Podcasts about Tooling

All but the last 20 minutes of this episode should be comprehensible to non-physicists.Steve explains where frontier AI models are in understanding frontier theoretical physics. The best analogy is to a “brilliant but unreliable genius colleague”!He describes a specific example: the use of AI in recent research in quantum field theory (Tomonaga-Schwinger integrability conditions applied to state-dependent modifications of quantum mechanics), work now accepted for publication in Physics Letters B after peer review. Remarkably, the main idea in the paper originated de novo from GPT-5.Links:X discussion - https://x.com/hsu_steve/status/1996034522308026435Companion paper: Theoretical Physics With Generative AI -  https://drive.google.com/file/d/16sxJuwsHoi-fvTFbri9Bu8B9bqA6lr1H/viewPhysics paper - https://arxiv.org/abs/2511.15935 | https://www.sciencedirect.com/science/article/pii/S0370269325008111Related discussion of AI and theoretical physics with Prof. Nirmalya Kajuri (IIT) and Prof. Jonathan Oppenheim (UCL) - https://youtu.be/BRuDd3l0e3kRelated video: AIs Win Math Olympiad Gold: Prof. Lin Yang (UCLA) – Manifold #97 - https://youtu.be/8JeRCqNg7RcChapter markers:(00:00) - Intro: AI discussion with specialized physics at the end (03:40) - The current AI landscape for science: frontier models, Co-Scientist, and recent math breakthroughs (11:01) - Why models help and why they fail: errors, deep confabulation, and the research risk (15:54) - The Generator–Verifier workflow: how chaining model inference suppresses mistakes (23:30) - Project origin: testing models on Hsu's older nonlinear QM/QFT work (30:35) - The “GPT-5 moment”: Tomonaga–Schwinger angle appears and produces the key equation (40:35) - Wild goose chases & a practical heuristic: axiomatic QFT detour; Generator-Verifier convergence (51:44) - Referee-driven test case: Kaplan–Rajendran model, past-lightcone geometry, and verification (55:55) - Tooling & outlook: automation prototype, chaining into “supermodels,” where this is headed (59:39) - Physics slides (advanced): TS integrability, microcausality, and why nonlinearity threatens locality –Steve Hsu is Professor of Theoretical Physics and of Computational Mathematics, Science, and Engineering at Michigan State University. Previously, he was Senior Vice President for Research and Innovation at MSU and Director of the Institute of Theoretical Science at the University of Oregon. Hsu is a startup founder (SuperFocus.ai, SafeWeb, Genomic Prediction, Othram) and advisor to venture capital and other investment firms. He was educated at Caltech and Berkeley, was a Harvard Junior Fellow, and has held faculty positions at Yale, the University of Oregon, and MSU. Please send any questions or suggestions to manifold1podcast@gmail.com or Steve on X @hsu_steve.

Why do so many AI rollouts stall right after the tools ship?In this episode of Alexa's Input (AI), Alexa talks with Melissa Reeve, author of the book Hyper Adaptive: Rewiring the Enterprise to Become AI Native, about what it actually takes to get AI adopted in large organizations.Melissa shares how her background in lean, Agile, and DevOps transformation shaped her view that AI adoption is less about “buying the tool” and more about rewiring how work happens. Together, they break down why many AI initiatives fail (and why ROI is slow), the FOCUS framework, the “AI time paradox,” and how support structures like AI activation hubs, social learning, and better success metrics can raise quality and accelerate impact.A must-listen for engineering leaders, product teams, and executives trying to move beyond pilots and turn AI into real operational leverage.Learn more about Melissa and Hyper Adaptive below.LinksWatch: ⁠⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠⁠Read: ⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠Listen:⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠More: ⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠Website: ⁠⁠⁠https://alexagriffith.com/⁠⁠⁠LinkedIn: ⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠Find out more about the guest at:LinkedIn: https://www.linkedin.com/in/melissamreeve/Book: https://itrevolution.com/product/hyperadaptive/KeywordsAI adoption, enterprise transformation, Hyper Adaptive model, organizational change, DevOps, Lean, Agile, AI integration, customer-centricity, innovation accounting, social learningChapters00:00 Introduction to AI Adoption in Enterprises03:00 Melissa's Journey and the Foundation of AI Thinking06:06 The Analogy of DevOps and AI Implementation08:47 Cultural Shifts vs. Tooling in AI Adoption11:49 The Hyper Adaptive Model for AI Integration14:48 Sociology of Workflows and Organizational Change17:49 Understanding AI Initiative Failures21:00 Customer Centricity in AI Solutions23:58 The AI Time Paradox and Learning26:58 AI Activation Hubs and Their Role30:54 The Role of Human Oversight in AI Automation34:03 Incentivizing AI Engagement in Organizations35:59 Social Learning and AI: The Power of Collaboration40:57 Practical Applications of AI in Daily Life44:44 Quality vs. Productivity: The AI Dilemma46:13 The Focus Framework: Prioritizing AI Use Cases48:23 Influencing AI Adoption in Organizations51:07 The Future of Hyper Adaptive Organizations55:08 Decision-Making in the Age of AI57:37 Key Takeaways for Leaders in the AI Revolution

In dieser Folge spreche ich mit Sander Rotmensen, Head of Business Line Cybersecurity Software for OT bei Siemens. Gemeinsam beschäftigen wir uns mit einem Problem, das viel zu oft ignoriert wird: In der Industrie heißt es häufig „Never change a running system“ patchen wäre zu riskant für Produktion und Sicherheit.Sander erklärt, warum genau dieses Mindset in der OT-Welt problematisch ist: Weil Transparenz über Assets, Firmwarestände und Häufigkeit von Updates fehlt, ist es oft unmöglich, Schwachstellen zuverlässig zu identifizieren und zu beheben.Wir gehen dabei folgende Themen durch:Warum klassische IT-Methoden (automatisches Scannen, schnelle Patches) in Industriesteuerungen selten funktionierenWie wichtig eine gründliche Inventarisierung aller OT-Komponenten ist aktiv und auch Geräte, die selten online sindWie man Schwachstellen-Management in der OT pragmatisch angeht: mit Tools, passenden Prozessen oder mit spezialisierten DienstleisternWarum nicht jedes System einfach gepatcht werden kann Zertifizierungen, Safety-Regeln und Produktionszwänge sind entscheidendWie moderne Ansätze aussehen können: Asset-Mapping + Risiko-Analyse + gezielte Updates oder Kompensationsmaßnahmen (Segmentierung, Monitoring, passive Überwachung)Warum OT-Security ein kontinuierlicher Prozess sein muss nicht ein einmaliges ProjektAm Ende geben wir einen pragmatischen Leitfaden: Transparenz schaffen, mit externen Integratoren oder Dienstleistern starten, Prozesse und Tooling etablieren und Sicherheit zur Chefsache machen.____________________________________________

In dieser Folge spreche ich mit Sander Rotmensen, Head of Business Line Cybersecurity Software for OT bei Siemens. Gemeinsam beschäftigen wir uns mit einem Problem, das viel zu oft ignoriert wird: In der Industrie heißt es häufig „Never change a running system“ patchen wäre zu riskant für Produktion und Sicherheit.Sander erklärt, warum genau dieses Mindset in der OT-Welt problematisch ist: Weil Transparenz über Assets, Firmwarestände und Häufigkeit von Updates fehlt, ist es oft unmöglich, Schwachstellen zuverlässig zu identifizieren und zu beheben.Wir gehen dabei folgende Themen durch:Warum klassische IT-Methoden (automatisches Scannen, schnelle Patches) in Industriesteuerungen selten funktionierenWie wichtig eine gründliche Inventarisierung aller OT-Komponenten ist aktiv und auch Geräte, die selten online sindWie man Schwachstellen-Management in der OT pragmatisch angeht: mit Tools, passenden Prozessen oder mit spezialisierten DienstleisternWarum nicht jedes System einfach gepatcht werden kann Zertifizierungen, Safety-Regeln und Produktionszwänge sind entscheidendWie moderne Ansätze aussehen können: Asset-Mapping + Risiko-Analyse + gezielte Updates oder Kompensationsmaßnahmen (Segmentierung, Monitoring, passive Überwachung)Warum OT-Security ein kontinuierlicher Prozess sein muss nicht ein einmaliges ProjektAm Ende geben wir einen pragmatischen Leitfaden: Transparenz schaffen, mit externen Integratoren oder Dienstleistern starten, Prozesse und Tooling etablieren und Sicherheit zur Chefsache machen.____________________________________________

Hyperion Materials & Technologies reveals its latest innovation — the DZ23 carbide grade — a major leap forward for canmaking efficiency, tooling durability, and high-speed line performance.In this episode of The Metal Pack Pod, host Alex Fordham sits down with Lluís Miñarro, the Global Segment Director for Can Tooling Products and Services at Hyperion Materials & Technologies, to explore how advanced carbide grades, global supply chain strategies, and customer-driven engineering are shaping the future of metal packaging.Lluís breaks down:Why lighter carbide grades improve punch dynamicsHow tooling innovation reduces spoilage, energy use, and maintenanceThe real impact of APT / tungsten supply constraints on cost and productionHyperion's expanded global footprint — including new US manufacturing facilitiesWhat canmakers can expect from the next wave of tooling R&DIf you're in canmaking, tooling, operations, sustainability, or metal forming, this episode delivers real-world insight from one of the industry's leading experts.00:00 Intro & episode overview01:20 Industry trends: sustainability, demand rebound & regional shifts03:50 Hyperion's role in global can tooling06:00 Luis Minero's background & passion for metallurgy08:00 Why tooling innovations matter more than ever11:00 Introducing the DZ23 carbide grade13:45 Performance benefits: wear resistance, weight reduction & line efficiency17:20 Understanding APT and tungsten supply chain challenges19:55 How raw material pricing affects tooling costs20:20 Hyperion's new US production capabilities22:00 What's next for Hyperion and can tooling innovation

