Podcasts about S3

IPv8 уже в драфте, Terraform хоронят в блогах, а одна компания жжёт $7M в год на Claude Code. Собрали новости DevOps, которые вы накидали через бота. О ЧЁМ ВЫПУСК Новостной выпуск: накопилось 63 новости, разобрали самое горячее. И снова с нами Ярослав. В этом выпуске: • IPv8: драфт в IETF - ASN в первых 32 битах, старый IPv4 во вторых. Без NAT и dual-stack, плюс токен-идентичность на каждый девайс • Terraform 1.15: переменные в source и version модулей, отдельная аутентификация для S3 backend • "Terraform is dead": разбираем хайповую статью - спека как desired state, Pulumi, CDK и причём тут AI • Terragrunt 1.0: units, stacks и фильтр по affected-ресурсам через git worktree • NGINX 1.30: sticky sessions, keep-alive и HTTP/2 к апстримам, Early Hints (103), Encrypted Client Hello • Экономика AI: Semi-Analysis масштабировала Claude Code до $7M/год, дефицит RAM • Google Agent Sandbox: новый Kubernetes CRD между StatefulSet и Deployment Сквозная мысль выпуска: AI ускоряет всё, но без понимания, как работают системы, спека и вайб-кодинг рано или поздно стреляют в ногу. ГОСТЬ Ярослав Бледковский - Un-principal SRE, Wargaming ССЫЛКИ Все новости выпуска (тезисы, голосование, ссылки): https://dkt-ai.github.io/episodes-news/episodes/episode-97-ru Присылайте новости через бота: @dkt_news_bot Упомянутые ресурсы: • IPv8 draft (IETF): https://www.ietf.org/archive/id/draft-thain-ipv8-00.html • Terraform 1.15: https://github.com/hashicorp/terraform/releases/tag/v1.15.0 • "Terraform is dead" (статья): https://grahamgilbert.com/blog/2026/04/20/terraform-is-dead/ • Terragrunt 1.0: https://github.com/gruntwork-io/terragrunt/releases/tag/v1.0.0 • NGINX 1.30: https://github.com/nginx/nginx/releases/tag/release-1.30.0 • AI tokens (Dylan Patel / Semi-Analysis): https://www.youtube.com/watch?v=LF3aUIM57uw • Kubernetes Agent Sandbox: https://github.com/kubernetes-sigs/agent-sandbox ПОДКАСТ YouTube - www.youtube.com/@DevOpsKitchenTalks Apple Podcasts - https://apple.co/41O6mqA Spotify - https://t.ly/Jg5_2 Yandex Music - https://music.yandex.ru/album/10151746 PodBean - https://devopskitchentalks.podbean.com НАВИГАЦИЯ 00:00 - Интро: вы уже на IPv6 или ещё IPv4? И снова в гостях Ярослав 02:53 - Anthropic и 200K карточек от Маска: лимиты Claude отпустило 04:26 - Адженда из 63 новостей через наш Telegram-бот

S3-016 30代40代必聴！お金やキャリアだけじゃない幸福度の高め方 恋愛と仕事、両方で「満ち足りた状態」を築くヒントを探る回。パーソナリティたちが経験したキャリア観や人生の価値観の変化、お金や地位だけではない「本当の幸せ」とは何かを深掘りします。30代・40代の恋愛でよくある「女性が求める上昇婚」のリアルや、男性が自分磨きを通じて魅力を高める方法を具体的に解説。経験が人間的な厚みとなり、理想のパートナーシップに繋がる秘訣を学び、あなたの恋愛と人生を豊かにする考え方を見つけましょう。 ◆━━━━━━━━━━━━━━━━━━━━◆ Discordへのご参加お待ちしています。 トーマスや、ムギちゃん、ひのりほと交流して欲しいのです。Discordの盛り上げに協力して！お願い！ 覗いてみる /https://discord.gg/nxq7UADbtn ◆━━━━━━━━━━━━━━━━━━━━◆ ひのりほのLINE公式アカウントにも登録！ ひのりほが運営するLINE公式アカウントも特典たくさん。 今ずぐ登録する /https://saruwaka.jp/s/1543/1WRY5Q43mAbm/93838783 ◆━━━━━━━━━━━━━━━━━━━━◆ 番組のフォローをお願いいたします|恋愛のヒント ポッドキャストアプリで番組を「フォロー（登録）」すると、最新のエピソードが公開された瞬間に通知が届きます。 Spotify、Apple Podcasts、Amazon Music、YouTubeなど、使い慣れたアプリで『恋愛のヒント』をお聴きください。隙間時間や寝る前の時間が、学びの時間に変わります。以下のボタンから各アプリに飛ぶことができます。 ◆━━━━━━━━━━━━━━━━━━━━◆ 今週のハイライト キャリアの目的は「野望」から「幸福」へ 物欲より「心の充実」が真の幸せ お金より時間を重視する現代の価値観 女性はパートナーに「上昇婚」を求める理由 男性は「自己成長」で恋愛の魅力を高める 仕事も遊びも「経験」が人生の財産 「欠乏感」ではなく「満ち足りた状態」で仕事 パートナーシップを深めるコミュニケーション術 人間的な「厚み」が理想の相手を引き寄せる 自分を磨き、恋愛も人生も豊かにするヒント ◆━━━━━━━━━━━━━━━━━━━━◆ Chapters 00:00 – イントロダクション：番組紹介とパーソナリティ挨拶 01:10 – ひのりほのキャリア論：年収1億の野望から心境の変化 05:00 – 時代の価値観：若者の「お金より時間」思考 06:30 – トーマスのキャリア遍歴：モテたい一心でアパレルへ 09:30 – 接客からマネジメントへ：キャリアの転機と退職理由 10:30 – フォトグラファー時代：チヤホヤされたい欲求と仕事 12:00 – 恋愛と仕事の共通点：満ち足りた人生の追求 14:00 – 女性が求める「上昇婚」の真実 16:00 – 男性が磨くべき「経験値」の重要性 20:00 – エンディング：番組からのメッセージとリスナーへのご案内 ◆━━━━━━━━━━━━━━━━━━━━◆ コミュニケーション ❶Discordに参加する 番組を運営するLifebloom.funが開設しているDiscordへご参加をお待ちしております。番組リスナーだけのチャンネルを覗いてみてください！ https://discord.gg/nxq7UADbtn ❷お便り募集 番組へのお便りは以下のお便りフォームまたは、renai@lifebloom.funまでメールにてお寄せください。 https://tmsk.jp/renai/letter ❸レビューとコメント お聴きのPODCASTアプリで、番組の評価やコメントをお願いいたします。あなたの評価やコメントで、より良い番組へと育って参ります。お気軽に思いのままの評価をいただきましたら幸いです。 Spotifyでのレビューの書き方解説を見る /https://lifebloom.fun/spotify-reviews/ ❹番組のシェア あなたのご家族や友人、同僚や先輩後輩など、番組に共感をいただけそうな方に向けて、番組のシェアをお願いいたします。SNSで「#恋愛のヒント」をつけての投稿もお待ちしております。 以下のリンクもシェア /https://tmsk.jp/renai/podcastpage ◆━━━━━━━━━━━━━━━━━━━━◆ 今回も最後までお聴きいただきありがとうございました。この番組は、人生に花を咲かせるPODCAST番組をお届けするLifebloom.funの制作でお送りいたしました。

S3.76 - The Chicago Table - Natasha & the Crystal Form of Auril by Wererat Studios

There's a gorgeous new Good Omens story coming soon - not quite a fix-it, but also really a great (better) alternative to S3!Music: 2 days left until Christmas by Sascha Ende (⁠⁠⁠CC-BY 4.0⁠⁠⁠)

After a historic market comeback over the last 60 days we evaluate where we are today. Which sectors have benefitted? Will Cannabis ever get some love? Are we bearish/bullish and is this an AI Fueled bubble? Is Cannabis still interesting with an S3 catalyst upcoming?Charting Man Dan joins us to answer all of these questions and reflect on how things have changed since the start the year.Follow Dan / The Chart GuysOn Twitter: ⁠⁠⁠⁠⁠⁠⁠https://twitter.com/ChartGuys⁠⁠⁠⁠⁠⁠⁠The Chart Guys on Youtube: ⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/channel/UCnqZ2hx679DqRi6khRUNw2g⁠⁠⁠⁠⁠

S3-015 恋愛と結婚をこじらせてない？30代40代が実践すべき愛を叶える秘訣 恋愛と結婚、実は難しくない？パーソナリティのひのりほが放った「恋愛と結婚ほど簡単なものはない」発言の真意を徹底解剖。30代〜40代の男女が恋愛をこじらせる原因を深掘りし、自信を持って異性と向き合うための具体的な秘訣を語ります。外見磨きから内面の充実まで、恋愛成就に向けた効果的なステップと、幸せな結婚生活のリアルな喜びを共有。恋愛は人生を豊かにする最高の経験です。もう「どうせ無理」とは言わせない！あなたも今日から愛を掴む実践者になろう。 ◆━━━━━━━━━━━━━━━━━━━━◆ Discordへのご参加お待ちしています。 トーマスや、ムギちゃん、ひのりほと交流して欲しいのです。Discordの盛り上げに協力して！お願い！ 覗いてみる /https://discord.gg/nxq7UADbtn ◆━━━━━━━━━━━━━━━━━━━━◆ ひのりほのLINE公式アカウントにも登録！ ひのりほが運営するLINE公式アカウントも特典たくさん。 今ずぐ登録する /https://saruwaka.jp/s/1543/1WRY5Q43mAbm/93838783 ◆━━━━━━━━━━━━━━━━━━━━◆ 番組のフォローをお願いいたします|恋愛のヒント ポッドキャストアプリで番組を「フォロー（登録）」すると、最新のエピソードが公開された瞬間に通知が届きます。 Spotify、Apple Podcasts、Amazon Music、YouTubeなど、使い慣れたアプリで『恋愛のヒント』をお聴きください。隙間時間や寝る前の時間が、学びの時間に変わります。以下のボタンから各アプリに飛ぶことができます。 ◆━━━━━━━━━━━━━━━━━━━━◆ 今週のハイライト 恋愛と結婚は驚くほどシンプルにできる 好意は素直に伝えてこそ相手に届く 自分を好きになることが恋愛成功の鍵 「どうせ無理」思考は恋愛を遠ざける 見た目の努力は自信に繋がり関係を深める 恋愛は人生を劇的に充実させる最高の経験 相手の気持ちを勝手に妄想しすぎない 恋愛にもPDCAを回す習慣が大切 運命の相手は必ずこの世に存在する 結婚生活は想像以上に幸せで満ちている ◆━━━━━━━━━━━━━━━━━━━━◆ Chapters 00:00 – OP〜ひのりほの鼻炎と大きいクシャミ 03:55 – 女性のくしゃみ問題と男性との違い 05:15 – トーマスの恋愛遍歴「両思いになれてきた」 06:18 – ひのりほの持論「恋愛と結婚以上に簡単なものはない」 08:24 – なぜ両思いになれる？恋愛に大切な２つのこと 10:49 – 恋愛をこじらせる「どうせ無理」思考を捨てる 13:17 – 人生を充実させる「恋愛と結婚」の魅力 15:58 – 恋愛と結婚はもっとシンプルでいい 18:27 – 恋愛成就に向けた自分磨きと努力の楽しさ 20:56 – 動物占いと理想の相手を見つけるPDCA ◆━━━━━━━━━━━━━━━━━━━━◆ コミュニケーション ❶Discordに参加する 番組を運営するLifebloom.funが開設しているDiscordへご参加をお待ちしております。番組リスナーだけのチャンネルを覗いてみてください！ https://discord.gg/nxq7UADbtn ❷お便り募集 番組へのお便りは以下のお便りフォームまたは、renai@lifebloom.funまでメールにてお寄せください。 https://tmsk.jp/renai/letter ❸レビューとコメント お聴きのPODCASTアプリで、番組の評価やコメントをお願いいたします。あなたの評価やコメントで、より良い番組へと育って参ります。お気軽に思いのままの評価をいただきましたら幸いです。 Spotifyでのレビューの書き方解説を見る /https://lifebloom.fun/spotify-reviews/ ❹番組のシェア あなたのご家族や友人、同僚や先輩後輩など、番組に共感をいただけそうな方に向けて、番組のシェアをお願いいたします。SNSで「#恋愛のヒント」をつけての投稿もお待ちしております。 以下のリンクもシェア /https://tmsk.jp/renai/podcastpage ◆━━━━━━━━━━━━━━━━━━━━◆ 今回も最後までお聴きいただきありがとうございました。この番組は、人生に花を咲かせるPODCAST番組をお届けするLifebloom.funの制作でお送りいたしました。

Escandalo!   Join Luke and Indigo as they round off the discussions about the opening episode of S3 of YR, including talking fashion, scandals and complicated feelings.   Songs for the playlist: Get Mine by Slinger & Jae Scream by WATRGRL & Zhone Say Less by Graham Lake Sweating by Alewya   As always, if you'd like to join the discussion on Insta or in the Facebook group, buy us a coffee or find our Redbubble merch store then follow the links at linktr.ee/aheartstopperpodcast

S3.75 - The Chicago Table - A Clockwork Dragon Infested with Fungus by Wererat Studios

Jason Goldschmidt and Nick Connolly, co-chairs of SNIA's Accelerated Object TWG, discussed the importance of S3 over RDMA for AI processing. SNIAs work addresses industries need for faster data transfer to improve GPU utilization during model training and inferencing.

