. In the prepared data process, we clean, organize, and label the data. AI can't learn from messy spreadsheets. We try to make the data more understandable and organized, like removing duplicates, filling missing values in the data with some default values or formatting dates. All these comes under organization of the data and give a label to the data, so that the data becomes more supervised. After preparing the data, I go for selecting the model to train. So now, we pick what type of model fits your goals. It can be a traditional ML model or a deep learning network model, or it can be a generative model. The model is chosen based on the business problems and the data we have. So, we train the model using the prepared data, so it can learn the patterns of the data. Then after the model is trained, I need to evaluate the model. You check how well the model performs. Is it accurate? Is it fair? The metrics of the evaluation will vary based on the goal that you're trying to reach. If your model misclassifies emails as spam and it is doing it very much often, then it is not ready. So I need to train it further. So I need to train it to a level when it identifies the official mail as official mail and spam mail as spam mail accurately.  After evaluating and making sure your model is perfectly fitting, you go for the next step, which is called the deploy model. Once we are happy, we put it into the real world, like into a CRM, or a web application, or an API. So, I can configure that with an API, which is application programming interface, or I add it to a CRM, Customer Relationship Management, or I add it to a web application that I've got. Like for example, a chatbot becomes available on your company's website, and the chatbot might be using a generative AI model. Once I have deployed the model and it is working fine, I need to keep track of this model, how it is working, and need to monitor and improve whenever needed. So I go for a stage, which is called as monitor and improve. So AI isn't set in and forget it. So over time, there are lot of changes that is happening to the data. So we monitor performance and retrain when needed. An e-commerce recommendation model needs updates as there might be trends which are shifting.  So the end user finally sees the results after all the processes. A better product, or a smarter service, or a faster decision-making model, if we do this right. That is, if we process the flow perfectly, they may not even realize AI is behind it to give them the accurate results.  04:59 Nikita: Got it. So, everything in AI begins with data. But what are the different types of data used in AI development?  Yunus: We work with three main types of data: structured, unstructured, and semi-structured. Structured data is like a clean set of tables in Excel or databases, which consists of rows and columns with clear and consistent data information. Unstructured is messy data, like your email or customer calls that records videos or social media posts, so they all comes under unstructured data.  Semi-structured data is things like logs on XML files or JSON files. Not quite neat but not entirely messy either. So they are, they are termed semi-structured. So structured, unstructured, and then you've got the semi-structured. 05:58 Nikita: Ok… and how do the data needs vary for different AI approaches?  Yunus: Machine learning often needs labeled data. Like a bank might feed past transactions labeled as fraud or not fraud to train a fraud detection model. But machine learning also includes unsupervised learning, like clustering customer spending behavior. Here, no labels are needed. In deep learning, it needs a lot of data, usually unstructured, like thousands of loan documents, call recordings, or scan checks. These are fed into the models and the neural networks to detect and complex patterns. Data science focus on insights rather than the predictions. So a data scientist at the bank might use customer relationship management exports and customer demographies to analyze which age group prefers credit cards over the loans. Then we have got generative AI that thrives on diverse, unstructured internet scalable data. Like it is getting data from books, code, images, chat logs. So these models, like ChatGPT, are trained to generate responses or mimic the styles and synthesize content. So generative AI can power a banking virtual assistant trained on chat logs and frequently asked questions to answer customer queries 24/7. 07:35 Lois: What are the challenges when dealing with data?  Yunus: Data isn't just about having enough. We must also think about quality. Is it accurate and relevant? Volume. Do we have enough for the model to learn from? And is my data consisting of any kind of unfairly defined structures, like rejecting more loan applications from a certain zip code, which actually gives you a bias of data? And also the privacy. Are we handling personal data responsibly or not? Especially data which is critical or which is regulated, like the banking sector or health data of the patients. Before building anything smart, we must start smart.  08:23 Lois: So, we've established that collecting the right data is non-negotiable for success. Then comes preparing it, right?  Yunus: This is arguably the most important part of any AI or data science project. Clean data leads to reliable predictions. Imagine you have a column for age, and someone accidentally entered an age of like 999. That's likely a data entry error. Or maybe a few rows have missing ages. So we either fix, remove, or impute such issues. This step ensures our model isn't misled by incorrect values. Dates are often stored in different formats. For instance, a date, can be stored as the month and the day values, or it can be stored in some places as day first and month next. We want to bring everything into a consistent, usable format. This process is called as transformation. The machine learning models can get confused if one feature, like example the income ranges from 10,000 to 100,000, and another, like the number of kids, range from 0 to 5. So we normalize or scale values to bring them to a similar range, say 0 or 1. So we actually put it as yes or no options. So models don't understand words like small, medium, or large. We convert them into numbers using encoding. One simple way is assigning 1, 2, and 3 respectively. And then you have got removing stop words like the punctuations, et cetera, and break the sentence into smaller meaningful units called as tokens. This is actually used for generative AI tasks. In deep learning, especially for Gen AI, image or audio inputs must be of uniform size and format.  10:31 Lois: And does each AI system have a different way of preparing data?  Yunus: For machine learning ML, focus is on cleaning, encoding, and scaling. Deep learning needs resizing and normalization for text and images. Data science, about reshaping, aggregating, and getting it ready for insights. The generative AI needs special preparation like chunking, tokenizing large documents, or compressing images. 11:06 Oracle University's Race to Certification 2025 is your ticket to free training and certification in today's hottest tech. Whether you're starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That's education.oracle.com/race-to-certification-2025. 11:50 Nikita: Welcome back! Yunus, how does a user choose the right model to solve their business problem?  Yunus: Just like a business uses different dashboards for marketing versus finance, in AI, we use different model types, depending on what we are trying to solve. Like classification is choosing a category. Real-world example can be whether the email is a spam or not. Use in fraud detection, medical diagnosis, et cetera. So what you do is you classify that particular data and then accurately access that classification of data. Regression, which is used for predicting a number, like, what will be the price of a house next month? Or it can be a useful in common forecasting sales demands or on the cost. Clustering, things without labels. So real-world examples can be segmenting customers based on behavior for targeted marketing. It helps discovering hidden patterns in large data sets.  Generation, that is creating new content. So AI writing product description or generating images can be a real-world example for this. And it can be used in a concept of generative AI models like ChatGPT or Dall-E, which operates on the generative AI principles. 13:16 Nikita: And how do you train a model? Yunus: We feed it with data in small chunks or batches and then compare its guesses to the correct values, adjusting its thinking like weights to improve next time, and the cycle repeats until the model gets good at making predictions. So if you're building a fraud detection system, ML may be enough. If you want to analyze medical images, you will need deep learning. If you're building a chatbot, go for a generative model like the LLM. And for all of these use cases, you need to select and train the applicable models as and when appropriate. 14:04 Lois: OK, now that the model's been trained, what else needs to happen before it can be deployed? Yunus: Evaluate the model, assess a model's accuracy, reliability, and real-world usefulness before it's put to work. That is, how often is the model right? Does it consistently perform well? Is it practical in the real world to use this model or not? Because if I have bad predictions, doesn't just look bad, it can lead to costly business mistakes. Think of recommending the wrong product to a customer or misidentifying a financial risk.  So what we do here is we start with splitting the data into two parts. So we train the data by training data. And this is like teaching the model. And then we have got the testing data. This is actually used for checking how well the model has learned. So once trained, the model makes predictions. We compare the predictions to the actual answers, just like checking your answer after a quiz. We try to go in for tailored evaluation based on AI types. Like machine learning, we care about accuracy in prediction. Deep learning is about fitting complex data like voice or images, where the model repeatedly sees examples and tunes itself to reduce errors. Data science, we look for patterns and insights, such as which features will matter. In generative AI, we judge by output quality. Is it coherent, useful, and is it natural?  The model improves with the accuracy and the number of epochs the training has been done on.  15:59 Nikita: So, after all that, we finally come to deploying the model… Yunus: Deploying a model means we are integrating it into our actual business system. So it can start making decisions, automating tasks, or supporting customer experiences in real time. Think of it like this. Training is teaching the model. Evaluating is testing it. And deployment is giving it a job.  The model needs a home either in the cloud or inside your company's own servers. Think of it like putting the AI in place where it can be reached by other tools. Exposed via API or embedded in an app, or you can say application, this is how the AI becomes usable.  Then, we have got the concept of receives live data and returns predictions. So receives live data and returns prediction is when the model listens to real-time inputs like a user typing, or user trying to search or click or making a transaction, and then instantly, your AI responds with a recommendation, decisions, or results. Deploying the model isn't the end of the story. It is just the beginning of the AI's real-world journey. Models may work well on day one, but things change. Customer behavior might shift. New products get introduced in the market. Economic conditions might evolve, like the era of COVID, where the demand shifted and the economical conditions actually changed. 17:48 Lois: Then it's about monitoring and improving the model to keep things reliable over time. Yunus: The monitor and improve loop is a continuous process that ensures an AI model remains accurate, fair, and effective after deployment. The live predictions, the model is running in real time, making decisions or recommendations. The monitor performance are those predictions still accurate and helpful. Is latency acceptable? This is where we track metrics, user feedbacks, and operational impact. Then, we go for detect issues, like accuracy is declining, are responses feeling biased, are customers dropping off due to long response times? And the next step will be to reframe or update the model. So we add fresh data, tweak the logic, or even use better architectures to deploy the uploaded model, and the new version replaces the old one and the cycle continues again. 18:58 Lois: And are there challenges during this step? Yunus: The common issues, which are related to monitor and improve consist of model drift, bias, and latency of failures. In model drift, the model becomes less accurate as the environment changes. Or bias, the model may favor or penalize certain groups unfairly. Latency or failures, if the model is too slow or fails unpredictably, it disrupts the user experience. Let's take the loan approvals. In loan approvals, if we notice an unusually high rejection rate due to model bias, we might retrain the model with more diverse or balanced data. For a chatbot, we watch for customer satisfaction, which might arise due to model failure and fine-tune the responses for the model. So in forecasting demand, if the predictions no longer match real trends, say post-pandemic, due to the model drift, we update the model with fresh data.  20:11 Nikita: Thanks for that, Yunus. Any final thoughts before we let you go? Yunus: No matter how advanced your model is, its effectiveness depends on the quality of the data you feed it. That means, the data needs to be clean, structured, and relevant. It should map itself to the problem you're solving. If the foundation is weak, the results will be also. So data preparation is not just a technical step, it is a business critical stage. Once deployed, AI systems must be monitored continuously, and you need to watch for drops in performance for any bias being generated or outdated logic, and improve the model with new data or refinements. That's what makes AI reliable, ethical, and sustainable in the long run. 21:09 Nikita: Yunus, thank you for this really insightful session. If you're interested in learning more about the topics we discussed today, go to mylearn.oracle.com and search for the AI for You course.  Lois: That's right. You'll find skill checks to help you assess your understanding of these concepts. In our next episode, we'll discuss the idea of buy versus build in the context of AI. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 21:39 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