What's up everyone, today we have the pleasure of sitting down with Anna Aubuchon, VP of Operations at Civic Technologies.(00:00) - Intro (01:15) - In This Episode (04:15) - How AI Flipped the Build Versus Buy Decision (07:13) - Redrawing What “Complex” Means (12:20) - Why In House AI Provides Better Economics And Control (15:33) - How to Treat AI as an Insourcing Engine (21:02) - Moving BI Workloads Out of Dashboards and Into LLMs (31:37) - Guardrails That Keep AI Querying Accurate (38:18) - Using Role Based AI Guardrails Across MCP Servers (44:43) - Ops People are Creators of Systems Rather Than Maintainers of Them (48:12) - Why Natural Language AI Lowers the Barrier for First-Time Builders (52:31) - Technical Literacy Requirements for Next Generation Operators (56:46) - Why Creative Practice Strengthens Operational Leadership Summary: AI has reshaped how operators work, and Anna lays out that shift with the clarity of someone who has rebuilt real systems under pressure. She breaks down how old build versus buy habits hold teams back, how yearly AI contracts quietly drain momentum, and how modern integrations let operators assemble powerful workflows without engineering bottlenecks. She contrasts scattered one-off AI tools with the speed that comes from shared patterns that spread across teams. Her biggest story lands hard. Civic replaced slow dashboards and long queues with orchestration that pulls every system into one conversational layer, letting people get answers in minutes instead of mornings. That speed created nerves around sensitive identity data, but tight guardrails kept the team safe without slowing anything down. Anna ends by pushing operators to think like system designers, not tool babysitters, and to build with the same clarity her daughter uses when she describes exactly what she wants and watches the system take shape.About AnnaAnna Aubuchon is an operations executive with 15+ years building and scaling teams across fintech, blockchain, and AI. As VP of Operations at Civic Technologies, she oversees support, sales, business operations, product operations, and analytics, anchoring the company's growth and performance systems.She has led blockchain operations since 2014 and built cross-functional programs that moved companies from early-stage complexity into stable, scalable execution. Her earlier roles at Gyft and Thomson Reuters focused on commercial operations, enterprise migrations, and global team leadership, supporting revenue retention and major process modernization efforts.How AI Flipped the Build Versus Buy DecisionAI tooling has shifted so quickly that many teams are still making decisions with a playbook written for a different era. Anna explains that the build versus buy framework people lean on carries assumptions that no longer match the tool landscape. She sees operators buying AI products out of habit, even when internal builds have become faster, cheaper, and easier to maintain. She connects that hesitation to outdated mental models rather than actual technical blockers.AI platforms keep rolling out features that shrink the amount of engineering needed to assemble sophisticated workflows. Anna names the layers that changed this dynamic. System integrations through MCP act as glue for data movement. Tools like n8n and Lindy give ops teams workflow automation without needing to file tickets. Then ChatGPT Agents and Cloud Skills launched with prebuilt capabilities that behave like Lego pieces for internal systems. Direct LLM access removed the fear around infrastructure that used to intimidate nontechnical teams. She describes the overall effect as a compression of technical overhead that once justified buying expensive tools.She uses Civic's analytics stack to illustrate how she thinks about the decision. Analytics drives the company's ability to answer questions quickly, and modern integrations kept the build path light. Her team built the system because it reinforced a core competency. She compares that with an AI support bot that would need to handle very different audiences with changing expectations across multiple channels. She describes that work as high domain complexity that demands constant tuning, and the build cost would outweigh the value. Her team bought that piece. She grounds everything in two filters that guide her decisions: core competency and domain complexity.Anna also calls out a cultural pattern that slows AI adoption. Teams buy AI tools individually and create isolated pockets of automation. She wants teams to treat AI workflows as shared assets. She sees momentum building when one group experiments with a workflow and others borrow, extend, or remix it. She believes this turns AI adoption into a group habit rather than scattered personal experiments. She highlights the value of shared patterns because they create a repeatable way for teams to test ideas without rebuilding from scratch.She closes by urging operators to update their decision cycle. Tooling is evolving at a pace that makes six month old assumptions feel stale. She wants teams to revisit build versus buy questions frequently and to treat modern tools as a prompt to redraw boundaries rather than defend old ones. She frames it as an ongoing practice rather than a one time decision.Key takeaway: Reassess your build versus buy decisions every quarter by measuring two factors. First, identify whether the workflow strengthens a core competency that deserves internal ownership. Second, gauge the domain complexity and decide whether the function needs constant tuning or specialized expertise. Use modern integration layers, workflow builders, and direct LLM access to assemble internal systems quickly. Build the pieces that reinforce your strengths, buy the pieces that demand specialized depth, and share internal workflows so other teams can expand your progress.Why In House AI Provides Better Economics And ControlAI tooling has grown into a marketplace crowded with vendors who promise intelligence, automation, and instant transformation. Anna watches teams fall into these patterns with surprising ease. Many of the tools on the market run the same public models under new branding, yet buyers often assume they are purchasing deeply specialized systems trained on inaccessible data. She laughs about driving down the 101 and seeing AI billboards every few minutes, each one selling a glossy shortcut to operational excellence. The overcrowding makes teams feel like they should buy something simply because everyone else is buying something, and that instinct shifts AI procurement from a strategic decision into a reflex."A one year agreement might as well be a decade in AI right now."Anna has seen how annual vendor contracts slow companies down. The moment a team commits to a year long agreement, the urgency to evaluate alternatives vanishes. They adopt a “set it and forget it” mindset because the tool is already purchased, the budget is already allocated, and the contract already sits in legal. AI development moves fast. Contract cycles do not. That mismatch creates friction that becomes expensive, especially when new models launch every few weeks and outperform the ones you purchased only months earlier. Teams do not always notice the cost of stagnation because it creeps in quietly.Anna lays out a practical build versus buy framework. Teams should inspect whether the capability touches their core competency, their customer experience, or their strategic distinctiveness. If it does, then in house AI provides more long term value. It lets the company shape the model around real customer patterns. It keeps experimentation in motion instead...

Some conversations feel scripted. This one… absolutely did not. Larry Robbins walked in ready to talk life, passion, family, culture, workholding, philosophy, and whatever else popped into his head — and somehow it all connected back to manufacturing. This episode of MakingChips is one of the most unhinged, hilarious, honest, and wisdom-packed conversations we've ever recorded. Larry has been in the industry for nearly 46 years, and he's collected enough stories, scars, and laughs for ten careers. From his father dragging him into the business ("long hair doesn't work here") to his famous explanation that SMW makes "magic hands," Larry blends humor and experience into lessons every shop owner needs to hear. His passion for the industry is unmatched — and his candor is even better. Throughout the episode, the crew dives into culture, leadership, lying (don't), modularity, flexibility, high-density workholding, predictable setups, financing equipment, and why you should stop crawling across a dollar to pick up a dime. Larry opens up about the future of manufacturing, warns against bad advice, and reminds everyone that machining touches every single thing in the world. If you're ready for an episode that's equal parts educational and unhinged in the best possible way, buckle up — Larry Robbins is in rare form. Segments (1:00) Larry's background, early failures, and the stories that shaped his approach to leadership (3:31) An investment in ProShop is an investment in your business (3:32) Culture, loving your work, and leadership lessons (5:07) Entering the family business, retirement humor, and long-term commitment (7:23) The reality of workplace culture, honesty, and handling difficult employees (10:02) Integrity, truth-telling, and early lessons on character (13:18) Appreciating machinists and the unseen parts of manufacturing (15:05) Workholding vs. cutting tools and why workholding matters more than people think (16:09) "Magic hands" — Larry's explanation of workholding for a 5-year-old (17:20) Workholding misconceptions and the cost of poor setups (19:00) Vendor trust, trying equipment, and choosing partnerships wisely (20:22) Setup reduction, rigidity vs. flexibility, and predictable processes (22:12) Cutting 12-hour setups and the value of internal vs. external setups (24:16) Why we love Phoenix Heat Treating for Outside Processing (25:24) Expensive machines + cheap vices = lost potential (27:26) Modular workholding, infinite adjustment, and the origins of the industry (29:18) When not to sell a customer — long-term trust over short-term gain (30:19) Why shops "don't know what they don't know" about proper workholding (31:58) Financing workholding and proving ROI to shop owners (33:09) Tooling certs and buying the solution, not just the machine (35:24) High-density workholding and maximizing machine real estate (37:12) Protecting customers from bad investments and the role of good vendors (38:01) The LEGO analogy and building reusable workholding systems (40:13) Trusting experts and using the right resources in decision-making (41:19) Grow your top and bottom line with CliftonLarsonAllen (CLA) (41:57) Buzzwords like Industry 4.0 vs. solving real problems (43:49) Competing with global labor costs and running unattended (44:19) Extending the life of old machines with better processes (46:41) Universal truth: If you're not making chips, you're not making money  Resources mentioned on this episode Connect with Larry Robbins and SMW Autoblok An investment in ProShop is an investment in your business Why we love Phoenix Heat Treating for Outside Processing Grow your top and bottom line with CliftonLarsonAllen (CLA) Smart Money Moves: Equipment Financing Tips with Ty Willis Connect With MakingChips www.MakingChips.com On Facebook On LinkedIn On Instagram On Twitter On YouTube

