Podcasts about software engineers

良いデザインドキュメントの書き方について話しました。How to design document https://grantslatton.com/how-to-design-documentAmazonのデザインドキュメントの書き方 https://medium.com/@apappascs/write-like-an-amazonian-14-tips-for-clear-and-persuasive-communication-e2a11afc7362Architectural Decision record https://martinfowler.com/articles/scaling-architecture-conversationally.htmlForensic Design Document https://littlegreenviper.com/miscellany/forensic-design-documentation/感想をぜひハッシュタグ #todayILearnedFM #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

while(true) ist der Podcast rund um Informatik – im Studium, in der Forschung und im Alltag. Dr. Sebastian Hahner arbeitet als Software Engineer in der Automobilindustrie und entwickelt in seiner Freizeit im Rahmen zahlreicher Open-Source Projekte Werkzeuge für Content-Creator & Streamer. Zuvor forschte er als PostDoc am Karlsruher Institut für Technologie (KIT) an der Schnittstelle von Software-Architektur und Sicherheit. Der Podcast verbindet praxisnahe Einblicke, spannende Forschung und persönliche Erfahrungen aus der Welt der Informatik.

BONUS: Agile Tour Vienna 2025—Building Community-Driven Agile Excellence In this BONUS episode, we explore the upcoming Agile Tour Vienna 2025 (get your ticket now!) with three passionate organizers who are bringing together the Austrian agile community for a day of learning, networking, and innovation. Join us as we dive into what makes this community-driven event special, the challenges facing today's agile practitioners, and why local connections matter more than ever in our evolving professional landscape. The Heart of Community-Driven Events "For me, it's really about creating an event from the community for the community. So at the Agile Tour Vienna we really pay a lot of attention that the contributions are made by community members." - Sabina Lammert The foundation of Agile Tour Vienna lies in its commitment to authentic community engagement. Unlike corporate-led conferences focused on sales and marketing, this event prioritizes genuine knowledge sharing and peer-to-peer learning. The organizers emphasize creating space for meaningful conversations, where participants don't just consume content but actively contribute to discussions and support one another with real-world challenges. This approach fosters an intimate atmosphere where attendees leave with valuable professional connections and practical insights they can immediately apply. Balancing Local Expertise with Global Perspectives "This local aspect is very important, but then it needs to be enhanced by bringing in ideas from people from the outside world." - Robert Ruzitschka Agile Tour Vienna strikes a unique balance between showcasing local Austrian talent and bringing in internationally renowned speakers. The event features a carefully curated mix of practical experiences from Vienna-based practitioners working directly with teams and companies, combined with keynotes from global thought leaders. This blend creates opportunities for attendees to understand both the local context of agile implementation and broader industry trends, making the learning experience both immediately relevant and strategically valuable. A Thoughtfully Designed Experience "We make sure we have a good diversity within the speakers. We also take care that we have a good mix, because for me, agile started with the engineering practices." - Richard Brenner The 2025 program demonstrates attention to creating a comprehensive learning experience. The organizers ensure language accessibility by maintaining at least one English track throughout the day while also offering German sessions. The content spans from technical engineering practices to team coaching and business strategy, reflecting agile's evolution across organizational levels. The event takes place in a stunning castle location (Auersperg Palace) that enhances the intimate, family-like atmosphere the organizers work hard to cultivate. World-Class Content in an Intimate Setting "Agile Tour Vienna is never aiming to go big, but to stay small and familiar. By the end of the day, you know new people." - Sabina Lammert This year's highlights include keynotes from Dave Farley on engineering excellence and Mirella Muse on product operations, plus an innovative Comic Agile storytelling workshop. The organizers deliberately limit attendance to maintain the conference's intimate character, ensuring meaningful networking opportunities rather than overwhelming crowds. Additional touches like a professional barista bar and ample space for informal conversations between sessions create an environment where genuine professional relationships can develop. From Concept-Based to Context-Based Agility "The biggest challenge is that we go from concept-based agility to context-based agility. Companies realize the world is complex. There is no one framework to rule them all." - Richard Brenner The agile community faces a significant evolution as the methodology matures from underground movement to established practice. Organizations are moving away from rigid framework implementations toward contextual problem-solving approaches. This shift requires practitioners to focus on solving real business issues rather than introducing agile for its own sake. The challenge lies in maintaining agile's core values while adapting to diverse organizational contexts and avoiding the trap of seeking simple solutions for complex problems. Maintaining Values-Based Working "It's not about winning over something. It's about using common sense, getting into interaction and trying to find sometimes complex solutions for complex problems." - Sabina Lammert Rather than declaring agile "dead," the community must refocus on value-based working and continuous adaptation. The real challenge involves empowering people to constantly reevaluate situations and embrace the reality that today's solutions may not work in three weeks or three years. This requires normalizing the inspect-and-adapt mindset as standard practice rather than exception, moving beyond method-focused thinking toward principle-driven decision making. Sustaining Community Spirit Through Challenging Times "In times of crisis, people tend to fall back to old patterns of behavior. We need to keep the ideas that made us work in a specific way alive." - Robert Ruzitschka Economic and political uncertainties create pressure to abandon agile practices in favor of traditional command-and-control approaches. Community events like Agile Tour Vienna play a crucial role in maintaining momentum for collaborative, adaptive working methods. The discipline required for agile practices - continuous integration, experimental approaches, market-driven feedback collection - represents a more sophisticated and ultimately more sustainable way of working than traditional project management approaches. The Discipline of Adaptability The discussion revealed an important distinction about discipline in agile environments. Agile teams demonstrate remarkable discipline through practices like continuous integration, experimental product development, and systematic feedback collection. This represents a more humane form of discipline that acknowledges complexity and enables adaptation, contrasting sharply with the rigid discipline of following predetermined plans regardless of changing circumstances. About Robert Ruzitschka, Sabina Lammert, and Richard Brenner Robert Ruzitschka is a Senior Principal Engineer at Raiffeisen Bank International and leads a team of Engineering Coaches. You can connect with Robert Ruzitschka on LinkedIn. Sabina Lammert is Founder and Agile Coach of Leadventure and supports Teams and organizations to improve their way of collaboration. You can connect with Sabina Lammert on LinkedIn. Richard Brenner is a previous guest, he started as Software Engineer and is now working as Agile Coach helping clients to adopt agile ways of working. You can connect with Richard Brenner on LinkedIn.