Nová řada pekla z kuchyně Medvěd, návrat do Západozemí v třetí sezóně Rodu draka, fantasy nářez Legenda jménem Vox Machina počtvrté, hvězdně obsazený Mys hrůzy s magorem Javierem Bardemem nebo návrat detektiva Colina Farrella v Sugar. Poslední předprázdninový měsíc bude pořádně narvaný, ale pokud máte i tak pocit, že vám streamovací platformy nenabízí dost, máme pro vás možná řešení.  FILMY Pracovní romance, 5.6., Netflix The Marked Woman, 5.6., Netflix Your Fault: London, 18.6., Prime Video Hlasovky pro Isabelle, 19.6., Netflix Malý bratr, 26.6., Netflix SERIÁLY Morfeusz, 2.6., SkyShowtime Not Suitable For Work, 2.6., Disney+ Legenda jménem Vox Machina S4, 3.6., Prime Video Mys hrůzy, 5.6., Apple TV+ Alice a Steve, 8.6., Disney+ Every Year After, 10.6., Prime Video X-Men 97 S2, 13.6., Disney+ Najdu si tě, 18.6., Netflix Sugar S2, 19.6., Apple TV+ Rod draka S3, 21.6., HBO Max Medvěd S5, 25.6., Disney+ Avatar: Legenda o Angovi S2 JINÉ Michael Jackson: Verdikt, 3.6., Netflix Clarksonova farma S5, 3.6., Prime Video MovieZone Extended Universe:  Web: https://www.moviezone.cz FB: https://www.facebook.com/moviezonecz IG: https://www.instagram.com/moviezonecz CSFD: https://www.csfd.cz/film/688751-moviezone-live-special HeroHero: https://herohero.co/moviezonelive Merch: https://www.blu-shop.cz/moviezone-merch/ Kniha Devadesátky ve filmu: https://www.xyz.cz/tituly/92360411/devadesatky-ve-filmu/ Kniha Encyklopedie sci-fi filmu: https://www.albatrosmedia.cz/tituly/92065999/encyklopedie-sci-fi-filmu/ #moviezone #moviezonecz #mzlive

