Podcasts about software engineers

CloudBees CEO Mo Plassnig is leading the CI/CD company through a major transformation as generative AI reshapes software development. Returning to CloudBees eight years after joining through its acquisition of CodeShip, which he co-founded, Plassnig says the emergence of generative AI renewed his interest in DevOps and the opportunities ahead.His central concern is the dramatic increase in code generated by AI. Rather than focusing on predictions that autonomous agents will replace developers, Plassnig argues that enterprises face a more immediate challenge: safely managing, governing, and deploying an unprecedented volume of machine-generated software.After meeting with Fortune 500 companies, public organizations, and global enterprises, Plassnig found a significant gap between AI hype and real-world adoption. Enterprises recognize the potential of agentic coding but must contend with complex process changes, governance requirements, and security concerns. His strategy is to reposition CloudBees as an AI-first company while rethinking how its Jenkins automation platform can support this new era of software development.Learn more from The New Stack around the latest update with CloudBees and CI/CD:CloudBees CEO: Why Migration Is a Mirage Costing You MillionsWhy coding agents will break your CI/CD pipeline (and how to fix it)Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

Let's know what you liked and learnt! As AI takes on more of the coding, where can software engineers make the biggest difference?In this episode of ContraMinds, engineering leader and researcher Annie Vella joins Swami to explore a shift from writing code to understanding business problems, working closer to customers, and guiding AI to build software that meets their needs. Drawing on her research, Annie introduces “supervisory engineering”: directing AI, evaluating its output, and correcting it. She also examines a growing tension: engineers need experience to judge AI-generated code, but how do they gain that experience when AI does more of the work?A conversation about the future of engineering, the limits of manual code review, and why human judgment matters as much as ever.5 Key TakeawaysMove closer to the business: Understand customer needs and decide which problems software should solve.Build skills in supervising AI: Direct, evaluate, and correct its work.Rethink quality assurance: Manual code review cannot keep pace with AI-generated output.Keep developing expertise: Engineers need practical experience to judge whether AI's work is sound.Retain human judgment: Faster code generation doesn't guarantee the right solution.About Annie VellaAnnie Vella is an engineering leader, researcher, and advisor with more than two decades of experience across nearly a dozen industries and four countries. Formerly a Distinguished Engineer at Westpac New Zealand, she now works independently at the intersection of AI adoption, governance, software engineering, and systems thinking. Her research explores how AI coding assistants are reshaping engineers' work, skills, and careers, and she writes and speaks internationally on the future of the profession.Timestamps00:00 Introduction02:53 How Is AI Changing Software Engineering?05:26 What Does a Software Engineer's New Role Look Like?09:16 If AI Does the Work, How Will Engineers Gain Experience?14:37 How Do We Ensure Quality When AI Generates Code Faster Than Humans Can Review It?20:32 Will Engineers Move From Writing Code to Solving Business Problems?26:29 Will Tomorrow's Software Engineers Become Systems Engineers?33:28 What Makes Software Engineering Feel Like a Passion Rather Than a Job?

“Vibe coding is already present in your company,” according to David Hsu, CEO of Retool and governance is the only way to secure it.Let's put this into context. Think of a Fortune 500 company's CIO who has recently come across a vibe-coded application spotted on the public internet. This app was developed by an employee of the Fortune 500 company in question. The developer obtained a data dump from Salesforce pertaining to customers, then fed this data to an external AI tool not part of the company's set of AI tools. The employee prompted the tool to host the resulting application on a public URL.Since it was an external AI tool, a security review couldn't be carried out, nor could the IT department view it. Without any internal visibility into a tool created by the employee, the actual customer data was live on the open web. It's a scary situation, a breach of trust, so imagine the CIO's wrath upon seeing it.This is why, in the recent episode of the Tech Transformed podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, sat down for a conversation with David Hsu, CEO and Founder of Retool. They talk about the repercussions of vibe coding in addition to examining why most enterprises have no conception of how much of it is already taking place within their own enterprise.The discussion then turns from the immediate issue of governance concerning uncontrolled AI app-building to Hsu's prediction that software engineering as a profession could vanish within two to three years.“All the CIOs that I speak to are genuinely frightened about vibe coding, and even if you don't want to admit it, vibe coding is already present in your company,” he tells Dua. This is partly why Hsu founded Retool.Retool secures vibe coding for enterprises. Hsu explains that Retool can be connected to a company's Snowflake database, for instance or their Databricks data lake. “Users should be able to have access to particular tables only,” he says. Access controls are required at the group level.“If you're in this particular Okta group, you can only access this table, and it's read-only. Whereas if you're in this other Okta group, you can actually write back to this other Salesforce over here." So one Okta group could be granted read-only access to a particular table, while another group might be given write access to a connected Salesforce instance. All applications that are built on top of that connection automatically inherit the same restrictions.Once rules of such a nature are put in place, people could then be set free, explaining why he views governance as something that enables freedom rather than something that restricts.TakeawaysVibe coding is already happening inside most enterprises, sanctioned or notA Fortune 500 CIO found a vibe-coded app exposed on the public internetOne customer's AI coding spend hit 30x their original budget estimateTwo employees independently built duplicate apps that returned different numbersHsu forecasts fully automated coding arriving by late 2027 or early 2028By 2030, Hsu estimates 90% of company work will be done by AI employeesThe CIO's future role: a "chief people officer" governing AI agents, not humansChapters00:00 The Rise of No-Code and Low-Code Development04:52 Governance in the Age of Rapid Development12:40 Centralisation and Control in Software Development20:04 The Future of Software Engineering and AI25:13 The Role of Humans in an AI-Driven Worldvibe coding, AI governance, enterprise AI, CIO, AI agents, software engineering, Retool, David Hsu, enterprise software, AI security

ネットワーク越しの時刻同期の仕組みやNTPの次世代プロトコル達を紹介します。感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

Neste episódio do podcast OsProgramadores, conversamos com Luiz Aoqui, Software Engineer na Oxide Computer, sobre engenharia de software, sistemas, infraestrutura, programação e carreira.Luiz tem uma longa trajetória na área de desenvolvimento de software, passando por empresas como IBM e HashiCorp, onde trabalhou na equipe do Nomad, além de ter atuado como Founding Engineer na Loophole Labs.Conversamos sobre carreira, aprendizado e sobre como experiências acumuladas ao longo dos anos podem abrir novos caminhos na engenharia de software.