While many people talk about “agents,” Shreya Shankar (UC Berkeley) has been building the systems that make them reliable. In this episode, she shares how AI agents and LLM judges can be used to process millions of documents accurately and cheaply. Drawing from work on projects ranging from databases of police misconduct reports to large-scale customer transcripts, Shreya explains the frameworks, error analysis, and guardrails needed to turn flaky LLM outputs into trustworthy pipelines. We talk through: - Treating LLM workflows as ETL pipelines for unstructured text - Error analysis: why you need humans reviewing the first 50–100 traces - Guardrails like retries, validators, and “gleaning” - How LLM judges work — rubrics, pairwise comparisons, and cost trade-offs - Cheap vs. expensive models: when to swap for savings - Where agents fit in (and where they don't) If you've ever wondered how to move beyond unreliable demos, this episode shows how to scale LLMs to millions of documents — without breaking the bank. LINKS Shreya's website (https://www.sh-reya.com/) DocETL, A system for LLM-powered data processing (https://www.docetl.org/) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Watch the podcast video on YouTube (https://youtu.be/3r_Hsjy85nk) Shreya's AI evals course, which she teaches with Hamel "Evals" Husain (https://maven.com/parlance-labs/evals?promoCode=GOHUGORGOHOME)

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. 

Mentor Sessions Ep. 026: Bitcoin's $2M Gold Parity, ETF Surge, MicroStrategy Risks & Network Threats | Vijay BoyapatiIs Bitcoin set for a historic bull market? In this explosive BTC Sessions interview, Vijay Boyapati, author of The Bullish Case for Bitcoin, reveals why ETF approval is fueling massive institutional adoption, potentially ending Bitcoin cycles. He predicts Bitcoin hitting gold parity at $1.5M–$2M within 5–10 years! Vijay dissects MicroStrategy's financial engineering, NAV premium risks, and treasury companies' threat to Bitcoin's decentralization. He warns of quantum computing challenges, advocates for Bitcoin Core ossification, and shares Ron Paul-inspired libertarian insights on Bitcoin as the global reserve currency. For Bitcoiners seeking bull market analysis this episode is a must-watch! Join the Bitcoin revolution, learn why Vijay champions self-custody, and discover how Bitcoin could reshape finance. Like, subscribe, and secure your financial future now!Key Topics:Bitcoin bull market and ETF approvalVijay Boyapati on institutional adoptionGold parity and $1.5M–$2M price predictionMicroStrategy's financial engineering risksTreasury companies and centralizationQuantum computing threats to BitcoinBitcoin Core ossification debateRon Paul and global reserve currencyChapters:00:00 Intro & Episode Summary01:16 Institutional Adoption02:02 ETF Approval Impact04:13 Gold ETF Comparison & Bull Market05:00 Are Bitcoin Cycles Dead?06:52 Gold Parity Price Prediction10:57 Gold to Bitcoin Shift12:30 Nation States & Bitcoin Reserves13:48 MicroStrategy's Bitcoin Strategy15:30 Financial Engineering & NAV Premium18:49 Centralization Risks24:27 NAV Discount Dangers36:40 Bitcoin Core Ossification41:50 Quantum Computing ThreatsAbout Vijay Boyapati:Author of The Bullish Case for Bitcoin, Software Engineer & Bitcoin AdvocateX: @real_vijayWebsite: bullishcaseforbitcoin.com

Neste episódio, recebemos Reginaldo Barros e Tiago Aguiar, para discutir como a inteligência artificial está transformando a forma como desenvolvedores aprendem e evoluem na carreira.Partimos da ideia de vibe coding e dos artigos The Junior Developer Extinction e Senior project legacy para refletir sobre questões importantes: até que ponto a IA pode acelerar o aprendizado de um júnior e em que momento ela pode atrapalhar?

在外邦人の一時帰国というトピックについて雑談しました。感想をぜひハッシュタグ #tilfm でつぶやいてください!Your co-hosts:Tomoaki Imai, Noxx CTO  https://twitter.com/tomoaki_imaiRyoichi Kato, Software Engineer ⁠https://twitter.com/ryo1kato

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Anthropic's Model Context Protocol (MCP) has become the standard for connecting AI agents to tools and data, but its security has lagged behind. In The New Stack Agents podcast, Tzvika Shneider, CEO of API security startup Pynt, discussed the growing risks MCP introduces. Shneider sees MCP as a natural evolution from traditional APIs to LLMs and now to AI agents. However, MCP adds complexity and vulnerability, especially as agents interact across multiple servers. Pynt's research found that 72% of MCP plugins expose high-risk operations, like code execution or accessing privileged APIs, often without proper approval or validation. The danger compounds when untrusted inputs from one agent influence another with elevated permissions. Unlike traditional APIs, MCP calls are made by non-deterministic agents, making it harder to enforce security guardrails. While MCP exploits remain rare for now, most companies lack mature security strategies for it. Shneider believes MCP merely highlights existing API vulnerabilities, and organizations are only beginning to address these risks. Learn more from The New Stack about the latest in Model Context Protocol: Model Context Protocol: A Primer for the Developers Building With MCP? Mind the Security Gaps MCP-UI Creators on Why AI Agents Need Rich User InterfacesJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Rahul Auradkar, executive VP and GM at Salesforce, grew up in India with a deep passion for cricket, where his love for the game sparked an early interest in data. This fascination with statistics laid the foundation for his current work leading Salesforce's Data Cloud and Einstein (Unified Data Services) team. Auradkar reflects on how structured data has evolved—from relational databases in enterprise applications to data warehouses, data lakes, and lakehouses. He explains how initial efforts focused on analyzing structured data, which later fed back into business processes. Eventually, businesses realized that the byproducts of data—what he calls "data exhaust"—were themselves valuable. The rise of "old AI," or predictive AI, shifted perceptions, showing that data exhaust could define the application itself. As varied systems emerged with distinct protocols and SQL variants, data silos formed, trapping valuable insights. Auradkar emphasizes that the ongoing challenge is unifying these silos to enable seamless, meaningful business interactions—something Salesforce aims to solve with its Data Cloud and agentic AI platform.Learn more from The New Stack about the evolution of structured data and agent AI: How Enterprises and Startups Can Master AI With Smarter Data Practices Enterprise AI Success Demands Real-Time Data PlatformsJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