428: David Wright and HFG Architecture's place in the world by Wichita Business Journal

Living and Working in Spain with David Wright: The Ultimate Expat Guide Special Guests in This Episode:Josh Williams (Ambient Wealth) and Paul Burt (Indalo Transport) Welcome to Living and Working in Spain with David Wright — the leading radio show and podcast for expats in Spain and anyone planning to relocate. Whether you're preparing to move to Spain, applying for Spanish residency or visas, looking for employment opportunities, or simply want insider tips on everyday life abroad, this is your trusted guide. David Wright shares over 23 years of real-life experience in Spain, combined with expert interviews and actionable advice to make your relocation stress-free and successful.

Join hosts Lois Houston and Nikita Abraham, along with Principal AI/ML Instructor Himanshu Raj, as they discuss the transformative world of Generative AI. Together, they uncover the ways in which generative AI agents are changing the way we interact with technology, automating tasks and delivering new possibilities.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead of Editorial Services.   Nikita: Hi everyone! Last week was Part 2 of our conversation on core AI concepts, where we went over the basics of data science. In Part 3 today, we'll look at generative AI and gen AI agents in detail. To help us with that, we have Himanshu Raj, Principal AI/ML Instructor. Hi Himanshu, what's the difference between traditional AI and generative AI?  01:01 Himanshu: So until now, when we talked about artificial intelligence, we usually meant models that could analyze information and make decisions based on it, like a judge who looks at evidence and gives a verdict. And that's what we call traditional AI that's focused on analysis, classification, and prediction.  But with generative AI, something remarkable happens. Generative AI does not just evaluate. It creates. It's more like a storyteller who uses knowledge from the past to imagine and build something brand new. For example, instead of just detecting if an email is spam, generative AI could write an entirely new email for you.  Another example, traditional AI might predict what a photo contains. Generative AI, on the other hand, creates a brand-new photo based on description. Generative AI refers to artificial intelligence models that can create entirely new content, such as text, images, music, code, or video that resembles human-made work.  Instead of simple analyzing or predicting, generative AI produces something original that resembles what a human might create.   02:16 Lois: How did traditional AI progress to the generative AI we know today?  Himanshu: First, we will look at small supervised learning. So in early days, AI models were trained on small labeled data sets. For example, we could train a model with a few thousand emails labeled spam or not spam. The model would learn simple decision boundaries. If email contains, "congratulations," it might be spam. This was efficient for a straightforward task, but it struggled with anything more complex.  Then, comes the large supervised learning. As the internet exploded, massive data sets became available, so millions of images, billions of text snippets, and models got better because they had much more data and stronger compute power and thanks to advances, like GPUs, and cloud computing, for example, training a model on millions of product reviews to predict customer sentiment, positive or negative, or to classify thousands of images in cars, dogs, planes, etc.  Models became more sophisticated, capturing deeper patterns rather than simple rules. And then, generative AI came into the picture, and we eventually reached a point where instead of just classifying or predicting, models could generate entirely new content.  Generative AI models like ChatGPT or GitHub Copilot are trained on enormous data sets, not to simply answer a yes or no, but to create outputs that look and feel like human made. Instead of judging the spam or sentiment, now the model can write an article, compose a song, or paint a picture, or generate new software code.  03:55 Nikita: Himanshu, what motivated this sort of progression?   Himanshu: Because of the three reasons. First one, data, we had way more of it thanks to the internet, smartphones, and social media. Second is compute. Graphics cards, GPUs, parallel computing, and cloud systems made it cheap and fast to train giant models.  And third, and most important is ambition. Humans always wanted machines not just to judge existing data, but to create new knowledge, art, and ideas.   04:25 Lois: So, what's happening behind the scenes? How is gen AI making these things happen?  Himanshu: Generative AI is about creating entirely new things across different domains. On one side, we have large language models or LLMs.  They are masters of generating text conversations, stories, emails, and even code. And on the other side, we have diffusion models. They are the creative artists of AI, turning text prompts into detailed images, paintings, or even videos.  And these two together are like two different specialists. The LLM acts like a brain that understands and talks, and the diffusion model acts like an artist that paints based on the instructions. And when we connect these spaces together, we create something called multimodal AI, systems that can take in text and produce images, audio, or other media, opening a whole new range of possibilities.  It can not only take the text, but also deal in different media options. So today when we say ChatGPT or Gemini, they can generate images, and it's not just one model doing everything. These are specialized systems working together behind the scenes.  05:38 Lois: You mentioned large language models and how they power text-based gen AI, so let's talk more about them. Himanshu, what is an LLM and how does it work?  Himanshu: So it's a probabilistic model of text, which means, it tries to predict what word is most likely to come next based on what came before.  This ability to predict one word at a time intelligently is what builds full sentences, paragraphs, and even stories.  06:06 Nikita: But what's large about this? Why's it called a large language model?   Himanshu: It simply means the model has lots and lots of parameters. And think of parameters as adjustable dials the model fine tuned during learning.  There is no strict rule, but today, large models can have billions or even trillions of these parameters. And the more the parameters, more complex patterns, the model can understand and can generate a language better, more like human.  06:37 Nikita: Ok… and image-based generative AI is powered by diffusion models, right? How do they work?  Himanshu: Diffusion models start with something that looks like pure random noise.  Imagine static on an old TV screen. No meaningful image at all. From there, the model carefully removes noise step by step to create something more meaningful and think of it like sculpting a statue. You start with a rough block of stone and slowly, carefully you chisel away to reveal a beautiful sculpture hidden inside.  And in each step of this process, the AI is making an educated guess based on everything it has learned from millions of real images. It's trying to predict.   07:24 Stay current by taking the 2025 Oracle Fusion Cloud Applications Delta Certifications. This is your chance to demonstrate your understanding of the latest features and prove your expertise by obtaining a globally recognized certification, all for free! Discover the certification paths, use the resources on MyLearn to prepare, and future-proof your skills. Get started now at mylearn.oracle.com.  07:53 Nikita: Welcome back! Himanshu, for most of us, our experience with generative AI is with text-based tools like ChatGPT. But I'm sure the uses go far beyond that, right? Can you walk us through some of them?  Himanshu: First one is text generation. So we can talk about chatbots, which are now capable of handling nuanced customer queries in banking travel and retail, saving companies hours of support time. Think of a bank chatbot helping a customer understand mortgage options or virtual HR Assistant in a large company, handling leave request. You can have embedding models which powers smart search systems.  Instead of searching by keywords, businesses can now search by meaning. For instance, a legal firm can search cases about contract violations in tech and get semantically relevant results, even if those exact words are not used in the documents.  The third one, for example, code generation, tools like GitHub Copilot help developers write boilerplate or even functional code, accelerating software development, especially in routine or repetitive tasks. Imagine writing a waveform with just a few prompts.  The second application, is image generation. So first obvious use is art. So designers and marketers can generate creative concepts instantly. Say, you need illustrations for a campaign on future cities. Generative AI can produce dozens of stylized visuals in minutes.  For design, interior designers or architects use it to visualize room layouts or design ideas even before a blueprint is finalized. And realistic images, retail companies generate images of people wearing their clothing items without needing real models or photoshoots, and this reduces the cost and increase the personalization.  Third application is multimodal systems, and these are combined systems that take one kind of input or a combination of different inputs and produce different kind of outputs, or can even combine various kinds, be it text image in both input and output.  Text to image It's being used in e-commerce, movie concept art, and educational content creation. For text to video, this is still in early days, but imagine creating a product explainer video just by typing out the script. Marketing teams love this for quick turnarounds. And the last one is text to audio.  Tools like ElevenLabs can convert text into realistic, human like voiceovers useful in training modules, audiobooks, and accessibility apps. So generative AI is no longer just a technical tool. It's becoming a creative copilot across departments, whether it's marketing, design, product support, and even operations.  10:42 Lois: That's great! So, we've established that generative AI is pretty powerful. But what kind of risks does it pose for businesses and society in general?  Himanshu: The first one is deepfakes. Generative AI can create fake but highly realistic media, video, audios or even faces that look and sound authentic.  