Podcasts about ai engineer

GPUs dominate today's AI landscape, but Google argues they are not necessary for every workload. As AI adoption has grown, customers have increasingly demanded compute options that deliver high performance with lower cost and power consumption. Drawing on its long history of custom silicon, Google introduced Axion CPUs in 2024 to meet needs for massive scale, flexibility, and general-purpose computing alongside AI workloads. The Axion-based C4A instance is generally available, while the newer N4A virtual machines promise up to 2x price performance.In this episode, Andrei Gueletii, a technical solutions consultant for Google Cloud joined Gari Singh, a product manager for Google Kubernetes Engine (GKE), and Pranay Bakre, a principal solutions engineer at Arm for this episode, recorded at KubeCon + CloudNativeCon North America, in Atlanta. Built on Arm Neoverse V2 cores, Axion processors emphasize energy efficiency and customization, including flexible machine shapes that let users tailor memory and CPU resources. These features are particularly valuable for platform engineering teams, which must optimize centralized infrastructure for cost, FinOps goals, and price performance as they scale.Importantly, many AI tasks—such as inference for smaller models or batch-oriented jobs—do not require GPUs. CPUs can be more efficient when GPU memory is underutilized or latency demands are low. By decoupling workloads and choosing the right compute for each task, organizations can significantly reduce AI compute costs.Learn more from The New Stack about the Axion-based C4A: Beyond Speed: Why Your Next App Must Be Multi-ArchitectureArm: See a Demo About Migrating a x86-Based App to ARM64Join our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Enterprises are racing to deploy AI services, but the teams responsible for running them in production are seeing familiar problems reemerge—most notably, silos between data scientists and operations teams, reminiscent of the old DevOps divide. In a discussion recorded at AWS re:Invent 2025, IBM's Thanos Matzanas and Martin Fuentes argue that the challenge isn't new technology but repeating organizational patterns. As data teams move from internal projects to revenue-critical, customer-facing applications, they face new pressures around reliability, observability, and accountability.The speakers stress that many existing observability and governance practices still apply. Standard metrics, KPIs, SLOs, access controls, and audit logs remain essential foundations, even as AI introduces non-determinism and a heavier reliance on human feedback to assess quality. Tools like OpenTelemetry provide common ground, but culture matters more than tooling.Both emphasize starting with business value and breaking down silos early by involving data teams in production discussions. Rather than replacing observability professionals, AI should augment human expertise, especially in critical systems where trust, safety, and compliance are paramount.Learn more from The New Stack about enabling AI with silos: Are Your AI Co-Pilots Trapping Data in Isolated Silos?Break the AI Gridlock at the Intersection of Velocity and TrustTaming AI Observability: Control Is the Key to SuccessJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Rob Whiteley, CEO of Coder, argues that the biggest winners in today's AI boom resemble the “picks and shovels” sellers of the California Gold Rush: companies that provide tools enabling others to build with AI. Speaking onThe New Stack Makersat AWS re:Invent, Whiteley described the current AI moment as the fastest-moving shift he's seen in 25 years of tech. Developers are rapidly adopting AI tools, while platform teams face pressure to approve them, as saying “no” is no longer viable. Whiteley warns of a widening gap between organizations that extract real value from AI and those that don't, driven by skills shortages and insufficient investment in training. He sees parallels with the cloud-native transition and predicts the rise of “AI-native” companies. As agentic AI grows, developers increasingly act as managers overseeing many parallel AI agents, creating new challenges around governance, security, and state management. To address this, Coder introduced Mux, an open source coding agent multiplexer designed to help developers manage and evaluate large volumes of AI-generated code efficiently.Learn more from The New Stack about AI Parallelization The Production Generative AI Stack: Architecture and ComponentsEnable ParallelFrontend/Backend Development to Unlock VelocityJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Nvidia Distinguished Engineer Kevin Klues noted that low-level systems work is invisible when done well and highly visible when it fails — a dynamic that frames current Kubernetes innovations for AI. At KubeCon + CloudNativeCon North America 2025, Klues and AWS product manager Jesse Butler discussed two emerging capabilities: dynamic resource allocation (DRA) and a new workload abstraction designed for sophisticated AI scheduling.DRA, now generally available in Kubernetes 1.34, fixes long-standing limitations in GPU requests. Instead of simply asking for a number of GPUs, users can specify types and configurations. Modeled after persistent volumes, DRA allows any specialized hardware to be exposed through standardized interfaces, enabling vendors to deliver custom device drivers cleanly. Butler called it one of the most elegant designs in Kubernetes.Yet complex AI workloads require more coordination. A forthcoming workload abstraction, debuting in Kubernetes 1.35, will let users define pod groups with strict scheduling and topology rules — ensuring multi-node jobs start fully or not at all. Klues emphasized that this abstraction will shape Kubernetes' AI trajectory for the next decade and encouraged community involvement.Learn more from The New Stack about dynamic resource allocation: Kubernetes Primer: Dynamic Resource Allocation (DRA) for GPU WorkloadsKubernetes v1.34 Introduces Benefits but Also New Blind SpotsJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Chris and Daniel talk with returning guest, Ramin Mohammadi, about how those seeking to get into AI Engineer/ Data Science jobs are expected to come in a mid level engineers (not entry level). They explore this growing gap along with what should (or could) be done in academia to focus on real world skills vs. theoretical knowledge. Featuring:Ramin Mohammadi – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XSponsors:Shopify – The commerce platform trusted by millions. From idea to checkout, Shopify gives you everything you need to launch and scale your business—no matter your level of experience. Build beautiful storefronts, market with built-in AI tools, and tap into the platform powering 10% of all U.S. eCommerce. Start your one-dollar trial at shopify.com/practicalaiUpcoming Events: Register for upcoming webinars here!