In this episode of the Don't Panic, It's Just Data podcast, Kevin Petrie, VP of Research at BARC and the podcast host, is joined by Dainius Jocas, Search Engineer at Vinted, and Radu Gheorghe, Software Engineer at Vespa.ai. They discuss how Vinted, an online marketplace for secondhand products, modernised its data architecture to address new AI search use cases and the challenges faced with Elasticsearch. From the switch to Vespa and the advantages of supporting multiple languages and complex queries, the podcast offers insights on the trade-offs organisations must think about when updating their search systems, especially regarding AI and machine learning applications.Vinted Elasticsearch ChallengesVinted's search architecture was built on Elasticsearch before they switched to Vespa. Elasticsearch is a functional system that presents a few major challenges. With over 20 supported languages, the company's "index per language" approach created significant sharding problems, leading to infrastructure imbalances and constant adjustments."The index for the French language, the biggest language that we support, was more than three times bigger than the second biggest language, which created imbalances in the Elasticsearch data nodes' load," Jocas explained.In addition to these technical obstacles, organisational issues arose as teams responsible for different parts of the search process found themselves "pointing fingers at each other at an increasing rate." The need for a more integrated, effective solution became clear.The Solution: A New Platform for a New EraThe search for a better solution led Vinted to Vespa. The initial adoption was a "one success story" when a machine learning engineer, working on recommendations, discovered that Vespa was ten times faster than Elasticsearch for their use case. This initial benchmark, run on a single decommissioned server, was a "true testament to how efficient Vespa is when it comes to serving requests,” Jocas told Petrie.Vespa helped Vinted solve their language problem by allowing it to set a language per document. Thus, it eliminates the need for separate indexes and the associated sharding headaches. As Jocas put it, "We got out of the sharding problem once and for all."TakeawaysVinted faced challenges with its initial Elasticsearch architecture.The need for better integration between matching and ranking was identified.Vespa outperformed Elasticsearch in handling image search and recommendations.Transitioning to Vespa involved significant learning and support from developers.Vespa allows for language-specific document handling, simplifying architecture.Organisations must evaluate the complexity and volume of their data before transitioning.Vespa is optimised for query performance, while Elasticsearch excels in data writing.The learning curve for Vespa can be steep, but support is available.It's important to focus on optimising new systems rather than emulating old ones.Partial updates in Vespa are more efficient than in Elasticsearch.Chapters00:00 Introduction to Vinted and...

最近開発した Agentic AI についての学びについて話しました。Agentic AI https://blogs.nvidia.com/blog/what-is-agentic-ai/ カスケードvsシングルエージェントAI セントリックな仕様設計Criteria Driftを用いた評価 https://arxiv.org/pdf/2404.12272 感想をぜひハッシュタグ #todayILearnedFM #tilfm でつぶやいてください!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

In this week's episode ofThe New Stack Agents, Scott Carey, editor-in-chief of LeadDev, discussed their first AI Impact Report, which explores how engineering teams are adopting AI tools. The report shows that two-thirds of developers are actively using AI, with another 20% in pilot stages and only 2% having no plans to use AI — a group Carey finds particularly intriguing. Popular tools include Cursor (43%) and GitHub Copilot (37%), with others like OpenAI, Gemini, and Claude following, while Amazon Q and Replit lag behind.Most developers use AI for code generation, documentation, and research, but usage for DevOps tasks like testing, deployment, and IT automation remains low. Carey finds this underutilization frustrating, given AI's potential impact in these areas. The report also highlights concern for junior developers, with 54% of respondents expecting fewer future hires at that level. While many believe AI boosts productivity, some remain unsure — a sign that organizations still struggle to measure developer performance effectively.Learn more from The New Stack about the latest insights about the AI tool adoption: AI Adoption: Why Businesses Struggle to Move from Development to Production3 Strategies for Speeding Up AI Adoption Among DevelopersAI Everywhere: Overcoming Barriers to AdoptionJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

While much of the AI world chases ever-larger models, Ravin Kumar (Google DeepMind) and his team build across the size spectrum, from billions of parameters down to this week's release: Gemma 270M, the smallest member yet of the Gemma 3 open-weight family. At just 270 million parameters, a quarter the size of Gemma 1B, it's designed for speed, efficiency, and fine-tuning. We explore what makes 270M special, where it fits alongside its billion-parameter siblings, and why you might reach for it in production even if you think “small” means “just for experiments.” We talk through: - Where 270M fits into the Gemma 3 lineup — and why it exists - On-device use cases where latency, privacy, and efficiency matter - How smaller models open up rapid, targeted fine-tuning - Running multiple models in parallel without heavyweight hardware - Why “small” models might drive the next big wave of AI adoption If you've ever wondered what you'd do with a model this size (or how to squeeze the most out of it) this episode will show you how small can punch far above its weight. LINKS Introducing Gemma 3 270M: The compact model for hyper-efficient AI (Google Developer Blog) (https://developers.googleblog.com/en/introducing-gemma-3-270m/) Full Model Fine-Tune Guide using Hugging Face Transformers (https://ai.google.dev/gemma/docs/core/huggingface_text_full_finetune) The Gemma 270M model on HuggingFace (https://huggingface.co/google/gemma-3-270m) The Gemma 270M model on Ollama (https://ollama.com/library/gemma3:270m) Building AI Agents with Gemma 3, a workshop with Ravin and Hugo (https://www.youtube.com/live/-IWstEStqok) (Code here (https://github.com/canyon289/ai_agent_basics)) From Images to Agents: Building and Evaluating Multimodal AI Workflows, a workshop with Ravin and Hugo (https://www.youtube.com/live/FNlM7lSt8Uk)(Code here (https://github.com/canyon289/ai_image_agent)) Evaluating AI Agents: From Demos to Dependability, an upcoming workshop with Ravin and Hugo (https://lu.ma/ezgny3dl) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Watch the podcast video on YouTube (https://youtu.be/VZDw6C2A_8E)

In this episode of Android Back Stage, Hosts Tor, and Chet are joined by Melba and Ralston, Software Engineers on the Compose Team, to talk about Autofill in Compose. Learn about Autofill services, best practices, and more! Chapters: 0:00 - Intro 0:40 - What is Autofill? 1:21 - Autofill team journey and semantics 2:15 - Defining semantics in Compose 3:29 - Bridging views and Compose for Autofill 4:23 - Developer Experience: Early autofill in Compose 7:02 - Autofill services and communication flow 7:59 - User authentication and multiple autofill services 12:53 - The Autofill flow: From tap to fill 21:56 - Handling list types and saving credentials (commit) 30:11 - The “Asteriks” bug and semantics refractor 31:09 - Performance improvements in Autofill 37:48 - Compose's architectural advantages (composition over inheritance) 40:30 - Best practices and future improvements  

Dario Gieselaar, Principal Software Engineer from Elastic gets brutally honest about what it really takes to succeed in a top tech company. We uncover the uncomfortable truths about the promotion game, why direct feedback is crucial (even when it hurts), and why a "winning" mindset is everything. This is the conversation about career growth they don't have in HR meetings.In this episode, we cover:Navigating promotion cycles and advocating for yourselfGiving and receiving critical feedback without being seen as "negative"The importance of accountability and managing low-performing colleaguesWhy a top-down approach can be more effective than team democracyHow to "disagree and commit" to keep projects moving forwardThis video is for any software engineer who wants to level up their career, navigate corporate dynamics, and become a high-impact performer in a competitive environment.Full episode on YouTube ▶️https://youtu.be/1q7Jjbi5GJ4Beyond Coding Podcast with ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

On this Salcedo Storm Podcast:Andy Hopper is a Software Engineer, Texas Guardsman, and conservative activist who unseated a RINO incumbent and now represents Texas House District 64 in the Texas State House.ANDRepresentative Brian Harrison represents the 10th district in the Texas State House. Prior to that he was President Trump's Chief of Staff at HHS.

