Podcasts about Walmart Labs

Send us feedback or episode suggestions.This week, we're digging into the Design Systems Podcast archives. Guest host Richard Banfield, VP of Design Leadership at Knapsack, revisits a 2020 conversation between host Chris Strahl and Rick Rodriguez, then Head of Design Systems at Walmart Labs.Rick shares how his team developed Living Design, Walmart's internal design system, to support both customer-facing and associate-facing digital products. The conversation explores what it takes to design for scale across a massive enterprise ecosystem, how to navigate legacy technologies while planning for the future, and how to engage people across your organization to drive alignment and adoption.You'll also hear about:Lessons in contribution, ownership, and iteration within a federated design organizationThe ambassador program that helped evangelize and align teams across the enterpriseInsights into how data and qualitative feedback drive system decisions — especially around complex components like carouselsAlthough this conversation originally aired five years ago, the lessons Rick shares remain strikingly relevant. As design systems continue to mature, this episode offers a timeless perspective on scaling thoughtfully, building collaboratively, and evolving with intention.View the transcript of this episode.Check out our upcoming events.If you want to get in touch with the show, ask some questions, or tell us what you think, send us a message over on LinkedIn.GuestRick Rodriguez is currently a Product Design Manager at Meta, but at the time of our original episode he wast the Head of Design Systems at Walmart Labs.  He is an avid runner, hand letterer, and superfan of cappuccinos and donut breaks. You can find Rick on Twitter as @rickrodriguez, and on LinkedIn.HostsRichard Benfield is the VP of Design Leadership at Knapsack. He's acted as an advisor and interim executive to category-leading organizations, ia a best selling author, been a founder/CEO, and built and sold several businesses over the last 20+ years.Chris Strahl is co-founder and CEO of Knapsack, host of @TheDSPod, DnD DM, and occasional river guide. You can find Chris on Twitter as @chrisstrahl and on LinkedIn.SponsorSponsored by Knapsack, the design system platform that brings teams together. Learn more at knapsack.cloud.

Podcast Episode 212 of the Make Each Click Count Podcast features Kausambi Manjita, the Co-Founder of Mason. Mason is an innovative company that's transforming online retail through AI technology. Mason's flagship product, Getmason.io, is an AI-shopping engine designed to boost conversions, enhance merchandising, and engage customers with unprecedented precision.Kausambi shares her journey from working with Walmart Labs to creating Mason and reveals how their AI engine seamlessly integrates with platforms like Shopify to drive personalized shopping experiences. Discover how Mason's AI-driven approach can dramatically increase conversion rates, optimize customer interactions, and propel online retail success. Plus, don't miss an exclusive holiday offer for our listeners! Join us for an engaging conversation that will provide valuable insights into leveraging AI technology for your e-commerce business.Learn more:LinkedInABOUT THE HOST:Andy Splichal is the World's Foremost Expert on Ecommerce Growth Strategies. He is the acclaimed author of the Make Each Click Count Book Series, the Founder & Managing Partner of True Online Presence, and the Founder of Make Each Click Count University. Andy was named to The Best of Los Angeles Award's Most Fascinating 100 List in both 2020 and 2021.New episodes of the Make Each Click Count Podcast, are released each Friday and can be found on Apple Podcast, iHeart Radio, iTunes, Spotify, Stitcher, Amazon Music, Google Podcasts and www.makeeachclickcount.com.

Welcome back to The Wellfuel Podcast! Ever wondered why your stomach feels like it's doing somersaults after certain foods? Or why your little one can't seem to catch a break from those pesky allergies? Well, buckle up, because our latest podcast episode is about to take you on a wild ride through the microscopic universe living inside us all! Today we've got Cheryl Sew Hoy, the brilliant mind behind Tiny Health, spilling the beans on why our gut microbiome is basically the cool kid on the block when it comes to our overall health. Trust me, you'll never look at your belly the same way again! Here's a sneak peek of what's in store: The shocking link between your gut and... well, pretty much everything else in your body Why your mom's vaginal microbiome might be the MVP you never knew you had The C-section conundrum: What it means for your baby's tiny tummy tenants Probiotics: Not all heroes wear capes (but they do come in capsules) Plus, Cheryl drops some serious knowledge bombs about why your baby's gut should be throwing a party for bifidobacteria. Spoiler alert: It's a big deal for their immune system and overall health! So, whether you're a science nerd, a health enthusiast, or just someone who wants to understand why your body does what it does, this episode is for you. It's time to get up close and personal with your microbiome! Ready to dive in? Click that play button and prepare to have your mind (and maybe your gut) blown! Happy listening! P.S. Don't forget to share this episode with your friends. After all, good gut health is meant to be shared (but maybe not the bacteria themselves - let's not get crazy here)! --------------- About Cheryl Sew Hoy: Cheryl is an accomplished and repeat founder of multiple companies, including the nonprofit Moving Forward, as well as a successful consumer software startup that was acquired by Walmart Labs in 2013. In 2014, she was headhunted by the White House to become CEO of Magic - a $30 million funded agency to spur the innovation of ecosystems in Malaysia and Southeast Asia. In 2020, Cheryl founded Tiny Health to take charge of her family's microbiome health after giving birth to her two children when she realized that early life microbiome imbalances are linked to many chronic conditions later in life. Cheryl has extensive expertise in building consumer brands and has raised a total of $34 million. She's an angel investor and advisor to many startups. Cheryl earned her master's degree in engineering management and data mining and her bachelor's degree in operations research and industrial engineering from Cornell. The Tiny Health team includes leading microbiologists, scientists, and physicians with backgrounds from the Mayo Clinic, Johns Hopkins, Wash U, Stanford, Cornell, Boston Children's Hospital, and UCSF. You can find Cheryl at her website, TinyHealth.com and on Instagram @tiny.health DON'T MISS OUT: Get a $20 discount on your first order with TinyHealth.com with discount code ISABEL - just for our Wellfuel Podcast listeners! ---------------------- Want to learn more about how Isabel Smith Nutrition can help support you on your journey to better health?  Book a call with us today! ---------------------- Join us next week for the next episode of The Wellfuel Podcast!  Be sure to like, share and subscribe to The Wellfuel Podcast for more great nutritional content in the future!   You can learn more about Isabel Smith Nutrition by following us on Instagram: ⁠@isabelsmithnutrition or checking out our website. To good health, The Isabel Smith Nutrition Team

Send us a Text Message.Jack Herrington is the mastermind behind the YouTube channel Blue Collar Coder where he does deep dives on React, JavaScript and front end concepts. He shares his impressive 40-year journey in coding that began at age 13 and has remained close to the front end and excelled in software with only a high school diploma! From working at Nike and Walmart Labs to becoming a prominent tech content creator, Jack opens up about balancing his YouTube channel, a high-level tech role, and personal life.Jack offers invaluable career tips, opens up about dealing with dyslexia, and advice for junior developers looking to thrive in the tech industry.Check out his YouTube channel if you're learning JS, React or NextJS, it's a no brainer: https://www.youtube.com/@jherrShameless Plugs

On today's episode Cheryl Sew Hoy, found of Tiny Health, joins me to chat all about the gut microbiome and why it can be so beneficial to test your infant's gut health. We discuss: What is the gut microbiome and why does it matter How a baby's gut microbiome is formed/seeded How mothers' gut health during pregnancy and breastfeeding impacts their baby's gut health How gut health can cause a variety of chronic health issues such as eczema, reflux, colic, gassiness, food sensitivities and more. How we can correct gut imbalances early on to help our babies have a strong health foundation and more! Find Tiny Health: Tiny Health testing- save $20 off first purchase with code TAYLOR How gut health can impact sleep Cheryl Sew Hoy is an accomplished and repeat founder of multiple companies including the non-profit #MovingForward as well as a successful consumer software startup that was acquired by Walmart Labs in 2013. In 2014, she was head-hunted by the White House to become CEO of MaGIC, a $30M-funded agency to spur the innovation ecosystem in Malaysia and Southeast Asia. In 2020, Cheryl founded Tiny Health to take charge of her family's microbiome health after giving birth to her 2 children, when she realized that early life microbiome imbalances are linked to many chronic conditions later in life. Cheryl has extensive expertise in building consumer brands and has raised a total of $34M. She is an angel investor and advisor to many startups. Cheryl earned her master's degree in Engineering Management and Data Mining and her bachelor's degree in Operations Research & Industrial Engineering from Cornell University, NY, both on full scholarships.The Tiny Health team includes leading microbiologists, scientists, and physicians with backgrounds from Mayo Clinic, John Hopkins, WashU, Stanford, Cornell, Boston Children's Hospital (Harvard Medical), and UCSF. --- Support this podcast: https://podcasters.spotify.com/pod/show/taylorkulik/support

Josh Fraser is the Co-Founder of Origin Protocol. Prior to Origin, he co-founded three other venture-backed companies: EventVue, Torbit (acquired by Walmart Labs) & Forage. Josh started coding at the age of 10.In this conversation, we discuss:- DeFi industry- Automated Redemption Manager (ARM)- Liquid staking- Transparency and integrity in crypto- Crypto industry's volatility- Staking and Ethereum- Learning to code at age 10- Getting better yields in crypto- The future of yield is multi-chainOrigin ProtocolWebsite: www.originprotocol.com X: @OriginProtocolTelegram: t.me/originprotocolJosh FraserX: @joshfraserLinkedIn: Josh Fraser ---------------------------------------------------------------------------------  This episode is brought to you by PrimeXBT.  PrimeXBT offers a robust trading system for both beginners and professional traders that demand highly reliable market data and performance. Traders of all experience levels can easily design and customize layouts and widgets to best fit their trading style. PrimeXBT is always offering innovative products and professional trading conditions to all customers.   PrimeXBT is running an exclusive promotion for listeners of the podcast. After making your first deposit, 50% of that first deposit will be credited to your account as a bonus that can be used as additional collateral to open positions.  Code: CRYPTONEWS50  This promotion is available for a month after activation. Click the link below:  PrimeXBT x CRYPTONEWS50

Wellness + Wisdom | Episode 638 How is Tiny Health helping parents and children optimize their gut health? Cheryl Sew Hoy, Founder of Tiny Health, joins Josh Trent on the Wellness + Wisdom Podcast, episode 638, to explain the correlation between parents' gut microbiome and their baby's gut health, why the first months of a baby's life define their health for the rest of their life, and how microbiome testing can prevent chronic diseases in children. "If your child has poor and imbalanced gut health in the first 6-12 months of life, we can predict if the child is at higher risk for atopic march symptoms; eczema, allergy, and asthma." - Cheryl Sew Hoy $20 Off Tiny Health Tests $20 off with code "JOSH20" Tiny Health is the first gut health test for moms and babies 0-3 years. Detect gut imbalances and course correct early on. Now offering microbiome tests for the whole family! Get to the root cause of chronic conditions: Reduce microbiome risk and relieve eczema, colic, allergies, constipation, and other gut-related issues Recover from antibiotic exposure: Get tailored recommendations to offset the negative impacts of antibiotics Track and optimize your family's health: Test proactively and take action early to support a lifetime of better health Learn the best ways to support the gut: Get the tests, tools, and insights you need to improve wellness over time In This Episode, Cheryl Sew Hoy Uncovers: [01:30] Healing The Baby Microbiome Cheryl Sew Hoy Tiny Health - Use code "JOSH20" for $20 OFF all single kits The different microbiomes in the human body. Why some microbes are beneficial to your health. You get the first microbes from your mother through the vaginal canal and then breast milk. C-section doesn't allow for the baby to acquire certain microbes. How gut microbiome imbalances show up in babies. [08:45] C-Section Affects The Baby's Gut Health The health system is made to make money. How breech birth taught Cheryl to advocate for herself. The impact of C-section birth on the baby's health. How to provide vaginal microbiomes for the baby when a mother gets a C-section. [17:50] Optimizing The Mother's Microbiome A mother should make sure her gut and vaginal microbiomes are healthy before passing them on to her baby. Testing can help you optimize microbiome health. You can take probiotics and pass them on to your baby through breastfeeding. It takes much longer for adults to correct their microbiome. [23:15] The Journey with Tiny Health Why akkermansia is an essential intestinal bacteria. The bacterias that are missing in the mother can be passed on to the baby from the father. Chewing food for your baby can help their microbiome. The infant microbiome development: mom matters How Cheryl developed Tiny Health. [30:15] Nature Heals Your Microbiome Contact with animals makes children's microbiome more diverse. We used to live 90% outdoors, but now we spend 90% of our time indoors.  You can intentionally expose your children to different microbes. How Tiny Health is helping people to go back to nature. [35:05] Asthma Prevention in Infants 236 Healing The Second Brain: Dr. Michael Ruscio Academic research takes 10 years to reach medical practice. How asthma can be prevented by healing the microbiome in early childhood. Why Tiny Health got casted for Shark Tank but decided not to take the opportunity. [39:40] The Gut-Brain Axis Connection Even successful people can be miserable. How stress impacts the gut microbiome. The mother's butyrate function affects her baby's behavior. It has been proven that the gut influences allergies, eczema, and asthma in babies. Why we're in a pediatric chronic condition crisis. [44:00] New Technology for Microbiome Testing Doctors are not taught about the importance of nutrition and the microbiome. How the modern medicine is developing. Why Tiny Health uses metagenomic sequencing to test the microbiome. Each strain of bacteria has a different purpose. 048 Nir Eyal: Breaking Bad Habits, Technology Addiction, & Emotional Triggers [49:15] How to Heal Gut Dysbiosis How Tiny Health achieved to make the test affordable. Everything changes when you have a child. Why Josh's son had gut dysbiosis. The adult gut needs different foods than the baby gut. You should test, not guess when you supplement. What type of probiotics will actually help you improve your gut. How Tiny Health is tracking strains of bacteria. [56:00] Most Supplements Don't Absorb Well 616 Alex Wolfe | Legal Psychedelic Microdosing For Anxiety + How To Take Your Sleep/Psyche to The Next Level Why the body doesn't absorb most oral supplements. Supplementation can get you back to the baseline and a good diet will maintain it. Take your health in your hands. [59:05] Antibiotics VS Gut Health How to restore a baby's microbiome if you had a C-section. The microbes you want to see in your child. Baby can regain the good bacteria within a month of taking probiotics. Most children take antibiotics within the first year of life. Ear infection doesn't need to be treated with antibiotics because it's viral, not bacterial. Understanding what supplements are going to help or hurt your child. Do your own research to advocate for your and your child's health. [01:08:30] Excuses + Fear + Stress Unconscious behavior gives people an excuse to say they don't have time and money. How the medical system is making you react out of fear. Join The Liberated Life Community - LiberatedLife.Me Stress and emotions impact your gut. 581 Healthspan Revolution: Game-Changing Biomarker Blood Testing (SiPhox) | Michael Dubrovsky [01:14:50] Healthy Microbiome = Healthy Lifestyle How Tiny Health mixes the knowledge of academic professionals and holistic practitioners. Why women should start restoring their microbiome before they start trying to get pregnant. A healthy microbiome is a lifestyle, not a one-time thing. Why Cheryl's family's gut health got worse when they moved to Austin. Why people often ask Cheryl about children's eczema and sleep. [01:23:50] Tiny Health for Wellness Why Tiny Health's reports are easy to understand for anyone. How household cleaning products can negatively impact your baby's gut health. Why Cheryl picks a different aspect of wellness to focus on during each season. The social constructs we're facing in the world. Leave Wellness + Wisdom a Review on Apple Podcasts ❄️ Biohack Your Mind & Body with Plunge Ice Baths! Save $150 on your PLUNGE order with code "WELLNESSFORCE" As seen on Shark Tank, Plunge's revolutionary Cold Plunge uses powerful cooling, filtration, and sanitation to give you cold, clean water whenever you want it, making it far superior to an ice bath or chest freezer. ☀️ Live Life Well from Sunrise to Sunset Save 20% with code "WELLNESSFORCE" on everyone's favorite Superfoods brand, ORGANIFI, including their Sunrise to Sunset Bundle and their Women's Power Stack that includes HARMONY + GLOW for true hormonal balance and great health radiating through your beautiful skin. Click HERE to order your Organifi today.

Fiona is the Chief Technology Officer at Wayfair, an American e-commerce company that is the destination for all things home  – a place to find the right furniture and home goods online. With 25+ years of experience leading technology teams, Fiona began her professional career working at top technical companies, including Oracle, TIBCO Software and Ariba. At TIBCO, she rose up to be the Vice President of Engineering in her 16-year tenure at this intelligence cloud data company. Prior to Wayfair, Fiona served in executive leadership roles at Walmart – first as the Senior VP of Engineering for Customer Technology at WalmartLabs and then as the Senior Vice President of U.S. Technology at Walmart.Fiona earned her bachelor's degree in Computer Science and Engineering from MIT and her masters in computer science from Stanford University. In this episode, we cover the following topics:1. Fiona's childhood and the emphasis on education2. Studying Computer Science at MIT3. Gender disparity in Computer Science4. Transition from college into the professional workforce5. Her long tenure at TIBCO software6. Transition from Consumer to Enterprise Software companies 7. Impact and challenges working at Walmart8.  Hiring and building a team 9. Transitioning from coding into managerial role10. CTO role 11. Balancing perfection from good enough12. Reflecting and learning from previous mistakes13. Women in STEM Fiona's life-time craft she is honing? Cooking▶️ Video interview available on Youtube.If you're enjoying the show, please share it with a friend and leave a review!

Anuj Rathi is the Chief Product and Marketing Officer at Jupiter Money, where he leads product management, marketing, design, growth, and analytics. Before Jupiter Money, Anuj served as the Senior Vice President of Revenue and Growth at Swiggy, VP of Product at SnapDeal, a Senior PM at Walmart Labs and the first-ever PM at Flipkart. He's also one of the most beloved and respected product leaders in India. In this episode, we discuss:• How product management is different in India• How to rethink your approach to new users• How Anuj operationalizes the “working backwards” framework• Why Anuj thinks PMs should be more full-stack than they are• How to use Anuj's “4BB” framework to get better at product strategy and prioritization• Advice on developing innovative roadmap ideas• The three essential skills of a successful PM• Three reasons why leadership fails• Why OKRs don't work in marketplaces—Brought to you by Sanity—The most customizable content layer to power your growth engine | Vanta—Automate compliance. Simplify security | Wix Studio—The web creation platform built for agencies—Find the transcript for this episode and all past episodes at: https://www.lennyspodcast.com/episodes/. Today's transcript will be live by 8 a.m. PT.—Where to find Anuj Rathi:• X: https://twitter.com/anujrathi• LinkedIn: https://www.linkedin.com/in/anujrathi1—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Anuj's background(04:28) How product differs in India (08:34) When modern product thinking started to gain traction in India(14:01) How Anuj thinks about new-user experiences(15:07) Scott Belsky's “lazy, vain, and selfish” framework (19:59) Why PMs must understand category consumers(22:30) Anuj's philosophy on the PM job(23:59) How Anuj applies the working-backwards framework(28:36) The importance of FAQs(30:10) The full-stack PM mindset(33:06) Anuj's “show don't tell” framework(36:19) How to use the show-don't-tell framework(39:14) The impact of using this framework(41:27) Anuj's “4BB framework” for product strategy(48:59) Contrarian corner(50:49) Anuj's “framework of 3” for great PMs(52:34) How to develop grit and influence(54:00) Three reasons why leaders fail (56:21) AI corner(57:51) Lessons from building a successful marketplace(1:02:19) How to balance and maintain stability on all sides of a marketplace(1:07:48) Lightning round—Referenced:• MakeMyTrip: https://www.makemytrip.com/• Shaadi.com: https://www.shaadi.com/• Bharat Matrimony: https://www.bharatmatrimony.com/• Flipkart: https://www.flipkart.com/• Ola: https://www.olacabs.com/mobile• Swiggy: https://www.swiggy.com/• Jio: https://www.jio.com/• UPI: http://cashlessindia.gov.in/upi.html• The First 15 Seconds by Scott Belsky: https://medium.com/positiveslope/the-first-15-seconds-9590d7dabc• Jupiter: https://jupiter.money/• How to get better at influence: https://www.lennysnewsletter.com/p/how-to-get-better-at-influence#• Working Backwards: https://www.workingbackwards.com/• Range: Why Generalists Triumph in a Specialized World: https://www.amazon.com/Range-Generalists-Triumph-Specialized-World/dp/0735214484• In Search of Greatness on Prime Video: https://www.amazon.com/Search-Greatness-Wayne-Gretzky/dp/B07P5X99P5• Team Topologies: Organizing Business and Technology Teams for Fast Flow: https://www.amazon.com/Team-Topologies-Organizing-Business-Technology/dp/1942788819• Conway's Law: https://www.atlassian.com/blog/teamwork/what-is-conways-law-acmi• Lessons from scaling Spotify: The science of product, taking risky bets, and how AI is already impacting the future of music | Gustav Söderström (Co-President, CPO, and CTO at Spotify): https://www.lennyspodcast.com/lessons-from-scaling-spotify-the-science-of-product-taking-risky-bets-and-how-ai-is-already-impac/• Taobao: https://world.taobao.com/• Alibaba: https://offer.alibaba.com/• Working Backwards: https://www.amazon.com/Working-Backwards-PB/dp/1529033845• How Brands Grow: What Marketers Don't Know: https://www.amazon.com/How-Brands-Grow-What-Marketers/dp/0195573560• The Luxury Strategy: Break the Rules of Marketing to Build Luxury Brands: https://www.amazon.com/Luxury-Strategy-Break-Marketing-Brands/dp/0749464917• The Office on Peacock: https://www.peacocktv.com/stream-tv/the-office• Rise: https://www.risescience.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Anand Rajaraman is a Partner at Rocketship VC and co-owner of SF Unicorns cricket team. Previously, he co-founded Junglee (acquired by Amazon.com) and Kosmix (acquired by Walmart). Anand has helped create Amazon's Marketplace, co-invented Mechanical Turk, and created and co-headed WalmartLabs. He is an early investor in Facebook, Lyft, AppNexus, Aster Data, and Efficient Frontier among others. He is a Professor at Stanford University, and co-author of the popular textbook Mining of Massive Datasets. --- Support this podcast: https://podcasters.spotify.com/pod/show/theindustryshow/support

In this episode, join us for a unique conversation as Bryan MacDonald interviews Alex Alexander, a seasoned CTO with over two decades of experience at global brands like Walmart, YOOX NET-A-PORTER and The Emirates Group. Alex shares captivating stories from his pioneering days in internet banking at Egg, emphasising the importance of boldness and seizing early opportunities. From leveraging innovation at Walmart Labs to navigating cultural landscapes in Italy at YOOX NET-A-PORTER, Alex delves into the challenges of influencing leaders, highlighting the value of aligning IT actions with business objectives. His insights on change management underline the significance of relationship-building, emotional connections and offer invaluable advice for current as well as aspiring tech leaders. Ultimately, Alex's focus remains on fostering a work environment where teams thrive, highlighting the crucial role of storytelling in influencing leadership and implementing transformative changes.