Imagine a fake video of a political leader announcing a policy, they never approved. This could cause mass confusion or even impact elections. In case of business, deepfakes can be also used in scams where a CEO's voice is faked to approve fraudulent transactions.  Number two, bias, if AI is trained on biased historical data, it can reinforce stereotypes even when unintended. For example, a hiring AI system that favors male candidates over equally qualified women because of historical data was biased.  And this bias can expose companies to discrimination, lawsuits, brand damage and ethical concerns. Number three is hallucinations. So sometimes AI system confidently generate information that is completely wrong without realizing it.   Sometimes you ask a chatbot for a legal case summary, and it gives you a very convincing but entirely made up court ruling. In case of business impact, sectors like health care, finance, or law hallucinations can or could have serious or even dangerous consequences if not caught.  The fourth one is copyright and IP issues, generative AI creates new content, but often, based on material it was trained on. Who owns a new work? A real life example could be where an artist finds their unique style was copied by an AI that was trained on their paintings without permission.  In case of a business impact, companies using AI-generated content for marketing, branding or product designs must watch for legal gray areas around copyright and intellectual properties. So generative AI is not just a technology conversation, it's a responsibility conversation. Businesses must innovate and protect.  Creativity and caution must go together.   12:50 Nikita: Let's move on to generative AI agents. How is a generative AI agent different from just a chatbot or a basic AI tool?  Himanshu: So think of it like a smart assistant, not just answering your questions, but also taking actions on your behalf. So you don't just ask, what's the best flight to Vegas? Instead, you tell the agent, book me a flight to Vegas and a room at the Hilton. And it goes ahead, understands that, finds the options, connects to the booking tools, and gets it done.   So act on your behalf using goals, context, and tools, often with a degree of autonomy. Goals, are user defined outcomes. Example, I want to fly to Vegas and stay at Hilton. Context, this includes preferences history, constraints like economy class only or don't book for Mondays.  Tools could be APIs, databases, or services it can call, such as a travel API or a company calendar. And together, they let the agent reason, plan, and act.   14:02 Nikita: How does a gen AI agent work under the hood?  Himanshu: So usually, they go through four stages. First, one is understands and interprets your request like natural language understanding. Second, figure out what needs to be done, in this case flight booking plus hotel search.  Third, retrieves data or connects to tools APIs if needed, such as Skyscanner, Expedia, or a Calendar. And fourth is takes action. That means confirming the booking and giving you a response like your travel is booked. Keep in mind not all gen AI agents are fully independent.  14:38 Lois: Himanshu, we've seen people use the terms generative AI agents and agentic AI interchangeably. What's the difference between the two?  Himanshu: Agentic AI is a broad umbrella. It refers to any AI system that can perceive, reason, plan, and act toward a goal and may improve and adapt over time.   Most gen AI agents are reactive, not proactive. On the other hand, agentic AI can plan ahead, anticipate problems, and can even adjust strategies.  So gen AI agents are often semi-autonomous. They act in predefined ways or with human approval. Agentic systems can range from low to full autonomy. For example, auto-GPT runs loops without user prompts and autonomous car decides routes and reactions.  Most gen AI agents can only make multiple steps if explicitly designed that way, like a step-by-step logic flows in LangChain. And in case of agentic AI, it can plan across multiple steps with evolving decisions.  On the memory and goal persistence, gen AI agents are typically stateless. That means they forget their goal unless you remind them. In case of agentic AI, these systems remember, adapt, and refine based on goal progression. For example, a warehouse robot optimizing delivery based on changing layouts.  Some generative AI agents are agentic, like auto GPT. They use LLMs to reason, plan, and act, but not all. And likewise not all agentic AIs are generative. For example, an autonomous car, which may use computer vision control systems and planning, but no generative models.  So agentic AI is a design philosophy or system behavior, which could be goal-driven, autonomous, and decision making. They can overlap, but as I said, not all generative AI agents are agentic, and not all agentic AI systems are generative.  16:39 Lois: What makes a generative AI agent actually work?  Himanshu: A gen AI agent isn't just about answering the question. It's about breaking down a user's goal, figuring out how to achieve it, and then executing that plan intelligently. These agents are built from five core components and each playing a critical role.  The first one is goal. So what is this agent trying to achieve? Think of this as the mission or intent. For example, if I tell the agent, help me organized a team meeting for Friday. So the goal in that case would be schedule a meeting.  Number 2, memory. What does it remember? So this is the agent's context awareness. Storing previous chats, preferences, or ongoing tasks. For example, if last week I said I prefer meetings in the afternoon or I have already shared my team's availability, the agent can reuse that. And without the memory, the agent behaves stateless like a typical chatbot that forgets context after every prompt.  Third is tools. What can it access? Agents aren't just smart, they are also connected. They can be given access to tools like calendars, CRMs, web APIs, spreadsheets, and so on.  The fourth one is planner. So how does it break down the goal? And this is where the reasoning happens. The planner breaks big goals into a step-by-step plans, for example checking team availability, drafting meeting invite, and then sending the invite. And then probably, will confirm the booking. Agents don't just guess. They reason and organize actions into a logical path.  And the fifth and final one is executor, who gets it done. And this is where the action takes place. The executor performs what the planner lays out. For example, calling APIs, sending message, booking reservations, and if planner is the architect, executor is the builder.   18:36 Nikita: And where are generative AI agents being used?  Himanshu: Generative AI agents aren't just abstract ideas, they are being used across business functions to eliminate repetitive work, improve consistency, and enable faster decision making. For marketing, a generative AI agent can search websites and social platforms to summarize competitor activity. They can draft content for newsletters or campaign briefs in your brand tone, and they can auto-generate email variations based on audience segment or engagement history.  For finance, a generative AI agent can auto-generate financial summaries and dashboards by pulling from ERP spreadsheets and BI tools. They can also draft variance analysis and budget reports tailored for different departments. They can scan regulations or policy documents to flag potential compliance risks or changes.  For sales, a generative AI agent can auto-draft personalized sales pitches based on customer behavior or past conversations. They can also log CRM entries automatically once submitting summary is generated. They can also generate battlecards or next-step recommendations based on the deal stage.  For human resource, a generative AI agent can pre-screen resumes based on job requirements. They can send interview invites and coordinate calendars. A common theme here is that generative AI agents help you scale your teams without scaling the headcount.   20:02 Nikita: Himanshu, let's talk about the capabilities and benefits of generative AI agents.  Himanshu: So generative AI agents are transforming how entire departments function. For example, in customer service, 24/7 AI agents handle first level queries, freeing humans for complex cases.  They also enhance the decision making. Agents can quickly analyze reports, summarize lengthy documents, or spot trends across data sets. For example, a finance agent reviewing Excel data can highlight cash flow anomalies or forecast trends faster than a team of analysts.  In case of personalization, the agents can deliver unique, tailored experiences without manual effort. For example, in marketing, agents generate personalized product emails based on each user's past behavior. For operational efficiency, they can reduce repetitive, low-value tasks. For example, an HR agent can screen hundreds of resumes, shortlist candidates, and auto-schedule interviews, saving HR team hours each week.  21:06 Lois: Ok. And what are the risks of using generative AI agents?  Himanshu: The first one is job displacement. Let's be honest, automation raises concerns. Roles involving repetitive tasks such as data entry, content sorting are at risk. In case of ethics and accountability, when an AI agent makes a mistake, who is responsible? For example, if an AI makes a biased hiring decision or gives incorrect medical guidance, businesses must ensure accountability and fairness.  For data privacy, agents often access sensitive data, for example employee records or customer history. If mishandled, it could lead to compliance violations. In case of hallucinations, agents may generate confident but incorrect outputs called hallucinations. This can often mislead users, especially in critical domains like health care, finance, or legal.  So generative AI agents aren't just tools, they are a force multiplier. But they need to be deployed thoughtfully with a human lens and strong guardrails. And that's how we ensure the benefits outweigh the risks.  22:10 Lois: Thank you so much, Himanshu, for educating us. We've had such a great time with you! If you want to learn more about the topics discussed today, head over to mylearn.oracle.com and get started on the AI for You course.  Nikita: Join us next week as we chat about AI workflows and tools. Until then, this is Nikita Abraham…  Lois: And Lois Houston signing off!  22:32 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.  