Data Science (click here to comment) 8. Dezember 2025, Jochen

Adrian is joined by Sofeast Group Head of New Product Development, Paul Adams, to unpack the brutal truth behind the question: “Can you actually afford to manufacture your new product idea?” They bust some of the most dangerous myths (like “MOQ × unit price is my total cost” and “we'll fix reliability later”), then walk through Sofeast/Agilian's 6-phase NPI process for electromechanical products and show how your budget is really consumed; from feasibility and prototyping through to tooling, pilot runs, and mass production. If you're planning to launch a new product, this episode is your reality check and roadmap.   Episode Sections: 00:00 – Intro & who this episode is for  07:02 – Mythbusting: YouTube & “$10k product launch” myths  12:13 – The Sofeast/Agilian 6-phase NPI process  21:18 – How your budget is split across the phases  29:00 – What to expect in each phase & readiness checks  37:31 – Tooling, NRE, and why half a tooling budget is worse than none  43:42 – Budgeting properly and adding contingency  45:21 – Call to action & how Sofeast/Agilian can help   Related content... How to Calculate the Cash Needed to Prototype & Launch your New Product Why does new product development take so long? What is an NRE Cost (Non-Recurring Engineering)? 10 Factors Affecting Electronic Product Design Costs Costs and Milestones to go from Product Concept to Market? The New Product Development Process in Electronics New Product Development In China: 4 Tips To Go Faster Get in touch with us Connect with us on LinkedIn Contact us via Sofeast's contact page Subscribe to our YouTube channel Prefer Facebook? Check us out on FB

What drives execution velocity—better tools or better clarity? Loïc Houssier, CTO of Superhuman Mail (post-Grammarly acquisition), argues that most velocity problems stem from unclear team missions, not inadequate tooling. From steering DocuSign's French acquisition through complex carve-out negotiations to building Superhuman's offline-first architecture with a 100-millisecond interaction rule, Loïc shares hard-won lessons about engineering metrics that actually matter (PR per engineer per week trends over absolutes), when to resist microservices (until it's genuinely painful), and why promotion frameworks determine product quality. Technical leaders will learn how vertical team alignment eliminates dependencies, why guild structures maintain consistency without blocking speed, and how European safety nets create under-appreciated opportunities for technical risk-taking.

Jacob Effron of Redpoint joins Nick to discuss How Model Progress Shifts the Goalposts, Why The Death of Software Is Overstated, and How to Diligence Hypergrowth Without Getting Burned. In this episode we cover: Investing in AI and Vertical Applications Model Layer Advancements and Future Milestones Challenges and Opportunities in Agentic AI Investing in Tooling and Middleware Product Market Fit and Defensibility in AI Applications Verticals with Real Product Market Fit The Evolution of AI Investing Metrics Future Trends in AI and Robotics Guest Links: Jacob's LinkedIn Jacob's X Redpoint's LinkedIn Redpoint's Website The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

Adrian and Paul break down why molding costs “balloon” (over-tight tolerances and cosmetic overkill) and then walk through three practical levers to cut costs safely: smarter tooling design & DFM (wall thickness, draft, gates, material choice), good tooling decisions (steel grades like P20 vs H13, cavity count, hot vs cold runners), and production/process tweaks (machine tonnage matching, sensible regrind use, SPC/sensors, in-tool de-gating). They finish with some tooling-costs myth-busting (cheap tools, mirror finishes, family molds).   Episode Sections: 00:00 Intro & today's topic 01:58 Why costs balloon: tolerances & cosmetics  06:52 Lever #1 — Design & DFM (wall thicknesses, material choice)  14:40 Lever #2 — Tooling decisions (steel grades, cavities)  22:44 Lever #3 — Processing & production setup  27:35 Myth-busting: cheap tools, mirror finishes, family molds  31:23 Recap & where the biggest savings really are   Related content... Product Tooling: Possible To Avoid Paying for it in Full? Common Design For Manufacture Improvements On Plastic Injection Molded Parts When To Sign Off On Injection Mold Tooling? Inside the Journey from DFM to T0→T2 [Podcast] Plastic Playbook: Choosing The Right Polymer [Podcast] Mold Tooling Ownership: The term Chinese suppliers push for will shock you! The Conundrum of Investing in Tooling Before a Final Prototype Get in touch with us Connect with us on LinkedIn Contact us via Sofeast's contact page Subscribe to our YouTube channel Prefer Facebook? Check us out on FB

Matthew Elder joins Tomi and I on the show this week. Matthew is a technician out of Texas, He is just beginning his path in the automotive diagnostic world. We'll share advice for being successful, Tooling options, & finding a good shop. Website- https://autodiagpodcast.com/Facebook Group- https://www.facebook.com/groups/223994012068320/YouTube- https://www.youtube.com/@automotivediagnosticpodcas8832Email- STmobilediag@gmail.comPlease make sure to check out our sponsors!SJ Auto Solutions- https://sjautosolutions.com/Automotive Seminars- https://automotiveseminars.com/L1 Automotive Training- https://www.l1training.com/Autorescue tools- https://autorescuetools.com/   

In this episode, Pooja Ranjan and Leo Alt discuss the innovative project powdr, which aims to enhance the developer experience in the ZK ecosystem through automatic precompilation. They explore the motivations behind the project, its economic implications, and the challenges faced in the adoption of zero-knowledge technology. The conversation also touches on community contributions, early adopters, and the future of powdr in the evolving landscape of ZK projects.

Blockiert dein Code Review gerade mal wieder den Release oder ist es der unsichtbare Klebstoff, der Wissen im Team verteilt? In dieser Episode gehen wir der Frage auf den Grund, warum Reviews weit mehr sind als ein einfaches “looks good to me” und was sie mit sozialer Interaktion, Teamdynamik und Wissensverteilung zu tun haben. Wir sprechen mit Prof. Michael Dorner, Professor für Software Engineering an der TH Nürnberg, der seit Jahren zur Rolle von Code Reviews in der Softwareentwicklung forscht: mit Code Review Daten von Microsoft, Spotify oder trivago. Überall zeigt sich: Pull Requests sind mehr als technische Checks, sie sind Kommunikationsnetzwerke. Gemeinsam beleuchten wir, warum Tooling oft zweitrangig ist, wie sich Review-Praktiken historisch entwickelt haben und was das alles mit Ownership, Architektur und sogar Steuern zu tun hat. Ein Blick auf Code Reviews, der dir definitiv eine neue Perspektive eröffnet.Bonus: Wir erklären, warum alle Informatiker Doktoren auch Philosophen sind ;)Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

TakeawaysThe importance of community in creative challenges like Arch Inktober.Using prompts can enhance creativity and push artistic boundaries.AI tools can assist in the design process but require careful curation.Sketching techniques vary and can be influenced by technology.The significance of architectural history in contemporary design.Engaging with prompts allows for experimentation and exploration.Collaboration between artists can lead to richer outcomes.Reflection on one's work is crucial for growth and improvement.The balance between traditional and digital methods in art is evolving.Encouragement to embrace challenges and not overthink the creative process.TitlesNavigating the Challenges of Live RecordingCelebrating Arch Inktober: A Creative JourneyChapters00:00 Introduction and Technical Glitches05:11 Coffee Talk and Trends in Technology09:33 The Importance of Community and Student Engagement13:52 Arch Inktober: A Creative Challenge18:41 Creative Challenges and Experimentation23:07 Engaging with Artistic Prompts25:33 Sketching Techniques and Processes37:05 AI in Art: Mid-Journey Exploration44:41 Curating Artistic Outputs52:53 Reflections on Artistic Growth and LearningSend Feedback :) Support the showBuy some Coffee! Support the Show!https://ko-fi.com/coffeesketchpodcast/shop Our Links Follow Jamie on Instagram - https://www.instagram.com/falloutstudio/ Follow Kurt on Instagram - https://www.instagram.com/kurtneiswender/ Kurt's Practice - https://www.instagram.com/urbancolabarchitecture/ Coffee Sketch on Twitter - https://twitter.com/coffeesketch Jamie on Twitter - https://twitter.com/falloutstudio Kurt on Twitter - https://twitter.com/kurtneiswender

Sign up for Alex's first live cohort, about Hierarchical Model building!Get 25% off "Building AI Applications for Data Scientists and Software Engineers"Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Why GPs still matter: Gaussian Processes remain a go-to for function estimation, active learning, and experimental design – especially when calibrated uncertainty is non-negotiable.Scaling GP inference: Variational methods with inducing points (as in GPflow) make GPs practical on larger datasets without throwing away principled Bayes.MCMC in practice: Clever parameterizations and gradient-based samplers tighten mixing and efficiency; use MCMC when you need gold-standard posteriors.Bayesian deep learning, pragmatically: Stochastic-gradient training and approximate posteriors bring Bayesian ideas to neural networks at scale.Uncertainty that ships: Monte Carlo dropout and related tricks provide fast, usable uncertainty – even if they're approximations.Model complexity ≠ model quality: Understanding capacity, priors, and inductive bias is key to getting trustworthy predictions.Deep Gaussian Processes: Layered GPs offer flexibility for complex functions, with clear trade-offs in interpretability and compute.Generative models through a Bayesian lens: GANs and friends benefit from explicit priors and uncertainty – useful for safety and downstream decisions.Tooling that matters: Frameworks like GPflow lower the friction from idea to implementation, encouraging reproducible, well-tested modeling.Where we're headed: The future of ML is uncertainty-aware by default – integrating UQ tightly into optimization, design, and deployment.Chapters:08:44 Function Estimation and Bayesian Deep Learning10:41 Understanding Deep Gaussian Processes25:17 Choosing Between Deep GPs and Neural Networks32:01 Interpretability and Practical Tools for GPs43:52 Variational Methods in Gaussian Processes54:44 Deep Neural Networks and Bayesian Inference01:06:13 The Future of Bayesian Deep Learning01:12:28 Advice for Aspiring Researchers

Why does every second LinkedIn post promise an “AI system” that will fix your marketing overnight?

