Podcasts about snowflakes

Robert DeNiro said something about Trump on MS-NOW that Bill O'Reilly didn't like so now he thinks DeNiro should catch a charge

Carl Quintanilla, Jim Cramer and David Faber delved into Nvidia's blowout quarter and upbeat guidance fueled by the AI boom — plus why the stock swung into negative territory at the opening bell. It was a different story for Salesforce, which posted better-than-expected Q4 results and erased its pre-market losses at the open. The CEOs of both companies spoke to CNBC: Nvidia's Jensen Huang on what the market got "wrong" — and Salesforce's Marc Benioff on the "SaaS-pocalypse" that has sent shares of the company and its software rivals tumbling. Also in focus: Snowflake heats up, the earnings chapter in the battle for Warner Bros. Discovery, the automaker that posted its first-ever annual loss, robots in China. Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Chris Degnan was the first sales hire at Snowflake and spent 11 years scaling the company from zero to $3.5 billion in revenue as its CRO, working alongside four different CEOs and learning from each one. In this episode, Chris breaks down what it actually takes to scale an enterprise sales organization, why MEDDIC is the methodology every founder should know, and what working under Frank Slootman taught him about firing fast, taking feedback and finding the fakers in your team. In today's episode, we discuss: What the CRO job looks like at $10M vs. $1B+ Why sales leaders must know how to sell the product themselves The MEDDIC methodology and why it's a founder's best insurance policy How to find the fakers, manage-uppers and passengers in your org What Frank Slootman got right — and wrong — about scaling Snowflake Why most AI companies will face a go-to-market reckoning References: Amazon: https://www.amazon.com/ Bob Muglia: https://www.linkedin.com/in/bob-muglia-714ba592/ Carl Eschenbach: https://www.linkedin.com/in/carl-eschenbach-980543/ Christian Kleinerman: https://www.linkedin.com/in/christian-kleinerman-a973102/ Denise Persson: https://www.linkedin.com/in/denisepersson/ Dell: https://www.dell.com/ Frank Slootman: https://www.linkedin.com/in/frankslootman/ John McMahon: https://www.linkedin.com/in/johnmcmahon1/ Michael Scarpelli: https://www.linkedin.com/in/michael-scarpelli-1b289b9/ Microsoft: https://www.microsoft.com/ Oracle: https://www.oracle.com/ Salesforce: https://www.salesforce.com/ Snowflake: https://www.snowflake.com/ Sridhar Ramaswamy: https://www.linkedin.com/in/sridhar-ramaswamy/ Stanford Graduate School of Business: https://www.gsb.stanford.edu/ Where to find Chris: LinkedIn: https://www.linkedin.com/in/chris-degnan/ Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 00:00 What is the job of a CRO? 01:12 What excellence looks like at different revenue stages 02:59 Sales leaders need to know how to sell the product 04:52 The hardest skill leaders have to learn 08:17 You need to stay open to feedback - at all levels 14:01 Sales, segmentation, and international expansion 16:17 Why MEDDIC is the foundation for every sales org 20:32 The metrics that actually matter 22:56 A week in the life of a CRO at scale 28:32 Navigating compensation at a GTM organization 31:45 What technical CEOs get wrong about GTM 36:01 The role of hunger in great sales leaders 40:35 What makes an exceptional IC sales rep 46:41 Dysfunctional vs. high-performing executive teams 48:01 Chris' most impactful decisions at Snowflake 49:53 "When there's doubt, there's no doubt" 54:49 Learning from world-class leaders

Snowflake (SNOW) and Salesforce (CRM) are signaling a shift in software economics as AI moves from hype to real revenue impact. Stephanie Walter explains how consumption-based and subscription models are giving way to pricing tied to digital workload scale and data activation. The advantage is consolidating around platforms that can turn clean data into automated workflows, not just store it.======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Snowflake expects fiscal 2027 product revenue of $5.7 billion, above analysts’ average estimate of $5.5 billion compiled by LSEG, driven by rising AI demand. CEO Sridhar Ramaswamy said the company signed its largest deal ever, over $400 million, without naming the client. He speaks with Bloomberg's Ed Ludlow and Caroline Hyde. See omnystudio.com/listener for privacy information.

Realities Remixed, formerly know as Cloud Realities, launches a new season exploring the intersection of people, culture, industry, and tech. Energy transportation is a deeply local business, safely delivering gas and electricity, more and more from renewable sources, directly to the communities it serves. Technology and AI help make that possible by strengthening safety, bringing companies closer to customers, and enabling teams to build the future together. This week, Dave, Esmee, and Rob are joined by John Koerwer, CIO of UGI Corporation, to explore explore why “the business” and tech still struggle to speak the same language, nd what helps close the gap.TLDR00:35 – Introduction01:17 – Hang out: new toys and coffee07:55 – Dig in: the business - tech divide21:07 – Conversation with John Koerwer59:40 – The amazing AI technology in The Sphere's version of The Wizard of OzGuestJohn Koerwer: https://www.linkedin.com/in/john-koerwer-46102127/HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/ SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:  https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

The Information's Sri Muppidi talks with TITV Host Akash Pasricha about Amazon's potential $50 billion OpenAI deal and its AGI-triggered terms. We also talk with Wedbush Managing Director Matt Bryson about Nvidia's blowout quarter, stock selloff, China export risks and margins, and reporter Anita Ramaswamy about how AI is reshaping Salesforce and Snowflake's growth and how Alphabet, Amazon and Meta are using debt to fund AI capex. Lastly, we get into autonomous warships and defense investing with Deputy Bureau Chief of Finance Cory Weinberg and the new data infrastructure stack for humanoid robots with Encord Co-CEOs Ulrik Stig Hansen and Eric Landau.Articles discussed on this episode: https://www.theinformation.com/articles/amazons-50-billion-investment-openai-hinge-ipo-agihttps://www.theinformation.com/articles/alphabet-big-tech-borrow-hundreds-billionshttps://www.theinformation.com/articles/autonomous-warship-startup-saronic-raising-7-5-billion-valuationhttps://www.theinformation.com/newsletters/ai-agenda/robot-data-startup-raises-60-millionSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/

Scott Wapner and the Investment Committee debate the tech sector as Nvidia, Salesforce and Snowflake all reporting earnings tonight. CNBC's Kristina Partsinevelos joins us with the latest from Nvidia. Plus, the Committee share their latest portfolio moves. And later, we get to the Setup on some key Committee names reporting earnings tonight and tomorrow.  Investment Committee Disclosures Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Los Angeles man arrested for serving alcohol to a hawk. How much cocaine is in the Nantucket sewage? New York City Police investigating after officers hit with snowballs during a snowball fight in the park. Weird AF News is the only daily weird news podcast in the world. Weird news 5 days/week and on Friday it's only Floridaman. SUPPORT by joining the Weird AF News Patreon http://patreon.com/weirdafnews - OR buy Jonesy a coffee at http://buymeacoffee.com/funnyjones Buy MERCH: https://weirdafnews.merchmake.com/ - Check out the official website https://WeirdAFnews.com and FOLLOW host Jonesy at http://instagram.com/funnyjones - wants Jonesy to come perform standup comedy in your city? Fill out the form: https://docs.google.com/forms/d/e/1FAIpQLSfvYbm8Wgz3Oc2KSDg0-C6EtSlx369bvi7xdUpx_7UNGA_fIw/viewform

The future of the tech trade is on the line tonight as Nvidia, Salesforce and Snowflake report. We discuss with our mega-panel: Star Bernstein analyst Stacy Rasgon, Capital Area Planning's Malcolm Ethridge, Requisite Capital's Bryn Talkington, CNBC's Kristina Partsinevelos and Seema Mody. Plus, private credit concerns are front and center again today. We discuss these new developments with our Leslie Picker. And, the battle between Anthropic and the government is heating up. We break down all the details – and what's at stake for the big AI battle. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Software faces it latest test with results from Workday. A look ahead to earnings from Salesforce and Snowflake. Plus, the CEO of Cava with his first reaction to earnings. The stock up more than 20% after the company says they are bridging the K-shaped economy. And the Department of Defense pressing anthropic for full access to its AI tools. The company's response and why it may not be so straight forward. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

APAC stocks traded higher as the region took impetus from the rebound on Wall Street after Anthropic's presentation helped soothe some AI/software concerns, and with tech also bolstered by the USD 60bln Meta-AMD chip deal; Euro Stoxx 50 futures up 0.2% after the cash market closed flat on Tuesday.US President Trump talked up the economy in his State of the Union Address, saying that the nation is back, bigger, better and stronger than before, while he added that we've seen nothing yet.Regarding tariffs, Trump said the Supreme Court decision on tariffs is very unfortunate but added that tariffs will remain in place and nearly all countries want to keep the trade deals.Trump also commented on Iran, which he claimed is working on missiles that could soon reach the US, and noted Iran wants to make a deal but hasn't yet said that it won't pursue nuclear weapons.Antipodeans were firmer amid the positive risk appetite, and with AUD/USD leading the advances following firmer-than-expected monthly CPI data from Australia.Looking ahead, highlights include German GfK (Mar), GDP Final (Q4), Swiss Sentiment (Feb), EZ HICP Final (Jan). Speakers include RBA's Bullock, Fed's Musalem, Barkin & Schmid. Supply from Germany & US. Earnings from NVIDIA, Salesforce, Snowflake, TJX Companies, Lowe's, Synopsys & Bayer.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

US President Trump talked up the economy in his State of the Union Address, saying that the nation is back, bigger, better and stronger than before, while he added that we've seen nothing yet.Regarding tariffs, Trump said the Supreme Court decision on tariffs is very unfortunate but added that tariffs will remain in place and nearly all countries want to keep the trade deals.European bourses firmer as HSBC lifts the banking sector; US equity futures hold onto Anthropic- driven gains. DXY flat, Aussie outpaces peers post CPI while JPY lags in continuation of recent weakness.JGBs underperform on Takaichi's "reflationist" BoJ candidates; USTs await Fed speak & NVDA.Crude prices rangebound; Spot XAU holds above USD 5200/oz. Looking ahead, highlights include Fed's Musalem, Barkin & Schmid. Supply from the US. Earnings from NVIDIA, Salesforce, Snowflake, TJX Companies, Lowe's & Synopsys.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