The new AIEWF website is live! CFPs close in 2 days and we will run our first New Engineer Orientation this weekend, get your tickets booked ASAP as they -will- sell out. Take the AI Engineering Survey and get >$2k in credits and free AIE WF tickets!One of the central tensions in the agents industry is that even while there are major decacorn agent labs like Sierra, Decagon, Notion and Cursor being built up, it is also true that it has never been easier to DIY agents, with a plethora of agent frameworks like LangGraph and Pydantic and Flue, and managed agents from Anthropic and Gemini and Amazon. There has been a wave of companies building their own background agents from Shopify to Stripe to Paradigm to Razorpay, and even Cognition's friends Ramp have built their own coding agent with other friend Modal.You'd think Cognition might feel a bit threatened, but they're not - even after all this, they were way oversubscribed for the $1B Series D they just announced:Walden Yan, coiner of context engineering and Chief Product Officer/Cofounder of Cognition, invited OpenInspect's Cole Murray to talk about why the Devin is in the Details.Full conversation live on the pod today: In retrospect, async agents were the most AGI pilled bet you could make in 2024 - the models weren't good enough yet to vibecode, and people didn't trust AI enough to let it rip, nobody (including early Cognition) was sure about the form factors. Now it is obvious:* The first wave of AI coding tools made the developer faster but remain heavily in the loop. Copilor and Cursor's tab autocomplete are prime examples However, the workflow was still heavily centered around and bottlenecked by the developer's local workflow: a developer in an IDE, watching the model, accepting or rejecting changes, and pushing code one interaction at a time.* The second wave was local agents: Claude Code, Windsurf, Cursor's agents pane: first one and increasingly many terminals all running concurrently.* The current Age of Async Agents points to a different future focused more on agent orchestration which drives end-to-end development.According to previous guest Steve Yegge, there are finer-grained 8 levels to agent adoption, but we have collapsed it into three.As Cursor's Michael Truell put it in The third era of AI software development:Cursor is no longer primarily about writing code. It is about helping developers build the factory that creates their software. This factory is made up of fleets of agents that they interact with as teammates: providing initial direction, equipping them with the tools to work independently, and reviewing their work.The agent should not sit solely inside the developer's flow. It should be setup to work in the background so that you can give it a task, a repo, a machine, a shell, a browser, tests, memory, and review loops to go do the work somewhere else.In less than a year, the sentiment has shifted from avoiding multi-agent systems:to suggesting approaches that actually work:From coining “context engineering” to building the infrastructure behind Devin's 7x PR growth and jump from 16% to 80% of commits across Cognition repos, Walden Yan has had a front-row seat to the background-agent shift. In this episode, Cognition co-founder and CPO Walden Yan joins swyx alongside Cole Murray, creator of OpenInspect, to unpack why everyone is building their own Devin, what changed after the December 2025 model inflection, and why “spec to pull request” is now becoming a real production workflow.We go deep on the architecture of background agents: harness-in-the-box vs out-of-the-box, why Devin separates the “brain” from the machine, why repo setup is still one of the hardest problems, why Docker is not always enough, and how full VMs, snapshots, scoped secrets, GitHub bots, Slack integrations, and video-based testing all fit together. Walden and Cole also dig into memory, MCP limitations, multi-agent orchestration, AI code review, SRE auto-triage, PMs shipping code from Slack, Windsurf 2.0, hybrid frontier/sub-frontier systems, and the real failure mode of uncontrolled vibe coding: your codebase regressing to your worst engineer.And as agents eat software… and software eats the world… you can draw the conclusion on what is next:We discuss:* Why the engineering world is waking up to background agents and cloud agents* The December 2025 model inflection that made spec-to-PR workflows practical* Devin's 7x merged PR growth and rise from 16% to 80% of commits* Why Cole built OpenInspect as an open-source background-agent system* The economics of $20/seat agent products and why monetization is tricky* What Cognition actually sells beyond Devin: infra, onboarding, integrations, and adoption* Harness in the box vs out of the box, and why architecture matters* Why Devin separates the brain from the machine for security and permissions* Repo setup, scoped secrets, Docker Compose, and agent-ready dev environments* Why full VMs matter when agents need to run real applications and test them* Android, macOS, Windows, nested virtualization, and machine-specific agent work* Why testing is much harder than “computer use”* Screenshots, video verification, and the “I know it works” merge moment* GitHub UX, Devin Review, AI reviewers, and agents responding to PR comments* Why MCP alone is not enough for first-class Slack and enterprise integrations* Memory, Knowledge, skills, Claude.md, and why retrieval is still unsolved* Devin's auto-generated memories and the challenge of memory pruning* Always-on agents as permanent PMs for issues, tickets, and product areas* Sub-agents, meta-Devin management, and what multi-agent systems actually add* Why pure auto-merge vibe coding breaks down after about two weeks* AI code smells, lint rules, reward hacking, and Semgrep for agent-written code* GitAI, inline context, and preserving the “why” behind code changes* Local testing, mock servers, older codebases, and preparing companies for agents* Windsurf 2.0 and the handoff between local foreground agents and cloud background agents* SRE auto-triage, support workflows, and agents as first responders* PMs, marketing, and non-engineers creating pull requests from Slack* AI agent budgets, $1k-$5k per engineer spend, and hybrid frontier/sub-frontier systems* The rise of autonomous coding factories and who Cognition is hiringWalden Yan* X: https://x.com/walden_yan* LinkedIn: https://www.linkedin.com/in/waldenyan/Cole Murray* X: https://x.com/_colemurray* LinkedIn: https://www.linkedin.com/in/colemurray/* OpenInspect / Background Agents: https://github.com/ColeMurray/background-agentsTimestamps00:00:00 Introduction00:00:43 Why Everyone Is Building Their Own Devin00:01:57 Devin's 2025 Ramp: 7x PR Growth and 80% of Commits00:03:49 OpenInspect and the Rise of Open-Source Background Agents00:07:59 What Cognition Actually Sells Beyond Devin00:09:56 Background Agent Architecture: Harness In vs Out of the Box00:12:08 Separating the Brain from the Machine00:14:07 Repo Setup, Secrets, Docker, and Full VMs00:19:13 Why Testing Is Harder Than Computer Use00:22:40 Video Verification and the “I Know It Works” Merge Moment00:23:19 GitHub UX, Devin Review, and AI Code Review00:25:42 MCP, Slack, and Enterprise Agent Integrations00:28:59 Memory, Knowledge, and Always-On Agents00:36:16 Sub-Agents, Multi-Agent Orchestration, and Meta-Devin00:43:55 Vibe Coding, Auto-Merge, and Codebase Decay00:48:38 Agent Infra, VPCs, Cloud Providers, and Fast VM Restore00:52:25 AI Code Smells, Reward Hacking, and Code Review Systems00:56:10 Making Codebases Agent-Ready00:58:30 Windsurf 2.0 and the Local-to-Cloud Agent Handoff01:01:15 SRE Auto-Triage, PMs Shipping Code, and Agent Use Cases01:04:32 Agent Budgets, Hybrid Models, and Autonomous Coding Factories01:06:51 Hiring at Cognition and OpenInspect Consulting01:07:45 OutroTranscriptIntroduction: Walden Yan, Cole Murray, and Context EngineeringSwyx [00:00:00]: All right, we're in the studio with Walden Yan, co-founder of Cognition, CPO.Walden [00:00:08]: Happy to be here.Swyx [00:00:09]: Which is a cool title. And coiner of context engineering.Walden [00:00:15]: Although I think there are many people who'd used the terms in various ways beforehand, but I did find that people, both internally and externally, enjoyed the upgrade from prompt engineering or model wrapping into maybe a more thoughtful way to build agents.Swyx [00:00:33]: For those who haven't caught up on that, I have on screen the Don't Build Multi-Agents post, which you should go read on and we might refer to, and Cole Murray, who created OpenInspect.Cole [00:00:43]: Great to be here.Swyx [00:00:43]: So let's talk about it. Everyone is building their own Devins. What's going on?The December Shift: From Handholding Models to Autonomous PRsCole [00:00:51]: So I think the engineering world is waking up to this idea of background agents, cloud agents, whatever you'd like to call it. And I think we saw a shift around the December timeframe of 2025, where the models Opus 4.5 and GPT 5.2, they reached a capability where we moved away from handholding the model and being able to actually more or less autonomously drive the model. And what I mean by that is that we could pretty much go from a specification to a completed pull request, assuming the spec was good enough, with very little friction. And that paradigm alone, I think, changed a lot of how we interact with agents, and opened this world where background agents became more practical.Swyx [00:01:41]: I think for Cole, everyone experienced this in December, but I feel like there was just this increasing ramp, right? There was this moment which was, I think, Sonnet 3.7, where, You guys rewrote Devin in one night or something. So describe 2025 or how it felt from your side.Walden [00:02:01]: In retrospect, we always thought it was ramping up, but then even now, over the last three, four months from today, it's been ramping up even faster. So it's almost funny to be talking about how, big of a leap Sonnet 3.7 was, and honestly, a lot of it was stripping out parts of Devin that were no longer needed with that jump in of intelligence. But I also just think that a lot of the recent leaps, especially, you look at, models like Opus and the latest GPT models, they are reaching levels of autonomy where people are actually finding that they actually can just be hands-off. And people who were once debating, “Oh, do I need to be in the weeds with my model in the IDE? Can I just completely move it off into the cloud?” That's a more serious conversation, and we've seen that in all of our growth charts. Internally there's this funny graph where our usage has, of PRs, our merged PRs, has grown 7X since I forget what it was called.Swyx [00:02:57]: I think Dev, maybe tweeted that. Yes.Walden [00:03:01]: it grew like 7X over, the last, I think it was, two months, three months, something like that. And then you see our engineering headcount growth. It's, gone up by, 10% or something.Swyx [00:03:11]: We were, we were afraid To release this. So this is Devin commit percentages on all Devin repos, was 16% in January and now 80% in March.Walden [00:03:25]: It's a big shift right now. And so it makes sense that a lot of people are now thinking about, buying Devin, but also maybe, trying to build their own and there's Lots of I have a lot of fun building Devin, so I can see why other people would want to build their own cloud agents as well. Matt, well, maybe it's good to hear, what initially inspired you to try to build OpenInspect?OpenInspect: Ramp, Cloud Agents, and Open SourceCole [00:03:49]: OpenInspect came about, through primarily my clients observing how they were using tools like Claude, OpenAI's Codex at the time, and seeing some of the friction that they were having with it. Primarily the Claude was being used through Slack, and a big issue they ran into was that the sessions that were launched were specific to whoever called it via Slack. And so if a PM was the one who invoked the session and they would then go to pass context to engineering can't see the session. And that in itself was a deal breaker because the PM, “Hey, engineering, can you jump in?” But there's nothing to jump in on unless they're copy-pasting out or the single response that came back. And so seeing some of these problems, I had built a similar architecture internally, just to experiment with, test out different ideas as this trend of moving off of localhost was starting to become, And as Ramp released their blog post, I had a lot of the pieces for this already in place, and just thought it would be funny to, see what Claude could do just purely from the blog post. And on my X account, there's actually a thread of where I live tweeted, going through thisCole [00:05:14]: comparing GPT and Claude as both of them are going through it.Swyx [00:05:17]: On the announcement thing or something else?Cole [00:05:19]: right after it got released. We can put it in the show notes. Yeah, it was helpful that I had already knew how to verify the system. I knew what I was looking for. I think Ramp did a great job of really illustrating, the technical aspects of how to build something. It was much more than just like, “Hey, we built a great system.” It was, “And here's how you can build it too.” And so, I resonated a lot with that, just with the problems that I was already seeing, and I thought that, looking around, I didn't really see anything in the open source community that, met this type of system. I think there's a lot that run, in localhost like Superset, Conductor, and many others.But nothing that was actually running in the cloud. And so, I built it, and I thought it was interesting to just open source it and allow anyone to then have a foundation that they can mix and match on top of.The Business of Background Agents: Open Source vs. DevinSwyx [00:06:16]: So literally after Devin was launched was, there was OpenDevin Which became All Hands. I don't know if you tried that orWalden [00:06:22]: I was going to say, one of the things that interested me a lot with OpenInspect was, you didn't try to go make it then something you monetize. There are a lot of, I think, these open source projects would then go and really try to, raise VSwyx [00:06:36]: That's why no OpenDevin. Yeah.Walden [00:06:38]: yeah, and how did you think about that? I thought that was very interesting.Cole [00:06:44]: I thought, and just what I had seen across my clients, was that having a background agent system is going to become a critical infrastructure within their company. And so because of that, I think that I wanted to open source it so that they could fork it and put in whatever customization they wanted. To that question though, I get asked all, “Oh, are you going to raise? Are you going to turn this into a service?”Walden [00:07:08]: I'm sure you've gotten offers.Cole [00:07:09]: but primarily I don't want to do that for a few reasons. One, I think that I don't want to compete for, $20 a seat. I think that is just a really difficult business. I think it's very easy to copy the main pieces of it. Again, I built this fairly quickly. And I think because you are not owning, I guess, the entire stack, it's hard to monetize. You have money being made at the sandbox layer with Daytona, E2b, many other players. You have money being made at the model layer. And you sit in this weird in-between gray area where what are you actually selling? You're selling, I guess, the infrastructure. You're selling, the integrations maybe.Swyx [00:07:55]: let's ask the guy. What are you What are you selling?Walden [00:07:59]: Well, yeah, there's multiple layers to this in practice, and actually it's funny you mentioned the infrastructure, ‘cause when we got started building Devin as well, we had to go figure out how to make the infrastructure as well because,Swyx [00:08:10]: You had to build this two years before everyone else,?Swyx [00:08:15]: Including, the model sideWalden [00:08:17]: It was not, it was not very polished at the start, when we just built it off of raw VMs from cloud providers like EC2, the boot up time was so slow, I think, And especially then, turning off the machines, saving them, and then to be able to bring them back up again when the, when you want Devin to wake up again later. It would just be out cold for like 10 minutes because that's just how long these systems took. They were not built for this repeated down and up usage. And so we actually had to go do all of that. And as a result now, one thing we offer when we go and sell Devin to people is, you don't have to worry about all the compute side of things. We'll make it work. We'll make it work in your cloud if you want it to. But aside from the product, and I want to go into the agents and the tuning of the intelligence part later, but I think a big part of what we do at Cognition as well is to just make sure that your company learns and uses and adopts these coding agents. ‘Cause I think for especially the largest enterprises in the world, you find that there is a lot of people who want to move over to using AI for their day-to-day workloads. But because of the way projects are planned, because, not everyone is literate in using AI in these ways, having a team of engineers who can actually go in and onboard you, set up all the integrations you need, the automations you need to really get to that level of, leverage with AI, is super helpful. And so We do that. We show thought partners to the customers that we work with as well.Swyx [00:09:56]: So let's talk about, architectural stuff. I think that's always, that is something that was the topic of conversation between the two of you. Is this, the mental model that you want to start with or something else? I'll just leave the floor open to you guys.Agent Architecture: Harness in the Box vs. Out of the BoxCole [00:10:11]: I think, maybe we can start here as just a general what are the pieces of a background agent system. And then maybe we can go into some of the nuances of, Decisions that you can make.Swyx [00:10:22]: But I guess I also Like, what, maybe what Walden is saying is the agent is like in this open code box, I guess. Right? This is infra, and then there's, that's the agent. And you had this discussion about whether you put the agent in here or in Out externally. Can you tease that out?Cole [00:10:39]: In a background agent systems, you have a decision to make of where the agent is actually going to run. This is typically described as the harness in the box or out of the box. With running the agent in the box, you're making some trade-offs by doing that. The negative trade-off you're making is primarily security. Because the agent is running in that box, unless you otherwise design it, all of your secrets need to go into that box as well. And given the nature of AI, it can be unpredictable, and you could very easily end up accidentally exfilling your secrets, or other unintended behavior. Now, the out of the box is the idea that we are going to have the actual agent running not directly in the sandbox, and we will have, quote-unquote, the brain of the agent running in some type of worker, control plane. That sandbox then is going to serve as the hands where the brain is basically operating and making tool calls into that environment to manipulate it. I guess other trade-off that you're making between the two systems is that, in my opinion, running it out of the box is much more complex because, you have state that has to be managed, whereas if you're running it in the box, all of the state of that agent is actually in the box, and yes, it's you could persist it elsewhere, but it's all localized and you have less concerns to worry about.Walden [00:12:08]: I think a lot of that, what you mentioned, is why we actually from the start built Devin to what we called separate the brain from the machine. The other thing that this allows you to do is reuse any existing infrastructure you have for dev boxes Perhaps. And so you don't have to worry as much about making a new type of dev box that has all the dependencies the brain needs, as you mentioned, the secrets the brain needs as well. One thing that we've seen some customers run into is, you have a GitHub app and you want Devin, your agent, whatever, be able to interact with GitHub through this application, but then you have different users with different actual permissions. If they are all interacting through the same GitHub app and there's no actual, separation between the system that decides, what it does and the actual secrets on the machine, then you run into an issue where, okay, it's hard to do the separation. But in practice, with Devin, it's much easier because we just say whatever you put on the machine, that is, the scope of basically what the user is free to do, what the agent is free to do. So only put the most scoped secrets on that machine, and then the brain is fully not accessible from the machine. So you don't have to worry about messing with the, any of the most secure parts of the brain if the user is free to do whatever they want with the machine.Swyx [00:13:31]: I was going to just bring, I have this, chart from OpenAI, where I don't know if this is, in the box, out of the box. That is something that they do use to describe it. And then also recently Anthropic did, managed agentsSwyx [00:13:44]: Which is, this is their thing. I don't know. It's all, it's all variations of the same pattern, right?Cole [00:13:49]: So this would be out of the box.Swyx [00:13:51]: Which, is preferable for them because it's less work?Cole [00:13:56]: I would say it's more work.Swyx [00:13:58]: It's more work?Cole [00:13:58]: But it, in my opinion, it is the better architecture of the two. It's just, you're taking on a bit of complexity by doing that.Repo Setup, Docker, and VM-Based Development EnvironmentsWalden [00:14:07]: One thing I've not seen a lot of other players do well is how do you manage what's actually on the box? And this can be complex for many reasons. Let's say you have a big repository that's changing and updating a lot with changing dependencies. How do you make sure that the working environment of the agent actually stays up to date, has all the credentials it needs to, let's say, run the app and test it, and all the things you want your autonomousSwyx [00:14:34]: So a repo setup.Walden [00:14:35]: Exactly. So in, internally At Cognition, we call this repo setup.Cole [00:14:39]: The hardest part ofWalden [00:14:40]: It's been a perennial problem since the start of the company, of how do we help people get this set up? Because not everyone just has, working cloud environments working out of the box. And do you find this to be a common problem withSwyx [00:14:53]: How do you solve it?Walden [00:14:53]: Your clients?Cole [00:14:54]: This is a very common problem, and through my consulting, this is a lot of what I help teams do. A lot of teams don't really have great developer environment setups, if any. A lot of the times it's, “Go talk to Bob and get the secrets,” and that obviously doesn't work when the agent needs to actually set this up. And so a lot of that, most teams are using Docker Compose or some type of microservices. And so for theSwyx [00:15:19]: Even in prod?Cole [00:15:20]: Not in prod. With the OpenInspect, you are using this primarily to interact, and make code changes. There is other use cases, but you can hook, whether through CLI, MCPs, other tools, you can then hook that into your production systems primarily for, SRE type use cases. But you are not, necessarily, trying to test your prod internal microservice through the system.Walden [00:15:48]: And you mentioned Docker Compose. I think one direction we saw some of our friends take early on was, using Docker containers as the level of abstraction for their models. There's lots of reasons, I think, why Docker containers are not great. One thing is, Docker container's not really a true security boundary, for one. But the other is, if you are running real applications, a lot of times those applications use Docker, and then you have to think about Docker in Docker, which is, really weird. And so I think part of, the really hard challenge of getting VMs to work, why did we do that? Well, it was because we realized that you actually needed, full VMs to be able to do these types of things. And especially nowadays where there's actually value in running the application and clicking around and sending you screen recordings of these things. The value just, keeps adding on top of that. But it is a decision I see people run into when they try to build their own systems, is, “Oh, do we, in addition to this, do we put the agent in the machine or out of the machine? Do we use Docker? Do we use something else?” What do you recommend people nowadays?Cole [00:16:57]: I think Docker is a good solution for maybe not running the agent, but running your infrastructure, because that is more or less the same setup your engineers are probably already using. If they're not, then I don't know what they're using. But they're probably already using Docker Compose.Swyx [00:17:14]: I've always had a small candle for web containers. I don't know if you guys have tried them before.Swyx [00:17:19]: To me, they were, supposed to be like Docker Light.Cole [00:17:22]: Is it?Swyx [00:17:22]: I don't know.Cole [00:17:22]: No, I haven't tried it. But yeah, I think any environment that you've set up that is a good experience for your developer naturally lends itself to being easy to set up for the agent. And once you figure out that local developer story, you've more or less solved the agent in a sandbox, environment setup. OpenInspect does have hooks as well, where you can, run a setup SH script that will pre-install everything. You can then pre-snapshot that build so it starts instantly, and then there is a second hook to actually then, restore the state of the sandbox when it comes back. And so you can already have all of those microservices running and basically get the same experience that you would on your machine within the sandbox.Testing Agents: Computer Use, Screenshots, and Real App WorkflowsWalden [00:18:08]: Another thing that we've been thinking a lot about is like Different VM service offerings. Have you had customers where they needed like macOS specific VMs or like Windows specificWalden [00:18:20]: VMs?Walden [00:18:22]: There are like many technologies in the world that only work on specific types of machines, right? If you're building a.NET application that has to run on Windows or like, maybe more commonly if you want to build iOS or macOS Does that workSwyx [00:18:32]: Does Commission supportSwyx [00:18:33]: Choices like that?Walden [00:18:35]: The fundamental architecture we do, because we do the separation, it does support, but the actual work in progress is happening right now on these. Another thing that we've actually recently added support now for, it's in beta, is doing Android development. To do that, we needed to support, I think, nested virtualization within our machines because the VM itself is like a, is a virtualized Firecracker instance, and then you had to then run another Android emulator inside. And there's like weird performance issues that like, it, which is why it's like still in beta. We have to think through these problems, but it unlocks a lot for anyone who wants to do Android development.Swyx [00:19:13]: I was trying to find like a reference video for the testing thing. I couldn't find it, but I think you worked on the testing, capability. Why call it testing and not like computer use or I don't know, it's, what's the general Category of problem?Walden [00:19:26]: I think that when people think about the ability of an AI to run your app and test it, I think they actually over-index on the computer use part of it because computer use in my mind is the literal, okay, you want what button you want to click. Can you emit the right coordinates to go click that button? I think testing is actually a really interesting likeWalden [00:19:48]: Problem-solving, challenge for these AIs because if you wanted to do arbitrary testing, imagine you make a change that spans the frontend and the backend, maybe, even some other like even more deeply nested service. To actually test that change, we have to reason through what-- how do you first run these applications to orchestrate with each other with the right version of the code? Then, okay, how do I trigger the feature or how do I make the thing actually happen? And this can get arbitrarily hard, maybe you have to be an admin. Maybe a certain thing has to be feature flagged on. Maybe, you have to like run two sessions and then send us a very specific word into one of them to trigger a specific behavior. And figuring out how do you do that requires a lot of code base context, requires, a lot of orchestration that we've specifically done. And in some cases, we found that you actually, no one frontier model can actually do this full end-to-end task itself.Walden [00:20:42]: We've seen cases where we actually had to orchestrate different frontier models together to solve this problem together. That is where we spend most of our time when we think about this testing problem, not so much the computer use part. Computer use for what it's worth has gotten a lot better with recent models and it's made that part of the job certainly easier.Swyx [00:20:58]: Especially with like even 4.7, that they released yesterday, apparently like way better in terms of the vision stuff, which is going to be encompassing computer use.Walden [00:21:08]: Having evals for all these as well is something that like takes a while to build up. And having the evals be right is tricky as well. Do you ever see like, clients who are building their own agents have to start standing up evals to make sure things don't regress?Swyx [00:21:25]: Not so much evals in the traditional sense, but specific to the testing part that has just gone in. I just added support for screenshots And in theory you can also do video. I need to put in a plugin to do that. But they do show up natively, and it was a very heavily requested feature, especially after Cursor's recording came out. I think that was very enlightening for everyone of like, “Oh, this is a very good feature to actually have.”, I think with Devin you guys have had this for a while.Swyx [00:21:57]: Oh, yeah. See how screenshots work. Yeah, I don't know if there's anything, super and not obvious. It's like once what feature to build, you can just prompt it and it Will mostly work.Walden [00:22:09]: I think to Walden's point, though, the computer use is a subset of the larger testing problem, and I think that's very specific to the code base that you're working and it's not something that, out of the box that you could just solve it. The-- you do need the code base context to actually know how to test it. And I think in the case of a background agent system, you fortunately do have that code base locally that what is changing and could then inspect it and use that to drive the model.Swyx [00:22:40]: For those who haven't seen it before, this is an example of how it works. You, after the PR is done, you click testing approved, and then it sends you back a video. What I really like is that it labels, It's very small here, but it actually labels what it's testing. And then it-- and then you actually see the cursor and everything. So I don't know, yeah, the engineering in this, just Whatever you want to show. ‘cause this is like, this is one of those like, oh, few of the AGI moments, right? ‘cause Once I look at this, I actually don't I wish I can just merge inside Of Slack instead of going to GitHub ‘cause I don't need to see the code. I know it works.Walden [00:23:19]: Maybe a new feature in Cursor. Yeah, the annotations at the bottom was also a big difference for me when I, when I added those.Swyx [00:23:27]: It's just like, what am I looking at? What are you trying to demonstrate?Walden [00:23:30]: Exactly. There's a surprisingly long tail of small details that ends up making a big difference for this end metric of like how fast do you actually merge the code in. One experience that we spent a lot of time tuning early on was what is the right experience on GitHub for these tools. Because I think, most tools out there when you build the agent, you'll think about, oh, it'll create the PR for you. We try to take that a step further and say, “Oh, what if we actually made sure