At KubeCon North America 2025, GitLab's Emilio Salvador outlined how developers are shifting from individual coders to leaders of hybrid human–AI teams. He envisions developers evolving into “cognitive architects,” responsible for breaking down large, complex problems and distributing work across both AI agents and humans. Complementing this is the emerging role of the “AI guardian,” reflecting growing skepticism around AI-generated code. Even as AI produces more code, humans remain accountable for reviewing quality, security, and compliance.Salvador also described GitLab's “AI paradox”: developers may code faster with AI, but overall productivity stalls because testing, security, and compliance processes haven't kept pace. To fix this, he argues organizations must apply AI across the entire development lifecycle, not just in coding. GitLab's Duo Agent Platform aims to support that end-to-end transformation.Looking ahead, Salvador predicts the rise of a proactive “meta agent” that functions like a full team member. Still, he warns that enterprise adoption remains slow and advises organizations to start small, build skills, and scale gradually.Learn more from The New Stack about the evolving role of "cognitive architects":The Engineer in the AI Age: The Orchestrator and ArchitectThe New Role of Enterprise Architecture in the AI EraThe Architect's Guide to Understanding Agentic AIJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Jonathan Bryce, the new CNCF executive director, argues that inference—not model training—will define the next decade of computing. Speaking at KubeCon North America 2025, he emphasized that while the industry obsesses over massive LLM training runs, the real opportunity lies in efficiently serving these models at scale. Cloud-native infrastructure, he says, is uniquely suited to this shift because inference requires real-time deployment, security, scaling, and observability—strengths of the CNCF ecosystem. Bryce believes Kubernetes is already central to modern inference stacks, with projects like Ray, KServe, and emerging GPU-oriented tooling enabling teams to deploy and operationalize models. To bring consistency to this fast-moving space, the CNCF launched a Kubernetes AI Conformance Program, ensuring environments support GPU workloads and Dynamic Resource Allocation. With AI agents poised to multiply inference demand by executing parallel, multi-step tasks, efficiency becomes essential. Bryce predicts that smaller, task-specific models and cloud-native routing optimizations will drive major performance gains. Ultimately, he sees CNCF technologies forming the foundation for what he calls “the biggest workload mankind will ever have.” Learn more from The New Stack about inference: Confronting AI's Next Big Challenge: Inference Compute Deep Infra Is Building an AI Inference Cloud for Developers Join our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Cloud Native Computing Foundation has introduced the Certified Kubernetes AI Conformance Program to bring consistency to an increasingly fragmented AI ecosystem. Announced at KubeCon + CloudNativeCon North America 2025, the program establishes open, community-driven standards to ensure AI applications run reliably and portably across different Kubernetes platforms. VMware by Broadcom's vSphere Kubernetes Service (VKS) is among the first platforms to achieve certification.In an interview with The New Stack, Broadcom leaders Dilpreet Bindra and Himanshu Singh explained that the program applies lessons from Kubernetes' early evolution, aiming to reduce the “muddiness” in AI tooling and improve cross-platform interoperability. They emphasized portability as a core value: organizations should be able to move AI workloads between public and private clouds with minimal friction.VKS integrates tightly with vSphere, using Kubernetes APIs directly to manage infrastructure components declaratively. This approach, along with new add-on management capabilities, reflects Kubernetes' growing maturity. According to Bindra and Singh, this stability now enables enterprises to trust Kubernetes as a foundation for production-grade AI. Learn more from The New Stack about Broadcom's latest updates with Kubernetes: Has VMware Finally Caught Up with Kubernetes?VMware VCF 9.0 Finally Unifies Container and VM ManagementJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Aydin sits down with Mike Potter, CEO and co-founder of Rewind, to talk about how AI is changing both the risk and opportunity landscape for SaaS companies. They cover how AI agents are now deleting real customer data, why backup is more critical than ever, and how Rewind became an AI-native org with dedicated AI ownership, monthly Lunch & Learns, and real internal workflows.Mike walks through the exact N8N workflows he uses to:Auto-triage his Gmail into multiple inboxes using AIGenerate a daily AI brief based on tasks, calendar events, and past email contextAnalyze churn, win/loss, and internal product data using Claude and MCPThey close with Mike's “dream automation”: a full AI-generated business review that looks across financials, CRM data, and benchmarks.Timestamps:0:00 — Welcome to the show0:31 — Mike's intro & what Rewind backs up across SaaS ecosystems1:40 — AI agents as a new failure mode and how Rewind “saves you from your AI”4:05 — Turning Rewind into an AI-native company early on4:53 — First attempt at AI-built integrations (why it failed then, why it might work now)7:23 — Developers trading tedious integration maintenance for more interesting AI work9:45 — Code vs architecture: the Shopify webhooks story and handling 1.1B+ events14:03 — Hiring an AI Engineer: scope, responsibilities, and why background mattered15:33 — How Rewind drove AI adoption: Lunch & Learns, “use it in your personal life,” experimentation20:53 — How AI Lunch & Learns actually run across multiple offices and remote folks23:10 — Examples: CS tools, Alloy prototypes, AI video voiceovers, end-to-end workflows25:13 — Churn workflows: combining uninstall reasons from multiple marketplaces into Claude27:06 — Win/loss and internal analytics using Claude Projects + MCP server into an internal DB29:14 — Choosing between Claude, ChatGPT, and Gemini depending on the task (and re-testing every few months)31:23 — Mike's Gmail system: multiple inboxes + N8N + AI classification36:07 — Inside the email-classifier prompt and AI-powered spam that beats Gmail filters41:34 — The “Daily AI Brief”: pulling tasks, meetings, and prior email threads into a single morning email45:02 — Letting AI write and debug N8N workflows (and how assistants in tools are getting better)48:58 — Wishlist: automated AI business review across finance, Salesforce, and SaaS benchmarks51:23 — Closing thoughts: so many useful tools are possible, but GTM is the hard partTools & Technologies MentionedRewind – Backup and restore for mission-critical SaaS applications.Claude – LLM used for analysis, projects, agents, and internal tools.ChatGPT / OpenAI (GPT-4.1, GPT-4.1 mini) – LLMs used for code, prompts, and workflow JSON.N8N – Automation platform used to build email and daily-brief workflows.Gmail – Email client where AI-powered labels drive multiple inboxes.Google Calendar – Calendar data powering the daily AI agenda.Google Tasks – Task list feeding into the morning brief email.MCP (Model Context Protocol) – Connects Claude to Rewind's internal databases.Alloy – Tool for building interactive product UI prototypes.Salesforce – CRM used for pipeline, churn, and win/loss analysis.Gumloop – Workflow tool with an embedded AI assistant.Zapier – Automation platform referenced for plain-English workflow creation.Fellow – AI meeting assistant for summaries, action items, and insights.Subscribe at⁠ thisnewway.com⁠ to get the step-by-step playbooks, tools, and workflows.

A carreira de AI Engineer se consolidou como uma das mais disputadas do mercado de tecnologia. Mas afinal, o que realmente é esperado desse profissional na prática?Neste episódio do Data Hackers, discutimos em profundidade o caminho para se tornar um AI Engineer, analisando as principais habilidades técnicas, as diferenças em relação a outros cargos da área de dados e engenharia, a formação acadêmica versus experiência prática, a rotina nas empresas e o impacto da IA Generativa, RAG e AI Agents no dia a dia da função.Para enriquecer o debate, utilizamos dados da pesquisa State of Data Brazil como base para entender o cenário atual do mercado brasileiro, identificar tendências de demanda por habilidades, perfis profissionais mais buscados e os principais desafios enfrentados por quem deseja ingressar ou evoluir nessa carreira.Se você quer migrar para IA, se preparar para oportunidades reais ou entender se esse é o próximo passo profissional em dados, este episódio é para você. Não se esqueça de preencher a pesquisa State of Data Brazil: https://www.stateofdata.com.br/Nossa Bancada Data Hackers:Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart.Gabriel Lages — Co-founder da Data Hacker e Diretor de Dados & AI da Hotmart