Ep. 35 - Snow Day! - Music with Miss Jen - An Early Childhood Music Class PodcastWelcome to the Music with Miss Jen podcast, an engaging early childhood music class full of playful songs, imaginative lyrics, and music that will make your child want to clap, dance, and sing along! While designed especially for the younger listener, this music class is one the whole family can enjoy, with simple instrumentation and a focus on high-quality music selections.In this episode, we are singing winter songs for our snow days this week, including:"Let's Sing Hello Together" - words © 2000 Music with Miss Jen, traditional music“Snow on the Rooftops” - music © Kathy Reid-Naiman from Sing the Cold Winter Away“Move to the Beat” - © Stephanie Leavell (www.musicforkiddos.com)“If All the Snowflakes” - traditional, additional words © 2025 Music with Miss Jen"Winter Weather” - words © 2024 Music with Miss Jen, accompaniment music licensed from Pixabay“Bluesy Shaker Song” - words and music © 2025 Music with Miss Jen“Windshield Wiper” - traditional first verse, additional words © 2025 Music with Miss Jen“Chubby Little Snowman” - traditional words, music © 2025 Music with Miss JenFind my Chubby Little Snowman video here: https://youtu.be/cVxV7A1gJ5s“S is for Snowman” - words © 2024 Music with Miss Jen, accompaniment music licensed from PixabayFind my S is for Snowman video here: https://youtu.be/xAlDxSh5N28“Goodbye, My Friends” - - words and music © 2025 Music with Miss JenVisit my website for printable song pages to go along with some of today's songs: https://www.musicwithmissjen.com/podcast/ep-35You can find more songs in my digital products available in my Teachers Pay Teachers store or on Etsy.Where to find more Music with Miss Jen:Website: https://www.musicwithmissjen.comYouTube: https://www.youtube.com/@musicwithmissjenInstagram: https://www.instagram.com/musicmissjen/About Miss Jen -Miss Jen has been making music with young children for over 25 years. While she has taught all ages, early childhood has been her area of expertise for her entire teaching career. She has taught in both public and independent schools in a number of urban, suburban, and rural settings in 3 states. For the past 20 years, she has taught music outreach programs in preschools and day care centers, as well as conservatory-based music programs for infants up through fourth grade. She still actively teaches in multiple preschools and daycare centers, working with nearly 300 students and 45 teachers each year.

We're coming to the cottage and we want you to join us! Our much-anticipated Heated Rivalry episode is finally here. This conversation is formatted a little differently than usual, as we recorded our thoughts after watching each episode to give you a watch-along experience. This will also make it easy to avoid spoilers if you somehow haven't seen Heated Rivalry yet. Just a heads up that since this was a less formal recording set up, we used different mics, so the sound quality is a little different from our usual episodes.Let us know what you think! How many times have you reheated? We can't wait to hear from you.Check out our Patreon for some of our favorite HR related videos and internet content! ⁠https://www.patreon.com/thebipod⁠⁠We also wanted to share some resources for protecting your communities from ICE:You can learn more or get involved with Órale (local to Long Beach, CA) here: https://www.orale.org/Immigrant Defense Project has toolkits for defending against ICE raids and community arrests: https://www.immigrantdefenseproject.org/raids-toolkit/

KI-Agenten von OpenAI & Co. setzen die Softwarebranche massiv unter Druck. Aktien wie Salesforce, Snowflake und ServiceNow geraten ins Wanken, während Analysten vor tiefgreifenden Folgen für Arbeitsmarkt und Konsum warnen. Gleichzeitig sorgt eine mögliche PayPal-Übernahme für Fantasie im Payment-Sektor. Alle Hintergründe, Marktreaktionen und der Trade der Woche im Überblick.

In dieser Folge analysieren wir die laufende Berichtssaison mit Schwerpunkt auf Künstliche Intelligenz und deren Einfluss auf die Märkte. Im Fokus stehen die Quartalszahlen von Nvidia, Salesforce und Snowflake sowie die Diskussion um steigende KI-Investitionen und Margenentwicklung bei OpenAI. Zudem betrachten wir die Lage am Kryptomarkt mit Bitcoin und dem Strategiewechsel großer Miner hin zu KI-Services. Abgerundet wird die Analyse durch einen Blick auf Novo Nordisk, US-Zölle, geopolitische Risiken und die Entwicklung des Ölpreises..

In this episode of The Effortless Podcast, Dheeraj Pandey speaks with Dr. Abhishek Bhowmick about how quantum mechanics reshaped our understanding of determinism and why that shift matters for AI today.  From the Einstein–Bohr debates to the idea that nature is fundamentally probabilistic, they explore how the collapse of “if-then” thinking began nearly a century ago. The discussion draws parallels between quantum superposition and modern LLM behavior. At its core, the episode reframes AI as a rediscovery of how reality computes. The conversation then moves from physics to computing architecture, tracing the evolution from scalar CPUs to GPUs, TPUs, tensors, and eventually quantum computing. They examine why probabilistic systems and vector math feel more natural than purely deterministic software. Hybrid computing models show that classical systems still matter. The episode also unpacks what quantum computers are truly good at, especially in cryptography and simulation. Ultimately, it reflects on whether the future of computing lies in embracing probability rather than resisting it. Key Topics & Timestamps 00:00 – Welcome, context, and how Dheeraj & Abhishek met 04:00 – Abhishek's journey: IIT, Princeton, Apple, Snowflake 08:00 – The 1927 Solvay Conference and physics at a crossroads 12:00 – Einstein vs. Bohr: determinism vs. probability 16:00 – Superposition and the collapse of the wave function 20:00 – Fields vs. particles: what is an electron really? 25:00 – Matter particles, force particles, and the Standard Model 30:00 – Transistors, voltage, and the rise of deterministic computing 35:00 – From scalar CPUs to vectors and matrices 40:00 – Tensors, linear algebra, and modern AI systems 45:00 – Principle of Least Action and gradient descent parallels 50:00 – Hallucinations, probability mass, and LLM behavior 55:00 – Vector databases, embeddings, and KNN search 59:00 – GPUs vs. TPUs: matrix vs. tensor architectures 1:05:00 – What quantum computers are actually good at 1:10:00 – Post-quantum cryptography and the future of computing Host -  Dheeraj Pandey Co-founder & CEO at DevRev. Former Co-founder & CEO of Nutanix. A systems thinker and product visionary focused on AI, software architecture, and the future of work. Guest -  Dr Abhishek Bhowmick                                                                                                                                                                                                                Co-Founder and CTO of Samooha, a secure data collaboration platform acquired by Snowflake. He previously worked at Apple as Head of ML Privacy and Cryptography, System Intelligence, and Machine Learning, and earlier at Goldman Sachs. He attended Princeton University and was awarded IIT Kanpur's Young Alumnus Award in 2024. Follow the Host and Guest - Dheeraj Pandey: LinkedIn - https://www.linkedin.com/in/dpandey Twitter - https://x.com/dheeraj Abhishek Bhowmik  LinkedIn –  https://www.linkedin.com/in/ab-abhishek-bhowmick Twitter/X – https://x.com/bhowmick_ab Share Your Thoughts Have questions, comments, or ideas for future episodes?

Fluent Fiction - Korean: From Screens to Snowflakes: A Seollal Family Reconnection Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ko/episode/2026-02-22-23-34-02-ko Story Transcript:Ko: 나미섬에서는 눈이 고즈넉이 내리고 있었다.En: In Namiseom, snow was quietly falling.Ko: 뽀얀 눈 속에 포근하게 자리 잡은 아늑한 오두막이 있었다.En: Nestled cozily in the white snow was a snug cabin.Ko: 그 오두막에는 미소 가족이 머물고 있었다.En: In that cabin, the Miso family was staying.Ko: 미소는 17살이었다.En: Miso was 17 years old.Ko: 요즘 가족과 어울리는 시간이 적었다.En: Lately, she had been spending less time with her family.Ko: 대신 스마트폰을 들여다보며 시간을 보내곤 했다.En: Instead, she would spend her time staring at her smartphone.Ko: "미소 누나, 같이 눈사람 만들래?" 지수는 그녀의 동생이었다.En: "Miso nuna, do you want to build a snowman together?" Jisoo, her younger sibling, asked.Ko: 지수는 언제나 미소를 바라보았다.En: Jisoo always admired Miso.Ko: "조금 이따가," 미소는 핸드폰을 내려다보며 답했다.En: "In a little while," Miso replied, looking down at her phone.Ko: 그러자, 아빠 대현이 한숨을 쉬며 말했다. "미소야, 이번 설날에는 가족과 함께해주길 바란다.En: Then, her father, Daehyun, sighed and said, "Miso, I hope you'll spend time with the family this Seollal.Ko: 새해를 함께 맞이하는 건 특별한 일이란다."En: Celebrating the new year together is a special thing."Ko: 미소는 잠시 마음이 울렸다.En: Miso felt a momentary tug at her heart.Ko: 그 말은 그녀에게 큰 부담으로 다가왔다.En: His words felt like a heavy burden to her.Ko: 하지만 대현의 진심이 담긴 눈빛을 보고, 미소는 결심했다.En: But seeing the sincerity in Daehyun's eyes, Miso made a decision.Ko: "핸드폰은 잠시 두고, 가족과 함께할래요." 미소는 핸드폰을 내려놓고, 지수에게 말했다.En: "I'll put my phone down and spend time with the family." Miso set her phone aside and spoke to Jisoo.Ko: 지수는 기뻐하며 손을 잡아 끌었다.En: Jisoo joyfully grabbed her hand and pulled her along.Ko: 그날 오후, 가족들은 모두 둘러앉아 설날의 전통적인 만두를 만들었다.En: That afternoon, the family gathered around to make traditional dumplings for Seollal.Ko: 미소는 처음에는 어색했지만, 점점 익숙해졌다.En: Initially awkward, Miso gradually became more comfortable.Ko: 만두를 만들던 중 웃음소리가 끊이지 않았다.En: Laughter echoed as they made dumplings.Ko: 대현과 지수가 장난을 치며 만두를 얼굴에 묻혔다.En: Daehyun and Jisoo playfully smeared dumpling dough on each other's faces.Ko: 미소는 그 모습을 보며 웃었다.En: Watching them, Miso laughed.Ko: 그런 미소의 모습에 대현도 미소 지었다.En: Seeing her smile, Daehyun also smiled.Ko: 저녁이 되자, 가족들은 강 근처에서 불꽃놀이를 시작했다.En: As evening came, the family started a fireworks display near the river.Ko: 반짝이는 불꽃이 하늘을 수놓자, 미소는 깊은 감정에 사로잡혔다.En: As the sparkling fireworks painted the sky, Miso found herself deeply moved.Ko: 가족과 함께 있는 시간이 이렇게 소중할 줄 미처 몰랐다.En: She hadn't realized just how precious time with her family could be.Ko: "가족이 최고야," 미소는 입으로, 그리고 마음으로 속삭였다.En: "Family is the best," Miso whispered both out loud and in her heart.Ko: 이제 미소는 무엇이 중요한지 깨달았다.En: Miso now understood what was truly important.Ko: 가족과 함께하는 지금 이 순간, 미소는 진정한 온기와 사랑을 느꼈다.En: In these moments shared with her family, she felt genuine warmth and love.Ko: 하늘에 불꽃이 사라지고, 미소와 가족들은 그렇게 특별한 설날을 맞이했다.En: As the fireworks disappeared into the sky, Miso and her family welcomed a special Seollal.Ko: 미소는 그 때의 감정을 평생 기억할 것이다.En: Miso would remember the emotions of that time for the rest of her life.Ko: 새해의 시작은 그렇게 따뜻하게 다가왔다.En: The start of the new year had come with a warm embrace. Vocabulary Words:nestled: 자리 잡은cozily: 포근하게snug: 아늑한cabin: 오두막lately: 요즘admired: 바라보았다momentary: 잠시tug: 울렸다sincerity: 진심embrace: 포옹awkward: 어색한gradually: 점점comfortable: 익숙해졌다playfully: 장난을 치며smeared: 묻혔다display: 불꽃놀이sparkling: 반짝이는painted: 수놓자deeply moved: 깊은 감정에 사로잡혔다precious: 소중할genuine: 진정한warmth: 온기echoed: 끊이지 않았다fireworks: 불꽃disappeared: 사라지고special: 특별한emotions: 감정burden: 부담echoed: 끊이지 않았다welcomed: 맞이했다