The Mets free fall since June 13th is discussed in detail on this weeks show. From the starting rotation's inability to go deep in games, the recent bullpen melt downs, to the streaky offense - we go over it all here with top baseball insiders Andy Martino and Laura Albanese. We also look at the phenomenal debut of Nolan McLean on August 16th and discuss why he was not brought up sooner (the real insider will surprise you). The Mets fed off the energy of the kids at the Little League Classic - against the Seattle Mariners - but lose Francisco Alvarez in the process! Plus Juan Soto shuffling for the kids! August 2015 was a much different story for the Mets. We discuss that month with the Mets marching to the NL Championship with Terry Collins, Mets VP of Alumni relations Jay Horwitz and Andy Martino. Tunnel to Towers provides stories of inspiration with David Wright and Sylvester Stallone. Watch the entire episode on our YouTube channel - which includes highlights of August 2015! https://youtu.be/qhYovs8DLCU?si=uy40FHbaZasSdLGc Subscribe to our YouTube Channel here: https://www.youtube.com/@TheTerryCollinsShow Subscribe to the Terry Collins show on your favorite podcast platform. Like and Subscribe to our YouTube channel: / @theterrycollinsshow Follow The Terry Collins Show: X: https://x.com/TerryCollins_10 Instagram: / terrycollins_10 Facebook: https://www.facebook.com/profile.php?... Follow John Arezzi on X: ⁠⁠⁠⁠https://x.com/johnarezzi⁠⁠⁠⁠ Follow John Arezzi on Instagram: ⁠⁠⁠⁠ / johnarezzi Donate $11 a month to now help first responders, veterans and our military heroes. Go to Tunnel to Towers and help them do good: ⁠⁠⁠⁠https://t2t.org/⁠⁠⁠⁠ Host: Terry Collins Co-Host: John Arezzi Creative Director: Marsh Researcher - Dominic DiBiase Executive Producer: John Arezzi Learn more about your ad choices. Visit megaphone.fm/adchoices