Helm — originally a hackathon project called Kate's Place — turned 10 in 2025, marking the milestone with the release of Helm 4, its first major update in six years. Created by Matt Butcher and colleagues as a playful take on “K8s,” the early project won a small prize but quickly grew into a serious effort when Deus leadership recognized the need for a Kubernetes package manager. Renamed Helm, it rapidly expanded with community contributors and became one of the first CNCF graduating projects.Helm 4 reflects years of accumulated design debt and evolving use cases. After the rapid iterations of Helm 1, 2, and 3, the latest version modernizes logging, improves dependency management, and introduces WebAssembly-based plugins for cross-platform portability—addressing the growing diversity of operating systems and architectures. Beyond headline features, maintainers emphasize that mature projects increasingly deliver “boring” but essential improvements, such as better logging, which simplify workflows and integrate more cleanly with other tools. Helm's re-architected internals also lay the foundation for new chart and package capabilities in upcoming 4.x releases. Learn more from The New Stack about Helm: The Super Helm Chart: To Deploy or Not To Deploy?Kubernetes Gets a New Resource Orchestrator in the Form of KroJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Kubernetes has relied on role-based access control (RBAC) since 2017, but its simplicity limits what developers can express, said Micah Hausler, principal engineer at AWS, on The New Stack Makers. RBAC only allows actions; it can't enforce conditions, denials, or attribute-based rules. Seeking a more expressive authorization model for Kubernetes, Hausler explored Cedar, an authorization engine and policy language created at AWS in 2022 and later open-sourced. Although not designed specifically for Kubernetes, Cedar proved capable of modeling its authorization needs in a concise, readable way. Hausler highlighted Cedar's clarity—nontechnical users can often understand policies at a glance—as well as its schema validation, autocomplete support, and formal verification, which ensures policies are correct and produce only allow or deny outcomes.Now onboarding to the CNCF sandbox, Cedar is used by companies like Cloudflare and MongoDB and offers language-agnostic tooling, including a Go implementation donated by StrongDM. The project is actively seeking contributors, especially to expand bindings for languages like TypeScript, JavaScript, and Python.Learn more from The New Stack about Cedar:Ceph: 20 Years of Cutting-Edge Storage at the Edge The Cedar Programming Language: Authorization SimplifiedJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hey, Alex here, I recorded these conversations just in front of the AI Engineer auditorium, back to back, after these great folks gave their talks, and at the epitome of the most epic AI week we've seen since I started recording ThursdAI.This is less our traditional live recording, and more a real podcast-y conversation with great folks, inspired by Latent.Space. I hope you enjoy this format as much as I've enjoyed recording and editing it. AntiGravity with KevinKevin Hou and team just launched Antigravity, Google's brand new Agentic IDE based on VSCode, and Kevin (second timer on ThursdAI) was awesome enough to hop on and talk about some of the product decisions they made, what makes Antigravity special and highlighted Artifacts as a completely new primitive. Gemini 3 in AI StudioIf you aren't using Google's AI Studio (ai.dev) then you're missing out! We talk about AI Studio all the time on the show, and I'm a daily user! I generate most of my images with Nano Banana Pro in there, most of my Gemini conversations are happening there as well! Ammaar and Kat were so fun to talk to, as they covered the newly shipped “build mode” which allows you to vibe code full apps and experiences inside AI Studio, and we also covered Gemini 3's features, multimodality understanding, UI capabilities. These folks gave a LOT of Gemini 3 demo's so they know everything there is to know about this model's capabilities! Tried new things with this one, multi camera angels, conversation with great folks, if you found this content valuable, please subscribe :) Topics Covered:* Inside Google's new “AntiGravity” IDE* How the “Agent Manager” changes coding workflows* Gemini 3's new multimodal capabilities* The power of “Artifacts” and dynamic memory* Deep dive into AI Studio updates & Vibe Coding* Generating 4K assets with Nano Banana ProTimestamps for your viewing convenience. 00:00 - Introduction and Overview01:13 - Conversation with Kevin Hou: Anti-Gravity IDE01:58 - Gemini 3 and Nano Banana Pro Launch Insights03:06 - Innovations in Anti-Gravity IDE06:56 - Artifacts and Dynamic Memory09:48 - Agent Manager and Multimodal Capabilities11:32 - Chrome Integration and Future Prospects20:11 - Conversation with Ammar and Kat: AI Studio Team21:21 - Introduction to AI Studio21:51 - What is AI Studio?22:52 - Ease of Use and User Feedback24:06 - Live Demos and Launch Week26:00 - Design Innovations in AI Studio30:54 - Generative UIs and Vibe Coding33:53 - Nano Banana Pro and Image Generation39:45 - Voice Interaction and Future Roadmap44:41 - Conclusion and Final ThoughtsLooking forward to seeing you on Thursday

AWS's approach to Elastic Kubernetes Service has evolved significantly since its 2018 launch. According to Mike Stefanik, Senior Manager of Product Management for EKS and ECR, today's users increasingly represent the late majority—teams that want Kubernetes without managing every component themselves. In a conversation onThe New Stack Makers, Stefanik described how AI workloads are reshaping Kubernetes operations and why AWS open-sourced an MCP server for EKS. Early feedback showed that meaningful, task-oriented tool names—not simple API mirrors—made MCP servers more effective for LLMs, prompting AWS to design tools focused on troubleshooting, runbooks, and full application workflows. AWS also introduced a hosted knowledge base built from years of support cases to power more capable agents.While “agentic AI” gets plenty of buzz, most customers still rely on human-in-the-loop workflows. Stefanik expects that to shift, predicting 2026 as the year agentic workloads move into production. For experimentation, he recommends the open-source Strands SDK. Internally, he has already seen major productivity gains from BI agents that automate complex data analysis tasks.Learn more from The New Stack about Amazon Web Services' approach to Elastic Kubernetes ServiceHow Amazon EKS Auto Mode Simplifies Kubernetes Cluster Management (Part 1)A Deep Dive Into Amazon EKS Auto (Part 2)Join our community of newsletter subscribers to stay on top of the news and at the top of your game.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AWS re:Invent has long featured CTO Werner Vogels' closing keynote, but this year he signaled it may be his last, emphasizing it's time for “younger voices” at Amazon. After 21 years with the company, Vogels reflected on arriving as an academic and being stunned by Amazon's technical scale—an energy that still drives him today. He released his annual predictions ahead of re:Invent, with this year's five themes focused heavily on AI and broader societal impacts.Vogels highlights technology's growing role in addressing loneliness, noting how devices like Alexa can offer comfort to those who feel isolated. He foresees a “Renaissance developer,” where engineers must pair deep expertise with broad business and creative awareness. He warns quantum-safe encryption is becoming urgent as data harvested today may be decrypted within five years. Military innovations, he notes, continue to influence civilian tech, for better and worse. Finally, he argues personalized learning can preserve children's curiosity and better support teachers, which he views as essential for future education.Learn more from The New Stack about evolving role of technology systems from past to future: Werner Vogels' 6 Lessons for Keeping Systems Simple50 Years Later: Remembering How the Future Looked in 1974Join our community of newsletter subscribers to stay on top of the news and at the top of your game.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hey everyone, Alex here

In this episode, Kristina McMillan, Executive in Residence at Scale Venture Partners, shares what she's seeing across Scale's portfolio when it comes to AI adoption in revenue teams. From the rise of the go-to-market engineer to the three levels of AI maturity, Kristina breaks down what's working, what's hype, and why RevOps needs to lead with strategy, not just tools. We also get into AI's real impact on metrics like ARR per employee, the role of internal AI hackathons, and how top teams are choosing between building and buying. If you're feeling overwhelmed by the pace of change, this episode will give you clarity and a tactical playbook.