Fluent Fiction - Swedish: Lost in Snowflakes: An Architect's Journey Back to Joy Find the full episode transcript, vocabulary words, and more:fluentfiction.com/sv/episode/2026-02-21-08-38-20-sv Story Transcript:Sv: Utan förvarning började snöflingorna tumla över de kullerstensbelagda gatorna i Gamla Stan.En: Without warning, snowflakes began to tumble over the cobblestone streets of Gamla Stan.Sv: Inne i den lilla kaféet spred sig aromen av färskt kaffe i den varma och hemtrevliga atmosfären.En: Inside the little café, the aroma of fresh coffee spread in the warm and cozy atmosphere.Sv: Kaféet var fyllt av mjukt ljus som reflekterades i de gamla träbalkarna.En: The café was filled with soft light that reflected off the old wooden beams.Sv: Elin satt vid ett hörnbord och väntade nervöst.En: Elin sat at a corner table, waiting nervously.Sv: Elin hade alltid varit fokuserad på sin karriär.En: Elin had always been focused on her career.Sv: Som arkitekt tillbringade hon timmar på kontoret.En: As an architect, she spent hours at the office.Sv: Men ibland, när snön föll och staden blev en idyllisk snöglob, längtade Elin efter ett enklare liv.En: But sometimes, when the snow fell and the city became an idyllic snow globe, Elin longed for a simpler life.Sv: Hon tänkte ofta på sin barndomsvän Magnus.En: She often thought about her childhood friend, Magnus.Sv: Magnus hade valt en annan väg i livet.En: Magnus had chosen a different path in life.Sv: Han drev en liten bokhandel, precis runt hörnet.En: He ran a small bookstore just around the corner.Sv: De hade känt varandra sedan de var barn och nu, efter alla dessa år, hade de bestämt sig för att träffas igen.En: They had known each other since they were children, and now, after all these years, they had decided to meet again.Sv: Magnus anlände med ett leende.En: Magnus arrived with a smile.Sv: Hans kindrosor vittnade om den kalla vintervinden.En: His rosy cheeks testified to the cold winter wind.Sv: De beställde kaffe och någonstans mellan första och andra klunken kaffe, började prata.En: They ordered coffee, and somewhere between the first and second sip, they began to talk.Sv: De delade minnen och skratt, men inombords kämpade Elin.En: They shared memories and laughter, but inside, Elin was struggling.Sv: Hon ville berätta om sina känslor, sin stress och sitt behov av förändring.En: She wanted to talk about her feelings, her stress, and her need for change.Sv: Men vad skulle Magnus tycka?En: But what would Magnus think?Sv: Elins hjärta bultade när Magnus plötsligt ställde en fråga som träffade henne mitt i hjärtat: "Hur mår du egentligen, Elin?"En: Elin's heart pounded when Magnus suddenly asked a question that hit her right in the heart: "How are you really doing, Elin?"Sv: Hon tvekade, men de var ju vänner.En: She hesitated, but they were friends after all.Sv: "Magnus, jag... ibland känns det som om jag har tappat bort mig själv i allt jobb."En: "Magnus, I... sometimes it feels like I've lost myself in all the work."Sv: Magnus nickade förstående och lutade sig lite närmare.En: Magnus nodded understandingly and leaned in a little closer.Sv: "Du behöver tid för det som gör dig glad, Elin."En: "You need time for what makes you happy, Elin."Sv: Elin kände sig plötsligt lättare.En: Elin suddenly felt lighter.Sv: Det var som om en börda hade lyfts från hennes axlar.En: It was as if a burden had been lifted from her shoulders.Sv: Magnus talade vidare om att följa sin passion och inte glömma bort livets små glädjeämnen.En: Magnus continued talking about following one's passion and not forgetting the little joys of life.Sv: Kaféets varma ljus och känslan av en gammal väns förståelse trängde undan vinterkylan utanför.En: The warm light of the café and the feeling of an old friend's understanding pushed away the winter cold outside.Sv: Elin log, tacksam för Magnus uppriktighet.En: Elin smiled, thankful for Magnus' honesty.Sv: De pratade vidare och innan de skildes åt, hade Elin bestämt sig: hon skulle fokusera mer på det som gjorde henne lycklig.En: They talked further, and before they parted ways, Elin had made up her mind: she would focus more on what made her happy.Sv: När de lämnade kaféet och snöflingorna fortsatte sin dans genom luften, visste Elin att det var början på en ny balans i hennes liv.En: As they left the café and the snowflakes continued their dance through the air, Elin knew that it was the beginning of a new balance in her life.Sv: Hon såg inte längre på framtiden med samma bekymrade blick.En: She no longer looked at the future with the same worried gaze.Sv: Istället följde hon snöflingornas dans med ett nyfunnet lugn.En: Instead, she followed the dance of the snowflakes with a newfound calm.Sv: Och Magnus, han visste att han hade hjälpt en gammal vän att hitta vägen till sig själv.En: And Magnus, he knew he had helped an old friend find her way back to herself. Vocabulary Words:tumble: tumlacobblestone: kullerstensbelagdaaroma: aromencozy: hemtrevligbeam: träbalkarnervously: nervöstidyllic: idyllisklonged: längtadetestified: vittnadehesitated: tvekadeburden: bördashoulders: axlarpassion: passionjoys: glädjeämnenunderstanding: förståelsethankful: tacksamhonesty: uppriktighetbalance: balansgaze: blickcalm: lugnspread: spredfocused: fokuseradcareer: karriärsip: klunkenstruggling: kämpadepounded: bultadeleaned: lutademakes: görlifted: lyftsparted: skildes

Совместный с ИИ ковер на композицию Alexy.Nov - White Waltz of Snowflakes

Fluent Fiction - Danish: Secrets, Snowflakes, and Twinkling Lights at Tivoli Gardens Find the full episode transcript, vocabulary words, and more:fluentfiction.com/da/episode/2026-02-20-08-38-20-da Story Transcript:Da: Tivoli Gardens var klædt i vinterens smukke lys.En: Tivoli Gardens was adorned with the beautiful lights of winter.Da: Sneen dalede blidt fra himlen, og luften var fyldt med duften af brændte mandler og varm kakao.En: Snow fell gently from the sky, and the air was filled with the scent of roasted almonds and hot cocoa.Da: Magnus gik hurtigt gennem menneskemængden.En: Magnus hurried through the crowd.Da: Han var bekymret.En: He was worried.Da: Han havde mistet et brev.En: He had lost a letter.Da: Et vigtigt brev.En: An important letter.Da: Brevet indeholdt en hemmelighed om en nær ven.En: The letter contained a secret about a close friend.Da: Hvis nogen fandt det, kunne det bringe stor skam.En: If someone found it, it could bring great shame.Da: Magnus kunne ikke lade det ske.En: Magnus could not let that happen.Da: Han måtte finde det hurtigst muligt.En: He had to find it as quickly as possible.Da: Pludselig hørte han en velkendt stemme.En: Suddenly, he heard a familiar voice.Da: "Magnus?En: "Magnus?Da: Hvad laver du her?"En: What are you doing here?"Da: Det var Stine, hans kollega fra arbejdet.En: It was Stine, his colleague from work.Da: Hun var der tilfældigt, nyde de smukke lys.En: She was there by chance, enjoying the beautiful lights.Da: Magnus tøvede et øjeblik, men besluttede at fortælle hende alt.En: Magnus hesitated for a moment but decided to tell her everything.Da: "Jeg har brug for din hjælp," sagde han.En: "I need your help," he said.Da: Stine forstod straks alvoren.En: Stine immediately understood the gravity of the situation.Da: "Selvfølgelig, lad os lede sammen," sagde hun og tog fat i hans arm.En: "Of course, let's search together," she said, taking hold of his arm.Da: Sammen bevægede de sig gennem folkemængden som et målrettet hold.En: Together, they maneuvered through the crowd like a determined team.Da: De kiggede under bænke og langs stierne, mens musikken spillede stille i baggrunden.En: They looked under benches and along the paths while music played softly in the background.Da: Da de nærmede sig den centrale sø, hvor lyshjerter var sat op på vandet, så Magnus noget ud af øjenkrogen.En: As they approached the central lake, where light hearts were set up on the water, Magnus spotted something out of the corner of his eye.Da: "Der!"En: "There!"Da: råbte han.En: he shouted.Da: En fremmed stod med brevet i hånden, klar til at åbne det.En: A stranger stood with the letter in hand, ready to open it.Da: Magnus' hjerte bankede.En: Magnus' heart pounded.Da: Han kunne konfrontere personen direkte, men det kunne skabe en scene.En: He could confront the person directly, but it could create a scene.Da: Stine lagde en hånd på hans skulder.En: Stine placed a hand on his shoulder.Da: "Vi laver en distraktion," hviskede hun.En: "We'll create a distraction," she whispered.Da: Mens Magnus gik tættere på, sørgede Stine for at vælte en lille taske ved et kiksebageri.En: As Magnus moved closer, Stine made sure to knock over a small bag at a cookie bakery.Da: Folk vendte sig om for at se på det lille optrin.En: People turned to look at the small commotion.Da: I det øjeblik snuppede Magnus brevet, stadig uåbnet, fra den fremmede forskrækket over tumulten.En: At that moment, Magnus snatched the letter, still unopened, from the stranger startled by the brouhaha.Da: Han satte hurtigt hen til Stine.En: He quickly went back to Stine.Da: "Lad os gå," sagde hun smilende.En: "Let's go," she said, smiling.Da: De gik begge mod udgangen fra de lysende haver, solen gik ned, og lyset blev stærkere omkring dem.En: They both walked towards the exit from the illuminated gardens, the sun setting, and the lights growing stronger around them.Da: "Tak, Stine," sagde Magnus hengivent.En: "Thank you, Stine," said Magnus gratefully.Da: Han indså, at han ikke altid behøvede at gøre alting selv.En: He realized that he didn't always have to do everything himself.Da: Under sneen og de blinkende lys fik deres venskab ny styrke.En: Beneath the snow and the twinkling lights, their friendship gained new strength.Da: Kristian, en gammel ven af Magnus, gik pludselig forbi dem.En: Kristian, an old friend of Magnus, suddenly walked past them.Da: Han vinkede og smilede varmt.En: He waved and smiled warmly.Da: Måske betød dette tilfældige møde, at en ny begyndelse var mulig.En: Perhaps this random meeting signaled that a new beginning was possible.Da: Magnus følte håb.En: Magnus felt hopeful.Da: Tivoli Gardens, med sine lys, sne og varme venskaber, føltes som en perfekt baggrund for Magnus' nye indsigt.En: Tivoli Gardens, with its lights, snow, and warm friendships, felt like a perfect backdrop for Magnus' new insight.Da: Han var ikke alene.En: He was not alone.Da: Han havde venner, og de var der for ham, når det virkelig galdt.En: He had friends, and they were there for him when it truly mattered.Da: Dette var kun begyndelsen på mange flere gode øjeblikke.En: This was only the beginning of many more good moments. Vocabulary Words:adorned: klædtscent: duftenroasted: brændteworried: bekymretcontained: indeholdtsecret: hemmelighedshame: skamhesitated: tøvedeabsolute: alvormaneuvered: bevægededetermined: målrettetbenches: bænkepaths: stiernespotted: såstranger: fremmedconfront: konfronterescene: scenecommotion: optrinstartled: forskrækketsnatched: snuppederealized: indsåinsight: indsigtilluminated: lysendehopeful: håbbackdrop: baggrundhappened: sketepounded: bankededistraction: distraktionknocked: væltedetumult: tumulten