Traditional software expects 100% passing tests. In LLM-powered systems, that's not just unrealistic — it's a feature, not a bug. Eric Ma leads research data science in Moderna's data science and AI group, and over breakfast at SciPy we explored why AI products break the old rules, what skills different personas bring (and miss), and how to keep systems alive after the launch hype fades. You'll hear the clink of coffee cups, the murmur of SciPy in the background, and the occasional bite of frittata as we talk (hopefully also a feature, not a bug!) We talk through: • The three personas — and the blind spots each has when shipping AI systems • Why “perfect” tests can be a sign you're testing the wrong thing • Development vs. production observability loops — and why you need both • How curiosity about failing data separates good builders from great ones • Ways large organizations can create space for experimentation without losing delivery focus If you want to build AI products that thrive in the messy real world, this episode will help you embrace the chaos — and make it work for you. LINKS Eric' Website (https://ericmjl.github.io/) More about the workshops Eric and Hugo taught at SciPy (https://hugobowne.substack.com/p/stress-testing-llms-evaluation-frameworks) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)

In a world where "AI" gets thrown around constantly, what does it really mean for those working in tech? What are the moments at work that actually feel fun—or genuinely rewarding? The idea of a “Cat in a Black Box” feels strangely fitting for engineers: so much of what they build is unseen, uncertain, until it's opened. Amid hectic days, she finds joy in dancing K-pop and hip-hop—movements that become more than just choreography. A dance instructor for over four years, she leads with quiet confidence behind the scenes, while holding onto a traditional Korean mindset she hopes never fades.

2021年に発生した5時間以上にわたるFacebookの大規模障害というトピックについて話していきます。Meta Tech Blog https://engineering.fb.com/2021/10/05/networking-traffic/outage-details/Wikipedia https://en.wikipedia.org/wiki/2021_Facebook_outageCloudflare の https://blog.cloudflare.com/october-2021-facebook-outage/感想をぜひハッシュタグ #todayILearnedFM #tilfm でつぶやいてください!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

While AI training garners most of the spotlight — and investment — the demands ofAI inferenceare shaping up to be an even bigger challenge. In this episode ofThe New Stack Makers, Sid Sheth, founder and CEO of d-Matrix, argues that inference is anything but one-size-fits-all. Different use cases — from low-cost to high-interactivity or throughput-optimized — require tailored hardware, and existing GPU architectures aren't built to address all these needs simultaneously.“The world of inference is going to be truly heterogeneous,” Sheth said, meaning specialized hardware will be required to meet diverse performance profiles. A major bottleneck? The distance between memory and compute. Inference, especially in generative AI and agentic workflows, requires constant memory access, so minimizing the distance data must travel is key to improving performance and reducing cost.To address this, d-Matrix developed Corsair, a modular platform where memory and compute are vertically stacked — “like pancakes” — enabling faster, more efficient inference. The result is scalable, flexible AI infrastructure purpose-built for inference at scale.Learn more from The New Stack about inference compute and AIScaling AI Inference at the Edge with Distributed PostgreSQLDeep Infra Is Building an AI Inference Cloud for DevelopersJoin our community of newsletter subscribers to stay on top of the news and at the top of your game  

In the latest episode ofThe New Stack Agents, Naveen Rao, VP of AI at Databricks and a former neuroscientist, reflects on the evolution of AI, neural networks, and the energy constraints that define both biological and artificial intelligence. Rao, who once built circuit systems as a child and later studied the brain's 20-watt efficiency at Duke and Brown, argues that current AI development—relying on massive energy-intensive data centers—is unsustainable. He believes true intelligence should emerge from low-power, efficient systems, more aligned with biological computing.Rao warns that the industry is headed toward “model collapse,” where large language models (LLMs) begin training on AI-generated content instead of real-world data, leading to compounding inaccuracies and hallucinations. He stresses the importance of grounding AI in reality and moving beyond brute-force scaling. Rao sees intelligence not just as a function of computing power, but as a distributed, observational system—“life is a learning machine,” he says—hinting at a need to fundamentally rethink how we build AI.Learn more from The New Stack about the latest insights about the evolution of AI and neural networks: The 50-Year Story of the Rise, Fall, and Rebirth of Neural NetworksThe Evolution of the AI Stack: From Foundation to AgentsJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

IVS京都というイベントに参加してNotion/VercelのCEOから創業について聴いた話、イベント開催、日本からのリモートワークについて話しました。IVS京都 https://www.ivs.events/ファウンディングエンジニア・デイ~起業・スタートアップに飛び込む技術者の集い~ **https://lu.ma/jv2e4ewr登壇スライド: シリコンバレーでスタートアップを共同創業したファウンディングエンジニアとしての学びhttps://speakerdeck.com/tomoima525/sirikonbaredesutatoatupuwogong-tong-chuang-ye-sitahuaundeinguenziniatositenoxue-bi感想をぜひハッシュタグ #todayILearnedFM #tilfm でつぶやいてください!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

Art Hu, Global CIO at Lenovo, shares proven strategies for implementing AI at scale in one of the world's largest technology companies. Learn how to navigate uncertainty, build organizational agility, and drive real business value from AI investments.In this episode, you'll learn:Why "no regret" AI investments beat waiting for perfect solutions • How to transform fear of job loss into workforce empowerment • The framework Lenovo uses to evaluate AI opportunities across every business function • Why pull-based learning environments outperform top-down AI mandates • How software engineers are expanding beyond code to become business architectsKey insights covered:✓ Agility as competitive advantage: Accept that AI technologies chosen today won't remain cutting-edge in six months. Build organizational agility instead of seeking guaranteed outcomes.✓ Reframe the AI conversation: AI automates specific tasks within jobs, not entire positions. Leaders must help teams decompose roles and reconstruct them around uniquely human contributions.✓ Create environments, not mandates: Lenovo built hundreds of approved AI agents across legal, marketing, finance, and HR. When employees experiment with relevant tools, they naturally request advanced training.✓ Leadership requires hands-on experience: Senior executives must personally engage with AI tools to lead effectively. You cannot manage what you don't understand.Art Hu oversees technology strategy for a company selling four devices per second globally. His dual perspective as both Global CIO and Chief Delivery & Technology Officer provides unique insights into bridging the gap between AI potential and practical business outcomes.Perfect for: CIOs, CTOs, business executives, and technology leaders navigating AI transformation in their organizations.