Wilmer Flores relives the night at Citi Field—finding out on his phone mid-game that he'd been traded, breaking down on the field, and the private words David Wright shared with him in the tunnel. He opens up about Jeff Wilpon telling him there was no deal, the next-day walk-off that made him a Mets legend, and why the love from Queens still follows him on every road trip. Plus: 2015 World Series takeaways (that Royals defense!), how Wright treated rookies, life now with family, and the story behind the name “Wilmer.” Mets fans, bring the tissues.  Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Lois Houston and Nikita Abraham continue their discussion on AI fundamentals, diving into Data Science with Principal AI/ML Instructor Himanshu Raj. They explore key concepts like data collection, cleaning, and analysis, and talk about how quality data drives impactful insights.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast. I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me today is Nikita Abraham, Team Lead: Editorial Services.  Nikita: Hi everyone! Last week, we began our exploration of core AI concepts, specifically machine learning and deep learning. I'd really encourage you to go back and listen to the episode if you missed it.   00:52 Lois: Yeah, today we're continuing that discussion, focusing on data science, with our Principal AI/ML Instructor Himanshu Raj.  Nikita: Hi Himanshu! Thanks for joining us again. So, let's get cracking! What is data science?  01:06 Himanshu: It's about collecting, organizing, analyzing, and interpreting data to uncover valuable insights that help us make better business decisions. Think of data science as the engine that transforms raw information into strategic action.  You can think of a data scientist as a detective. They gather clues, which is our data. Connect the dots between those clues and ultimately solve mysteries, meaning they find hidden patterns that can drive value.  01:33 Nikita: Ok, and how does this happen exactly?  Himanshu: Just like a detective relies on both instincts and evidence, data science blends domain expertise and analytical techniques. First, we collect raw data. Then we prepare and clean it because messy data leads to messy conclusions. Next, we analyze to find meaningful patterns in that data. And finally, we turn those patterns into actionable insights that businesses can trust.  02:00 Lois: So what you're saying is, data science is not just about technology; it's about turning information into intelligence that organizations can act on. Can you walk us through the typical steps a data scientist follows in a real-world project?  Himanshu: So it all begins with business understanding. Identifying the real problem we are trying to solve. It's not about collecting data blindly. It's about asking the right business questions first. And once we know the problem, we move to data collection, which is gathering the relevant data from available sources, whether internal or external.  Next one is data cleaning. Probably the least glamorous but one of the most important steps. And this is where we fix missing values, remove errors, and ensure that the data is usable. Then we perform data analysis or what we call exploratory data analysis.  Here we look for patterns, prints, and initial signals hidden inside the data. After that comes the modeling and evaluation, where we apply machine learning or deep learning techniques to predict, classify, or forecast outcomes. Machine learning, deep learning are like specialized equipment in a data science detective's toolkit. Powerful but not the whole investigation.  We also check how good the models are in terms of accuracy, relevance, and business usefulness. Finally, if the model meets expectations, we move to deployment and monitoring, putting the model into real world use and continuously watching how it performs over time.  03:34 Nikita: So, it's a linear process?  Himanshu: It's not linear. That's because in real world data science projects, the process does not stop after deployment. Once the model is live, business needs may evolve, new data may become available, or unexpected patterns may emerge.  And that's why we come back to business understanding again, defining the questions, the strategy, and sometimes even the goals based on what we have learned. In a way, a good data science project behaves like living in a system which grows, adapts, and improves over time. Continuous improvement keeps it aligned with business value.   Now, think of it like adjusting your GPS while driving. The route you plan initially might change as new traffic data comes in. Similarly, in data science, new information constantly help refine our course. The quality of our data determines the quality of our results.   If the data we feed into our models is messy, inaccurate, or incomplete, the outputs, no matter how sophisticated the technology, will be also unreliable. And this concept is often called garbage in, garbage out. Bad input leads to bad output.  Now, think of it like cooking. Even the world's best Michelin star chef can't create a masterpiece with spoiled or poor-quality ingredients. In the same way, even the most advanced AI models can't perform well if the data they are trained on is flawed.  05:05 Lois: Yeah, that's why high-quality data is not just nice to have, it's absolutely essential. But Himanshu, what makes data good?   Himanshu: Good data has a few essential qualities. The first one is complete. Make sure we aren't missing any critical field. For example, every customer record must have a phone number and an email. It should be accurate. The data should reflect reality. If a customer's address has changed, it must be updated, not outdated. Third, it should be consistent. Similar data must follow the same format. Imagine if the dates are written differently, like 2024/04/28 versus April 28, 2024. We must standardize them.   Fourth one. Good data should be relevant. We collect only the data that actually helps solve our business question, not unnecessary noise. And last one, it should be timely. So data should be up to date. Using last year's purchase data for a real time recommendation engine wouldn't be helpful.  06:13 Nikita: Ok, so ideally, we should use good data. But that's a bit difficult in reality, right? Because what comes to us is often pretty messy. So, how do we convert bad data into good data? I'm sure there are processes we use to do this.  Himanshu: First one is cleaning. So this is about correcting simple mistakes, like fixing typos in city names or standardizing dates.  The second one is imputation. So if some values are missing, we fill them intelligently, for instance, using the average income for a missing salary field. Third one is filtering. In this, we remove irrelevant or noisy records, like discarding fake email signups from marketing data. The fourth one is enriching. We can even enhance our data by adding trusted external sources, like appending credit scores from a verified bureau.  And the last one is transformation. Here, we finally reshape data formats to be consistent, for example, converting all units to the same currency. So even messy data can become usable, but it takes deliberate effort, structured process, and attention to quality at every step.  07:26 Oracle University's Race to Certification 2025 is your ticket to free training and certification in today's hottest technology. Whether you're starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That's education.oracle.com/race-to-certification-2025. 08:10 Nikita: Welcome back! Himanshu, we spoke about how to clean data. Now, once we get high-quality data, how do we analyze it?  Himanshu: In data science, there are four primary types of analysis we typically apply depending on the business goal we are trying to achieve.  The first one is descriptive analysis. It helps summarize and report what has happened. So often using averages, totals, or percentages. For example, retailers use descriptive analysis to understand things like what was the average customer spend last quarter? How did store foot traffic trend across months?  The second one is diagnostic analysis. Diagnostic analysis digs deeper into why something happened. For example, hospitals use this type of analysis to find out, for example, why a certain department has higher patient readmission rates. Was it due to staffing, post-treatment care, or patient demographics?  The third one is predictive analysis. Predictive analysis looks forward, trying to forecast future outcomes based on historical patterns. For example, energy companies predict future electricity demand, so they can better manage resources and avoid shortages. And the last one is prescriptive analysis. So it does not just predict. It recommends specific actions to take.  So logistics and supply chain companies use prescriptive analytics to suggest the most efficient delivery routes or warehouse stocking strategies based on traffic patterns, order volume, and delivery deadlines.   09:42 Lois: So really, we're using data science to solve everyday problems. Can you walk us through some practical examples of how it's being applied?  Himanshu: The first one is predictive maintenance. It is done in manufacturing a lot. A factory collects real time sensor data from machines. Data scientists first clean and organize this massive data stream, explore patterns of past failures, and design predictive models.  The goal is not just to predict breakdowns but to optimize maintenance schedules, reducing downtime and saving millions. The second one is a recommendation system. It's prevalent in retail and entertainment industries. Companies like Netflix or Amazon gather massive user interaction data such as views, purchases, likes.  Data scientists structure and analyze this behavioral data to find meaningful patterns of preferences and build models that suggest relevant content, eventually driving more engagement and loyalty. The third one is fraud detection. It's applied in finance and banking sector.  Banks store vast amounts of transaction record records. Data scientists clean and prepare this data, understand typical spending behaviors, and then use statistical techniques and machine learning to spot unusual patterns, catching fraud faster than manual checks could ever achieve.  The last one is customer segmentation, which is often applied in marketing. Businesses collect demographics and behavioral data about their customers. Instead of treating all the customers same, data scientists use clustering techniques to find natural groupings, and this insight helps businesses tailor their marketing efforts, offers, and communication for each of those individual groups, making them far more effective.  Across all these examples, notice that data science isn't just building a model. Again, it's understanding the business need, reviewing the data, analyzing it thoughtfully, and building the right solution while helping the business act smarter.  11:44 Lois: Thank you, Himanshu, for joining us on this episode of the Oracle University Podcast. We can't wait to have you back next week for part 3 of this conversation on core AI concepts, where we'll talk about generative AI and gen AI agents.     Nikita: And if you want to learn more about data science, visit mylearn.oracle.com and search for the AI for You course. Until next time, this is Nikita Abraham…  Lois: And Lois Houston signing off!  12:13 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Living and Working in Spain with David Wright:The Ultimate Expat Guide Special guests today: Josh Williams from Ambient Wealth and Paul Burt from Indalo Transport. Welcome to Living and Working in Spain with David Wright, the leading radio show and podcast for anyone dreaming of a new life abroad. If you're planning to move to Spain, searching for reliable advice on Spanish residency, visas, employment, or lifestyle tips, this is your go-to resource. David Wright shares practical guidance, expert interviews, and insider knowledge to help you make your move stress-free and successful.

Barstool Sports' Chris Klemmer is back on the show talking all things Mets Baseball. Chris joins Dylan Campione & Matt Potter to discuss the recent cold streak, trade deadline, Pete Alonso's record setting Home Run and David Wright's jersey retirement! All that & more packed into our annual episode with Chris! Thanks so much for joining us again, appreciate all the time & insight as always.   

The Mets and Yankees both suffered frustrating losses. Gio and Jerry agree the Yankees' offensive struggles against Joe Ryan are déjà vu. Gio believes Aaron Boone will be fired if the Yankees miss the playoffs, while Jerry is unsure. A positive Mets fan from LA, Gio's critique of the Infinity Sports Network talent draft, and C-Lo's updates are also discussed. Gio is hesitant to ask Boomer for a favor. David Wright dislikes the Mets' home pinstripes. C-Lo explains a TikTok trend Brian Daboll referenced. Taylor Swift discussed her relationship with Travis and Jason Kelce on their podcast. Eddie undermines Gio's caddy story. The Moment of the Day: "Down goes Straw" and Jerry is "our Jeff McNeil." The hour concludes with discussions about watching Hard Knocks and CBS celebrating "The NFL Today."

Hour 1 Returning from California, Gio and Jerry discuss the Mets' 6-0 lead blown last night, attributing the loss to David Peterson's poor performance and the team's recent pitching struggles. They question the Mets' future, especially with the pitching staff. C-Lo's update covers the Mets' loss, the Brewers' 12th consecutive win, the Yankees' inability to sweep, Giancarlo Stanton's strong play, injuries at the Jets and Giants joint practice, Breece Hall's contract extension comments, and Jerry Jones's experimental drug trial. The hour ends with Gio and Jerry still struggling with the Mets' ongoing issues. Hour 2 Gio and Jerry discuss their trip to Pebble Beach and their golf games. C-Lo provides an update, and they talk about the Mets' blown lead and Pete Alonso's out at the plate. C-Lo explains the Brewers' free burger promotion. They also discuss Cam Schlittler's early exit, Paul Goldschmidt's potential injury, and a spat between The 7 Line and BT & Sal. Finally, Gio praises Jaxson Dart as an NFL QB prospect, and a caller thanks them for the Pebble Beach trip. Hour 3 Gio thinks Jerry will have an issue with Pete Alonso writing “down goes Straw” on his 253rd home run ball but Jerry is ok with it. Gio says there was no reason at all to include that. They agree that Pete definitely didn't have any negative intentions when he wrote it. Gio wasn't a big fan of Gary Cohen's call of the home run but Jerry liked it. C-Lo returns for an update but first Gio asks him to chime in on the Pete and Darryl Strawberry debate. The Mets and Yankees are a combined 6-17 since the trade deadline. They're hanging onto playoff spots by one game. It's been a dark two weeks. Chris “Mad Dog” Russo was bothered by the Mets celebration of Pete Alonso's 253rd HR and Gary Cohen's call of it. Gio doesn't agree with Dog but he thinks the pregame celebration after the record-breaking game is unnecessary. In the final segment of the hour, Gio and Jerry wonder if Pete's home run ball will stay in the Mets Hall of Fame at Citi Field or at his home. A caller wants Jeremy Hefner fired. Gio says it could happen after the season but Jerry would put the blame on the players and David Stearns before Hefner. Hour 4 The Mets and Yankees both suffered frustrating losses. Gio and Jerry agree the Yankees' offensive struggles against Joe Ryan are déjà vu. Gio believes Aaron Boone will be fired if the Yankees miss the playoffs, while Jerry is unsure. A positive Mets fan from LA, Gio's critique of the Infinity Sports Network talent draft, and C-Lo's updates are also discussed. Gio is hesitant to ask Boomer for a favor. David Wright dislikes the Mets' home pinstripes. C-Lo explains a TikTok trend Brian Daboll referenced. Taylor Swift discussed her relationship with Travis and Jason Kelce on their podcast. Eddie undermines Gio's caddy story. The Moment of the Day: "Down goes Straw" and Jerry is "our Jeff McNeil." The hour concludes with discussions about watching Hard Knocks and CBS celebrating "The NFL Today."