 In this episode, join us for a unique conversation as Bryan MacDonald interviews Alex Alexander, a seasoned CTO with over two decades of experience at global brands like Walmart, YOOX NET-A-PORTER and The Emirates Group. Alex shares captivating stories from his pioneering days in internet banking at Egg, emphasising the importance of boldness and seizing early opportunities. From leveraging innovation at Walmart Labs to navigating cultural landscapes in Italy at YOOX NET-A-PORTER, Alex delves into the challenges of influencing leaders, highlighting the value of aligning IT actions with business objectives. His insights on change management underline the significance of relationship-building, emotional connections and offer invaluable advice for current as well as aspiring tech leaders. Ultimately, Alex's focus remains on fostering a work environment where teams thrive, highlighting the crucial role of storytelling in influencing leadership and implementing transformative changes. 

It's 1999. I'm attending Pepperdine University for my MBA program when I ask myself, “What else can I do?” I decide to learn computer programming at UCLA. In my first class there, I'm sitting in a room filled with other students and realize two things: First, I'm one of only three female students in a class of over 40 people. And second, I love everything about this training and want to learn more! So I start reading books and learning as fast as I can, noticing all the while that there aren't many female authors and role models in tech. Yet, I'm still hungry to learn as much as I can and want to accelerate my training. Thanks to a magazine ad, I sign up for the Web 99 Conference in San Francisco and listen in fascination to Lynda Weinman talk about Flash technology. It makes me realize that I want to do this for my career. I walk up to her after her Talk to introduce myself and discover she's holding her first-ever workshop on Flash in Ojai, California. She personally invites me to sign up, and I go for it! Thanks to Lynda's guidance, I move from that workshop to teaching classes for her, writing two books on Flash technology, running a Flash-focused tech event, and co-founding my own software company. My story changed just from casually taking a computer class in college… and all because someone believed in and opened doors for me. And my special guest today has made it his business to do the same for others. Mike Roberts helps underrepresented people break into tech and companies build high-performance engineering teams out of often overlooked talent. In this episode of the Storytelling School Podcast, you'll learn about how creating opportunities for the marginalized can change the trajectory of their story and get answers to questions like: Why does storytelling help those with social anxiety? How does having different skill sets affect the future of your story's path? And why is software engineering both a science and, like storytelling, an art? What you will learn in this episode: How being a trailblazer can influence other people's stories (even for generations) How learning to tell stories is like learning how to play an instrument Why it's better to tell your story in the present tense Who is Mike? Mike Roberts is the founder and CEO of Creating Coding Careers (CCC), an innovative nonprofit organization committed to diversifying the tech community and creating equitable opportunities for individuals pursuing a career in the industry. He is passionate about helping underrepresented people break into tech and helping companies build high-performance engineering teams out of often-overlooked talent. Mike has launched more than 100 student careers and has grads working at IBM/RedHat, Apple, WalmartLabs, Sony, AWS, Facebook, Deloitte, and many more amazing tech companies. His superpower is helping gritty people grow and get better at writing quality software. Links and Resources: Creating Coding Careers @merobertsjr on LinkedIn Storytelling School Website @storytellingschool on Instagram @storytellingSchool on Facebook

Dr. Elana interviews Cheryl Sew Hoy, the CEO & Founder of Tiny Health, a gut microbiome startup that focuses on expecting parents and their babies. Tiny Health is the first ever baby gut health test to ever come to the market where parents can order directly, without the need of a doctor's order.  During this interview, we dive deep into what to expect from a baby's microbiome from birth and through the first 1,000 days of life. We review how a baby's microbiome differs from adults' and what we can do to optimize a child's microbiome to help with lifelong optimal health!  If interested in testing any of your family members, from pregnancy, newborn, and even adults, Tiny Health offers $20 off each kit using code ELANA20. You never know what you will find until you test!  Topics Discussed: How the first 1,000 days of a child's life are so crucial in creating a balanced microbiome The biggest differences between baby gut health and adult gut health and why the science for the former is much clearer than the latter Surprising finds from testing thousands of babies' gut microbiomes that all moms want to know! Fermented foods and how to best use them for optimal health Future research in baby's microbiome  How a baby's microbiome can influence immune disorders as adults What Tiny Health tests for and how to get the support you need when reviewing results Show Notes: Get tested with Tiny Health - Get $20 off each kit using code ELANA20 Visit Tiny Health's Website Follow @tiny.health on Instagram Check out @tiny_health on Tiktok Click here to learn more about Dr. Elana Roumell's Doctor Mom Membership, a membership designed for moms who want to be their child's number one health advocate! Click here to learn more about Steph Greunke, RD's online nutrition program and community, Postpartum Reset, an intimate private community and online roadmap for any mama (or mama-to-be) who feels stuck, alone, and depleted and wants to learn how to thrive in motherhood. Listen to today's episode on our website Cheryl Sew Hoy is CEO & Founder of Tiny Health, a gut microbiome startup that focuses on expecting parents and their babies.  Cheryl is an accomplished CEO and serial entrepreneur who co-founded multiple companies including the non-profit #MovingForward as well as a successful consumer software startup that was acquired by Walmart Labs in 2013. In 2014, she was head-hunted by the White House team and Prime Minister of Malaysia to become CEO of MaGIC, a $30M-funded agency to spur the innovation ecosystem in Malaysia and Southeast Asia.  In 2020, Cheryl founded Seeding Inc (dba Tiny Health).  Cheryl has extensive expertise in building consumer brands and has experience fundraising a total of $21M. She is an angel investor and advisor to many startups. Cheryl earned her master's degree in Engineering Management and Data Mining and her bachelor's degree in Operations Research & Industrial Engineering from Cornell University, NY, both on full scholarships. This Episode's Sponsors  Enjoy the health benefits of PaleoValley's products such as their supplements, superfood bars and meat sticks.  Receive 15% off your purchase by using code DOCTORMOM at checkout or head to paleovalley.com/doctormom  Discover for yourself why Needed is trusted by women's health practitioners and mamas alike to support optimal pregnancy outcomes. Try their 4 Part Complete Nutrition plan which includes a Prenatal Multi, Omega-3, Collagen Protein, and Pre/Probiotic. To get started, head to thisisneeded.com, and use code DOCTORMOM100 for $100 off your first 3 months of Needed's Complete Plan! Active Skin Repair is a must-have for everyone to keep themselves and their families healthy and clean.  Keep a bottle in the car to spray your face after removing your mask, a bottle in your medicine cabinet to replace your toxic first aid products, and one in your outdoor pack for whatever life throws at you.  Use code DOCTORMOM to receive 20% off your order + free shipping (with $35 minimum purchase). Visit BLDGActive.com to order. INTRODUCE YOURSELF to Steph and Dr. Elana on Instagram. They can't wait to meet you! @stephgreunke @drelanaroumell Please remember that the views and ideas presented on this podcast are for informational purposes only.  All information presented on this podcast is for informational purposes and not intended to serve as a substitute for the consultation, diagnosis, and/or medical treatment of a healthcare provider. Consult with your healthcare provider before starting any diet, supplement regimen, or to determine the appropriateness of the information shared on this podcast, or if you have any questions regarding your treatment plan.  

Jeremy King leads a team of 1,400 passionate engineers working on the continuous improvement of Pinterest's image-driven platform. With a background that includes heading up a translation team at eBay and overseeing the technology behind Walmart's U.S. retail stores and e-commerce business, Jeremy is now responsible for technology operations at Pinterest. To support the company's mission to inspire people to “create a life that they love,” he and his team rely on advanced AI, machine learning, and a graph database to index and build a network of images so users can find inspiration — particularly when they aren't completely sure what they're looking for. On this episode, Jeremy joins Sam and Shervin to talk about some recent advances Pinterest has made in the image-recognition space and shares his views on how generative AI will transform image-based content like Pinterest's. Read the episode transcript here. Me, Myself, and AI is a collaborative podcast from MIT Sloan Management Review and Boston Consulting Group and is hosted by Sam Ransbotham and Shervin Khodabandeh. Our engineer is David Lishansky, and the coordinating producers are Allison Ryder and Sophie Rüdinger. Stay in touch with us by joining our LinkedIn group, AI for Leaders at mitsmr.com/AIforLeaders or by following Me, Myself, and AI on LinkedIn. Guest bio: Jeremy King is senior vice president of technology at Pinterest, where he leads the company's technical vision and the engineering organization responsible for building and scaling a visual discovery engine. Before joining Pinterest, he was CTO and senior vice president at Walmart, where he led the team responsible for the technology behind U.S. retail stores and e-commerce for Walmart and Jet, and oversaw customer, merchant, and supply chain technologies across cloud and data platforms. King has also held executive-level technology roles at Walmart Labs, LiveOps, and eBay. We encourage you to rate and review our show. Your comments may be used in Me, Myself, and AI materials.

Ashutosh Kaushik is a UX design leader originally from Mumbai, India. He holds a Bachelor's degree in Electronics Engineering and a Master's degree in Human-Computer Interaction from the University of Michigan, Ann Arbor. Starting his career as a User Experience Designer at Ford Motors, Ashu later joined Walmart.com/Walmartlabs as an Interaction Designer, where he led multiple successful projects, including Pharmacy and Search redesigns. He then worked at Intuit Inc, where he played a significant role in growing the apps ecosystem for Quickbooks, followed by leading the redesign of Quickbooks.com into a successful e-commerce site. In 2020, he joined ServiceNow as a Senior Group Manager Design & Research, where he established the research team and created the design system from scratch. Currently, Ashutosh is the Director of UX at Indeed.com, leading the growth and Monetization team and driving the evolution of the business model for the billion-dollar job website.