Vincent Heuschling reçoit Hayssam Saleh, créateur de **Starlake**, une plateforme data open source française née de la factorisation de projets clients depuis 2017-2018. L'épisode intervient dans un contexte de consolidation du marché (rachat de DBT et de SQLMesh par Fivetran), qui invite à challenger les solutions établies.Starlake se distingue par une approche **entièrement déclarative** (YAML + SQL natif, sans Jinja) couvrant toute la chaîne data engineering : ingestion, transformation, orchestration et qualité des données. L'outil s'appuie sur les moteurs sous-jacents des plateformes cibles (Snowflake, BigQuery, Spark) et génère automatiquement les DAGs pour les orchestrateurs du marché (Airflow, Dagster, Snowflake Tasks).Parmi les fonctionnalités marquantes : le **data branching** (branches de données à la manière de Git), l'inférence automatique de schémas YAML à partir de fichiers sources, un **transpiler SQL** multi-plateformes, et l'extraction du lineage depuis du SQL brut sans annotation. L'intégration récente de **DuckLake** ouvre la voie à des architectures on-premise souveraines à coût maîtrisé (sous 300 €/mois sur OVH, Scaleway, Clever Cloud).Le modèle économique repose sur le support, la formation, et le consulting : Starlake s'installe dans le cloud du client, avec mise à jour automatique gérée par l'équipe, sans accès aux données.**Chapitres****00:00:27** – Introduction : consolidation du marché data (rachat de DBT et SQLMesh par Fivetran) et présentation de l'épisode**00:03:13** – Hayssam et la genèse de Starlake : parcours Spark/Scala, POC à 4 000 formats de fichiers (2017-2018)**00:09:51** – Architecture et philosophie : load, transform, orchestration unifiés en déclaratif (YAML + SQL natif, pas de Jinja)**00:00:18:18** – Starlake vs DBT : différences philosophiques, composabilité, fonctionnalités 100 % open source**00:00:22:20** – Data branching, Starlake Labs (pipe syntax, transpiler SQL, lineage) et expérience développeur (DuckDB local, UI point-and-click)**00:36:35** – Modèle open source et économique : licence Apache, support, formation, marketplace cloud souveraine**00:43:42** – DuckLake : alternative on-premise/cloud souverain (OVH, Scaleway, Clever Cloud) et comment contribuer / démarrer**Le BigdataHebdo**Le BigdataHebdo est le podcast Francophone de la Data et de l'IA.Retrouvez plus de 200 épisodes https://bigdatahebdo.comRejoignez la communauté sur le Slack https://join.slack.com/t/bigdatahebdo/shared_invite/zt-a931fdhj-8ICbl9dbsZZbTcze61rr~Q

#331 | Dave is joined by a group of marketing leaders from Ramp, Snowflake, and Hightouch for a discussion about ABM and their plans for 2026. Casey Patterson (Director of ABM, Snowflake), Drew Pinta (Director of Growth Data Science, Ramp), and Brian Kotlyar (CMO, Hightouch) break down what ABM actually looks like in 2026 and what's working right now inside of their companies. They share how they're picking target accounts, aligning with sales, and building programs that go way beyond running ads. The group also digs into measurement, personalization, and how teams are using better data and AI to scale ABM without wasting budget. If you need a deeper dive on ABM tactics right now, this is the episode for youTimestamps(00:00) - - Why ABM is still a top topic in 2026 (04:31) - - Intros: Snowflake, Ramp, and Hightouch (07:51) - - Defining ABM (and why sales alignment is everything) (14:01) - - The “stop list”: ABM tactics they've killed (16:31) - - Why paid social “ABM awareness” is overrated (19:51) - - Shifting ABM to in-person and physical plays (24:01) - - Budgeting for ABM and how to start small (28:59) - - Why ABM measurement is different than traditional demand gen (34:19) - - How they build ABM audiences using data + signals (39:39) - - Scaling personalization without making it manual (45:19) - - Final takeaways and wrap-up Join 50,0000 people who get Dave's Newsletter here: https://www.exitfive.com/newsletterLearn more about Exit Five's private marketing community: https://www.exitfive.com/***Brought to you by:Knak - A no-code, campaign creation platform that lets you go from idea to on-brand email and landing pages in minutes, using AI where it actually matters. Learn more at knak.com/exitfive.Optimizely - An AI platform where autonomous agents execute marketing work across webpages, email, SEO, and campaigns. Get a free, personalized 45-minute AI workshop to help you identify the best AI use cases for your marketing team and map out where agents can save you time at optimizely.com/exitfive (PS - you'll get a FREE pair of Meta Ray Bans if you do). Customer.io - An AI powered customer engagement platform that help marketers turn first-party data into engaging customer experiences across email, SMS, and push. Learn more at customer.io/exitfive.  ***Thanks to my friends at hatch.fm for producing this episode and handling all of the Exit Five podcast production.They give you unlimited podcast editing and strategy for your B2B podcast.Get unlimited podcast editing and on-demand strategy for one low monthly cost. Just upload your episode, and they take care of the rest.Visit hatch.fm to learn more

Realities Remixed, formerly know as Cloud Realities, launches a new season exploring the intersection of people, culture, technology, and society. Hosts Dave Chapman, Esmee van de Giessen, and Rob Kernahan unpack 2026's defining trends, from AI and sovereignty to adaptability and automation, offering fresh insight, candid reflections, and forward‑looking conversations shaping the year ahead. TLDR00:20 – Introduction of Realities Remixed02:30 – Why the show evolved?04:50 – Dig in with the team: Predictions for 202606:40 – Macro trends13:00 – Sovereignty 17:40 – Agentic AI22:17 – Human–AI interaction26:06 – Cloud trends30:42 – AI scaling, domain‑specific models35:03 – Adoption lag39:34 – Physical AI43:47 – Quantum computing48:21 – Hardware acceleration50:30 – Cybersecurity52:38 – Season outlook HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

What if your data platform could power both critical business decisions and real-time product features at scale? In this episode, host Benjamin sits down with Magnus Dahlbäck, Senior Director of Data and Platform at Voi, to explore how a metrics-first approach and semantic layers transform data accessibility, why traditional ML and LLMs require different strategies for different problems, and how to balance FinOps costs while processing billions of IoT events daily. Whether you're building data infrastructure for a high-growth company or rethinking how your organization consumes data, this conversation is packed with practical strategies for unlocking data value and preparing your platform for AI. Tune in to discover how Voi ditched traditional BI tools and revolutionized their approach to enterprise analytics.