Ryan is a current student at Parsity who build an app for his employer, Smoothie King, to suggest drinks in a chat interface using a powerful and lesser-known AI technology: RAG.RAG stands for retrieval augmented generation. Basically, providing information (like smoothie recipes) to an AI model so it can return a highly specific response.Ryan breaks down how he finds the time to build side projects like this and how he built this app.Want to build your own AI-powered app? Check out this project: parsity.io/ai-with-ragConnect with Ryan here: https://www.linkedin.com/in/rhardin378/Send us a textShameless Plugs

AI-assisteret softwareudvikling er rykket fra eksperiment til virkelighed. Men hvad virker – og hvad er bare hype?Kasper Junge og Christian Bech Nørhave tager dig med ind i maskinrummet, hvor AI allerede er en del af udviklingsteamets hverdag. De deler erfaringer med AI i praksis.Det handler ikke om hype, men om hvad der virker i praksis.Hvad AI faktisk kan (og ikke kan) i softwareudviklingFælles sprog og processer: gør AI til en kollega, ikke en gadgetFart kræver retning: klare mål, kodekvalitet og ansvarBrug AI som kraftforstærker – uden at miste kontrollenMedvirkende:Christian Bech Nørhave+20 års erfaring med Digitaliseringsrådgivning+200 foredrag omkring AIBygger nordisk MSP i samarbejde med DevoteamKasper JungeAI Engineer hos DineroVært på Verbos PodcastNordic AI Influencer DAIR Award WinnerLink:vibe-coding.dk

Building Career Resilience in the Age of Generative AI Every week, we explore how AI and technology are changing the way we work and learn. This episode dives into the question I get asked the most, How is Generative AI changing every career? Let's unpack why it matters, how it's shifting roles and skills, and what you can do to lead this change instead of chasing it In this solo episode of Open Tech Talks, host Kashif Manzoor, AI Engineer and Strategiest, and author of AI Tech Circle, dives deep into one of the biggest career questions of our time: How is Generative AI reshaping every profession? Whether you're a developer, analyst, marketer, finance expert, or operations lead, the rise of Gen AI is transforming how work gets done. Kashif combines real-world enterprise experience, current research from McKinsey and Goldman Sachs, and his personal journey building the Gen AI Maturity Framework and Portal to uncover how you can stay relevant, resilient, and ready for AI-driven change. He shares first-hand stories from his own AI adoption journey, how enterprise teams are shifting from cloud architecture to AI architecture, from isolated use-cases to full-scale agentic AI strategies and the lessons learned while guiding organizations through transformation. This episode is both a roadmap and a reflection: how to experiment weekly, build your portfolio, upskill smartly, reposition your role, and teach and share as you grow. Episode # 173 What You'll Learn Why Generative AI matters now and how it differs from traditional AI How tasks, roles, and careers are evolving across industries Real-world examples from finance, marketing, and software engineering The five practical steps to future-proof your career with Gen AI Insights from McKinsey, ResearchGate, and Goldman Sachs on AI productivity impact How to move from "knowing AI tools" to using AI strategically in daily work A behind-the-scenes look at the creation of the Gen AI Maturity Framework Why the future of work is not about jobs lost but roles transformed   External References   McKinsey Global Institute – Generative AI and the Future of Work  Deloitte – Generative AI and the Future of Work  Goldman Sachs – How Will AI Affect the Global Workforce  Robert Half – How GenAI Is Changing Creative Careers Mäkelä & Stephany (2024) – Complement or Substitute?

Welcome back to The Multifamily Podcast with Ronn and Martin, powered by ApartmentSEO.com. Today, we're diving into a topic that's moving faster than almost anything in history—artificial intelligence. Our guest is Ben, an AI Engineer with Apartment SEO, who's been right in the thick of these changes. From the big bang ChatGPT moment to the new era of GPT-5, agent mode, and the future of work, we'll unpack what all of this means not just for tech, but for industries like multifamily real estate. Welcome to The Multifamily Podcast, Ben!

Asai さんをゲストにお呼びしました。Asaiさんの近況についてキャッチアップしました。前半では第二子出産、ジュネーブとロンドンの保育園事情についてお話しました。その後、AsaiさんのSREからAI Engineerへの転身について決断した背景等をお伺いしました。SREからAI Engineerへ - 初週の感想経営戦略を問いなおすご意見・ご感想など、お便りはこちらの⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠Google Form⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ で募集しています。

On this episode of DGTL Voices, Ed interviews Eduardo Conrado, the incoming CEO of Ascension, discussing his journey from engineering to healthcare leadership. They explore the role of data-driven insights, and strategies for career growth. Eduardo shares his experiences and insights on how CIOs and technology leaders can effectively connect with operations to drive transformation in the healthcare sector.

Is there a defined pathway to becoming an AI engineer? While school curriculums are still inchoate, must-have skills have been, more or less, identified; the major ones, we tackle in this segment.Find out more at https://interviewcamp.ai/ interviewcamp.ai City: New York Address: 430 Park Ave Website: https://interviewcamp.ai

Full show notes, transcript and AI chatbot - http://bit.ly/4gXOEJaWatch on YouTube - https://www.youtube.com/watch?v=dcb_WFxQGZg-----Episode Summary:In this episode of The Measure Pod, Dara and Matthew sit down with Mark Edmondson, AI Engineer, founder of Sunholo and Aitana, and board member at 8-bit-sheep. They explore the rapidly evolving landscape of AI in business and analytics, from career progression in the age of AI to the practical realities of implementing AI-driven company strategies. Mark shares insights on DevOps, data pipelines, and the distinction between data engineering and data science, while discussing why so many AI projects fail and what it takes to succeed. The conversation tackles the human side of technological change, including productivity expectations, burnout, and how we should be educating future generations for an AI-driven world.-----About The Measure Pod:The Measure Pod is your go-to fortnightly podcast hosted by seasoned analytics pros. Join Dara Fitzgerald (Co-Founder at Measurelab) & Matthew Hooson (Head of Engineering at Measurelab) as they dive into the world of data, analytics and measurement, with a side of fun.-----If you liked this episode, don't forget to subscribe to The Measure Pod on your favourite podcast platform and leave us a review. Let's make sense of the analytics industry together!

L'IA entre-t-elle dans son automne ?

The European Union's upcoming Cyber Resilience Act (CRA) goes into effect in  October 2026, with the remainder of the requirements going into effect in December 2027, and introduces significant cybersecurity compliance requirements for software vendors, including those who rely heavily on open source components. At the Open Source Summit Europe, Christopher "CRob" Robinson of the Open Source Security Foundation highlighted concerns about how these regulations could impact open source maintainers. Many open source projects begin as personal solutions to shared problems and grow in popularity, often ending up embedded in critical systems across industries like automotive and energy. Despite this widespread use—Robinson noted up to 97% of commercial software contains open source—these projects are frequently maintained by individuals or small teams with limited resources.Developers often have no visibility into how their code is used, yet they're increasingly burdened by legal and compliance demands from downstream users, such as requests for Software Bills of Materials (SBOMs) and conformity assessments. The CRA raises the stakes, with potential penalties in the billions for noncompliance, putting immense pressure on the open source ecosystem. Learn more from The New Stack about Open Source Security:Open Source Propels the Fall of Security by ObscurityThere Is Just One Way To Do Open Source Security: TogetherJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Send us a textAs a security researcher who specializes in application security and AI, Joseph Thacker shares his knowledge on the growing influence of AI in various aspects of our culture. He's the principal AI Engineer at AppOmni and has helped multiple Fortune 500 companies find vulnerablities that could have cost them millions. He is incredibly knowledgable and offers great insight into this growing industry._______________________________Find Joseph Thacker onLinkedIn:https://www.linkedin.com/in/josephthacker?original_referer=https%3A%2F%2Fwww.google.com%2FHis New website and course for parents:https://aisafetyforparents.com/X:@rez0__Instagram:@thackandforthWebsite:https://josephthacker.com/_______________________________Show hosted by Landry Fieldshttps://www.x.com/landryfieldz'https://www.linkedin.com/in/landryfields/https://www.instagram.com/landryfields_https://www.youtube.com/@landryfields_www.novainsurancegroup.com859-687-2004