Latent Space is popping off! Welcome to the over 8500 latent space explorers who have joined us. Join us this month at various events in SF and NYC, or start your own!This post spent 22 hours at the top of Hacker News.As announced during their Developer Day celebrating their $100m fundraise following their Google partnership, Replit is now open sourcing its own state of the art code LLM: replit-code-v1-3b (model card, HF Space), which beats OpenAI's Codex model on the industry standard HumanEval benchmark when finetuned on Replit data (despite being 77% smaller) and more importantly passes AmjadEval (we'll explain!)We got an exclusive interview with Reza Shabani, Replit's Head of AI, to tell the story of Replit's journey into building a data platform, building GhostWriter, and now training their own LLM, for 22 million developers!8 minutes of this discussion go into a live demo discussing generated code samples - which is always awkward on audio. So we've again gone multimodal and put up a screen recording here where you can follow along on the code samples!Recorded in-person at the beautiful StudioPod studios in San Francisco.Full transcript is below the fold. We would really appreciate if you shared our pod with friends on Twitter, LinkedIn, Mastodon, Bluesky, or your social media poison of choice!Timestamps* [00:00:21] Introducing Reza* [00:01:49] Quantitative Finance and Data Engineering* [00:11:23] From Data to AI at Replit* [00:17:26] Replit GhostWriter* [00:20:31] Benchmarking Code LLMs* [00:23:06] AmjadEval live demo* [00:31:21] Aligning Models on Vibes* [00:33:04] Beyond Chat & Code Completion* [00:35:50] Ghostwriter Autonomous Agent* [00:38:47] Releasing Replit-code-v1-3b* [00:43:38] The YOLO training run* [00:49:49] Scaling Laws: from Kaplan to Chinchilla to LLaMA* [00:52:43] MosaicML* [00:55:36] Replit's Plans for the Future (and Hiring!)* [00:59:05] Lightning RoundShow Notes* Reza Shabani on Twitter and LinkedIn* also Michele Catasta and Madhav Singhal* Michele Catasta's thread on the release of replit-code-v1-3b* Intro to Replit Ghostwriter* Replit Ghostwriter Chat and Building Ghostwriter Chat* Reza on how to train your own LLMs (their top blog of all time)* Our Benchmarks 101 episode where we discussed HumanEval* AmjadEval live demo* Nat.dev* MosaicML CEO Naveen Rao on Replit's LLM* MosaicML Composer + FSDP code* Replit's AI team is hiring in North America timezone - Fullstack engineer, Applied AI/ML, and other roles!Transcript[00:00:00] Alessio Fanelli: Hey everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO in residence at Decibel Partners. I'm joined by my co-host, swyx, writer and editor of Latent Space.[00:00:21] Introducing Reza[00:00:21] swyx: Hey and today we have Reza Shabani, Head of AI at Replit. Welcome to the studio. Thank you. Thank you for having me. So we try to introduce people's bios so you don't have to repeat yourself, but then also get a personal side of you.[00:00:34] You got your PhD in econ from Berkeley, and then you were a startup founder for a bit, and, and then you went into systematic equity trading at BlackRock in Wellington. And then something happened and you were now head of AI at Relet. What should people know about you that might not be apparent on LinkedIn?[00:00:50] One thing[00:00:51] Reza Shabani: that comes up pretty often is whether I know how to code. Yeah, you'd be shocked. A lot of people are kind of like, do you know how to code? When I was talking to Amjad about this role, I'd originally talked to him, I think about a product role and, and didn't get it. Then he was like, well, I know you've done a bunch of data and analytics stuff.[00:01:07] We need someone to work on that. And I was like, sure, I'll, I'll do it. And he was like, okay, but you might have to know how to code. And I was like, yeah, yeah, I, I know how to code. So I think that just kind of surprises people coming from like Ancon background. Yeah. Of people are always kind of like, wait, even when people join Relet, they're like, wait, does this guy actually know how to code?[00:01:28] Is he actually technical? Yeah.[00:01:30] swyx: You did a bunch of number crunching at top financial companies and it still wasn't[00:01:34] Reza Shabani: obvious. Yeah. Yeah. I mean, I, I think someone like in a software engineering background, cuz you think of finance and you think of like calling people to get the deal done and that type of thing.[00:01:43] No, it's, it's not that as, as you know, it's very very quantitative. Especially what I did in, in finance, very quantitative.[00:01:49] Quantitative Finance and Data Engineering[00:01:49] swyx: Yeah, so we can cover a little bit of that and then go into the rapid journey. So as, as you, as you know, I was also a quantitative trader on the sell side and the buy side. And yeah, I actually learned Python there.[00:02:01] I learned my, I wrote my own data pipelines there before airflow was a thing, and it was just me writing running notebooks and not version controlling them. And it was a complete mess, but we were managing a billion dollars on, on my crappy code. Yeah, yeah. What was it like for you?[00:02:17] Reza Shabani: I guess somewhat similar.[00:02:18] I, I started the journey during grad school, so during my PhD and my PhD was in economics and it was always on the more data intensive kind of applied economic side. And, and specifically financial economics. And so what I did for my dissertation I recorded cnbc, the Financial News Network for 10 hours a day, every day.[00:02:39] Extracted the close captions from the video files and then used that to create a second by second transcript of, of cmbc, merged that on with high frequency trading, quote data and then looked at, you know, went in and did some, some nlp, tagging the company names, and and then looked at the price response or the change in price and trading volume in the seconds after a company was mentioned.[00:03:01] And, and this was back in. 2009 that I was doing this. So before cloud, before, before a lot of Python actually. And, and definitely before any of these packages were available to make this stuff easy. And that's where, where I had to really learn to code, like outside of you know, any kind of like data programming languages.[00:03:21] That's when I had to learn Python and had to learn all, all of these other skills to work it with data at that, at that scale. So then, you know, I thought I wanted to do academia. I did terrible on the academic market because everyone looked at my dissertation. They're like, this is cool, but this isn't economics.[00:03:37] And everyone in the computer science department was actually way more interested in it. Like I, I hung out there more than in the econ department and You know, didn't get a single academic offer. Had two offer. I think I only applied to like two industry jobs and got offers from both of them.[00:03:53] They, they saw value in it. One of them was BlackRock and turned it down to, to do my own startup, and then went crawling back two and a half years later after the startup failed.[00:04:02] swyx: Something on your LinkedIn was like you're trading Chinese news tickers or something. Oh, yeah. I forget,[00:04:07] Reza Shabani: forget what that was.[00:04:08] Yeah, I mean oh. There, there was so much stuff. Honestly, like, so systematic active equity at, at BlackRock is, was such an amazing. Group and you just end up learning so much and the, and the possibilities there. Like when you, when you go in and you learn the types of things that they've been trading on for years you know, like a paper will come out in academia and they're like, did you know you can use like this data on searches to predict the price of cars?[00:04:33] And it's like, you go in and they've been trading on that for like eight years. Yeah. So they're, they're really ahead of the curve on, on all of that stuff. And the really interesting stuff that I, that I found when I went in was all like, related to NLP and ml a lot of like transcript data, a lot of like parsing through the types of things that companies talk about, whether an analyst reports, conference calls, earnings reports and the devil's really in the details about like how you make sense of, of that information in a way that, you know, gives you insight into what the company's doing and, and where the market is, is going.[00:05:08] I don't know if we can like nerd out on specific strategies. Yes. Let's go, let's go. What, so one of my favorite strategies that, because it never, I don't think we ended up trading on it, so I can probably talk about it. And it, it just kind of shows like the kind of work that you do around this data.[00:05:23] It was called emerging technologies. And so the whole idea is that there's always a new set of emerging technologies coming onto the market and the companies that are ahead of that curve and stay up to date on on the latest trends are gonna outperform their, their competitors.[00:05:38] And that's gonna reflect in the, in the stock price. So when you have a theory like that, how do you actually turn that into a trading strategy? So what we ended up doing is, well first you have to, to determine what are the emergent technologies, like what are the new up and coming technologies.[00:05:56] And so we actually went and pulled data on startups. And so there's like startups in Silicon Valley. You have all these descriptions of what they do, and you get that, that corpus of like when startups were getting funding. And then you can run non-negative matrix factorization on it and create these clusters of like what the various Emerging technologies are, and you have this all the way going back and you have like social media back in like 2008 when Facebook was, was blowing up.[00:06:21] And and you have things like mobile and digital advertising and and a lot of things actually outside of Silicon Valley. They, you know, like shale and oil cracking. Yeah. Like new technologies in, in all these different types of industries. And then and then you go and you look like, which publicly traded companies are actually talking about these things and and have exposure to these things.[00:06:42] And those are the companies that end up staying ahead of, of their competitors. And a lot of the the cases that came out of that made a ton of sense. Like when mobile was emerging, you had Walmart Labs. Walmart was really far ahead in terms of thinking about mobile and the impact of mobile.[00:06:59] And, and their, you know, Sears wasn't, and Walmart did well, and, and Sears didn't. So lots of different examples of of that, of like a company that talks about a new emerging trend. I can only imagine, like right now, all of the stuff with, with ai, there must be tons of companies talking about, yeah, how does this affect their[00:07:17] swyx: business?[00:07:18] And at some point you do, you do lose the signal. Because you get overwhelmed with noise by people slapping a on everything. Right? Which is, yeah. Yeah. That's what the Long Island Iced Tea Company slaps like blockchain on their name and, you know, their stock price like doubled or something.[00:07:32] Reza Shabani: Yeah, no, that, that's absolutely right.[00:07:35] And, and right now that's definitely the kind of strategy that would not be performing well right now because everyone would be talking about ai. And, and that's, as you know, like that's a lot of what you do in Quant is you, you try to weed out other possible explanations for for why this trend might be happening.[00:07:52] And in that particular case, I think we found that, like the companies, it wasn't, it wasn't like Sears and Walmart were both talking about mobile. It's that Walmart went out of their way to talk about mobile as like a future, mm-hmm. Trend. Whereas Sears just wouldn't bring it up. And then by the time an invest investors are asking you about it, you're probably late to the game.[00:08:12] So it was really identifying those companies that were. At the cutting edge of, of new technologies and, and staying ahead. I remember like Domino's was another big one. Like, I don't know, you[00:08:21] swyx: remember that? So for those who don't know, Domino's Pizza, I think for the run of most of the 2010s was a better performing stock than Amazon.[00:08:29] Yeah.[00:08:31] Reza Shabani: It's insane.[00:08:32] swyx: Yeah. Because of their investment in mobile. Mm-hmm. And, and just online commerce and, and all that. I it must have been fun picking that up. Yeah, that's[00:08:40] Reza Shabani: that's interesting. And I, and I think they had, I don't know if you, if you remember, they had like the pizza tracker, which was on, on mobile.[00:08:46] I use it[00:08:46] swyx: myself. It's a great, it's great app. Great app. I it's mostly faked. I think that[00:08:50] Reza Shabani: that's what I heard. I think it's gonna be like a, a huge I don't know. I'm waiting for like the New York Times article to drop that shows that the whole thing was fake. We all thought our pizzas were at those stages, but they weren't.[00:09:01] swyx: The, the challenge for me, so that so there's a, there's a great piece by Eric Falkenstein called Batesian Mimicry, where every signal essentially gets overwhelmed by noise because the people who wants, who create noise want to follow the, the signal makers. So that actually is why I left quant trading because there's just too much regime changing and like things that would access very well would test poorly out a sample.[00:09:25] And I'm sure you've like, had a little bit of that. And then there's what was the core uncertainty of like, okay, I have identified a factor that performs really well, but that's one factor out of. 500 other factors that could be going on. You have no idea. So anyway, that, that was my existential uncertainty plus the fact that it was a very highly stressful job.[00:09:43] Reza Shabani: Yeah. This is a bit of a tangent, but I, I think about this all the time and I used to have a, a great answer before chat came out, but do you think that AI will win at Quant ever?[00:09:54] swyx: I mean, what is Rentech doing? Whatever they're doing is working apparently. Yeah. But for, for most mortals, I. Like just waving your wand and saying AI doesn't make sense when your sample size is actually fairly low.[00:10:08] Yeah. Like we have maybe 40 years of financial history, if you're lucky. Mm-hmm. Times what, 4,000 listed equities. It's actually not a lot. Yeah, no, it's,[00:10:17] Reza Shabani: it's not a lot at all. And, and constantly changing market conditions and made laden variables and, and all of, all of that as well. Yeah. And then[00:10:24] swyx: retroactively you're like, oh, okay.[00:10:26] Someone will discover a giant factor that, that like explains retroactively everything that you've been doing that you thought was alpha, that you're like, Nope, actually you're just exposed to another factor that you're just, you just didn't think about everything was momentum in.[00:10:37] Yeah. And one piece that I really liked was Andrew Lo. I think he had from mit, I think he had a paper on bid as Spreads. And I think if you, if you just. Taken, took into account liquidity of markets that would account for a lot of active trading strategies, alpha. And that was systematically declined as interest rates declined.[00:10:56] And I mean, it was, it was just like after I looked at that, I was like, okay, I'm never gonna get this right.[00:11:01] Reza Shabani: Yeah. It's a, it's a crazy field and I you know, I, I always thought of like the, the adversarial aspect of it as being the, the part that AI would always have a pretty difficult time tackling.[00:11:13] Yeah. Just because, you know, there's, there's someone on the other end trying to out, out game you and, and AI can, can fail in a lot of those situations. Yeah.[00:11:23] swyx: Cool.[00:11:23] From Data to AI at Replit[00:11:23] Alessio Fanelli: Awesome. And now you've been a rep almost two years. What do you do there? Like what does the, the team do? Like, how has that evolved since you joined?[00:11:32] Especially since large language models are now top of mind, but, you know, two years ago it wasn't quite as mainstream. So how, how has that evolved?[00:11:40] Reza Shabani: Yeah, I, so when I joined, I joined a year and a half ago. We actually had to build out a lot of, of data pipelines.[00:11:45] And so I started doing a lot of data work. And we didn't have you know, there, there were like databases for production systems and, and whatnot, but we just didn't have the the infrastructure to query data at scale and to process that, that data at scale and replica has tons of users tons of data, just tons of ripples.[00:12:04] And I can get into, into some of those numbers, but like, if you wanted to answer the question, for example of what is the most. Forked rep, rep on rep, you couldn't answer that back then because it, the query would just completely time out. And so a lot of the work originally just went into building data infrastructure, like modernizing the data infrastructure in a way where you can answer questions like that, where you can you know, pull in data from any particular rep to process to make available for search.[00:12:34] And, and moving all of that data into a format where you can do all of this in minutes as opposed to, you know, days or weeks or months. That laid a lot of the groundwork for building anything in, in ai, at least in terms of training our own own models and then fine tuning them with, with replica data.[00:12:50] So then you know, we, we started a team last year recruited people from, you know from a team of, of zero or a team of one to, to the AI and data team today. We, we build. Everything related to, to ghostrider. So that means the various features like explain code, generate code, transform Code, and Ghostrider chat which is like a in context ide or a chat product within the, in the ide.[00:13:18] And then the code completion models, which are ghostwriter code complete, which was the, the very first version of, of ghostrider. Yeah. And we also support, you know, things like search and, and anything in terms of what creates, or anything that requires like large data scale or large scale processing of, of data for the site.[00:13:38] And, and various types of like ML algorithms for the site, for internal use of the site to do things like detect and stop abuse. Mm-hmm.[00:13:47] Alessio Fanelli: Yep. Sounds like a lot of the early stuff you worked on was more analytical, kind of like analyzing data, getting answers on these things. Obviously this has evolved now into some.[00:13:57] Production use case code lms, how is the team? And maybe like some of the skills changed. I know there's a lot of people wondering, oh, I was like a modern data stack expert, or whatever. It's like I was doing feature development, like, how's my job gonna change? Like,[00:14:12] Reza Shabani: yeah. It's a good question. I mean, I think that with with language models, the shift has kind of been from, or from traditional ml, a lot of the shift has gone towards more like nlp backed ml, I guess.[00:14:26] And so, you know, there, there's an entire skill set of applicants that I no longer see, at least for, for this role which are like people who know how to do time series and, and ML across time. Right. And, and you, yeah. Like you, you know, that exact feeling of how difficult it is to. You know, you have like some, some text or some variable and then all of a sudden you wanna track that over time.[00:14:50] The number of dimensions that it, that it introduces is just wild and it's a totally different skill set than what we do in a, for example, in in language models. And it's very it's a, it's a skill that is kind of you know, at, at least at rep not used much. And I'm sure in other places used a lot, but a lot of the, the kind of excitement about language models has pulled away attention from some of these other ML areas, which are extremely important and, and I think still going to be valuable.[00:15:21] So I would just recommend like anyone who is a, a data stack expert, like of course it's cool to work with NLP and text data and whatnot, but I do think at some point it's going to you know, having, having skills outside of that area and in more traditional aspects of ML will, will certainly be valuable as well.[00:15:39] swyx: Yeah. I, I'd like to spend a little bit of time on this data stack notion pitch. You were even, you were effectively the first data hire at rep. And I just spent the past year myself diving into data ecosystem. I think a lot of software engineers are actually. Completely unaware that basically every company now eventually evolves.[00:15:57] The data team and the data team does everything that you just mentioned. Yeah. All of us do exactly the same things, set up the same pipelines you know, shop at the same warehouses essentially. Yeah, yeah, yeah, yeah. So that they enable everyone else to query whatever they, whatever they want. And to, to find those insights that that can drive their business.[00:16:15] Because everyone wants to be data driven. They don't want to do the janitorial work that it comes, that comes to, yeah. Yeah. Hooking everything up. What like, so rep is that you think like 90 ish people now, and then you, you joined two years ago. Was it like 30 ish people? Yeah, exactly. We're 30 people where I joined.[00:16:30] So and I just wanna establish your founders. That is exactly when we hired our first data hire at Vilify as well. I think this is just a very common pattern that most founders should be aware of, that like, You start to build a data discipline at this point. And it's, and by the way, a lot of ex finance people very good at this because that's what we do at our finance job.[00:16:48] Reza Shabani: Yeah. Yeah. I was, I was actually gonna Good say that is that in, in some ways, you're kind of like the perfect first data hire because it, you know, you know how to build things in a reliable but fast way and, and how to build them in a way that, you know, it's, it scales over time and evolves over time because financial markets move so quickly that if you were to take all of your time building up these massive systems, like the trading opportunities gone.[00:17:14] So, yeah. Yeah, they're very good at it. Cool. Okay. Well,[00:17:18] swyx: I wanted to cover Ghost Writer as a standalone thing first. Okay. Yeah. And then go into code, you know, V1 or whatever you're calling it. Yeah. Okay. Okay. That sounds good. So order it[00:17:26] Replit GhostWriter[00:17:26] Reza Shabani: however you like. Sure. So the original version of, of Ghost Writer we shipped in August of, of last year.[00:17:33] Yeah. And so this was a. This was a code completion model similar to GitHub's co-pilot. And so, you know, you would have some text and then it would predict like, what, what comes next. And this was, the original version was actually based off of the cogen model. And so this was an open source model developed by Salesforce that was trained on, on tons of publicly available code data.[00:17:58] And so then we took their their model, one of the smaller ones, did some distillation some other kind of fancy tricks to, to make it much faster and and deployed that. And so the innovation there was really around how to reduce the model footprint in a, to, to a size where we could actually serve it to, to our users.[00:18:20] And so the original Ghost Rider You know, we leaned heavily on, on open source. And our, our friends at Salesforce obviously were huge in that, in, in developing these models. And, but, but it was game changing just because we were the first startup to actually put something like that into production.[00:18:38] And, and at the time, you know, if you wanted something like that, there was only one, one name and, and one place in town to, to get it. And and at the same time, I think I, I'm not sure if that's like when the image models were also becoming open sourced for the first time. And so the world went from this place where, you know, there was like literally one company that had all of these, these really advanced models to, oh wait, maybe these things will be everywhere.[00:19:04] And that's exactly what's happened in, in the last Year or so, as, as the models get more powerful and then you always kind of see like an open source version come out that someone else can, can build and put into production very quickly at, at, you know, a fraction of, of the cost. So yeah, that was the, the kind of code completion Go Strider was, was really just, just that we wanted to fine tune it a lot to kind of change the way that our users could interact with it.[00:19:31] So just to make it you know, more customizable for our use cases on, on Rep. And so people on Relet write a lot of, like jsx for example, which I don't think was in the original training set for, for cogen. And and they do specific things that are more Tuned to like html, like they might wanna run, right?[00:19:50] Like inline style or like inline CSS basically. Those types of things. And so we experimented with fine tuning cogen a bit here and there, and, and the results just kind of weren't, weren't there, they weren't where you know, we, we wanted the model to be. And, and then we just figured we should just build our own infrastructure to, you know, train these things from scratch.[00:20:11] Like, LMS aren't going anywhere. This world's not, you know, it's, it's not like we're not going back to that world of there's just one, one game in town. And and we had the skills infrastructure and the, and the team to do it. So we just started doing that. And you know, we'll be this week releasing our very first open source code model.[00:20:31] And,[00:20:31] Benchmarking Code LLMs[00:20:31] Alessio Fanelli: and when you say it was not where you wanted it to be, how were you benchmarking[00:20:36] Reza Shabani: it? In that particular case, we were actually, so, so we have really two sets of benchmarks that, that we use. One is human eval, so just the standard kind of benchmark for, for Python, where you can generate some code or you give you give the model a function definition with, with some string describing what it's supposed to do, and then you allow it to complete that function, and then you run a unit test against it and and see if what it generated passes the test.[00:21:02] So we, we always kind of, we would run this on the, on the model. The, the funny thing is the fine tuned versions of. Of Cogen actually did pretty well on, on that benchmark. But then when we, we then have something called instead of human eval. We call it Amjad eval, which is basically like, what does Amjad think?[00:21:22] Yeah, it's, it's exactly that. It's like testing the vibes of, of a model. And it's, it's cra like I've never seen him, I, I've never seen anyone test the model so thoroughly in such a short amount of time. He's, he's like, he knows exactly what to write and, and how to prompt the model to, to get you know, a very quick read on, on its quote unquote vibes.[00:21:43] And and we take that like really seriously. And I, I remember there was like one, one time where we trained a model that had really good you know, human eval scores. And the vibes were just terrible. Like, it just wouldn't, you know, it, it seemed overtrained. So so that's a lot of what we found is like we, we just couldn't get it to Pass the vibes test no matter how the, how[00:22:04] swyx: eval.[00:22:04] Well, can you formalize I'm jal because I, I actually have a problem. Slight discomfort with human eval. Effectively being the only code benchmark Yeah. That we have. Yeah. Isn't that[00:22:14] Reza Shabani: weird? It's bizarre. It's, it's, it's weird that we can't do better than that in some, some way. So, okay. If[00:22:21] swyx: I, if I asked you to formalize Mja, what does he look for that human eval doesn't do well on?[00:22:25] Reza Shabani: Ah, that is a, that's a great question. A lot of it is kind of a lot of it is contextual like deep within, within specific functions. Let me think about this.[00:22:38] swyx: Yeah, we, we can pause for. And if you need to pull up something.[00:22:41] Reza Shabani: Yeah, I, let me, let me pull up a few. This, this[00:22:43] swyx: is gold, this catnip for people.[00:22:45] Okay. Because we might actually influence a benchmark being evolved, right. So, yeah. Yeah. That would be,[00:22:50] Reza Shabani: that would be huge. This was, this was his original message when he said the vibes test with, with flying colors. And so you have some, some ghostrider comparisons ghost Rider on the left, and cogen is on the right.[00:23:06] AmjadEval live demo[00:23:06] Reza Shabani: So here's Ghostrider. Okay.[00:23:09] swyx: So basically, so if I, if I summarize it from a, for ghosting the, there's a, there's a, there's a bunch of comments talking about how you basically implement a clone. Process or to to c Clooney process. And it's describing a bunch of possible states that he might want to, to match.[00:23:25] And then it asks for a single line of code for defining what possible values of a name space it might be to initialize it in amjadi val With what model is this? Is this your, this is model. This is the one we're releasing. Yeah. Yeah. It actually defines constants which are human readable and nice.[00:23:42] And then in the other cogen Salesforce model, it just initializes it to zero because it reads that it starts of an int Yeah, exactly. So[00:23:51] Reza Shabani: interesting. Yeah. So you had a much better explanation of, of that than than I did. It's okay. So this is, yeah. Handle operation. This is on the left.[00:24:00] Okay.[00:24:00] swyx: So this is rep's version. Yeah. Where it's implementing a function and an in filling, is that what it's doing inside of a sum operation?[00:24:07] Reza Shabani: This, so this one doesn't actually do the infill, so that's the completion inside of the, of the sum operation. But it, it's not, it's, it, it's not taking into account context after this value, but[00:24:18] swyx: Right, right.[00:24:19] So it's writing an inline lambda function in Python. Okay.[00:24:21] Reza Shabani: Mm-hmm. Versus[00:24:24] swyx: this one is just passing in the nearest available variable. It's, it can find, yeah.[00:24:30] Reza Shabani: Okay. So so, okay. I'll, I'll get some really good ones in a, in a second. So, okay. Here's tokenize. So[00:24:37] swyx: this is an assertion on a value, and it's helping to basically complete the entire, I think it looks like an E s T that you're writing here.[00:24:46] Mm-hmm. That's good. That that's, that's good. And then what does Salesforce cogen do? This is Salesforce cogen here. So is that invalidism way or what, what are we supposed to do? It's just making up tokens. Oh, okay. Yeah, yeah, yeah. So it's just, it's just much better at context. Yeah. Okay.[00:25:04] Reza Shabani: And, and I guess to be fair, we have to show a case where co cogen does better.[00:25:09] Okay. All right. So here's, here's one on the left right, which[00:25:12] swyx: is another assertion where it's just saying that if you pass in a list, it's going to throw an exception saying in an expectedly list and Salesforce code, Jen says,[00:25:24] Reza Shabani: This is so, so ghost writer was sure that the first argument needs to be a list[00:25:30] swyx: here.[00:25:30] So it hallucinated that it wanted a list. Yeah. Even though you never said it was gonna be a list.[00:25:35] Reza Shabani: Yeah. And it's, it's a argument of that. Yeah. Mm-hmm. So, okay, here's a, here's a cooler quiz for you all, cuz I struggled with this one for a second. Okay. What is.[00:25:47] swyx: Okay, so this is a four loop example from Amjad.[00:25:50] And it's, it's sort of like a q and a context in a chat bot. And it's, and it asks, and Amjad is asking, what does this code log? And it just paste in some JavaScript code. The JavaScript code is a four loop with a set time out inside of it with a cons. The console logs out the iteration variable of the for loop and increasing numbers of of, of times.[00:26:10] So it's, it goes from zero to five and then it just increases the, the delay between the timeouts each, each time. Yeah.[00:26:15] Reza Shabani: So, okay. So this answer was provided by by Bard. Mm-hmm. And does it look correct to you? Well,[00:26:22] the[00:26:22] Alessio Fanelli: numbers too, but it's not one second. It's the time between them increases.[00:26:27] It's like the first one, then the one is one second apart, then it's two seconds, three seconds. So[00:26:32] Reza Shabani: it's not, well, well, so I, you know, when I saw this and, and the, the message and the thread was like, Our model's better than Bard at, at coding Uhhuh. This is the Bard answer Uhhuh that looks totally right to me.[00:26:46] Yeah. And this is our[00:26:47] swyx: answer. It logs 5 5 55, what is it? Log five 50. 55 oh oh. Because because it logs the state of I, which is five by the time that the log happens. Mm-hmm. Yeah.[00:27:01] Reza Shabani: Oh God. So like we, you know we were shocked. Like, and, and the Bard dancer looked totally right to, to me. Yeah. And then, and somehow our code completion model mind Jude, like this is not a conversational chat model.[00:27:14] Mm-hmm. Somehow gets this right. And and, you know, Bard obviously a much larger much more capable model with all this fancy transfer learning and, and and whatnot. Some somehow, you know, doesn't get it right. So, This is the kind of stuff that goes into, into mja eval that you, you won't find in any benchmark.[00:27:35] Good. And and, and it's, it's the kind of thing that, you know, makes something pass a, a vibe test at Rep.[00:27:42] swyx: Okay. Well, okay, so me, this is not a vibe, this is not so much a vibe test as the, these are just interview questions. Yeah, that's, we're straight up just asking interview questions[00:27:50] Reza Shabani: right now. Yeah, no, the, the vibe test, the reason why it's really difficult to kind of show screenshots that have a vibe test is because it really kind of depends on like how snappy the completion is, how what the latency feels like and if it gets, if it, if it feels like it's making you more productive.[00:28:08] And and a lot of the time, you know, like the, the mix of, of really low latency and actually helpful content and, and helpful completions is what makes up the, the vibe test. And I think part of it is also, is it. Is it returning to you or the, the lack of it returning to you things that may look right, but be completely wrong.[00:28:30] I think that also kind of affects Yeah. Yeah. The, the vibe test as well. Yeah. And so, yeah, th this is very much like a, like a interview question. Yeah.[00:28:39] swyx: The, the one with the number of processes that, that was definitely a vibe test. Like what kind of code style do you expect in this situation? Yeah.[00:28:47] Is this another example? Okay.[00:28:49] Reza Shabani: Yeah. This is another example with some more Okay. Explanations.[00:28:53] swyx: Should we look at the Bard one[00:28:54] Reza Shabani: first? Sure. These are, I think these are, yeah. This is original GT three with full size 175. Billion[00:29:03] swyx: parameters. Okay, so you asked GPC three, I'm a highly intelligent question answering bot.[00:29:07] If you ask me a question that is rooted in truth, I'll give you the answer. If you ask me a question that is nonsense I will respond with unknown. And then you ask it a question. What is the square root of a bananas banana? It answers nine. So complete hallucination and failed to follow the instruction that you gave it.[00:29:22] I wonder if it follows if one, if you use an instruction to inversion it might, yeah. Do what better?[00:29:28] Reza Shabani: On, on the original[00:29:29] swyx: GP T Yeah, because I like it. Just, you're, you're giving an instructions and it's not[00:29:33] Reza Shabani: instruction tuned. Now. Now the interesting thing though is our model here, which does follow the instructions this is not instruction tuned yet, and we still are planning to instruction tune.[00:29:43] Right? So it's like for like, yeah, yeah, exactly. So,[00:29:45] swyx: So this is a replica model. Same question. What is the square of bananas? Banana. And it answers unknown. And this being one of the, the thing that Amjad was talking about, which you guys are. Finding as a discovery, which is, it's better on pure natural language questions, even though you trained it on code.[00:30:02] Exactly. Yeah. Hmm. Is that because there's a lot of comments in,[00:30:07] Reza Shabani: No. I mean, I think part of it is that there's a lot of comments and there's also a lot of natural language in, in a lot of code right. In terms of documentation, you know, you have a lot of like markdowns and restructured text and there's also just a lot of web-based code on, on replica, and HTML tends to have a lot of natural language in it.[00:30:27] But I don't think the comments from code would help it reason in this way. And, you know, where you can answer questions like based on instructions, for example. Okay. But yeah, it's, I know that that's like one of the things. That really shocked us is the kind of the, the fact that like, it's really good at, at natural language reasoning, even though it was trained on, on code.[00:30:49] swyx: Was this the reason that you started running your model on hella swag and[00:30:53] Reza Shabani: all the other Yeah, exactly. Interesting. And the, yeah, it's, it's kind of funny. Like it's in some ways it kind of makes sense. I mean, a lot of like code involves a lot of reasoning and logic which language models need and need to develop and, and whatnot.[00:31:09] And so you know, we, we have this hunch that maybe that using that as part of the training beforehand and then training it on natural language above and beyond that really tends to help. Yeah,[00:31:21] Aligning Models on Vibes[00:31:21] Alessio Fanelli: this is so interesting. I, I'm trying to think, how do you align a model on vibes? You know, like Bard, Bard is not purposefully being bad, right?[00:31:30] Like, there's obviously something either in like the training data, like how you're running the process that like, makes it so that the vibes are better. It's like when it, when it fails this test, like how do you go back to the team and say, Hey, we need to get better[00:31:44] Reza Shabani: vibes. Yeah, let's do, yeah. Yeah. It's a, it's a great question.[00:31:49] It's a di it's very difficult to do. It's not you know, so much of what goes into these models in, in the same way that we have no idea how we can get that question right. The programming you know, quiz question. Right. Whereas Bard got it wrong. We, we also have no idea how to take certain things out and or, and to, you know, remove certain aspects of, of vibes.[00:32:13] Of course there's, there's things you can do to like scrub the model, but it's, it's very difficult to, to get it to be better at something. It's, it's almost like all you can do is, is give it the right type of, of data that you think will do well. And then and, and of course later do some fancy type of like, instruction tuning or, or whatever else.[00:32:33] But a lot of what we do is finding the right mix of optimal data that we want to, to feed into the model and then hoping that the, that the data that's fed in is sufficiently representative of, of the type of generations that we want to do coming out. That's really the best that, that you can do.[00:32:51] Either the model has. Vibes or, or it doesn't, you can't teach vibes. Like you can't sprinkle additional vibes in it. Yeah, yeah, yeah. Same in real life. Yeah, exactly right. Yeah, exactly. You[00:33:04] Beyond Code Completion[00:33:04] Alessio Fanelli: mentioned, you know, co being the only show in town when you started, now you have this, there's obviously a, a bunch of them, right.[00:33:10] Cody, which we had on the podcast used to be Tap nine, kite, all these different, all these different things. Like, do you think the vibes are gonna be the main you know, way to differentiate them? Like, how are you thinking about. What's gonna make Ghost Rider, like stand apart or like, do you just expect this to be like table stakes for any tool?[00:33:28] So like, it just gonna be there?[00:33:30] Reza Shabani: Yeah. I, I do think it's, it's going to be table stakes for sure. I, I think that if you don't if you don't have AI assisted technology, especially in, in coding it's, it's just going to feel pretty antiquated. But but I do think that Ghost Rider stands apart from some of, of these other tools for for specific reasons too.[00:33:51] So this is kind of the, one of, one of the things that these models haven't really done yet is Come outside of code completion and outside of, of just a, a single editor file, right? So what they're doing is they're, they're predicting like the text that can come next, but they're not helping with the development process quite, quite yet outside of just completing code in a, in a text file.[00:34:16] And so the types of things that we wanna do with Ghost Rider are enable it to, to help in the software development process not just editing particular files. And so so that means using a right mix of like the right model for for the task at hand. But but we want Ghost Rider to be able to, to create scaffolding for you for, for these projects.[00:34:38] And so imagine if you would like Terraform. But, but powered by Ghostrider, right? I want to, I put up this website, I'm starting to get a ton of traffic to it and and maybe like I need to, to create a backend database. And so we want that to come from ghostrider as well, so it can actually look at your traffic, look at your code, and create.[00:34:59] You know a, a schema for you that you can then deploy in, in Postgres or, or whatever else? You know, I, I know like doing anything in in cloud can be a nightmare as well. Like if you wanna create a new service account and you wanna deploy you know, nodes on and, and have that service account, kind of talk to those nodes and return some, some other information, like those are the types of things that currently we have to kind of go, go back, go look at some documentation for Google Cloud, go look at how our code base does it you know, ask around in Slack, kind of figure that out and, and create a pull request.[00:35:31] Those are the types of things that we think we can automate away with with more advanced uses of, of ghostwriter once we go past, like, here's what would come next in, in this file. So, so that's the real promise of it, is, is the ability to help you kind of generate software instead of just code in a, in a particular file.[00:35:50] Ghostwriter Autonomous Agent[00:35:50] Reza Shabani: Are[00:35:50] Alessio Fanelli: you giving REPL access to the model? Like not rep, like the actual rep. Like once the model generates some of this code, especially when it's in the background, it's not, the completion use case can actually run the code to see if it works. There's like a cool open source project called Walgreen that does something like that.[00:36:07] It's like self-healing software. Like it gives a REPL access and like keeps running until it fixes[00:36:11] Reza Shabani: itself. Yeah. So, so, so right now there, so there's Ghostrider chat and Ghostrider code completion. So Ghostrider Chat does have, have that advantage in, in that it can it, it knows all the different parts of, of the ide and so for example, like if an error is thrown, it can look at the, the trace back and suggest like a fix for you.[00:36:33] So it has that type of integration. But the what, what we really want to do is is. Is merge the two in a way where we want Ghost Rider to be like, like an autonomous agent that can actually drive the ide. So in these action models, you know, where you have like a sequence of of events and then you can use you know, transformers to kind of keep track of that sequence and predict the next next event.[00:36:56] It's how, you know, companies like, like adapt work these like browser models that can, you know, go and scroll through different websites or, or take some, some series of actions in a, in a sequence. Well, it turns out the IDE is actually a perfect place to do that, right? So like when we talk about creating software, not just completing code in a file what do you do when you, when you build software?[00:37:17] You, you might clone a repo and then you, you know, will go and change some things. You might add a new file go down, highlight some text, delete that value, and point it to some new database, depending on the value in a different config file or in your environment. And then you would go in and add additional block code to, to extend its functionality and then you might deploy that.[00:37:40] Well, we, we have all of that data right there in the replica ide. And and we have like terabytes and terabytes of, of OT data you know, operational transform data. And so, you know, we can we can see that like this person has created a, a file what they call it, and, you know, they start typing in the file.[00:37:58] They go back and edit a different file to match the you know, the class name that they just put in, in the original file. All of that, that kind of sequence data is what we're looking to to train our next model on. And so that, that entire kind of process of actually building software within the I D E, not just like, here's some text what comes next, but rather the, the actions that go into, you know, creating a fully developed program.[00:38:25] And a lot of that includes, for example, like running the code and seeing does this work, does this do what I expected? Does it error out? And then what does it do in response to that error? So all, all of that is like, Insanely valuable information that we want to put into our, our next model. And and that's like, we think that one can be way more advanced than the, than this, you know, go straighter code completion model.[00:38:47] Releasing Replit-code-v1-3b[00:38:47] swyx: Cool. Well we wanted to dive in a little bit more on, on the model that you're releasing. Maybe we can just give people a high level what is being released what have you decided to open source and maybe why open source the story of the YOLO project and Yeah. I mean, it's a cool story and just tell it from the start.[00:39:06] Yeah.[00:39:06] Reza Shabani: So, so what's being released is the, the first version that we're going to release. It's a, it's a code model called replica Code V1 three B. So this is a relatively small model. It's 2.7 billion parameters. And it's a, it's the first llama style model for code. So, meaning it's just seen tons and tons of tokens.[00:39:26] It's been trained on 525 billion tokens of, of code all permissively licensed code. And it's it's three epox over the training set. And And, you know, all of that in a, in a 2.7 billion parameter model. And in addition to that, we, for, for this project or, and for this model, we trained our very own vocabulary as well.[00:39:48] So this, this doesn't use the cogen vocab. For, for the tokenize we, we trained a totally new tokenize on the underlying data from, from scratch, and we'll be open sourcing that as well. It has something like 32,000. The vocabulary size is, is in the 32 thousands as opposed to the 50 thousands.[00:40:08] Much more specific for, for code. And, and so it's smaller faster, that helps with inference, it helps with training and it can produce more relevant content just because of the you know, the, the vocab is very much trained on, on code as opposed to, to natural language. So, yeah, we'll be releasing that.[00:40:29] This week it'll be up on, on hugging pace so people can take it play with it, you know, fine tune it, do all type of things with it. We want to, we're eager and excited to see what people do with the, the code completion model. It's, it's small, it's very fast. We think it has great vibes, but we, we hope like other people feel the same way.[00:40:49] And yeah. And then after, after that, we might consider releasing the replica tuned model at, at some point as well, but still doing some, some more work around that.[00:40:58] swyx: Right? So there are actually two models, A replica code V1 three B and replica fine tune V1 three B. And the fine tune one is the one that has the 50% improvement in in common sense benchmarks, which is going from 20% to 30%.[00:41:13] For,[00:41:13] Reza Shabani: for yes. Yeah, yeah, yeah, exactly. And so, so that one, the, the additional tuning that was done on that was on the publicly available data on, on rep. And so, so that's, that's you know, data that's in public res is Permissively licensed. So fine tuning on on that. Then, Leads to a surprisingly better, like significantly better model, which is this retuned V1 three B, same size, you know, same, very fast inference, same vocabulary and everything.[00:41:46] The only difference is that it's been trained on additional replica data. Yeah.[00:41:50] swyx: And I think I'll call out that I think in one of the follow up q and as that Amjad mentioned, people had some concerns with using replica data. Not, I mean, the licensing is fine, it's more about the data quality because there's a lot of beginner code Yeah.[00:42:03] And a lot of maybe wrong code. Mm-hmm. But it apparently just wasn't an issue at all. You did[00:42:08] Reza Shabani: some filtering. Yeah. I mean, well, so, so we did some filtering, but, but as you know, it's when you're, when you're talking about data at that scale, it's impossible to keep out, you know, all of the, it's, it's impossible to find only select pieces of data that you want the, the model to see.[00:42:24] And, and so a lot of the, a lot of that kind of, you know, people who are learning to code material was in there anyway. And, and you know, we obviously did some quality filtering, but a lot of it went into the fine tuning process and it really helped for some reason. You know, there's a lot of high quality code on, on replica, but there's like you, like you said, a lot of beginner code as well.[00:42:46] And that was, that was the really surprising thing is that That somehow really improved the model and its reasoning capabilities. It felt much more kind of instruction tuned afterward. And, and you know, we have our kind of suspicions as as to why there's, there's a lot of like assignments on rep that kind of explain this is how you do something and then you might have like answers and, and whatnot.[00:43:06] There's a lot of people who learn to code on, on rep, right? And, and like, think of a beginner coder, like think of a code model that's learning to, to code learning this reasoning and logic. It's probably a lot more valuable to see that type of, you know, the, the type of stuff that you find on rep as opposed to like a large legacy code base that that is, you know, difficult to, to parse and, and figure out.[00:43:29] So, so that was very surprising to see, you know, just such a huge jump in in reasoning ability once trained on, on replica data.[00:43:38] The YOLO training run[00:43:38] swyx: Yeah. Perfect. So we're gonna do a little bit of storytelling just leading up to the, the an the developer day that you had last week. Yeah. My understanding is you decide, you raised some money, you decided to have a developer day, you had a bunch of announcements queued up.[00:43:52] And then you were like, let's train the language model. Yeah. You published a blog post and then you announced it on Devrel Day. What, what, and, and you called it the yolo, right? So like, let's just take us through like the[00:44:01] Reza Shabani: sequence of events. So so we had been building the infrastructure to kind of to, to be able to train our own models for, for months now.[00:44:08] And so that involves like laying out the infrastructure, being able to pull in the, the data processes at scale. Being able to do things like train your own tokenizes. And and even before this you know, we had to build out a lot of this data infrastructure for, for powering things like search.[00:44:24] There's over, I think the public number is like 200 and and 30 million res on, on re. And each of these res have like many different files and, and lots of code, lots of content. And so you can imagine like what it must be like to, to be able to query that, that amount of, of data in a, in a reasonable amount of time.[00:44:45] So we've You know, we spent a lot of time just building the infrastructure that allows for for us to do something like that and, and really optimize that. And, and this was by the end of last year. That was the case. Like I think I did a demo where I showed you can, you can go through all of replica data and parse the function signature of every Python function in like under two minutes.[00:45:07] And, and there's, you know, many, many of them. And so a and, and then leading up to developer day, you know, we had, we'd kind of set up these pipelines. We'd started training these, these models, deploying them into production, kind of iterating and, and getting that model training to production loop.[00:45:24] But we'd only really done like 1.3 billion parameter models. It was like all JavaScript or all Python. So there were still some things like we couldn't figure out like the most optimal way to to, to do it. So things like how do you pad or yeah, how do you how do you prefix chunks when you have like multi-language models, what's like the optimal way to do it and, and so on.[00:45:46] So you know, there's two PhDs on, on the team. Myself and Mike and PhDs tend to be like careful about, you know, a systematic approach and, and whatnot. And so we had this whole like list of things we were gonna do, like, oh, we'll test it on this thing and, and so on. And even these, like 1.3 billion parameter models, they were only trained on maybe like 20 billion tokens or 30 billion tokens.[00:46:10] And and then Amjad joins the call and he's like, no, let's just, let's just yolo this. Like, let's just, you know, we're raising money. Like we should have a better code model. Like, let's yolo it. Let's like run it on all the data. How many tokens do we have? And, and, and we're like, you know, both Michael and I are like, I, I looked at 'em during the call and we were both like, oh God is like, are we really just gonna do this?[00:46:33] And[00:46:34] swyx: well, what is the what's the hangup? I mean, you know that large models work,[00:46:37] Reza Shabani: you know that they work, but you, you also don't know whether or not you can improve the process in, in In important ways by doing more data work, scrubbing additional content, and, and also it's expensive. It's like, it, it can, you know it can cost quite a bit and if you, and if you do it incorrectly, you can actually get it.[00:47:00] Or you, you know, it's[00:47:02] swyx: like you hit button, the button, the go button once and you sit, sit back for three days.[00:47:05] Reza Shabani: Exactly. Yeah. Right. Well, like more like two days. Yeah. Well, in, in our case, yeah, two days if you're running 256 GP 100. Yeah. Yeah. And and, and then when that comes back, you know, you have to take some time to kind of to test it.[00:47:19] And then if it fails and you can't really figure out why, and like, yeah, it's, it's just a, it's kind of like a, a. A time consuming process and you just don't know what's going to, to come out of it. But no, I mean, I'm Judd was like, no, let's just train it on all the data. How many tokens do we have? We tell him and he is like, that's not enough.[00:47:38] Where can we get more tokens? Okay. And so Michele had this you know, great idea to to train it on multiple epox and so[00:47:45] swyx: resampling the same data again.[00:47:47] Reza Shabani: Yeah. Which, which can be, which is known risky or like, or tends to overfit. Yeah, you can, you can over overfit. But you know, he, he pointed us to some evidence that actually maybe this isn't really a going to be a problem.[00:48:00] And, and he was very persuasive in, in doing that. And so it, it was risky and, and you know, we did that training. It turned out. Like to actually be great for that, for that base model. And so then we decided like, let's keep pushing. We have 256 TVs running. Let's see what else we can do with it.[00:48:20] So we ran a couple other implementations. We ran you know, a the fine tune version as I, as I said, and that's where it becomes really valuable to have had that entire pipeline built out because then we can pull all the right data, de-dupe it, like go through the, the entire like processing stack that we had done for like months.[00:48:41] We did that in, in a matter of like two days for, for the replica data as well removed, you know, any of, any personal any pii like personal information removed, harmful content, removed, any of, of that stuff. And we just put it back through the that same pipeline and then trained on top of that.[00:48:59] And so I believe that replica tune data has seen something like 680. Billion tokens. And, and that's in terms of code, I mean, that's like a, a universe of code. There really isn't that much more out there. And, and it, you know, gave us really, really promising results. And then we also did like a UL two run, which allows like fill the middle capabilities and and, and will be, you know working to deploy that on, on rep and test that out as well soon.[00:49:29] But it was really just one of those Those cases where, like, leading up to developer day, had we, had we done this in this more like careful, systematic way what, what would've occurred in probably like two, three months. I got us to do it in, in a week. That's fun. It was a lot of fun. Yeah.[00:49:49] Scaling Laws: from Kaplan to Chinchilla to LLaMA[00:49:49] Alessio Fanelli: And so every time I, I've seen the stable releases to every time none of these models fit, like the chinchilla loss in, in quotes, which is supposed to be, you know, 20 tokens per, per, what's this part of the yo run?[00:50:04] Or like, you're just like, let's just throw out the tokens at it doesn't matter. What's most efficient or like, do you think there's something about some of these scaling laws where like, yeah, maybe it's good in theory, but I'd rather not risk it and just throw out the tokens that I have at it? Yeah,[00:50:18] Reza Shabani: I think it's, it's hard to, it's hard to tell just because there's.[00:50:23] You know, like, like I said, like these runs are expensive and they haven't, if, if you think about how many, how often these runs have been done, like the number of models out there and then, and then thoroughly tested in some forum. And, and so I don't mean just like human eval, but actually in front of actual users for actual inference as part of a, a real product that, that people are using.[00:50:45] I mean, it's not that many. And, and so it's not like there's there's like really well established kind of rules as to whether or not something like that could lead to, to crazy amounts of overfitting or not. You just kind of have to use some, some intuition around it. And, and what we kind of found is that our, our results seem to imply that we've really been under training these, these models.[00:51:06] Oh my god. And so like that, you know, all, all of the compute that we kind of. Through, with this and, and the number of tokens, it, it really seems to help and really seems to to improve. And I, and I think, you know, these things kind of happen where in, in the literature where everyone kind of converges to something seems to take it for for a fact.[00:51:27] And like, like Chinchilla is a great example of like, okay, you know, 20 tokens. Yeah. And but, but then, you know, until someone else comes along and kind of tries tries it out and sees actually this seems to work better. And then from our results, it seems imply actually maybe even even lla. Maybe Undertrained.[00:51:45] And, and it may be better to go even You know, like train on on even more tokens then and for, for the[00:51:52] swyx: listener, like the original scaling law was Kaplan, which is 1.7. Mm-hmm. And then Chin established 20. Yeah. And now Lama style seems to mean 200 x tokens to parameters, ratio. Yeah. So obviously you should go to 2000 X, right?[00:52:06] Like, I mean, it's,[00:52:08] Reza Shabani: I mean, we're, we're kind of out of code at that point, you know, it's like there, there is a real shortage of it, but I know that I, I know there are people working on I don't know if it's quite 2000, but it's, it's getting close on you know language models. And so our friends at at Mosaic are are working on some of these really, really big models that are, you know, language because you with just code, you, you end up running out of out of context.[00:52:31] So Jonathan at, at Mosaic has Jonathan and Naveen both have really interesting content on, on Twitter about that. Yeah. And I just highly recommend following Jonathan. Yeah,[00:52:43] MosaicML[00:52:43] swyx: I'm sure you do. Well, CAGR, can we talk about, so I, I was sitting next to Naveen. I'm sure he's very, very happy that you, you guys had such, such success with Mosaic.[00:52:50] Maybe could, could you shout out like what Mosaic did to help you out? What, what they do well, what maybe people don't appreciate about having a trusted infrastructure provider versus a commodity GPU provider?[00:53:01] Reza Shabani: Yeah, so I mean, I, I talked about this a little bit in the in, in the blog post in terms of like what, what advantages like Mosaic offers and, and you know, keep in mind, like we had, we had deployed our own training infrastructure before this, and so we had some experience with it.[00:53:15] It wasn't like we had just, just tried Mosaic And, and some of those things. One is like you can actually get GPUs from different providers and you don't need to be you know, signed up for that cloud provider. So it's, it kind of detaches like your GPU offering from the rest of your cloud because most of our cloud runs in, in gcp.[00:53:34] But you know, this allowed us to leverage GPUs and other providers as well. And then another thing is like train or infrastructure as a service. So you know, these GPUs burn out. You have note failures, you have like all, all kinds of hardware issues that come up. And so the ability to kind of not have to deal with that and, and allow mosaic and team to kind of provide that type of, of fault tolerance was huge for us.[00:53:59] As well as a lot of their preconfigured l m configurations for, for these runs. And so they have a lot of experience in, in training these models. And so they have. You know, the, the right kind of pre-configured setups for, for various models that make sure that, you know, you have the right learning rates, the right training parameters, and that you're making the, the best use of the GPU and, and the underlying hardware.[00:54:26] And so you know, your GPU utilization is always at, at optimal levels. You have like fewer law spikes than if you do, you can recover from them. And you're really getting the most value out of, out of the compute that you're kind of throwing at, at your data. We found that to be incredibly, incredibly helpful.[00:54:44] And so it, of the time that we spent running things on Mosaic, like very little of that time is trying to figure out why the G P U isn't being utilized or why you know, it keeps crashing or, or why we, you have like a cuda out of memory errors or something like that. So like all, all of those things that make training a nightmare Are are, you know, really well handled by, by Mosaic and the composer cloud and and ecosystem.[00:55:12] Yeah. I was gonna[00:55:13] swyx: ask cuz you're on gcp if you're attempted to rewrite things for the TPUs. Cause Google's always saying that it's more efficient and faster, whatever, but no one has experience with them. Yeah.[00:55:23] Reza Shabani: That's kind of the problem is that no one's building on them, right? Yeah. Like, like we want to build on, on systems that everyone else is, is building for.[00:55:31] Yeah. And and so with, with the, with the TPUs that it's not easy to do that.[00:55:36] Replit's Plans for the Future (and Hiring!)[00:55:36] swyx: So plans for the future, like hard problems that you wanna solve? Maybe like what, what do you like what kind of people that you're hiring on your team?[00:55:44] Reza Shabani: Yeah. So We are, we're currently hiring for for two different roles on, on my team.[00:55:49] Although we, you know, welcome applications from anyone that, that thinks they can contribute in, in this area. Replica tends to be like a, a band of misfits. And, and the type of people we work with and, and have on our team are you know, like just the, the perfect mix to, to do amazing projects like this with very, very few people.[00:56:09] Right now we're hiring for the applied a applied to AI ml engineer. And so, you know, this is someone who's. Creating data pipelines, processing the data at scale creating runs and and training models and you