If AI is better than your developers, why are your best engineers still writing code?Software engineering is the only engineering discipline where we make the engineer also turn the wrenches. Nicky Pike, Field CTO at Coder, argues that AI is finally letting your best engineers become architects while agents do the mechanic work — and lays out exactly what has to change about how you hire, govern, and provision your teams to make it real.Nicky has 20+ years in developer infrastructure — Xbox Live, private cloud at CVS Health, Cloud Foundry at VMware/Pivotal, and now the 'first mile' problem at Coder. He's a self-described 'platform guy, not a developer' who's watched this pattern play out through four platform shifts and sees the same arc happening faster. What you'll learn• Why the developer laptop is the worst possible endpoint for running AI agents• The identity split that fixes 'AI slop' overnight — human vs. agent permissions• Why Uber blew through their entire AI budget in four months (and how to avoid it)• How the software bottleneck moved from the inner loop to the outer loop — and why GitHub can't keep up• What to actually tell junior devs asking 'am I cooked?' ConnectNicky Pike on LinkedInCoder Chapters• 0:00 Introduction• 1:15 From Air Force crew chief to platform CTO — the 'works on my machine' problem• 2:44 Why this is the most interesting moment in tech since the cloud• 4:46 AI is Home Depot for software — the citizen developer reframe• 8:33 The homegrown-app problem coming back to bite us• 10:16 How Minecraft kids built Coder — and the 'first mile' problem• 13:00 Why the developer laptop is your biggest AI security hole• 16:17 AI is a Mensa student on caffeine — the identity fix that changes everything• 19:31 Uber blew through their AI budget in 4 months — the tokenomics trap• 20:36 Sovereign AI, open weights, and self-hosting the future• 29:02 The bottleneck moved — why GitHub can't keep up• 31:46 The airplane magazine trap — why bolting AI on will destroy you• 36:33 Software engineers have been doing two jobs this whole time• 40:27 GTG 1002 and why the disease is also the cure• 43:02 What happens to open source when AI writes the code• 48:19 What to tell junior devs asking 'am I cooked?' -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ashwin Ballal never spent a day in IT before Freshworks handed him the CIO title. He trained as a materials scientist, then worked his way through marketing, product, and engineering roles at KLA-Tencor before the CEO asked him to run IT. Three CIO jobs later, now at Freshworks, he's become the company's single most important customer, shaping its product roadmap from inside the org he runs.In this conversation, Ashwin explains why AI-generated code needs more human verification, not less, why counting token usage tells you nothing about whether AI is actually working, and how Freshworks tore apart a "Frankenstack" of roughly 400 disconnected systems by refusing to customize its own workflows. He also gets candid about what worries him most heading into the next phase of enterprise AI: agents running with privileged access and few real guardrails. Listen in for an unfiltered read on where AI is actually delivering, and where it's just noise.Listen or subscribe wherever you get your podcasts. Visit ciopod.com for more episodes.3 Takeaways:Ashwin argues AI-generated code needs more engineers, not fewer, because someone still has to verify what the model produces before it reaches production.He says most companies are tracking the wrong AI metric, counting token usage instead of asking what revenue or savings actually resulted.Freshworks cut a "Frankenstack" of roughly 400 disconnected systems down by implementing tools out of the box instead of customizing them.Chapters:(00:00) Introduction(03:16) Early Life & What Drives Ashwin(05:06) From PhD to CIO(07:51) Career Lessons & Leadership Style(10:56) Why Freshworks, Why Now(13:57) The CIO as Product Customer(17:05) AI Adoption & Rethinking Workflows(22:24) Killing the Frankenstack(25:30) Agent Risk & Guardrails(27:20) Lightning RoundAbout Ashwin: Ashwin Ballal joined Freshworks in May 2024 as Chief Information Officer. His significant leadership experience of over 30 years in technology and business strategy will now be channeled to spearhead all facets of IT infrastructure, systems, and operations within Freshworks. Prior to this new responsibility, Ballal served as the Senior Vice President and Chief Information Officer at Medallia. He successfully led and revamped the IT and security, workplace services, corporate procurement, and travel divisions during his tenure there. His preceding role was as the Chief Information, Intelligence, and Data Officer of KLA Corp.Guest Highlights:"Oh, 90% of my code is written by AI. How do you know if it's right? Who verified it, right?""Let's count token usage. I mean, that's not a behavior you want. What outcomes change?""If you thought the SaaS crawl was the Wild Wild West, Yousuf, you ain't seen anything yet."Get Connected:Yousuf Kahn: https://www.linkedin.com/in/yousufakhan/ Ashwin Ballal: https://www.linkedin.com/in/ashwin-ballal-9a89961/ Our Sponsor: Want to accelerate software development by 500%? Meet Blitzy, the only autonomous code generation platform with infinite code context, purpose-built for large, complex enterprise-scale codebases.While other AI coding tools provide snippets of code and struggle with context, Blitzy ingests millions of lines of code and orchestrates thousands of agents that reason for hours to map every line-level dependency.With a complete contextual understanding of your codebase, Blitzy is ready to be deployed at the beginning of every sprint. Blitzy handles the heavy lifting, delivering over 80% of the work autonomously. The platform plans, builds, and validates premium-quality code at the speed of compute, turning months of engineering into a matter of days.It's the secret weapon for Fortune 500 companies globally. To hear how engineering leaders are transforming the way they deliver software, visit blitzy.com. Schedule a meeting with their consultants to enable an AI-Native SDLC in your organization today. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Send us Fan MailVibe coding can patch almost anything, but a duct-tape fix only holds until the water pressure changes. Amith Nagarajan and Mallory Mejias welcome Han-Shen (Han) Yuan, who built mobile at eBay and Netflix, led engineering at Upwork through its IPO, grew up alongside Amith in 1980s Silicon Valley, and is back at Berkeley earning a master's in data science. For association leaders weighing how much technical talent they need, they unpack where AI-built tools break down, explain why judgment under ambiguity still belongs to engineers, and challenge the assumption that every organization needs a full-time CTO. Han makes the case that the bigger gap may be product leadership, and he breaks down how tech companies use guilds, a one-thing-per-quarter rule, and an interview built around self-awareness, drive, and grit. He also argues that nobody has ever saved their way to prosperity and that AI's real return lies in growth.

The AI industry has faced a growing enterprise dilemma: companies want access to powerful proprietary AI models without risking sensitive data or intellectual property, while AI labs want to protect their model weights from being exposed to customers. Traditionally, businesses had to choose between proprietary models with potential data-leakage concerns or open-weight models that lagged behind the frontier. Vast Data co-founder Jeff Denworth argues that a new approach can address both sides of the trust problem.Vast Data's DataEnclave uses Nvidia's Confidential Computing technology to let enterprises run proprietary AI models securely on their own infrastructure, while preventing either the company's data or the AI lab's model weights from being exposed. Denworth says the timing reflects rapidly increasing enterprise AI adoption, particularly after agentic coding tools drove demand and usage. As AI agents create new requirements at the data layer, the podcast explores how enterprises are approaching AI, the security challenges involved, and the untapped potential of enterprise data.Learn more from The New Stack around the latest in AI trust:VAST Data tackles the enterprise AI trust gapGoogle, Microsoft, and OpenAI join forces to help create AI's missing trust layerJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

Send us Fan MailAlbert Wan is a San Gabriel Valley native, raised in Arcadia, and a second-generation business owner leading his family's enterprise software company. After studying at UC Berkeley and working as a software engineer at Apple Inc., where he contributed to software projects including Chinese keyboards and CarPlay, he returned home in 2018. Today, he focuses on modernizing his family's business, which has provided business and warehousing software technologies to customers across the San Gabriel Valley and beyond for over three decades.In this episode, Albert shares his journey from attending local schools in Arcadia and participating in marching band and Scouting to earning an engineering role at Apple. He discusses his experience inside a major technology organization, earning an MBA at UCLA Anderson, and deciding to leave Silicon Valley to preserve his family's business legacy. Albert also details the practical steps involved in updating legacy Enterprise Resource Planning (ERP) and warehousing software, transitioning to cloud-based systems, and maintaining trust with long-time clients.This conversation offers real-world insights for San Gabriel Valley viewers interested in local business growth, tech professionals exploring career transitions, and second-generation entrepreneurs managing corporate succession. Viewers searching for topics around software modernization, family business leadership, and digital transformation will find clear perspectives rooted in steady progress and long-term strategy.If you enjoyed this episode, please subscribe and share this video with others to support local stories on the MySGV Podcast._______________Music CreditsIntroEuphoria in the San Gabriel Valley, Yone OGStingerScarlet Fire (Sting), Otis McDonald, YouTube Audio LibraryOutroEuphoria in the San Gabriel Valley, Yone OG__________________My SGV Podcast:Website: www.mysgv.netNewsletter: Beyond the MicPatreon: MySGV Podcastinfo@sgvmasterkey.com