Joe Cuevas Jr. is a seasoned software engineer based in San Antonio, Texas, with a strong focus on front-end development and user experience. At Southwest Research Institute, he contributes to innovative engineering solutions that span industries. With a background that blends technical precision and creative problem-solving, Joe brings a thoughtful perspective to the evolving world of software development. His passion for clean design, scalable code, and collaborative innovation makes him a standout voice in today's tech landscape.   Topics of Discussion: [1:46] Why Joe fell in love with programming after a single VB.NET class. [5:00] What guided Joe towards the enterprise side of .NET. [6:25] Joe's favorite foundational .NET and ASP.NET Core books. [9:06] Learning algorithms, concurrency, and testing to make up for no CS degree. [10:00] Why test automation is essential and doing testing immediately after writing the code. [12:14] Thoughts on test-driven development vs. testing right after coding. [14:18] Previewing Microsoft Semantic Kernel in Action, by Daniel Costa. [15:38] How Joe became a Manning book reviewer, and what he's learned. [17:18] Prompt engineering and agent instructions: what's ready now. [18:21] Codex and the moment that blew Joe's mind. [22:26] How Joe sees software architecture evolving in the future. [24:09] Large language models vs. older code bases, and where they shine. [27:57] Dependency injections. [32:43] Joe's advice for junior developers and why persistence beats perfection. Mentioned in this Episode: Clear Measure Way Architect Forum Software Engineer Forum Programming with Palermo — New Video Podcast! Email us at programming@palermo.net. Joe Cuevas LinkedIn ASP.NET Core in Action ASP.NET Core in Action, Third Edition Unit Testing Principles, Practice, and Patterns Unit Testing Principles, Practices, and Patterns: Effective testing styles, patterns, and reliable automation for unit testing, mocking, and integration testing with examples in C# Dependency Injection in .NET Microsoft Semantic Kernel in Action Explore — LeetCode Joe on GitHub   Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.

A senior software engineer for Turso, Thorpe is part of an experimental program in the Maine state prison system that allows incarcerated people to work remote jobs from custody. Learn more about your ad choices. Visit podcastchoices.com/adchoices

断片化に強いという特徴があるmallocメモリアロケーターの実装のひとつ、jemallocの20年の歴史について紹介しました。jemalloc Postmortem by Jason Evansブログ https://jasone.github.io/2025/06/12/jemalloc-postmortem/Hacker Newsの議論 https://news.ycombinator.com/item?id=44264958感想をぜひハッシュタグ #todayILearnedFM #tilfm でつぶやいてください!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

Fal.ai, once focused on machine learning infrastructure, has evolved into a major player in generative media. In this episode of The New Stack Agents, hosts speak with Fal.ai CEO Burkay Gur and investor Glenn Solomon of Notable Capital. Originally aiming to optimize Python runtimes, Fal.ai shifted direction as generative AI exploded, driven by tools like DALL·E and ChatGPT. Today, Fal.ai hosts hundreds of models—from image to audio and video—and emphasizes fast, optimized inference to meet growing demand.Speed became Fal.ai's competitive edge, especially as newer generative models require GPU power not just for training but also for inference. Solomon noted that while optimization alone isn't a sustainable business model, Fal's value lies in speed and developer experience. Fal.ai offers both an easy-to-use web interface and developer-focused APIs, appealing to both technical and non-technical users.Gur also addressed generative AI's impact on creatives, arguing that while the cost of creation has plummeted, the cost of creativity remains—and may even increase as content becomes easier to produce.Learn more from The New Stack about AI's impact on creatives:AI Will Steal Developer Jobs (But Not How You Think) How AI Agents Will Change the Web for Users and Developers Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Sze Wong is the Founder and CEO of Zerion Software, a software development company building technology to help teams perform better field data collection.Listen to Sze talk about how he turned teaching java into a business, how he nearly went bankrupt after failing to recognize the macroeconomics of his business, how he founded and pivoted Zerion Software to become an idea factory, how he embraces and nurtures new forms of software development such as vibe coding, and much more. Hosted by Perry Tiu.Episode Links:• Sze's LinkedIn: https://www.linkedin.com/in/szeywong• Sze's Website: https://www.szewong.com/• Zenith Venture Studio: https://www.zenithstudio.io/• Craft your world: Create a life worth living through entrepreneurship: https://www.amazon.com/Craft-your-world-through-entrepreneurship/dp/B0BCD4ZY3P—Interested being on the show? contact@perrytiu.comSponsorship enquiries: sponsor@perrytiu.comFollow Podcast Ruined by a Software Engineer and leave a review• Apple Podcasts: https://apple.co/3RASg8x• Spotify: https://spoti.fi/3RBAXEw• Youtube: https://youtube.com/@perrytiuMore Podcast Ruined by a Software Engineer• Website: https://perrytiu.com/podcast• Merch: https://perrytiu.com/shop• RSS Feed: https://perrytiu.com/podcast/rss.xmlFollow Perry Tiu• Twitter: https://twitter.com/perry_tiu• LinkedIn: https://linkedin.com/in/perrytiu• Instagram: https://instagram.com/doctorpoor

In this episode of Clocking Out, we meet Carla Stickler, a former Broadway actor who once brought Elphaba to life in Wicked and now writes code as a web engineer at Spotify.After landing her first big tour right out of college, Carla returned to New York expecting stardom. Only to find herself waiting tables, battling rejection, and facing the hard truth that talent alone doesn't guarantee success. That humbling chapter forced her ego to take a back seat and ultimately reshaped her definition of success.That shift in perspective made her not only more grounded but also a better colleague, and it led to a decade on stage. Landing big roles on Broadway and building a career many people dream about. But behind the curtain, the reality was far more complicated. The physical toll of performing eight shows a week mounted, and when Carla transitioned to teaching at the college level, she found herself struggling to inspire students to enter an industry she no longer felt supported by.Burnout, financial instability, and a deepening disillusionment led her to make a bold pivot: software engineering. With no tech background but plenty of grit, Carla enrolled in a coding bootcamp and discovered a surprising new outlet for her creativity. Today, she thrives in tech and champions artists looking to reinvent their careers. She proves that there's always overlap in every profession, no skill or experience is ever wasted. When you set your mind to it, you really can do anything.In this conversation, Carla reflects on ambition, ego, rejection, and what it means to let go of the dream you thought you had to chase. If you've ever questioned whether it's too late, or too risky, to start over. Carla's story is your proof that it's not. Tune in for a masterclass in reinvention.Connect with Carla at:TikTok: https://www.tiktok.com/@sticklercarla Instagram: https://www.instagram.com/sticklercarla/ LinkedIn: https://www.linkedin.com/in/carlastickler/Personal website: www.carlastickler.comCompany website: www.creators.spotify.comFollow Raymond: LinkedIn: Raymond LeeInstagram: @raymondlee.coTiktok: @clockingoutpodX: @hrentrepreneurYoutube: @clockingoutwithraymondleeClick Here to become a guest on Clocking Out

Jack Landry is a former Division I athlete and software engineer turned men's mindset and fitness coach. After working at companies like Fidelity and Amazon, he left the corporate world to pursue a more meaningful path—helping high-achieving men who feel unfulfilled create lives of purpose, discipline, and freedom. ⁣ Drawing from his own transformation through fitness, faith, and personal development, Jack now runs a thriving coaching business and creates inspiring content for his 70,000+ followers on Instagram. ⁣ His mission is to guide others in stepping into their God-given potential while building a strong foundation for their future—personally, spiritually, and professionally.

