Player FM - Internet Radio Done Right
14 subscribers
Checked 7d ago
تمت الإضافة منذ قبل three أعوام
المحتوى المقدم من Jonathan Stephens. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Jonathan Stephens أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.
Player FM - تطبيق بودكاست
انتقل إلى وضع عدم الاتصال باستخدام تطبيق Player FM !
انتقل إلى وضع عدم الاتصال باستخدام تطبيق Player FM !
المدونة الصوتية تستحق الاستماع
برعاية
B
B2B Agility with Greg Kihlström™: MarTech, E-Commerce, & Customer Success


1 #52: Navigating the effect of AI on marketing jobs and the job market with Sue Keith, Landrum Talent Solutions 19:09
19:09
التشغيل لاحقا
التشغيل لاحقا
قوائم
إعجاب
احب19:09
This episode is brought to you by Landrum Talent Solutions, a national recruiting firm specializing in marketing and HR positions. Our guest today has been keeping us up to date with the current state of hiring for marketers on a quarterly basis, which has taken us on quite a roller coaster ride. Today we’re going to look at how marketing and communication execs are responding to the latest developments in the world while still needing to get their work done. To take a look at the latest here, I’d like to welcome back to the show Sue Keith, Corporate Vice President at Landrum Talent Solutions. About Sue Keith Sue Keith is Corporate Vice President at Landrum Talent Solutions. With deep expertise in navigating complex labor markets, Sue has a front-row seat to the evolving dynamics of marketing roles, hiring trends, and the broader implications of AI and economic uncertainty. RESOURCES Landrum Talent Solutions: https://www.landrumtalentsolutions.com Catch the future of e-commerce at eTail Boston, August 11-14, 2025. Register now: https://bit.ly/etailboston and use code PARTNER20 for 20% off for retailers and brands Online Scrum Master Summit is happening June 17-19. This 3-day virtual event is open for registration. Visit www.osms25.com and get a 25% discount off Premium All-Access Passes with the code osms25agilebrand Don't Miss MAICON 2025, October 14-16 in Cleveland - the event bringing together the brights minds and leading voices in AI. Use Code AGILE150 for $150 off registration. Go here to register: https://bit.ly/agile150 Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstrom Don't miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.show Check out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com…
Computer Vision Decoded
وسم كل الحلقات كغير/(كـ)مشغلة
Manage series 3364101
المحتوى المقدم من Jonathan Stephens. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Jonathan Stephens أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.
A tidal wave of computer vision innovation is quickly having an impact on everyone's lives, but not everyone has the time to sit down and read through a bunch of news articles and learn what it means for them. In Computer Vision Decoded, we sit down with Jared Heinly, the Chief Scientist at EveryPoint, to discuss topics in today’s quickly evolving world of computer vision and decode what they mean for you. If you want to be sure you understand everything happening in the world of computer vision, don't miss an episode!
…
continue reading
18 حلقات
وسم كل الحلقات كغير/(كـ)مشغلة
Manage series 3364101
المحتوى المقدم من Jonathan Stephens. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Jonathan Stephens أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.
A tidal wave of computer vision innovation is quickly having an impact on everyone's lives, but not everyone has the time to sit down and read through a bunch of news articles and learn what it means for them. In Computer Vision Decoded, we sit down with Jared Heinly, the Chief Scientist at EveryPoint, to discuss topics in today’s quickly evolving world of computer vision and decode what they mean for you. If you want to be sure you understand everything happening in the world of computer vision, don't miss an episode!
…
continue reading
18 حلقات
كل الحلقات
×C
Computer Vision Decoded

1 The Evolution of Image Based 3D Reconstruction 32:55
32:55
التشغيل لاحقا
التشغيل لاحقا
قوائم
إعجاب
احب32:55
In this episode of Computer Vision Decoded, we bring to you a live recording of Jared Heinly presentation on the evolution of image based 3D reconstruction. This recording was from a Computer Vision Decoded meetup in Pittsburgh with a visual component. If you would like to tune in with the visuals, we recommend watching the episode on our YouTube channel: https://youtu.be/Gwib5IcTKHI Follow: Jared on X: https://x.com/JaredHeinly Jonathan on X: https://x.com/jonstephens85 This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services. Learn more at https://www.everypoint.io…
C
Computer Vision Decoded