Fluent Fiction - Danish: From Snowflakes to Spotlight: Mikkel's Winter Festival Triumph Find the full episode transcript, vocabulary words, and more:fluentfiction.com/da/episode/2026-02-17-08-38-20-da Story Transcript:Da: Mikkel stod ved indgangen til Tivoli og så ud over den sneklædte park.En: Mikkel stood at the entrance to Tivoli and looked out over the snow-covered park.Da: Tidlig morgen, og snefnuggene dalede stille ned.En: It was early morning, and snowflakes were silently falling.Da: Han havde altid drømt om at lave den perfekte festival.En: He had always dreamed of creating the perfect festival.Da: En festival, der lokkede folk til på trods af vinterens kolde greb.En: A festival that would attract people despite the cold grip of winter.Da: "Vi skal tænke stort, Mikkel," sagde Freja, da hun kom gående forbi med en lang to-do-liste i hånden.En: "We need to think big, Mikkel," said Freja as she walked by with a long to-do list in hand.Da: "Folk er ikke vant til festivaler om vinteren."En: "People aren't used to festivals in winter."Da: "Jeg ved det," svarede Mikkel med et smil.En: "I know," replied Mikkel with a smile.Da: "Men tænk på, hvad vi kan gøre.En: "But think about what we can do.Da: Lysinstallationer, interaktive kunstværker, lokale kunstnere.En: Light installations, interactive artworks, local artists.Da: Alt indendørs, hvis vi skal, for vejret kan være lunefuldt."En: Everything indoors, if we must, because the weather can be unpredictable."Da: Lars ankom kort efter, pænt pakket ind i hans store frakke.En: Lars arrived shortly after, neatly bundled in his large coat.Da: "Husk, vi har ikke råd til at gå over budget," sagde han med et bekymret blik.En: "Remember, we can't afford to go over budget," he said with a worried look.Da: "En storm kan nemt koste os dyrt."En: "A storm can easily cost us dearly."Da: Mikkel nikkede forstående.En: Mikkel nodded understandingly.Da: Han var under pres.En: He was under pressure.Da: Han måtte finde en balance mellem sine drømme og realiteterne.En: He had to find a balance between his dreams and reality.Da: Han præsenterede sin plan for Freja og Lars med entusiasme.En: He presented his plan to Freja and Lars with enthusiasm.Da: Freja så skeptisk ud, men Mikkels detaljerede udlægning og vilje til at lytte vandt hende over.En: Freja looked skeptical, but Mikkel's detailed explanation and willingness to listen won her over.Da: Lars, der fokuserede på budgettet, gav et nik og en lille, acceptabel smil.En: Lars, focused on the budget, gave a nod and a small, acceptable smile.Da: Dagene gik hurtigt, og festivalen nærmede sig.En: The days went by quickly, and the festival approached.Da: Parken blev langsomt forvandlet til et vintereventyr, med lys i hver krog og varm chokolade klar.En: The park was slowly transformed into a winter wonderland, with lights in every corner and hot chocolate ready.Da: Selvom sneen fortsat dalede, var stemningen magisk.En: Although the snow continued to fall, the atmosphere was magical.Da: Den store dag kom.En: The big day arrived.Da: Men morgenen bød på en overraskelse: en voldsom snestorm.En: But the morning brought a surprise: a severe snowstorm.Da: Mikkel så bekymret på Freja og Lars.En: Mikkel looked worriedly at Freja and Lars.Da: "Plan B?"En: "Plan B?"Da: spurgte Freja kort.En: Freja asked curtly.Da: Mikkel reagerede hurtigt.En: Mikkel reacted quickly.Da: "Vi rykker det hele indenfor.En: "We'll move everything indoors.Da: Vi har forberedt os, lad os gøre det."En: We've prepared for this, let's do it."Da: Personalet arbejdede hurtigt med at flytte aktiviteterne ind i parkens eventhaller.En: The staff worked quickly to move activities into the park's event halls.Da: Besøgende, der kæmpede mod sneen, fandt vej ind og blev betaget af de kreative indendørs oplevelser.En: Visitors, who battled against the snow, found their way inside and were captivated by the creative indoor experiences.Da: Interaktive installationer, kunstudstillinger fra lokale talenter, og varme drinks bragte smil til de besøgendes ansigter.En: Interactive installations, art exhibitions from local talents, and warm drinks brought smiles to the visitors' faces.Da: Mikkel så ud over mængden, lettet.En: Mikkel looked out over the crowd, relieved.Da: Sneen kunne ikke knuse hans drømme, og festivalen summede af liv.En: The snow couldn't crush his dreams, and the festival buzzed with life.Da: Lars kom og klappede ham på skulderen.En: Lars came over and patted him on the shoulder.Da: "Godt arbejde, Mikkel," sagde han med et oprigtigt smil.En: "Great job, Mikkel," he said with a sincere smile.Da: Freja tilføjede: "Jeg troede måske ikke helt på det først, men din plan holdt."En: Freja added, "I may not have fully believed in it at first, but your plan held."Da: Mikkel smilte tilbage.En: Mikkel smiled back.Da: Han havde lært værdien af at være fleksibel og samarbejde med sine kollegaer.En: He had learned the value of being flexible and collaborating with his colleagues.Da: Tivoli Gardens blændede stadig i vinterlandskabet, men indendørs var hjerter varme og kreativiteten i fuldt flor.En: Tivoli Gardens still dazzled in the winter landscape, but indoors, hearts were warm and creativity flourished.Da: Festivalen blev en succes.En: The festival was a success.Da: En symfoni af lys, kunst og glæde.En: A symphony of lights, art, and joy.Da: Og for Mikkel, Freja og Lars, var det starten på noget nyt og lovende i Tivoli.En: And for Mikkel, Freja, and Lars, it was the beginning of something new and promising in Tivoli. Vocabulary Words:entrance: indgangsnowflakes: snefnuggeneattract: lokkedespite: på trods afinstallations: installationerinteractive: interaktiveartworks: kunstværkerunpredictable: lunefuldtbudget: budgetcost dearly: koste dyrtunder pressure: under presbalance: balanceenthusiasm: entusiasmeskeptical: skeptisktransformed: forvandletmagical: magisksevere: voldsomreacted quickly: reagerede hurtigtevent halls: eventhallerbattled: kæmpedecaptivated: betagetexhibitions: udstillingerrelieved: lettetcrush: knusesincere smile: oprigtigt smilflexible: fleksibelcollaborating: samarbejdedazzled: blændedeflourished: i fuldt florpromising: lovende

Tonia Krügers „Love Songs in London“-Reihe hat mich durch meine Trennung begleitet. Dann habe ich „Kisses in the Snow“* und „Snow Flakes and Heartbeats“* von ihr in Zusammenarbeit mit Leonie Lastella und Valentina Fast gehört und mich gefragt: Wer hat was geschrieben? Wie geht das, die Arbeit von drei Autorinnen unter einen Hut zu bekommen, sodass auch alles wie aus einem Guss erscheint? Wie kam es zu dem Projekt, und was ist noch geplant? All das, aber auch alles über die individuellen Schreibroutinen der drei, über ihre Anfänge als Autorinnen, und auch zu ihrem eigenen Podcast mit Schreibfokus erfahrt ihr in diesem Interview. Dabei erwähnen wir: „I give you my body: How I write sex scenes“ von Diana Gabaldon „Somewhere in summer“ von Tonia Krüger „Two steps away“ von Valentina Fast Die „Outer Banks“-Saga von Emma Cole und Joanne St. Lucas bzw. Leonie Lastella und Jana Lukas „Das Licht von tausend Sternen“ von Leonie Lastella „Ein Leben aus Glas“ von Valentina Fast „All I (don't) want for Christmas“ von Tonia Krüger „Found“ aus der „Lake of Lies“-Reihe von Leonie Lastella Die „Secret Academy“-Reihe von Valentina Fast Die „Royal“-Reihe von Valentina Fast Die „Meereswelten“-Saga von Valentina Fast „Mate“ und „Problematic Summer Romance“ von Ali Hazelwood „Miss Moons höchst geheimer Club für ungewöhnliche Hexen“ von Sangu Mandanna „Regretting you/All das Ungesagte zwischen uns“ und „Verity“ von Colleen Hoover „Three words unspoken“  und „Four nights together“  aus der „London Hearts“-Reihe von Valentina Fast und Lorena Schäfer Ein audible-Original von Leonie Lastella „Bitten“ von Jordan Stephanie Gray „Heartless Hunter“ von Kristen Ciccarelli „A duet of fear and trust“ von Jenny Krone Den „Bookish Delight Bookclub“ Viel Spaß beim Hören des Interviews mit Leonie Lastella, Tonia Krüger & Valentina Fast! Wenn ihr mehr über die Autorinnen erfahren wollt, hört unbedingt auch mal in ihren Podcast „The Book Hangover“ rein. Eure Ilana Entschuldigt bitte, dass die Tonqualität leider nicht auf dem üblichen Niveau ist, da das Interview digital geführt und aufgenommen wurde. *Das Buch wurde mir als Rezensionsexemplar vom Verlag oder dem Autor/der Autorin zur Verfügung gestellt. Ich benutze teilweise Affiliate Links von Amazon.de. Näheres siehe "Impressum und Rechtliches".

Kennst du diese Situation im Team: Jemand sagt "das skaliert nicht", und plötzlich steht der Datenbankwechsel schneller im Raum als die eigentliche Frage nach dem Warum? Genau da packen wir an. Denn in vielen Systemen entscheidet nicht das nächste hippe Tool von Hacker News, sondern etwas viel Grundsätzlicheres: Datenlayout und Zugriffsmuster.In dieser Episode gehen wir einmal tief runter in den Storage-Stack. Wir schauen uns an, warum Row-Oriented-Datastores der Standard für klassische OLTP-Workloads sind und warum "SELECT id" trotzdem oft fast genauso teuer ist wie "SELECT *". Danach drehen wir die Tabelle um 90 Grad: Column Stores für OLAP, Aggregationen über viele Zeilen, Spalten-Pruning, Kompression, SIMD und warum ClickHouse, BigQuery, Snowflake oder Redshift bei Analytics so absurd schnell werden können.Und dann wird es file-basiert: CSV bekommt sein verdientes Fett weg, Apache Parquet seinen Hype, inklusive Row Groups, Metadaten im Footer und warum das für Streaming und Object Storage so gut passt. Mit Apache Iceberg setzen wir noch eine Management-Schicht oben drauf: Snapshots, Time Travel, paralleles Schreiben und das ganze Data-Lake-Feeling. Zum Schluss landen wir da, wo es richtig weh tut, beziehungsweise richtig Geld spart: Storage und Compute trennen, Tiered Storage, Kafka Connect bis Prometheus und Observability-Kosten.Wenn du beim nächsten "das skaliert nicht" nicht direkt die Datenbank tauschen willst, sondern erst mal die richtigen Fragen stellen möchtest, ist das deine Folge.Bonus: DuckDB als kleines Taschenmesser für CSV, JSON und SQL kann dein nächstes Wochenend-Experiment werden.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

Send a textBark und Jack's Adrian Barker hat eine Kollaboration mit Christopher Ward auf die Beine gestellt und eine recht coole Taucheruhr erschaffen, mit kleinen aber wichtigen Änderungen an der C60.Indische Uhren Szene (nicht die Sammlerszene)....Eine neue Snowflake und Skyflake von Grand Seiko in 33mm zum ersten Mal mit dem 9F Quartzwerk (+- 10 Sekunden pro Jahr) - ein Hit für die Damen.Interview mit Sebastian Vivas, dem Heritage und Musuems-Direktor von Audemars Piguet (in Englisch).Viel Spass!! Danke für Deine Zeit und für's Zuhören. Sendet mir eine Voicemail und wir hören uns im Podcast:https://www.speakpipe.com/opportunistischesdurcheinanderBitte folgt mir/uns auf instagram IG: @leuchtmasse_podcast oder schreibt mir: opportunistischesdurcheinander@gmail.com