On the latest episode of The Mets Pod presented by Tri-State Cadillac, Connor Rogers and Joe DeMayo have the perfect diversion from the struggling Mets - an exclusive interview with Mets legend David Wright!  The guys talk to Number Five about stories from his number retirement day, behind the scenes tales from the production of his SNY documentary, the 2015 trade deadline, his thoughts on the current team, his choice of the best Mets uniform ever, and all the details of the Battle of the Badges Game between the NYPD and FDNY that David is hosting at Citi Field on Sunday August 17th. Later, Connor and Joe dive down deep (and low) to talk about the current mess that is the Mets, including the pitching problems, the hitting problems, and all the other problems.  The show also goes Down on the Farm to reveal what's behind the recent success of Brandon Sproat, and opens up a loud Mailbag to let the listeners let it all out as well. Be sure to subscribe to The Mets Pod at Apple Podcasts, Spotify, or wherever you get your podcasts.Today's Show:00:00 Welcome to the show00:20 David Wright joins the pod!01:15 Stories from the number retirement day03:05 How did David end up with number 5 in the first place?05:15 David hosts Battle of the Badges, NYPD vs FDNY, at Citi Field 8/17!07:10 Thoughts on SNY's documentary, “The Wright Way”08:45 Behind the scenes with the crew making the show10:25 Thoughts on the trade deadline, and adding Yoenis Cespedes in 201512:00 The modern MLB14:00 Could David have stolen 40 bases with today's rules?15:20 The big answer: what's the best Mets uniform?17:10 The meaning of being a captain17:20 Current Mets are in the rough, what should a captain do?19:30 Recounting the catch: how the bare hand dive in SD went down21:00 The Mets Pod Mount Rushmore: David's 4 favorite Shea Stadium memories23:55 Goodbye to David Wright26:00 The Week That Was…just terrible in every way39:50 Mailbag – Ranking collapses43:05 Mailbag – What can change to shake things up?49:10 Mailbag – Questioning the starting pitching strategy of David Stearns?56:25 The Scoreboard: last week's recap59:50 Mailbag/Down on the Farm: Brandon Sproat deep dive01:05:00 Any way to piggyback Brandon Sproat and Nolan McLean?

David Wright joins Jay Horwitz for a special Amazin' Conversation ahead of the 2025 Battle of the Badges at Citi Field — the annual showdown between New York's police and firefighters. Wright opens up about: How Tom Seaver inspired one of his most memorable on-field moments Why honoring first responders means so much to him and his family The fierce competitiveness (and trash talk) at Battle of the Badges His reflections on his jersey retirement day and the bond with Mets fans The lasting friendships from his 15-year career in Queens

My guest today is third generation baker, writer and teacher David Wright author of the excellent book Breaking Bread: How Baking Shaped our World published by Aurum.We talk about the social benefits of bread making, milling grain into flour, the anatomy of a grain, roller mills, the Chorleywood process and why gluten can be compared to Arnold Schwarzenegger and Danny DeVito.Those listening to the secret podcast: you get a little over 15 minutes of bonus material that includes additives that don't have be named on ingredients lists, flatbreads, the National Loaf, the value of bread and more!Follow David on Instagram @thebreaducatorBreaking Bread: How Baking Shaped Our World is published by AurumMore on the Pump Street workshopsMore about David's Earth's Crust Bakery at Camp BestivalRemember: Fruit Pig are sponsoring the 9th season of the podcast and Grant and Matthew are very kindly giving listeners to the podcast a unique special offer 10% off your order until the end of October 2025 – use the offer code Foodhis in the checkout at their online shop, www.fruitpig.co.uk.The Serve it Forth Food History Festival website is now live and tickets are available on Eventbrite.If you can, support the podcast and blogs by becoming a £3 monthly subscriber, and unlock lots of premium content, including bonus blog posts and recipes, access to the easter eggs and the secret podcast, or treat me to a one-off virtual pint or coffee: click here.This episode was mixed and engineered by Thomas Ntinas of the Delicious Legacy podcast.Things mentioned in today's episodeServe it Forth websiteServe it Forth Eventbrite pageAgainst the Grain by James C. Scott (2018)Knead to Know: A History of Baking by Neil Buttery (2023)My blog post and recipe for a cobMy blog post and recipe for a cottage loafPertinent previous podcast episode:

In today's episode we look back at two Hall of Fame interviews with Mets legend, David Wright, and NFL legend Eric Allen. Hosts: Cousin Sal Guest: David Wright, Eric Allen Producer: Michael Szokoli The Ringer is committed to responsible gaming, please visit theringer.com/RG to learn more about the resources and helplines available, and listen to the end of the episode for additional details. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Mike Francesa reaches into his inbox and reacts to listener emails. You'll hear his thoughts on David Wright's career, Caitlin Clark's cultural impact, favorite Florida golf courses, and much more. Get more Mike! Subscribe to the free Mike Francesa newsletter at mikefrancesapodcast.com

In this jam-packed episode of Giant Mess, Neal Lynch digs deep into the biggest stories and turning points from July of the New York Mets' 2025 season. We break down the good, the bad, and the ugly: eking out wins against struggling teams, bouncing back from a brutal June, and surviving a turbulent All-Star break marked by Francisco Lindor's starter nod and Pete Alonso's near-MVP performance.Has Lindor finally won over the entire fanbase? All that, plus signature rants and hilarious Home Run Derby grievances.#Mets #MLB #DavidWright #FranciscoLindor #PeteAlonso #baseballNew York Giants Fan Rants & Analysis from Giant Mess Podcast - https://bit.ly/NYGiantsYTPlaylist NY Mets Fan Rants & Analysis from Giant Mess - https://bit.ly/MetsYTPlaylist Movie Reviews from Giant Mess Podcast - https://bit.ly/GiantMessMovieReviews TV Show Reactions from Giant Mess Podcast - https://bit.ly/GiantMessTV Funny Stories from Giant Mess Podcast -  https://bit.ly/GiantMessFunnyStoriesABOUT NEAL LYNCH:Irish-Italian-American who graduated from a Catholic high school (even though I'm not Catholic), and a college known for producing doctors and lacrosse players, then became neither. Former 4th string quarterback and middle relief pitcher at a D3 school. Degrees in Film & Media Studies and Communications. Worked for Condé Nast, New York Post, SportsNet New York, and Hearst Television.Divorced dad who blogs, podcasts, writes, edits, optimizes, strategizes, and over-analyzes.  ABOUT "GIANT MESS":"Giant Mess" is a weird sports and entertainment comedy podcast hosted by a giant mess, the Real Cinch Neal Lynch. Neal covers New York Giants football, Mets baseball, movies, and TV shows, mixing in funny life stories along the way. Episodes focus on movie reviews, tv show recaps, post-game analysis, predictions, reactions, and funny stories about parenting.Subscribe to Giant Mess on YouTube: ⁠⁠https://bit.ly/GiantMessYT⁠⁠ Follow me on:* Link Tree - ⁠⁠https://linktr.ee/neallynch⁠⁠  * My Official Blog - ⁠⁠http://bit.ly/neallynchBLOG⁠⁠ * Giant Mess Facebook Page - ⁠⁠http://bit.ly/GiantMessFB⁠⁠    * Twitter - ⁠⁠http://bit.ly/NealLynchTW⁠⁠     * Personal Instagram - ⁠⁠http://bit.ly/NealLynchIG⁠⁠    * Giant Mess Instagram - ⁠⁠https://bit.ly/GiantMessInstagram⁠⁠  * Subscribe to Giant Mess on Apple Podcasts - ⁠⁠http://bit.ly/GiantMessApple⁠⁠  * Subscribe to Giant Mess on Spotify - ⁠⁠http://bit.ly/GiantMessSpotify⁠⁠ 