As AI accelerates development, many companies are halting junior hiring, believing AI tools can replace them. Shahin Shahkarami, Director of Data & AI at Ikea Retail, argues this is a massive mistake and that now is actually the best time to invest in new talent.In this episode/video, we cover:Why companies should hire junior talent despite the rise of AI.How the role of a data scientist is evolving with generative AI.The most valuable business use cases for AI beyond chatbots.This conversation is for tech leaders, hiring managers, and aspiring developers looking to understand how to build and grow their careers in the age of AI.Connect with Shahin:https://www.linkedin.com/in/shahin-shahkaramiFull episode on YouTube ▶️https://youtu.be/Jui-8Lx6kvkBeyond Coding Podcast with ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Is the AI industry an unsustainable bubble built on burning billions in cash? We break down the AI hype cycle, the tough job market for developers, and whether a crash is on the horizon. In this panel discussion with Josh Goldberg, Paige Niedringhaus, Paul Mikulskis, and Noel Minchow, we tackle the biggest questions in tech today. * We debate if AI is just another Web3-style hype cycle * Why the "10x AI engineer" is a myth that ignores the reality of software development * The ethical controversy around AI crawlers and data scraping, highlighted by Cloudflare's recent actions Plus, we cover the latest industry news, including Vercel's powerful new AI SDK V5 and what GitHub's leadership shakeup means for the future of developers. Resources Anthropic Is Bleeding Out: https://www.wheresyoured.at/anthropic-is-bleeding-out The Hater's Guide To The AI Bubble: https://www.wheresyoured.at/the-haters-gui No, AI is not Making Engineers 10x as Productive: https://colton.dev/blog/curing-your-ai-10x-engineer-imposter-syndrome Cloudflare Is Blocking AI Crawlers by Default: https://www.wired.com/story/cloudflare-blocks-ai-crawlers-default Perplexity is using stealth, undeclared crawlers to evade website no-crawl directives: https://blog.cloudflare.com/perplexity-is-using-stealth-undeclared-crawlers-to-evade-website-no-crawl-directives GitHub just got less independent at Microsoft after CEO resignation: https://www.theverge.com/news/757461/microsoft-github-thomas-dohmke-resignation-coreai-team-transition Chapters 0:00 Is the AI Industry Burning Cash Unsustainably? 01:06 Anthropic and the "AI Bubble Euphoria" 04:42 How the AI Hype Cycle is Different from Web3 & VR 08:24 The Problem with "Slapping AI" on Every App 11:54 The "10x AI Engineer" is a Myth and Why 17:55 Real-World AI Success Stories 21:26 Cloudflare vs. AI Crawlers: The Ethics of Data Scraping 30:05 Vercel's New AI SDK V5: What's Changed? 33:45 GitHub's CEO Steps Down: What It Means for Developers 38:54 Hot Takes: The Future of AI Startups, the Job Market, and More 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)

Enterprise AI is still in its infancy, with less than 1% of enterprise data currently used to fuel AI, according to Raj Verma, CEO of SingleStore. While consumer AI is slightly more advanced, most organizations are only beginning to understand the scale of infrastructure needed for true AI adoption. Verma predicts AI will evolve in three phases: first, the easy tasks will be automated; next, complex tasks will become easier; and finally, the seemingly impossible will become achievable—likely within three years. However, to reach that point, enterprises must align their data strategies with their AI ambitions. Many have rushed into AI fearing obsolescence, but without preparing their data infrastructure, they're at risk of failure. Current legacy systems are not designed for the massive concurrency demands of agentic AI, potentially leading to underperformance. Verma emphasizes the need to move beyond siloed or "swim lane" databases toward unified, high-performance data platforms tailored for the scale and complexity of the AI era.Learn more from The New Stack about the latest evolution in AI infrastructure: How To Use AI To Design Intelligent, Adaptable InfrastructureHow to Support Developers in Building AI Workloads Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

What does it actually mean to be an “AI Engineer”? Honestly—not much. The title is overloaded and vague. But what is meaningful right now is knowing how to build real projects with AI that go beyond toy chatbots and portfolio fluff.In this episode, I walk you through the exact project I've been building at two different AI startups: a Retrieval Augmented Generation (RAG) app. You'll learn how to:Scrape and store content in a vector databaseUse embeddings to turn your text into something a model can understandStream responses back to your frontend with Next.js + TypeScriptReduce hallucinations and add structured, reliable outputsUnderstand why this is the skillset employers are actually hiring for right now

Новое исследование Stack Overflow 2025 Developer Survey, в котором приняли участие более 49,000 разработчиков из 177 стран, выявило парадоксальную проблему: 45% программистов сообщают, что отладка ИИ-сгенерированного кода занимает больше времени, чем ожидалось 84% of developers use AI, yet most don't trust it!. https://survey.stackoverflow.co/2025/ai#2-accuracy-of-ai-toolsОсновная причина — "ИИ-решения, которые почти правильные, но не совсем", с которыми сталкиваются 66% разработчиков. Такой код выглядит работоспособным, но требует тщательной проверки и исправления скрытых ошибок, что превращает обещанную экономию времени в дополнительную нагрузку.Эти данные контрастируют с недавними заявлениями CEO OpenAI Сэма Альтмана, который в своем последнем блог-посте "The Gentle Singularity" утверждает, что "2025 год ознаменовался появлением агентов, которые могут выполнять настоящую когнитивную работу; написание компьютерного кода уже никогда не будет прежним". Однако в том же посте Альтман признает постепенность изменений, отмечая что "мир не изменится сразу" и люди найдут "новые способы быть полезными друг другу", хотя эти способы "могут не очень походить на сегодняшние рабочие места". https://blog.samaltman.com/the-gentle-singularityПока что реальность показывает обратное — ИИ создает дополнительную работу вместо её сокращения, заставляя разработчиков тратить время на верификацию и исправление "почти правильного" кода.Евгений Волчков, Engineering Manager в iManage (ex-Bank of America и Verizon).LinkedIn: https://www.linkedin.com/in/valchkou/ Эпизоды по теме:- AI Engineer - это будущее или модный хайп? Какие программисты будут в спросе, а какие за бортом? Евгений Волчков https://youtube.com/live/5T6be4jjzrY- Калифорнийский парадокс: почему местные AI-таланты с дипломом Berkeley не нужны? Савва Вяткин https://youtu.be/PAJ_R2hBie8- Новая эра: AI, работа и профессии будущего. Как ИИ меняет правила игры на рынке труда. Ник Береза https://youtube.com/live/eO9PghMknOY- Тренды IT 2025: венчур, стартапы, искусственный интеллект. Алексей Моисеенков. https://youtube.com/live/1d7hRZrJTkM- Что нас ждет в 2025? Кризис, массовые увольнения, крах стартапов. Где искать работу? Денис Калышкин https://youtube.com/live/ZbYm10zrfEA***Записаться на карьерную консультацию (резюме, LinkedIn, карьерная стратегия, поиск работы в США) https://annanaumova.comКоучинг (синдром самозванца, прокрастинация, неуверенность в себе, страхи, лень) https://annanaumova.notion.site/3f6ea5ce89694c93afb1156df3c903abВидео курс по составлению резюме для международных компаний "Идеальное американское резюме": https://go.mbastrategy.com/resumecoursemainГайд "Идеальное американское резюме" https://go.mbastrategy.com/usresumeПодписывайтесь на мой Телеграм канал: https://t.me/prodcastUSAПодписывайтесь на мой Инстаграм https://www.instagram.com/prodcast.us Гайд "Как оформить профиль в LinkedIn, чтобы рекрутеры не смогли пройти мимо" https://go.mbastrategy.com/linkedinguide⏰ Timecodes ⏰00:00 Начало15:12 Парадокс ИИ. Заменит ли он разработчиков?30:50 Изменятся ли финансовые рынки?41:08 Рынок IT в кризисе?54:59 Как найти удаленку в ИИ в США?1:01:26 Заменит ли ИИ дата-саентистов?1:11:07 Куда вывозят из США? 1:12:27 Как изменятся зарплаты специалистов?1:17:14 Про лейоффы и кризисы1:21:28 Что ждёт QA специалистов?1:30:18 AI Action Plan 1:34:25 Что посоветуешь тем кто боится Ai?