a16z Board Partner and former Microsoft Windows president Steven Sinofsky joins Theo Jaffee and Sofia Puccini on MTS to argue that the language we use to describe AI failures is making it harder to understand what's actually going wrong.Steven takes aim at terms like “alignment,” “goal-seeking,” and “rogue agents,” arguing that they can anthropomorphize problems that software engineers have dealt with for decades. His framing is simpler: when software doesn't do what it's supposed to do, it has a bug. And as AI becomes more widely deployed, labs need the same kind of telemetry, debugging, incident reporting, and operational discipline that previous generations of software eventually developed.Drawing on everything from early computer hacking and Microsoft's response to major software failures to Y2K and cybersecurity standards, Steven makes the case for treating AI reliability as an engineering problem. They also discuss what AI labs can learn from CVE reporting, why industry has a responsibility to make its systems safer, and how confusing terminology can lead to equally confused regulation.Resources:Follow Steven Sinofsky on X: https://x.com/stevesiFollow Theo Jaffee on X: https://x.com/theojaffeeFollow Sofia Puccini on X: https://x.com/schisofrenia Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

チャンスは、変な顔をしてやってくる https://note.com/maxmatsuura/n/n04f20fbdce8eAIに書かせたら、前より休めなくなった https://note.com/maxmatsuura/n/ne59969fd6e71感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

チームにおけるAI開発の生産性について話しました。Your job is to deliver code you have proven to work　https://simonwillison.net/2025/Dec/18/code-proven-to-work/感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5スポンサーの問い合わせは todayilearnedfm@gmail.com へYour co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

This week Chris sits down with Zane Hengsperger, Founder and CEO of NOX Metals, a metal supplier in Detroit he started in 2025. NOX buys metal from the mill, cuts it to the exact size a machine shop needs, and ships it out. The whole operation runs on software the company writes itself. Zane is 26, and his first job was scrubbing the floors of his dad's machine shop at 14. He went into software, then came back to run a machine shop. That is where he saw that metal was the bottleneck for everyone making parts in America. The incumbents have been around for 200 years and have never put software to work. NOX is a year old and has raised an $11.5M seed round. It has written a million lines of code and quotes an order in 10 seconds. They discuss:• The 18-minute call with Y Combinator that put him on a plane the next day• Why his dad hired software engineers for a machine shop back in 2017• How one piece of software does the work of 20 salespeople• Why two of his engineers moved their desks onto the factory floor• Why the forklift driver gets equity after three months• The 7pm order that was in Dallas by 10 the next morning• Why he says America needs 100x more factories• Where AI has let him down so far This one is for anyone betting on American manufacturing. Timestamps(00:00) Intro(01:10) Reading Alex Karp and Walking Back Into a Machine Shop(06:06) Hiring Young Technicians Who Want Hard Work(09:10) Why the Brand Is the Moat(14:28) Lessons From a Dad Who Ran a Machine Shop(16:14) Getting Into Y Combinator With One Day's Notice(22:06) Buy a Saw and Write Instant Quoting Software(27:37) Software Engineers on the Factory Floor(34:29) Is America Actually Reindustrializing?(40:55) America Needs 100 Times More Factories(45:26) Quotes in 10 Seconds Instead of Eight Hours(50:09) Delta Cargo Deliveries and Why Detroit Is Ripping(56:07) Where AI Falls Short and What the Next 24 Months Hold(59:13) The Customer Is the Core Thing Left in America === Presented by True North Advisors: True North AdvisorsTrue North Advisors is a multi-family office and private wealth advisory firm serving business owners, entrepreneurs, and families since 2000. With over $5.6 billion under management, they're real investors offering conflict-free counsel and portfolios built around your life. Learn more at https://truenorthadvisors.com === Sponsored by: AirshareAirshare is trusted across the country for fractional ownership, jet cards, charter, and aircraft management. They give you a smarter way to fly private - over 25 years of experience, operating their own fleet, with the top safety ratings in the industry. Drive up to the FBO, walk on, and go. Go to flyairshare.com to learn more. Collateral PartnersCollateral Partners builds institutional-grade investor materials for private credit, private equity, real estate, and family office firms, the kind of marketing collateral that helps you close capital. Learn more at https://collateral.com/powers Relay Human CloudRelay Human Cloud gives you pre-vetted, fully managed global talent for up to 75% less than hiring locally. Your best people stop doing repetitive work and get back to the work that moves your company forward. Learn more at https://www.relayhumancloud.com/powers === Chris on Social Media:X: https://x.com/fortworthchrisInstagram: https://www.instagram.com/thepowerspodcastLinkedIn: https://bit.ly/45gIkFd Watch POWERS on YouTube: https://bit.ly/3oynxNXVisit our website: https://www.powerspod.com/Leave a review on Apple: https://bit.ly/45crFD0Leave a review on Spotify: https://bit.ly/3Krl9jO

What's the one rule that matters most when AI writes most of your code? Emilie says it comes down to a single line: don't outsource your thinking.In this episode, Emilie Schario, co-founder of Kilo Code, unpacks what it actually means to lead in a world where AI can write most of your code. She explains why Kilo went all-in on being model agnostic, supporting over 500 models instead of betting on a single lab. Emilie shares her core rule for using AI responsibly: never outsource your thinking, especially when it comes to auth, billing, or security. She introduces a metric more companies should be tracking, spend per merged pull request, as a better signal of value than raw AI cost. Emilie also talks about the “killing problem” that comes with software becoming nearly free to ship, and why teams now discover product-market fit after shipping rather than before. She closes with how engineers are shifting from producers to reviewers, and what that means for anyone now managing a team of AI agents instead of just their own code.Key topics discussed:Why Kilo Code refuses to bet on a single AI modelThe one rule for not losing control to AI agentsA better way to measure AI spend: cost per merged PRWhy shipping software is nearly free, and why that's riskyHow AI-native teams find product-market fit after shippingWhy senior engineers adapt to AI agents the fastestBuilding a council of reviewers for high-risk code changesWhat the Anaconda acquisition means for Kilo CodeTimestamps:(00:00) Trailer & Intro(02:27) What Is Kilo Code All About?(03:31) How Do You Avoid Getting Overwhelmed by 500 Plus AI Models?(04:34) How Does a Multi-Platform Approach Benefit Developers?(07:19) How Does Kilo's Agent Harness Differ From Other Tools?(09:51) Why Will Model Choice Matter Less Over Time?(11:20) What Tools Help You Pick the Right Model for Each Task?(14:16) What Does the Anaconda Acquisition Mean for Kilo Code?(15:43) Why Should You Measure AI Spend Per Merged Pull Request?(18:54) How Do You Balance Speed and Control in AI Development?(20:28) Why Should Companies Treat AI as Operational Infrastructure?(23:31) How Do AI-Native Organizations Run Their Day-to-Day Workflows?(27:58) How Will High-Performing AI Organizations Differentiate Themselves in the Future?(30:11) What Is the “Killing Problem” Now That Shipping Software Is Free?(32:40) How Do You Shift to a Metrics-First Mindset?(34:24) How Is the Product Manager's Role Changing in AI-Native Teams?(37:06) How Do You Balance Rapid Feature Delivery With Product Quality?(41:48) How Is the Software Engineer's Role Evolving in the Age of AI?(43:57) What Is the Best Advice for Junior Engineers Entering an AI-Native World?(45:51) How Can Developers Handle the Cognitive Load of Code Reviews?(49:11) How Should Docs and Code Attribution Evolve With AI Agents?(53:06) How Can AI Improve Your Life Outside of Work?(55:04) 3 Tech Lead Wisdom_____Emilie Schario's BioEmilie is VP, Engineering at Anaconda and the former cofounder of Kilo (acquired by Anaconda), where she focuses on turning AI investment into real engineering output. She operates at the intersection of AI tooling, engineering org design, and the day-to-day realities of running high-performing teams, helping organizations translate innovation into measurable impact.Follow Emilie:LinkedIn – linkedin.com/in/emiliescharioPlaybooks and Priority newsletter – emilie.substack.comKilo Code – kilo.ai/Like this episode?Show notes & transcript: techleadjournal.dev/episodes/268.Follow @techleadjournal on LinkedIn and Instagram.Buy me a coffee or become a patron.