On the latest episode of The Mets Pod presented by Tri-State Cadillac, SNY Mets play-by-play broadcaster Gary Cohen joins Connor Rogers and Joe DeMayo to talk about the results of the trade deadline and the road ahead for the Mets. The crew covers the team's new additions, plans for the starting rotation, the streakiness of the Mets and their younger players, David Wright, Pete Alonso, plus Gary answers a listener's question about the idea of Juan Soto as a leadoff hitter. Later, Connor and Joe go Down on the Farm for a Carson Benge deep dive and a check-in on Jett Williams, then score the Scoreboard and open the Mailbag for questions answered about the race for the NL East and the Mets future starting staffs. Be sure to subscribe to The Mets Pod at Apple Podcasts, Spotify, or wherever you get your podcasts.Today's Show:00:00 Welcome to the show, Gary Cohen joins the pod!00:35 Thoughts on the trade deadline 02:15 The starting rotation going forward03:40 The Mets are hot and cold06:10 Balancing Mauricio/Vientos/Baty playing time07:55 What makes Carlos Mendoza a successful leader09:15 Takeaways from David Wright's number retirement10:35 What is different about Francisco Alvarez after return?12:25 Pete Alonso chasing history, free agency again14:20 Mailbag for Gary: Should the Mets hit Juan Soto in the leadoff spot?15:20 Goodbye to Gary Cohen15:30 A rough week, a tight playoff race21:35 When to call up Brandon Sproat and/or Nolan McLean?27:50 Mailbag/Down on the Farm: Carson Benge deep dive30:25 Mailbag/Down on the Farm: With Drew Gilbert gone, does Jett Williams go full CF?31:55 The Scoreboard: last week's recap33:40 The Scoreboard: making this week's bets40:05 Mailbag – Why does Joe mispronounce “platoon?”42:05 Mailbag – Can the Marlins threaten for the NL East?46:45 Mailbag – Fitting Mets starters into future rotations

The Mets were quite active during the Trade Deadline, shoring up their bullpen and adding a centerfielder. The team continues to struggle with the lack of starting pitching going deep in the games, and with the top of the lineup struggling to produce with runners on base. Terry Collins goes over it all with Baseball Insider, SNY.TV writer and Analyst Andy Martino. On Talkin with TC - Mets VP of Alumni Relations - Jay Horwitz joins Terry to discuss the recent David Wright ceremony, and the upcoming Alumni Classic Game. Tunnel to Towers provides stories of inspiration with David Wright and Sylvester Stallone. Watch the entire episode which includes the music video of David Wright's HOF induction and number retirement ceremony here: https://youtu.be/FV10VvnOk9E Subscribe to the Terry Collins show on your favorite podcast platform. Like and Subscribe to our YouTube channel: / @theterrycollinsshow Follow The Terry Collins Show: X: https://x.com/TerryCollins_10 Instagram: / terrycollins_10 Facebook: https://www.facebook.com/profile.php?... Follow John Arezzi on X: ⁠⁠⁠⁠https://x.com/johnarezzi⁠⁠⁠⁠ Follow John Arezzi on Instagram: ⁠⁠⁠⁠ / johnarezzi Donate $11 a month to now help first responders, veterans and our military heroes. Go to Tunnel to Towers and help them do good: ⁠⁠⁠⁠https://t2t.org/⁠⁠⁠⁠ Host: Terry Collins Co-Host: John Arezzi Creative Director: Marsh Researcher - Dominic DiBiase Executive Producer: John Arezzi Learn more about your ad choices. Visit megaphone.fm/adchoices

Former Mets first baseman Ike Davis sits down with Jay Horwitz for an unforgettable Amazin' Conversations episode — diving deep into his career highs, crushing injuries, and life after baseball. Ike opens up about battling Valley Fever, his ankle injury with David Wright, playing first base during Johan Santana's historic no-hitter, and hitting big home runs for R.A. Dickey's 20-win season. He shares behind-the-scenes stories of playing with Jose Reyes and David Wright, reflects on his 2014 trade from the Mets, and reveals what he's doing now in commercial real estate. Plus — an unbelievable childhood encounter with Joe DiMaggio you have to hear.

Two of the most memorable managers in Mets history—Terry Collins and Bobby Valentine—sit down with Jay Horwitz for a no-holds-barred conversation about their wildest moments in Queens. From the infamous “our asses are in the jackpot” rant to Ricky Henderson myths, World Series heartbreak, and managing generational talent like David Wright and Jacob deGrom, this episode is packed with stories fans have never heard before.

In Episode 535 of For All You Kids Out There, Jeffrey and Jarrett chat about the David Wright number retirement and Mark Vientos trade rumors, before Michael Baumann (we'll leave you in suspense which one) joins the show to chat about college baseball and the Tour de France. We even have a little time to answer some correspondence, but hope you folks like cycling chat.

From 'Rico Brogna' (subscribe here): It was a tough 3-game series for the Mets as they return from all-star break. David Wright's retirement of his number 5 distracted the fans from losing the series to the Reds. A victory on Sunday at least allowed the Mets to avoid being swept at home. To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

On the latest episode of The Mets Pod presented by Tri-State Cadillac, Connor Rogers and Joe DeMayo look back at the week that was coming off of the All-Star break. Leading off, Connor and Joe talk about the Mets offense playing small ball, Francisco Alvarez' return to the big league club, and Brett Baty's contributions to the lineup. Then, the guys discuss the upcoming trade deadline and how president of baseball operations David Stearns plans to approach next Thursday for the Mets. Connor and Joe also share their reaction to David Wright's number retirement and go Down on the Farm to discuss potential position player call ups. They wrap the show with their scoreboard predictions and some Mailbag questions answered about Seth Lugo and potential prospects in centerfield. Be sure to subscribe to The Mets Pod at Apple Podcasts, Spotify, or wherever you get your podcasts. Today's Show: 00:00 Welcome to the show! 0:54 The Week That Was: Reds and Angels 5:44 The Bottom of the Lineup Comes Through 8:12 Francisco Alvarez returns 10:31 Trade Deadline Chatter 21:08 David Stearns trade deadline approach 25:11 David Wright Day Reaction 27:35 Down on the Farm: Position Player September Call Ups? 31:18 The Scoreboard 41:12 Mailbag: Is Seth Lugo a trade deadline option? 42:58 Mailbag: Any chance the Mets see how prospects handle centerfield before the deadline?

David Wright To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

Evan, Tommy and Rosie discuss their experiences at David Wright's Number Retirement Ceremony at CitiField this past Saturday.

Evan and Tiki talk about the WFAN Quarter Century team results focused on the Jets & Giants. They then give their thoughts on David Wright's Number Retirement Ceremony this past weekend.

On Monday's ENN, Jerry Jones responds to Micah, Bengals president Mike Brown on Shemar Stewart and David Wright number retirement. Learn more about your ad choices. Visit podcastchoices.com/adchoices

We heard from David Wright as he addressed the crowd when the Mets retired his number. Scottie Scheffler talked about staying calm as he won the Open Championship.

Boomer discusses Netflix's 'Quarterback', admiring Jared Goff and empathizing with Kirk Cousins, while finding Joe Burrow unique. He notes Aaron Glenn followed Dan Campbell's strategy of replacing the existing quarterback. Netflix is focusing on cruises, releasing 'Poop Cruise' and a documentary about a missing woman sold into sex trafficking. Jerry provides updates: Yankees beat Braves, MLB investigates a Braves coach's threat, Mets avoided a sweep against the Reds, and David Wright's number was retired. Scottie Scheffler calmly won the Open Championship. The hour concluded with a caller urging Gio to handle a wasp nest, prompting calls from two exterminators named Vinny.