you could interact Devin, with direct Devin directly on GitHub?” And so we made sure that you can comment on GitHub, and Devin would actually receive those comments and address them back. But there's actually quite a bit of tuning you have to do here because you can imagine that actually like-We recently have Devin Review, for example. Devin Review will post comments on his own PR And then Devin has to then goGitHub Workflows: Devin Review, Comments, and PR AutomationSwyx [00:24:23]: He answers his own comments, which is Really loopy. So like, yeah, I like that it just updates here that it's, that I have commented But usually it's just me saying like, “Hey, merged, fix any merge conflicts.”Walden [00:24:37]: The, so when Devin fixes his own comments, you might be scared that, oh, maybe I'll infinite loop. But we've put a lot of work into making sure it doesn't, both by making sure that the comments are high signal, but also that the agent is thoughtful about what comments it immediately goes and tries to fix, and what comments it's like, “Wait a second, I think you're wrong.” Actually, that's one of my favorite moments is when Devin tells me that I'm wrong, when I try to get it to do something different. But tuning that behavior, actually makes a big difference in terms of how useful the actual GitHub experience is.Cole [00:25:06]: I think to touch on that as well, I think having the AI reviewer integrated into the system is a critical part of this background system. OpenInspect does have that. It has a GitHub code reviewer that you can control the prompt. It does do comments as well. It doesn't do them automatically yet. The capability is there, but it's not fully used.Swyx [00:25:27]: So you have to ask for it?Cole [00:25:28]: you do, yeah. You can tag it on GitHub, and then whatever you named your, GitHub bot, it will then follow up on it. It will then, if you have merge conflicts or whatever you have asked it to resolve, it will then resolve it, but it doesn't do it automatically yet.Integrations: Slack, MCP, and First-Party Agent InterfacesWalden [00:25:42]: Well, I'm curious, what is, the most common thing that people end up requesting, that they still need on top of OpenInspect when you help them go implement it?Cole [00:25:52]: I think a lot of it comes down to actually integrating it into the company. It's one thing to have the background agent system set up, but if it isn't actually integrated into your larger ecosystem, it isn't that useful. It is useful to be able to kick off sessions, but what we really want to be able to do is hook it into all of our other systems, whether that is the production database with read-only credentials, the logs, a Confluence or internal knowledge-based system. I think that is where I see the huge leap for companies, and that can be a challenge for companies as well who are maybe not familiar with exactly how to approach it, especially if they're in environments that have more compliance type things where, access control can be pretty big and how do you deliberately think about these problems, I find to be, one of the problems that comes with a system like this.Walden [00:26:46]: The thing we found is So, MCPs, obviously it has been like this, really big explosion of, oh, you can go, integrate it with all these different things. But to actually get the integration right and the and get the right experience, oftentimes we found that we had to go build our own ad hoc things. I think Slack is a great example of this. You could give your agent a Slack MCP and okay, it can post messages back to you on Slack. But we actually use Devin like a coworker in Slack, and that's how it's been built from the ground up. But to do that, you actually need to, support webhooks that come back, right? And then Devin has to respond in a natural way and then hopefully don't spam your threads too much and annoy the people in your company. So you got to tune that experience just right. Especially when there's a lot of back and forths, we find that we actually have to go beyond the simple MCP integrations in these places.Swyx [00:27:39]: I just pulled up the MCP marketplace. I know this is a Fair amount of work. Is the answer to eventually take first party control of all the top MCPs? Is that theWalden [00:27:48]: I would love a world where you could have something that's more expressive than MCP. That, goes both ways, not just a set of tools, but a proper system that interacts back and lets it Have the right experience with all these interfaces.Swyx [00:28:03]: So there actually is sampling in the MCP spec, but nobody Uses it, right?Walden [00:28:07]: And so I think that's the other part is, actually we found that when the MCP spec starts to get too complicated, it starts to lose its original promise of Being like a simple one-step connect. Now then we have to go figure out how to support all these different variations of things and It starts to look a lot like just building the first party integrations in a lot of these cases now.Cole [00:28:29]: I think it matters, too, how critical it is to your company, right? If this is something that nearly every session is going through, it probably makes sense to own it so that you can make optimizations on top of it Versus just whatever is off the shelf.Swyx [00:28:43]: Awesome. Other than MCPs, what else, sorry, well, I don't know if that's Narrowing in too much on, integrations. But what else? What other elements of building OpenInspect or Devin that you guys really sink on?Memory and Knowledge: What Agents Should RememberCole [00:28:59]: I think, a problem that comes up very frequently is this idea of memories or knowledge base.Swyx [00:29:05]: Oh, boy. How do you solve it?Cole [00:29:08]: so not solved yet, is the short answer.Cole [00:29:11]: it's something, there's a open issue for it, someone asking about it.Swyx [00:29:16]: There's, I, D Wiki hasn't indexed anything about memory yet.Cole [00:29:20]: how I'm seeing it solved across my clients is primarily through skills. I find that skills can be a good gap within that or updating Claude MD, but I think memory as a whole is a pretty unsolved problem, and it is why I've been hesitant to add it. I think there is parts of memory and that can be addressed, but I think as a whole it's a very difficult retrieval problem.Swyx [00:29:44]: Oh my God. RAMP didn't write anything about memory? I see zero search results.Walden [00:29:50]: No. Memory can be quite tricky to get right because it's the retrieval, but also the generation of the memories that can be really tricky. You don't want it to just like Remember very specific details.Swyx [00:29:59]: Walk us through the Devin memory journey because I know there's been a journey.Walden [00:30:03]: the first version of memory that like stuck around for a while was A system we have called Knowledge. And the idea was we wanted it to pick up things over time and not need the user to be proactive about teaching Devin things. So, okay, any time you remind Devin, “Wait, no, that's not quite the way you're supposed to use Git”Like, we actually want Devin to say, “Hey, do you want me to actually just remember this for the future?” And for you to just basically quickly approve or reject and for it to build up over time. ‘Cause I find that, 95%, I think, or some crazy stat like that of the memories that Devin has are all through these auto-generated things. Very few people actually just want to sit down and write big docs on Here's how you're supposed to work with the technology, et cetera. The generation and the retrieval has been something that we've been trying to tune a lot over the years. Generation, you don't want it to remember something like, if you asked one time to like, “Oh, please open as a draft PR,” you don't want to be like, “Oh, everyone forever now should get their PRs as draft PRs.” But you do want some, conveyor. Maybe you want to say like, “Oh, Cole generally likes, things to be created as draft PRs.” Same with retrieval, if you have thousands of these memories, how do you actually make sure they're retrieved at the right time? And that can be quite tricky to do right without exploding the context with a bunch of useful yeah, useless information. Surprising amount of just, eval work to just make sure that, memory is, remains a reliable system as new models come and go.Cole [00:31:31]: Do you have anything that you could share on, memory pruning? And like the temporal aspect of memory?Swyx [00:31:36]: Deleting and forgetting?Walden [00:31:39]: The, today, the, So the things they could do is it could edit memories. And so if your memory used to say like, “Oh, Cole likes to open everything as like a draft PR,” then you can imagine, “No, don't do that.” And then it'll say, “Oh, do you want me to update the memory to be Cole now want everything as, open PRs?” I think that at the same time we don't know if this is going to be the final version of the system. Whatever we have here will probably, translate into the new system that we'll be coming up with. But I think one big difference between two years ago and today is these agents are really good at using anything that resembles a file system natively. And so part of us are, is thinking, “Oh, should we rebuild memories to feel more like a file system that we let the agent navigate on its own?” That's been an interesting exploration. Also similar ideas in the scale space.Swyx [00:32:35]: I am pulling up OpenClaude's memory thing right now. So memory, OpenClaude has like this like daily memory journal thing, right? And you can I mean, that is a file system you can grep through and is a source of truth. I don't know if it's the best. It's probably super noisy, but at least, if you lose something you can discover it or you can apply some, forgetting algorithm to, more ancient memories that don't get recalled again or something. I don't know.Walden [00:33:01]: One thing we've been trying to do to push the boundaries of how you use agents at your company is letting an agent basically have a very similar file, a memory.md or something, and just like be your permanent PM for a specific set of issues maybe. So we have like some Slack channels internally, maybe a Slack channel dedicated to, a specific product like DeepWiki maybe. And you can imagine that, or you want a Devin that never stops, it's just always awake, but it has this like memory dock that it can just maintain for itself about, okay, what are like the number one priorities of what we have to fix and prioritize? Who is responsible for some upcoming work? Maybe they'll even Devin will even tag you on some recurring basis. And so it's been an interesting move to see, okay, how can we actually use Devin for more than just engineering? Can we actually upstream above the engineering process and maybe it's just Devin creating tickets, which then maybe some humans do, but then maybe other Devins do.Swyx [00:34:00]: One of my more fun automations is go research competitors and just suggest stuff to me on a weekly basis. That's the automation. I can't find it right now, but basically it just like, “Look at competitors and suggest things.” “And here are three things that you've suggested that I don't want any more of,” and you just stick that in the prompts. But like I wish actually So for like when I, for example, when I reject a PR, I wish that it updated memory so that I can then just not have to go up, go back and update the scheduled, sync, but anyway, feature request.Walden [00:34:31]: what? We might change it soon. I guess OpenInspect, in the time you've been around, has there been anything you tried to implement but then you had to like undo and like do a different way?OpenInspect Architecture: Webhooks, Control Planes, and Agent StateCole [00:34:41]: Nothing yet, but something that is on my mind. The initial way that I built it was that each of the integrations lives as its own package. And so you have The Slack bot, which is what's handling the webhooks, and then is basically interacting with the control plane. As I'm seeing the system starting to be more integrated, specifically with the GitHub bot integration, I'm considering bringing that all into the central control plane because especially now I want to start, And a request that I'm getting is the ability to monitor, the actual, pull requests being merged, as well as just tracking ofSwyx [00:35:19]: What do I have open?Cole [00:35:21]: What do I have open? How many of these are getting merged? How many comments are showing up? To just understand the health of the system. And so in the case of a GitHub app, you only have one webhook. And so then it's a question of do I put that webhook in that GitHub bot package? That's weird. It doesn't really make sense to live there because that package is more for like the code reviewer. Or do I like centralize it? So that's something that's on my mind of, making that decision. I think the other one we touched on earlier is the harness in the box versus out of the box. I think long term the architecture will eventually come back out of the box. Some of the newer tools that I've added are calling back into the control plane so that you don't have the secrets in the sandbox. And so I think long term I probably will pull the actual, agent out of the box, but I think for now it's fine.Subagents and Multi-Agent Systems: When Parallelism Helps or HurtsSwyx [00:36:16]: Just, a quick question on pulling the agent out of the box. I'm One thing I'm very bullish on this year is agents calling other agents or spawning sub-agents or Whatever you want to call it. Does that make it harder or easier? I can't tell. Because if the harness is in the box, you can just spin up more boxes. If the harness is outside the box, then you're, it's less easy because you are, you have a unicorn pet of a, of a harness that's, living outside the box.Cole [00:36:45]: In theory it would be the same way, right? Whether, one agent has launched many, sub-sessions within it, OpenInspect, for example, can launch sub-sessions and actually create other environments and then monitor them. In the case where it is out of the box, that would basically just be an additional session that's running. And so that session is also running outside of the box. It's running in your worker plane, wherever you're running this. And then you really just have to think about how does your top level agent then interact with it. I do think it can be more complex, just ‘cause again, you have now a more difficult architecture. But I think if you figured it out once, it's probably fine.Swyx [00:37:26]: Well, then I'm just, throwing it open to you in terms of, I call this like meta Devin management. Which is like the, Devin's calling Devins or Devin scheduling Devins or querying trajectories or anything like that. What have you built or unshipped, anything?Cole [00:37:46]: I think one of the surprising things we've seen is that a lot of the ways that, these, separate agents work with each other, and you want them to, parallelize their work, has still mostly followed the same manager sub-agents regime. And a lot of people I think are excited about this world where you have swarms of agents that, talk with each other all over the place. We've actually given Devin an MCP so they can just go arbitrarily message other Devins And create new Devins, et cetera. But I guess, it somehow creates, a really chaotic world in that sense. And so we've still found that most practical use on a day-to-day basis has been one single Devin.Cole [00:38:33]: Figuring out how to segregate the work and get, have other Devins work on it in, a relatively isolated sense, each with their own boxes Not sharing machines, so there's, a very little room for conflict is the regime that you have to create today.Swyx [00:38:50]: I'll call out, the experiments from Cursor, right? This is Wilson Lin's work on Single agent to multi-agent, and you're obviously famously on the side of don't build multi-agent. But they went through the whole thing, only to arrive at, this Which is exactly what Devin has, I think.Cole [00:39:08]: I think there will be a revision to that post at some point AboutSwyx [00:39:12]: Tell us about itCole [00:39:12]: I think multi-agents were very much not at all possible a year ago. You do see more multi-agent experiments today, but you can argue, are they really multi-agents, or are they just just, tool calls,? There are people who, will create sub-agents to go look for XYZ file, XYZ implementation. Has really nice context management benefits because all of the tool calls and tokens that it spends then get collapsed back to just the answer for the main agent. There's a lot of benefits to doing this. We basically have Devin do this with Deep Bookie, make a call out to Deep Bookie, give you back the results, but that feels like a tool call,? It's not like these, two collaborators actually talking back with each, back and forth with each other. But I think the thing that gives me the most bullishness that multi-agents might actually be possible is actually what I said earlier about Devin will actually sometimes tell me I'm wrong and push back, and I think that demonstrates a level of maturity and communication today that makes a multi-agent world possible. One, can two agents who have seen different information come back to each other and actually figure out who is right, what is the correct implementation? They're not just, yes men. Claude, I guess is like, used to just say, what is it? “You're right,” or,Swyx [00:40:25]: “You're absolutely right.”Cole [00:40:26]: “You're absolutely right.” Yeah.Swyx [00:40:28]: The Have you seen, did you seeCole [00:40:29]: The age is overSwyx [00:40:30]: The Codex app troll in Topic? This is the Codex app. Inside of Settings, there's a little, there's a little Easter egg, right? So if you go to, the Themes or Appearance, right? There's all these, color codes, and the top is absolutely, and it's the Topic's colors. Which is such a troll. Anyway.Model Behavior: Pushback, Adversarial Prompts, and Agent SkepticismCole [00:40:53]: I love that Easter egg. Did you discover that yourself?Swyx [00:40:54]: No, it was, someone was, tweeting about it And I was like, I was like, “Is this true?” Because, sometimes people just tweet stuff to, get a rise out of you. But yeah, there you go, in Topic colors.Cole [00:41:06]: Yeah. So yeah, we're out of this regime where, it just says you're absolutely right, and they can have real conversations and real back and forths.Swyx [00:41:13]: You can prompt it as well to be more adversarial or whatever. Yeah. Okay. Yeah, that, I mean, to me, that is more intelligence, right? That is not just something that's, a dumb tool, it's actually pushing back on you I think. Yeah.Cole [00:41:24]: when you mentioned, of course, the blog posts. There was one blog they had where they fed a swarm of agents together and built a browser.Swyx [00:41:34]: That was I think that was the one.Cole [00:41:36]: You can have, likeSwyx [00:41:37]: I think it's the same oneCole [00:41:37]: Creation of it. We found a surprising success of, don't do a swarm or anything, just have one Devin, it does its own context management. Just let it keep running for a while and give it some crazy tasks. I think we asked it to, rebuild, a Windows OS system. And it managed to do it just like, going on for long enough. It'sSwyx [00:41:55]: Was this Andrew's thing?Cole [00:41:58]: there were lots of demos that we ended up not posting, ‘cause at some point we'd just be posting way too much a bunch of, Demos. But I love that because it shows that I think the multi-agent thing still has, a bit of exciting sexiness to it, which is maybe still beyond still, the actual delta it adds to the capabilities of these systems. But it's absolutely the future. I think we're heading in that direction and we can see the progress being made there already.Swyx [00:42:25]: If I were to, make one super minor pushback because I don't feel that confident about it yetCole [00:42:33]: Go for itSwyx [00:42:33]: But I've had Ryan Lopopolo from OpenAI on the pod And he's a super slop cannon, right? Oh my God, that's my coding agent being done. I downloaded this, Peon Ping. I don't know if you guys have heard this. It takes like-, sound packs from popular games like, Command and Conquer and Warcraft, and then it plays it whenever it's done. And so it's like, “Work,” or whatever, “At your command,” or something. Anyway, what I got from the Cursor code base and from Ryan's thing was that there's a slop cannon approach where you try to loosen the single agent's, bottleneck, and I feel like that is, probably an, a very important thing to try to figure out. I don't think anyone's, really solved it. Because then you just have more reviewer slop on top of the agent slop To try to wrangle it all. Ryan will probably very strongly object that I say that he hasn't solved it, but he thinks he's He thinks he's completely solved it. But I think it's still I think it's, very important, ‘cause, that is a bottleneck, right? I feel Devin is slow sometimes Because I'm like, well, yeah, this is very readable and very sensible, but also it is slower than it could be if I just, I want a button to just say, “Just ramp this up 1,000 next parallel, in parallel and just, see what happens,”? And I don't know if that's, feasible at some point in the future.Code Review, Entropy, and AI SlopWalden [00:43:55]: I And we've also run experiments internally where we've basically tried to build entire products, true products that we knew we would eventually ship, but for now, let's try to see if we can do it just by purely, vibe coding on top of each other, auto merge, no code review at all. And then there's this benchmark of how many weeks can you go onto this for Before you say, “We have the trashiest code base.”Walden [00:44:18]: “Let's actually rewrite it from scratch.”Swyx [00:44:19]: Start a new factory, yeah. What'd you find?Walden [00:44:21]: I think we found that the state-of-the-art in December was you can probably, run this for about two weeks. By the end of those two weeks, you'd find that, hey, you want to, change the color of a button. Well, it turns out this button is implemented in, 10 different places, and they, have All these different variations, and oh, you forgot one of them, and actually it's a slightly different color in one spot. And you're like, “Okay, this is too much to work with. Let's actually try to do code review at the same time.” And make sure that we're on top of our software, actually cleaning it up a bit And making sure it's done in a scalable way.Cole [00:44:54]: I think building on that, the idea of, you don't have to look at code, I think is generally a bad idea. And the meme that I have for thatWalden [00:45:03]: What timeline, all right, is Do you think that statement will be true on?Cole [00:45:06]: I think probably for a while it'll be true that you should continue to look at your code. A problem that I see a lot of teams run into that I work with who are embracing AI native, AI first coding, is The meme that I have is that your code base regresses to your worst engineer, because that engineer who is, very gung-ho about AI and is not auditing their code, their pattern starts cementing into the code, and now the AI is referencing their patterns. And so now their if/else block that, is 20 if/elses back and forth, the AI is seeing that as the pattern of how things are done and starts to then exponentially grow this slop. And I find to your point, a pretty good approach to that is having scheduled cleanup, whether by humans or through systems, that are looking for duplication. They then address that. You'll end up with like 12 helpers for how to format a date. And you need to address that, because otherwise it will continue to sprawl.Swyx [00:46:09]: Within balance, I think it's fine to have some duplication, and then sometimes To have garbage collection, right? Yeah. The What I've been, talking about with a lot of engineering leaders is that you want to be very strict about the boundaries between modules, and it's your job as an architect, as a CTO, whatever, to say like, “Okay, here's the hard contract between you guys and you guys. Whatever you do inside this black box is your business. You do whatever. But between these guys, let's be, really damn clear, and any movement must be signed off by a human or me,” or. Then, and like that's that. I don't know if you have any other modifications or advice.Walden [00:46:44]: Well, I guess generally on the topic of, where humans can be useful, I found that ‘cause, some of these, really deep infra problems, sometimes just having a human that just has, really deep expertise can make a big difference. I've actually seen this come into play when actually building agents. So we've had a few friends now, try building their own coding agents, and I think one same problem that I recurringly heard a lot of them run into was this problem of like, “Oh, Grep is really slow on our agents' machines.” And so a lot of them, I assume because they're using AI and they themselves don't have, super deep infra background knowledge, say, “Okay, we're going to go build our own custom Grep index. It's going to be really fast,” and use that as a way around this problem. When we ran into this problem About like, maybe like a year and a half ago when we were, in the early days of building Devin, we obviously didn't have AI then. We just asked our, how to, how to do this. You can just swap out a new Grep index, so.Infrastructure Details: Grep, File Systems, and SandboxesSwyx [00:47:45]: What do you mean you hand-coded Devin? What?Walden [00:47:48]: It's like, can you believe we hand-wrote this code? And we had, our infra people who are really amazing, they were looking into it and they're like, “Oh, what? We realized that actually the root cause of this problem is actually super simple, but like fine-grain detail,” which is that a lot of these virtual machines actually underlying them don't use real file systems. They use these, network file systems where things are actually cached over the network actually in S3. So when you're Grepping, you're actually making network calls Every time you're doing these things, and that's why Grep is extremely slow on these machines. And so again, goes back to, what is all of the crazy infra work that we had to do to actually get these machines working. If you try to do this yourself, there are tons of small details like this, and so we had to eventually go swap out that network file system. ButSwyx [00:48:35]: I think there's a write-up about it, right? Silas did one about the virtual file system.Walden [00:48:38]: Oh, that was a whole other thing. TheSwyx [00:48:39]: Oh, that's a different thingWalden [00:48:40]: The BlockDev file storage formatSwyx [00:48:42]: I'll bring it upWalden [00:48:42]: Which is, a file system format that we built so that the VMs could be spun up and down very quickly. Basically, the intuition behind this is-Imagine you have, a terabyte of disk, and your agent only, wrote, a hundred lines of code on top of that disk. How long does it, say, take to, save and re-bring up that disk? And most systems, because you're not optimizing for this case, it's just, on the order of a terabyte of work because you have to Save all of that and bring it back up. In our system, we try to build a file system that incrementally builds on top of each other. So every time you save and bring the machine back up, you're only doing work that is proportional to effectively the diff in the file system. And so this, shaves off a lot of time in the boot-up process of Devin. I think we This is actually now outdated. We have a newer system inside of Devin. But yeah, there's a lot of tiny details you have to get right here to actually get the day-to-day experience of Devin to be good.Swyx [00:49:39]: It's, not technically agents, but it is agent infra, and when you sell an agent as a company, you sell agent plus agent infra.Walden [00:49:46]: At least the way we do it be And the other The nice thing about having the agent infra being done together is, you We get to deploy Devin in whatever environment we want now. We don't need to wait for some underlying infra provider to also go and support VPC or on-prem or FedGovCloud, for instance. So we can actually go and figure out, okay, since we own the infrastructure, how can we get that set up for you?Cloud Providers: Modal, Daytona, and Enterprise SandboxesSwyx [00:50:12]: Whereas you're Cloudflare dependent.Cole [00:50:15]: so Cloudflare runs the control plane. The sandboxes, Modal is supported. A contributor just added Daytona. E2B is on the roadmap, and I think there's an abstraction in place that if any contributor wants to add a new provider, they can add that in.Walden [00:50:32]: Well, what are, How are the customers you work with Do they generally try to then go set up a contract with another one of these third-party providers? Do they try to do the VMs in-house?Cole [00:50:44]: most of them I see using Modal. I think Modal has a greatWalden [00:50:48]: Shout out Modal.Swyx [00:50:48]: Shout out Modal.Cole [00:50:50]: I think Modal has a great offering. It captures all of the sandbox pieces you need, snapshots being a pretty big piece of that, and given that they also offer GPUs, I think it's a pretty nice offering as a whole.Swyx [00:51:04]: no debate there.Walden [00:51:07]: Modal is great, especially, I think their container offering is, the most natural, and so especially if you are willing to, forego, the full VM requirements Modal is, a really vast place you can spin something up on.Swyx [00:51:20]: Is there a point So Modal's very Python, and I feel like most workload, has really shifted to JavaScript. I don't know if you guys Get the same feeling. So, okay, when I started Landspace and IE and all these things, I was like 50/50 Python and JS, right? That's roughly. I think that's wrong now. I think JS has won. I don't know if you guys Like, I Maybe I'm overstating it, and maybe for cognition, there's, C# and Java and what have you. But for, new greenfield apps, do you feel that Do you get that sense? Does it matter?Cole [00:51:52]: I think that most of the libraries that I see in this space are Python native first, especially in theCole [00:51:58]: Observability space. That said, I think that there is a pretty big appeal of having your entire system in one language. Especially when you have both your frontend and backend communicating, you can have one central type Which is very nice.Swyx [00:52:11]: That's my case against Modal, which is Then you have to run JS. You can run JS inside Modal. It's just, one extra step That, isn't native to the runtime. I don't know ifWalden [00:52:22]: I don't knowSwyx [00:52:23]: Reviews. Do you have numbers? I don't know.Walden [00:52:25]: the one thing I don't like about Python is whenever AI, whenever it writes Python, it always does, the weirdest patterns, andSwyx [00:52:32]: Oh, because it's, mixing two and three or what?Walden [00:52:34]: I think it's something mixing two and three, yeah. The I don't know if you see this. It always tries to do, has attribute on objects as likeCole [00:52:41]: Oh, my God.Walden [00:52:41]: But it's like But that you shouldn't be doing that. It should error if there wasSwyx [00:52:45]: Because it's training on library code?Cole [00:52:47]: I think it's more of, likeCole [00:52:48]: From what I've seen, it's more of, a reward hacking mechanism where it doesn't want to basicallyWalden [00:52:54]: It'll never error.Cole [00:52:54]: It doesn't want the code to fail. And so it Even when it knows it has the attribute, it'll call getattr on a, and for a lot of my clients who have moved towards more autonomous coding, we've put that in as a lint rule That if you do getattr, your pull request is going to fail.Slop Signatures: Comments, Backwards Compatibility, and TypesSwyx [00:53:12]: Ooh, this is a fun topic. Can you tell me more about this? What else is a sign of AI coding that you have to put guards in?Walden [00:53:21]: So we were talking just before this about Opus 4.7. One of the things this new model likes to do is it writes lots of comments. Not like, it'll, comment every line, but it'll write, paragraph, PRDs, on top of every function. But I will say, to its credit, these aren't slop, descriptions like they were before. “Oh, here's what this function does.” It's like, “Oh, here's actually the r