Harness Field CTO Martin Reynolds joins The New Stack to talk about what happens after coding agents start opening pull requests faster than anyone can review them. He explains how he first saw the bottleneck during early GitHub Copilot trials, the three ways enterprises are coping with the volume now, and why Harness rebuilt its Code Repository and launched AI Code Review for agent traffic. The conversation also covers GitHub's recent outages, the software delivery knowledge graph behind Harness's reviewer, and how much of the delivery pipeline should stay deterministic. Learn more from The New Stack around the latest in coding agents:AI coding agents can write code, Crafting wants to help them ship itGit real: AI agents aren't just for solo developers anymoreJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

OSSのKVストア・DBエンジン、FoundationDBについて取り上げました。- https://www.foundationdb.org/- SIGMOD21 の論文 https://dl.acm.org/doi/10.1145/3542700.3542707感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

Traces provide a detailed view of a request's journey through data, microservices and applications, helping SREs pinpoint where failures occur and resolve issues faster. But while tracing can reduce downtime and developer burnout, collecting every trace creates its own problems. Storing massive volumes of data is expensive, can burden the systems being monitored and makes it harder to find the information that actually matters.The solution isn't abandoning tracing, but being smarter about what gets retained. Head sampling captures only a portion of traces upfront, while tail sampling evaluates completed traces and keeps those most valuable for troubleshooting. Dynamic sampling goes further by filtering repetitive or nearly identical traces before they overwhelm storage.On The New Stack podcast, Sarah Hudspeth of Chronosphere, a Palo Alto Networks company, explains how teams can build a more effective tracing strategy. She breaks down how thoughtful sampling and observability design can turn tracing from a data-hoarding problem into a practical tool for production troubleshooting. Learn more from The New Stack around the latest in tracing:Sampling: the philosopher's stone of distributed tracingHow OpenTelemetry Works: Tracing, Metrics and Logs on KubernetesWhy Synthetic Tracing Delivers Better Data, Not Just More DataJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

Need financing for your next investment property? Visit: https://www.academyfund.com/ Want to join us in Washington, D.C. on Sept 28th & 29th? Visit: https://www.10xvets.com/events ____ Matthew Filbert is the founder of Reunion Baths, a modern bathhouse concept reimagining how people connect through wellness, community, and shared experience. After serving in the U.S. Army and later working as a software engineer, Matthew set out to build something entirely different: a place where people could disconnect from technology, slow down, and reconnect with one another. Inspired by years of visiting bathhouses throughout New York City and around the world, Matthew saw an opportunity to bring a centuries-old tradition into a modern setting. Reunion Baths blends communal bathing, thoughtful design, and hospitality to create spaces where meaningful conversations, genuine relationships, and everyday wellness naturally come together.     In this episode of the SABM podcast, Scott chats with Matthew about: An Unconventional Entrepreneurial Journey: How an unexpected passion for bathhouses led Matthew from software engineering to launching Reunion Baths. More Than a Wellness Trend: Why Matthew believes communal bathing fills a growing need for deeper human connection in an increasingly digital world. Designing an Experience People Remember: The vision behind Reunion Baths and how every detail is intended to encourage conversation, relaxation, and community. Bringing a Big Idea to Life: Matthew shares what it takes to raise capital, navigate development, and turn an ambitious concept into a reality. Building for the Long Term: From expanding into major cities to helping shape America's culture around communal bathing, Matthew shares the vision guiding Reunion Baths beyond its first location.  Timestamps: 00:41 Spa Love Story Begins 02:26 Russian Turkish Baths Spark 05:40 Why Bathhouses Are Back 09:37 Funding and Brooklyn Location 11:13 Inside the Bathhouse Design 13:54 Timeline and Build Plan 15:49 Big Vision and Expansion 19:03 How to Help and Invest Connect with Matthew LinkedIn | Matthew Filbert reunionbaths.com matthew@reunionbaths.com  If you found value in today's episode, don't keep it to yourself—share it with a colleague or friend who could benefit. And if you're a Service Academy graduate ready to elevate your business, we'd love for you to join our community and get started today. Make sure you never miss an episode. Subscribe now and help support the show: Apple Podcasts Spotify Leave us a 5-star review! A special thank you to Matthew for joining me this week. Until next time! -Scott Mackes, USNA '01  

When you think about high-paying jobs in the cleared community, it's easy to jump straight to software engineering, cloud computing, or other highly technical careers.But coding isn't the only path to a lucrative cleared career.Across the national security workforce, employers need professionals who can analyze intelligence, manage programs, design training, communicate across languages, understand military operations, conduct research, and even bring creative ideas to life.If you have an active security clearance and the right combination of experience, education, and specialized skills, there are plenty of career paths worth exploring. Here are 12 cleared roles that demonstrate just how diverse the six-figure job market can be. Hosted on Acast. See acast.com/privacy for more information.

Show NoteHow Anthropic's product team moves faster than anyone else | Cat Wu (Head of Product, Claude Code) https://www.youtube.com/watch?v=PplmzlgE0kg感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

Suggest Guests For Our Podcast: https://forms.gle/ytt6a9jxe8YwzsGD6 In this episode of the Sushant Pradhan Podcast, we sit down with *Swikar Sharma (Swikar Codes), a Software Engineer, Instructor and Content Creator, for a deep dive into **Artificial Intelligence, Claude AI, Large Language Models, prompt engineering and the future of technology*. Swikar explains why he started using Claude AI, how he entered the tech industry, and how AI and LLMs are changing the way we learn, work and create content. The conversation goes deep into *Claude AI prompting, including how to write better prompts and use AI at its fullest. Swikar breaks down important Claude features such as **Claude Skills, Projects, Artifacts, Connectors and Custom Instructions*, while also explaining which AI model is best for different types of work. We also discuss practical ways to use AI for productivity, content creation and learning. The episode explores bigger questions surrounding *Artificial Intelligence, including whether AI will replace humans, how AI companies train their models, why AI development is accelerating so quickly, and how **China is developing its AI ecosystem*. Swikar also shares insights into AI vision capabilities and why students should have greater access to AI tools. Whether you're a software engineer, student, content creator, entrepreneur or simply interested in *AI and LLMs*, this episode offers practical insights into using modern AI tools effectively. Learn about Claude AI, prompt engineering, AI productivity, Claude Projects, Claude Skills and the future of AI with Swikar Codes. GET CONNECTED WITH Swikar Sharma (Swikar Codes): Instagram - https://www.instagram.com/swikar.codes/?hl=en Facebook - https://www.facebook.com/swikar.code/ YouTube - https://www.youtube.com/@swikarcodes TikTok - https://www.tiktok.com/@swikar.codes  