1 Understanding Gaussian Splatting w/ NVIDIA's Ruilong Li 1:18:53
1:18:53
التشغيل لاحقا
التشغيل لاحقا
قوائم
إعجاب
احب1:18:53
In this episode of Computer Vision Decoded, our hosts Jonathan Stephens and Jared Heinly are joined by Ruilong Li, a researcher at NVIDIA and key contributor to both Nerfstudio and gsplat, to dive deep into 3D Gaussian Splatting. They explore how this relatively new technology works, from the fundamentals of gaussian representations to the optimization process that creates photorealistic 3D scenes. Ruilong explains the technical details behind gaussian splatting, and discusses the development of the popular gsplat library. The conversation covers practical advice for capturing high-quality data, the iterative training process, and how Gaussian splatting compares to other 3D representations like meshes and NeRFs. Links: gsplat: https://github.com/nerfstudio-project/gsplat Nerfstudio: https://docs.nerf.studio/ Follow: Ruilong on X: https://x.com/ruilong_li Jared on X: https://x.com/JaredHeinly Jonathan on X: https://x.com/jonstephens85 This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services. Learn more at https://www.everypoint.io…
C
Computer Vision Decoded

1 Camera Types for 3D Reconstruction Explained 1:15:35
1:15:35
التشغيل لاحقا
التشغيل لاحقا
قوائم
إعجاب
احب1:15:35
In this episode of Computer Vision Decoded, hosts Jonathan Stephens and Jared Heinly explore the various types of cameras used in computer vision and 3D reconstruction. They discuss the strengths and weaknesses of smartphone cameras, DSLR and mirrorless cameras, action cameras, drones, and specialized cameras like 360, thermal, and event cameras. The conversation emphasizes the importance of understanding camera specifications, metadata, and the impact of different lenses on image quality. The hosts also provide practical advice for beginners in 3D reconstruction, encouraging them to start with the cameras they already own. Takeaways Smartphones are versatile and user-friendly for photography. RAW images preserve more data than JPEGs, aiding in post-processing. Mirrorless and DSLR cameras offer better low-light performance and lens flexibility. Drones provide unique perspectives and programmable flight paths for capturing images. 360 cameras allow for quick scene capture but may require additional processing for 3D reconstruction. Event cameras capture rapid changes in intensity, useful for robotics applications. Thermal and multispectral cameras are specialized for specific applications, not typically used for 3D reconstruction. Understanding camera metadata is crucial for effective image processing. Choosing the right camera depends on the specific needs of the project. Starting with a smartphone is a low barrier to entry for beginners in 3D reconstruction. This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services: https://www.everypoint.io…
C
Computer Vision Decoded

1 Understanding 3D Reconstruction with COLMAP 57:02
57:02
التشغيل لاحقا
التشغيل لاحقا
قوائم
إعجاب
احب57:02
In this episode, Jonathan Stephens and Jared Heinly delve into the intricacies of COLMAP, a powerful tool for 3D reconstruction from images. They discuss the workflow of COLMAP, including feature extraction, correspondence search, incremental reconstruction, and the importance of camera models. The conversation also covers advanced topics like geometric verification, bundle adjustment, and the newer GLOMAP method, which offers a faster alternative to traditional reconstruction techniques. Listeners are encouraged to experiment with COLMAP and learn through hands-on experience. This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services: https://www.everypoint.io…
C
Computer Vision Decoded

1 Tips and Tricks for 3D Reconstruction in Different Environments 1:21:23
1:21:23
التشغيل لاحقا
التشغيل لاحقا
قوائم
إعجاب
احب1:21:23
In this episode, we discuss practical tips and challenges in 3D reconstruction from images, focusing on various environments such as urban, indoor, and outdoor settings. We explore issues like repetitive structures, lighting conditions, and the impact of reflections and shadows on reconstruction quality. The conversation also touches on the importance of camera motion, lens distortion, and the role of machine learning in enhancing reconstruction processes. Listeners gain insights into optimizing their 3D capture techniques for better results. Key Takeaways Repetitive structures can confuse computer vision algorithms. Lighting conditions greatly affect image quality and reconstruction accuracy. Wide-angle lenses can help capture more unique features. Indoor environments present unique challenges like textureless walls. Aerial imaging requires careful management of lens distortion. Understanding the application context is crucial for effective 3D reconstruction. Camera motion should be varied to avoid distortion and drift. Planning captures based on goals can lead to better results. This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services. Learn more at https://www.everypoint.io…
C
Computer Vision Decoded