Recorded live at MBA Annual25 in Las Vegas, host Rebecca Kritzman and guests Ashley Sellers, Elaina McFarland, and Bobby Deery break down what lenders are asking for right now: AI-driven workflow efficiency, expanding use of soft-pull strategies, and dual processing to analyze Vantage Score alongside existing scores. Who are the speakers?Rebecca Kritzman – SVP, Experience & Partner Marketing, EquifaxAshley Sellers – VP, Mortgage Sales, EquifaxElaina McFarland – Leader, Solution Sales Experts (Credit & Verification), EquifaxBobby Deery – SVP, Product, Credit Division, EquifaxTogether, they explore the intersection of innovation, compliance, and customer trust.What were the major insights from Day Two?AI and Automation in Workflows: Lenders are adopting AI to streamline process flows and improve efficiency from application through close.Rising Interest in Dual Processing: Many lenders are testing Vantage Score alongside existing models to compare outcomes and assess portfolio risk.Soft Pull Momentum: Equifax's soft-pull tools are helping lenders pre-qualify borrowers and protect consumers' credit scores, especially under the new trigger law.Voice of the Customer: Product teams are incorporating direct lender feedback to guide new innovations such as income qualify and telco/pay-TV/utility data integrations.Education and Clarity: With rapid industry change — from FICO model updates to 1B vs. 3B credit reporting — customers are asking for clear, data-driven guidance. What challenges did attendees highlight?Widespread uncertainty dominated discussions — from pricing implications and trigger-law timing to confusion around single- vs. tri-bureau models. Customers expressed concern about misinformation and asked for help educating both lenders and consumers on what these changes truly mean.What recommendations did Equifax leaders share?Stand up dual-score processing to compare outcomes between Vantage and FICO models.Collaborate with Equifax product teams to provide feedback that shapes future solutions.Audit your process flows to align products (credit, verification, income qualify) with milestones that deliver the most value.Prioritize education and communication — both internally and with consumers — to navigate market shifts confidently. 

Imagine a world where handoff no longer exists and designers are moving fluidly in code…[Drew Wilson](https://x.com/drewwilson) is one of the people pulling that future into the present so this week's episode is a deep dive into his vision for the new design tool [Opacity](https://opacity.app/).Some highlights:- How team structures are changing- How to stand out when everyone is a builder- What design's “Github moment” will look like- The fracturing of the market for design talent- How Drew is approaching this startup differently- Where the new technical threshold is for designers- + a lot moreDrew is also building his new IDE called Loop - https://loupe.build/

Dominic Gannaway joins us to talk about Ripple.js, a new TypeScript-first UI framework built with its own templating language and a focus on clarity and reactivity. We explore how Ripple.js handles fine-grained updates through its track and block system, why it avoids global state, and how context plays a key role. Dominic also walks us through the developer experience, from the language server and VS Code integration to syntax highlighting and the Prettier plugin, plus how the framework handles error boundaries, server-side rendering, future plans, and more. Links Twitter: https://x.com/trueadm Github: https://github.com/trueadm LinkedIn: https://www.linkedin.com/in/dominic-gannaway-414b7750 Resources RippleJS GitHub: https://ripplejs.github.io RippleJS website: https://www.ripplejs.com/ We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey (https://t.co/oKVAEXipxu)! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com (mailto:elizabeth.becz@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Check out our newsletter (https://blog.logrocket.com/the-replay-newsletter/)! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. (https://logrocket.com/signup/?pdr) Chapters 00:00 – Intro & What is RippleJS 01:00 – The Origins and Naming of Ripple 02:00 – A New UI Framework Built on TypeScript 03:30 – Creating a Custom Language and Templating System 05:00 – Building Ripple's Tooling and Language Server 06:00 – The Team, Open Source Growth, and Early Feedback 07:00 – From UI Framework to Meta Framework 09:00 – Integrating AI into the Dev Server 10:30 – Handling Controversy and Changing the Status Quo 11:30 – How Ripple Was Built in a Week 13:00 – Redesigning the Reactivity System 16:00 – Why Ripple Doesn't Use Global State 19:00 – Lessons Learned from Other Frameworks 21:00 – Naming Conventions and API Design Decisions 22:30 – Error Boundaries and Async Patterns in Ripple 24:00 – Accessibility and ByteDance Native App Integration 25:00 – The Team's Workflow and Contributor Culture 27:00 – Building TypeScript-First from Scratch 29:00 – Language Server, Source Maps, and VS Code Integration 31:00 – Building in Public and Open Source Collaboration 32:30 – The Future of Frontend Frameworks 34:00 – How Ripple's Ideas Might Influence Others 35:00 – AI, Security, and the Road Ahead 36:00 – Closing Thoughts & How to Get Involved

In this episode, Darko welcomes Brian Douglas, Head of Developer Experience at Continue and longtime open source advocate. They talk about the rise of the AI engineer, how AI agents are reshaping developer workflows, and what's next for open source infrastructure. Enjoy the episode!Read the blog post: https://semaphore.io/blog/brian-douglasLike this episode? Be sure to leave a ⭐️⭐️⭐️⭐️⭐️ review on the podcast player of your choice and share it with your friends.

In this episode of Engineering Enablement, Laura Tacho and Abi Noda discuss how engineering leaders can plan their 2026 AI budgets effectively amid rapid change and rising costs. Drawing on data from DX's recent poll and industry benchmarks, they explore how much organizations should expect to spend per developer, how to allocate budgets across AI tools, and how to balance innovation with cost control.Laura and Abi also share practical insights on building a multi-vendor strategy, evaluating ROI through the right metrics, and ensuring continuous measurement before and after adoption. They discuss how to communicate AI's value to executives, avoid the trap of cost-cutting narratives, and invest in enablement and training to make adoption stick.Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda  • Substack: ​​https://substack.com/@abinoda  Where to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro: Setting the stage for AI budgeting in 2026(01:45) Results from DX's AI spending poll and early trends(03:30) How companies are currently spending and what to watch in 2026(04:52) Why clear definitions for AI tools matter and how Laura and Abi think about them(07:12) The entry point for 2026 AI tooling budgets and emerging spending patterns(10:14) Why 2026 is the year to prove ROI on AI investments(11:10) How organizations should approach AI budgeting and allocation(15:08) Best practices for managing AI vendors and enterprise licensing(17:02) How to define and choose metrics before and after adopting AI tools(19:30) How to identify bottlenecks and AI use cases with the highest ROI(21:58) Key considerations for AI budgeting (25:10) Why AI investments are about competitiveness, not cost-cutting(27:19) How to use the right language to build trust and executive buy-in(28:18) Why training and enablement are essential parts of AI investment(31:40) How AI add-ons may increase your tool costs(32:47) Why custom and fine-tuned models aren't relevant for most companies today(34:00) The tradeoffs between stipend models and enterprise AI licensesReferenced:DX Core 4 Productivity FrameworkMeasuring AI code assistants and agents2025 State of AI Report: The Builder's PlaybookGitHub Copilot · Your AI pair programmerCursorGleanClaude CodeChatGPTWindsurfTrack Claude Code adoption, impact, and ROI, directly in DXMeasuring AI code assistants and agents with the AI Measurement FrameworkDriving enterprise-wide AI tool adoptionSentryPoolside

Anthony and Katie are joined by Andreas Møller, co-founder of Nordcraft. Nordcraft aims to bring design and development closer together, and as you can imagine Andreas has a unique perspective on design tools. What happens to "hand-off" when designers can get their ideas 90% there?Find Andreas on BlueskyHosts:Anthony Hobday, Generalist Product Designer: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/hobdaydesign⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Katie Langerman, Systems Designer: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/KatieLangerman

Elixir creator, José Valim, is throwing his hat into the coding agent ring with Tidewave –a coding agent for full-stack web development. Tidewave runs in the browser alongside your app, but it's also deeply integrated into Rails and Phoenix. On this episode, José tells us all about it. Also: his agent flow, YOLO mode, an MCP hot take, and more.

Elixir creator, José Valim, is throwing his hat into the coding agent ring with Tidewave –a coding agent for full-stack web development. Tidewave runs in the browser alongside your app, but it's also deeply integrated into Rails and Phoenix. On this episode, José tells us all about it. Also: his agent flow, YOLO mode, an MCP hot take, and more.