On this episode of Unsupervised Learning, in the wake of Elon Musk's xAI Grok chatbot turning anti-Semitic following a recent update, Razib catches up with Nikolai Yakovenko about the state of AI in the summer of 2025. Nearly three years after their first conversations on the topic, the catch up, covering ChatGPT's release and the anticipation of massive macroeconomic transformations driven by automation of knowledge-work. Yakovenko is a former professional poker player and research scientist at Google, Twitter (now X) and Nvidia (now the first $4 trillion company). With more than a decade on the leading edge computer science, Yakovenko has been at the forefront of the large-language-model revolution that was a necessary precursor to the rise of companies like OpenAI, Anthropic and Perplexity, as well as hundreds of smaller startups. Currently, he is the CEO of DeepNewz, an AI-driven news startup that leverages the latest models to retrieve the ground-truth on news-stories. Disclosure: Razib actively uses and recommends the service and is an advisor to the company. Razib and Yakovenko first tackle why Mark Zuckerberg's Meta is offering individual pay packages north of $200 million, poaching some of OpenAI's top individual contributors. Yakovenko observes that it seems Meta is giving up on its open-source Llama project, their competitor to the models that underpin OpenAI and ChatGPT (he also comments that it seems that engineers at xAI are disappointed in the latest version of Grok). Overall, though the pay-packages of AI engineers and researchers are high; there is now a big shakeout as massive companies with the money and engineering researchers pull away from their competitors. Additionally, in terms of cutting-edge models, the US and China are the only two international players (Yakovenko notes parenthetically that Chinese engineers are also the primary labor base of American AI firms). They also discuss how it is notable that almost three years after the beginning of the current booming repeated hype-cycles of artificial intelligence began to crest, we are still no closer to “artificial general intelligence” and the “intelligence super-explosion” that Ray Kurzweil has been predicting for generations. AI is partially behind the rise of companies like Waymo that are on the verge of transforming the economy, but overall, even though AI is still casting around for its killer app, big-tech has fully bought in and believes that the next decade will determine who wins the future.

Как очистить резюме от цифрового мусора и привлечь HR-ботов?Действительно ли удаленка умерла и теперь правит гибрид?Превратился ли LinkedIn в Tinder для программистов?Почему $200k стали новыми $100k в IT-зарплатах?Выбирают ли стартапы теперь vibe важнее технических скиллов?Нужно ли фронтендеру знать DevOps или это просто способ сэкономить на зарплате?Маша (Мария) Подоляк (Marsha Podolyak)Автор Телеграм канала "

Find out more about Jashan on Instagram @Jashan_KalekaYou + Happy podcast on Instagram @YouPlusHappy Host @Selena_MarshaeFuture of AI: Job Impact, Career Success, and More with AI Engineer & Comedian Jashan Kaleka

Заменит ли AI всех разработчиков или создаст миллионы новых рабочих мест? Какие навыки программиста станут бесполезными уже через два года? Почему получить диплом в 30 лет стало нормой в IT? Почему junior с тремя языками программирования - это красный флаг? Что важнее в 2025 году - диплом или реальный опыт в IT? Какие AI скилы стоит изучать прямо сейчас, чтобы не остаться за бортом? Что делать продактам, проджектам, маркетологам: QA? Повторится ли история доткомов с AI стартапами или это разные времена?Евгений Волчков, Engineering Manager в iManage (ex-Bank of America и Verizon).LinkedIn: https://www.linkedin.com/in/valchkou/ Видео по теме:- Найм сломан. Тысячи кандидатов, а подходящих нет? Почему так сложно найти программиста в 2025? Юлия Тарасова https://youtube.com/live/6uVCZsF4aQE- Новая эра: AI, работа и профессии будущего. Как ИИ меняет правила игры на рынке труда. Ник Береза. https://youtube.com/live/eO9PghMknOY- Тренды IT 2025: венчур, стартапы, искусственный интеллект. Алексей Моисеенков. https://youtube.com/live/1d7hRZrJTkM- Аутсорсинг в IT. Дешевый код - это новая реальность? Кто кого вытеснит с рынка разработки? Валерий Широков и Евгений Волчков https://youtube.com/live/LVrEzC3zai4 ***Записаться на карьерную консультацию (резюме, LinkedIn, карьерная стратегия, поиск работы в США) https://annanaumova.comКоучинг (синдром самозванца, прокрастинация, неуверенность в себе, страхи, лень) https://annanaumova.notion.site/3f6ea5ce89694c93afb1156df3c903abВидео курс по составлению резюме для международных компаний "Идеальное американское резюме": https://go.mbastrategy.com/resumecoursemainГайд "Идеальное американское резюме" https://go.mbastrategy.com/usresumeПодписывайтесь на мой Телеграм канал: https://t.me/prodcastUSAПодписывайтесь на мой Инстаграм https://www.instagram.com/prodcast.us Гайд "Как оформить профиль в LinkedIn, чтобы рекрутеры не смогли пройти мимо" https://go.mbastrategy.com/linkedinguide⏰ Timecodes ⏰00:00 Начало9:10 Что изменилось на рынке найма в США?24:20 Вопросы из чата31:12 Что нужно учить в AI сейчас?1:12:56 Кого заменит AI?

The AI Engineer World's Fair highlighted key AI and agent world shifts. Top themes: evals, tiny teams, agent swarms, and the rise of coding agents. NLW breaks down the key trends and the alpha that exists in the program. Get Ad Free AI Daily Brief: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://patreon.com/AIDailyBrief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Brought to you by:KPMG – Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://kpmg.com/ai⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to learn more about how KPMG can help you drive value with our AI solutions.Blitzy.com - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to build enterprise software in days, not months AGNTCY - The AGNTCY is an open-source collective dedicated to building the Internet of Agents, enabling AI agents to communicate and collaborate seamlessly across frameworks. Join a community of engineers focused on high-quality multi-agent software and support the initiative at ⁠⁠⁠⁠agntcy.org ⁠⁠⁠⁠ -  ⁠⁠⁠⁠https://agntcy.org/?utm_campaign=fy25q4_agntcy_amer_paid-media_agntcy-aidailybrief_podcast&utm_channel=podcast&utm_source=podcast⁠⁠⁠⁠ Vanta - Simplify compliance - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://vanta.com/nlw⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Plumb - The automation platform for AI experts and consultants ⁠⁠⁠⁠https://useplumb.com/⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Subscribe to the newsletter: https://aidailybrief.beehiiv.com/Join our Discord: https://bit.ly/aibreakdownInterested in sponsoring the show? nlw@breakdown.network

 It's been a while since I've released an episode. I'm heading to the AI Engineer World's Fair tomorrowv If you're going to be there, I am going to be wearing a Superman shirt, so come say hi! I would love to talk to listeners and hear how your prompting journey has been going.I'm also planning on restarting the podcast, with one of a couple different directions: agents, vibe coding, or using reasoning models. Which one would you find most useful? Poll here: https://forms.gle/fLqiKeouDPazuU3s5Stay in touch on:Youtube: youtube.com/@PromptEngineeringPodcastTelegram: https://t.me/PromptEngineeringMastermindLinkedIn: https://www.linkedin.com/groups/14231334/Support the show