Sneha Narahalli, VP Head of Product at Sephora, joins E-Commerce with Coffee?! host Nate Svoboda to talk about what successful technology transformations look like. Sneha artfully outlines answers to each question that are each mini-systems in and of themselves. How does a brand avoid acquiring too much overlapping technology? She has a system for that. What does personalization in e-commerce require? She has a system for that, too. Sneha's expertise covers the biggest pain points that brands look to technology to solve: customer acquisition and client experience. She's also extraordinarily insightful in how to apply technology to business goals in a meaningful way. Listen to the full interview to hear everything she has to say! What to listen for: As usual, Nate opens with a question about coffee. To his delight, it turns out that Sneha's mother grows coffee in the richest coffee region of India, and Sneha gets ground coffee from her mom exclusively! Sneha then walks us through her professional trajectory. She started working for the technology department of Sears, and then later moved to Walmart and Walmart Labs. After that, she moved to Sephora. Her focus has always been on the customer experience, including end customers and internal teams. Listen to the full episode to hear her describe how the brands have differed. “As I continue to grow,” she comments, “I've come to understand how important the leadership team is...much more important than the role itself.” When asked what motivates her, Sneha answers, “ambiguity!” Any process (or lack of process) inside of a team or organization where steps are not laid out is her cup of tea (or coffee, as it were). She's a clean-up artist with no shortage of curiosity. As the interview turns to tech trends, Sneha notes how companies today have learned to “reinvent themselves as tech companies” instead of business-focused companies. “The importance of technology driving business strategy,” she explains, “is no longer about ‘here's our business goal, let's see whether technology can achieve it or not.'” Instead, technology can drive business goals in some areas—and in all others, it's about the partnership between technology and business goals. Listen to the full interview to hear Sneha expand on this compelling concept. Sustainability in tech is a conversation you'll be glad Nate had with Sneha. Questions came up like, “what needs to be true of a tech solution for it to be useful today and in 10 years?” Sneha's expertise shines brilliantly as she breaks her answer down into bite-size pieces. She starts, “a technology solution needs a strong foundation that's able to adapt to change.” Solutions designed for customer problems, she adds, don't usually do that. Tech creators instead need to look at the current use case that they're solving and then ask what use cases are likely in the near and distant future. Listen to the full interview to hear what that looks like. When asked about personalization in the consumer experience, Sneha puts her “consumer” hat on and muses, “you need to know what I'm doing… and if I'm changing, adapt to the way that I'm changing and make my life easier. And, if possible, delight me.” Removing friction is the key. Sneha talks about the essential systems that brands need to support personalization, too. Listen to the full interview to hear what she says. As a closing thought, Nate asks Sneha about being a woman in the tech world, which is still largely dominated by men. She advises people to think of it this way: “it's my job to tell you [the employer, the supervisor, etc.] ‘I'm OK with this' or ‘I'm not OK with this.'” Businesses also need to create environments that allow people to “show up as their true, authentic selves,” she says, by tolerating no biases. Listen to her final thoughts on the episode.