Andreas Rossberg unpacks WASM 3.0, covering new capabilities like garbage collection, exception handling, tail calls, and support for 64-bit addressing with multiple memories. The discussion explores deterministic profiles following relaxed sim, WebAssembly's capability-based security model, and advances in sandboxing and module design. Andreas connects these features to practical use cases in JavaScript engines and applications like Google Sheets, then looks ahead to experimental work on threading, stack switching, and async programming models shaping the next phase of the WebAssembly ecosystem. Links Website: https://people.mpi-sws.org/~rossberg GitHub: https://github.com/rossberg Resources WASM 3.0 Completed: https://webassembly.org/news/2025-09-17-wasm-3.0 Chapters 00:00 Intro – Andreas Rossberg and the WebAssembly 3.0 Update 01:05 The State of WebAssembly Today 02:15 Why WebAssembly Exists Beyond the Web 03:20 From WebAssembly 2.0 to 3.0 – What's Actually New 04:30 Garbage Collection: A Game-Changer for Managed Languages 06:00 The Vision of WebAssembly as a Universal Compilation Target 07:40 How GC Support Unlocks Java, Kotlin, and Dart on WASM 09:10 Expanding to 64-bit Memory – Performance and Limits 10:40 WebAssembly for Databases, AI, and LLMs 12:00 Sandboxing and Security by Design 13:10 How Capabilities and Static Analysis Keep WASM Safe 14:30 Multi-Memory Support and Real-World Use Cases 16:00 Developer Ergonomics vs. Specification Purity 17:20 Tail Calls and Functional Programming Benefits 18:40 Function Tables and Secure Indirection 20:00 Exception Handling Finally Arrives 21:10 Determinism, Efficiency, and Why It Matters for Blockchain 22:30 SIMD and Hardware Divergence Across Platforms 24:00 Balancing Portability with Performance 25:20 The Design Philosophy Behind WebAssembly 26:30 Why WASM Rejects Language-Specific Features 27:40 Proposal Process: Who Decides What Gets In 29:00 Browser Vendors and Implementation Challenges 30:10 Early Deployments: GC, Tooling, and Adoption Stories 31:30 Threads, Stack Switching, and the Future of Concurrency 33:00 Async/Await and Coroutines on WebAssembly 34:30 What's Coming Next for WASM Developers 35:40 How to Get Involved – Working Groups and Proposals 37:00 Closing Thoughts and Thanks We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey (https://t.co/oKVAEXipxu)! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabet.becz@logrocket.com (mailto:elizabeth.becz@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. (https://logrocket.com/signup/?pdr)

AI Weekly Rundown From September 29 to October 05th, 2025:

Hi, Spring fans! In this installment we talk to Spring tooling legend Dr. Kris De Volder

IntroductionOne of the great joys of hosting my Fireside PM podcast is the opportunity to reconnect with people I've known for years and go deep into the mechanics of business building. Recently, I sat down with Jason Stoffer, partner at Maveron Capital, a venture firm with a laser focus on consumer companies. Jason and I go way back to my Seattle days, so this was both a reunion and an education. Our conversation turned into a masterclass on scaling consumer businesses, the art of finding moats, and the brutal realities of marketplaces.But beyond the case studies, what stood out were the actionable insights PMs can apply right now. If you're an early or mid-career product manager in Silicon Valley, there are playbooks here you can borrow—not in theory, but in practice.Jason summed up his approach to analyzing companies like this: “So many founders can get caught in the moment that sometimes it's best when we're looking at a new investment to talk about if things go right, what can happen. What would an S-1 or public filing look like? What would the company look like at a big M&A event? And then you work backwards.” That mindset—begin with the end in mind—is as powerful for a product manager shipping features as it is for a VC evaluating billion-dollar bets.In this post, I'll share:* The key lessons from Jason's breakdown of Quince and StubHub* How these lessons apply directly to your PM career* Tactical moves you can make to future-proof your trajectory* Reflections on what surprised me most in this conversationAnd along the way, I'll highlight specific frameworks and examples you can put into action this week.Part 1: Quince and the Power of Supply Chain InnovationWhen Jason first explained Quince's model, I'll admit I was skeptical. On its face, it sounds like yet another DTC apparel play. Sell cheap cashmere sweaters online? Compete with incumbents like Theory and Away? It didn't sound differentiated.Jason disagreed. “Most people know Shein, and Shein was kind of working direct with factories. Quince's innovation was asking, what do factories in Asia have during certain times of the year? They have excess capacity. Those are the same factories who are making a Theory shirt or an Away bag. Quince went to those factories and said, hey, make product for us, you hold the inventory, we'll guarantee we'll sell it.”That's not a design tweak—it's a supply chain disruption. Costco built an empire on this principle. TJX did the same. Walmart before them. If you can structurally rewire how goods get to consumers, you've got the foundation for a massive business.Lesson for PMs: Sometimes the real innovation isn't visible in the interface. It's hidden in the plumbing. As PMs, we often obsess over UI polish, onboarding flows, or feature prioritization. But step back and ask: what's the equivalent of supply chain disruption in your domain? It might be a new data pipeline, a pricing model, or even a workflow that cuts out three layers of manual steps for your users. Those invisible shifts can unlock outsized value.Jason gave the example of Quince's $50 cashmere sweater. “Anyone in retail knows that if you're selling at a 12% gross margin and it's apparel with returns, you're making no money on that. What is it? It's an alternative method of customer acquisition. You hook them with the sweater and sell them everything else.” In other words, they turned a P&L liability into a marketing hack.Actionable move for PMs: Identify your “$50 sweater.” What's the feature you can offer that might look unprofitable or inconvenient in isolation, but serves as an on-ramp to deeper engagement? Maybe it's a generous free tier in SaaS, or an intentionally unscalable white-glove onboarding process. Don't dismiss those just because they don't scale on day one.Part 2: Moats, Marketing, and Hero SKUsJason emphasized that great retailers pair supply chain execution with marketing innovation. Costco has rotisserie chickens and $2 hot dogs. Quince has $50 cashmere sweaters. These “hero SKUs” create shareable moments and lasting brand associations.“You're pairing supply chain innovation with marketing innovation, and it's super effective,” Jason explained.Lesson for PMs: Don't just think about your feature set—think about your hero feature. What's the one thing that makes users say, “You have to try this product”? Too often, PM roadmaps are a laundry list of incremental improvements. Instead, design at least one feature that can carry your brand in conversations, tweets, and TikToks. Think about Figma's multiplayer cursors or Slack's playful onboarding. These are features that double as marketing.Part 3: StubHub and the Economics of TrustAfter Quince, Jason shifted to a very different case study: StubHub. Here, the lesson wasn't about supply chain but about moats built on trust, liquidity, and cash flow mechanics.“Customers will pay for certainty even if they hate you,” Jason said. Think about that. StubHub's fees are infamous. Buyers grumble, sellers grumble. And yet, if you need a Taylor Swift ticket and want to be sure it's legit, you go to StubHub. That reliability is the moat.Lesson for PMs: Trust is an underrated product feature. In consumer software, this might mean uptime and reliability. In enterprise SaaS, it might mean compliance and security certifications. In AI, it could mean interpretability and guardrails. Don't underestimate how much people will endure friction if they can be sure you'll deliver.Jason also pointed out StubHub's cash flow hack: “StubHub gets money from buyers up front and then pays the sellers later. That's a beautiful business model. If you create a cash flow cycle where you're getting the money first and delivering later, you raise a lot less equity and get diluted less.”This is a reminder that product decisions can have financial implications. As PMs, you may not directly set billing cycles, but you can influence monetization models, free trial design, or even refund policies—all of which affect working capital.Actionable move for PMs: Partner with finance. Ask them: what product levers could improve cash conversion cycles? Could prepayment discounts, annual billing, or usage-based pricing reduce working capital strain? Thinking beyond the feature spec makes you more valuable to your company—and accelerates your own career.Part 4: Five Takeaways from StubHub Jason listed five lessons from StubHub:* Trust is a moat – Even if users complain, reliability keeps them loyal.* Liquidity is a moat – Scale compounds, especially in marketplaces.* Cash flow mechanics matter – Payment terms can determine survival.* Tooling locks in supply – Seller-facing tools create stickiness.* Scale itself compounds – Once you're ahead, momentum carries you.Part 5: What Surprised Me MostAs I listened back to this conversation, two surprises stood out.First, the sheer size of value retail. Jason noted that TJX is worth $157 billion. Burlington, $22 billion. Costco, $418 billion. These aren't sexy tech names, but they are empires. It made me rethink my assumptions about what “boring” industries can teach us.Second, Jason's humility about being wrong. “Reddit might be one,” he admitted when I asked about his biggest misses. “I had no idea that LLMs would use their data in a way that would make it incredibly important. I was dead wrong. I said sit on the sidelines.” That candor is refreshing—and a reminder that even seasoned investors get it wrong. The key is to keep learning.Lesson for PMs: Admit your misses. Write them down. Share them. Don't hide them. Your credibility grows when you own your blind spots and show how you've adjusted.Closing ThoughtsTalking with Jason felt like being back in business school—but with sharper edges. These aren't abstract frameworks. They're battle-tested strategies from companies that scaled to billions. As PMs, our job isn't just to ship features. It's to build businesses. That requires thinking about supply chains, trust, cash flow, and marketing moats.If you found this helpful and want to go deeper, check out Jason's Substack, Ringing the Bell, where he publishes his case studies. And if you want to level up your own career trajectory, I offer 1:1 executive, career, and product coaching at tomleungcoaching.com.Shape the Future of PMAnd if you haven't yet, I'd love your input on my Future of Product Management survey. It only takes about 5 minutes, and by filling it out you'll get early access to the results plus an invitation to a live readout with a panel of top product leaders. The survey explores how AI, team structures, and skill sets are reshaping the PM role for 2026 and beyond.OK. Let's ship greatness. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

Charlie Marsh built Ruff (an extremely fast Python linter written in Rust) and uv (an extremely fast Python package manager written in Rust) because he believes great tools can have an outsized impact. He believes it so much, in fact, that he started an entire company that builds next-gen Python tooling. On this episode, Charlie joins us to tell us all about it: why Python, why Rust, how they make everything so fast, how they're starting to make money, what other products he's dreaming up, and more.