One of the new tracks at next week's AI Engineer conference in SF is a new focus on LLMs + Robotics, ft. household names like Waymo and Physical Intelligence. However there are many other companies applying LLMs and VLMs in the real world! CloudChef, the first industrial-scale kitchen robotics company with one-shot demonstration learning and an incredibly simple business model, will be serving tasty treats all day with Zippy (https://www.cloudchef.co/zippy ) their AI Chef platform. This is a lightning pod with CEO Nikhil Abraham to preview what Zippy is capable of! https://www.cloudchef.co/platform See a real chef comparison: https://www.youtube.com/watch?v=INDhZ7LwSeo&t=64s See it in the AI Engineer Expo at SF next week: https://ai.engineer Chapters 00:00 Welcome and Introductions 00:58 What is Cloud Chef? 01:36 How the Robots Work: Culinary Intelligence 05:57 Commercial Applications and Early Success 07:02 The Software-First Approach 10:09 Business Model and Pricing 13:10 Demonstration Learning: Training the Robots 16:03 Call to Action and Engineering Opportunities 18:45 Final Thoughts and Technical Details

This podcast discuss Deep Research, defining it as a comprehensive engagement with information beyond superficial inquiry. It contrasts this with surface learning and simple information gathering, emphasizing the need for rigor and critical analysis. The emergence of AI-powered deep research tools, such as Grok, ChatGPT, and Gemini, is explored as a new dimension, capable of automating and enhancing research processes with unprecedented speed and scale, although they introduce challenges related to accuracy, bias, and ethical considerations. Ultimately, the text argues that while AI can significantly augment human capabilities, the core principles of deep understanding and ethical conduct remain fundamentally reliant on human intellect and oversight, essential for advancing knowledge and tackling complex global issues across various domains.

This episode from the week of 11-18 May 2025 cover a range of AI developments, highlighting major model releases and updates from companies like OpenAI, Google, and Anthropic, as well as the strategic deployment of AI in various sectors, including healthcare, law, education, and content creation. They also touch upon significant ethical and regulatory considerations, such as data privacy concerns raised by international partnerships, debates over copyright protection for artists, the persistent issue of AI "hallucinations," and discussions around government approaches to AI regulation. The reports also reflect on the evolving capabilities of AI agents in tasks from software engineering to web research and customer service, alongside breakthroughs in AI-assisted scientific discovery.

Theis episode and sources collectively offer a snapshot of the AI landscape on a May 16th 2025 highlighting diverse developments. One major theme is advancements in AI model capabilities and applications, with Windsurf launching specialised models for software development, OpenAI introducing a coding agent in ChatGPT, and a new AI model, YingLong, focusing on rapid, high-resolution local weather forecasts. Simultaneously, the texts reveal challenges in AI reliability and deployment, including Anthropic's Claude hallucinating a legal citation, Meta delaying a major model release due to insufficient improvements, and research indicating current LLMs struggle with coherence in multi-turn conversations. Finally, the articles touch on practical AI integrations and evaluations, such as Zapier automating legal document analysis, a pilot for an "AI doctor" clinic in Saudi Arabia, and OpenAI releasing a benchmark specifically for healthcare AI, demonstrating the ongoing effort to apply and rigorously assess AI in various fields.

AI, Marketing, and Human Decision Making // MLOps Podcast #313 with Fausto Albers, AI Engineer & Community Lead at AI Builders Club.Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractDemetrios and Fausto Albers explore how generative AI transforms creative work, decision-making, and human connection, highlighting both the promise of automation and the risks of losing critical thinking and social nuance.// BioFausto Albers is a relentless explorer of the unconventional—a techno-optimist with a foundation in sociology and behavioral economics, always connecting seemingly absurd ideas that, upon closer inspection, turn out to be the missing pieces of a bigger puzzle. He thrives in paradox: he overcomplicates the simple, oversimplifies the complex, and yet somehow lands on solutions that feel inevitable in hindsight. He believes that true innovation exists in the tension between chaos and structure—too much of either, and you're stuck.His career has been anything but linear. He's owned and operated successful restaurants, served high-stakes cocktails while juggling bottles on London's bar tops, and later traded spirits for code—designing digital waiters, recommender systems, and AI-driven accounting tools. Now, he leads the AI Builders Club Amsterdam, a fast-growing community where AI engineers, researchers, and founders push the boundaries of intelligent systems.Ask him about RAG, and he'll insist on specificity—because, as he puts it, discussing retrieval-augmented generation without clear definitions is as useful as declaring that “AI will have an impact on the world.” An engaging communicator, a sharp systems thinker, and a builder of both technology and communities, Fausto is here to challenge perspectives, deconstruct assumptions, and remix the future of AI.// Related LinksWebsite: aibuilders.clubMoravec's paradox: https://en.wikipedia.org/wiki/Moravec%27s_paradox?utm_source=chatgpt.comBehavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311: https://youtu.be/jJXee5rMtHI~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Fausto on LinkedIn: /stepintoliquidTimestamps:[00:00] Fausto's preferred coffee[00:26] Takeaways[01:18] Automated Ad Creative Generation[07:14] AI in Marketing Workflows[13:23] MCP and System Bottlenecks[21:45] Forward Compatibility vs Optimization[29:57] Unlocking Workflow Speed[33:48] AI Dependency vs Critical Thinking[37:44] AI Realism and Paradoxes[42:30] Outsourcing Decision-Making Risks[46:22] Human Value in Automation[49:02] Wrap up

AI, Marketing, and Human Decision Making // MLOps Podcast #313 with Fausto Albers, AI Engineer & Community Lead at AI Builders Club.Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractDemetrios and Fausto Albers explore how generative AI transforms creative work, decision-making, and human connection, highlighting both the promise of automation and the risks of losing critical thinking and social nuance.// BioFausto Albers is a relentless explorer of the unconventional—a techno-optimist with a foundation in sociology and behavioral economics, always connecting seemingly absurd ideas that, upon closer inspection, turn out to be the missing pieces of a bigger puzzle. He thrives in paradox: he overcomplicates the simple, oversimplifies the complex, and yet somehow lands on solutions that feel inevitable in hindsight. He believes that true innovation exists in the tension between chaos and structure—too much of either, and you're stuck.His career has been anything but linear. He's owned and operated successful restaurants, served high-stakes cocktails while juggling bottles on London's bar tops, and later traded spirits for code—designing digital waiters, recommender systems, and AI-driven accounting tools. Now, he leads the AI Builders Club Amsterdam, a fast-growing community where AI engineers, researchers, and founders push the boundaries of intelligent systems.Ask him about RAG, and he'll insist on specificity—because, as he puts it, discussing retrieval-augmented generation without clear definitions is as useful as declaring that “AI will have an impact on the world.” An engaging communicator, a sharp systems thinker, and a builder of both technology and communities, Fausto is here to challenge perspectives, deconstruct assumptions, and remix the future of AI.// Related LinksWebsite: aibuilders.clubMoravec's paradox: https://en.wikipedia.org/wiki/Moravec%27s_paradox?utm_source=chatgpt.comBehavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311: https://youtu.be/jJXee5rMtHI~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Fausto on LinkedIn: /stepintoliquid