John Mille, Principal Cloud Engineer at Sainsbury's UK joins Corey on Screaming in the Cloud to discuss how retail companies are using cloud services. John describes the lessons he's learned since joining the Sainsbury's UK team, including why it's important to share knowledge across your team if you don't want to be on call 24/7,  as well as why he doesn't subscribe to the idea that every developer needs access to production. Corey and John also discuss an open-source project John created called ECS Compose-X.About JohnJohn is an AWS Community Builder (devtools), Open Source enthusiast, SysAdmin born in the cloud, and has worked with AWS since his very first job. He enjoys writing code and creating projects. John likes to focus on automation & architecture that delivers business value, and has been dabbling with data & the wonderful world of Kafka for the past 3 years.Links Referenced: AWS Open-Source Roundup newsletter blog post about ECS Compose-X: https://aws.amazon.com/blogs/opensource/automating-your-ecs-container-architecture-deployments-with-ecs-composex/ ECS Compose-X: https://docs.compose-x.io/ LinkedIn: https://www.linkedin.com/in/john-mille/ Twitter: https://twitter.com/JohnPre32286850 TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: It's easy to **BEEP** up on AWS. Especially when you're managing your cloud environment on your own!Mission Cloud un **BEEP**s your apps and servers. Whatever you need in AWS, we can do it. Head to missioncloud.com for the AWS expertise you need. Corey: Do you wish your developers had less permanent access to AWS? Has the complexity of Amazon's reference architecture for temporary elevated access caused you to sob uncontrollably? With Sym, you can protect your cloud infrastructure with customizable, just-in-time access workflows that can be setup in minutes. By automating the access request lifecycle, Sym helps you reduce the scope of default access while keeping your developers moving quickly. Say goodbye to your cloud access woes with Sym. Go to symops.com/corey to learn more. That's S-Y-M-O-P-S.com/coreyCorey: Welcome to Screaming in the Cloud. I'm Corey Quinn. Today my guest is a long-time listener, first-time caller. John Mille is a Principal Cloud Engineer at Sainsbury's, which is UK-speak for ‘grocery store.' John, thank you for joining me.John: Hi, Corey. Thanks for having me.Corey: So, I have to begin with, I guess, the big question that I used to run into people in San Francisco with all the time. They would work at Walmart Labs and they would mention in conversation that they work at Walmart, and people who weren't aware that there was a labs out here figured they were a greeter at the grocery store. Do you ever wind up with people making that sort of fundamental assumption around the fact, oh, you work at Sainsbury's as a checker or whatnot?John: No. But it actually is one of the—if you look at one of the job descriptions from Sainsbury's, the first thing is, why would you join a retail company to do tech? And as it turns out, tech—I mean, I think retail companies, as any other companies in the world, rely on Cloud more and more and more. And I think that one of the things that is interesting today is, if you look at the landscape of retailers, I've heard many times people saying, “We don't want to go for AWS because we're giving money to the competition.” And actually, I think AWS does a fantastic job overall giving you all the tools to actually beat them as your competition. And as it turns out, we've had really, really great success running a lot of our workloads on AWS for many, many years now.Corey: On some level, if you can't come to terms with the idea of Amazon as competition, you shouldn't be using AWS, regardless of what industry you're in, because their entire company strategy is yes. It's very hard to start to even come up with industries that they don't have some form of presence within. On some level, that's a problem. In fact a lot of levels, that's something of a problem.Everyone tends to wind up viewing the world in a bunch of different ways. I like to divide companies into two groups. More or less it's, is the AWS bill one of the top three line items at the company? And if the answer's no, on some level, you know, that usually is an indicator that there's a sustainable business there that, you know, both our grandparents and our grandchildren will be able to recognize, in the fullness of time. You absolutely have a business that winds up falling into that category, whereas, “Oh yeah, I fix the AWS bill,” yeah, my parents would have no idea what I do and my kids don't have much of a better one. It feels like it's very point-in-time type of problem. At least I hope.Technology is not the core of what grocery stores tend to do, but I also don't get the sense that what you're doing is sitting there doing the back office corporate IT style of work, either. How do you use technology in the overall context of the business?John: Well, so we use it in a very wide variety of sense. So, you obviously have everything that has to do with online shopping, orders and all of those sort of things, which obviously, especially with the drive of Covid and being everybody from home, has been a huge driver to improve our ability to deliver to customers. But certainly, I think that Sainsbury's sees AWS as a key partner to be able to go and say we want to deliver more value. And so, there's been a number of transformation over the years to—and one of the reasons I was hired is actually to be part of one of those transformation, where we're going to take existing infrastructure servers that literally—I usually say to people, “Oh, are we doing an upgrade this month? Has somebody gotten their little brush to go and brush onto the hard drives to make sure that nothing is going to die?” And actually do that transformation and move over to the cloud in order to never have to really worry about whether or not they have to manage hardware and infrastructure.Corey: It's strange in that I never got very deep into containers until I was no longer hands-on hardware, managing things. I was more or less doing advisory work and then messing around with them. And you'd think given my proclivities historically, of being very unlucky when it comes to data, you would think that this would be great because, oh yeah, you blow away an ephemeral container? Well, that's kind of the point. We'll all laugh and it'll re-instantiate itself and life goes on.But no. Making fun of them was more or less how I tended to do approach them for the longest time until I started to see them a little bit… well I guess less as a culture, less as a religion, and more as an incredibly versatile packaging format, which is probably going to annoy the people I know who are the packaging [unintelligible 00:04:58] for Linux distributions. How do you tend to view them? And how did you start using them?John: Right. So, that's a great question. So historically, I was a student at, I think the school were one of the original creators of Docker were. And one of the things that you learn when you do development at the school is that, you know, containers [unintelligible 00:05:18] new invention. Docker, I think, came on the platform as the way to, you know, give everybody a great framework, a great API, to drive the deployment of containers in the world and bundle them and ship them around the world, on your laptop and somebody else's, and help a little bit with, you know, solving the problem of it works on my laptop, but not just on the laptop properly. Maybe.It's obviously gone viral over the years and I really enjoy containers; I quite like containers. What I find interesting is what people are going to do with. And I think that over the last few years, we've seen a number of technologies such as Kubernetes and others come into the scene and say—and trying to solve people's problem, but everybody seems to be doing, sort of, things on their own way. And historically, I started off using ECS, when it was terrible and you didn't have security groups per containers and all of this. But over the years, you know, you learn, and AWS has improved the service quite significantly with more and more features.And I think we are today in the place where there's this landscape, I think, where a lot of workloads are going to be extremely ephemeral and you can go [unintelligible 00:06:28], you know, wherever you want and you have a bit—if you have a platform or workflow that you need to have working in different places, maybe Kubernetes could be an easy way to have a different sort of sets of features that allows you to move around in maybe an easier way. But that also comes with a set of drawbacks. Again, I look at using EKS, for example, and I see okay, I have to manage IAM in our back now, whereas if I used something like ECS, for the whatever the [unintelligible 00:06:56] cloud vendor of choice, I don't have to deal with any of this. So, I think it's finding the fine balance between how you do orchestration of containers now and what works for you and is any sustainable over the time, more than about are you going to use containers? Because the chances are, somebody is using containers.Corey: My experiences and workflows and constraints are radically different than that of other folks because for a lot of the things I'm building, these are accounts that are I'm the only person that has access to them. It is me. So, the idea of fine-grained permissions for users from an ARBAC perspective doesn't really factor into it. Yes, yes, in theory, I should have a lot of the systems themselves with incidents roles being managed in safe and secure ways, but in many cases, the AWS account boundary is sufficient for that, depending on what it is we're talking about. But that changes when you start having a small team of people working with you and having to collaborate on these things.And we do a little bit of that with some of our consulting stuff that isn't just the shitpost stuff I build for fun. But there's multiple levels beyond that. You are clearly in a full-blown enterprise at this point where there are a bunch of different teams working on different things, all ideally going in the same direction. And it's easy to get stuck in the weeds of having to either go through central IT for these things, which gives rise to shadow IT every time you find a corporate credit card in the wild, or it winds up being everyone can do what they want, but then there's no consensus, there's no control, there's no architectural similarity. And I'm not sure which path is worse in some respects. How do you land on it?John: Right. So, what I've seen done in companies that works very well—and again, to the credit of my current company—is one of the things they've done really well is build a hub of people who are going to manage solely everything that has to do with accounts access, right? So, the control, IAM, Security Hub, all of those sorts of things, for you. There's things that are mandatory that you can't deal without, you have permissions boundary, that's it, you have to use those things, end of story. But beyond that point, once you have access to your accounts, you've been given all of the access that is necessary for you to deliver application and deploy them all the way up to production without asking permission for anybody else apart from your delivery managers, potentially.And I think from there, because there is the room to do all of this, one of the things that we've done within my business unit is that we've put in place a framework that enables developers—and when I say that it really is a question of allowing them to do everything they have to do, focus on the code, and I know it's a little catchy [unintelligible 00:09:33] a phrase that you hear these days, but the developers really are the customers that we have. And all that we do is to try to make sure that they have a framework in place that allows them to do what they need and deploy the applications in a secure fashion. And the only way to do that for us was to build the tools for them that allows them to do all of that. And I honestly haven't checked a single service IAM policies in a very are longtime because I know that by providing the tools to developers, they don't have this [will 00:10:05] to go and mess with the permissions because their application suddenly doesn't have the permissions. They just know that with the automation we've providing them, the application gets the access it needs and no more.Corey: On some level, it feels like there's a story around graduated development approach where in a dev environment you can do basically whatever you want with a big asterisk next to it. That's the same asterisk, by the way, next to the AWS free tier. But as you start elevating things into higher environments, you start to see gating around things like who has access to what, security reviews, et cetera, et cetera, and ideally, by the time you wind up getting into production, almost no one should have access and that access that people do have winds up being heavily gated. That is, of course, the vision that folks have. In practice, reality is what happens instead of what we plan on. The idea of it works in theory, but not in production is of course, why I call my staging environment ‘theory.' Does that tend to resonate as far as what you've seen in the wild?John: Yeah. Very much so. And when I joined the company, and we put together our [standard 00:11:11] pipelines for developers to be able to do everything, the rule that I would give to my team—so I manage a small team of cloud engineers—the one rule I would say is, “We have access to prod because we need to provision resources, but when we're going to build the pipelines for the developers, you have to build everything in such a way that the developers will only have read-only access to the production environment, and that is only to go and see their logs.” And at least try to foster this notion that developers do not need access to production, as much as possible because that avoids people going and do something they shouldn't be doing in those production environments.Now, as the pipeline progresses and applications get deployed to production, there are some operational capabilities that people need to have, and so in that case, what we do is we try to fine-tune what do people need to do and grant those people access to the accounts so that they can perform the jobs and I don't have to be woken up at two in the morning. The developers are.Corey: One thing that I think is going to be a cause of some consternation for folks—because I didn't really think about this in any meaningful sense until I started acting as a consultant, which means you're getting three years of experience for every year that you're in the wild, just by virtue of the variety of environments you encounter—on some level, there's a reasonable expectation you can have when you're at a small, scrappy startup, that everyone involved knows where all the moving parts live. That tends to break down with scale. So, the idea of a Cloud Center of Excellence has been bandied around a lot. And personally, I hate the term because it implies the ‘Data Center of Mediocrity,' which is a little on the nose for some people at times. So, the idea of having a sort of as a centralized tiger team that has the expertise and has the ability to go on deep dives and sort of loan themselves out to different teams seems to be a compromise between nobody knows what they're doing and, every person involved should have an in-depth knowledge of the following list of disciplines.For example, most folks do not need an in-depth primer on AWS billing constructs. They need about as much information fits on an index card. Do you find that having the centralized concentration of cloud knowledge on a particular team works out or do you find that effectively doing a rotating embedding story is the better answer?John: It varies a lot, I think, because it depends on the level of curiosity of the developers quite a lot. So, I have a huge developer background. People in my team are probably more coming from ex-IT environments or this sort of operation and then it just naturally went into the cloud. And in my opinion, is fairly rare to find somebody that is actually good at doing both AWS and coding. I am by no means really, really great at coding. I code pretty much every day but I wouldn't call myself a professional developer.However, it does bring to my knowledge the fact that there are some good patterns and good practices that you can bring into building your applications in the cloud and some really bad ones. However, I think it's really down to making sure that the knowledge is here within the team. If there's a specialized team, those really need to be specialists. And I think the important thing then is to make sure that the developers and the people around you that are curious and want to ask questions know that you're available to them to share that knowledge. Because at the end of the day, if I'm the only one with the knowledge, I'm going to be the one who is always going to be on call for this or doing that and this is no responsibility that I want. I am happy with a number of responsibilities, but not to be the only person to ever do this. I want to go on holidays from time to time.So, at the end of the day, I suppose it really is up to what people want or expect out of their careers. I do a job that it was a passion for me since I was about 14 years old. And I've always been extremely curious to understand how things work, but I do draw the line that I don't write anything else than Python these days. And if you ask me to write Java, I'll probably change job in the flip of a second. But that's the end of it. But I enjoy understanding how Java things work so that I can help my developers make better choices with what services in AWS to use.Corey: On some level, it feels like there's a, I guess, lack of the same kind of socialization that startups have sort of been somewhat guided by as far as core ethos goes, where, oh whatever I'm working on, I want to reach out to other people, and, “Hey, I'm trying to solve this problem. What is it that you have been working on that's germane to this and how can we collaborate together?” It has nothing to do, incidentally, with the idea that, oh, big company people aren't friendly or are dedicated or aren't good or aren't well-connected; none of that. But there are so many people internally that you're spending your time focusing on and there's so much more internal context that doesn't necessarily map to anything outside of the company that the idea of someone off the street who just solved a particular problem in a weird way could apply to what a larger company with, you know, regulatory burdens, starts to have in mind, it becomes a little bit further afield. Do you think that that's accurate? Do you think that there's still a strong sense of enterprise community that I'm just potentially not seeing in various ways because I don't work at big companies?John: It's a very fine line to walk. So, when I joined the company, I was made aware that there's a lot of Terraform and Kubernetes, which I went [unintelligible 00:16:28] all the way with CloudFormation is yes. So, that was one of the changes I knew I would have. But I can move an open mind and when I looked around at, okay, what are the Terraform modules—because I used Terraform with anger for an entire year of suffering—and I thought, “Okay, well, maybe people have actually got to a point where they've built great modules that I can just pick up off the shelf and reuse or customize only a tiny little bit, add maybe a couple of features and that's, it move on; it's good enough for me.” But as it turns out, there is I think, a lot of the time a case where the need for standardization goes against the need for business to move on.So, I think this is where you start to see silos start to being built within the company and people do their own thing and the other ones do their own. And I think it's always a really big challenge for a large company with extremely opinionated individuals to say, “All right, we're going to standardize on this way.” And it definitely was one of the biggest challenge that I had when I joined the company because again, big communities and Terraform place, we're going to need to do something else. So, then it was the case of saying, “Hey, I don't think we need Kubernetes and I definitely don't think we need Terraform for any the things—for any of those reasons, so how about we do something a little different?”Corey: Speaking of doing things a little bit different, you were recently featured in an AWS Open-Source Roundup newsletter that was just where you, I think, came across my desk one of the first times, has specifically around an open-source project that you built: ECS Compose-X.So, I assume it's like, oh, it's like Docker Compose for ECS and also the ‘X' implies that it is extreme, just, like, you know, snack foods at the convenience store. What does it do and where'd it come from?John: Right. So, you said most of it, right? It literally is a question where you take a Docker Compose file and you want to deploy your services that you worked on and all of that together, and you want to deploy it to AWS. So, ECS Compose-X is a CLI tool very much like the Copilot. I think it was released about four months just before Copilots came out—so, sorry, I beat you to the ball there—but with the Docker Compose specification supported.And again, it was really out of I needed to find a neat way to take my services and deploy them in AWS. So, Compose-X is just a CLI tool that is going to parse your Docker Compose file and create CloudFormation templates out of it. Now, the X is not very extreme or anything like that, but it's actually coming from the [finite 00:18:59] extension fields, which is something supported in Docker Compose. And so, you can do things like x-RDS, or x-DynamoDB, which Docker Compose on your laptop will totally ignore, but ECS Compose-X however will take that into account.And what it will do is if you need a database or a DynamoDB table, for example, in your Docker Compose file, you do [x-RDS, my database, some properties, 00:19:22]—exactly the same properties as CloudFormation, actually—and then you say, “I want this service to have access to it in read-only fashion.” And what ECS Compose-X is going to do is just understand what it has to do when—meaning creating IAM policies, opening security groups, all of that stuff, and make all of that available to the containers in one way or another.Corey: It feels like it's a bit of a miss for Copilot not to do this. It feels like they wanted to go off in their own direction with the way that they viewed the world—which I get; I'm not saying there's anything inherently wrong with that. There's a reason that I point kubernetestheeasyway.com to the ECS marketing site—but there's so much stuff out there that is shipped or made available in other ways with a Docker Compose file, and the question of okay, how do I take this and run it in Fargate or something because I don't want to run it locally for whatever reason, and the answer is, “That's the neat part. You don't.”And it just becomes such a clear miss. There have been questions about this Since Copilot launched. There's a GitHub issue tracking getting support for this that was last updated in September—we are currently recording this at the end of March—it just doesn't seem to be something that's a priority. I mean, I will say the couple of times that I've used Copilot myself, it was always for greenfield experiments, never for adopting something else that already existed. And that was… it just felt like a bit of a heavy lift to me of oh, you need to know from the beginning that this is the tool you're going to use for the thing. Docker Compose is what the ecosystem has settled on a long time ago and I really am disheartened by the fact that there's no direct ECS support for it today.John: Yeah, and it was definitely a motivation for me because I knew that ECS CLI version 1 was going into the sunset, and there wasn't going to be anything supporting it. And so, I just wanted to have Docker Compose because it's familiar to developers and again, if you want to have adoption and have people use your thing, it has to be easy. And when I looked at Copilot the first time around, I was extremely excited because I thought, “Yes, thank you, Amazon for making my life easy. I don't have to maintain this project anymore and I'm going to be able to just lift and shift, move over, and be happy about it.” But when the specification for Copilot was out and I could go for the documentation, I was equally disheartened because I was like, “Okay, not for me.”And something very similar happened when they announced Proton. I was extremely excited by Proton. I opened a GitHub issue on the roadmap immediately to say, “Hey, are you going to support to have some of those things together or not?” And the fact that the Proton templates—I mean, again, it was, what, two, three years ago now—and I haven't looked at Proton since, so it was a very long time now.Corey: The beta splasher was announced in 2020 and I really haven't seen much from it since.John: Well, and I haven't done anything [unintelligible 00:22:07] with it. And literally, one of the first thing did when the project came out. Because obviously, this is an open-source project that we use in Sainsbury's, right because we deploy everything in [ECS 00:22:17] so why would I reinvent the wheel the third time? It's been done, I might as well leverage it. But every time something on it came out, I was seeing it as the way out of nobody's going to need me anymore—which is great—and that doesn't create a huge potential dependency on the company for me, oh, well, we need this to, you know, keep working.Now, it's open-source, it's on the license you can fork it and do whatever you want with it, so from that point of view, nobody's going to ask me anything in the future, but from the point of view where I need to, as much as possible, use AWS native tools, or AWS-built tools, I differently wanted every time to move over to something different. But every time I tried and tiptoed with those alternative offerings, I just went back and said, “No, this [laugh] either is too new and not mature enough yet, or my tool is just better.” Right? And one of the things I've been doing for the past three years is look at the Docker ECS plugin, all of the issues, and I see all of the feature requests that people are asking for and just do that in my project. And some with Copilots. The only thing that Copilot does that I don't do is tell people how to do CI/CD pipelines.Corey: One thing you said a second ago just sort of, I guess, sent me spiraling for a second because I distinctly remember this particular painful part. You're right, there was an ECS CLI for a long time that has since been deprecated. But we had internal tooling built around that. When there was an issue with a particular task that failed, getting logs out of it was non-trivial, so great. Here's the magic incantation that does it.I still haven't found a great way to do that with the AWS v2 CLI and that feels like it's a gap where yes, I understand, old tools go away and new ones show up, but, “Hey, I [unintelligible 00:24:05] task. Can you tell me what the logs are?” “No. Well, Copilot's the new answer.” “Okay. Can I use this to get logs from something that isn't Copilot?” “Oh, absolutely not.” And the future is inherently terrible as a direct result.John: Yeah. Well, I mean, again, the [unintelligible 00:24:20]—the only thing that ECS Compose-X does is create all the templates for you so you can, you know, then just query it and know where everything has been created. And one of the things it definitely does create is all of the log groups. Because again, least-privileged permissions being something that is very dear to me, I create the log groups and just allow the services to only write in those log groups and that's it.Now, typically this is not a thing that I've thought Compose-X was going to do because that's not its purpose. It's not going to be an operational tool to troubleshoot all the things and this is where I think that other projects are much better suited and I would rather use them as an extension or library of the project as opposed to reinvent them. So, if you're trying to find a tool for yourself to look at logs, I highly recommend something called ‘AWS logs,' which is fantastic. You just say, “Hey, can you list the groups?” “Okay.” “Can you get me the groups and can I tell them on a terminal?”And that's it. Job done. So, as much as I enjoy building new features into the project, for example, I think that there's a clear definition between what the project is for and what it's not. And what it's for is giving people CloudFormation templates they can reuse in any region and deploy their services and not necessarily deal with their operations; that's up to them. At the end of the day, it's really up to the user to know what they want to do with it. I'm not trying to force anybody into doing something specific.Corey: I would agree. I think that there's value to there's more than one way to do it. The problem is, at some point, there's a tipping point where you have this proliferation of different options to the point where you end up in this analysis paralysis model where you're too busy trying to figure out what is the next clear step. And yes, that flexibility is incredibly valuable, especially when you get into, you know, large, sophisticated enterprises—ahem, ahem—but when you're just trying to kick the tires on something new, I feel like there's a certain lack of golden path where in the event of not having an opinion on any of these things, this is what you should do just to keep things moving forward, as opposed to here are two equal options that you can check with radio boxes and it's not at all clear what you which does what or what the longer-term implications are. We've all gotten caught with the one-way doors we didn't realize we were passing through at the time and then had to do significant technical debt repayment efforts to wind up making it right again.I just wish that those questions would be called out, but everything else just, it doesn't matter. If you don't like the name of the service that you're creating, you can change it later. Or if you can't, maybe you should know now, so you don't have—in my case—a DynamoDB table that is named ‘test' running in production forever.John: Yeah. You're absolutely right. And again, I think it goes back to one of the biggest challenges that I had when I joined the company, which was when I said, “I think we should be using CloudFormation, I think we should be using ECS and Terraforming Kubernetes for those reasons.” And one of the reasons was, the people. Meaning we were a very small team, only five cloud engineers at the time.And as I joined the company, they were already was three different teams using four different CI/CD tools. And they all wanted to use Kubernetes, for example, and they were all using different CI/CD—like I said, just now—different CI/CD tools. And so, the real big challenge for me was how do I pitch that simplicity is what's going to allow us to deliver value for the business? Because at the end of the day, like you said many, many times before, the AWS bill is a question of architecture, right? And there's a link and intricacy between the two things.So, the only thing that really mattered for me and the team was to find a way, find the service that was going to allow to do a number of things, A, delivering value quickly, being supported over time. Because one of the things that I think people forget these days—well, one of the things I'm allergic to and one of the things that makes me spiral is what I call CV-driven tech choices where people say, “Hey, I love this great thing I read about and I think that we should use that in production. How great idea.” But really, I don't know anything about it and is then up to somebody else to maintain it long-term.And that goes to the other point, which is, turnover-proof is what I call it. So, making tech choices that are going to be something that people will be able to use for many, many years, there is going to be a company behind the scenes that he's going to be able to support you as well as you go and use the service for the many, many years to go.Corey: I really want to thank you for taking the time to speak with me today. If people want to learn more, where's the best place for them to find you?John: So, people can find me on LinkedIn. I'm also around on Twitter these days, although I probably about have nine followers. Well, probably shouldn't say that [laugh] and that doesn't matter.Corey: It's fine. We'll put a link into it—we'll put a link to that in the [show notes 00:29:02] and maybe we'll come up with number ten. You never know. Thanks again for your time. I really appreciate it.John: Thanks so much, Corey, for having me.Corey: John Mille, Principal Cloud Engineer at Sainsbury's. I'm Cloud Economist Corey Quinn and this is Screaming in the Cloud. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice along with an angry comment that you go to great pains to type out but then fails to post because the version of the tool you use to submit it has been deprecated without a viable replacement.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business and we get to the point. Visit duckbillgroup.com to get started.