This week on The Data Stack Show, Alexander Patrushev joins John to share his journey from working on mainframes at IBM to leading AI infrastructure innovation at Nebius, with stops at VMware and AWS along the way. The discussion explores the evolution of AI and cloud infrastructure, the five pillars of successful machine learning projects, and the unique challenges of building and operating modern AI data centers—including energy consumption, cooling, and networking. Alexander also delves into the practicalities of infrastructure as code, the importance of data quality, and offers actionable advice for those looking to break into the AI field. Key takeaways include the need for strong data foundations, thoughtful project selection, and the value of leveraging existing skills and tools to succeed in the rapidly evolving AI landscape. Don't miss this great conversation.Highlights from this week's conversation include:Alexander's Background and Early Career at IBM (1:06)Moving From Mainframes to Virtualization at VMware (4:09)Transitioning to AWS and Machine Learning Projects (8:22)What Was Missed From Mainframes and the Rise of Public Cloud (9:03)Security, Performance, and Economics in Cloud Infrastructure (12:40)The Five Pillars of Successful Machine Learning Projects (15:02)Choosing the Right ML Project: Data, Impact, and Existing Solutions (18:01)Real-World AI and ML Use Cases Across Industries (19:42)Building Specialized AI Clouds Versus Hyperscalers (22:08)Performance, Scalability, and Reliability in AI Infrastructure (25:18)Data Center Energy Consumption and Power Challenges (28:41)Cooling, Networking, and Supporting Systems in AI Data Centers (30:06)Infrastructure as Code and Tooling in AI (31:50)Lowering Complexity for AI Developers and the Role of Abstraction (34:08)Startup Opportunities in the AI Stack (38:53)When to Fine-Tune or Post-Train Foundation Models (43:41)Comparing and Testing Models With Tool Use (47:49)Skills and Advice for Entering the AI Field (49:18)Final Thoughts and Encouragement for AI Newcomers (52:31)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. Each week, we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Charlie Marsh built Ruff (an extremely fast Python linter written in Rust) and uv (an extremely fast Python package manager written in Rust) because he believes great tools can have an outsized impact. He believes it so much, in fact, that he started an entire company that builds next-gen Python tooling. On this episode, Charlie joins us to tell us all about it: why Python, why Rust, how they make everything so fast, how they're starting to make money, what other products he's dreaming up, and more.

AI Weekly Rundown From September 21 to September 28th, 2025:

Quinn Slack (CEO) and Thorsten Ball (Amp Dictator) from SourceGraph join the show to talk about Amp Code, how they ship 15x/day with no code reviews, and why subagents and prompt optimizers aren't a promising direction for coding agents. Amp Code: https://ampcode.com/ Latent Space: https://latent.space/ 00:00 Introduction 00:41 Transition from Cody to Amp 03:18 The Importance of Building the Best Coding Agent 06:43 Adapting to a Rapidly Evolving AI Tooling Landscape 09:36 Dogfooding at Sourcegraph 12:35 CLI vs. VS Code Extension 21:08 Positioning Amp in Coding Agent Market 24:10 The Diminishing Importance of Model Selectors 32:39 Tooling vs. Harness 37:19 Common Failure Modes of Coding Agents 47:33 Agent-Friendly Logging and Tooling 52:31 Are Subagents Real? 56:52 New Frameworks and Agent-Integrated Developer Tools 1:00:25 How Agents Are Encouraging Codebase and Workflow Changes 1:03:13 Evolving Outer Loop Tasks 1:07:09 Version Control and Merge Conflicts in an AI-First World 1:10:36 Rise of User-Generated Enterprise Software 1:14:39 Empowering Technical Leaders with AI 1:17:11 Evaluating Product Without Traditional Evals 1:20:58 Hiring

Topics covered in this episode: * pandas is getting pd.col expressions* * Cline, At-Cost Agentic IDE Tooling* * uv cheatsheet* Ducky Network UI Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: pandas is getting pd.col expressions Marco Gorelli Next release of Pandas will have pd.col(), inspired by some of the other frameworks I'm guessing Pandas 2.3.3? or 2.4.0? or 3.0.0? (depending on which version they bump?) “The output of pd.col is called an expression. You can think of it as a delayed column - it only produces a result once it's evaluated inside a dataframe context.” It replaces many contexts where lambda expressions were used Michael #2: Cline, At-Cost Agentic IDE Tooling Free and open-source Probably supports your IDE (if your IDE isn't a terminal) VS Code VS Code Insiders Cursor Windsurf JetBrains IDEs (including PyCharm) You pick plan or act (very important) It shows you the price as the AI works, per request, right in the UI Brian #3: uv cheatsheet Rodgrigo at mathspp.com Nice compact cheat sheet of commands for Creating projects Managing dependencies Lifecycle stuff like build, publish, bumping version uv tool (uvx) commands working with scripts Installing and updating Python versions plus venv, pip, format, help and update Michael #4: Ducky Network UI Ducky is a powerful, open-source, all-in-one desktop application built with Python and PySide6. It is designed to be the perfect companion for network engineers, students, and tech enthusiasts, combining several essential utilities into a single, intuitive graphical interface. Features Multi-Protocol Terminal: Connect via SSH, Telnet, and Serial (COM) in a modern, tabbed interface. SNMP Topology Mapper: Automatically discover your network with a ping and SNMP sweep. See a graphical map of your devices, color-coded by type, and click to view detailed information. Network Diagnostics: A full suite of tools including a Subnet Calculator, Network Monitor (Ping, Traceroute), and a multi-threaded Port Scanner. Security Toolkit: Look up CVEs from the NIST database, check password strength, and calculate file hashes (MD5, SHA1, SHA256, SHA512). Rich-Text Notepad: Keep notes and reminders in a dockable widget with formatting tools and auto-save. Customizable UI: Switch between a sleek dark theme and a clean light theme. Customize terminal colors and fonts to your liking. Extras Brian: Where are the cool kids hosting static sites these days? Moving from Netlify to Cloudflare Pages - Will Vincent from Feb 2024 Traffic is a concern now for even low-ish traffic sites since so many bots are out there Netlify free plan is less than 30 GB/mo allowed (grandfathered plans are 100 GB/mo) GH Pages have a soft limit of 100 GB/mo Cloudflare pages says unlimited Michael: PyCon Brazil needs some help with reduced funding from the PSF Get a ticket to donate for a student to attend (at the button of the buy ticket checkout dialog) I upgraded to macOS Tahoe Loving it so far. Only issue I've seen so far has been with alt-tab for macOS Joke: Hiring in 2025 vs 2021 2021: “Do you have an in-house kombucha sommelier?” “Let's talk about pets, are you donkey-friendly?”, “Oh you think this is a joke?” 2025: “Round 8/7” “Out of 12,000 resumes, the AI picked yours” “Binary tree? Build me a foundational model!” “Healthcare? What, you want to live forever?”

Creating 3D assets can be daunting, but does it have to be? Mahima and Rakesh are on a quest to democratize 3D content creation with AssetGen, a foundation model for 3D. They discuss the challenges of training such a model given the scarcity of available data and how large language models have unlocked key solutions. As if that weren't enough, they're also tackling the ambitious goal of generating entire worlds from a simple prompt. Tune in to learn more! Got feedback? Send it to us on Threads (https://threads.net/@metatechpod), Instagram (https://instagram.com/metatechpod) and don't forget to follow our host Pascal (https://mastodon.social/@passy, https://threads.net/@passy_, @passy.bsky.social). Fancy working with us? Check out https://www.metacareers.com/. Links Horizon Worlds Desktop Editor: https://developers.meta.com/horizon-worlds/advanced-tools  Horizon Worlds Studio: https://developers.meta.com/horizon-worlds/studio/application Meta Ray-Ban Display: https://www.meta.com/gb/ai-glasses/meta-ray-ban-display/  MTP 77 - How to build a human-computer interface for everyone: https://engineering.fb.com/2025/08/04/virtual-reality/building-a-human-computer-interface-for-everyone-meta-tech-podcast/  Timestamps Intro 0:06 Introduction Mahima 1:39 Introduction Rakesh 2:57 Team mission 3:26 Why is 3D content hard to create? 5:15 The Metaverse 7:49 Tooling vision in Horizon Worlds 10:31 AssetGen Architecture 15:27 Consolidating models 18:25 From assets to worlds 19:22 Time to generate 24:46 Feedback loop 26:41 What's the market for AssetGen 29:49 What's available today? 31:26 What's next? 32:11 Outro 35:24

Razor Tooling is evolving! Carl and Richard talk to David Wengier about the changes coming for Razor Pages in the next version of Visual Studio. David talks about the realization that much of the new work in Razor ties closely to Roslyn, which has resulted in a new co-hosting model that means higher performance and reliability for your web pages! The conversation delves into how capabilities in Visual Studio Code are shared with Visual Studio and vice versa, as well as the role of the Language Service Protocol in making it easier to bring more powerful tools to you.