HOUR 3: If the software engineers don't even know what the future of AI looks like... full 2288 Tue, 18 Aug 2026 21:00:00 +0000 Zzw8gNV7xnCIckwSUg8XqRUtqcI52Zjd news The Dana & Parks Podcast news HOUR 3: If the software engineers don't even know what the future of AI looks like... You wanted it... Now here it is! Listen to each hour of the Dana & Parks Show whenever and wherever you want! © 2025 Audacy, Inc. News https://p

In this episode, Dave and Jamison answer these questions: Hi Dave & Jamison! I've been listening for almost a year now and have worked through nearly your entire back catalog in reverse. The podcast has genuinely helped me become a better engineer, so thank you! I'm two years into my software engineer career at a large infrastructure company. I was promoted to SWE II last year, and this year my manager told me she felt my work was promotion-worthy, but because I'd just been promoted and she had others on the team to consider, she wants to target me for next year's cycle instead. She gave me a spot bonus and said she'd try to make up for it in my raise. I understand and appreciate the honesty, but I'm also a little disappointed because I've worked hard to hit every milestone on the roadmap she gave me, and career progression does matter to me. My question is: How should I navigate the next year? What should I focus my 1:1s on? Should I push for a mid-year promotion if that's an option, or instead focus on building an undeniable case for next year? How would you approach this if you were in my shoes? Hey Dave and Jamison, Long time listener. First time question asker :). I recently joined a major tech company. Before this, I worked at a mid-sized company with about 200 engineers. In my old role, I built customer-facing products. The work was challenging and exciting, and I genuinely enjoyed it. I ended up moving to a new city to be closer to family and started looking for a job. The job search was brutal. Honestly, by the end of it, I would have accepted just about anything. Luckily, a major tech company extended an offer. The money was great, and I thought it would be a huge plus to have a major tech name on my resume. It is a fully remote role, but they did mention that they could go hybrid in the future. I knew ahead of time that the team built internal tools instead of customer-facing products. But after joining, I realized most of what we do is closer to operations than actual software engineering. I know I should have screened the team better. I was just so relieved to get a good offer from a top company that I accepted without thinking twice. I am not enjoying the work at all. I am not planning to quit immediately. However, I want to be ready to leave in about a year or earlier if they decide to lay us all off. I want to make sure I utilize my time here as best as possible. Do you have any suggestions on how I can best position myself for an internal transfer or an eventual external move? Also, I have been in this role for close to 2 months now. I did a 1-1 with the manager in my first week, but after that, have not had any 1-1s. He has not scheduled anything. I was thinking of scheduling it myself but not sure if that can be seen as too forward. Btw this is outside US, if that matters :) Thanks again for your advice and laughs.

技術力が高い企業が、なぜ新しい市場で簡単に負けてしまうのか？Anshu Sharma氏のブログ Why Big Companies Keep Failing: The Stack Fallacy について取り上げました。https://techcrunch.com/2016/01/18/why-big-companies-keep-failing-the-stack-fallacy/ 感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

Before introducing their guest, Capers Jones, Hosts Llewellyn King and Adam Clayton Powell III talk about the possibility that AI could help middle-income and poor countries leapfrog older infrastructure and catch up to high-income economies. Joining in later, Jones, enthuses about both AI and robotics, but worries about the displacement of human workers. While he favors open source and broad sharing of knowledge, he also favors legislation to limit harmful uses.

As AI evolves from conversational chatbots to autonomous agents, CPUs are becoming an increasingly important part of the infrastructure equation. In this episode, The New Stack speaks with Bhumik Patel of Arm and Mo Farhat of Google about how CPUs act as an “air traffic controller” for agentic workloads, handling orchestration, data preparation, semantic search, vector databases, code execution and API calls alongside GPUs and TPUs. Smaller AI models, including summarizers and evaluators, can also run effectively on CPUs for specialized tasks.  As agents increasingly generate and execute code, secure sandboxing becomes critical. Google's gVisor and GKE Agent Sandbox provide isolation and scalable environments, with the latter supporting up to 300 sandboxes per second per cluster. The discussion also explores efficiency and cost, with Google highlighting Axion's price-performance and energy-efficiency advantages across different workload types. Ultimately, the shift toward agentic AI is creating a more diverse compute environment where CPUs, GPUs and TPUs each play complementary roles in delivering scalable, efficient AI applications. Learn more from The New Stack around the latest in CPUs in the world of AI agents:  AI Agents Will Eat Enterprise Software, Just Not in One Bite  How to ground AI agents in accurate, context-rich data  Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Does moving faster with AI mean you get to skip the laws that governed every software project before it? Milan Milanovic argues the opposite, the old laws of software engineering now apply twice as hard.In this episode, Milan Milanovic, CTO and author of Laws of Software Engineering, returns to unpack why the laws that have quietly governed software projects for decades are more relevant than ever in the AI era. He walks through Gall's Law and why AI lets teams generate complex systems on unvalidated assumptions faster than ever, Conway's Law and how it now runs twice, shaping both human organizations and agent topologies, and Goodhart's Law and the trap of “tokenmaxxing” as a metric. Milan also covers Hofstadter's Law and the 90-90 rule, explaining why the last 10% of a project still takes as long with AI in the loop, and the Dunning-Kruger effect, where vibe coders overestimate their skills while senior engineers underestimate theirs. The conversation closes with his advice for juniors entering the field, his top five must-read books, and how he personally uses AI for research and planning rather than implementation.Key topics discussed:Gall's Law: the trap of generating complex systems with AIConway's Law now runs twice, on humans and on AI agent topologyWhy software architects should help design your org chartGoodhart's Law and the danger of “tokenmaxxing” as a metricThe 90-90 rule: why AI's last 10% still costs half the projectDunning-Kruger effect on vibe coders and expertsWhy less code beats more, even with AI writing it for freeTimestamps:(00:00) Trailer & Intro(03:02) What Inspired Milan to Write Laws of Software Engineering?(05:32) How Did a Book on Software Laws Reach Such a Global Audience?(06:49) How Should You Read the Laws of Software Engineering Book?(08:51) Why Does the Book Cover People and Planning, Not Just Technical Laws?(11:07) What Is Gall's Law in Software Engineering?(14:46) How Can You Apply Gall's Law When Using AI?(17:34) What Is Conway's Law in Software Engineering?(19:34) How Should Big Corporations Build New Products Without Structural Reorganization?(21:26) How Does Conway's Law Apply to Small AI-Powered Product Teams?(23:25) What Is Goodhart's Law in Software Engineering?(25:52) What Are the Best Examples of Counterbalance Metrics for Leaders Today?(28:06) What Kind of Outcomes Should You Actually Measure?(31:01) What Is Hofstadter's Law in Software Engineering?(33:35) How Does Hofstadter's Law Apply in the Age of AI?(36:41) What Is the Dunning-Kruger Effect?(40:10) How Does the Dunning-Kruger Effect Apply to Experienced Engineers Learning AI?(42:46) Are Any of These Laws Becoming Less Relevant Because of AI?(44:32) What Does the Lindy Effect Say About the Fate of Software Developers?(47:49) What Is the Best Career Advice for Junior Developers Entering the AI Landscape?(50:34) What Are the Top Five Must-Read Books for Software Engineers?(55:46) How Can Software Engineers Apply These Laws in Their Daily Work?(57:26) 3 Tech Lead Wisdom_____Milan Milanovic's BioMilan Milanović is the CTO and the author of Laws of Software Engineering. He holds a PhD in Computer Science, has more than 20 years of experience across .NET, Azure, and mobile development, and is a Microsoft MVP. He runs Tech World With Milan, a software engineering newsletter and community followed by more than 400,000 engineers. He writes about architecture, engineering leadership, and how AI is changing the way software gets built.Follow Milan:LinkedIn – linkedin.com/in/milanmilanovicTwitter / X – @milan_milanovicPersonal website – milan.milanovic.org Book's website - lawsofsoftwareengineering.com Newsletter - newsletter.techworld-with-milan.comLike this episode?Show notes & transcript: techleadjournal.dev/episodes/266.Follow @techleadjournal on LinkedIn and Instagram.Buy me a coffee or become a patron.