Disaster recovery is no longer just about backups. It is about resiliency, recovery speed, cyber readiness, and operational flexibility.In this episode of Nutanix Weekly, Phil Sellers is joined by Andy Greene and Chris Calhoun from XenTegra to break down Nutanix Multi-Cloud Snapshot Technology (MST) and how organizations are using it to modernize disaster recovery without overspending on infrastructure.The conversation explores how MST enables organizations to replicate snapshots to S3-compatible storage providers like AWS S3, Azure Blob Storage, Google Cloud, Wasabi, Backblaze, and Nutanix Objects to improve resiliency, optimize storage costs, and simplify long-term retention.The team also discusses:Nutanix Instant Restore in NCI 7.5.1Faster VM recovery and improved availabilityRansomware and clean room recovery strategiesPilot light vs. zero compute DR modelsHybrid cloud resiliencyLong-term snapshot retentionBalancing recovery objectives with budget realitiesWhether you are building a modern DR strategy or evaluating new approaches to cyber resilience, this episode provides practical insight into how Nutanix MST helps organizations stay available when it matters most.

S3-014 停滞する恋愛を変える！人生を好転させる習慣と行動の秘訣 「恋愛のヒント」パーソナリティのトーマスが語るPODCAST制作秘話から、人生を豊かにするヒントが満載。一見、恋愛とは無関係に見えるM&Aや健康管理の話題も、実は「行動すること」や「習慣化」の大切さを教えてくれます。恋愛がなかなかうまくいかない、新しい出会いが見つからない30代・40代のあなたへ。今の自分を変える一歩を踏み出すことで、やがて恋にもポジティブな変化が訪れるかもしれません。リスナー同士の交流や番組へのフィードバックも、新たな繋がりを築くきっかけに。あなたの人生と恋愛を好転させるためのマインドセットをぜひ見つけてください。 ◆━━━━━━━━━━━━━━━━━━━━◆ Discordへのご参加お待ちしています。 トーマスや、ムギちゃん、ひのりほと交流して欲しいのです。Discordの盛り上げに協力して！お願い！ 覗いてみる /https://discord.gg/nxq7UADbtn ◆━━━━━━━━━━━━━━━━━━━━◆ ひのりほのLINE公式アカウントにも登録！ ひのりほが運営するLINE公式アカウントも特典たくさん。 今ずぐ登録する /https://saruwaka.jp/s/1543/1WRY5Q43mAbm/93838783 ◆━━━━━━━━━━━━━━━━━━━━◆ 番組のフォローをお願いいたします|恋愛のヒント ポッドキャストアプリで番組を「フォロー（登録）」すると、最新のエピソードが公開された瞬間に通知が届きます。 Spotify、Apple Podcasts、Amazon Music、YouTubeなど、使い慣れたアプリで『恋愛のヒント』をお聴きください。隙間時間や寝る前の時間が、学びの時間に変わります。以下のボタンから各アプリに飛ぶことができます。 ◆━━━━━━━━━━━━━━━━━━━━◆ 今週のハイライト 人生を豊かにするPODCASTの力 恋にも繋がるM&A的思考とは 行動と習慣が未来を創る 健康管理が恋愛にもたらす変化 恋愛における「継続力」の真髄 自己肯定感を高める食の記録術 新しい出会いを呼ぶ交流の場 恋愛成就に必須の「諦めない」心 恋愛がうまくいかない時の処方箋 自信に繋がる新しい挑戦のヒント ◆━━━━━━━━━━━━━━━━━━━━◆ Chapters 00:00 – オープニング 00:30 – PODCASTプロデューサー・トーマスの日常 01:06 – 恋活中なら聴くべき？M&Aサバイバル 05:07 – 健康で恋活！踊る内臓マニアの魅力 07:07 – SpotifyとDiscordで恋活を加速させよう！ 11:00 – 人生と恋愛に共通する「継続」の重要性 12:35 – アスケンと食事記録！自己管理が恋を呼ぶ 14:15 – 頑張るあなたへ！恋愛も人生も「継続」がカギ 15:30 – Discordへお悩み相談！ 16:16 – エンディング ◆━━━━━━━━━━━━━━━━━━━━◆ コミュニケーション ❶Discordに参加する 番組を運営するLifebloom.funが開設しているDiscordへご参加をお待ちしております。番組リスナーだけのチャンネルを覗いてみてください！ https://discord.gg/nxq7UADbtn ❷お便り募集 番組へのお便りは以下のお便りフォームまたは、renai@lifebloom.funまでメールにてお寄せください。 https://tmsk.jp/renai/letter ❸レビューとコメント お聴きのPODCASTアプリで、番組の評価やコメントをお願いいたします。あなたの評価やコメントで、より良い番組へと育って参ります。お気軽に思いのままの評価をいただきましたら幸いです。 Spotifyでのレビューの書き方解説を見る /https://lifebloom.fun/spotify-reviews/ ❹番組のシェア あなたのご家族や友人、同僚や先輩後輩など、番組に共感をいただけそうな方に向けて、番組のシェアをお願いいたします。SNSで「#恋愛のヒント」をつけての投稿もお待ちしております。 以下のリンクもシェア /https://tmsk.jp/renai/podcastpage ◆━━━━━━━━━━━━━━━━━━━━◆ 今回も最後までお聴きいただきありがとうございました。この番組は、人生に花を咲かせるPODCAST番組をお届けするLifebloom.funの制作でお送りいたしました。

Find out why the world's largest banks and enterprises trust CockroachDB for mission-critical infrastructure, and what a decade of AWS partnership means for the future of cloud-native data.Topics Include:Cockroach Labs makes CockroachDB, a distributed SQL database built for resilience.It delivers cloud-native consistency that legacy relational databases simply cannot match.The name "cockroach" reflects survivability — it's designed to never go down.Target customers include major banks, trading platforms, retailers, and gaming companies.AI is forcing enterprises to accelerate database modernization from the board level down.AWS has been a foundational cloud partner for Cockroach Labs for a decade.The CockroachDB-AWS integration spans EC2, S3, Bedrock, and Amazon Q-Transform.AWS partnership shapes both product roadmap decisions and go-to-market execution.New partners should educate themselves first — AWS programs are deep and extensive.CockroachDB now supports native vector search for RAG and generative AI applications.Agentic AI could mean trillions of digital agents demanding real-time data infrastructure.Database modernization and AI adoption will only accelerate dramatically through 2027.Participants:Cassie Zimmerman – Senior Director, Global Strategic Partnerships, Cockroach LabsSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

We take a deep dive into Amazon S3 Files, AWS's exciting new managed file system backed by S3! We kick things off by exploring why S3 isn't a traditional file system, covering everything from the lack of true directories and atomic renames to immutable objects and POSIX access control differences. We then walk through the existing solutions people have used to bridge that gap, like S3FS FUSE, MountPoint for S3, FSx for Lustre, and Storage Gateway. From there, we get into the heart of the episode: how S3 Files works, how to set it up, and how it uses EFS under the hood as a caching layer. We share our own real-world benchmarking results comparing S3 Files against various EFS configurations across Lambda and Fargate, and we discuss a real customer project where we put S3 Files to the test. We also cover the important caveats like eventual consistency, the 60-second write-back delay, the lack of cross-account bucket support, and the cost model so you can make an informed decision.Resources mentionedEpisode 124: S3 PerformanceEpisode 95: Mounting S3 as a FilesystemAmazon S3 FAQs: S3 FilesfourTheorem S3 Files demo code on GitHubAmazon documentation: Understanding how synchronization worksSponsor Thanks to fourTheorem for powering AWS Bites. We help teams build cloud systems that are simple, scalable, and cost effective. Visit fourtheorem.com.Chapters00:00 Introduction: Why S3 is amazing but not a file system, and what S3 Files promises to solve01:47 Why S3 is not a file system: no true directories, immutable objects, no atomic renames, expensive listings, and POSIX differences05:23 Existing solutions for mounting S3 as a file system: S3FS FUSE, Python fsspec, Hadoop S3A, MountPoint, FSx for Lustre, File Cache, and Storage Gateway07:16 How S3 Files works: NFS-based access, EFS caching layer, streaming from S3, and supported compute services like EC2, ECS, EKS, and Lambda09:49 Setting up S3 Files: buckets, file system resources, import and expiration rules, mount targets, access points, VPC requirements, and NFS port configuration13:42 S3 Files performance numbers from AWS documentation: throughput, IOPS, latency figures, and why real-world benchmarking is recommended15:39 Benchmarking S3 Files vs EFS configurations on Lambda and Fargate: small and large file reads and writes, memory/CPU impact, and key findings19:48 Downsides and limitations: NFS only, no hard links, no atomic renames, eventual consistency, the 60-second write-back delay, and large-scale rename performance warnings23:05 Real-world project experience: a SaaS multi-tenant architecture, cross-account bucket limitation discovered, and how the team worked around it27:52 Cost breakdown: EFS-equivalent cache pricing, S3 storage costs, reads from cache vs. S3 directly, and how S3 access tiers still apply29:50 Final recap and take: when S3 Files shines, when to be cautious, mixed access pattern warnings, and an invitation to share your own experiences33:42 ClosingSend us your AWS questions Do you have any AWS questions you would like us to address? Leave a comment here or connect with us on X/Twitter, Bluesky, or LinkedIn: Eóin: Bluesky | LinkedIn Luciano: X/Twitter | Bluesky | LinkedIn

Greg Drexler from Boardriding Maui joins the show for a deep dive into style, simplicity, and the evolution of wind-powered foiling. We get into Greg's roots in windsurfing and kiting, the design philosophy behind BRM, the early days of parawings, why the new S3 became the benchmark, and how stripping gear down to its essence can unlock more freedom on the water. This one is a great look inside the mind of a designer who has helped shape kites, wings, and now parawings with a focus on feel, function, and keeping the stoke alive.