AirbnbのCEOがスタンフォード大学で話したプロダクト/デザイン/グロースの哲学を紹介しました。https://www.youtube.com/watch?v=V6h_EDcj12k感想をぜひハッシュタグ #todayILearnedFM #tilfm でつぶやいてください!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

Traditional headless browsers weren't built for AI agents, often breaking when web elements shift even slightly. Paul Klein IV, founder of Browserbase and its open-source tool Stagehand, is tackling this by creating a browser infrastructure designed specifically for AI control. On The New Stack Agents podcast, Klein explained that Stagehand enables AI agents to interpret vague, natural-language instructions and still function reliably—even when web pages change. This flexibility contrasts with brittle legacy tools built for deterministic testing. Instead of writing 100 scripts for 100 websites, one AI-powered script can now handle thousands.Klein's broader vision is a world where AI can fully operate the web on behalf of users—automating tasks like filing taxes without human input. He acknowledges the technical challenges, from running browsers on servers to handling edge cases like time zones and emojis. The episode also touches on Klein's concerns with AWS, which he says held a “partnership” meeting that felt more like corporate espionage. Still, Klein remains confident in Browserbase's community-driven edge.Learn more from The New Stack about the latest insights in AI browser based tools: Why Headless Browsers Are a Key Technology for AI Agents Ladybird: That Rare Breed of Browser Based on Web Standards Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

Colab is cozy. But production won't fit on a single GPU. Zach Mueller leads Accelerate at Hugging Face and spends his days helping people go from solo scripts to scalable systems. In this episode, he joins me to demystify distributed training and inference — not just for research labs, but for any ML engineer trying to ship real software. We talk through: • From Colab to clusters: why scaling isn't just about training massive models, but serving agents, handling load, and speeding up iteration • Zero-to-two GPUs: how to get started without Kubernetes, Slurm, or a PhD in networking • Scaling tradeoffs: when to care about interconnects, which infra bottlenecks actually matter, and how to avoid chasing performance ghosts • The GPU middle class: strategies for training and serving on a shoestring, with just a few cards or modest credits • Local experiments, global impact: why learning distributed systems—even just a little—can set you apart as an engineer If you've ever stared at a Hugging Face training script and wondered how to run it on something more than your laptop: this one's for you. LINKS Zach on LinkedIn (https://www.linkedin.com/in/zachary-mueller-135257118/) Hugo's blog post on Stop Buliding AI Agents (https://www.linkedin.com/posts/hugo-bowne-anderson-045939a5_yesterday-i-posted-about-stop-building-ai-activity-7346942036752613376-b8-t/) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Hugo's recent newsletter about upcoming events and more! (https://hugobowne.substack.com/p/stop-building-agents)

Faaaaala Dev! Começamos a 7ª temporada do #FalaDev com um papo de peso, diretamente do nosso novo formato de áudio e também com vídeo!Nesta nova temporada você verá muito conhecimento técnico através de convidados super renomados no mercado tech. Bora!No episódio de estreia, o PV Faria, nosso host, recebe Fábio Vedovelli, Software Engineer, para uma conversa cheia de reflexões sobre tecnologia, carreira, inteligência artificial e arquitetura de software.Neste bate-papo, o Vedovelli compartilha sua trajetória desde os primórdios da web até a criação de seus próprios produtos com IA, além de trazer uma visão prática e apaixonada sobre o framework Remix / React Router 7, e como ele se tornou referência ao ensinar sobre tecnologias antes mesmo de virarem tendência.Se você curte conteúdo técnico, histórias reais de quem vive o mercado e dicas valiosas sobre evolução na carreira, esse episódio é para você.Dá o play e já se inscreve no canal, ein!Host: PV FariaConvidado: Fábio Vedovelli • X• Youtube• Curso React Router 7 + IAIndependente do seu momento de carreira e objetivos, temos o conteúdo ideal

In this episode, you'll meet Mariena Quintanilla, a Los Angeles-based career flipper who proves you can pivot, even within the same industry.After spending nearly two decades in tech, moving through roles in software engineering, product management, solutions architecture, and customer experience (plus navigating a layoff), Mariena had a realization: she didn't want to keep climbing the corporate ladder but she still loved tech.So, she made the leap. Today, Mariena runs Mellonhead.co, an AI education and consulting company helping others make sense of emerging technology.In our conversation, we talk about:The inner work that happens before, during, and after a big leapBuilding a financial safety net and jumping anywayThe identity shifts that come with walking away from a titleWhy knowing who you are matters more than knowing where you're goingMariena doesn't sugarcoat the messy middle. She shares what it really felt like to start over, the fears she faced, and the courage it took to rebuild on her own terms.Connect with MarienaLinkedIn: https://www.linkedin.com/in/mariena/Website: https://mellonhead.co/ Thanks for listening to The Career Flipper!If you enjoyed this episode, let's spread the word! Share it with a friend, subscribe, and leave a review—it helps other career flippers find the show.Let's Stay Connected:Join the community: thecareerflipper.comTikTok: @thecareerflipperInstagram: @thecareerflipperpodGot a career flip story? I'd love to hear it—and maybe even have you on the podcast! Whether you've completed your flip, are just starting, or are in the thick of it, submit your story here: https://www.thecareerflipper.com Want to support the show?Looking for a speaker? I'd love to talk about career changes at your next event.Collaborate through sponsorships or affiliates! Let's work together.Email me: hello@thecareerflipper.comCheck Out My Customer Service CoursesBefore my career flip, I led customer experience teams and created online courses that have helped over 12,000 students worldwide. Whether you're switching to customer service or sharpening your skills to run your own business, these courses are packed with practical tips. Learn more at thecareerflipper.com/courses.Other Ways to Get Involved:Buy me a coffee!Explore my furniture flipsMusic CreditsSeason 1: Intro and outro music by audionautix.com. Season 2: Intro and outro original music by Jenny Dempsey, recorded in a home studio.What's the best that could happen?