No one is hiring. AI took your job. Junior developers aren't needed anymore.I can't tell you how sick I am of hearing this but there is some truth to it.Things aren't roses and candy canes but they're not all doom and gloom either.Tom graduated from Parsity's coding bootcamp in 2025 and got hired faster than you might expect.There's no secret to his success but there are clues. He's got nothing to sell you but a lot to tell you.

ワールドモデルを理解するためがんばりました。A Functional Taxonomy of World Model https://substack.com/home/post/p-200386248感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

"AI scribes are just a feature. The record of clinical judgment is the moat."I talked with Kush Shah, founder and CEO of Noted Dent, on Startup Hustle this week. Noted Dent uses AI to handle clinical documentation for dentists, so practitioners aren't stuck writing notes at 11pm or on the weekend, and so they're covered when the auditors and health funds come knocking.Here's what you'll get out of it:⚡ Why vibe coding gets you to an MVP and no further, and why Kush deleted 8,000 lines of AI-generated code in three months⚡ Why the job of a software engineer has changed, and how AI has actually made it more human⚡ Why demand, not product, is the hardest thing in any business, even eight years in⚡ Why startups are the best place for young engineers to learn, and why job-title silos hold them back⚡ What actually makes the leap to CEO so hard when you come from a technical backgroundIf you're a technical founder, an engineer eyeing your next move, or anyone building in a regulated space, this one is worth your time.Listen to the Startup Hustle podcast anywhere you listen to podcasts!⏱️ Episode Breakdown01:06 Introduction and Anthony Jackson's background02:47 Is software development dead again?03:48 The impact of AI on coding and human judgment04:41 The importance of responsibility in software development06:33 The challenge of product management and customer feedback10:52 Transitioning from individual contributor to manager12:00 Building effective teams and leadership culture15:14 Anthony Jackson's leadership OS book and approach19:00 The significance of company and team culture21:41 The impact of good and bad managers22:06 Anthony Jackson's newsletter and coaching23:48 Connecting software developers with customers25:40 Empathy for users and understanding their experience29:06 Where to find Anthony Jackson's resources and booksLinks & ResourcesConnect with Kush Shah on LinkedIn - https://www.linkedin.com/in/kush-shah-kush/Noted Dent - https://noteddent.com/What Smart CTOs Are Doing Differently With Offshore Teams in 2025Subscribe to the Global Talent SprintFull Scale – Build your dev team quickly and affordablyIf you're trying to get your team out of the basement and into real product ownership, this episode is your playbook. Stop being a ticket factory. Build teams that think, create, and lead.Follow the show, rate it, and send this to someone who's still trying to do “real Scrum.” They need it more than you do.

Answering engineer questions on AI pressure, career growth, product thinking and impact. Including the production incident I'm glad happened, and the mindset I refuse to accept when things break.In this video, we cover:- Whether managers are really demanding more output because of AI- Balancing fundamentals with AI coding tools and agents early in your career- Specialist vs generalist and when to lean into each- Visibility, personal branding and who gets credit for your work- Product thinking, evaluating impact and what I got wrong about content being kingFor software engineers at any level who want honest answers on career strategy in the agent era, from someone doing both engineering and product.Timestamps:00:00:00 - How to Spot the Next Big Thing00:03:15 - The Saying I Hate Most00:04:27 - The Production Mistake I'm Glad I Made00:07:32 - Are Managers Demanding More Because of AI?00:13:39 - Learning Fundamentals vs AI Coding Tools00:19:00 - Will AI Ever Get Good at Distributed Systems?00:20:51 - Specialist vs Generalist: When to Lean In00:26:35 - How to Become More Visible in Your Org00:31:49 - I Was Wrong: Content Isn't King00:35:03 - Workflows, Priorities and Hiring an Editor00:37:08 - What Being a Force Multiplier Really Means00:41:26 - How to Evaluate What's Worth Building00:45:01 - Product Thinking Without Years of Experience00:48:13 - Energy Management, Curiosity and Defining Success00:54:21 - Hair Talk

Intelの元CEOによる科学的マネジメント手法の伝説的な名著「High Output Management」について話しました。邦訳: ハイアウトプット マネジメント) 人を育て、成果を最大にするマネジメント https://amzn.to/4bPmZIQHigh Output Management https://amzn.to/3REtYxw感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

As AI coding agents accelerate software development, they also create new challenges for site reliability engineers (SREs), who are increasingly responsible for debugging systems that no single human fully understands. In this episode ofThe New Stackpodcast, Sam Farid and Nate Heinrich of Chronosphere argue that AI agents should also be used for root-cause analysis, helping teams diagnose failures more quickly as model capabilities continue to improve.  Rather than immediately purchasing a commercial solution, they recommend organizations first build an in-house AI SRE. The process of documenting systems, dependencies, and operational knowledge creates valuable context that enables AI agents to troubleshoot effectively while improving institutional knowledge. Although Chronosphere offers its own AI SRE platform, the hosts emphasize that building an internal prototype helps teams understand their needs before evaluating vendor tools. As AI-generated code becomes more common, organizations that invest in mapping their systems and leveraging AI for operations will be better equipped to reduce downtime and support increasingly complex software environments. Learn more from The New Stack around AI SREs:  5 ways SRE AI agents are set to augment human capabilities The Future of AI in SRE: Preventing Failures, Not Fixing Them AI Reliability Engineering: Welcome to the Third Age of SRE Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Voice AI & Voice Agents https://voiceaiandvoiceagents.com/2025年の音声エージェント https://open.spotify.com/episode/31yLX95GkRdQInVsh21M3p?si=nEe8GMAoQm-5GBs4VyVieA感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

In this episode with The New Stack Agents, Frederic Lardinois, NVIDIA's Joey Conway says advances in AI over the past year have dramatically improved the capabilities of local models, making them practical for enterprise and personal use alongside frontier cloud models. Rather than replacing large models, Conway envisions a “system of models” where specialized local models handle routine, cost-sensitive, or privacy-focused tasks, while larger frontier models tackle more complex reasoning. He explains that organizations can fine-tune smaller open models using domain-specific data, creating expert AI agents that reflect the specialized roles found within businesses.  NVIDIA supports this ecosystem through open models, training tools, and software such as NeMo, Dynamo, and Nemotron. Conway also highlights the growing importance of agentic harnesses, which give AI models access to tools, memory, and iterative workflows, significantly improving performance and reducing costs. Looking ahead, he expects AI orchestration to become increasingly important, with intelligent routing systems selecting the right model for each task based on complexity, cost, latency, and data governance requirements, enabling enterprises to balance performance, security, and efficiency. Learn more from The New Stack around NVIDIA's latest updates in AI: Palantir and Nvidia want to change who owns government AI  Nvidia's best model is now live Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Hoje o papo é sobre interpretabilidade em modelos de inteligência artificial! Neste episódio, conversamos sobre como pesquisas recentes, em especial da Anthropic, vêm buscando entender o que acontece por baixo do capô dos grandes modelos de linguagem, além de como eles chegam a determinadas respostas. De alinhamento a alucinações, passando por reasoning, emoções, J-Space, comportamentos emergentes e os desafios de abrir a caixa-preta das grandes IAs, a conversa é bastante abrangente e repleta de boas referências. Vem ver quem participou desse papo: Paulo Silveira, o host que usa a palavra governança Fernanda Kipper, Software Engineer & Founder na kipper.dev Vinny Neves, cohost, dev e professor na Alura Fabrício Carraro, co-host do IA Sob Controle, Program Manager da Alura, autor de IA e host do podcast Carreira Sem Fronteiras  Links:  Gemini Notebook (ex-NotebookLM) Podcast Naruhodo! O Império da IA IA Sob Controle 213: O Império da IA – Sonhos e Pesadelos na OpenAI de Sam Altman Logits: o que acontece antes da IA escolher a próxima palavra Estudo: A global workspace in language models Estudo: Emotion concepts and their function in a large language model Estudo: Reasoning models don’t always say what they think Projeto Subliminal Learning: Language Models Transmit Behavioral Traits via Hidden Signals in Data Estudo: Persona vectors: Monitoring and controlling character traits in language models Estudo: Teaching Claude why Estudo: Mapping the mind of a large language model Estudo: Tracing the thoughts of a large language model Estudo: Agentic misalignment: How LLMs could be insider threats Estudo: Alignment faking in large language models Estudo: Measuring Faithfulness in Chain-of-Thought Reasoning Estudo: LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples Toda revolução tecnológica começa com quem antecipa o futuro e transforma ideias em soluções de alto impacto. Conheça os cursos da Alura + FIAP Skills & Go: Agentic Engineering, Building AI Products, e AI Data Strategy. Saiba mais sobre o Skills & Go. Vá para o Vale do Silício com Paulo Silveira, Marcell Almeida, Fabrício Carraro e Marcus Mendes na “Imersão IA Sob Controle e Alura no Vale do Silício“! Vagas limitadas, corra para reservar a sua. TechGuide.sh, um mapeamento das principais tecnologias demandadas pelo mercado para diferentes carreiras, com nossas sugestões e opiniões. #7DaysOfCode: Coloque em prática os seus conhecimentos de programação em desafios diários e gratuitos. Acesse https://7daysofcode.io/ Produção e conteúdo: Alura Cursos de Tecnologia – https://www.alura.com.br Edição e sonorização: Rede Gigahertz de Podcasts

感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

In this episode, Mark Russinovich, CTO of Microsoft Azure revealed Brain, the AI-powered AIOps system that continuously monitors Azure's health, detects incidents, identifies root causes, and increasingly automates responses such as pausing problematic deployments and notifying affected customers. Built on Azure Resource Graph, Brain creates a real-time digital twin of Azure, mapping dependencies across hundreds of services, data centers, and regions. Although Brain predates the generative AI boom, years of data engineering, standardized service-level indicators (SLIs), and machine learning laid the foundation for today's capabilities.  Brain combines standardized SLIs, service-specific monitoring, and third-party signals to detect anomalies, while ML models dynamically establish service baselines and correlate outages with software rollouts. Microsoft says automated notifications have reduced customer support tickets by four to six times, with 80–90% of Brain-covered services receiving notifications within 15 minutes, often in under five. The company is also layering LLM-powered agents, called Triangle, on top of Brain to streamline incident routing and eventually enable AI agents to autonomously troubleshoot and remediate outages.   Learn more from The New Stack around the latest in Microsoft Azure: Meet Brain, the AI that decides when Azure is officially down  Microsoft's pitch to enterprises: Ditch Azure Repos for GitHub, despite its rocky reliability record  Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Coding agents have gotten really good at one kind of work. You scope a feature, edit some files, run the tests, ship it. It all happens on disk. But that is not how data work feels. You load something, you look at it, you run a cell, you watch how it responds, and you decide the next move from whatever is sitting in memory. And until now, your agent couldn't see any of that. It only saw the files. Never the live state. This episode, that wall comes down. marimo pair drops a coding agent right inside a running notebook, with full access to every variable Python is holding in memory. The notebook becomes a shared canvas. You point, it runs the code. You tell it to zoom in on the Picasso paintings, and the chart just updates. No MCP tools to wire up, no schema to describe. Just Python, and an agent that can finally see what you see. Trevor Manz is back to walk us through it. Episode sponsors Sentry Error Monitoring, Code talkpython26 Talk Python Courses Links from the show marimo pair: marimo.io/pair Course transcripts announcement: talkpython.fm/blog anywidget: Jupyter Widgets made easy: talkpython.fm marimo: marimo.io blog: marimo.io GitHub: github.com given this: martinalderson.com llms.txt: talkpython.fm mcp: talkpython.fm cli: talkpython.fm open issues: github.com Discord: marimo.io Marimo Pair: marimo.io OpenCode: opencode.ai AI Tooling for Software Engineers in 2026: newsletter.pragmaticengineer.com Watch this episode on YouTube: youtube.com Episode #555 deep-dive: talkpython.fm/555 Episode transcripts: talkpython.fm Theme Song: Developer Rap

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We live in an age of rampant tribalism and knee-jerk reactions, and discussions surrounding AI generated media is no exception. I have been fairly apprehensive about this emerging technology up to this points, but I want to make sure that I am thinking in a rational and principled way.Josh Daws is a former Disney software Engineer and is currently the Vice President of Technology for The Blaze. He views AI as an artistic tool that is allow him to pick up his dream of directing film. You can find Josh on X at x.com/JoshDaws and watch his videos at https://www.youtube.com/@JoshDawsTV.Michael Kane is the author of the Western sci-fi series, After Moses, and he is currently running a Kickstarter campaign for his new book, The Witch of Hobomock Pass. You can find him on X at x.com/MichaelFKane and contribute to his campaign here: https://www.kickstarter.com/projects/michaelfkane/the-witch-of-hobomock-pass-a-frontier-fantasy-novelWatcht the video for this episode and subscribe to my channel here: https://youtu.be/1jTkmfAQ9SIBecome a patron of Mythic Mind at patreon.com/mythicmindListen to all THREE Mythic Mind podcasts:Mythic MindMythic Mind GamesMythic Mind Movies & Shows(or become a patron to get all three shows in one ad-free feed)Become a supporter of this podcast: https://www.spreaker.com/podcast/mythic-mind--5808321/support.