Scott Wu is the co-founder and CEO of Cognition, the company behind Devin—the world's first autonomous AI software engineer. Unlike other AI coding tools, Devin works like an autonomous engineer that you can interact with through Slack, Linear, and GitHub, just like with a remote engineer. With Scott's background in competitive programming and a previous AI-powered startup, Lunchclub, teaching AI to code has become his ultimate passion.What you'll learn:1. How a team of “Devins” are already producing 25% of Cognition's pull requests, and they are on track to hit 50% by year's end2. How each engineer on Cognition's 15-person engineering team works with about five Devins each3. How Devin has evolved from a “high school CS student” to a “junior engineer” over the past year4. Why engineering will shift from “bricklayers” to “architects”5. Why AI tools will lead to more engineering jobs rather than fewer6. How Devin creates its own wiki to understand and document complex codebases7. The eight pivots Cognition went through before landing on their current approach8. The cultural shifts required to successfully adopt AI engineers—Brought to you by:Enterpret—Transform customer feedback into product growthParagon—Ship every SaaS integration your customers wantAttio—The powerful, flexible CRM for fast-growing startups—Where to find Scott Wu:• X: https://x.com/scottwu46• LinkedIn: https://www.linkedin.com/in/scott-wu-8b94ab96/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Scott Wu and Devin(09:13) Scaling and future prospects(10:23) Devin's origin story(17:26) The idea of Devin as a person(22:19) How a team of “Devins” are already producing 25% of Cognition's pull requests(25:17) Important skills in the AI era(30:21) How Cognition's engineering team works with Devin's(34:37) Live demo(42:20) Devin's codebase integration(44:50) Automation with Linear(46:53) What Devin does best(52:56) The future of AI in software engineering(57:13) Moats and stickiness in AI(01:01:57) The tech that enables Devin(01:04:14) AI will be the biggest technology shift of our lives(01:07:25) Adopting Devin in your company(01:15:13) Startup wisdom and hiring practices(01:22:32) Lightning round and final thoughts—Referenced:• Devin: https://devin.ai/• GitHub: https://github.com/• Linear: https://linear.app/• Waymo: https://waymo.com/• GitHub Copilot: https://github.com/features/copilot• Cursor: https://www.cursor.com/• Anysphere: https://anysphere.inc/• Bolt: https://bolt.new/• StackBlitz: https://stackblitz.com/• Cognition: https://cognition.ai/• v0: https://v0.dev/• Vercel: https://vercel.com/• Everyone's an engineer now: Inside v0's mission to create a hundred million builders | Guillermo Rauch (founder and CEO of Vercel, creators of v0 and Next.js): https://www.lennysnewsletter.com/p/everyones-an-engineer-now-guillermo-rauch• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder and CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons• Assembly: https://en.wikipedia.org/wiki/Assembly_language• Pascal: https://en.wikipedia.org/wiki/Pascal_(programming_language)• Python: https://www.python.org/• Jevons paradox: https://en.wikipedia.org/wiki/Jevons_paradox• Datadog: https://www.datadoghq.com/• Bending the universe in your favor | Claire Vo (LaunchDarkly, Color, Optimizely, ChatPRD): https://www.lennysnewsletter.com/p/bending-the-universe-in-your-favor• OpenAI's CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai• Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad• Windsurf: https://windsurf.com/• COBOL: https://en.wikipedia.org/wiki/COBOL• Fortran: https://en.wikipedia.org/wiki/Fortran• Magic the Gathering: https://magic.wizards.com/en• Aura frames: https://auraframes.com/• AirPods: https://www.apple.com/airpods/• Steven Hao on LinkedIn: https://www.linkedin.com/in/steven-hao-160b9638/• Walden Yan on LinkedIn: https://www.linkedin.com/in/waldenyan/—Recommended books:• How to Win Friends & Influence People: https://www.amazon.com/How-Win-Friends-Influence-People/dp/0671027034• The Power Law: Venture Capital and the Making of the New Future: https://www.amazon.com/Power-Law-Venture-Capital-Making/dp/052555999X• The Great Gatsby: https://www.amazon.com/Great-Gatsby-F-Scott-Fitzgerald/dp/0743273567—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Beyond the Matrix: AI and the Future of Human Creativity // MLOps Podcast #300 with Fausto Albers, AI Engineer & Community Lead at AI Builders Club.Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractFausto Albers discusses the intersection of AI and human creativity. He explores AI's role in job interviews, personalized AI assistants, and the evolving nature of human-computer interaction. Key topics include AI-driven self-analysis, context-aware AI systems, and the impact of AI on optimizing human decision-making. The conversation highlights how AI can enhance creativity, collaboration, and efficiency by reducing cognitive load and making intelligent suggestions in real time.// BioFausto Albers is a relentless explorer of the unconventional—a techno-optimist with a foundation in sociology and behavioral economics, always connecting seemingly absurd ideas that, upon closer inspection, turn out to be the missing pieces of a bigger puzzle. He thrives in paradox: he overcomplicates the simple, oversimplifies the complex, and yet somehow lands on solutions that feel inevitable in hindsight. He believes that true innovation exists in the tension between chaos and structure—too much of either, and you're stuck.His career has been anything but linear. He's owned and operated successful restaurants, served high-stakes cocktails while juggling bottles on London's bar tops, and later traded spirits for code—designing digital waiters, recommender systems, and AI-driven accounting tools. Now, he leads the AI Builders Club Amsterdam, a fast-growing community where AI engineers, researchers, and founders push the boundaries of intelligent systems.Ask him about RAG, and he'll insist on specificity—because, as he puts it, discussing retrieval-augmented generation without clear definitions is as useful as declaring that “AI will have an impact on the world.” An engaging communicator, a sharp systems thinker, and a builder of both technology and communities, Fausto is here to challenge perspectives, deconstruct assumptions, and remix the future of AI.// Related LinksWebsite: aibuilders.club~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Fausto on LinkedIn: /stepintoliquid

Zara Hajihashemi, AI Engineer and Founder of Cybele Health, joins the podcast to share her journey from Apple tech lead to femtech entrepreneur, driven by a mission to revolutionize women's health with AI-driven insights. With a PhD in machine learning, Zara spent six years at Apple leading cross-functional AI projects before founding Cybele Health to address the inefficiencies in healthcare for professional women and working mothers.In this episode, you'll discover:The Evolution from AI Engineer to Founder: Learn how Zara's experience at Apple, coupled with her PhD research, shaped her vision for Cybele Health and the need for AI-powered, personalized healthcare solutions.Bridging the Healthcare Gap with AI: Zara discusses how Cybele Health is leveraging AI to provide 360-degree visibility into women's health, improving communication between patients and providers to create personalized wellness strategies.The Importance of Personalized Health: Discover how diet, mental health, and physical activity should be aligned with a woman's biological cycle to optimize well-being and productivity.The Role of Functional Medicine and Preventative Care: Zara explains why being proactive rather than reactive in healthcare is crucial, and how AI can assist in creating sustainable, individualized health plans.The Future of AI in Femtech: Explore how AI is revolutionizing the health industry by acting as a 24/7 health assistant, providing predictive insights, and closing gaps in traditional medical care.Building a Health-Tech Startup: Zara shares her journey of founding Cybele Health, securing early users, and the marketing strategies she is employing to drive adoption among both providers and consumers.Zara's Top Health and Wellness Tips:Read labels and avoid processed foods with unrecognizable ingredients.Sync your diet, workouts, and daily habits with your biological cycle for optimal results.Prioritize functional medicine approaches for proactive rather than reactive health management.Connect with Zara and Learn More:Website join the waitlist: Cybele Health LinkedIn: Zara Hajihashemi

Welcome to another episode of The Elite Expert Insider! Today, we're turning the tables as Melanie Johnson, our usual host, steps into the spotlight as our guest, interviewed by her co-owner Jenn Foster. Learn the power of AI in business, especially focusing on practical applications and debunking the fears surrounding artificial intelligence.