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

In this episode, we welcome Emmy-nominated director Andrew Bernstein. Known for his work on acclaimed series including Mad Men, Ozark, The Americans, The Diplomat, and Fear the Walking Dead, Andrew has built a remarkable career crafting visually dynamic and emotionally grounded stories across film and television. In our conversation, we discuss Tom Clancy's Jack Ryan: Ghost War, his collaboration with writer and star John Krasinski, shooting on location in New York City, and the creative process behind bringing the latest chapter of the iconic franchise to life.The Making Of is presented by AJA:AJA solves IP, sync gen hurdles at NABFrom remote production to monitoring, IP introduces new challenges across productions. Get ahead of them with AJA's latest ST 2110 solutions, including BRIDGE LIVE IP and an upcoming IP25-R firmware update. The company also unveiled a new OG-GEN10 solution bringing its GEN10 Mini-Converter functionality to an openGear format. Find out more.Upcoming Event:ATX TV Festival | May 28–31TV Camp for Grown Ups returns with ATX TV Festival: Season 15 happening in downtown Austin on May 28–31 — and it is packed full of TV goodness.This year's lineup includes Friday Night Lights 20 Year Reunion, a celebration with Phil Rosenthal & Ray Romano for Everybody (Still) Loves Raymond‘s 30th anniversary, HBO's House of the Dragon returns for S3, Apple TV brings us the enigmatic Tatiana Maslany, Jake Johnson & Murray Bartlett for Maximum Pleasure Guaranteed,and the return of Steve Zahn and Rick Gomez. British TV invasion with Jane Austin in Austin courtesy of Britbox, Universal TV celebrates NBC's 100th Anniversary with The Paper, Funny AF and Procedurals, and the Mark Duplass led inaugural Indie TV Pilot Competition.Whether you're a die-hard fan or a TV industry insider, there's a seat at the campfire for you. Badges on sale now — don't miss the weekend where TV people come to celebrate the medium they love most. 20% off Camp, GP, TV Pass with code: atxtvpartnerMAKE — expires 5/20/26. Visit hereThunderbolt 5 Speed. DIY RAID Without Limits.The OWC Express 4M2 Ultra is a next-gen Thunderbolt 5 NVMe enclosure built for serious post workflows. Delivering up to 6622MB/s, it lets you use your own drives to create a high-performance RAID with up to 32TB—and beyond via daisy chaining. Compact, powerful, and scalable for 8K+ and VFX workflows. Available for pre-order now, shipping in late June. Browse hereZEISS LA Event | June 4thJoin ZEISS Camera Lenses and Beers & Cameras LA for a special evening of photography, lenses, lighting and community before we kick off CineGear LA 2026!Whether it was a hand-me-down SLR, a thrift store point-and-shoot, or your grandpa's coveted rangefinder, every filmmaker begins their journey examining light and shape through the viewfinder of a still camera. ZEISS Camera Lenses is honored to have spent the last 130 years supporting the Photography and Filmmaking community on sets in LA and abroad and is thrilled to join Beers and Cameras LA to continue our legacy of community building and image making.The ZEISS and B&C:LA teams invite all LA imagemakers to Arts District Brewing Co on Thursday June 4th from 6:30-9:30 PM for an evening of lens testing, portrait shooting, analog geekery, and so much more, including: Otus ML, ZEISS ZM, Batis, Touit, and Milvus lenses will be available for testing at the ZEISS Lens Bar!A series of beautifully lit portrait bays will be provided by our friends and sponsors Harlowe Lighting! Additional event support provided by our friends at LA Film Lab and The Darkroom! With a special guest appearance by Photographer, Filmmaker, Analog Enthusiast, and YouTuber, Caleb Knueven! (@BadFlashes)RSVP hereMeet the YoloCam S7The YoloCam S7 paired with the included YoloLiv MFT 18mm F1.4 Lens gives creators a complete professional video solution right out of the box — all for just $799. Featuring stunning 4K60FPS video, real-time autofocus, interchangeable lenses, simultaneous HDMI and USB-C output, and seamless integration with YoloBox and YoloLiv workflows, the YoloCam S7 delivers incredible flexibility for livestreaming, content creation, and video production. Whether you're using it as a high-end webcam or a full live production camera, this bundle gives you everything you need to get started. Learn more today by contacting Videoguys at 800-323-2325. Visit here Podcast Rewind:May 2026 - Ep. 133.Advertise in The Making Of:Promote your products or services to 260K film industry pros and content creators reading this newsletter. To explore a partnership, email mvalinsky@me.com Get full access to The Making Of at themakingof.substack.com/subscribe

The culture is obsessed with speed, scale, lean teams, massive output, and automation, but faster output does not automatically mean better direction. In part two of this connected conversation, the focus shifts to the missing human layer underneath AI-powered growth and the belief that more content, more automation, and more velocity always lead to better outcomes. Using S³ Growth Streams™ as the operating lens, you will learn how to strategize before accelerating, synergize before scaling, and systemize before sprinting forever. More importantly, you will see how to separate signal from noise, align tools with real human capacity, and build repeatable rhythms that support growth without requiring constant sacrifice. This episode is not anti-AI, anti-hustle, or anti-ambition. It is anti-default sacrifice, anti-permanent sprint, and anti-output without awareness. It also challenges the obsession with flow state by introducing five operating states that support sustainable growth: capture, clarity, commitment, flow, and reflection. Because flow is not the whole game. Sometimes the most productive thing you can do is capture the signal, get clarity, commit to the right action, recover, reflect, and return to baseline before sprinting again. The goal is not to become more machine-like. The goal is to become more intentionally human while using machines wisely. Beyond The Episode Gems: Buy My Book, Strategize Up: The Blueprint To Scale Your Business: StrategizeUpBook.com Discover All Podcasts On The HubSpot Podcast Network Get Free HubSpot Marketing Tools To Help You Grow Your Business Grow Your Business Faster Using HubSpot's CRM Platform Support The Podcast & Connect With Troy:  Rate & Review iDigress: iDigress.fm/Reviews Follow Troy's Socials @FindTroy: LinkedIn, Instagram, Threads, TikTok Subscribe to Troy's YouTube Channel For Strategy Videos & See Masterclass Episodes Need Growth Strategy, A Keynote Speaker, Or Want To Sponsor The Podcast? Go To FindTroy.com

In this episode, we welcome cinematographer Armando Salas, ASC. Armando has shot acclaimed projects including Ozark, Griselda, Cocaine Cowboys, The Terminal List, Mr. Mercedes, Lanterns, and Normal. In our conversation, he shares his upbringing, path into cinematography, experiences working across various projects, and all about the making of Normal. He also offers invaluable advice for the next generation of cinematographers and filmmakers today.“The Making Of” is presented by AJA:AJA solves IP, sync gen hurdles at NABFrom remote production to monitoring, IP introduces new challenges across productions. Get ahead of them with AJA's latest ST 2110 solutions, including BRIDGE LIVE IP and an upcoming IP25-R firmware update. The company also unveiled a new OG-GEN10 solution bringing its GEN10 Mini-Converter functionality to an openGear format. Find out more.Upcoming Event: ATX TV Festival | May 28–31TV Camp for Grown Ups returns with ATX TV Festival: Season 15 happening in downtown Austin on May 28–31 — and it is packed full of TV goodness.This year's lineup includes Friday Night Lights 20 Year Reunion, a celebration with Phil Rosenthal & Ray Romano for Everybody (Still) Loves Raymond‘s 30th anniversary, HBO's House of the Dragon returns for S3, Apple TV brings us the enigmatic Tatiana Maslany, Jake Johnson & Murray Bartlett for Maximum Pleasure Guaranteed, and the return of Steve Zahn and Rick Gomez. British TV invasion with Jane Austin in Austin courtesy of Britbox, Universal TV celebrates NBC's 100th Anniversary with The Paper, Funny AF and Procedurals, and the Mark Duplass led inaugural Indie TV Pilot Competition. Whether you're a die-hard fan or a TV industry insider, there's a seat at the campfire for you. Badges on sale now — don't miss the weekend where TV people come to celebrate the medium they love most. 20% off Camp, GP, TV Pass with code: atxtvpartnerMAKE — expires 5/20/26. Visit here Thunderbolt 5 Speed. DIY RAID Without Limits.The OWC Express 4M2 Ultra is a next-gen Thunderbolt 5 NVMe enclosure built for serious post workflows. Delivering up to 6622MB/s, it lets you use your own drives to create a high-performance RAID with up to 32TB—and beyond via daisy chaining. Compact, powerful, and scalable for 8K+ and VFX workflows. Available for pre-order now, shipping in late June. Browse hereZEISS CinCraft LensCore: Cinema Lens Looks for CompositingZEISS announces the launch of CinCraft LensCore, a novel solution for creating physically based cinematic lens looks for visual effects and animation. Built on the Virtual Lens Technology introduced at FMX in 2025, this new Nuke plugin brings decades of ZEISS optical expertise into post-production, bridging the gap between on-set lens choices and the VFX pipeline. Visit hereMeet the Elgato Prompter XLThe Elgato Prompter XL is an all-in-one studio teleprompter designed for larger, more professional setups, featuring a 15.6” removable display and an extended reading range of up to 15 feet so talent can stay on script even at a distance. With ultra-wide compatibility, simple USB 3.2 connectivity, and included software, it easily integrates into multi-camera productions, live shows, and content creation workflows—while also doubling as a secondary monitor between shoots. Available at Videoguys.com, call us at 800-323-2325 for more information. Browse herePodcast Rewind:May 2026 - Ep. 132.Advertise in The Making Of:Promote your products or services to 250,000 film and TV industry pros reading this newsletter each week. To explore a partnership, please email mvalinsky@me.com Get full access to The Making Of at themakingof.substack.com/subscribe

This clip is from S3 of our BibleLine LIVE show. S4 will begin June 7th 8PM Sunday EST. HOW TO HAVE ETERNAL LIFE : https://www.youtube.com/watch?v=vX6NdGnm_vASUBSCRIBE https://www.youtube.com/c/biblelineLIKE https://www.facebook.com/biblelineminCOMMENT ask us a question!SHARE with all your friends and familyHave a Bible question? The questions@biblelineministries.org email address is not longer in use, but you can:- Explore Pastor Jesse's full teaching library: https://www.youtube.com/@BibleLine/playlists- Watch a clear gospel presentation: https://www.youtube.com/watch?v=vX6NdGnm_vA- Ask your question live on air during our YouTube call-in show:https://www.youtube.com/playlist?list=PLElaVGv3oAZ6Y9q4uV9TOX5PMEYimFXqgSupport Bibleline - https://www.calvaryoftampa.org/donate/Bibleline is a ministry of Calvary Community Church in Tampa, Florida and is hosted by Pastor Jesse Martinez.LIKE THIS? CHECK THESE GUYS OUT:@Northlandchurchstc@YankeeArnoldMinistries@focusevangelisticministriesinc@TheKeesBoerMinistryChannel@FishersWithFaithMinistries@QuentinRoad@NorthsideChurchAthens@C4CApologetics@OnoDiamante#bibleline #salvation #homosexual #homosexuality #condemnation #condemned #heaven #hell #evanglism #gospel #truth #real

S3.74 - The Chicago Table - Faebane by Wererat Studios

Steve finally fixed phillycocoa.org, and the journey from broken CircleCI pipelines and hijacked S3 buckets to a blazing-fast Cloudflare Pages site took one Side Project Saturday and an embarrassing number of Codex tokens. Then The Trio turns to the AI hype machine, and they're tired: tired of opaque token costs, tired of reviewing generated code that complicates everything it touches, and tired of an industry that mistakes syntax speed for software engineering. Fred Brooks called it in 1986, and The Trio is calling it now.## Chapters00:00 Introductions01:47 The Journey of Updating the Website06:38 Challenges with CircleCI and S3 Buckets09:23 Exploring Cloudflare Pages11:14 Navigating Cloudflare's User Interface14:22 Setting Up Automatic Deployments17:35 Managing DNS and SSL with Cloudflare23:07 LLM Development Fatigue26:15 Navigating Concerns and Costs in AI Usage29:11 LLMs are No Silver Bullet31:57 The Exhaustion of Code Review and Architectural Decisions36:25 Token Management and Cost Awareness in AI Tools40:07 The Economics of AI and Software Development42:45 The Hype vs. Reality of AI Tools46:34 Future Prospects of LLMs and Universal UI50:16 The Future of Edge Computing with LLMs53:08 The Evolution of Software Development and AI Integration54:17 AI in Sci-Fi: Myths vs. Reality57:54 The Challenges of Local Models and Hardware Limitations01:03:21 Outro & Upcoming Event01:09:21 Tag## Show Notes- Steve spent Side Project Saturday migrating phillycocoa.org from a broken CircleCI/S3 setup to Cloudflare Pages, burning his entire weekly Codex token budget in about three hours.- Cloudflare Pages handles Hugo builds automatically and manages SSL and CDN without manual config, all on a free tier that's plenty for the site.- Cloudflare's UI hides the Pages "Get Started" link below giant worker buttons, which Kotaro calls "the weirdest dark pattern."- Steve argues that syntax generation was never the real bottleneck in software engineering, citing Fred Brooks' 1986 essay "No Silver Bullet."- Aaron is worn out from reviewing AI-generated code and still having to make every architectural decision himself.- LLM costs are nearly impossible to forecast: a single prompt can burn a significant chunk of your plan, depending on model, tool calls, and context.- The Trio sees firms rushing to adopt LLM tooling before the ROI math makes sense, driven by hype rather than evidence.- ThePrimeagen's recent take on the shifting AI economy lines up with what Steve sees at work: token-based billing is starting to expose the real cost.- The Trio agrees local models running on personal hardware are the interesting long-term play, but RAM shortages make even basic setups expensive.- Kotaro closes with a dad joke: he thought his LLM skills landed him his current job, but it turns out...## Links**PhillyCocoa.org Update**Website: https://phillycocoa.org**Articles & Essays**"Let's talk about LLMs" by James Bennett: https://www.b-list.org/weblog/2026/apr/09/llms/"No Silver Bullet" by Fred Brooks: https://www.cs.unc.edu/techreports/86-020.pdf**Videos**"The AI economy is about to change" by ThePrimeagen: https://youtu.be/_Q-e_nczWqM**One More Thing**"Beyond the Simulator: Perspectives on Modern App Development": https://luma.com/i00ll61z**PhillyCocoa:** https://phillycocoa.orgIntro music: "When I Hit the Floor", © 2021 Lorne Behrman. Used with permission of the artist.

Euphoria Season 3 is officially in its wildest era yet and we're diving headfirst into our biggest midseason theories, predictions, and fan conspiracies! On this episode of the Previously On podcast, Jillian and her husband Tyler break down everything we think is happening in Season 3. Could Nate Jacobs survive this season after all? What's Rue's fate? Why does Laurie keep watching episodes of Have Gun–Will Travel… and what does it reveal about where this season is headed? Tyler does a full deep-dive investigation into the exact episodes being referenced and how they may spoil the ending of the show.We also discuss theories about Lexi secretly writing the entire season as a screenplay, Cassie spiraling into Hollywood chaos on LA Nights, Rue's religious journey through her celebrity-filled Bible audiobook, and whether someone is about to rob Wayne's safe. Plus: Nate becoming his father, one wild face-swapping plastic surgery theory, Wizard of Oz symbolism at the Silver Slipper, and the internet's most unhinged Reddit predictions.Whether our predictions end up genius-level correct or completely crash and burn by the finale, we're having way too much fun trying to figure out where this chaotic season is headed.00:00:00 Intro to Pod00:02:19 Is this the final season of Euphoria?00:06:55 Jillian and Tyler's Theories00:07:06 Will Rue or Nate Jacobs die?00:13:43 Cassie gets LA Nights and hooks up with Dylan Reid00:18:35 Have Gun – Will Travel Theory00:29:57 S3 is a fictional Western Lexi's writing00:33:23 The Word of Promise Audio Bible00:39:12 Black Gunn (1972) and Alamo00:42:13 The Silver Slipper club and the Wizard of Oz00:47:12 Jules' plastic surgeon sugar daddy changes Nate or Rue's face00:48:46 Angel is coming back to help Rue00:50:06 Bishop is an undercover cop00:52:56 Cassie gets sold00:59:24 Magick (Rosalía) is an informantThank you to Matt Buechele (@mattbooshell) for creating our new theme song. You can listen to "Sunscreen" on Spotify: https://open.spotify.com/artist/1gFHHF3QyQxjbbKXV3qLu9Buy our merch: ⁠https://www.etsy.com/shop/PreviouslyOnTeenTV⁠Follow Previously On Teen TV on Instagram: ⁠⁠https://www.instagram.com/previouslyon_teentv/Follow Previously On Teen TV on TikTok: ⁠⁠https://www.tiktok.com/@previouslyon_teentv⁠⁠Subscribe to our YouTube: ⁠⁠https://www.youtube.com/channel/UCe2lgvvZGKMrQ8v24FmDdWQ?sub_confirmation=1⁠

This week, host Jorden Guth is joined by SoundStage! founder Doug Schneider and SoundStage! Access editor Dennis Burger to discuss the remarkable technology behind JL Audio's new fully active, DSP-optimized Primacy loudspeakers, including the three-way T6 tower and two-way T3 standmount model. Do these highly integrated speakers point toward the future of high-end audio, or are they a radical detour from traditional hi-fi thinking? Along with debating the concept itself, the group also digs into Doug's early listening impressions and what makes these speakers so different from most of today's high-end designs. Sources: “JL Audio's First Hi-Fi Speakers Rethink Stereo—Primacy T6 and S3” by SoundStage Network: https://youtu.be/m2yuMHwr8TQ?si=rpIeewakT7ulK7Ja Chapters: 00:00:00 Announcement 00:00:36 Introductions 00:03:06 The rumours of JL Audio's demise… 00:16:47 A radical approach? 00:23:08 We can't show, but we can tell 00:26:08 Yeah, they cost that much! 00:27:40 The real future of real high-end? 00:32:48 Achille's heel or nah? 00:33:45 OK, but do they even sound good? 00:42:28 Outro music: "Flying Above the Sun" by Yehezkel Raz feat. Sivan Talmor

Today I'm joined by Hagen Bach, star of Vanderpump Villa, to talk about his experience S3 and catch us up on things that we missed. This episode we talk Hagen's promotion to Butler, how he spent his $50k, where him and Hannah stand today, and what his experience of being the only openly Queer cast member was like this season. After you finish this episode, make sure to follow Hagen on all socials! Follow Hagen on Instagram: https://www.instagram.com/hagen_bach/Follow Hagen on TikTok: https://www.tiktok.com/@hagen_bachFollow Positively Uncensored on Instagram: https://www.instagram.com/positivelyuncensored/Follow Positively Uncensored on TikTok: https://www.tiktok.com/@positivelyuncensored?lang=en

S2 Finale｜你不需要更多改變，你需要的是整合 為什麼「接下來的一年」，你不會再用舊方式活著▷ 本集定位｜這一集寫給誰如果你最近開始出現這些感覺——你沒有更激進，但你更清楚：有些活法回不去了你不再想用消耗換安全，不再想用撐住證明價值你對「再調整自己一點」開始感到疲乏，甚至覺得哪裡不對勁你感覺自己站在一個門口：不是要更努力，而是要換一套活法那這一集就是寫給你的。這一集不是教新工具、不是加新方法。Season Finale 的意義只有一個：

Join Dave and Wayne for genre television show news, a glimpse into what the hosts are watching, listener feedback, and analysis of the HBO series Station Eleven. This week on the SciFi TV Rewatch podcast we discuss the series' finale in which several characters receive redemptive treatments, and our protagonist finally feels able to let her guard down twenty years after her heroic journey began.  In our What We're Watching segment, Dave returned to Netflix for S3 of The Law According to Lidia Poet, and Wayne watched Star Wars: Maul-Shadow Lord. In Listener Feedback, Fred from the Netherlands, Alan in Missouri and Alan in England provide audio feedback, and Cincinnati Joe checks in via email.  Remember to join the genre television and film discussion on the SciFi TV Rewatch Facebook group for the latest genre television show news and podcast releases. Episode Grade: Dave 9.8  Wayne  8.6 Series Grade: Dave 9.2  Wayne 9.0

The episode explores why modern databases keep reinventing the same distributed-systems machinery and argues that a major part of database cost is the operational tax of running replication-heavy systems. Our guest, Almog Gavra, co-founder of Responsive, explains how his team pivoted from operating Kafka Streams as a service to building SlateDB and the “Open Data” manifesto: an object-storage-native LSM foundation that can power multiple database types (vector, time series, logs, key-value) with shared tuning knobs and failure modes. They discuss why distributed-systems complexity is often harder than query engines, how LSM trees provide a tunable tradeoff between read/write/space amplification, caching layers and cost transparency, separating readers/writers, stateless ingest, single-writer availability and fencing via S3 compare-and-set, offloading compaction, and how the architecture enables near-free snapshots. They also cover when this approach doesn't fit: OLTP that can stay on Postgres and ultra-low-latency workloads where cold object-store misses are unacceptable.Chapters:00:00 Introduction08:36 Open Data Manifesto18:34 Specialized vs General25:10 SlateDB Architecture32:51 LSM Trees as Tuning Dial38:58 Tuning Without Overload39:46 Cost Aware Config Knobs41:51 Latency Cost Durability Tradeoffs46:46 Caching Strategies And Layers50:23 Split Readers And Writers52:43 Single Writer Versus Multi Writer55:16 Scaling And Partitioning Writes58:58 Failure Modes And Fencing01:05:23 Compaction As Separate Worker01:09:28 Snapshots And Garbage Collection01:10:25 When Open Data Is Not FitImportant links and references:OpenData: http://github.com/opendata-oss/opendataOpenData manifesto: https://www.opendata.dev/blog/manifestoReach out to Almog: https://www.linkedin.com/in/agavra/ or https://x.com/almoggavraDostovesky paper on LSM: https://nivdayan.github.io/dostoevsky.pdfLatency/Cost/Durability Triad: https://materializedview.io/p/cloud-storage-triad-latency-cost-durabilitySlateDB: https://github.com/slatedb/slatedb"how SSTs work": https://www.bitsxpages.com/p/sorted-string-tables-sst-from-firstFor memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!

For the first time in what feels like ages all of The FullyGeeked Boys are back together for Episode 327. With Merv fresh back from his holiday we were eager to understand if he let his hair down in the paradise that is St Lucia? With Boys S5 being the end of the Boys series and it being announced that Gen V will not be returning for S3 we discuss what that means or was this always the case? Someone got arrested for leaking Avatar Ang Animated Movie and is potentially facing 7 yrs in prison and/or $50K fine - is it that serious?? (16:00) Trailer/Teaser of the week Clayface (#WarnerBrosPictures). Before we look into what's been hot over the past few weeks in TV and Films: The Boys S5E4 (⁠⁠⁠⁠⁠⁠#PrimeVideo⁠⁠⁠⁠) (⁠⁠⁠⁠⁠⁠20:25⁠⁠⁠⁠⁠⁠), Invincible S4 E8 (⁠⁠⁠⁠⁠⁠⁠#PrimeVideo⁠⁠⁠⁠⁠) (⁠⁠⁠⁠⁠⁠⁠28:04⁠⁠⁠⁠⁠⁠⁠), Daredevil: Born Again S2 E6 (⁠⁠⁠⁠⁠⁠⁠#DisneyPlus⁠⁠⁠⁠) (⁠⁠⁠⁠⁠⁠⁠42:30⁠⁠⁠⁠⁠), Michael (⁠⁠⁠⁠⁠⁠⁠#UniversalPictures) (⁠⁠⁠⁠⁠⁠⁠51:55⁠⁠⁠⁠⁠⁠⁠), among many more.......⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠#Podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠#TheFullyGeekedPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠#Films⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠#TV⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠#Review⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠#GuysThatPodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠#Like⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠#Movies⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠#Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠#Comment⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠#Subscribe⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠#Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠#FYP⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠#PodcastReady⁠⁠⁠⁠

We're back on a roll baby, and there's lots to share! Heroes eating good on this one, and I get a great question about the S3 release date! —--------------------- Want more 7th Valkyrie? Check out our Patreon to become a Hero of Edara, where you can shape the future of the series, decide on merch drops and incentives, get early access to new episodes, enjoy bonus features and content, and help us hit the major checkpoints on the Path of Heroes!  https://www.patreon.com/7thvalkyrie  

S3.73 - The Chicago Table - Witches Rise by Wererat Studios

為什麼世界開始用不同方式回應你？當你走到這個人生階段，穩定本身就會改變整個場域The Field Effect｜Presence-Based Feminine Leadership & Embodied Stability▷ 本集定位｜這一集寫給誰如果你最近發現——世界變得比較安靜，但不是退場有些合作自然淡掉，有些機會卻不用爭取就出現人們開始等你說完話，而不是急著接話你不再被默認為「一定會補位、一定會配合的人」你可能會困惑：我沒有刻意改變什麼，但整個場域好像不一樣了你不是變得比較強，也不是進入某種境界，你只是走到一個階段：穩定本身，開始影響世界▷ 本集核心洞見這一集要談的，不是吸引力，也不是顯化 ; 而是一個很實際、卻很少被說清楚的現象：

В этом выпуске: пробуем 3D-принтер Snapmaker U1 и Wi-Fi роутер GL.iNet GL-MT3000; переписываем etcd так, чтобы он работал поверх S3; пытаемся понять, какая связь между марксизмом и OpenAI; а также другие темы. [00:01:40] Чему мы научились за неделю https://redis.io/docs/latest/operate/oss_and_stack/reference/cluster-spec/ Snapmaker U1 Color 3D Printer — Snapmaker Global [00:27:36] t4db/t4: S3-durable key-value storage with etcd v3… Читать далее →

I'm watching this season of Vanderpump Villa so you don't have to and filling you in on all of Lisa Vanderpump and her staff's shenanigans. The most interesting thing I learned while researching this episode is that there is no Rosecroft Park, the location they filmed at for S3 is actually called Tyringham Hall....why did they change it? Who knows??? But that makes the ceiling fiasco 10x worse when I saw it really is one of England's oldest buildings lmao. I'll be back when I finish the season and again after the reunion which premieres 4/29!

S3.72 - The Chicago Table - The Fungus Among Us - A 420 Smoketacular by Wererat Studios

Joanna Robinson and Rob Mahoney reinvent themselves to recap the ‘Euphoria' Season 3 premiere. (0:00) Intro (1:57) Is this the final season of ‘Euphoria'? (5:26) Previously on… (28:08) Reactions to the S3 premiere (31:55) Rue's reintroduction (38:23) Lexi, Maddy, and L.A. Nights (43:58) Cassie and Nate (49:48) The Bible audiobook (54:00) Alamo Brown and his entourage (58:15) We need more Jules this season Email us! prestigetv@spotify.com Follow us on IG and TikTok! Call (909) 313-4046 for a chance to receive a personalized TV rec! Subscribe to the Ringer TV YouTube channel here for full episodes of ‘The Prestige TV Podcast' and so much more! Hosts: Joanna Robinson and Rob Mahoney Producer: Kai Grady and Devon Renaldo Additional Production Support: Justin Sayles Learn more about your ad choices. Visit podcastchoices.com/adchoices

AWS Morning Brief for the week of April, 13th with Corey Quinn. Links:AWS Certificate Manager now supports native certificate searchAmazon S3 Lifecycle pauses actions on objects that are unable to replicateAmazon Bedrock now offers Claude Mythos Preview (Gated Research Preview)Amazon OpenSearch Serverless now supports Zstandard (zstd) codec for index compressionAWS Secrets Manager console now supports custom input for AWS KMS keysAmazon Bedrock now supports cost allocation by IAM user and roleAmazon S3 starts rolling out new security best practice to new and existing buckets by defaultIntroducing AI-Powered Cost Analysis in AWS Cost ExplorerLaunching S3 Files, making S3 buckets accessible as file systemsThe future of managing agents at scale: AWS Agent Registry now in previewUnderstanding Amazon Bedrock model lifecycleIntroducing OpenTelemetry & PromQL support in Amazon CloudWatch

#345: Vibe coding works fine until your project gets complicated. That's the gap Amit Patel and his team at AWS built Kiro to fill. The tool launched with about six people in mid-2024, hit GA around October 2025, and the team still fits in a single room -- maybe a seven-pizza team by Darin's math. The core idea is spec-driven development, but not the kind where business analysts disappear for five years and come back with a document nobody needs anymore. Amit's version: you tell the agent what you want in a chat prompt, it writes the spec for you, and you iterate on it. Twenty minutes of back and forth and you've got requirements, a design, and a task breakdown. Then the agent executes. Two to three days later, working software. Here's where it gets interesting. Amit frames the human role as bookends. At the front, you define intent -- what needs to exist and why. At the back, you verify that what got built actually matches. Everything in the middle? That's where the tooling lives. And that middle is getting wider every month as agents run longer, handle more turns, and start working in parallel. But the gap between 'I can build it' and 'I built it right' is real. Amit's S3 example nails it. Ask an LLM to build a file upload app and you'll get one that works. Encryption at rest, encryption in transit, KMS, bucket policies -- none of that shows up unless you know to ask for it. The LLM will generate all of it on request. It just won't volunteer it. That's the experience gap, and it's why junior developers still need to become senior developers the old-fashioned way. One story that landed: a product manager on Amit's team used Kiro to go from conversation to working prototype overnight. Not a wireframe. Not a doc. A demo the engineering team could put into production. The roles aren't disappearing -- they're getting more fluid. The value was never in the writing. It was always in knowing what needed to be built. Kiro is now widely adopted inside AWS, with both an IDE and a CLI. Where it's headed next: agents that run in the background, handle multiple tasks at once, and get verified with formal methods instead of just hoping the output is right. But Amit's honest about the limits -- steering file adherence is, in his words, an art in itself. Non-deterministic LLMs will ignore your rules sometimes. Just like humans.   Amit's contact information: LinkedIn: https://www.linkedin.com/in/amit-patel-040453/   YouTube channel: https://youtube.com/devopsparadox   Review the podcast on Apple Podcasts: https://www.devopsparadox.com/review-podcast/   Slack: https://www.devopsparadox.com/slack/   Connect with us at: https://www.devopsparadox.com/contact/

S3.71 - The Chicago Table - Of Men and Demons by Wererat Studios

Jason Howell and Jeff Jarvis break down Anthropic's locked-down Mythos cybersecurity AI, OpenAI's New Deal-style economic policy vision, OpenAI's controversial podcast acquisition, the dueling takes on Google AI Overviews accuracy, a vibe-coded startup hitting $401 million in year one, and a speed round covering Broadcom's compute deal, Amazon's AI-era S3 update, Android XR spatial features, and Netflix's VOID video model. Note: Time codes subject to change depending on dynamic ad insertion by the distributor. CHAPTERS: 0:00 - Start 0:01:00 - Anthropic Claims Its New A.I. Model, Mythos, Is a Cybersecurity ‘Reckoning' 0:18:34 - OpenAI's Industrial Policy for the Intelligence Age 0:19:03 - What to Know About OpenAI's Ideas for a World With ‘Superintelligence' 0:27:49 - OpenAI isn't just buying a podcast — it's buying influence 0:30:44 - Why OpenAI's Purchase of a Big Tech Podcast Is So Sleazy 0:40:39 - Google's AI Overviews are correct nine out of ten times, study finds 0:41:46 - Testing suggests Google's AI Overviews tell millions of lies per hour 0:45:34 - How A.I. Helped One Man (and His Brother) Build a $1.8 Billion Company 0:52:15 - Anthropic expands partnership with Google and Broadcom for multiple gigawatts of next-generation compute 0:52:25 - Broadcom agrees to expanded chip deals with Google, Anthropic 0:53:20 - Amazon revamps S3 cloud storage for the AI era, removing a key barrier for apps and agents 0:54:18 - 5 new features for Android XR 0:57:14 - Netflix - yes Netflix - jumps on the AI bandwagon with video editor Hosts: Jason Howell and Jeff Jarvis Download and subscribe to AI Inside in audio and video: https://aiinside.show/ Support the podcast on Patreon for special perks: https/www.patreon.com/aiinsideshow You'll get ad-free episodes, members-only Discord, T-shirts and stickers you love, and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Learn more about your ad choices. Visit megaphone.fm/adchoices

S3.70 - The Chicago Table - Be Our Guest by Wererat Studios

At RSAC Conference 2026, Anthony Cusimano, Chief Evangelist and Director of Solutions Marketing at Object First, joins Sean Martin on the show floor to break down what separates truly immutable storage from the checkbox version. The answer comes down to zero access: no command line interface, no root access, no administrative back doors at any layer -- for customers or for Object First itself. Object First appliances are purpose-built for Veeam and ship with S3 protocol storage in automatic compliance mode, versioning, and object lock. Once data is written and a retention period is set, nothing -- no admin, no attacker, not even the vendor -- can touch it. Cusimano describes the architecture as a storage utility, not an administration platform: Veeam handles all backup policy and configuration; Object First handles one thing only, ensuring the data cannot be erased. The statistics behind the design are sobering. According to Cusimano, 96 percent of ransomware attacks specifically target backup data -- a figure validated across four independent industry surveys. Organizations that rely on encryption alone, without immutable storage, are leaving a critical gap that attackers have learned to exploit. Many do not discover that gap until recovery is already underway. Cusimano also makes the case for recovery testing as a security priority in its own right. He recommends full tabletop exercises that assume worst-case conditions: every admin credential compromised, active directory gone. Teams that run through this process discover gaps in their architecture that no amount of vendor documentation will surface. His practical tip -- collect coworkers' cell phone numbers before an incident -- reflects just how complete the communications blackout can be when directory services fail. Two capabilities from Object First round out the conversation. Fleet Manager, launching May 6th, gives managed service providers and large enterprises a single SaaS dashboard to manage all Object First instances with unified telemetry and honeypot visibility -- with no backup data leaving the appliance. And the honeypot feature, included on every device at no cost, simulates a Veeam backup and replication server as a decoy. When agentic AI-driven attacks probe the environment, they interact with the honeypot exactly as they would a real target, triggering alerts that can surface threats days or weeks before a full attack develops. This is a Brand Spotlight. A Brand Spotlight is a ~15 minute conversation designed to explore the guest, their company, and what makes their approach unique. Learn more: https://www.studioc60.com/creation#spotlight GUEST Anthony Cusimano, Chief Evangelist & Director of Solutions Marketing, Object First LinkedIn: https://www.linkedin.com/in/anthonycusimano89/ RESOURCES Object First website: https://objectfirst.com ITSPmagazine RSAC Conference 2026 coverage: https://www.itspmagazine.com/rsac-2026-conference-san-francisco-usa-cybersecurity-event-infosec-conference-coverage Are you interested in telling your story? ▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full ▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight ▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlight KEYWORDS Anthony Cusimano, Object First, Sean Martin, brand story, brand marketing, marketing podcast, brand spotlight, ransomware, immutable storage, backup security, Veeam, data protection, RSAC Conference 2026, cyber resilience, absolute immutability, ransomware recovery, Fleet Manager, honeypot detection, managed service providers, zero trust storage Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Our look back at Saturday Night Live's original era has come to an end. We wrap up our final episode of The Early Years of SNL, looking back at all five seasons of the original era. We revisit the journey the show took from a trying-it-out-in-real-time “variety” show (S1) to being a truly experimental and edgy platform (S2), exploding into a cultural juggernaut (S3&4), and eventual fall from grace (S5).Looking back, we remember some of our favorite hosts, musical guests, special guests, and pre-tapes. Of course, we also call out what we didn't like. We discuss what surprised us, disappointed us, and highlight our favorite forgotten and underrated sketches (we did a whole episode on best bits).To bring it all to a close, we select our best and worst episodes of the original era. And, we finally answer the question everyone's been asking – “What comes next?” Sort of.Thanks to everyone for listening. “Goodnight, and goodbye.”---------------------------------Subscribe & Follow today!And follow us on social media: Twitter: @NR4PTProject Instagram: @nr4ptprojectBluesky: @nr4ptproject.bsky.socialFacebook: The Not Ready for Prime Time ProjectContact Us: Website: https://www.nr4project.comEmail: nr4ptproject@gmail.com

Chris and Andy talk about the ‘Euphoria' S3 trailer (1:07) and ‘The White Lotus' S4 cast additions of Heather Graham and Rosie Perez (5:42). Next, they talk about the first episode of Netflix's ‘Something Very Bad Is Going to Happen' and why it is an excellent example of longform horror (10:14), before Andy shares his thoughts on ‘Project Hail Mary' (27:40). Later, they are joined by Mina Kimes to unpack the ‘Paradise' Season 2 finale, why she loves the show, what else she's been watching recently, and her take on the Eagles heading into next season (42:00). Drivers wanted. Learn more at http://vw.com Subscribe to the Ringer TV YouTube channel here for full episodes of The Watch and so much more! Hosts: Chris Ryan and Andy Greenwald Guest: Mina Kimes Producers: Kaya McMullen and Kai Grady Additional Video Supervision: Sarah Reddy Learn more about your ad choices. Visit podcastchoices.com/adchoices

At last, we're again joined by novelist Nancy Northcott to discuss the portrayal of women in Batman 66! We pick up near the start of Season Three, with the episodes "The Unkindest Tut of All" and "Louie, the Lilac." Can Tim, Paul, and Nancy survive S3? Have any three people used the word "cringe" more frequently in the space of 45 minutes? How is it that the Bat-season with more female villains and a female hero also seems to be the most sexist? Listen and find out, my dear. (cringe) Listen to Bat Bits by subscribing to our Patreon for at least $2 a month! At $4 a month you'll get that AND our monthly discussion of silver age Batman comics, as Paul or another in our stable of co-hosts joins me to examine individual Batman stories from the 1950s and '60s — most recently, the establishment of the idea that Bruce Wayne once wore the Robin costume! And coming up, Tim and Paul look at another comics story featuring Batwoman! So slide down your Batpole and join today!

This episode of the Pure Report podcast, features Solutions Director Andrew Sillifant where we dive into the implications of Oracle 26ai, focusing on what enterprises must do to prepare for this major long-term support release. Our discussion positions Oracle as part of the database industrial complex, noting its enduring dominance alongside Microsoft SQL Server, together accounting for over 50% of the enterprise market. Oracle 26ai is presented as the latest phase in the database lifecycle—following G for Grid and C for Cloud—which capitalizes on the momentum of artificial intelligence by repositioning the database as a full system. The new version embeds AI vector store capabilities and machine learning models, allowing organizations to combine structured data from legacy systems with unstructured data (like S3 tables) for better context awareness. Our conversation shifts to a look at the complexity and risk of database upgrades, which extends far beyond the DBA team to considerations around capacity planning, application integration, and platform choices. Andrew notes that while Oracle is mature and its upgrade cycle is well-known, infrastructure modernization, including decisions on virtualization and containers, is now taking precedence due to many economic and regulatory forces. The addition of new capabilities in 26ai —including OLTP, analytics, and vector data types means increased storage consumption and introduces new workload patterns that stress the compute layer. Enterprises face decision paralysis when considering the cost and multifaceted nature of these changes, making a simplified, reliable infrastructure foundation critical. We close with a look at how the Everpure platform is an essential risk reduction element in the upgrade process, simplifying the storage layer so it reduces complexity in the upgrade process. Key benefits discussed include de-risking capacity bloat through metadata-only snapshots for development, test, and QA copies, and offering extremely fast recovery speeds, with examples citing a two-node Oracle RAC database restore at 68 terabytes per hour. Non-disruptive risk reduction (NDU) capabilities and the Evergreen service model are emphasized as a significant moat against competitors, providing a low-risk platform that allows teams to pivot their focus to the database upgrade itself, rather than the underlying infrastructure. To learn more, visit: https://www.purestorage.com/solutions/databases/oracle.html Check out the new Everpure digital customer community to join the conversation with peers and Everpure experts: 
https://purecommunity.purestorage.com/ 00:00 Intro and Welcome 02:43 Background on Oracle Database 08:35 Upgrade Considerations 12:17 Maturing Oracle Features 15:16 Database Upgrade Process 21:17 Complex Factors to Consider 25:12 Handling Different Data Types 30:24 Everpure Value for Oracle Operations 36:30 Key Steps for Successful Upgrades

We discuss the diagnosis and treatment of one of EM's paradoxes: High-Output Heart Failure. Hosts: Nicolas Gonzalez, MD Brian Gilberti, MD https://media.blubrry.com/coreem/content.blubrry.com/coreem/HOHF.mp3 Download Leave a Comment Tags: Cardiology Show Notes Core EM Modular CME Course Maximize your commute with the new Core EM Modular CME Course, featuring the most essential content distilled from our top-rated podcast episodes. This course offers 12 audio-based modules packed with pearls! Information and link below.  Course Highlights: Credit: 12.5 AMA PRA Category 1 Credits™ Curriculum: Comprehensive coverage of Core Emergency Medicine,  with 12 modules spanning from Critical Care to Pediatrics. Cost: Free for NYU Learners $250 for Non-NYU Learners Click Here to Register and Begin Module 1 1. Core Definition & Hemodynamic Profile Clinical Paradox: Congestive symptoms (pulmonary edema, JVD, peripheral edema) in the setting of a hyperdynamic, supranormal cardiac function. Hemodynamic Criteria: Cardiac Index (CI): >4.0 L/min/m2. Cardiac Output (CO): >8 L/min. Systemic Vascular Resistance (SVR): Pathologically low (vasodilated or shunted state). The “Warm” Phenotype: Unlike standard HFrEF/HFpEF (often “Cold and Wet”), HOHF presents as “Warm and Wet” due to low SVR and bounding pulses. 2. Pathophysiology: The Hemodynamic Paradox Primary Insult: Decreased SVR (either via peripheral vasodilation or arteriovenous shunting). Effective Arterial Blood Volume: Paradoxically low despite high total CO. Neurohormonal Cascade: Activation of Renin-Angiotensin-Aldosterone System (RAAS). Increased Sympathetic Nervous System tone. Increased Antidiuretic Hormone (ADH) secretion. Resultant State: Avid renal salt and water retention leading to massive plasma volume expansion. Cardiac Response: Chronic volume overload → eccentric remodeling → chamber dilation → eventual secondary myocardial failure/dilated cardiomyopathy. 3. Differential Diagnosis: Etiological “Buckets” Category A: Increased Metabolic Demand (Systemic) Hyperthyroidism/Thyrotoxicosis: Direct T3 effects: increased chronotropy/inotropy. Indirect effects: metabolic byproduct accumulation causing peripheral vasodilation. Myeloproliferative Disorders: High cell turnover and increased oxygen consumption drive compensatory CO increase. Sepsis (Hyperdynamic Phase): Cytokine-mediated global vasodilation. Note: Often transient; may transition to sepsis-induced myocardial depression. Category B: Peripheral Vascular Effects (Shunting/Vasodilation) Arteriovenous Fistulas (AVF) / Malformations (AVM): Most Common Cause: Iatrogenic AVF for Hemodialysis (ESRD population). Bypasses high-resistance capillary beds, dumping arterial blood directly into venous circulation. Chronic Liver Disease (Cirrhosis): Formation of “spider angiomata” and internal AV shunts. Impaired clearance of endogenous vasodilators (e.g., Nitric Oxide). Thiamine Deficiency (Wet Beriberi): Accumulation of pyruvate/lactate → systemic vasodilation. Histopathology: Vacuolation, myofiber hypertrophy, and interstitial edema. Chronic Lung Disease: Hypoxia/Hypercapnia-driven systemic vasodilation. Concomitant pulmonary HTN (RV remodeling) but preserved/high LV output. Others: Paget's disease of bone (extensive micro-shunting), Carcinoid syndrome, Mitochondrial diseases, Acromegaly, Erythroderma. 4. Special Focus: Hemodialysis Access-Induced HOHF Physiologic Phases of AVF Creation: Acute Phase: Immediate ↓ SVR. ↑ Stroke volume and Heart Rate (SNS-mediated). Endothelial shear stress → Nitric Oxide release → further arterial dilation. Subacute Phase (Days to 2 Weeks): RAAS-driven volume expansion. ↑ Right Atrial, Pulmonary Artery, and LV End-Diastolic Pressures (LVEDP). Natriuretic peptide surge (BNP/ANP) peaks around Day 10. Chronic Phase (Weeks to Months): Adaptive hypertrophy. Decompensation occurs when dilation exceeds contractility limits. 5. Point-of-Care Physical Exam & Maneuvers Nicoladoni-Branham Sign (Pathognomonic for Shunt-driven HOHF): Maneuver: Manually compress the AVF (or inflate cuff to >50 mmHg above SBP) for 30 seconds. Positive Result: Reflexive bradycardia or a transient rise in systemic BP. Significance: Confirms the shunt is a major contributor to the cardiac workload. Peripheral Pulse Assessment: Water Hammer Pulses: Rapid upstroke and collapse. Quincke's Pulse: Visible capillary pulsations in the nail beds. Traube's Sign: “Pistol-shot” sounds auscultated over the femoral arteries. Volume Status: Rales, S3 gallop, peripheral edema (standard HF signs). 6. Diagnostic Workup (Technical Targets) POCUS / Echocardiography: Left Ventricle: Hyperdynamic function; EF typically >60%. Left Atrium: Significant dilation (Left Atrial Volume Index >34 mL/m2; Case study noted 72 mL/m2). IVC: Plethoric with minimal respiratory variation. Doppler: High flow velocities across the AV access if applicable. Laboratory Evaluation: BNP/NT-proBNP: Often markedly elevated (e.g., >70,000 in severe cases), though mean values in literature hover around 700–800 pg/mL. Hematology: CBC to evaluate for severe anemia (trigger for HOHF if Hgb7–8 g/dL to reduce demand. Beriberi: High-dose IV Thiamine (100–500 mg). Thyrotoxicosis: Beta-blockers (Propranolol) + Antithyroid meds (PTU/Methimazole). Phase 3: Surgical/Interventional Salvage (Refractory AVF Cases) Closure of Accessory Sites: If multiple fistulas exist, close the non-dominant/unused sites. Flow Reduction (Banding): Surgical narrowing of the fistula to target flow

Welcome back to The Snack – a lighter serving of Girls Gotta Eat. This week, we're talking about: Kristi and Desmond Scott's divorce and scandal Kyle and Amanda from Summer House divorce announcement Brooklyn Beckham airing his family's dirty laundry  Breaking down the 2016 trend and reminiscing  Tell Me Lies – review of S3 so far and the real life couples on the show Headlines: Euphoria trailer breaks records, Indiana University wins football championship, Luda pulls out of MAGA Fest  Follow us on Instagram @girlsgottaeatpodcast, Ashley @ashhess, and Rayna @rayna.greenberg. Visit girlsgottaeat.com for more. Thank you to Shopify: If 2026 is your year, go to shopify.com/gge and make your move. Download the Kitchen Sink app here.