When you think of financial services and healthcare, ease of transformation likely isn't the first to come to mind. There are privacy concerns, compliance issues, and security responsibilities. So how does a company that sits at the intersection of those two tackle radical digital transformation—all while already sitting at the top of its field? For this episode, we speak with Cory Gundberg about driving digital transformation in large organizations. Cory is the COO of Optum Financial, where he's driven their transformation strategy for close to a year. Prior to that, he was the Chief Digital Officer at Optum Rx, and he also held several leadership roles at Walmart, including SVP of Health and Wellness, SVP of Walmart Labs, VP of Walmart Technology, and Senior Director of Innovation. We discuss what drives innovation within his organization, how he filters through the noise to find where transformation is truly needed, and how UnitedHealth channels its digital innovations budget into new technologies that help its consumers.   Resources: Learn more at Optum.com Connect with Cory on LinkedIn   Learn more and get the full show notes at: 3PillarGlobal.com

Caroline is a senior product manager on the Etsy mobile apps team and previously worked at Walmart Labs. In this episode of the Product Science Podcast, we cover Caroline's career in product, how she uses story telling to align different teams, how to get buy in for continuous experimentation at companies large and small, and how even a failed experiment can yield positive results.

You're well aware that this podcast is about public policy and so often we focus on that, but today, we're also bringing you the creative side of policy making. So many of the policies that we fight to implement are created as a way to protect and preserve our ability to be creative. We know that for many professionals, it's hard to be able to live a completely creative life, while balancing it with work and earning a living.   In a podcast first, we begin today's episode with a poem by our guest, Anita Balaraman. The poem is called “Doubt” and you can read it on Medium.com.   Anita never specifically sought out a creative life. Anita is a technology product leader with more than 10 years of experience in building technology products that delight the customer both in the B2B and B2C domain. She is also an adjunct faculty at UC Berkeley, teaching and coaching hi-tech product management. She is currently the founder of an early stage ed-tech startup. Most recently she led the digital customer experience practice at Cisco Systems, designing and launching enterprise solutions for customer experience. Prior to that, she led the product team at WalmartLabs launching products that combine machine learning, predictive analytics and personalization. She consults independently and on the board of technology startups in the advertising, ecommerce, and ed-tech space. Anita received her MS in toxicology and applied statistics, and an MBA, both from the University of California, Berkeley.   Experiencing Creativity As Anita has gotten older, her view of creativity is much different than it would have been when she was in her 20's. Now, it's more of having the ability to move forward, regardless of the constraints that are imposed upon you. We all deal with different challenges and constraints, and Anita sees creativity as almost being a river which flows around the boulders and roadblocks in our way. Your roadblocks are what make your path unique, but it's also what allows you to tap into that creativity.   Rethinking Overly Technical Job Descriptions   Recently, Anita published some research indicating that overly technical job descriptions can actually discourage some of the most creative people from applying for the job. The problem with that is in tech and cyber security, many minority populations are already underserved and these highly technical job descriptions can further exacerbate the problem.    Translating the Technical-Speak   One of the issues that many newly minted interns are seeing in their job searches is that job postings tend to lean heavily on engineering and technical data, and it seems as if they are only wanting applicants with very specific majors. The reality is that the technical data in the job posting rarely captures what the job actually is, and it doesn't show the impact that the employee will have in their role. So it almost takes some translation to let the job posting paint the picture of the actual role.   Hard Skills, Competencies and Skill Sets There is little doubt that many of the hard skills and competencies that a company would want could be clearly articulated in a job posting, but so often we default to a technical context that only attracts applicants with certain degrees. The reality is that most of the hard skills and competencies that a company would desire in a role would be possessed by applicants with a range of degrees.    There is a plethora of anecdotal evidence that these types of highly technical job postings discourage even the most skilled and qualified women or minorities from applying for the job. So, this segment of the population has removed themselves from the job pool and it becomes increasingly homogenous over time.   Multiple Streams of Income Having multiple roles and multiple streams of income can really broaden your skill set. This is especially true if one role requires you to be in touch with the technology for the sake of technology, and then maybe another role is in product development which would involve technology for the sake of a social reason, or to solve a problem. Then it becomes critical to stay in touch with customers and users, in addition to having a handle on the technology, so it's very beneficial.   You Have A Much Right As Anyone Else Jane Goodall is a world renowned expert on chimpanzees and other wildlife that she works to preserve, but Jane Goodall never even got a college degree. Her natural curiosity in chimpanzees drew her into her work and research and she made herself an expert. Jane's admonition to women who find themselves in a workplace or collaboration where they feel insecure about their credentials, or even as if they don't have the same brilliant mind as everyone else in the room was, “You have just as much right to be in the room as anybody else.” It's important for women to realize this and to pursue jobs they would be qualified for.   Women Leaving Tech There are some inherent blind spots in the struggle for equity in the workplace. As much as companies or males in the workplace try, they don't always get it right. Men, be careful about validating an experience or feeling of another person in terms of relating your own experience. When women hear a man say, “I felt that way too.” or “The same thing happened to me”, we understand that there is a societal contract that wants us to find commonality with our peers, but you are discounting the different starting point of the other person. You need to get through the layers to fundamentally understand how the experience from the same trigger could be different for other people who are different from you.   As we approach the future of work in some ways, how we think about STEM, how we think about cybersecurity being one of the STEM areas, how we think about equity, how we think about the purpose for the technology that's being built, it's becoming more and more critical. And having technology be for technology's sake, in some senses is a moot point, especially when you have the demand for these roles outstripping the supply. We need to be smarter, better at attracting the talent to opt into these fields and keep them there and enable them to do the work that they do. And we don't have the luxury of writing job descriptions or fostering an environment which in some ways is a weed out rather than opt in kind of a frame. Links: Sapiens: A Brief History of Humankind Washingtech.org Berkeley College of Engineering Doubt, by Anita Balaraman An obedient child Never wild Begged to be schooled Never one to do, what she wants to. Somewhere in my teens I grew To my parents, a quarrelsome, defiant point of view. Aspired to cross the oceans blue To America for graduate school to pursue. Girls can't be safe, outside of parents' purview Unless she has a husband, never mind she is just twenty-two! In Berkeley, I was told you can be what you want to Even a brown girl with big starry eyes, can dream one day to be a researcher, a professional, or a professor someday. Worked hard, very hard, or at least I thought, For I've been given a chance, a really long shot. But told that I may never be a researcher sought There must be more than just the grades, I thought. Despite how hard I fought… Hiding my feminine brownness was like adding a nought[*]. Perhaps they are right, went my train of thought… Why else would I not see someone like me in doctoral gown? Oh don't be sad, said my loved ones around You can be happy, rich, and successful without a doctoral gown- hands down. Look at the valley of silicon and sand A dreamland of success, prestige and wealth For those that are committed to technology at hand. Yes, but my mind wandered… Where did I lose the defiance in my view? I really care about children and leukemia And I can build risk models that I learned in academia. But can you blame them if they did not trust The models I built that needed their process to adjust. I don't look like them, or speak like them The assumptions in my models are hard to trust. I found my kind, the brown variety, Who spoke bad English with no anxiety. The friends at home and those at work Looked and spoke like they belong to the same network. No apologies for being a vegetarian during team lunch Who clairvoyantly knew that salad wasn't a good munch. This must be beautiful- to feel like you belong Without having to rehearse your lines so I don't say something wrong. To work with the bunch where I hoped I belonged, I got another graduate degree, not the Ph.D. I longed. A business degree, hoping to correct the wronged. A Mom twice over, a wife and an employee, ‘you can't get promoted if you leave at 5', would annoy me. Benevolent prejudice, paternalism, and sexism: Belonging, I understood, with deep skepticism. A misfit perhaps, have always been A toxicologist, but not the wet-lab kind A technologist, but not an engineer's mind An entrepreneur, who venture capital declined An educator, living the adjunct grind A researcher, without the terminal degree- unrefined. Seeking belonging, but always unaligned. Perhaps down in my subconscious mind the fringes appeal more than the straight jacket kind? The fringes feed concern for mistakes, Suspended between two or more contradictory states. An indecision between belief and non-belief Hiding, somewhere, is a fictitious fig leaf? Belonging requires suspending the lunatic fringe To honor and reflect the collective doubt. But that is harder to live, day in and day out Easier it seems to simply not honor their doubts?

We had a chance to catch up with Prasad Kuchoor Rao, Director of Talent Acquisition at Adobe in India, who comes with a wealth of experience in recruiting early talent for companies like Google and Walmart Labs. Listen to Prasad as he walks us through best practices for finding the right talent on campus, boosting employer brand in universities to attract early talent and more.

Building connections is a critical skill set for career success. Most successful people would agree that the popular catchphrase, "it's who you know, not what you know," rings true in daily life. The fact is, “who you know” might matter more, or at least, be just as important as what you know in getting professional opportunities. Our guest, Rosa Gonzalez Welton, Director of Product Management, Digital Acquisition, Growth and Customer Success at ServiceNow, shares her career journey and why she believes creating and building connections is the key to learning, growing, and fostering relationships. As Director of Product Management, Rosa is responsible for creating experiences across the end-to-end customer journey, from acquisition and growth, through to customer success. Rosa also created her own LeanIn Circle, for Latinas working in tech, a safe space for real talk, inspiration, and support. In this episode, Rosa shares why the best way to learn is by seizing opportunities, the benefits of investing in yourself, her decision framework, and how she gained access to influential leaders, sponsors, and mentors. Visit https://www.iambeyondbarriers.com where you will find show notes and links to all the resources in this episode, including the best way to get in touch with Rosa. Highlights: [02:22] Rosa's story [04:30] Jumping at opportunities to learn [06:13] Gaining clarity on your career path [08:26] The ROI of investing in yourself [10:24] The importance of community [15:07] Pushing forward through barriers [16:58] Rosa's decision framework [18:34] Gaining access to influential leaders, sponsors, and mentors [20:43] Asking for help [22:29] Rosa's daily success habits [24:39] Staying ahead of the curve Quotes: “Sometimes, in an organization, you're in a situation where you're just not going to win. Go where you're valued.” – Rosa Welton “People generally want to help. There is a benefit they get simply by mentoring or coaching others.” – Rosa Welton “The big picture in trying something new is that it's going to somehow open the door for you to know if that's the direction you want to go or a direction you don't want to go in.” - Rosa Welton “When others are complimenting you or your work, listen to them with an open mind, it will help you to find clarity on your strengths.” - Rosa Welton “It's important to know when to move on from a company or role. It is equally important to be intentional about where you are going next.” - Rosa Welton About Rosa Welton: As Director of Product Management, Digital Acquisition, Growth and Customer Success at ServiceNow, Rosa Gonzalez Welton is responsible for creating experiences across the end-to-end customer journey, from acquisition and growth, through to customer success. Prior to joining ServiceNow in 2019, Rosa spent 7 years with eBay where she held leadership positions in the consumer selling business; leading teams in product management, strategy, and product marketing. Her experiences building consumer marketplaces include roles at Walmart Labs and TrueCar, where she was an instrumental part of the team that launched the car shopping platform. Earlier in her career, she worked at Forrester Research, the Food Network, and Hearst Interactive. Rosa is originally from Los Angeles and holds an MBA from UCLA Anderson and a BA from Yale University. In her free time Rosa sparks connections between people around her dinner table in a drive to cure loneliness. Links: Website: https://www.rosawelton.com/ LinkedIn: https://www.linkedin.com/in/rwelton/ Latinas In Tech: https://leanin.org/circles/latinas-in-tech

In Episode 5 of Summit Series, Mayank Khanduja (Partner, Elevation Capital) speaks to Siddharth Jain (CEO and Co-founder, Playsimple Games) on the journey of building a global gaming giant from India. Founded in 2014 by former Zynga executives Siddharth Jain, Preeti Reddy, and Suraj Nalin along with former WalmartLabs engineer Siddhanth Jain, PlaySimple operates multiple mobile games in the word gaming genre. Today, it is a leading company globally in the free-to-play gaming space, with over 75 million installs and 2 million daily active users. In July 2021, Playsimple was acquired by the Swedish gaming giant, MTG, in what was one of the biggest gaming exits in the Indian startup ecosystem. Elevation partnered with PlaySimple in November 2016 when we invested $3.2 Mn as part of a $4 Mn Series A round in the company. The company did not raise any subsequent capital until the acquisition. The journey with PlaySimple has been one of many firsts for Elevation, and is quite contrary to the way venture-funded businesses are usually imagined and built in India. In this episode, Sid uncovers the nuances behind his unconventional journey, how his time at Zynga helped him scale Playsimple, his frameworks for innovation, how he was able to build a culture of analytics and metrics orientation in the business, and more.

Big Data is overrated. We don't need more volume — we need more diversity We can do more with less. And someone finally needed to say it. That someone is Jennifer Prendki, Founder and CEO at Alectio , the world's first Data Prep Ops platform. Before that she held senior leadership positions in data science and machine learning at Figure Eight, Atlassian, Walmartlabs among others. She also has a PhD in particle physics, from Sorbonne in Paris, which greatly influenced her belief that we can move away from the brute force of Big Data into the precision of Smart Data. In this episode, we discuss: Transitioning from particle physics to entrepreneurship How to do more with less data Why Big Data is overrated (and why we should strive for Smart Data, instead) How principles of human learning apply to machine learning How to tackle Big Data using automated data curation The economics of machine learning The ins and outs of Data Prep Ops If you want to hear more, subscribe to Leading with Data on Apple Podcasts , Spotify , or here . Listening on a desktop & can't see the links? Just search for Leading with Data in your favorite podcast player.

Show Notes(01:46) Jennifer shared her formative experiences growing up in France and wanting to be a physicist.(03:04) Jennifer unpacked the evolution of her academic journey in France — getting Physics degrees at Louis Pasteur University, Paris-Sud University, and Sorbonne University.(06:44) Jennifer mentioned her time as a Postdoctoral Researcher in Neutrino Physics at Duke University, where her research group lacked the funding to carry on scientific projects.(09:35) Jennifer discussed her transition from academia to industry, working as a Quantitative Research Scientist at Quantlab Financial in Houston.(13:31) Jennifer went over her move to the Bay Area, working for YuMe and Ayasdi — growing and managing early-stage data science teams at both places.(19:19) Jennifer recalled her foray into becoming a Senior Data Science Manager of the Search team at Walmart Labs. She managed the Metrics-Measurements-Insights team and the Store-Search team.(23:59) Jennifer shared the business anecdote that made her obsessed with measuring the ROI of data science.(28:46) Jennifer reflected on the opportunity to give conference talks and become a thought leader in the data science community (watch her first industry talk, “Review Analysis: An Approach to Leveraging User-Generated Content in the Context of Retail” at MLconf 2016).(31:10) Jennifer unpacked her interest in active learning and outlined existing challenges of making active learning performant in real-world ML systems.(36:58) After 1.5 years with Walmart Labs, Jennifer became the Chief Data Scientist at Atlassian. She shared the tactics to grow the Search & Smarts team of scientists and engineers from 3 to 17 people in less than 6 months across 3 locations.(40:31) Jennifer discussed the organizational and operational challenges with making ML useful in enterprises and the importance of data preparation in the modern ML stack.(47:24) Jennifer elaborated on the topic of “Agile for Data Science Teams,” which discusses that organizations that invest in ML but do not get the organizational side of things right will fail.(53:09) Jennifer went over her decision to accept a VP of Machine Learning role at Figure Eight, then a frontier startup that offers enterprise-grade labeling solutions to ML teams.(57:56) Jennifer went over the inception of her startup Alectio, whose mission is to help companies do ML more efficiently with fewer data and help the world do ML more sustainably by reducing the industry's carbon footprint.(01:04:32) Jennifer unpacked her 4-part blog series about responsible AI that calls out the need to fight bias, increase accessibility, and create more opportunities in AI.(01:09:06) Jennifer discussed the hurdles she had to jump through to find early adopters of Alectio.(01:11:03) Jennifer emphasized the valuable lessons learned to attract the right people who are excited about Alectio's mission.(01:14:38) Jennifer cautioned the danger of taking advice without thinking through how it can be applied to one's career.(01:17:09) Jennifer condensed her decade of experience navigating the tech industry as a woman into concrete advice.(01:19:19) Closing segment.Jennifer's Contact InfoLinkedInTwitterMediumAlectio's ResourcesWebsiteTwitterLinkedInWhat Is Alectio? (Video)Is Big Data Dragging Us Towards Another AI Winter? (Article)Mentioned ContentTalksThe Day Big Data Died (Oct 2020 @ Interop Digital)The Importance of Ethics in Data Science (Keynote @ Women in Analytics Conference 2019)Introduction to Active Learning (ODSC London 2018)Agile for Data Science Teams (Strata Data Conf — New York 2018)Big Data and the Advent of Data Mixology (Interop ITX — The Future of Data Summit 2017)The Limitations of Big Data In Predictive Analytics (DataEngConf SF 2017)Review Analysis: An Approach to Leveraging User-Generated Content in the Context of Retail (MLconf 2016)Articles1 — Women vs. The Workplace SeriesGender Discrimination (Oct 2015)Why Leading By Example Matters (Jan 2017)Data Scientist: the SexISTiest Job of the 21st Century? (Feb 2017)The Role of Motherhood in Gender Discrimination (March 2017)The Biggest Challenges of the Female Manager (May 2017)Parity in the Workplace: Why We Are Not There Yet (Dec 2017)The Pyramid of Needs of Professional Women (Dec 2017)2 — Management SeriesThe Secrets to Successfully Managing an Underperformer (June 2017)The Top Secrets to Managing a Rockstar (July 2017)The Real Cost of Hiring Over-Qualified Candidates in Technology (March 2018)Team Culture (May 2018)3 — Responsible AI SeriesHow We Got Responsible AI All Wrong (Part 1)Impact, Bias, and Sustainability in AI (Part 2)Increasing Accessibility to AI (Part 3)Creating More Opportunities in AI (Part 4)Book“Managing Up” (by Rosanne Badowski and Roger Gittines)NotesJennifer told me that Alectio is about to launch a community version that people will be able to compete to get the best model with the minimum amount of data this fall. Be sure to check out their blog and follow them on LinkedIn!About the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing khanhle.1013@gmail.com.Subscribe by searching for Datacast wherever you get podcasts or click one of the links below:Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf you're new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.