Fluent Fiction - Hebrew: Snowflakes & Secrets: Love Amidst the City Grind Find the full episode transcript, vocabulary words, and more:fluentfiction.com/he/episode/2026-02-15-08-38-20-he Story Transcript:He: בחורף, כשהשלג כיסה את העיר הלבנה, משרד גבוה ומודרני במרכז העיר עמד מאורגן ויפה.En: In the winter, when the snow covered the white city, a tall and modern office in the city center stood organized and beautiful.He: בכניסה קרצו קישוטי ולנטיין עדינים, מוסיפים אווירה חמה באמצע הקרירות.En: At the entrance, delicate Valentine's decorations winked, adding a warm atmosphere in the midst of the chill.He: לוי, עובד חרוץ ושאפתן, עמד מול המחשב וסידר את המצגת שלו.En: Levi, a diligent and ambitious worker, stood in front of the computer arranging his presentation.He: המבט שלו נדד מדי פעם לרינה, הקולגה שלו, שהיא לא רק יפה אלא גם מקצוענית אמיתית.En: His gaze occasionally drifted to Rina, his colleague, who was not only beautiful but also a true professional.He: לוי היה מאוהב ברינה בסתר, אבל היום הוא היה חייב להתרכז במשימה: מצגת חשובה ללקוח גדול.En: Levi was secretly in love with Rina, but today he had to focus on the task: an important presentation for a major client.He: אבי, המנהל, היה שם לפקח.En: Avi, the manager, was there to supervise.He: הוא עמד בצד ודיבר עם רינה, מנסה להרגיע אותה.En: He stood to the side talking with Rina, trying to calm her.He: לוי הרגיש נקיפת קנאה קטנה, אך התנער מכך במהירות.En: Levi felt a slight twinge of jealousy but shook it off quickly.He: הייתה לו מטרה: להרשים את הלקוח, את רינה, ואת אבי.En: He had a goal: to impress the client, Rina, and Avi.He: כאשר הגיע הזמן לפגישה, לוי נשם עמוק ופנה לעבר חדר הישיבות.En: When the time for the meeting arrived, Levi took a deep breath and headed towards the conference room.He: רינה חייכה אליו בעידוד, ואבי הנהן בראשו באישור.En: Rina smiled at him in encouragement, and Avi nodded his head in approval.He: המצגת החלה.En: The presentation began.He: לוי דיבר בביטחון, אבל אז, לקוח שאל שאלה מפתיעה.En: Levi spoke confidently, but then a client asked a surprising question.He: לרגע, לוי הרגיש את הלחץ עולה, אך הוא נזכר בדבר אחד – הכנה.En: For a moment, Levi felt the pressure rising, but he remembered one thing—preparation.He: עם חיוך קל, הוא ענה על השאלה בצורה ברורה ומשכנעת.En: With a slight smile, he answered the question clearly and convincingly.He: בסוף המצגת, החדר היה מלא במחיאות כפיים.En: At the end of the presentation, the room was filled with applause.He: רינה פנתה ללוי, עיניה בורקות, "עשית עבודה נהדרת!En: Rina turned to Levi, her eyes sparkling, "You did a great job!"He: "לוי חייך, הקשיים נמסו כשלג באביב.En: Levi smiled, the struggles melted away like snow in the spring.He: הוא הבין שהיכולות המקצועיות שלו הן אלו שבולטות באמת.En: He realized that his professional abilities were what truly stood out.He: אולי, חשב, יש כאן יותר מרק עבודה.En: Maybe, he thought, there is more here than just work.He: לוי יצא מהמשרד עם רינה, השלג ירד שוב וגרם לעיר לנצוץ.En: Levi left the office with Rina, the snow fell again, causing the city to sparkle.He: שני הלבבות שמו את המקצועיות במרכז, אך אולי גם מקום לרגשות.En: The two hearts put professionalism in the center, but maybe there was also room for feelings. Vocabulary Words:diligent: חרוץambitious: שאפתןcolleague: קולגהprofessional: מקצועניתsecretly: בסתרsupervise: לפקחtwinge: נקיפהjealousy: קנאהencouragement: עידודapproval: אישורconfidently: בביטחוןpreparation: הכנהconvincingly: משכנעתapplause: מחיאות כפייםsparkling: בורקותstruggles: קשייםmelted: נמסוabilities: יכולותtruly: באמתrealized: הביןhearts: לבבותcover: כיסהdelicate: עדיניםatmosphere: אווירהchill: קרירותpresentation: מצגתgoal: מטרהpressure: לחץclear: ברורהtask: משימהBecome a supporter of this podcast: https://www.spreaker.com/podcast/fluent-fiction-hebrew--5818690/support.

BONUS: Why Embedding Sales with Engineering in Stealth Mode Changed Everything for Snowflake In this episode, we talk about what it really takes to scale go-to-market from zero to billions. We interview Chris Degnan, a builder of one of the most iconic revenue engines in enterprise software at Snowflake. This conversation is grounded in the transformation described in his book Make It Snow—the journey from early-stage chaos to durable, aligned growth. Embedding Sales with Engineering While Still in Stealth "I don't expect you to sell anything for 2 years. What I really want you to do is get a ton of feedback and get customers to use the product so that when we come out of stealth mode, we have this world-class product."   Chris joined Snowflake when there were zero customers and the company was still in stealth mode. The counterintuitive move of embedding sales next to engineering so early wasn't about driving immediate revenue, it was about understanding product-market fit. Chris's job was to get customers to try the product, use it for free, and break it. And break it they did. This early feedback led to material changes in the product before general availability. The approach helped shape their ideal customer profile (ICP) and gave the engineering team real-world validation that shaped Snowflake's technical direction. In a world where startups are pressured to show revenue immediately, Snowflake's investors took the opposite approach: focus on building a product people cannot live without first. Why Sales and Marketing Alignment Is Existential "If we're not driving revenue, if the revenue is not growing, then how are we going to be successful? Revenue was king."   When Denise Persson joined as CMO, she shifted the conversation from marketing qualified leads (MQLs) to qualified meetings for the sales team. This simple reframe eliminated the typical friction between sales and marketing. Both leaders shared challenges openly and held each other accountable. When someone in either organization wasn't being respectful to the other team, they addressed it directly. Chris warns founders against creating artificial friction between sales and marketing: "A lot of founders who are engineers think that they want to create this friction between sales and marketing. And that's the opposite instinct you should have." The key insight is treating sales and marketing as a symbiotic system where revenue is the shared north star. Coaching Leaders Through Hypergrowth "If there's a problem in one of our organizations, if someone comes with a mentality that is not great for us, we're gonna give direct feedback to those people."   Chris and Denise maintained tight alignment at the top level of their organizations through four CEO transitions. Their partnership created a culture of accountability that cascaded through both teams. When either hired senior people who didn't fit the culture, they investigated and addressed it. The coaching approach wasn't about winning by authority—it was about maintaining partnership and shared accountability for results. This required unlearning traditional management approaches that pit departments against each other and instead fostering genuine collaboration. Cultural Behaviors That Scale (And Those That Don't) "We got dumb and lazy. We forgot about it. And then we decided, hey, we're gonna go get a little bit more fit, and figure out how to go get the new logos again."   Chris describes himself as a "velocity salesperson" with a hyper-focus on new customer acquisition. This focus worked brilliantly during Snowflake's growth phase—land customers, and the high net retention rate would drive expansion. However, as Snowflake prepared to go public, they took their foot off the gas on new logo acquisition, believing not all new logos were equal. This turned out to be a mistake. In his final year at Snowflake, working with CEO Sridhar Ramaswamy, they redesigned the sales team to reinvigorate the new logo acquisition machine. The lesson: the cultural behaviors that fuel early success must be consciously maintained and sometimes redesigned as you scale. Keeping the Message Narrow Before Going Platform "Eventually, I know you want to be a platform. But having a targeted market when you're initially launching the company, that people are spending money on, makes it easier for your sales team."   Snowflake intentionally positioned itself in the enterprise data warehousing market—a $10-12 billion annual market with 5,000-7,000 enterprise customers—rather than trying to sound "bigger" as a platform play. The strategic advantage was accessing existing budgets. When selling to large enterprises that go through annual planning processes, fitting into an existing budget means sales cycles of 3-6 months instead of 9-18 months. Yes, competition eventually tried to corner Snowflake as "just a cute data warehouse," but by then they had captured significant market share and could stretch their wings into the broader data cloud opportunity. Selling Consumption-Based Products to Fixed-Budget Buyers "Don't believe anything I say, try it."   One of Snowflake's hardest challenges was explaining their elastic, consumption-based architecture to procurement and legal teams accustomed to fixed budgets. In 2013-2015, many CIOs still believed data would stay in their data centers. Snowflake's model—where customers could spin up a thousand servers for 4 hours, load data, while analysts ran queries without performance impact—seemed impossible. Chris's approach was simple: set up proof of concepts and pilots. Let the technology speak for itself. The shift from fixed resources to elastic architecture required changing not just technology but entire mindsets about how data infrastructure could work.   About Chris Degnan Chris Degnan is a builder of one of the most iconic revenue engines in enterprise software. As the first sales hire at Snowflake, he helped scale the company from zero customers to billions in revenue. Chris co-authored Make It Snow: From Zero to Billions with Denise Persson, documenting their journey of building Snowflake's go-to-market organization. Today, Chris advises early-stage startups on building their go-to-market strategies and works with Iconiq Capital, the venture firm that led Snowflake's Series D round.   You can link with Chris Degnan on LinkedIn and learn more about the book at MakeItSnowBook.com.

Church of England vicars with a difference Jamie Franklin and Daniel French talk about the big stories in Church and State. This time:Keir Starmer is clinging to power as folly of Epstein-linked Mandelson decision (and many other decisions) is being made increasingly apparent.It's the Church of England's General Synod and the new Archbishop Sarah Mullally promises to support the local and deprioritise central initiatives. But will she follow through on this promise and why is she still supporting £100 million slavery reparations initiative Project Spire?Project Spire itself takes a battering in question and answer session at Synod and should be renamed "Project Snowflake" as those working on the scheme are said to need special support because they can't handle questions and criticism.And the Living in Love and Faith gay relationships project is officially cancelled after years of fruitless toil...and then restarted again with a new "working group" to look at the same issues.We answer some questions on talking Bibles and the link between Lockdown and the Quiet Revival, plus a few other things as always.All that and much more as always. Please enjoy!You make this podcast possible. Support us and get episodes early, bonus Uncollared audio podcasts, monthly epic chats between Jamie and Nick Dixon and more!On Patreon - https://www.patreon.com/irreverendOn Substack - https://irreverendpod.substack.com/Buy Me a Coffee - https://www.buymeacoffee.com/irreverend To make a direct donation or to get in touch with questions or comments please email irreverendpod@gmail.com!For the Clergy Post at Holy Trinity, Stroud Green make enquiries with the Bishop of Fulham's office fulham.chaplain@london.anglican.org or phone 020 7932 1130.Notices:Join our Irreverend Telegram group: https://t.me/irreverendpodFollow us on Twitter: https://x.com/IrreverendPodBuy Jamie's Book! THE GREAT RETURNDaniel French Substack: https://undergroundchurch.substack.com/Jamie Franklin's "Good Things" Substack: https://jamiefranklin.substack.comIrreverend Substack: https://irreverendpod.substack.comFind me a church: https://irreverendpod.com/church-finder/Support the show

Sridhar Ramaswamy is the CEO of Snowflake. Ramaswamy joins Big Technology Podcast to break down the competitive dynamics in the AI race today, drawing from his experience working at Google and competing with it. We also cover the future of software, looking at whether AI will turn established software companies into "dumb backends." In the second half, we discuss “shadow AI” driving enterprise adoption from the bottom up, the risk of becoming a feature in someone else's platform, and why Chinese open-source models might actually be a net positive for the US. Hit play for a sharp, deeply informed conversation about where AI competition, enterprise software, and the future of work are heading. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b EXCLUSIVE NordVPN Deal ➼ https://nordvpn.com/bigtech.  Try it risk-free now with a 30-day money-back guarantee! Learn more about your ad choices. Visit megaphone.fm/adchoices

NFLSuper Bowl LX recapNews/NotesWinter OlympicsCurrent medal countLindsey Vonn updateCollege BasketballMens and Women's Week 15 AP Top 10Scores from the weekTop 25 schedule for the weekNews/NotesShow music by DJ Cam One: Twitter/Instagram/SpotifyDJ Cam One's label: Mysteryismusic.comCover art by Xclusive Packaging & Design: InstagramEmail: x.pac.design@gmail.com Your host Uncle Dub: Bluesky/Twitter/InstagramPodcast Instagram and YouTubeUntappd (Beer Check-in app): ItsUncle_DubEmail: sportswagonpodcast@gmail.comCheck out the Bald Head Logic podcast co-hosted with DJ Cam OnePlease consider supporting the podcast: Buy Me a CoffeeSend a voicemail, subscribe, rate and tell a friend about the show!Thank you for listening!