Metaの開発者生産性向上の取り組みと、コード変更の所要時間を14％（年間150億円）も削減した型付きモックフレームワークについて取り上げました。Meta Tech Podcast関連エピソードhttps://insidefacebookmobile.libsyn.com/67-measuring-developer-productivity-with-diff-authoring-timehttps://insidefacebookmobile.libsyn.com/69-to-type-or-not-to-type-measuring-productivity-impact-with-datep161. Metaをレイオフされたはなし https://open.spotify.com/episode/70ZKoPEkO1DpSf1Ox3NQeo感想をぜひハッシュタグ #todayILearnedFM #tilfm でつぶやいてください!Your co-hosts:Tomoaki Imai, Noxx CTO  https://twitter.com/tomoaki_imaiRyoichi Kato, Software Engineer ⁠https://twitter.com/ryo1kato

Meta Platforms is unlikely to alter its “pay-or-consent” model further, which could lead to new EU antitrust charges, Goldman Sachs is integrating an autonomous AI software engineer named Devin, and a new study found that using AI coding assistants actually slowed down developers by 19%. MP3 Please SUBSCRIBE HERE for free or get DTNS LiveContinue reading "Goldman Sachs Is Integrating An Autonomous AI Software Engineer Named Devin – DTH"

In a recent episode of The New Stack Agents livestream, Antje Barth, AWS Developer Advocate for Generative AI, discussed the growing developer interest in building agentic and multi-agent systems. While foundational model knowledge is now common, Barth noted that developers are increasingly focused on tools, frameworks, and protocols for scaling agent-based applications. She emphasized the complexity of deploying such systems, particularly around navigating human-centric interfaces and minimizing latency in multi-agent communication.Barth highlighted AWS's support for developers through tools like Amazon Q CLI and the newly launched open-source Strands SDK, which AWS used internally to accelerate development cycles. Strands enables faster, flexible agentic system development, while services like Bedrock Agents offer a managed, enterprise-ready solution.Security was another key theme. Barth stressed that safety must be a “day one” priority, with built-in support for authentication, secure communication, and observability. She encouraged developers to leverage AWS's GenAI Innovation Center and active open-source communities to build robust, scalable, and secure agentic systems.Learn more from The New Stack about AWS' support for developers through tools that support multiple agents: Code in Your Native Tongue: Amazon Q Developer Goes Global AWS Launches Its Take on an Open Source AI Agents SDKAmazon's Bedrock Can Now 'Check' AI for Hallucinations Join our community of newsletter subscribers to stay on top of the news and at the top of your game.     

Uma Subramanian is the Founder and CEO of Limitless Leaders, a personalized mentoring program for individuals willing to overcome their career plateau.Listen to Uma talk about her perspective on developer relations after doing it for over 20 years at Microsoft, what are the signs of a career plateau, how to overcome professional struggles with the help of mentors, how to build a circle of trust, and much more. Hosted by Perry Tiu.Episode Links:• Limitless Leaders: https://thelimitlessleaders.com• Uma's LinkedIn: https://www.linkedin.com/in/uma-subramanian-limitless—Interested being on the show? contact@perrytiu.comSponsorship enquiries: sponsor@perrytiu.comFollow Podcast Ruined by a Software Engineer and leave a review• Apple Podcasts: https://apple.co/3RASg8x• Spotify: https://spoti.fi/3RBAXEw• Youtube: https://youtube.com/@perrytiuMore Podcast Ruined by a Software Engineer• Website: https://perrytiu.com/podcast• Merch: https://perrytiu.com/shop• RSS Feed: https://perrytiu.com/podcast/rss.xmlFollow Perry Tiu• Twitter: https://twitter.com/perry_tiu• LinkedIn: https://linkedin.com/in/perrytiu• Instagram: https://instagram.com/doctorpoor

Demos are easy; durability is hard. Samuel Colvin has spent a decade building guardrails in Python (first with Pydantic, now with Logfire), and he's convinced most LLM failures have nothing to do with the model itself. They appear where the data is fuzzy, the prompts drift, or no one bothered to measure real-world behavior. Samuel joins me to show how a sprinkle of engineering discipline keeps those failures from ever reaching users. We talk through: • Tiny labels, big leverage: how five thumbs-ups/thumbs-downs are enough for Logfire to build a rubric that scores every call in real time • Drift alarms, not dashboards: catching the moment your prompt or data shifts instead of reading charts after the fact • Prompt self-repair: a prototype agent that rewrites its own system prompt—and tells you when it still doesn't have what it needs • The hidden cost curve: why the last 15 percent of reliability costs far more than the flashy 85 percent demo • Business-first metrics: shipping features that meet real goals instead of chasing another decimal point of “accuracy” If you're past the proof-of-concept stage and staring down the “now it has to work” cliff, this episode is your climbing guide. LINKS Pydantic (https://pydantic.dev/) Logfire (https://pydantic.dev/logfire) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Hugo's recent newsletter about upcoming events and more! (https://hugobowne.substack.com/p/stop-building-agents)

Is AI taking over the craft of coding? Many engineers now face an identity crisis.In the episode, Distinguished Engineer Annie Vella discusses her research on AI's impact on software development. She explores the “software engineering identity crisis” as the craft of coding becomes automated. Annie warns that the seductive speed of AI tools can lead to lower quality and delivery instability, a trend supported by reports from DORA and GitClear. She also cautions that over-reliance on AI prevents engineers from gaining the hands-on experience needed for deep skill acquisition.Key topics discussed:How AI is reshaping the software development lifecycleThe software engineer's professional identity crisisThe real danger of over-relying on AI toolsHow to balance the seduction of speed with long-term qualityCrucial advice for junior engineers entering the industryWhy leaders must shift focus from speed to qualityThe idea of treating AI as a team member instead of just a tool  Timestamps:(00:00:00) Trailer & Intro(00:02:32) AI Impact on Career and Software Engineering(00:07:00) The Future of AI-Driven Software Engineering(00:14:29) The Shift in the Role of Software Engineer(00:22:13) When Writing Code is Not the Bottleneck Anymore(00:32:04) The Danger of Over-Reliance on AI(00:38:51) The Software Engineering Identity Crisis(00:48:09) Advice for Junior Engineers in This Challenging Time(00:53:34) The Shift in the Role of Engineering Management(00:59:46) You Are Not Alone(01:00:50) 3 Tech Lead Wisdom_____Annie Vella's BioAnnie Vella is a Distinguished Engineer at Westpac NZ with two decades of experience in software engineering and technical leadership across various industries and countries.Vella has returned to an engineering role after a period in management and is also a part-time Master's student at the University of Auckland, researching the impact of AI on software engineering. She believes that technologies like Generative AI, LLMs, and Agentic AI will revolutionize the field and problem-solving in general.Follow Annie:LinkedIn – linkedin.com/in/annievellaX – x.com/codefrenzyWebsite – annievella.com/Like this episode?Show notes & transcript: techleadjournal.dev/episodes/223.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