Razor Tooling is evolving! Carl and Richard talk to David Wengier about the changes coming for Razor Pages in the next version of Visual Studio. David talks about the realization that much of the new work in Razor ties closely to Roslyn, which has resulted in a new co-hosting model that means higher performance and reliability for your web pages! The conversation delves into how capabilities in Visual Studio Code are shared with Visual Studio and vice versa, as well as the role of the Language Service Protocol in making it easier to bring more powerful tools to you.

Alexander Lichter joins the podcast to talk about Rolldown, a bundler built in Rust by Void Zero that aims to replace Rollup and ESBuild with faster builds and better enterprise scalability. He dives into the power of OXC and Oxlint, the push toward a unified JavaScript toolchain, and previews what to expect at ViteConf 2024. Links X: https://x.com/TheAlexLichter Website: https://www.lichter.io Mastodon: https://hachyderm.io/@manniL GitHub: https://github.com/manniL YouTube: https://www.youtube.com/@TheAlexLichter Twitch: https://www.twitch.tv/TheAlexLichter LinkedIn: https://www.linkedin.com/in/alexanderlichter Resources Rolldown: How Vite Bundles at the Speed of Rust: https://squiggleconf.com/2025/sessions#rolldown-how-vite-bundles-at-the-speed-of-rust Rolldown: https://rolldown.rs Rolldown-vite migration: https://vite.dev/guide/rolldown Oxlint Type Aware linting (preview) announcement: https://oxc.rs/blog/2025-08-17-oxlint-type-aware.html ViteConf: https://viteconf.amsterda Benchmarks: Minifier: https://github.com/privatenumber/minification-benchmarks Linter: https://github.com/oxc-project/bench-javascript-linter Parser: https://github.com/oxc-project/bench-javascript-parser-written-in-rust Transformer: https://github.com/oxc-project/bench-transformer/ Bundler: https://github.com/rolldown/benchmarks Chapters We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey (https://t.co/oKVAEXipxu)! Let us know by sending an email to our producer, Em, at emily.kochanek@logrocket.com (mailto:emily.kochanek@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. (https://logrocket.com/signup/?pdr) Special Guest: Alexander Lichter.

Willard and Dibs continue to debate if the 49ers are closer to a Super Bowl or to a complete re-tooling and discuss where Brock Purdy fits into this whole equation.

Web development is constantly evolving, and so are the tools we use to build. In this episode, Amy and Brad chat with the organizers of Squiggle Conf about the future of web dev tooling, how conferences shape the developer experience, and why community matters just as much as code.Chapters0:00 - Intro0:34 - Meet the Guests: Squiggle Conf OrganizersSquiggle Conf1:19 - What Makes Squiggle Conf Unique3:19 - Tooling and Developer Experience3:30 - Penguins, IMAX, and the Conference Venue4:18 - Who Should Attend Squiggle Conf5:31 - How Talks Are Selected and Curated6:51 - Social and Community Aspects of the Conference12:19 - Behind the Scenes of Organizing a Conference17:46 - Lessons Learned from Running Events23:30 - The Role of Tooling in Modern Development27:21 - Browser-Based Tools and Their Impact28:51 - Shoutout to Astro and Other FrameworksAstroStarlight - Astro's template for documentation33:51 - Comparing Different Conference Experiences38:55 - Building Momentum in the Developer Community40:45 - Looking Ahead: The Future of Squiggle Conf42:02 - Final Thoughts from the Organizers43:43 - Picks and PlugsAre the Types Wrong? — a package & CLI tool by Andrew Branch from the TypeScript teamThe Harry Potter movie seriesCloudflareOne Switch - Mac Menu Bar AppRedwoodSDK

Episode 289: Adrian is joined again by our head of New Product Development, Paul Adams, to explore the complexity of plastic injection mold tooling and what it takes to go from tooling design to mass production of plastic parts. They explore the full journey, from DFM and tool design through trial runs (T0, T1, T2) to final sign-off and mass production. Along the way, they highlight common pitfalls, golden samples, and why rushing into production can be a costly mistake. You'll love this episode if you're developing plastic products, as it will help you to avoid surprises and manage expectations in the new product introduction (NPI) process.   Episode Sections: (00:00:03) Introduction to episode 289 (00:00:13) Adrian welcomes back Paul Adams (00:00:35) Today's topic: plastic injection mold tooling and its complexity (00:01:11) From DFM to mass production – the journey explained (00:02:01) Why tooling is expensive and misunderstood (00:02:48) The role of DFM (Design for Manufacturing) in tooling (00:05:13) Customer involvement and asking the right questions (00:05:19) Tooling design: bolster set vs. core and cavity (00:06:21) Material procurement and standard vs. custom components (00:09:01) Machining the tool: CNC, EDM, wire cutting, polishing (00:11:12) Metal safe condition and first fitting (00:11:59) The T0 trial run explained (00:13:42) First look at molded parts and making big adjustments (00:15:09) The T1 trial run with virgin polymer (00:15:57) Inspection reports and customer sign-off (00:18:00) Surface texturing between T1 and T2 (00:18:14) T2 trial – final tuning and sign-off preparation (00:19:02) Phase gates link: tooling to mass production (00:20:19) Golden samples and color consistency checks (00:22:02) Why being on the ground in China helps with sign-off (00:23:23) Limit samples and customer approval process (00:23:55) The importance of T0–T2 for expectation management (00:24:58) Why not to rush into mass production (00:25:02) Links to prototypes and phase gate methodology (00:26:05) Don't sign off tooling until everything is consistent (00:26:59) Moving into mass production and ongoing monitoring (00:28:28) Tool lifespan and long-term considerations (00:28:48) Wrapping up: intricacies of tooling complexity (00:29:16) Sofeast NPI guide and related video resources (00:30:16) Looking ahead: polymers and material selection (00:30:45) Closing remarks and call to action   Related content... How We Work With You On New Product Development & Manufacturing Projects - Agilian NPI Process Tooling Management for Plastic Injection Molds in China 7 Key NPI Tasks Before Production The Conundrum of Investing in Tooling Before a Final Prototype Inside the Tooling: Common Plastic Injection Mold Components Explained Understanding Plastic Injection Mold Tooling Complexity, from DFM to T1, When Manufacturing in China (Video) Get in touch with us Connect with us on LinkedIn Contact us via Sofeast's contact page Subscribe to our YouTube channel Prefer Facebook? Check us out on FB

Inventory and materials management may not sound glamorous, but for us—and for any thriving shop—it's the difference between healthy cashflow and a financial chokehold. In this Machine Shop MBA conversation, we break down how smart inventory practices—both physical and digital—can free up space, cut costs, and improve delivery performance. From raw materials and finished goods to WIP and cutting tools, we share the financial, operational, and workflow implications of what you keep on the shelf (and what you shouldn't). You'll hear real-world examples of vendor-managed material programs, strategies for keeping traceability without burying yourself in admin work, and ways we've turned “dead” stock into real cash. We also dig into why inventory accuracy matters beyond just making parts—touching on tax implications, property valuations, and how inventory missteps can kill the value of your business in a sale. And if you've ever lost hours hunting for the right cutter or fixture, our storage and tracking advice might change the way you think about tooling forever. This episode isn't about counting nuts and bolts—it's about building an inventory strategy that supports your cashflow, your team's efficiency, and your long-term profitability. Segments (0:24) Paperless Parts: Quoting made simple, profitable, and powerful (3:09) Why inventory is “sneaky important” for cashflow, workflow, and profitability (4:00) Common categories: raw materials, finished goods, consumables, and workholding (6:30) “Part stock” vs. catalogued vs hybrid strategies (7:58) Calculating the real cost of capital when buying material in bulk (10:45) Consolidating material sizes to reduce stock complexity (13:07) Physical storage, traceability, and avoiding costly scrap from lost certs (15:07) Labeling and marking best practices—from PO numbers to color codes (19:05) Storage layouts that save space and speed up retrieval (22:28) FIFO, LIFO, and how inventory accounting can impact your taxes (24:07) Why you need to check out the SMW Autoblok Catalogue  (24:50) Why WIP can matter for accurate financials and business valuation (29:24) Cycle counting vs. painful year-end full inventory counts (33:26) Real-world wins from knowing exactly what's on your shelves (36:10) Avoiding the trap of overbuilding and obsolete finished goods (39:09) Using contracts and order commitments to protect yourself from rev changes (42:02) Inventory strategies for cutting tools—your most critical shop consumable (45:11) The value of having the right tool at the right time vs. lowest cost (49:45) Why random storage beats “organized” by type for cutting tools (52:19) Fixture storage, location tracking, and purging rarely used setups (54:39) How reviewing inventory can generate sales and free up cash (57:35) Key takeaways for building a smart, profitable inventory strategy  (58:42) Grow your top and bottom line with CLA Resources mentioned on this episode Tooling and the Demon of Chaos Unlocking Tax Savings: Essential Strategies You Can Implement Immediately Paperless Parts: Quoting made simple, profitable, and powerful Why you need to check out the SMW Autoblok Catalogue  Grow your top and bottom line with CLA Connect With MakingChips www.MakingChips.com On Facebook On LinkedIn On Instagram On Twitter On YouTube