I sat down with Paul Dudley (CEO) and Ricky Thomas (CTO) from StreamKap to catch up on where the world of streaming data is heading—and things have changed fast since we last spoke.We dive into the concept of "vibe coding" and how AI is radically accelerating how we build software (I even share a story about building a data analysis tool in an hour). But the real meat of this conversation is about the intersection of streaming data and AI agents. Everyone is building agents, but without real-time context, they're flying blind. We discuss why streaming is a missing link for agentic workflows, the shift from dashboards to automated decision-making, and why SaaS companies are racing to build walled gardens around their data.We also get into the nitty-gritty of the UK vs. US tech markets, the resurgence of PR in the AI era, and StreamKap's upcoming move into the Snowflake native app ecosystem.Streamkap: https://streamkap.com/

Collate is building a semantic intelligence platform that unifies fragmented metadata tooling across the modern data stack. With 12,000+ community members, 3,000+ open source deployments, and 400+ code contributors, the company has proven that open source can be a systematic GTM engine, not just a distribution tactic. In this episode of BUILDERS, I sat down with Suresh Srinivas, Co-Founder & CEO of Collate, to explore his journey from the Hadoop core team at Yahoo, through founding Hortonworks, to architecting data systems processing 4 trillion events daily at Uber—and why that experience led him to rebuild metadata infrastructure from scratch. Topics Discussed: Why platform builders at Yahoo and Hortonworks struggled to drive business value despite powerful technology The metadata fragmentation problem: how siloed tools lack unified vocabularies and end-to-end context Collate's contrarian decision to build Open Metadata from zero rather than spinning out Uber's internal tooling Engineering an open core GTM model that generates nearly 100% inbound sales from technical practitioners Scaling community contribution: moving from feedback loops to 400+ code contributors Hiring a CMO to translate technical value into business-leader messaging without losing practitioner trust The convergence thesis: structured data, knowledge graphs, and semantic layers as the foundation for reliable AI GTM Lessons For B2B Founders: Architect your open source for GTM leverage, not just distribution: Suresh built Open Metadata as a unified platform consolidating data discovery, observability, and governance—previously fragmented across multiple tools. This architectural decision created natural upgrade paths to Collate's managed offering. The lesson: open source architecture should solve a complete job-to-be-done that reveals commercial value through usage, not just demonstrate technical capability. 100+ daily practitioner conversations beats any user research: Collate maintains ongoing dialogue with their community across Snowflake, Databricks, and other integrations. Suresh called this "a product manager's dream"—immediate feedback on what breaks, what's missing, and what workflow improvements matter. For infrastructure startups, this beat rate of validated learning is nearly impossible to replicate through traditional customer development. High-velocity releases build credibility faster than pedigree: Starting from scratch without Yahoo or Uber's brand meant proving commitment through shipping cadence. Collate's strategy: demonstrate you'll be around and responsive before asking for production deployments. This matters more in open source than closed-source where sales cycles force commitment conversations earlier. Separate technical-buyer and business-buyer GTM motions explicitly: Collate's founding team spoke fluently to data engineers and architects who lived the metadata problem daily. Their CMO hire (after establishing product-market fit) brought expertise in articulating business impact—ROI on data initiatives, compliance risk reduction, AI readiness—without the founders faking business-speak. The timing matters: hire for the motion you're entering, not the one you're in. Play the long game with builder-culture companies: At Uber, internal tools were 2-3 years ahead of vendor solutions but became technical debt as teams moved to new problems. Suresh's advice: "Keep in touch with these larger companies. Your technology will improve and you will have better conversation with larger technical companies." The wedge is timing—catch them when maintenance burden outweighs building pride, typically 24-36 months post-launch. Design for all company scales from day one: Unlike Uber's internal metadata platform built for massive scale with corresponding complexity, Open Metadata works for small teams through enterprises. This wasn't just good design—it was GTM expansion strategy. Building only for scale locks you into enterprise-only sales. Building only for simplicity caps your ACV. The middle path requires architectural discipline upfront. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

The Information's Ann Gehan talks with TITV Host Akash Pasricha about OpenAI's rollout of ads and the massive tax challenges facing its future shopping ambitions. We also talk with Catherine Perloff about Amazon's secret plans to launch a content marketplace for publishers to sell data to AI companies, and Rishi Jaluria of RBC Capital Markets about the carnage in software stocks and the wave of CEO changes at companies like Workday and Snowflake. Lastly, we get into the technical mechanics of how OpenClaw agents learn new skills with our reporter Rocket Drew.Articles discussed on this episode: https://www.theinformation.com/articles/chatgpt-shopping-get-complicated-fasthttps://www.theinformation.com/articles/amazon-discusses-ai-content-marketplace-publishershttps://www.theinformation.com/newsletters/ai-agenda/openclaw-learns-new-thingshttps://www.theinformation.com/newsletters/the-briefing/workdays-ceo-shuffle-sign-things-comeSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/

Alex breaks down why Bad Bunny's Super Bowl halftime show was more than just a performance—it was a cultural statement that had MAGA conservatives whining about reggaetón, pronouns, and inclusivity. He contrasts the positive, unifying energy of the halftime show with TPUSA's low-turnout, divisive counter-event, unpacking what this says about America's political and cultural divides.

In this episode of Run the Numbers, CJ Gustafson sits down with Dan Miller, CFO at RightRev. They unpack why leasing is underused in software, how RevTech emerged, and why revenue recognition may be the next AI battleground. Dan also shares how he evaluates durable growth vs. hypergrowth.—SPONSORS:Rillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.com—LINKS: Dan on LinkedIn: https://www.linkedin.com/in/danmillercpa/RightRev: https://www.rightrev.com/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:00:00:00 Preview and Intro00:02:41 Why Operating Experience Matters for CFOs00:04:08 Defining Durable Growth00:06:06 Snowflake and Consumption Revenue Complexity00:10:17 Forecasting in Consumption Models00:11:29 AI's Role in Revenue Forecasting00:12:14 Sponsors — Rillet | Tabs | Abacus AI00:15:39 Comping Sales in Usage-Based Models00:18:15 Leasing as a Software Monetization Tool00:20:47 The CFO's Role in Sales and GTM00:22:29 How CFOs Help Close Deals00:24:14 Rev Tech vs RevOps00:26:20 Sponsors — Brex | Metronome | RightRev00:29:40 Where AI Actually Helps Rev Rec00:31:55 Deterministic vs Probabilistic AI00:33:05 Why Enterprises Hesitate on AI Agents00:34:18 Startups vs Incumbents in the AI Race00:35:13 FOMO, Overfunding, and Market Distortions00:38:13 CFO Playbooks Without Hypergrowth00:39:38 Finding PMF as a CFO00:41:15 Career Advice: Growth vs Shiny Objects00:42:00 Building the CEO–CFO Relationship00:42:49 Learning Beyond the Back Office00:43:22 Lightning Round00:44:28 Advice to My Younger Self00:45:09 Finance Tech Stack00:46:36 Credits

Maxima is building AI agents that automate enterprise accounting while maintaining the auditability and control standards finance teams require. In a recent episode of BUILDERS, we sat down with Yogi Goel, CEO and Co-Founder of Maxima, to explore his eight-year journey at Rubrik from Series C through IPO, and how those lessons shaped his approach to solving the 70-80% of finance time currently wasted on manual work. Topics Discussed: Why Rubrik's approach—entering stagnant markets with first-principles thinking—became Maxima's blueprint Securing $3K-$5K POC commitments from Figma mockups before writing code Why Scale AI and Rippling rejected a point solution and demanded 3-4 modules from day one The compound startup model: building multiple products simultaneously to meet buyer expectations How 17% of CFOs are adopting AI tools today (vs 51% in software development) Why finance teams view AI agents as "digital college freshmen" who need proof of work Hiring from YouTube Studios, Apple, and Robinhood instead of legacy finance software companies How NetSuite World conference booth sizes revealed the data integration infrastructure gap The $3K-$5K validation threshold that proved finance pain was urgent enough to pay pre-product GTM Lessons For B2B Founders: Demand generation unlocks engineering potential: Yogi learned from his Rubrik mentors: "focus on demand and if you have great engineers then they will solve the problems." Maxima built products in 2-3 months they didn't initially know were technically feasible—because customer demand pulled the engineering team forward. For founders with strong technical teams, customer demand should drive the roadmap, not engineering's comfort zone. Trust your engineers to solve hard problems when customers are waiting. $3K-$5K is the pre-product validation threshold: Before writing any code, Yogi secured POC commitments at this price point based solely on Figma mockups. This isn't about revenue—it's about proving urgency. Verbal interest means nothing. Small pilot commitments mean "we'll try it someday." But $3K-$5K pre-product means "this problem is urgent enough to pay before seeing a working solution." Use this threshold to separate real pain from polite interest. Sophisticated buyers will reject your narrow MVP: Scale AI and Rippling told Maxima explicitly: "If you will only build this one thing, we will not buy. You have to commit to building three, four modules." Conventional wisdom says start narrow, but enterprise buyers with complex workflows won't adopt point solutions that create new integration headaches. When sophisticated buyers articulate their real buying criteria, ignore the startup playbook. Yogi built a "compound startup" with 4-5 modules from day one because that's what the market demanded. Target acute pain over easy access: Early-stage companies (10-30 people) were easier to reach but finance wasn't urgent enough. At that scale, it's "build product, ship product"—finance operations aren't broken enough to warrant urgent attention. Companies at 500-1,000+ employees have finance teams drowning in manual work that prevents strategic contribution. Target where pain justifies urgent action and budget exists, not where calendar access is easiest. Hire intensity and first-principles thinking over domain knowledge: Maxima deliberately hired zero engineers from legacy finance software companies. Their frontend engineer came from YouTube Studios. Others came from Apple, Robinhood, Netflix—none with financial product experience. Yogi's three hiring criteria: "incredible intensity, huge confidence in themselves, and fast thinking mode." Domain expertise creates pattern-matching to old solutions. First-principles thinking creates breakthrough products. One team member didn't finish high school but is "one of the best out there." Make AI explainable or finance teams won't adopt: Finance teams adopted faster than expected because Maxima showed every calculation step. "If they can prove by looking at the Math, you know, 18 plus 88 plus 36 is X. And I can see the step of the work, they are willing to give it to them." This isn't about fancy UX—it's about auditor-grade proof of work. Finance professionals won't trust black box outputs. Build transparency into the product architecture, not as an afterthought. This explainability became Maxima's competitive moat. Conference booth sizes reveal infrastructure gaps: At NetSuite World, the largest booths weren't ERP vendors or payment processors—they were data integration companies. This single observation validated that enterprises are desperately solving data fragmentation problems. Companies manually download from Stripe, Snowflake, Salesforce weekly to build Excel pivots. Maxima invested in upstream integrations as core infrastructure from day one. Use industry conferences to validate where companies are spending money on workarounds—that's where infrastructure gaps exist. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Today's minisode features Chris Degnan, former CRO of Snowflake. In this clip, Chris explains what it really takes to grow with a company as it scales, and why earning your role does not stop once the title changes. He shares how treating every quarter like a 90-day contract, staying open to feedback, and knowing when to shift from grinding in the business to building leaders helped him navigate board pressure and scale through hypergrowth.If you're a sales leader navigating rapid growth, or questioning how to evolve without losing your edge, this is a perspective worth hearing.Chris Degnan is the former Chief Revenue Officer of Snowflake, where he helped build the company from zero to more than $1B in consumption revenue. He is known for his expertise in scaling go-to-market organizations through early-stage ambiguity, enterprise expansion, and consumption-based selling models.Connect with Chris:LinkedInFrom Zero to Billions: How Snowflake Scaled its Go-to-Market Organization by Denise Persson & Chris DegnanResources mentioned:Multiple Myeloma Research Foundation Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