Get 10% off Hugo's "Building LLM Applications for Data Scientists and Software Engineers" online course!Today's clip is from episode 135 of the podcast, with Teemu Säilynoja.Alex and Teemu discuss the importance of simulation-based calibration (SBC). They explore the practical implementation of SBC in probabilistic programming languages, the challenges faced in developing SBC methods, and the significance of both prior and posterior SBC in ensuring model reliability. The discussion emphasizes the need for careful model implementation and inference algorithms to achieve accurate calibration.Get the full conversation here.Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Today we're examining Palantir's "Forward Deployed Software Engineers" - and separating fact from the hype!Everything old is new again! Move over companies that have been doing this for decades such as SAP, IBM, and countless consulting firms!https://blog.palantir.com/a-day-in-the-life-of-a-palantir-forward-deployed-software-engineer-45ef2de257b1Listen as we break down Palantir's 2020 blog post about their Forward Deployed Software Engineers (FDSEs) and discover what these engineers actually do versus what the marketing claims...Key topics covered:What FDSEs actually do, day-to-dayHow this compares to traditional consulting rolesThe difference between software configuration and software engineeringWhy embedded customer roles aren't newCareer advice for aspiring technical professionalsIf you're interested in understanding the reality behind tech industry buzzwords, this is your episode!#ProductManagement #SoftwareEngineering #TechCareers #Consulting #Leadership #AgileCoachingLINKS= = = = = = = = = = = =YouTube: https://youtu.be/SGvJK-aruJ8Spotify: https://open.spotify.com/show/362QvYORmtZRKAeTAE57v3Apple: https://podcasts.apple.com/us/podcast/agile-podcast/id1568557596Website: http://arguingagile.com= = = = = = = = = = = =Toronto Is My Beat (Music Sample)By Whitewolf (Source: https://ccmixter.org/files/whitewolf225/60181)CC BY 4.0 DEED (https://creativecommons.org/licenses/by/4.0/deed.en)

Unsere beiden heutigen Gäste sind Gründer, Produktstrategen und leidenschaftliche Technologen, mit einem klaren Ziel: die Kundenkommunikation mit Hilfe von Künstlicher Intelligenz grundlegend neu zu denken. Der eine studierte an der WHU, an der University of Texas und in Shanghai, gründete bereits ein eigenes Start-up im Creator-Tech-Bereich und arbeitete bei Roland Berger, Infosys und Enpal. Was ihn auszeichnet: ein tiefes Verständnis für digitale Geschäftsmodelle und der Wille, komplexe Technologien so zu gestalten, dass sie echten Mehrwert schaffen. Der andere absolvierte seinen Bachelor in Computer Science an der Stanford University und startete dort auch einen Master, den er zur Hälfte abschloss, um sich ganz dem Unternehmertum zu widmen. Nach Stationen bei N26, BCG Digital Ventures, Circ und zuletzt als Software Engineer und Product Manager bei Pitch ist er heute Co-Founder und CTO von telli. Seine Schwerpunkte: künstliche Intelligenz, Human-Computer-Interaction und der Brückenschlag zwischen Technologie und Nutzererlebnis. Mit ihrem gemeinsamen Unternehmen telli entwickeln sie eine KI-gestützte Lösung, die es Unternehmen ermöglicht, ihre gesamte Kundenkommunikation über ein intelligentes System zu führen: automatisiert, empathisch und effizient. Ihr Ziel ist es, das Kundenerlebnis neu zu definieren und Menschen in Unternehmen zu entlasten, indem repetitive Kommunikationsaufgaben künftig von einer digitalen Instanz übernommen werden können. Seit über acht Jahren beschäftigen wir uns in diesem Podcast mit der Frage, wie Arbeit den Menschen stärkt, statt ihn zu schwächen. In fast 500 Gesprächen mit über 600 Menschen haben wir darüber gesprochen, was sich für sie geändert hat und was sich noch ändern muss. Was passiert mit Kundenbeziehungen, wenn Künstliche Intelligenz zum ersten Ansprechpartner wird? Wie gelingt es, technologische Effizienz mit menschlicher Empathie zu verbinden und ist das überhaupt möglich? Und was heißt „New Work“ in einer Zukunft, in der Kommunikation zunehmend automatisiert, aber dennoch bedeutungsvoll bleiben soll? Fest steht: Für die Lösung unserer aktuellen Herausforderungen brauchen wir neue Impulse. Und darum suchen wir weiter nach Methoden, Vorbildern, Erfahrungen, Tools und Ideen, die uns dem Kern von New Work näherbringen. Darüber hinaus beschäftigt uns von Anfang an die Frage, ob wirklich alle Menschen das finden und leben können, was sie im Innersten wirklich, wirklich wollen. Ihr seid bei On the Way to New Work, heute mit Finn zur Mühlen und Seb Hapte-Selassie von telli. [Hier](https://linktr.ee/onthewaytonewwork) findet ihr alle Links zum Podcast und unseren aktuellen Werbepartnern

Have you ever been cut off mid-sentence, had your idea hijacked, or been completely misunderstood with no chance to recover? If you're someone who already finds speaking up challenging, these moments can feel devastating. In this episode, I'm sharing exactly how to reclaim your voice and communicate with unshakeable confidence. What I'm Covering: Why interruptions hit socially reserved people especially hard (and why it's not about the value of what you're saying) The real psychology behind why people interrupt—from enthusiasm to power plays My proven strategies for recovering in-the-moment when someone cuts you off or misunderstands you How to have follow-up conversations that prevent future interruptions without damaging relationships My In-the-Moment Recovery Framework: Stay Centered and Reclaim the Floor - Confident phrases to get your voice back Use Your Body Language - How to project confidence even when you're feeling rattled Address Misunderstandings Immediately - Scripts for clarifying without being defensive Bridge Back to Your Point - Techniques for returning to your original contribution  **   Don't even know where to begin in improving your interpersonal skills? Are you ready to leave social stress behind and go from where you are to where you want to be? Book a Social Strategy Session HERE  Free Guide: The Social Skills Playbook Have a question that needs an answer. Email me at Hello@SocialConfidencePro.com   LinkedIn Instagram TikTok

On today's show Ben and Andrew answer questions about the future of engineering jobs, the definition of vibe coding, Meta's AI upside, ChatGPT-led fashion shows, xAI as a third-tier streamer, and bitter lessons as autonomous driving becomes more viable. At the end: An emailer follow-ups on last week's conversation about normies and AI risk.

Tim O'Hearn is a Software Engineer who wrote 'Framed: A Villain's Perspective on Social Media'. LinkedIn: https://www.linkedin.com/in/tohearn/ Website: https://timohearn.beehiiv.com/ Book: https://www.amazon.com/dp/B0DW2X8YSK CallumConnects Micro-Podcast is your daily dose of wholesome leadership inspiration. Hear from many different leaders in just 5 minutes what hurdles they have faced, how they overcame them, and what their key learning is. Be inspired, subscribe, leave a comment, go and change the world!

Tim O'Hearn is a Software Engineer who wrote 'Framed: A Villain's Perspective on Social Media'. LinkedIn: https://www.linkedin.com/in/tohearn/ Website: https://timohearn.beehiiv.com/ Book: https://www.amazon.com/dp/B0DW2X8YSK CallumConnects Micro-Podcast is your daily dose of wholesome leadership inspiration. Hear from many different leaders in just 5 minutes what hurdles they have faced, how they overcame them, and what their key learning is. Be inspired, subscribe, leave a comment, go and change the world!