1 Exploring Depth Maps in Computer Vision 57:31
57:31
التشغيل لاحقا
التشغيل لاحقا
قوائم
إعجاب
احب57:31
In this episode of Computer Vision Decoded, Jonathan Stephens and Jared Heinly explore the concept of depth maps in computer vision. They discuss the basics of depth and depth maps, their applications in smartphones, and the various types of depth maps. The conversation delves into the role of depth maps in photogrammetry and 3D reconstruction, as well as future trends in depth sensing and machine learning. The episode highlights the importance of depth maps in enhancing photography, gaming, and autonomous systems. Key Takeaways: Depth maps represent how far away objects are from a sensor. Smartphones use depth maps for features like portrait mode. There are multiple types of depth maps, including absolute and relative. Depth maps are essential in photogrammetry for creating 3D models. Machine learning is increasingly used for depth estimation. Depth maps can be generated from various sensors, including LiDAR. The resolution and baseline of cameras affect depth perception. Depth maps are used in gaming for rendering and performance optimization. Sensor fusion combines data from multiple sources for better accuracy. The future of depth sensing will likely involve more machine learning applications. Episode Chapters 00:00 Introduction to Depth Maps 00:13 Understanding Depth in Computer Vision 06:52 Applications of Depth Maps in Photography 07:53 Types of Depth Maps Created by Smartphones 08:31 Depth Measurement Techniques 16:00 Machine Learning and Depth Estimation 19:18 Absolute vs Relative Depth Maps 23:14 Disparity Maps and Depth Ordering 26:53 Depth Maps in Graphics and Gaming 31:24 Depth Maps in Photogrammetry 34:12 Utilizing Depth Maps in 3D Reconstruction 37:51 Sensor Fusion and SLAM Technologies 41:31 Future Trends in Depth Sensing 46:37 Innovations in Computational Photography This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services. Learn more at https://www.everypoint.io…
C
Computer Vision Decoded

1 What's New in 2025 for Computer Vision? 50:03
50:03
التشغيل لاحقا
التشغيل لاحقا
قوائم
إعجاب
احب50:03
After an 18 month hiatus, we are back! In this episode of Computer Vision Decoded, hosts Jonathan Stephens and Jared Heinly discuss the latest advancements in computer vision technology, personal updates, and insights from the industry. They explore topics such as real-time 3D reconstruction, computer vision research, SLAM, event cameras, and the impact of generative AI on robotics. The conversation highlights the importance of merging traditional techniques with modern machine learning approaches to solve real-world problems effectively. Chapters 00:00 Intro & Personal Updates 04:36 Real-Time 3D Reconstruction on iPhones 09:40 Advancements in SfM 14:56 Event Cameras 17:39 Neural Networks in 3D Reconstruction 26:30 SLAM and Machine Learning Innovation 29:48 Applications of SLAM in Robotics 34:19 NVIDIA's Cosmos and Physical AI 40:18 Generative AI for Real-World Applications 43:50 The Future of Gaussian Splatting and 3D Reconstruction This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services: https://www.everypoint.io…
C
Computer Vision Decoded

1 A Computer Vision Scientist Reacts to the iPhone 15 Announcement 42:17
42:17
التشغيل لاحقا
التشغيل لاحقا
قوائم
إعجاب
احب42:17
In this episode of Computer Vision Decoded, we are going to dive into our in-house computer vision expert's reaction to the iPhone 15 and iPhone 15 Pro announcement. We dive into the camera upgrades, decode what a quad sensor means, and even talk about the importance of depth maps. Episode timeline: 00:00 Intro 02:59 iPhone 15 Overview 05:15 iPhone 15 Main Camera 07:20 Quad Pixel Sensor Explained 15:45 Depth Maps Explained 22:57 iPhone 15 Pro Overview 27:01 iPhone 15 Pro Cameras 32:20 Spatial Video 36:00 A17 Pro Chipset This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services: https://www.everypoint.io…
C
Computer Vision Decoded

1 OpenMVG Decoded: Pierre Moulon's 10 Year Journey Building Open-Source Software 55:44
55:44
التشغيل لاحقا
التشغيل لاحقا
قوائم
إعجاب
احب55:44
In this episode of Computer Vision Decoded, we are going to dive into Pierre Moulon's 10 years experience building OpenMVG. We also cover the impact of open-source software in the computer vision industry and everything involved in building your own project. There is a lot to learn here! Our episode guest, Pierre Moulon, is a computer vision research scientist and creator of OpenMVG - a library for computer-vision scientists and targeted for the Multiple View Geometry community. The episode follow's Pierre's journey building OpenMVG which he wrote about as an article in his GitHub repository. Explore OpenMVG on GitHub: https://github.com/openMVG/openMVG Pierre's article on building OpenMVG: https://github.com/openMVG/openMVG/discussions/2165 Episode timeline: 00:00 Intro 01:00 Pierre Moulon's Background 04:40 What is OpenMVG? 08:43 What is the importance of open-source software for the computer vision community? 12:30 What to look for deciding to use an opensource project 16:27 What is Multi View Geometry? 24:24 What was the biggest challenge building OpenMVG? 31:00 How do you grow a community around an open-source project 38:09 Choosing a licensing model for your open-source project 43:07 Funding and sponsorship for your open-source project 46:46 Building an open-source project for your resume 49:53 How to get started with OpenMVG Contact: Follow Pierre Moulon on LinkedIn: https://www.linkedin.com/in/pierre-moulon/ Follow Jared Heinly on Twitter: https://twitter.com/JaredHeinly Follow Jonathan Stephens on Twitter at: https://twitter.com/jonstephens85 This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services: https://www.everypoint.io…
C
Computer Vision Decoded

1 Understanding Implicit Neural Representations with Itzik Ben-Shabat 55:22
55:22
التشغيل لاحقا
التشغيل لاحقا
قوائم
إعجاب
احب55:22
In this episode of Computer Vision Decoded, we are going to dive into implicit neural representations. We are joined by Itzik Ben-Shabat, a Visiting Research Fellow at the Australian National Universit (ANU) and Technion – Israel Institute of Technology as well as the host of the Talking Paper Podcast . You will learn a core understanding of implicit neural representations, key concepts and terminology, how it's being used in applications today, and Itzik's research into improving output with limit input data. Episode timeline: 00:00 Intro 01:23 Overview of what implicit neural representations are 04:08 How INR compares and contrasts with a NeRF 08:17 Why did Itzik pursued this line of research 10:56 What is normalization and what are normals 13:13 Past research people should read to learn about the basics of INR 16:10 What is an implicit representation (without the neural network) 24:27 What is DiGS and what problem with INR does it solve? 35:54 What is OG-I NR and what problem with INR does it solve? 40:43 What software can researchers use to understand INR? 49:15 What information should non-scientists be focused to learn about INR? Itzik's Website: https://www.itzikbs.com/ Follow Itzik on Twitter: https://twitter.com/sitzikbs Follow Itzik on LinkedIn: https://www.linkedin.com/in/yizhak-itzik-ben-shabat-67b3b1b7/ Talking Papers Podcast: https://talking.papers.podcast.itzikbs.com/ Follow Jared Heinly on Twitter: https://twitter.com/JaredHeinly Follow Jonathan Stephens on Twitter at: https://twitter.com/jonstephens85 Referenced past episode- What is CVPR: https://share.transistor.fm/s/15edb19d This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services: https://www.everypoint.io…
C
Computer Vision Decoded

1 From 2D to 3D: 4 Ways to Make a 3D Reconstruction from Imagery 54:29
54:29
التشغيل لاحقا
التشغيل لاحقا
قوائم
إعجاب
احب54:29
In this episode of Computer Vision Decoded, we are going to dive into 4 different ways to 3D reconstruct a scene with images. Our cohost Jared Heinly, a PhD in the computer science specializing in 3D reconstruction from images, will dive into the 4 distinct strategies and discuss the pros and cons of each. Links to content shared in this episode: Live SLAM to measure a stockpile with SR Measure: https://srmeasure.com/professional Jared's notes on the iPhone LiDAR and SLAM: https://everypoint.medium.com/everypoint-gets-hands-on-with-apples-new-lidar-sensor-44eeb38db579 How to capture images for 3D reconstruction: https://youtu.be/AQfRdr_gZ8g 00:00 Intro 01:30 3D Reconstruction from Video 13:48 3D Reconstruction from Images 28:05 3D Reconstruction from Stereo Pairs 38:43 3D Reconstruction from SLAM Follow Jared Heinly Twitter: https://twitter.com/JaredHeinly LinkedIn https://www.linkedin.com/in/jheinly/ Follow Jonathan Stephens Twitter: https://twitter.com/jonstephens85 LinkedIn: https://www.linkedin.com/in/jonathanstephens/ This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services: https://www.everypoint.io…
C
Computer Vision Decoded

1 From Concept to Reality: The Journey of Building Scaniverse 50:05
50:05
التشغيل لاحقا
التشغيل لاحقا
قوائم
إعجاب
احب50:05
Join our guest, Keith Ito, founder of Scaniverse as we discuss the challenges of creating a 3D capture app for iPhones. Keith goes into depth on balancing speed with quality of 3D output and how he designed an intuitive user experience for his users. In this episode, we discuss… 01:00 - Keith's Ito's background at Google 09:44 - What is the Scaniverse app 11:43 - What inspired Keith to build Scaniverse 17:37 - The challenges of using LiDAR in the early versions of Scaniverse 25:54 - How to build a good user experience for 3D capture apps 32:00 - The challenges of running photogrammetry on an iPhone 37:07 - The future of 3D capture 40:57 - Scaniverse's role at Niantic Learn more about Scaniverse at: https://scaniverse.com/ Follow Keith Ito on Twitter at: https://twitter.com/keeeto Follow Jared Heinly on Twitter: https://twitter.com/JaredHeinly Follow Jonathan Stephens on Twitter: https://twitter.com/jonstephens85 Follow Jonathan Stephens on LinkedIn: https://www.linkedin.com/in/jonathanstephens/ ----- This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services: https://www.everypoint.io…
C
Computer Vision Decoded

In this episode of Computer Vision Decoded, we are going to dive into one of the hottest topics in the industry: Neural Radiance Fields (NeRFs) We are joined by Matt Tancik, a student pursuing a PhD in the computer science and electrical engineering department at UC Berkeley. He has also contributed research to the original NeRF project in 2020 along with several others since then. Last but not least, he is building NeRFStudio - a collaboration friendly studio for NeRFs. In this episode you will learn about what NeRFs are and more importantly what they are not. Matt goes into the challenges of large scale NeRF creation with his experience with Block-NeRF. Follow Matt's work at https://www.matthewtancik.com/ Get started with Nerfstudio here: https://docs.nerf.studio/en/latest/ Block-NeRF details: https://waymo.com/research/block-nerf/ 00:00 Intro 00:45 Matt’s Background Into NeRF Research 04:00 What is a NeRF and how it is different from photogrammetry 11:57 Can geometry be extracted from NeRFs? 15:30 Will NeRFs supersede photogrammetry in the future? 22:47 Block-NeRF and the pros and cons of using 360 cameras 25:30 What is the goal of Block-NeRF 30:44 Why do NeRFs need large GPUs to compute? 35:45 Meshes to simulate NeRF visualizations 40:28 What is Nerfstudio? 47:40 How to get started with Nerfstudio Follow Jared Heinly on Twitter: https://twitter.com/JaredHeinly Follow Jonathan Stephens on Twitter at: https://twitter.com/jonstephens85 This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services: https://www.everypoint.io…
C
Computer Vision Decoded

1 How to Capture Images for 3D Reconstruction 1:23:29
1:23:29
التشغيل لاحقا
التشغيل لاحقا
قوائم
إعجاب
احب1:23:29
In this episode of Computer Vision Decoded, we are going to dive into image capture best practices for 3D reconstruction. At the end of this livestream, you will have learned the basics for capturing scenes and objects. We will also provide a downloadable visual guide for reference on your next 3D reconstruction project. Download the official guide here to follow along: https://tinyurl.com/4n2wspkn 00:00 Intro 04:40 Camera motion overview 07:15 Good camera motions 18:43 Transition camera motions 30:39 Bad camera motions 39:27 How to combine camera motions 49:16 Loop Closure 57:42 Image Overlap 1:14:00 Lighting and camera gear Watch out episode of Computer Vision in the Wild to learn more about capturing images outside and in busy locations: https://youtu.be/FwVBR6KFjPI Follow Jared Heinly on Twitter: https://twitter.com/JaredHeinly Follow Jonathan Stephens on Twitter at: https://twitter.com/jonstephens85 This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services: https://www.everypoint.io…
C
Computer Vision Decoded

1 Is The iPhone 14 Camera Any Good? 1:01:34
1:01:34
التشغيل لاحقا
التشغيل لاحقا
قوائم
إعجاب
احب1:01:34
In this episode of Computer Vision Decoded, we join Jared Heinly and Jonathan Stephens from EveryPoint for their live reaction to the iPhone 14 series announcement. They go in depth into what all the camera specs mean to the average person. We also explain basics of computational photography and how Apple is able to get great photos from a small camera sensor. 00:00 Intro 02:43 Apple Watch Review 06:58 Airpods Pro Review 09:40 iPhone 14 Initial Reaction 15:05 iPhone 14 Camera Specs Breakdown 37:13 iPhone 14 Pro Initial Reaction 40:47 iPhone 14 Pro Camera Specs Breakdown Follow Jared Heinly on Twitter Follow Jonathan Stephens on Twitter This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services: https://www.everypoint.io…
مرحبًا بك في مشغل أف ام!
يقوم برنامج مشغل أف أم بمسح الويب للحصول على بودكاست عالية الجودة لتستمتع بها الآن. إنه أفضل تطبيق بودكاست ويعمل على أجهزة اندرويد والأيفون والويب. قم بالتسجيل لمزامنة الاشتراكات عبر الأجهزة.