In this episode, Jason, Wesley, and Mary share some of our favorite tools of the trade—from live streaming setups and demo-building tricks to the software and hardware we rely on for recording videos and tracking metrics. Join us for a practical, behind-the-scenes look at the gear and workflows that help us connect with developers and communities every day. Categories Building & Managing Websites Hugo (https://gohugo.io/) Astro (https://astro.build/) Form Bricks (https://formbricks.com/) Local Recall (https://github.com/mudler/LocalRecall) SquareSpace (https://www.squarespace.com/) Eleventy (https://www.11ty.dev/) Data, metrics, and knowledge sharing Airtable (https://airtable.com/) Common Room (https://www.commonroom.io/) Metabase (https://www.metabase.com/) Scheduling meetings Cal.com (https://cal.com/) Fantastical (https://flexibits.com/fantastical) LiveStreaming & video recording and editing Streamyard (https://streamyard.com/) Riverside.fm (http://riverside.fm/) OBS (https://obsproject.com/) OpenShot (https://www.openshot.org/) Audacity (https://www.audacityteam.org/) VLC (https://www.videolan.org/vlc/) Descript (https://www.descript.com/) Otter Meeting Agent - AI Notetaker, Transcription, Insights (http://otter.ai/) Automation tools n8n (https://n8n.io/) Zapier (https://zapier.com/) IFTTT (https://ifttt.com/) Forums Slack (https://slack.com/) Discourse (https://www.discourse.org/) Podcast hosting Fireside (https://fireside.fm/) Building demos Claude Code (https://chat.chatbot.app/claude?utm_source=GoogleAds&utm_medium=cpc&utm_campaign={campaign}&utm_id=22665042439&utm_term=180325682866&utm_content=767386553008&gad_source=1&gad_campaignid=22665042439&gbraid=0AAAAA_a6ETtwr7jtRKa-4KqypAZlQydKF&gclid=CjwKCAjw49vEBhAVEiwADnMbbDl9w_QW525TCw1W56_NGJOqgGOZDKJopNiYSH_pc_yRGVDpUoZ1CxoCL1UQAvD_BwE) Lovable (https://lovable.dev/?via=promo80&via=promo80&gad_source=1) Cursor (https://cursor.com/en) LocalAI (https://localai.io/) Enjoy the podcast? Please take a few moments to leave us a review on iTunes (https://itunes.apple.com/us/podcast/community-pulse/id1218368182?mt=2) and follow us on Spotify (https://open.spotify.com/show/3I7g5W9fMSgpWu38zZMjet?si=eb528c7de12b4d7a&nd=1&dlsi=b0c85248dabc48ce), or leave a review on one of the other many podcasting sites that we're on! Your support means a lot to us and helps us continue to produce episodes every month. Like all things Community, this too takes a village. Photo by Todd Quackenbush on Unsplash.

In this episode Bill Kennedy and Kenneth Stott discuss the evolving role of AI in organizations, emphasizing the importance of human intelligence in leveraging AI tools effectively. They explore the challenges and opportunities presented by generative AI, the need for a structured data language within organizations, and the ethical considerations surrounding AI insights. Ken shares insights from his upcoming book on redefining organizational intelligence in the age of AI, highlighting strategies for effective human-AI collaboration.00:00 Introduction2:45 AI and Organizational Behavior5:30 Limitations of Generative AI11:00 Learning the AI Tooling15:00 Using AI Tools for Code20:00 Business Decisions / Analytics25:00 Trusting AI Insights 36:00 What is an AI Agent43:00 Future of Organizational Intelligence Connect with  Kenneth: Linkedin: https://www.linkedin.com/in/kenstott/Mentioned in this Episode:Hasura: https://hasura.io/Cursor: https://cursor.com/Claude: https://www.anthropic.com/claude-codeWant more from Ardan Labs? You can learn Go, Kubernetes, Docker & more through our video training, live events, or through our blog!Online Courses : https://ardanlabs.com/education/ Live Events : https://www.ardanlabs.com/live-training-events/ Blog : https://www.ardanlabs.com/blog Github : https://github.com/ardanlabs

In this episode, Dave and Jamison answer these questions: I'm a long time listener to the podcast. Thanks for reading and answering my question! I have over 20+ yrs experience as a manual QA and 6+ yrs experience as a SDET. I'm in a new role as a hybrid manual QA / SDET for a company that hasn't had QA for a few years. After a couple of months a new hire was added to support a new project in non-development or QA tasks. While waiting for the launch of the new project, senior leadership decided to have this new hire to help me with QA. They have no experience in QA or coding. I spent a considerable amount of time training them, and found it difficult. After a few months my manager told me the hire will transition to lead QA. They will NOT be my supervisor or manager. I will be answering directly to the manager as before. I feel sidelined since I didn't get hired on as a Sr. or Lead role. I've already been left out of numerous meetings catered to team leads only. The new hire is very vocal in meetings. They repeat my ideas as their own, and speak for me when I don't agree. It's exhausting to hold back ideas from the new hire or correct them and add context to the rest of the team when I disagree. I'm worried I'm training this new QA lead to be my replacement. What are your thoughts? I feel like the company culture is chaotic for the long term. Any thoughts what I should do in the short term and long term? Hi Dave and Jamison (as a unit would you answer to Davison?). Long time listener, first time caller. I recently joined a data-engineering team at chill 90s multi-national tech company. My boss and I are based in the UK, and two more junior engineers who do the bulk of the IC work are based in India. These two engineers seem to work hard, have far more domain knowledge and technical ability than me, and generally seem to do most of the work. There's also a senior engineer who's kind of absent. My boss is a ‘red personality' who's been at the org for at least a decade, who doesn't seem as close to the technical detail. He cares about the destination and wants to get there yesterday, but discussions about ‘ways of working' or the specifics of achieving the output seem to bore him. He characterizes such talk as risk-aversion. I'm shocked by some of the technical details. Tooling chosen specifically to bypass version control, editing Jupyter Notebooks to deploy changes to ‘production', dashboards that seem to have totally wrong data, etc. It seems like they will do the minimum required to make things ‘work' and then move on. Scalability or making things interpret-able for others just doesn't seem to weigh on their mind. It's then me as the new-joiner navigating their hacky code who inevitably wanders into all the pitfalls and gotchas. I've tried to advocate for better practices and lead by example. They nod along, but ultimately seem resistant to change. I need their help and experience with the codebase, but I also have this creeping sense that their working style is too sloppy and unprofessional. They don't report to me, and our mutual boss seems happy with the work. I feel a bit like the guy in Twilight Zone: I can see a gremlin wrecking the plane, but nobody else can see it, and my attempts to address the situation just seem a bit hysterical. What's worse, my gentle attempts at flagging the issues with my boss haven't gone down well. In my first performance review my boss mentioned something about a ‘us versus them attitude' and ‘assuming good intent'. What do you make of this situation? Am I the a-hole? Have you faced this sort of thing in the past? Is it time to consider old-reliable? Is 4 months too soon to quit a job?

Disaster Recovery with ZFS: A Practical Guide, The best interfaces we never built, Choose Tools That Make You Happy, open source has turned into two worlds, TrueNAS CORE is Dead – Long Live zVault, You should start a computer club in the place that you live, and more NOTES This episode of BSDNow is brought to you by Tarsnap (https://www.tarsnap.com/bsdnow) and the BSDNow Patreon (https://www.patreon.com/bsdnow) Headlines Disaster Recovery with ZFS: A Practical Guide (https://klarasystems.com/articles/disaster-recovery-with-zfs-practical-guide/?utm_source=BSD%20Now&utm_medium=Podcast) The best interfaces we never built (https://www.chrbutler.com/the-best-interfaces-we-never-built) News Roundup You Can Choose Tools That Make You Happy (https://borretti.me/article/you-can-choose-tools-that-make-you-happy) I feel open source has turned into two worlds (https://utcc.utoronto.ca/~cks/space/blog/tech/OpenSourceTwoWorlds) UPDATE 2 – TrueNAS CORE is Dead – Long Live zVault (https://vermaden.wordpress.com/2024/04/20/truenas-core-versus-truenas-scale/#truenas-core-dead-long-live-zvault) You should start a computer club in the place that you live (https://startacomputer.club) Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Brad - syslogng issue (https://github.com/BSDNow/bsdnow.tv/blob/master/episodes/618/feedback/Brad%20-%20syslogng%20issue.md) Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv (mailto:feedback@bsdnow.tv) Join us and other BSD Fans in our BSD Now Telegram channel (https://t.me/bsdnow)

AI coding agents are getting wild. Scott and Wes break down the latest tools that run in the background, write code across multiple steps, and charge you $200 a month to do it. From CLI-based primitives to full-on copilots, this episode covers the next wave of dev tools and what it takes to use them effectively. Show Notes 00:00 Welcome to Syntax! 03:13 Background Agents. 04:26 Appropriate tasks for background agents. 12:46 CLI tooling. 14:17 Claude Code Pricing. 18:20 Approaches to get the most from these tools. 19:56 PRD Documents. Atlasian What's a PRD Document. 20:50 Claude Taskmaster. Langflow. 25:29 Sick Picks & Shameless Plugs. Sick Picks Scott: RingConn. Wes: Dell Projector Shameless Plugs Scott: Syntax on YouTube. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads