Podcasts about senior software engineer

В этом выпуске у меня в гостях Маша Червякова - преподаватель английского языка с 15-летним опытом, которая взяла на себя полный цикл поиска работы в США для своего мужа, старшего разработчика с 15-летним опытом в IT.Мы поговорили о том, как семья переехала из Аргентины в Северную Каролину по гринкарте и почему поиск работы решили превратить в отдельный проект с четким разделением ролей.Разобрали, как Маша выстроила систему откликов, вела переписку от лица мужа, кастомизировала резюме и cover letter под конкретные вакансии, использовала LinkedIn Premium, Notion, нетворкинг и рефералы, а также скрывала отказы, чтобы снизить стресс и сохранить уверенность на собеседованиях. Отдельно обсудили, почему сработали точечные отклики вместо массовой рассылки, как выглядела воронка поиска - от 80 заявок до двух оферов за три месяца - и на что смотреть при выборе между офисной и удаленной работой в США.Маша Червякова - преподаватель английского языка с 15-летним опытом. Ее муж - Сергей Червяков, Senior Software Engineer с 15-летним опытом в IT. Переехали из Аргентины в Северную Каролину по гринкарте в конце 2024 года.LinkedIn: https://www.linkedin.com/in/wormer/Telegram: https://t.me/assistantUSAjobsearch***Записаться на карьерную консультацию (резюме, LinkedIn, карьерная стратегия, поиск работы в США):https://annanaumova.comКоучинг (синдром самозванца, прокрастинация, неуверенность в себе, страхи, лень):https://annanaumova.notion.site/3f6ea5ce89694c93afb1156df3c903abТелеграм: https://t.me/prodcastUSAИнстаграм: https://www.instagram.com/prodcast.usТикТок: https://www.tiktok.com/@us.job⏰ Timecodes ⏰00:00 Начало04:02 Хронология переездов (Россия - Аргентина - США).07:21 Почему важно найти оффер до перелета.09:43 Как жена-гуманитарий искала работу мужу-ИТшнику.16:31 Работа с резюме и преодоление сопротивления.23:11 Погружение в ИТ-термины и использование ИИ.31:24 Система трекинга вакансий в Notion.40:03 80 откликов, 13 скринингов и 2 оффера за 3 месяца.43:07 Оптимизация хедлайна LinkedIn и ключевые слова.48:05 Подготовка множества вариантов резюме и писем.53:40 Поддержка партнера при отказах.57:15 Репетиции самопрезентации на английском.1:02:45 Сравнение двух офферов и финальный выбор.1:08:20 Разделение обязанностей в поиске внутри пары.1:12:10 Почему системность важнее удачи.

In this episode Revathy Ramalingam, Senior Software Engineer and AI Engineer at a healthcare startup, shares her inspiring personal journey from over nine years in telecom software architecture to successfully transitioning back into the industry after a seven-year career break. We explore the evolution of the AI engineer role, the practical application of RAG pipelines, and the strategic use of AI tools to rebuild a technical career.You'll learn about:- AI Career Mapping: Using LLMs to design an upskilling roadmap.- Vibe Coding: Leveraging AI tools for rapid prototyping.- RAG Implementation: Building retrieval systems with LangChain.- Interview Strategy: Proving technical skills after a career gap.- Learning in Public: Building a network through community projects.TIMECODES:00:00 Why Move to AI? Using ChatGPT to Plan a Career Pivot11:00 Learning in Public: The Power of Community Support15:35 Telecom Capstone: Predicting Network Slices with ML22:15 "Vibe Coding" & Building Prototypes with AI Dev Tools28:00 The Interview Process: Navigating a 7-Year Career Break33:45 Practical Interview Tasks: Building a PDF Q&A Assistant39:40 Career Advice: Clear Plans, AI Mentors, and Hard Work44:30 Closing Thoughts: Scaling the Learning LadderThis talk is for developers and career-changers looking for a blueprint to enter the AI engineering space. It is ideal for those interested in RAG, healthcare tech, and practical career resets.Connect with Revathy- Github - https://github.com/RevathyRamalingam- Linkedin - https://www.linkedin.com/in/revathy-ramalingam/ Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

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Listen to Full Episode at: https://podcasts.apple.com/us/podcast/ai-daily-news-rundown-the-anthropic-blacklist-openais/id1684415169?i=1000752107128The headlines are explosive: President Trump has ordered a total phase-out of Anthropic from all federal agencies. The reason? CEO Dario Amodei refused to lower Claude's guardrails for autonomous weapons and domestic surveillance. But here is the twist that is setting Reddit on fire: OpenAI has immediately stepped into the vacuum.As OpenAI maneuvers to absorb those multi-billion dollar federal contracts, a massive grassroots 'Cancel OpenAI' movement has ignited on social media. Users in the r/ArtificialInteligence community are reporting a wave of subscription cancellations, with many citing OpenAI's willingness to drop safety standards for 'lawful military use' as a bridge too far.But as OpenAI's consumer base bleeds, Anthropic is seeing a historic surge. For every user leaving OpenAI, it seems two are joining Anthropic, viewing their defiance of the Pentagon as the ultimate proof of their 'Constitutional AI' mission. It's a market split we haven't seen before: OpenAI is becoming the 'Industrial-Military AI,' while Anthropic is being crowned the 'Ethical Consumer's Choice.'This episode is made possible by our sponsors:

Listen to Full Audio at https://podcasts.apple.com/us/podcast/scientist-vs-storyteller-benchmarking-gpt-5-2-claude/id1684415169?i=1000752001078For years, Latent Diffusion Models—the tech behind Stable Diffusion and DALL-E—have relied on a bit of an 'art form' called KL-regularization. Basically, researchers had to manually guess how much to compress an image before the AI started to lose the details. If you compressed too much, the image got blurry. Too little, and the model became too expensive to train.Enter Unified Latents, or UL.In a new paper out of DeepMind Amsterdam, researchers have introduced a framework that replaces that guesswork with a single, cohesive mathematical objective. Instead of training the compressor and the generator separately, UL trains the Encoder, the Prior, and the Decoder all at once.The 'Secret Sauce' here is something called Fixed Gaussian Noise Encoding. By injecting a constant, specific amount of noise during the encoding process, DeepMind has created a 'Maximum Precision Link.' This forces the encoder to be incredibly efficient, focusing only on the most important structures of an image.The results are staggering: UL achieved a state-of-the-art Video Distance score on the Kinetics-600 dataset and hit a competitive 1.4 FID on ImageNet—all while using significantly less computational power than traditional methods.This episode is made possible by our sponsors:

Listen to Full Episode at: https://podcasts.apple.com/us/podcast/ai-daily-news-rundown-the-anthropic-blacklist-openais/id1684415169?i=1000752107128India spent decades becoming the world's back office. Now, AI is starting to shrink that office into a single server rack. I'm Etienne Noumen, and this is your AI Unraveled Reddit Pulse.A viral discussion is erupting in the r/ArtificialInteligence community centered on a sobering report from the New York Times. The reality is stark: AI is no longer 'promising' to automate white-collar work—it is actively doing it. For a country like India, which built its tech powerhouse status on BPO, customer service, and entry-level coding, this isn't just a market shift; it's being described as a 'historical critical transition.'The Reddit community is calling this a 'Tsunami' that is hitting everywhere at once. One of the top-rated comments points out the 'Demographic Disaster' potential: India has a massive, young population that relies on these 'ladder' jobs to enter the middle class. If the bottom rungs of that ladder—data entry, basic debugging, and Level 1 support—are replaced by agents like Claude or Perplexity, the entire economic engine of the subcontinent faces a stall.Other users are noting that the 'Cheap Labor' advantage has officially evaporated. When an AI agent costs pennies an hour and never sleeps, even the most efficient human workforce cannot compete on price. The race is now on: can India upskill 5 million tech workers before the 'Back Office' disappears entirely?This episode is made possible by our sponsors:

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Teppeiさんをゲストに招待しました。Teppei さんがプロジェクトマネージャーとして関わった書籍出版と最近のロンドンの生活、Senior Software Engineerへのプロモーションについてお話ししました。世界で働くエンジニア - 10人のリアルな海外移住ストーリーご意見・ご感想など、お便りはこちらの⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠Google Form⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ で募集しています。メールアドレス london-tech-talk@proton.me にもお便りを送ってください。

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In this rapid-fire "Product Showdown," we test drive the two hottest coding models on the planet: OpenAI's GPT-5.3 Codex and Anthropic's Claude Opus 4.6.Are they direct competitors, or do they serve completely different masters? We break down the strengths of each: Codex for "execution" and Opus for "reasoning." If you are a developer trying to decide which subscription to keep, this 2-minute breakdown is for you.Key Takeaways:GPT-5.3 Codex: Best for fast iteration, terminal workflows, and shipping code quickly.Claude Opus 4.6: Best for deep reasoning, long-context architecture, and complex problem-solving.The Verdict: Stop looking for a winner. Use Codex for doing and Opus for thinking.Keywords: GPT-5.3 Codex, Claude Opus 4.6, AI Coding Benchmarks, Dev Tools, Agentic Workflows, OpenAI vs Anthropic

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Mike Sheffield is a 35-year Senior Software Engineer at Northrop Grumman Space Systems and a former U.S. Air Force Electronic Warfare Specialist. A passionate sci-fi fan, Mike joined the Heinlein Society in 2001, chaired its blood drive committee until 2010, has led the scholarship committee since 2012, served on the board from 2006–2014, and was the fourth Chair and President for its final four years. He discusses his love for science fiction, the origins of Heinlein-inspired blood drives at conventions (starting 1999 NASFiC), participation tips, the Heinlein for Heroes veteran book program, connections between L. Ron Hubbard and Robert A. Heinlein, thoughts on Final Blackout and Battlefield Earth, his favorite Heinlein works, the Writers of the Future legacy, and a message for global listeners.

Full Audio at https://podcasts.apple.com/us/podcast/product-showdown-gpt-5-3-codex-vs-claude-opus-4-6-the/id1684415169?i=1000748609126In this rapid-fire "Product Showdown," we test drive the two hottest coding models on the planet: OpenAI's GPT-5.3 Codex and Anthropic's Claude Opus 4.6.Are they direct competitors, or do they serve completely different masters? We break down the strengths of each: Codex for "execution" and Opus for "reasoning." If you are a developer trying to decide which subscription to keep, this 2-minute breakdown is for you.Key Takeaways:GPT-5.3 Codex: Best for fast iteration, terminal workflows, and shipping code quickly.Claude Opus 4.6: Best for deep reasoning, long-context architecture, and complex problem-solving.The Verdict: Stop looking for a winner. Use Codex for doing and Opus for thinking.Keywords: GPT-5.3 Codex, Claude Opus 4.6, AI Coding Benchmarks, Dev Tools, Agentic Workflows, OpenAI vs AnthropicTimestamps:00:00 – Intro: The Ultimate Product Showdown: GPT-5.3 Codex vs. Claude Opus 4.601:08 – Round 1: "The Doer" – GPT-5.3 Codex (Speed, Execution, & The Terminal)02:40 – Round 2: "The Thinker" – Claude Opus 4.6 (Depth, Reasoning, & Architecture)03:54 – Round 3: Real-World Use Case – The "Swarm CRM" (Orchestrating Agents)05:01 – Round 4: The Vibe Check – "Vibe Coding" vs. "Strict Engineering"06:21 – The Verdict: Why You Need Both (The Doer to Build, The Thinker to Plan)07:22 – Outro: The Era of Orchestration (Don't Just Code, Manage)Full Audio at https://podcasts.apple.com/us/podcast/product-showdown-gpt-5-3-codex-vs-claude-opus-4-6-the/id1684415169?i=1000748609126