Building a company from the ground up is rarely clean, fast, or glamorous. It requires leaders who are willing to earn their role repeatedly, adapt faster than the business evolves, and stay grounded in customer reality even as pressure to scale intensifies. In this replay of one of our favorite Revenue Builders Podcast conversations, Chris Degnan shares what it actually took to help build Snowflake from pre-product uncertainty into a billion-dollar revenue engine. Drawing on his experience joining the company two years before general availability, Chris breaks down the stages of growth, the discipline required to identify real product-market fit, and the leadership mindset needed to scale teams, go-to-market motion, and accountability without losing velocity or culture.Chris Degnan is the former Chief Revenue Officer of Snowflake, where he helped build the company from zero to more than $1B in consumption revenue. He is known for his expertise in scaling go-to-market organizations through early-stage ambiguity, enterprise expansion, and consumption-based selling models.Connect with Chris:LinkedInFrom Zero to Billions: How Snowflake Scaled its Go-to-Market Organization by Denise Persson & Chris DegnanResources mentioned:Multiple Myeloma Research FoundationIf you're responsible for scaling a go-to-market organization, drive predictability at scale with Force Management's Predictable Revenue Framework. Get the free guide: https://hubs.li/Q03-T6NH0Key takeaways from this episode:05:10 – Why joining an early-stage company means earning your role every quarter, not relying on past success or title10:25 – How defining a narrow and honest ideal customer profile creates momentum, while chasing outliers quietly destroys focus and capital16:45 – Why velocity and enterprise selling must coexist, and how overcommitting to one creates instability as companies scale20:05 – How coachability and adaptability determine whether leaders grow with the company or get replaced as scale increases21:55 – Why consumption-based selling demands accountability beyond the deal, and how reps must own customer success to earn full value26:30 – Why resisting the urge to replace leaders too early preserves institutional knowledge and strengthens culture during scale Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

In der heutigen Folge sprechen die Finanzjournalisten Anja Ettel und Lea Oetjen über die Durststrecke der Tech-Titel, Zweifel am Bitcoin-Narrativ und brisante Verbindungen von Jeffrey Epstein in die deutsche Wirtschaft. Außerdem geht es um SAP, Siemens, Infineon, Scout24, Snowflake, Palantir, CrowdStrike, AMD, Intel, Broadcom, Nvidia, Alphabet, Brenntag, BASF, Volkswagen, BMW, Mercedes-Benz Group, Lanxess, Wacker Chemie, Evonik, Heidelberg Materials, Uber, Bitcoin, Spotify, Alibaba, Deutsche Bank und Walmart. Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts und AAA-Newsletter. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Der Börsen-Podcast Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

This week's Tech Field Day News Rundown dives into the biggest AI, security, and enterprise shakeups. Tom Hollingsworth and Alastair Cooke deliver this week's Tech Field Day News Rundown, starting with a global push by actors and musicians calling for a “permission-first” approach to AI training, as unions accuse AI companies of using copyrighted works without consent. They also cover growing security concerns around agentic AI after researchers discovered serious vulnerabilities in MCP servers from Anthropic and Microsoft. Snowflake and OpenAI's $200 million partnership to bring governed, production-ready AI into the enterprise data cloud, mounting financial pressure on Oracle with potential mass layoffs and a possible sale of Cerner, high-severity vulnerabilities in the n8n AI automation platform that allow remote code execution. They also discuss a critical Broadcom Wi-Fi chipset flaw capable of taking entire 5 GHz networks offline and new warnings from researcher Jason Meller about AI agent “skills” being weaponized as malware—highlighting how quickly AI ecosystems are evolving into both powerful business tools and major security risks.This and more on the Tech Field Day News Rundown with Tom Hollingsworth and Alastair Cooke. Time Stamps: 0:00 - Cold Open0:25 - Welcome to the Tech Field Day News Rundown1:26 - Creative Industry Launches Global Push Against AI Training Practices5:17 - Anthropic and Microsoft MCP Server Flaws Expose Growing Security Risks in Agentic AI9:27 - Snowflake and OpenAI Sign $200M Partnership to Bring Enterprise AI Closer to Data12:48 - Oracle Weighs Massive Layoffs and Cerner Sale Amid AI Data Center Funding Crunch17:03 - Critical n8n AI Automation Flaws Expose Systems to Remote Code Execution21:21 - Broadcom Wi-Fi Flaw Highlights How One Wireless Bug Can Disrupt Entire Networks25:24 - A Closer Look: AI Agent Skills Turn Into a New Malware Supply Chain Risk35:36 - The Weeks Ahead: Upcoming Tech Field Day Events37:51 - Thanks for Watching the Tech Field Day News RundownFollow our hosts ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tom Hollingsworth⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Alastair Cooke⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Stephen Foskett⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow Tech Field Day ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X/Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bluesky⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mastodon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.

Arnnon Geshuri is the legendary Chief People Officer behind the world's most iconic workforces. From the early days of Google to the high-stakes scaling of Tesla under Elon Musk, and now leading the charge at Snowflake, Arnnon has mastered the art of "human engineering" at a scale very few on Earth have ever seen.In this conversation, Arnnon pulls no punches on what it actually takes to build a high-performance culture. We discuss the "performance DNA" required to survive in a hyper-growth environment, the truth about hiring for grit over skill, and why most leaders are too afraid to demand excellence.We discuss:The Elon Musk Era: What it's really like building a team at Tesla.The High-Performance Secret: Why some people thrive under pressure while others crack.The "Zero Gravity" Culture: How Snowflake maintains its hiring edge while scaling to billions.This is a masterclass in leadership, psychology, and the brutal reality of what it takes to build a world-class organization.

Text us your thoughts on the episode or the show!In this episode of Ops Cast, we dig into what it really takes to build demand generation and revenue marketing capability inside a large enterprise organization.Michael Hartmann is joined by Rachel Roundy, Product Marketing Lead for AI at Snowflake. Before Snowflake, Rachel spent more than four years inside a legacy enterprise technology company, where she helped lead a cross-functional tiger team tasked with building modern demand generation and revenue marketing capabilities at scale.This conversation explores the reality of enterprise marketing, where strategy and execution often live far apart, tech stacks are outdated, ownership is fragmented, and meaningful change must happen without direct authority. Rachel shares what it was like working inside systems that felt frozen in time, uncovering unused or partially implemented tools, and compensating for missing fundamentals like attribution and source tracking through manual processes and spreadsheets.You will hear how marketing and operations teams often struggle to understand each other's worlds, why that gap persists in large organizations, and what happens when those two sides finally align. Topics covered include: • Building demand generation inside large enterprises • Leading cross-functional change without formal authority • The gap between marketing strategy and operational execution • Working around outdated or underutilized tech stacks • Lessons from enterprise transformation efforts • How marketers and ops teams can become better partnersThis episode is especially relevant for Marketing Ops, Demand Gen, and Revenue Marketing leaders working inside complex, legacy organizations who are trying to modernize systems, processes, and mindsets.Episode Brought to You By MO Pros The #1 Community for Marketing Operations Professionals MarketingOps.com is curating the GTM Ops Track at Demand & Expand (May 19-20, San Francisco) - the premier B2B marketing event featuring 600+ practitioners sharing real solutions to real problems. Use code MOPS20 for 20% off tickets, or get 35-50% off as a MarketingOps.com member. Learn more at demandandexpand.com.Support the show

Part two of my chat with Faun.

We're diving into the latest in the auto world, bringing you essential car news and practical car tips. Whether you're navigating car sales or seeking car advice, our experts answer your car questions answered. Tune in to learn how to buy a car smart! Join us on the Jeep Talk Show for an epic conversation with Derek "Diesel" Meyer – lifelong Jeep fanatic, co-founder of the legendary Silver Lake Sand Dunes Jeep Invasion, and General Manager at Graff Chrysler Dodge Jeep Ram in Rockford, Michigan! In this fun, no-holds-barred interview, Diesel dives deep into: - The Jeep wave debate: Do you wave at Gladiators, XJs, or even 392s with those gold tow hooks?

Hosts Spencer Neuharth, Janis Putelis, and Seth Morris speak with Sue Richardson about her great-great uncle, Wilson "Snowflake" Bentley, and his groundbreaking nature photography, talk some gear, share Top 3s, and chat with Peace River K9 Search and Rescue's Michael Hadsell about the world's only search-and-rescue otter, Splash. Watch the live stream on the MeatEater Podcast Network YouTube channel. Subscribe to The MeatEater Podcast Network MeatEater on Instagram, Facebook, Twitter, and YouTubeSee omnystudio.com/listener for privacy information.