Show Notes(01:46) Jennifer shared her formative experiences growing up in France and wanting to be a physicist.(03:04) Jennifer unpacked the evolution of her academic journey in France — getting Physics degrees at Louis Pasteur University, Paris-Sud University, and Sorbonne University.(06:44) Jennifer mentioned her time as a Postdoctoral Researcher in Neutrino Physics at Duke University, where her research group lacked the funding to carry on scientific projects.(09:35) Jennifer discussed her transition from academia to industry, working as a Quantitative Research Scientist at Quantlab Financial in Houston.(13:31) Jennifer went over her move to the Bay Area, working for YuMe and Ayasdi — growing and managing early-stage data science teams at both places.(19:19) Jennifer recalled her foray into becoming a Senior Data Science Manager of the Search team at Walmart Labs. She managed the Metrics-Measurements-Insights team and the Store-Search team.(23:59) Jennifer shared the business anecdote that made her obsessed with measuring the ROI of data science.(28:46) Jennifer reflected on the opportunity to give conference talks and become a thought leader in the data science community (watch her first industry talk, “Review Analysis: An Approach to Leveraging User-Generated Content in the Context of Retail” at MLconf 2016).(31:10) Jennifer unpacked her interest in active learning and outlined existing challenges of making active learning performant in real-world ML systems.(36:58) After 1.5 years with Walmart Labs, Jennifer became the Chief Data Scientist at Atlassian. She shared the tactics to grow the Search & Smarts team of scientists and engineers from 3 to 17 people in less than 6 months across 3 locations.(40:31) Jennifer discussed the organizational and operational challenges with making ML useful in enterprises and the importance of data preparation in the modern ML stack.(47:24) Jennifer elaborated on the topic of “Agile for Data Science Teams,” which discusses that organizations that invest in ML but do not get the organizational side of things right will fail.(53:09) Jennifer went over her decision to accept a VP of Machine Learning role at Figure Eight, then a frontier startup that offers enterprise-grade labeling solutions to ML teams.(57:56) Jennifer went over the inception of her startup Alectio, whose mission is to help companies do ML more efficiently with fewer data and help the world do ML more sustainably by reducing the industry's carbon footprint.(01:04:32) Jennifer unpacked her 4-part blog series about responsible AI that calls out the need to fight bias, increase accessibility, and create more opportunities in AI.(01:09:06) Jennifer discussed the hurdles she had to jump through to find early adopters of Alectio.(01:11:03) Jennifer emphasized the valuable lessons learned to attract the right people who are excited about Alectio's mission.(01:14:38) Jennifer cautioned the danger of taking advice without thinking through how it can be applied to one's career.(01:17:09) Jennifer condensed her decade of experience navigating the tech industry as a woman into concrete advice.(01:19:19) Closing segment.Jennifer's Contact InfoLinkedInTwitterMediumAlectio's ResourcesWebsiteTwitterLinkedInWhat Is Alectio? (Video)Is Big Data Dragging Us Towards Another AI Winter? (Article)Mentioned ContentTalksThe Day Big Data Died (Oct 2020 @ Interop Digital)The Importance of Ethics in Data Science (Keynote @ Women in Analytics Conference 2019)Introduction to Active Learning (ODSC London 2018)Agile for Data Science Teams (Strata Data Conf — New York 2018)Big Data and the Advent of Data Mixology (Interop ITX — The Future of Data Summit 2017)The Limitations of Big Data In Predictive Analytics (DataEngConf SF 2017)Review Analysis: An Approach to Leveraging User-Generated Content in the Context of Retail (MLconf 2016)Articles1 — Women vs. The Workplace SeriesGender Discrimination (Oct 2015)Why Leading By Example Matters (Jan 2017)Data Scientist: the SexISTiest Job of the 21st Century? (Feb 2017)The Role of Motherhood in Gender Discrimination (March 2017)The Biggest Challenges of the Female Manager (May 2017)Parity in the Workplace: Why We Are Not There Yet (Dec 2017)The Pyramid of Needs of Professional Women (Dec 2017)2 — Management SeriesThe Secrets to Successfully Managing an Underperformer (June 2017)The Top Secrets to Managing a Rockstar (July 2017)The Real Cost of Hiring Over-Qualified Candidates in Technology (March 2018)Team Culture (May 2018)3 — Responsible AI SeriesHow We Got Responsible AI All Wrong (Part 1)Impact, Bias, and Sustainability in AI (Part 2)Increasing Accessibility to AI (Part 3)Creating More Opportunities in AI (Part 4)Book“Managing Up” (by Rosanne Badowski and Roger Gittines)NotesJennifer told me that Alectio is about to launch a community version that people will be able to compete to get the best model with the minimum amount of data this fall. Be sure to check out their blog and follow them on LinkedIn!About the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing khanhle.1013@gmail.com.Subscribe by searching for Datacast wherever you get podcasts or click one of the links below:Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf you're new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.

Abstract of the talk… Longhorn is a lightweight, reliable, and powerful distributed block storage system for Kubernetes. It is an open source tool that can be installed on any Kubernetes Cluster. It has features like incremental snapshots and backup that can be backed up to NFS or S3-compatible object storage. In this talk, you will learn about Longhorn, its features including backup/recovery, and how you can take maximum benefit for your persistent Kubernetes volumes. You will also be shown a UI to understand the features in a much better way. Bio… Saiyam is working as Director of Technical Evangelism at Civo with a focus on defining the Civo cloud platform for simplifying Kubernetes and making it accessible for developers. Previously at Walmart Labs, Oracle, and hp, Saiyam has worked on many facets of k8s including machine learning platform, scaling, multi-cloud, managed k8s services, and k8s documentation. He's worked on implementing Rancher and Influx in different organizations. When not coding, Saiyam works on contributing to the community by writing blogs and organizing local meetups for k8s, rancher, Influx. He is also an Influx ACE, Traefik Ambassador, CNCF ambassador, and can be reached on twitter @saiyampathak. Key take-aways from the talk… The audience will get to know about Longhorn, what it is, its features and how to use it. If time permits I can walk through the UI that will give more insights into the Product.

During the COVID-19 pandemic, while many of us were working remotely through 2020 and perhaps felt like we were putting our lives on pause, ambitious entrepreneurs were plowing ahead with their hopes, dreams and business plans. In the case of Stephanie's two guests on this edition of Out to Lunch Baton Rouge, this perseverance has turned out extremely well. Cool Heads Jack Karavich is owner of Tigeraire, a sports technology start-up founded at LSU's Innovation Park that has developed an in-helmet airflow system for use in sports helmets, construction hardhats, and military helmets. This high tech head wear utilizes a small device that leverages the air vents in a helmet using a small battery, a couple of tubes and a tiny fan. It enables air circulation that keeps athletes and other wearers cooler, drier, safer, and better able to perform. Jack developed the helmet-cooling technology in partnership with LSU which has since licensed the product for its football team. Several other universities, including Albama, Auburn, Clemson, and Texas A&M have since followed suit. Tigeraire is now designing batting helmets that incorporate the technology and has plans to tackle lacrosse helmets and ice hockey helmets after that. Tigeraire has opened new offices in Richmond Virginia, which will serve as its corporate headquarters. Prior to founding the company, Jack served as chief digital architect of Honeywell, GlaxoSmithKline, Walmart Labs, and Capital One.  Hot Bodies While Jack's technology is designed to keep its wearers cool, April Hill is focused on making them hot. April is owner of Yoga Studio 90, a health and wellness studio offering high intensity interval training, yoga and Pilates in one complete, full body workout that is intended to be done in a steamy 90-degree-room. April has owned the studio for more than a decade, but one month into the pandemic in 2020, when everyone was under lockdown and no one could go to the gym, April rebranded her studios and launched an online platform of virtual workouts available on a subscription basis that has enabled her to reach a much broader clientele from anywhere in the country.  This show was recorded over lunch at Mansurs on the Boulevard. You can find photos by Erick Otts at our website. And check out more lunch-table Tiger entrepreneur talk with Matt Flynn. See omnystudio.com/listener for privacy information.

UX-radio.com is a podcast about Information Architecture, User Experience, and Design. Hosts Lara Fedoroff and Chris Chandler talk with industry experts with the purpose to educate, inspire and share resources. In this episode, we talk with Stephanie Mencarelli, Senior Design Director at Walmart Labs, about her experience leading teams and designing delightful user experiences even in a pandemic.

Welcome to another episode of Develomentor. Today's guest is Andrei Lopatenko.Andrei is Vice President of Engineering at Zillow Group. He is the head of search and discovery engines. This including search science, development, infrastructure, and operations. As part of this role, he heads Zillow Group’s conversational AI efforts, which consist of initiatives around natural language processing platforms, speech analytics, call center AI, and conversational interfaces. The goal is improving Zillow’s business and customer services using natural language processing and speech understanding. Before joining Zillow in 2019, Andrei led search science teams within eBay and Walmart. Prior to Walmart he worked at Google and Apple, serving as a core contributor to products like Google web search, Apple Maps, Apple’s AppStore, and iTunes search engines. He previously led engineering efforts for Recruit Holdings’ AI Lab. He also sat on the advisory board of Ozlo, a Conversational AI startup acquired by Facebook in 2017. Andrei earned a Doctor of Philosophy in Computer Science from The University of Manchester, United Kingdom and Master of Science Degree from the Moscow Institute of Physics and Technology.If you are enjoying our content please leave us a rating and review or consider supporting usA note from GrantAndre Lopatenko has worked around the globe in his career as a search and natural language processing expert. He turned an undergraduate and master’s degree in physics and a PhD in Computer Science into a long and successful career as a software engineer and engineering leader. Over the years, his specialties have carried him from his early days in Moscow to Italy, Switzerland, the United Kingdom, Austria, California and now Seattle. Along his journey, Andrei has risen the ranks from software engineer to Principal Software Engineer, to Director of Engineering, Head of Search Science and now VP of Engineering for the likes of Apple, Google, Walmart Labs, eBay and Zillow. Andre was also the co-founder and advisor of Ozlo, a company focused on conversational artificial intelligence that was purchased by Facebook. Andrei is also a long time public speaker often giving talks on how to build a career in natural language processing or how artificial intelligence is used in search. Be sure to stay tuned as we catch up with Andrei Lopatenko and search for answers to his career in tech.You can find more resources in the show notesTo learn more about our podcast go to https://develomentor.com/To listen to previous episodes go to https://develomentor.com/blog/Connect with Andrei LopatenkoLinkedInTwitterConnect with Grant IngersollLinkedInTwitterSupport the show (https://www.patreon.com/develomentor)

Phillip Rossi, Head of Data Science at Shopify, Laya Shamgah, Data Scientist at Lowe's Company, Jeffrey Yau, Head of Data Science at Walmart Labs, Samantha Cvetkovski, Data Science Manager at Mindbody

As the engineering leader at Freshworks, STS Prasad plays an integral role in the development of the Freshworks suite of products. In the latest episode of the podcast, we talk to him about engineering at startups. We covered

In this episode, We have a chat with Michael Costello, a previous colleague from Walmart Labs about how User experience designers and Software engineers work together, some common mistakes made on both sides, and how to improve the working relationship in order to provide the best product to the user. SIck picks: Ionic Framework (https://ionicframework.com/) Notion (https://www.notion.so/) Eyedriven (https://eyedriven.tech/) Michael Costello: https://costello.io/ E - Twitter: https://twitter.com/_natural_e Instagram: https://www.instagram.com/_natural_e/ Website: emmanuella.tech Will - Twitter: https://twitter.com/therealkwao Instagram: https://www.instagram.com/therealkwao/ Website: kwao.io

Rick and Chris discuss deputizing evangelists to drive adoption and reduce fragmentation, contribution models and thinking about the areas to own vs. areas to share ownership vs. areas to give up ownership, using design systems to share innovation across teams, measuring design system efficacy, and more.Guest:Rick Rodriguez is Head of Design Systems at Walmart Labs, an avid runner, hand letterer and superfan of cappuccinos and donut breaks. You can find Rick on Twitter as @rickrodriguez, and on LinkedIn.Host:Chris Strahl is co-founder and CEO of Knapsack, host of @TheDSPod, DnD DM, and occasional river guide. You can find Chris on Twitter as @chrisstrahl and on LinkedIn.Sponsor:Knapsack — Build without rebuilding. Learn more at knapsack.cloud about Knapsack and getting your team to 80% design and code reusability.Links:View the transcript for this episode.Check out the Amazing Design People List to discover amazing designers worldwide on this community-led talent base during COVID-19.

This podcast is part of a series highlighting the finalist teams of the 2020 INFORMS Franz Edelman Award competition, to be held on September 29. Joining me for this episode are Yixian Chen, Senior Data Scientist and Prakhar Mehrotra, Senior Director of Machine Learning, both with Walmart Labs, to discuss Walmart’s finalist entry for the 2020 Franz Edelman Award.  

Welcome to SnackWalls #25.I admire the work that Himanshu has put into ensuring customers are surprised and delighted by digital products that just work.According to Himanshu knowing and managing the culture within the company really helps when managing diverse people. When it comes to evaluating experience companies should be open to adapting to what people are bringing with them. Spending many, many years as a software engineer is going to gain you lots and lots more opportunities to apply the skill of solving problems again in the future. So a college degree is not the only path to unlocking a successful career.Himanshu is a cross-functional leader at WalmartLabs and has over 14+ years of experience in software development and eCommerce. He has worked at large enterprise organizations like Qualcomm and SAP. Himanshu is a first-generation immigrant from India and obtained a Masters in Computer Science from San Diego State University. He is a great mentor and motivator. Himanshu has been responsible for automating modern feature testing and supervising large teams of high performing software engineers at companies that test and deliver software services Walmart scale.Himanshu Jain: https://www.linkedin.com/in/himanshujain60/More episodes of the SnackWalls Podcast: http://podcast.snackwalls.comSnackWalls is powered by San Diego Code School: https://sdcs.ioPlease share like and subscribe for more reach

Shalini Pasupuleti shares her stories of transition from Biotechnology to becoming a Technical Product Manager at Ola and Walmart Labs. She talks about how her experiments—some natural and some forced—made her find the right answers to the deepest questions she had as a product manager. You can reach out to Shalini Pasupuleti on LinkedIn. *** Topics: 1. Transitioning from Biotechnology to Technical Product Management 2. Product management experiences in Ola & Walmart Labs (based on her personal experiences) 3. Critical skills to navigate as a successful product manager 4. Customer insights and data-driven product management in Ola 5. Women in product management 6. Interview tips and resources to help people who are looking to transition to product management 7. Traits that a product manager appreciates in people managers and project/program managers *** Books/Resources: 1. Case in Point - Complete Case Interview Preparation Book by Marc Cosentino 2. Cracking PM Interview - Book by Gayle Laakmann McDowell and Jackie Bavaro 3. Decode and Conquer - Book by Lewis C. Lin 4. UX Hack Challenges *** You can reach out to me on LinkedIn or write an email to womenatlunchtable@gmail.com. I'll be happy to hear from you! *** Please subscribe to this podcast. This podcast is now available on Spotify, Apple Podcasts, Google Podcasts, RadioPublic, Pocket Casts and Breaker. *** Jeevitha Balakrishnan *** --- Support this podcast: https://podcasters.spotify.com/pod/show/jeevitha-balakrishnan/support

Sajida shares her unique viewpoint in this intriguing conversation - What happens when we speak up rather than sit quietly regarding issues that affect women in the workplace. From attending one of the most sought after colleges, BITS Pilani on a partial scholarship to working with strong masculine Wall Street personalities. She used each of her experiences to prepare herself and gain the courage to lead and address social norms. Sajida discusses how she developed her leadership style to make it her own, rather than conforming herself. _____________________________________________________________________________Bio: Sajida Kaliyadan lead a product team as the Head of Buyer Experience at Atlassian with an exciting charter in pioneering a unified buyer experience by taking world-class consumer capabilities and applying them to a B2B model.She said she has had a zig zag career path that she sought out consciously now and unconsciously at first, to further her growth and learning. She began as a developer and then engineering manager in banking technology for global banks such as Citibank and Deutsche Bank, working in India, Singapore and New York. Looking for a change from large organizations, she joined a privately held algorithmic trading systems provider in New York - where she was the first female employee wherer she worked in the demanding and challenging environs of Wall Street, a huge learning experience for her.Keen on building more customer facing products, she invested in a part time MBA at NYU Stern. She later transitioned to a career in product management which she loves in Walmart Labs after a move to the West Coast. Sajida led a portfolio that drove over $800MM in annual revenue. She was responsible for the digital transformation of gift registry and digital pharmacy including a patent pending innovation (Techcrunch, Fortune) for digital pharmacy refills. Seeking to further challenge herself and to learn new domains, she joined Atlassian. I am super pumped about the company’s mission to unleash the potential of all teams - whether its a small non profit or NASA’s projects to drive Rovers on Mars, Atlassian’s products have a huge impact.

A startup called Portobel is working to help food producers shift their businesses so that they can support direct-to-consumer deliveries. Portobel is backed by Heroic Ventures and led by Ranjith Kumaran, founder or co-founder of file-sharing company Hightail (acquired by OpenText) and loyalty startup PunchTab (acquired by Walmart Labs). Kumaran told me that he and his […]

Jamison quickly took us through his studies, how he zoomed in on computer sciences... not without a few detours beforehand. We talked about his part time jobs and how he learned what a bad manager can be. We then jumped forward a few years to talk about management, what it takes to do a good job there, the challenges and successes of the function. We finished talking about two dreaded responsibilities of a manager: hiring and firing and how Jamison made his way through this.Jamison is a code whisperer and experienced product engineer. He has led teams and been led. Jamison is currently an engineering manager at Walmart Labs leading a distributed team in building delightful APIs and UIs that configure Walmart’s performance systems. He also co-hosts the "Soft Skills Engineering podcast". If this name is new to you, you should definitely listen to Episode 77 of this very DevJourney podcast where I interviewed Dave Smith, the other half of the co-hosts of this show. Oh and Jamison thinks you are great!Here are the links of the show:https://www.twitter.com/jamison_dancehttps://jamison.dancehttps://softskills.audiohttps://devjourney.info/Guests/77_DaveSmith.htmlhttps://tinyurl.com/shitty11meetingsBook: The making of a manager by Julie Zhuo https://amzn.to/37VTt1cBook: An elegant puzzle by Will Larson https://amzn.to/31layz0https://conf.reactjs.orghttps://www.reactrally.comhttps://devjourney.info/Guests/52_CharityMajors.htmlhttps://en.wikipedia.org/wiki/Camille_FournierCreditsMusic Aye by Yung Kartz is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.Your hostSoftware Developer‘s Journey is hosted and produced by Timothée (Tim) Bourguignon, a crazy frenchman living in Germany who dedicated his life to helping others learn & grow. More about him at timbourguignon.fr.Want to be next?Do you know anyone who should be on the podcast? Do you want to be next? Drop me a line: info@devjourney.info or via Twitter @timothep.Gift the podcast a ratingPlease do me and your fellow listeners a favor by spreading the good word about this podcast. And please leave a rating (excellent of course) on the major podcasting platforms, this is the best way to increase the visibility of the podcast:Apple PodcastsStitcherGoogle PlayPatreonFinally, if you want to help produce the podcast, support me on Patreon. Every cent you pledge will help pay the hosting bills!Thanks!Support the show (http://bit.ly/2yBfySB)

Each month, “Ammirati on Innovation” episodes will look at ways that the disruptive-startup mentality is spreading beyond young entrepreneurs to big established corporations. Serial entrepreneur, venture capitalist and Carnegie Mellon B-school professor Sean Ammirati, who sits at the intersection of these high-change dynamics, provides insight.Episode 9In this episode, Sean and I discuss Slack. He says Slack has basically been slashed in half – $42 a share vs. $22 a share. Its market cap is $12 billion. Sean says what would these statistics be worth inside Oracle, Salesforce, Microsoft, or Google. He says that with some of the assets Microsoft has, LinkedIn plus Slack would be a very good combination. Sean says LinkedIn bought Lynda, a corporate education company, for $1.5 billion, but so far it’s been an accretive acquisition.He speculates Walmart has the potential to buy one or two very large companies – including FedEx, which he says would be a good acquisition – and maybe Shopify too. He says we’ll see a further investment in Walmart Labs, including new business models.He then turns to Amazon and AWS. He says he just can’t wrap his head around why Amazon won’t spin out AWS this year. He says he’s seen all the denials from the management team – but it just doesn’t make sense that they don’t want to do this. Sean says how does Walmart feel about cutting checks to AWS? I say that AWS will finish the calendar year with about $38 billion in revenue – placing them among the top seven tech companies.Sean says he’s not an economist, but his conclusion is that often companies are wrong. They often deliver with confidence and that confidence often turns out to be incorrect. The market almost certainly needs to correct in 2020.Sean’s podcast is Agile Giants, and it’s on all the major platforms. See acast.com/privacy for privacy and opt-out information.

Claude Jones is a man of many talents. His day job is as the site lead at WalMart Labs, but he is also the founder San Diego Tech Hub, the Elevate Foundation, and the Practical Leadership Guy. His talent for tech and public speaking is truly inspiring; I’ve heard him speak on several occasions. We talked about his involvement in tech, his motivation for his extracurricular endeavors, and his passion for public speaking. To learn more about Claude: claude@sandiegotechhub.com claudejones.me linkedin.com/in/claudejones/

Walmart Labs launched an HR experiment that lead to being more inclusive and getting a leg up in the war for talent. In this episode of the HR Exchange Podcast, host Mason Stevenson sits down with Julia Keintz, the Director of Insights and Analytics for Walmart eCommerce and Walmart Labs. During the discussion, Keintz explains how they put women with tech skills back to work.