Mike Sheffield is a 35-year Senior Software Engineer at Northrop Grumman Space Systems and a former U.S. Air Force Electronic Warfare Specialist. A passionate sci-fi fan, Mike joined the Heinlein Society in 2001, chaired its blood drive committee until 2010, has led the scholarship committee since 2012, served on the board from 2006–2014, and was the fourth Chair and President for its final four years. He discusses his love for science fiction, the origins of Heinlein-inspired blood drives at conventions (starting 1999 NASFiC), participation tips, the Heinlein for Heroes veteran book program, connections between L. Ron Hubbard and Robert A. Heinlein, thoughts on Final Blackout and Battlefield Earth, his favorite Heinlein works, the Writers of the Future legacy, and a message for global listeners.

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Charles Acknin is Co-Founder and CEO of Skyways.  Skyways is building unmanned aircraft systems designed to revolutionize autonomous cargo transportation. Unlike many competitors still in development, Skyways' aircraft are operational today — carrying meaningful payloads over significant distances for both military and commercial customers. Based in Austin, Skyways is now shifting toward mass production of its autonomous aircraft, building confidence through real-world cargo missions that could ultimately enable future passenger applications. At Skyways, Charles leads the company's strategic vision and oversees operations, product development, and growth initiatives in autonomous aviation. Prior to Skyways, Charles co-founded and served as CEO of Elixir Labs. Before that, he was a Senior Software Engineer at Google, where he worked on the company's search engine for nearly five years and spent a year advancing Google's mission of universal accessibility. Earlier in his career, Charles held engineering roles at Oracle, Cosworld-Asia in Shanghai, and Amadeus. He also participated in Google Summer of Code, contributing to the open-source version control system Subversion. Charles holds a Master of Science in Software Engineering from Oxford Brookes University and a Master of Science in Computer Science from SUPINFO. In this episode of the Drone Radio Show, Charles shares how Skyways is turning autonomous cargo drones from experimental technology into dependable, scalable logistics systems.

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At CES in Las Vegas, we caught up with Serhii Popov, Senior Software Engineer, and Pavlo Haidamak, Product Manager, both from MacPaw, to talk about Eney, “the world's first Computerbeing,” (also understood as a personal AI assistant). Eney is designed to let users control their Mac through natural voice interaction. Running primarily on-device for privacy, it executes tasks, manages files, summarizes content, and integrates with third-party apps via expandable skills, and is available as part of SetApp.  Show Notes: Support:      Become a MacVoices Patron on Patreon     http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:     http://macvoices.com      Twitter:     http://www.twitter.com/chuckjoiner     http://www.twitter.com/macvoices      Mastodon:     https://mastodon.cloud/@chuckjoiner      Facebook:     http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:     http://www.facebook.com/macvoices/      MacVoices Group on Facebook:     http://www.facebook.com/groups/macvoice      LinkedIn:     https://www.linkedin.com/in/chuckjoiner/      Instagram:     https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes     Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

At CES in Las Vegas, we caught up with Serhii Popov, Senior Software Engineer, and Pavlo Haidamak, Product Manager, both from MacPaw, to talk about Eney, "the world's first Computerbeing," (also understood as a personal AI assistant). Eney is designed to let users control their Mac through natural voice interaction. Running primarily on-device for privacy, it executes tasks, manages files, summarizes content, and integrates with third-party apps via expandable skills, and is available as part of SetApp.  Show Notes: Support:      Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:      http://macvoices.com      Twitter:      http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices      Mastodon:      https://mastodon.cloud/@chuckjoiner      Facebook:      http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:      http://www.facebook.com/macvoices/      MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice      LinkedIn:      https://www.linkedin.com/in/chuckjoiner/      Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

This week on DisrupTV, we go beyond the AI hype and into the decisions shaping 2026. Peter Danenberg, Senior Software Engineer at Google DeepMind, joins us to unpack the rapid evolution of multimodal models like Gemini, the race toward AGI, and what he's seeing from inside one of the world's most influential AI labs. We're also joined by David Bray, PhD, Distinguished Chair of the Accelerator at The Stimson Center & Principal/CEO, LDA Ventures Inc., to examine the dangerous assumptions executives are making post-Davos — from AI-driven workforce disruption and machine-speed cyber threats to hardware security risks and why geopolitics can no longer be ignored by business leaders. From meetups and models to geopolitics and governance, this episode is a must-watch for leaders navigating AI at scale.