In this week's MBA Admissions podcast we began by discussing the current state of the MBA admissions season. While activity for this season is winding down, we do still anticipate a little more waitlist movement in the weeks ahead. For upcoming events, Clear Admit is also hosting a special webinar event for international students, scheduled for July 16, at 10AM Central European time. This webinar will explore the value of a U.S.-based MBA for international students, as well as the logistics of moving to the United States, getting a student visa, and staying for post-MBA employment. Clear Admit's MBA Essay Workshop series, which has now expanded to five events, is scheduled for July 21, 22 and 23, and July 28 and 29. These events will bring together an increasing number of the top MBA programs to discuss both their written essay prompts as well as their video essays. Signups for all events are here: https://www.clearadmit.com/events Graham highlighted Clear Admit's weekly refresh series, which tracks the top MBA programs' announcements related to the upcoming admissions season. We then discussed an admissions tip that focuses on the impact of volunteer experience on the MBA admissions process. Clear Admit continues its Adcom Q&A series. This week Graham noted Q&As from Harvard Business School, Georgetown / McDonough and UNC / Kenan Flagler. Finally, Graham continued with the Real Humans Alumni series. This week focuses on an alumnus from Yale, working at Pepsi Co. For this week, for the candidate profile review portion of the show, Alex selected three ApplyWire entries. This week's first MBA admissions candidate is a chemical engineer by training, and wants to use the MBA to pivot to consulting. This week's second MBA applicant appears to have strong founder experience. We worry about the combination of their undergraduate record and their lower test score. This week's final MBA candidate is a software engineer from India. They have a 322 GRE. We think it makes sense to try to improve that score. This episode was recorded in Paris, France and Cornwall, England. It was produced and engineered by the fabulous Dennis Crowley in Philadelphia, USA. Thanks to all of you who've been joining us and please remember to rate and review this show wherever you listen!

I interviewed Trond Nilsen about reluctantly using AI Hyperscalers & the aspiration for open source alternatives on Thursday, June 18, 2026 at Augmented World Expo in Long Beach, California. This is a listener-supported podcast through the Voices of VR Patreon. Music: Fatality

In this talk, Aleksandr Kim, Senior Data Scientist at Intuit, shares his expertise in building AI-powered features in production from fine-tuning BERT models in cyber security to engineering scalable data verification platforms. We explore the reality of moving beyond messy research code to build observable, cost-effective AI agents and automated pipelines.You'll learn about:- Translating traditional machine learning metrics into actionable business outcomes- Validating large language model behavior through robust evaluation and alignment techniques- Pivoting from a generic chatbot project to high-value Slack automation workflows- Structuring outputs and guided reasoning layers to eliminate trivial AI summaries- Defining the overlapping skills between AI engineers, data scientists, and full-stack software engineers- Implementing multi-LLM routing logic and token caching to minimize enterprise API expenses- Identifying critical data infrastructure bottlenecks to determine when to pivot or drop an AI pilotTIMECODES:00:00 AI Engineering Production and Scalability06:12 Intuit Ecosystem and QuickBooks Products12:17 Aligning ML Metrics with Business Outcomes18:52 AI Engineers Conducting Customer Interviews25:13 Structured Output and Guided Reasoning31:13 Defining AI Engineering vs Software Engineering37:20 Cost Optimization and Multi LLM Routing43:26 UI Trends and Token Management in Industry49:33 Future Career Trends in AI Engineering55:46 Data Infrastructure Bottlenecks and ML FailuresThis session is designed for mid-to-senior level Data Scientists, Machine Learning Engineers, and Software Engineers who want to develop a highly practical, production-first approach to generative AI. It is especially useful for technology leads focused on reducing token overhead and building self-correcting agentic systems.Connect with Aleksandr- Website - https://alexkimds.github.io/- Linkedin - https://www.linkedin.com/in/aleksandrkim/

In this episode, Dave and Jamison answer these questions: Hi D&J! Question from Sweden! I'm a senior dev that have seen a lot of orgs without tech lead and/or staff engineer roles. I know some companies have them, but mostly newer more techy companies. Bigger older less techy companies usually have non-coding “architects”. Though that's one title I have no good experience of, just neutral ones or worse :) In particular I've been in many teams without a tech lead (all developers are equal, no tiebreaker vote). It's often successful due to a leader and/or consensus emerging organically and peacefully. I loathe the failure modes however: consensus abuse through veto (we can't move discussion forward because someone explicitly actively disagrees, a “keeping the meeting hostage” situation), disagreement on technical priorities and choices (lowest common denominator it is…) or that the team is bickering and bikeshedding during technical discussions. Is this just a regional/cultural thing, or is it more about the type of org? Also would you say it's to the detriment of these orgs to not have these “technical leadership hierarchies”? Thanks! Hey Dave and Jameson, love the show. I'm a Software Engineer at a major tech company. The org is non technical. I consider myself pretty well rounded. I consistently hit my sprint goals, crush my story points, and even put in weekend commits when needed as well as talk but I dont look for conversations into work im too far away involved in. My direct manager thinks my output is good. He told me to work on communication, but the work and my drive is good. I have a relaxed demeanor in a high stress environment. Recently, I learned there are concerns that my “vibe” seems lazy and unengaged, and those concerns may have been shared with my skip-level manager. When my friend literally pulled up my raw metrics and story points to defend me, the friend had no answer, but just reiterated that it's a “vibe thing.” It feels like a political target has been placed on my back by business people who don't understand my work but hold corporate influence. I told my manager and he says im doing fine, and people will say what they say. Everyone talks, etc, and just to focus on his feedback. My question is: How do I fight a “vibe” complaint when the data says I'm crushing it? Should I cave and alter my lifestyle to play the corporate theater game, or do I double down on my metrics, lean on my manager, or dare them to try and figure out how valuable I am? Thanks, An Engineer Who Refuses to Look Bored

Anish Acharya speaks with Microsoft VP of Design John Maeda and Impeccable founder and CEO Paul Bakaus about how AI is changing the practice of design. The conversation explores the relationship between design and technology, the rise of AI-powered creative tools, and whether automation raises the floor, the ceiling, or both. Maeda and Bakaus discuss software craftsmanship, taste, creative judgment, and why some aspects of design may become increasingly automated while others become more valuable. They also examine agentic workflows, the future of user experience, the role of designers in an AI-native world, and how new tools may reshape the relationship between designers, engineers, and software itself.   Resources: Follow Anish Acharya on X: https://x.com/illscience Follow John Maeda on X: https://x.com/johnmaeda Follow Paul Bakaus on X: https://x.com/pbakaus Get the GitHub Copilot app: gh.io/app Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Danny Yang and Sam Goldman, both Software Engineers at Meta, speak with host Gregory M. Kapfhammer about the Rust-based Pyrefly type checker for Python. After a look at the foundational concepts for annotating and checking types for Python programs, Danny and Sam present a deep dive of the implementation of Pyrefly. While comparing and contrasting against various type checkers, they also describe how Pyrefly implements the language server protocol (LSP) for Python. The episode explores a range of other topics, including how to balance the features, performance, and language integrations of a type checker.

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In this week's First $1,000 segment, we hear from an engineer who takes out his daytime frustrations on a series of side hustle balloon sculptures.Side Hustle School features a new episode EVERY DAY, featuring detailed case studies of people who earn extra money without quitting their job. This year, the show includes free guided lessons and listener Q&A several days each week.Show notes: SideHustleSchool.comEmail: team@sidehustleschool.comBe on the show: SideHustleSchool.com/questionsConnect on Instagram: @193countriesVisit Chris's main site: ChrisGuillebeau.comRead A Year of Mental Health: yearofmentalhealth.comIf you're enjoying the show, please pass it along! It's free and has been published every single day since January 1, 2017. We're also very grateful for your five-star ratings—it shows that people are listening and looking forward to new episodes.