Hour 1 Boomer, Gio, Jerry, Al & Eddie are back. Scottie Scheffler won The Open Championship; his dominance contrasts with his boring personality. Jerry's update included Scheffler's win. The Mets avoided a sweep, beating the Reds with Juan Soto scoring on an infield hit. The Yankees beat the Braves, winning the series as Aaron Judge homered. Gio saw a Joe Namath hearing aid commercial, leading to a discussion on Boomer doing a boner pill commercial. Hour 2 Boomer discusses Netflix's 'Quarterback' series, liking Jared Goff and sympathizing with Kirk Cousins, while finding Joe Burrow unique. He notes Aaron Glenn's approach mirrors Dan Campbell's in Detroit. Netflix is also targeting cruises with documentaries, including one about a missing woman sold into sex trafficking. Jerry's update covers the Yankees beating the Braves, MLB investigating a Braves coach, the Mets avoiding a sweep, and David Wright's number retirement speech. Scottie Scheffler reflected on staying calm during his Open Championship win. Finally, Gio's wasp nest issue was discussed, with two exterminators named Vinny calling in. Hour 3 Despite losing their series to the Reds, the Mets avoided a sweep. Boomer noted high-paid players underperforming, a sentiment echoed by a caller who highlighted the offense's season-long struggles, previously masked by dominant pitching. Jerry's update began with the Mets' win and then covered the Yankees taking 2-of-3 from the Braves before their Toronto series. Scottie Scheffler won the Open. The hour concluded with a discussion on the upcoming NFL season, focusing on the Jets' new GM, Head Coach, and Offensive Coordinator. Hour 4 Scottie Scheffler, dubbed "boring," won the Open Championship, prompting comparisons to Tiger Woods. Jerry's final update covered WFAN personalities' votes for NY's all-quarter-century teams. Aaron Judge tied A-Rod for sixth all-time Yankee homers. The Mets beat the Reds, with Juan Soto scoring on an infield single. Scheffler reacted to his win. The Moment of the Day: Boomer endorsing a "boner pill." The final segment revisited the WFAN quarter-century teams, including the hockey team. The building's fire alarm frequently goes off unnoticed.

The first series back after the All-Star break did not go as planned for the Los Angeles Dodgers. On the flip-side, it went as well as it could go for the Milwaukee Brewers. The Brewers swept the Dodgers, in LA, over the weekend as they extended their win streak to ten and climbed up the standings to tie the Cubs for the lead in the NL Central. The Dodgers remain 3.5 games up in the NL West, but everything is not going according to the script in Los Angeles. Mookie Betts' struggles continued, and he's now been moved into the leadoff spot, and Freddie Freeman left the game Sunday after getting hit on the wrist. Freeman's future availability is currently in question. Jake and Jordan discuss both the impressive success of the Brewers and the Dodgers struggles.That was not the only sweep of the weekend as the Blue Jays and Diamondbacks swept their series, and the Chicago White Sox notched their first sweep of the year by taking down the Pittsburgh Pirates. The guys dive into each of these series as well as the Cubs vs. Red Sox, Yankees vs. Braves and Reds vs. Mets. They wrap up by going Turbo Mode to chat about every other series over the weekend. Plus, the Mets retired David Wright's jersey number on Saturday and Jake and Jordan reflect on the former Met's impressive career. Start your week off getting caught up on all the action in baseball here on Baseball Bar-B-Cast. (1:28) - Brewers sweep Dodgers(22:34) - Blue Jays, Diamondbacks & White Sox sweep(31:29) - Cubs over Red Sox(36:08) - Yankees in Atlanta(46:06) - Reds ruin Wright weekend(53:16) - Turbo Mode  Subscribe to Baseball Bar-B-Cast on your favorite podcast app:

Hear the best interviews of the week on WFAN. David Wright joins Sal Licata to discuss the Mets retiring his No. 5 this weekend. Plus, Sal talks Mets and more with Terry Collins; Joe Torre joins to talk All-Star Game and Yankees stories, MLB trade deadline talk with SNY's Andy Martino, Knicks talk with John Starks and an entertaining chat with comedian Andrew Dice Clay.

Hear the best interviews of the week on WFAN. David Wright joins Sal Licata to discuss the Mets retiring his No. 5 this weekend. Plus, Sal talks Mets and more with Terry Collins; Joe Torre joins to talk All-Star Game and Yankees stories, MLB trade deadline talk with SNY's Andy Martino, Knicks talk with John Starks and an entertaining chat with comedian Andrew Dice Clay.

This week's Mets highlights include Sal Licata ripping Major League Baseball for not naming Juan Soto an All-Star, and for not naming Pete Alonso All-Star Game MVP. Plus, Boomer and Gio discuss Francisco Lindor potentially being named the next team captain, as do Tiki and Morash. Finally, Sal talks with David Wright ahead of his number retirement.

This week's Mets highlights include Sal Licata ripping Major League Baseball for not naming Juan Soto an All-Star, and for not naming Pete Alonso All-Star Game MVP. Plus, Boomer and Gio discuss Francisco Lindor potentially being named the next team captain, as do Tiki and Morash. Finally, Sal talks with David Wright ahead of his number retirement.

David Wright Doc To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

From 'Rico Brogna' (subscribe here): In lieu of the Mets retiring David Wright's number on Saturday, July 19th, Evan Roberts takes you through his top moments of David Wright's career. To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

Tommy has a ticket to David Wright Day, but he isn't sure if he wants to go, much to Shaun's surprise.

Hour 2: Tommy has not accepted free tickets to David Wright Day quite yet. Shaun argues that he has to go or he loses his Mets fan card. That and much more.

Hour 3: Shaun and Tommy discuss the importance of David Wright to the Mets, Shaun is frustrated by the constant Yankee injuries, and much more.

Shaun gives the Top-Five #5's in his lifetime in honor of David Wright. Also, some breaking injury news on Cam Schlittler.

David Wright, the ESPYS, and Led Zeppelin To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

On the latest episode of The Mets Pod presented by Tri-State Cadillac, Connor Rogers and Joe DeMayo look back at All-Star week and the MLB Draft, while looking ahead to the trade deadline and the second half of the season. Leading off, Connor and Joe talk about Pete and Peterson at the All-Star Game, Jonah Tong and Carson Benge at the Futures Game, David Wright getting his number retired, and what kind of talent the Mets added during this year's MLB Draft. Then the organization's first pick, Michigan infielder Mitch Voit, joins the show to talk about his development, his emotions about getting drafted by the Mets, and his career goals moving forward.The guys also go Down on the Farm to discuss the best time to call up pitchers Brandon Sproat and Nolan McLean, and wrap the show with Mailbag questions answered about potential trade ideas as the MLB Trade Deadline approaches. Be sure to subscribe to The Mets Pod at Apple Podcasts, Spotify, or wherever you get your podcasts.Today's Show:00:00 Welcome to the show!02:00 The Week That Was, Kodai Senga and Sean Manaea return03:25 MLB Trade deadline coming soon!04:40 Jonah Tong and Carson Benge at the MLB Futures Game09:00 Mets All-Stars showing out at the ASG11:55 David Peterson stepping up14:10 Cheers to the Captain, as David Wright's number gets retired16:50 Mets 2025 MLB Draft recap24:30 Mets first pick Mitch Voit joins the show!24:40 Voit's earliest baseball memory25:05 The path to playing at Michigan26:00 Favorite team and players growing up26:45 UCL injury and surgery27:45 2025 breakout season, giving up pitching28:35 Playing second base30:05 The Mitch Voit self-scouting report31:05 What needs to improve31:50 Draft day experience33:45 How would Mitch Voit the hitter do against Mitch Voit the pitcher?34:25 Goodbye to Mitch36:05 Down on the Farm: Brandon Sproat and Nolan McLean40:10 The Scoreboard: last week's recap41:55 Mailbag – Pitching trades, who to give up?48:00 Mailbag – Joe's mock trades

Hour 4: Shaun says that naming Lindor Captain would take away from David Wright's day. That and much more.

Today, Brandon talks to David Wright about Rick Devens' time on Survivor.