Rewrites are seductive. Clean slates promise clarity, speed, and “doing it right this time.” In practice, they're often late, over budget, and quietly demoralizing.In this episode of Maintainable, Robby sits down with Brittany Ellich, a Senior Software Engineer at GitHub, to talk about a different path. One rooted in stewardship, readability, and resisting the urge to start over.Brittany's career began with a long string of rebuild projects. Over time, she noticed a pattern. The estimates were wrong. Feature development stalled. Teams burned energy reaching parity with systems they'd already had. That experience pushed her toward a strong belief: if software is in production and serving users, it's usually worth maintaining.[00:00:57] What well-maintained software actually looks likeFor Brittany, readability is the first signal. If code can't be understood, it can't be changed safely. Maintenance begins with making systems approachable for the next person.[00:01:42] Rethinking technical debtShe explains how her understanding of technical debt has evolved. Rather than a fixed category of work, it's often anything that doesn't map directly to new features. Bugs, reliability issues, and long-term risks frequently get lumped together, making prioritization harder than it needs to be.[00:05:49] Why AI changes the maintenance equationBrittany describes how coding agents have made it easier to tackle small, previously ignored maintenance tasks. Instead of waiting for debt to accumulate into massive projects, teams can chip away incrementally. (Related: GitHub Copilot and the Copilot coding agent workflow she's explored.)[00:07:16] Context from GitHub's billing systemsWorking on metered billing at GitHub means correctness and reliability matter more than flash. Billing should be boring. When it's not, customers notice quickly.[00:11:43] Navigating a multi-era codebaseGitHub's original Rails codebase is still in active use. Brittany relies heavily on Git blame and old pull requests to understand why decisions were made, treating them as a form of living documentation.[00:25:27] Treating coding agents like teammatesRather than delegating massive changes, Brittany assigns agents small, well-scoped tasks. She approaches them the same way she would a new engineer: clear instructions, limited scope, and careful review.[00:36:00] Structuring the day to avoid cognitive overloadShe breaks agent interaction into focused windows, checking in a few times a day instead of constantly monitoring progress. This keeps deep work intact while still moving maintenance forward.[00:40:24] Low-risk ways to experimentImproving test coverage and generating repository instructions are safe entry points. These changes add value without risking production behavior.[00:54:10] Navigating team resistance and ethicsBrittany acknowledges skepticism around AI and encourages teams to start with existing backlog problems rather than selling AI as a feature factory.[00:57:57] Books, habits, and staying balancedOutside of software, Brittany recommends Atomic Habits by James Clear, sharing how small routines help her stay focused.The takeaway is clear. AI doesn't replace engineering judgment. Used thoughtfully, it can support the unglamorous work that keeps software alive.Good software doesn't need a rewrite.It needs caretakers.References MentionedGitHub – Brittany's current role and the primary environment discussedGitHub Universe – Where Brittany presented her coding agent workflowAtomic Habits by James Clear – Brittany's recommended book outside of techOvercommitted - Podcast Brittany co-hostsThe Balanced Engineer Newsletter – Brittany's monthly newsletter on engineering, leadership, and balanceBrittany Ellich's website – Central hub for her writing and linksGitHub Copilot – The AI tooling discussed throughout the episodeHow the GitHub billing team uses the coding agent in GitHub Copilot to continuously burn down technical debt – GitHub blog post referencedThanks to Our Sponsor!Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks.It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications.Keep your coding cool and error-free, one line at a time! Use the code maintainable to get a 10% discount for your first year. Check them out! Subscribe to Maintainable on:Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts.Keep up to date with the Maintainable Podcast by joining the newsletter.