This podcast interview focuses on product innovation that’s transforming impact marketers can make in cross-channel marketing scenarios. My guest is Vijay Chittoor, Co-founder and CEO of Blueshift. Vijay has a wealth of experience in AI, marketing technology and e-commerce domains. He was an early team member and the director of product management at Kosmix which was acquired by Walmart to become WalmartLabs. In 2010 he co-founded Mertado and led it as the CEO for two years until it was acquired by Groupon and became Groupon Goods. This prepared him for his new venture, Blueshift, which he co-founded in 2014. Vijay is a graduate of Harvard Business School’s MBA Program. He also holds Bachelor’s and Master's degrees in Electrical Engineering from the Indian Institute of Technology, Bombay. Blueshift enables B2C marketers to automate segment-of-one marketing on every channel.They’re on a mission to put AI in the Hands of Every Marketer. This triggered me, hence I invited Vijay to my podcast. We explore the transformation of digital marketing, and the scaled revenue opportunities that now provides to companies of all sizes. We also discuss how AI is enabling marketers to reclaim their creative and strategic role again, and create a flywheel of value that’s pretty hard to stop once in motion. Here are some of his quotes:Every few years a technology matures to a point when non-technical people can start using it, and when that happens that unlocks a lot of enterprise value What's happening is that you and I, as consumers, we are interacting with all these brands on digital and mobile and social. And when we do that, we are leaving behind 1000 times more data than we used to leave behind 10 years ago. With all the data we're leaving behind marketers, and brands now for the first time have a way of understanding us as the dynamic individuals that we've always been.Doing that is exciting, but it's also challenging, because dealing with thousand times more data, dealing with 10 to 15 times more channels, that is very difficult for marketers. Obviously, humans are not best equipped to operate at that scale. Humans are created for guiding how the engagement should be driven, how that engagement should be humanized. Where the machine can really come in is to help the human marketer become excellent at that scale of decision making and make each decision truly intelligent.That's what we do with the Blueshift platform. During this interview, you will learn three things:How thinking differently about key customer challenges allows you to build solutions that have transformative impact because of their simplicity.That AI alone is not the solution – the synergy really kicks in when humans and AI systems are working well together and even impress the customers’ customersThat to deliver remarkable solutions you have to stay true to your vision – no matter how enticing the short term opportunity is. See acast.com/privacy for privacy and opt-out information.

This retail podcast focuses on a topic that's been trending for quite a while now - and that's conversational technology. Most of us have heard about and experienced chatbots, Alexa, Google Home and waiting for a wider release of Google Duplex - Google's newest human-sounding assistant. Brands and Retailers are exploring how conversational technology applies to their business and the ways it could positively affect their bottom line. From conversational marketing to conversational commerce and customer support, what are the most effective ways businesses can utilize chatbots and voice interfaces? To discuss this topic in-depth with us, we have Ravi Raj, Co-Founder and CEO of Passage AI, a natural language understanding and processing platform that can be used to create deep conversational interfaces for any website or business. With over 20 years of experience in product development, Ravi has led teams at Yahoo, Kosmix, WalmartLabs and Bloomreach before he founded Passage AI. Listen to this episode to learn all about the future of conversational commerce through exclusive insights from leaders in the retail and technology space!

Consumers know Walmart as a retailing giant that has changed the face of retail in communities across America. But with a data store containing billions of queries and items, it’s also a laboratory for the company’s data scientists and IT professionals who mine and manage it. “We have data scientists embedded in every single team within the company,” says Sonu Durgia, group product manager for search and discovery at Walmart Labs. “Every function at Walmart, from the quality of groceries to the supply chain, has data science embedded in it,” she noted during an interview recorded for the Women in Data Science podcast at Stanford University. Because Walmart’s product catalog is immense, holding the attention of consumers and helping them find what they want to buy is a challenge. “We do not have your attention for the next several hours. We have to show you the right things very, very quickly. So it's a ranking and relevance problem right there, even though it's not coming from a query,” Durgia says. Explaining the insights of data scientists to the business and retail sides of Walmart, people who are not always conversant with technical issues is an important part of her job, she says. Her varied career path has provided her with the expertise to interact successfully with Walmart’s line of business executives. “My engineering degree gives me those tools to really understand the (algorithms) and work with these engineers and very savvy data scientists. My finance background gives me that bird's eye view, understanding what the key things are here,” she says. Because data science is still a male-dominated discipline, finding a role model can be difficult for women in the field. But technology, says Durgia, has enabled new ways for women to find role models. “Back in the day, you would just look at your peer group to find inspiration or even to solve some problems, ask about a concept you didn't get in class. But now YouTube is your teacher. Everything is available,” she says.

Howard Lewis Ship talks about Walmart Labs and their open source Clojure projects.. Pedestal table routing Lacinia GraphQL Joker Vizdeps Schematic

Recorded live at A Cloud Guru's ServerlessConf in San Francisco, here's a special Think FaaS episode from Walmart Labs engineer Leslie Pajuelo.

Recorded live at A Cloud Guru's ServerlessConf in San Francisco, here's a special Think FaaS episode from Walmart Labs engineer Leslie Pajuelo.

Panel: Charles Max Wood Guest: Fred Zirdung This week on My JavaScript Story, Charles speaks with Fred Zirdung. Fred is currently the head of curriculum at Hack Reactor, where he essentially builds all of the tools and learning materials for the students there. He is also an instructor and has been there for five years. Prior to that, he worked for multiple companies such as Walmart Labs as well as many small startups. He first got into programming with the Logo programming language in the 6th grade and he had always been interested in working with computers since a young age. They talk about what got him into web programming, what enthralled him about JavaScript and Ruby on Rails, and what he is proud of contributing to the JavaScript community. In particular, we dive pretty deep on: JavaScript Jabber Episode 76 Fred intro How did you first get into programming? Coding professionally for 20+ years Coding prior to college graduation Logo programming language QNX operating system Were you always interested in programming? Always interested in computers Commodore 64 Basic programming in high school Programming didn’t click for him until high school In college when the web became popular Computer engineering degree in college What was it that appealed to you about software over hardware? Software vs hardware Embedded systems software How did you get into web programming? Dolby Laboratories What technologies got you excited? JavaScript, Perl, and Ruby on Rails Loved the flexibility of JS and Rails Found something he could be productive with What are you proud of contributing to the JavaScript community? What are you working on now? And much, much more! Links: JavaScript Jabber Episode 76 Hack Reactor Walmart Labs Dolby Laboratories JavaScript Perl Ruby on Rails @fredzirdung Fred’s GitHub Fred’s Medium Picks Charles React Developer Tools plugin PluralSight React Round Up and Views on Vue Framework Summit Fred Navalia Koa Vue

Panel: Charles Max Wood Guest: Fred Zirdung This week on My JavaScript Story, Charles speaks with Fred Zirdung. Fred is currently the head of curriculum at Hack Reactor, where he essentially builds all of the tools and learning materials for the students there. He is also an instructor and has been there for five years. Prior to that, he worked for multiple companies such as Walmart Labs as well as many small startups. He first got into programming with the Logo programming language in the 6th grade and he had always been interested in working with computers since a young age. They talk about what got him into web programming, what enthralled him about JavaScript and Ruby on Rails, and what he is proud of contributing to the JavaScript community. In particular, we dive pretty deep on: JavaScript Jabber Episode 76 Fred intro How did you first get into programming? Coding professionally for 20+ years Coding prior to college graduation Logo programming language QNX operating system Were you always interested in programming? Always interested in computers Commodore 64 Basic programming in high school Programming didn’t click for him until high school In college when the web became popular Computer engineering degree in college What was it that appealed to you about software over hardware? Software vs hardware Embedded systems software How did you get into web programming? Dolby Laboratories What technologies got you excited? JavaScript, Perl, and Ruby on Rails Loved the flexibility of JS and Rails Found something he could be productive with What are you proud of contributing to the JavaScript community? What are you working on now? And much, much more! Links: JavaScript Jabber Episode 76 Hack Reactor Walmart Labs Dolby Laboratories JavaScript Perl Ruby on Rails @fredzirdung Fred’s GitHub Fred’s Medium Picks Charles React Developer Tools plugin PluralSight React Round Up and Views on Vue Framework Summit Fred Navalia Koa Vue

Panel: Charles Max Wood Guest: Fred Zirdung This week on My JavaScript Story, Charles speaks with Fred Zirdung. Fred is currently the head of curriculum at Hack Reactor, where he essentially builds all of the tools and learning materials for the students there. He is also an instructor and has been there for five years. Prior to that, he worked for multiple companies such as Walmart Labs as well as many small startups. He first got into programming with the Logo programming language in the 6th grade and he had always been interested in working with computers since a young age. They talk about what got him into web programming, what enthralled him about JavaScript and Ruby on Rails, and what he is proud of contributing to the JavaScript community. In particular, we dive pretty deep on: JavaScript Jabber Episode 76 Fred intro How did you first get into programming? Coding professionally for 20+ years Coding prior to college graduation Logo programming language QNX operating system Were you always interested in programming? Always interested in computers Commodore 64 Basic programming in high school Programming didn’t click for him until high school In college when the web became popular Computer engineering degree in college What was it that appealed to you about software over hardware? Software vs hardware Embedded systems software How did you get into web programming? Dolby Laboratories What technologies got you excited? JavaScript, Perl, and Ruby on Rails Loved the flexibility of JS and Rails Found something he could be productive with What are you proud of contributing to the JavaScript community? What are you working on now? And much, much more! Links: JavaScript Jabber Episode 76 Hack Reactor Walmart Labs Dolby Laboratories JavaScript Perl Ruby on Rails @fredzirdung Fred’s GitHub Fred’s Medium Picks Charles React Developer Tools plugin PluralSight React Round Up and Views on Vue Framework Summit Fred Navalia Koa Vue

Joining us this week is Jordan Rinke, Principal Software Engineer, Walmart Labs. Jordan offers his views on various technologies and open source projects as it relates to the scale and connectivity issues faced by Walmart. Highlights • Technical Gaps in Kubernetes Technologies and Installer Issues • Tooling and Orchestration Focus for Kubernetes and Other Tools • Core OS Model for Bootstrapping Kubernetes • Discussion on Immutability: Middle Ground for Jordan • Edge Computing – Emerging markets lead to disconnected edge sites • Data location challenges in edge and cloud services • Skills issues for medium sized clusters

Have you wondered if you get paid enough?  Or should you get a higher salary? Or maybe, you are wondering if "they" find out that your salary is too high?  The answers are coming.  Paysa is the AI-powered platform that helps you with your salary but also your skills. In my conversation with the CEO, Chris Bolte, we discuss how this can impact the labour market, how you can benefit today and almost more importantly, how you can benefit during a time of increasing disruption. At the moment of publication of the Episode, Paysa has helped clients to make $137m+ additional salary. Congratulations!  Enjoy the interview! More about Chris Bolte Chris is Co-Founder and CEO of Paysa.  He loves helping employees super-charge their careers. Chris is a serial Entrepreneur whose last company was acquired by Walmart. After spending some time Walmart Labs, it was time for the next company.  Connect with Chris and Paysa.com here Web: https://www.paysa.com/team LinkedIn: https://www.linkedin.com/in/chrisbolte/ Twitter: http://twitter.com/cb0lte       Are you a Hybreneuer? Find out here.     

Sara Khoury is an ArtCenter alum who specializes in user experience. She is now the Director of User Experience Design at Google, where she oversees product design for many of their apps including Google Hire. Previously, Sara led UED teams at Bank of America and Walmart Labs. With over 20 years experience at the intersection of design and technology in Silicon Valley, Sara continues to pioneer paths for female leadership in the tech field. She has cleared a path for women in Silicon Valley without losing sight of her values and commitments to positive change, pushing boundaries, and doing it all in a supportive, creative environment. In this episode Sara shares with us about her upbringing by urban parents in a rural setting, her role as a critical thinker, and how she finds being a woman in a male-dominated field. Follow Sara on Twitter @sokhoury Learn more about Design at Google www.design.google

My guest this week is Jennifer Prendki. That name might sound familiar, as she was one of the great speakers from my Future of Data Summit back in May. At the time, Jennifer was senior data science manager and principal data scientist at Walmart Labs, but she's since moved on to become head of data science at Atlassian. Back at the summit, Jennifer gave an awesome talk on what she calls Data Mixology, the slides for which you can find on the show notes page. My conversation with Jennifer begins with a recap of that talk. After that, we shift our focus to some of the practices she helped develop and implement at Walmart around the measurement and management of machine learning models in production, and more generally, building agile processes and teams for machine learning. The notes for this show can be found at twimlai.com/talk/46

We talk to Quentin Harley, the "crazy inventor" behind the locally built Morgan 3D Printers This episode is brought to you by OfferZen, a South African recruitment startup for developers. OfferZen inverts the normal recruitment process. Instead of applying for jobs, 350 tech companies in Cape Town, Johannesburg and Pretoria, send developers interview requests with upfront salary info. For developers it’s completely free to signup and use. In fact, you get R5000 if you take a job through them. Visit offerzen.com to sign up. Kenneth & Len chat to Quentin about the how the Morgan 3D printers came to be, building the impossible in 2 weeks, and how to the unique form factor of the Morgan came about in a dream! Quentin shares a lot of fascinating insights into the history of the printer and demystifies a lot of terms for us, including how you measure print quality & resolution, different types of filaments, the technology behind these printers, open sourcing the hardware and how 3D printing as a whole can be scaled up to the point of printing buildings. We also learned that the popular PLA filament is organic and a fully renewable material! Find and follow Quentin & the Morgan 3D printers online: * https://twitter.com/QuentinHarley * http://www.morgan3dp.com/ * https://github.com/qharley * https://github.com/RepRapMorgan Here are some resources mentioned in the show: * House4Hack - http://www.house4hack.co.za/ * E3D-v6 Hotend - http://e3d-online.com/E3D-v6 * XTC-3D - https://www.smooth-on.com/product-line/xtc-3d/ * RAPDASA - http://www.rapdasa.org/ * GAP (Gauteng Accelerator Program) - http://www.theinnovationhub.com/opportunities/gap-innovation-3 * Thingiverse - https://www.thingiverse.com/ * OpenSCAD - http://www.openscad.org/ * RapCAD - https://github.com/GilesBathgate/RapCAD * SketchUp - http://www.sketchup.com/ * Inventor - https://www.autodesk.co.za/products/inventor/overview * Simplify3D - https://www.simplify3d.com/ * MakerCon - http://themakerspace.co.za/makercon/ And finally our picks Len: * OneOps by WalmartLabs - http://oneops.com/ * Electrode - http://www.electrode.io/ Kenneth: * Art of Synergy - http://www.artofsynergy.co.za/ Thanks for listening! Stay in touch: * Website & newsletter - https://zadevchat.io * Socialize - https://twitter.com/zadevchat & http://facebook.com/ZADevChat/ * Suggestions and feedback - https://github.com/zadevchat/ping * Subscribe and rate in iTunes - http://bit.ly/zadevchat-itunes

Every week, 140 million people around the world set foot in a Walmart owned store. Easily taking first position as the world's largest retailer, its family of brands include Sam's Club, Jet, and a host of others. For the past 5-plus years, WalmartLabs has been the backbone of the company's innovation ecosystem, catalyzed by a a simple goal: create a seamless shopping experience so customers can save money and live better – anytime and anywhere – in retail stores, online, and through their mobile devices. On this installment of Innovation Crush, Walmart CTO, Jeremy King dishes on the strategies, hurdles, and opportunities he and his teams face, managing the perception of the Walmart brand, and how the company continues to lead the charge when it comes to awe inspiring cultural innovations. More info: http://www.walmartlabs.com/

In this week's episode we chat with Tim O'Brien (https://codemonkey.fm/guests/tim-o-brien) from WalmartLabs (https://medium.com/walmartlabs) about how they are reinventing development at the largest company in the world with OneOps (https://oneops.com). We also discuss the LEGO Boost (https://www.lego.com/en-us/boost), Adobe Acrobat Reader silently installs Chrome extension (https://it.slashdot.org/story/17/01/11/218254/latest-adobe-acrobat-reader-update-silently-installs-chrome-extension); Atlassian buys Trello (https://techcrunch.com/2017/01/09/atlassian-acquires-trello/) and the religion of JIRA. Links LEGO Boost (https://www.lego.com/en-us/boost) Adobe Acrobat Reader silently installs Chrome extension (https://it.slashdot.org/story/17/01/11/218254/latest-adobe-acrobat-reader-update-silently-installs-chrome-extension) Atlassian buys Trello (https://techcrunch.com/2017/01/09/atlassian-acquires-trello/) WalmartLabs Engineering Blog (https://medium.com/walmartlabs) Continuous Delivery by Jez Humble (https://www.amazon.com/Continuous-Delivery-Deployment-Automation-Addison-Wesley/dp/0321601912) Gyroscope App (https://gyrosco.pe) RescueTime (https://www.rescuetime.com/) Credits Opening Music: Another beek beep beer please (http://freemusicarchive.org/music/Rolemusic/gigs_n_contest/rolemusic_-_gigs_n_contest_-_03_Another_beek_beep_beer_please) by Rolemusic (http://freemusicarchive.org/music/Rolemusic/) Special Guest: Tim O'Brien.

Our Guest Julia Kaplan brings more than 20 years of leadership experience in product development, delivery and innovation. She is responsible for driving ThredUP product and user experience. She started her career as a software engineer in financial services and then moved on to work in technology for companies in healthcare, e-commerce, advertising, and media. Prior to joining thredUP, Julia held product leadership roles at @WalmartLabs, Kosmix, and Yahoo! Julia earned an undergraduate degree in Computer Science from University of California, Berkeley and an MBA from INSEAD in Fontainebleau, France. Here are the highlights of our conversation with our guest: Julia was born and raised in Eastern Europe and it took three months for her family to move to the US before collapse of Soviet Union in 1989. She shares how she did not speak a word of English when she arrived, how she worked to put herself through college, choosing Berkeley on price, how the educational system differs in the West and how, through it all, everything was still a rewarding experience. While she worked as a software engineer, Julia became interested in venture capitalism so she set her sights on entrepreneurial strategy and finance after school. Despite this, she ended up enrolling in product management, which she did not regret. Julia’s journey started with Visa followed by becoming a certified trainer for Microsoft where she taught people who wanted to learn web development when it was very new before. She then worked with Ford where she got exposed to product management. This is where she got the bug and decided to pursue her MBA. After business school, she came back to California to work for Visa as a contractor and in Yahoo where she took care of the platform side and eventually moved to the media side during the latter years. She joined Kosmix in 2010 where her team became WalmartLabs when it was acquired. Her core responsibilities as a VP of Product at ThredUP involves managing a small but dynamic team. Their tasks revolve around product culture and they maintain a very strong discipline around user research and user testing. They reiterate a lot and are pursuing the mobile first approach in line with their focus on amazing customer experience mainly through product findability. Julia gives us examples to show how her team uses research, user testing and iteration on one of their two-week engineering sprints. She shares take aways, challenges and unexpected surprises which can arise from this cycle. We learn about the key things that happened when they transitioned to mobile responsive and invested in mobile apps. Julia also shares why how ThredUP uses qualitative and quantitative data from user research and what KPIs they look at for the data to drive iterations. Rapid Fire Questions What is your definition of innovation? Innovation is thinking of things – may it be technology or process or something else – and applying this in a new way to create better customer experiences or better way to solve customer problems. Would you put more emphasis on the idea or the execution? How would you weigh each of them and why? I agree with the quote that an idea without execution is just a hallucination. Great ideas with bad execution are not going to succeed. Ideas are important but I’ll put more emphasis on execution. I would say 20% - 80%. What is your biggest learning lesson on your journey so far? Customers are going to go for better pastures. If somebody else gives them better experience, better price, better assortment…they are going to go there. So it’s very important to stay on top of things, to innovate and always listen to your customers. What is your favorite business book? Competing Against Luck by Clayton Christensen Inspired: How To Create Products Customers Love by Marty Cagan The Membership Economy by Robbie Baxter What is your favorite digital resource? Marvel UserTesting.com Looker What is your favorite app and why? Uber Houzz What is the coolest thing that you are working on right now that you want everyone to know about? We are about to launch a beta of a new app that some people will call comparative shopping but it’s more than that. It will inspire consumers to think 2nd hand first.

Yashaswini Kotresh, Data Scientist at WalmartLabs, talks at length about what the job of a Data Scientist entails. Some of the areas we touch upon in this episode include: 1. What is Data Science 2. Applications of Data Science in eCommerce 3. Sample project that a Data Science may work on 4. Fields with applications for Data Science 5. Qualities of a great Data Scientist 6. Interesting and challenging aspects of the job 7. Typical day for a Data Scientist 8. Useful resources for budding Data Scientists Thank you for listening!! Follow the show on Twitter @LED_Curator Like us on FaceBook at www.facebook.com/learneducatediscover/ Email us at learneducatediscover@gmail.com. We will reply!! Subscribe to the show on iTunes itunes.apple.com/us/podcast/learn…ver/id1049159321 Blog: bit.ly/1R1nTDk

Omnichannel UX requires device-specific web strategies, right? America's largest retailer says no. Mini Kurhan and Olawale Oladunni tell us responsive design works for Walmart. Read more »

In this episode, I interview Vidal Graupera - co author of "Pro Smartphone Cross-Platform Development”. Vidal is the Engineering Leader at WalmartLabs, Entrepreneur, and Completely Hands on Senior Ruby on Rails & Mobile Developer.

Dave is a software engineering manager on the Core Web team at WalmartLabs. We talk about his path to front-end development, the perils of functional testing, and a framework that his team is working on to help smooth some of those bumps.

A personal review of Hapi 2.0, the next major version of the fantastic Node.js web framework created by Walmart Labs

Stacked ranking, Amazon's distribution, WalmartLabs, deconstructing Digg, the fetishization of failure, Hudsons, and more. Host: Leo Laporte Guests: John C. Dvorak, Becky Worley, and Jolie O'Dell Download or subscribe to this show at https://twit.tv/shows/this-week-in-tech. For additional show notes, visit the wiki page for this episode. Sponsors: GoToMeeting Promo Code TWiT Freshbooks.com You heard it from TWiT! Gazelle Squarespace Promo Code twit7

Stacked ranking, Amazon's distribution, WalmartLabs, deconstructing Digg, the fetishization of failure, Hudsons, and more. Host: Leo Laporte Guests: John C. Dvorak, Becky Worley, and Jolie O'Dell Download or subscribe to this show at https://twit.tv/shows/this-week-in-tech. For additional show notes, visit the wiki page for this episode. Sponsors: GoToMeeting Promo Code TWiT Freshbooks.com You heard it from TWiT! Gazelle Squarespace Promo Code twit7