Podcasts about dynamodb

When a major Amazon cloud outage brings everything from smart mattresses to Snapchat grinding to a halt, what does it reveal about our digital fragility—and are we trusting the cloud a little too much? A Single Point of Failure Triggered the Amazon Outage Affecting Million Pluralistic: The mad king's digital killswitch (20 Oct 2025) Trump and Xi will 'consummate' TikTok deal on Thursday, treasury secretary says 3,000 YouTube Videos Exposed as Malware Traps in Massive Ghost Network Operation Can YouTube Replace 'Traditional' TV? All the implications of F1's game-changing TV move Foreign hackers breached a US nuclear weapons plant via SharePoint flaws Browser Promising Privacy Protection Contains Malware-Like Features, Routes Traffic Through China iCloud data helps crack NBA and mob poker scheme Rubbish IT systems cost the US at least $40bn during Covid: study Counter-Strike cosmetics economy loses nearly $2 billion in value overnight GM to introduce eyes-off, hands-off driving system in 2028 WordPress co-founder files countersuit against WP Engine over trademark violations a16z-Backed Startup Sells Thousands of 'Synthetic Influencers' to Manipulate Social Media as a Service Bill Gates-Backed 345 MWe Advanced Nuclear Reactor Secures Crucial US Approval Programmer Gets Doom Running On a Space Satellite Host: Leo Laporte Guests: Richard Campbell and Doc Rock Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: deel.com/twit zapier.com/twit helixsleep.com/twit expressvpn.com/twit zscaler.com/security

10 月 20 號星期一，亞馬遜雲端服務 AWS 的核心區域 us-east-1 爆出一個 Race Condition，導致 DynamoDB 的 DNS 被清空，結果連帶拖垮了 113 項內部與外部服務。從社群平台、交易所、航空公司、政府單位，甚至英超足球聯盟，全都中標。這場十五小時的大當機，不只是 AWS 的災難，更是「雲端集中化」的一次警訊。這集我們就來聊聊：☁️ 為什麼 us-east-1 這麼關鍵？⚙️ Race Condition 到底怎麼讓 DNS 全毀？

When a major Amazon cloud outage brings everything from smart mattresses to Snapchat grinding to a halt, what does it reveal about our digital fragility—and are we trusting the cloud a little too much? A Single Point of Failure Triggered the Amazon Outage Affecting Million Pluralistic: The mad king's digital killswitch (20 Oct 2025) Trump and Xi will 'consummate' TikTok deal on Thursday, treasury secretary says 3,000 YouTube Videos Exposed as Malware Traps in Massive Ghost Network Operation Can YouTube Replace 'Traditional' TV? All the implications of F1's game-changing TV move Foreign hackers breached a US nuclear weapons plant via SharePoint flaws Browser Promising Privacy Protection Contains Malware-Like Features, Routes Traffic Through China iCloud data helps crack NBA and mob poker scheme Rubbish IT systems cost the US at least $40bn during Covid: study Counter-Strike cosmetics economy loses nearly $2 billion in value overnight GM to introduce eyes-off, hands-off driving system in 2028 WordPress co-founder files countersuit against WP Engine over trademark violations a16z-Backed Startup Sells Thousands of 'Synthetic Influencers' to Manipulate Social Media as a Service Bill Gates-Backed 345 MWe Advanced Nuclear Reactor Secures Crucial US Approval Programmer Gets Doom Running On a Space Satellite Host: Leo Laporte Guests: Richard Campbell and Doc Rock Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: deel.com/twit zapier.com/twit helixsleep.com/twit expressvpn.com/twit zscaler.com/security

When a major Amazon cloud outage brings everything from smart mattresses to Snapchat grinding to a halt, what does it reveal about our digital fragility—and are we trusting the cloud a little too much? A Single Point of Failure Triggered the Amazon Outage Affecting Million Pluralistic: The mad king's digital killswitch (20 Oct 2025) Trump and Xi will 'consummate' TikTok deal on Thursday, treasury secretary says 3,000 YouTube Videos Exposed as Malware Traps in Massive Ghost Network Operation Can YouTube Replace 'Traditional' TV? All the implications of F1's game-changing TV move Foreign hackers breached a US nuclear weapons plant via SharePoint flaws Browser Promising Privacy Protection Contains Malware-Like Features, Routes Traffic Through China iCloud data helps crack NBA and mob poker scheme Rubbish IT systems cost the US at least $40bn during Covid: study Counter-Strike cosmetics economy loses nearly $2 billion in value overnight GM to introduce eyes-off, hands-off driving system in 2028 WordPress co-founder files countersuit against WP Engine over trademark violations a16z-Backed Startup Sells Thousands of 'Synthetic Influencers' to Manipulate Social Media as a Service Bill Gates-Backed 345 MWe Advanced Nuclear Reactor Secures Crucial US Approval Programmer Gets Doom Running On a Space Satellite Host: Leo Laporte Guests: Richard Campbell and Doc Rock Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: deel.com/twit zapier.com/twit helixsleep.com/twit expressvpn.com/twit zscaler.com/security

When a major Amazon cloud outage brings everything from smart mattresses to Snapchat grinding to a halt, what does it reveal about our digital fragility—and are we trusting the cloud a little too much? A Single Point of Failure Triggered the Amazon Outage Affecting Million Pluralistic: The mad king's digital killswitch (20 Oct 2025) Trump and Xi will 'consummate' TikTok deal on Thursday, treasury secretary says 3,000 YouTube Videos Exposed as Malware Traps in Massive Ghost Network Operation Can YouTube Replace 'Traditional' TV? All the implications of F1's game-changing TV move Foreign hackers breached a US nuclear weapons plant via SharePoint flaws Browser Promising Privacy Protection Contains Malware-Like Features, Routes Traffic Through China iCloud data helps crack NBA and mob poker scheme Rubbish IT systems cost the US at least $40bn during Covid: study Counter-Strike cosmetics economy loses nearly $2 billion in value overnight GM to introduce eyes-off, hands-off driving system in 2028 WordPress co-founder files countersuit against WP Engine over trademark violations a16z-Backed Startup Sells Thousands of 'Synthetic Influencers' to Manipulate Social Media as a Service Bill Gates-Backed 345 MWe Advanced Nuclear Reactor Secures Crucial US Approval Programmer Gets Doom Running On a Space Satellite Host: Leo Laporte Guests: Richard Campbell and Doc Rock Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: deel.com/twit zapier.com/twit helixsleep.com/twit expressvpn.com/twit zscaler.com/security

AWS Morning Brief for the week of October 27th, with Corey Quinn. Links:Streamline in-place application upgrades with Amazon VPC LatticeBuild a proactive AI cost management system for Amazon Bedrock – Part 2 -Overview and best practices of multithreaded replication in Amazon RDS for MySQL, Amazon RDS for MariaDB, and Amazon Aurora MySQL AWS announces Nitro Enclaves are now available in all AWS RegionsAmazon CloudWatch Synthetics now supports bundled multi-check canaries Amazon U7i instances now available in Europe (London) RegionAmazon Connect now supports automated follow-up evaluations triggered by initial evaluation resultsHow the Wildlife Conservation Society uses AWS to accelerate coral reef monitoring worldwideAmazon MQ is now available in AWS Asia Pacific (New Zealand) Region Amazon CloudWatch introduces interactive incident reportingAWS Secret-West Region is now availableCharting the life of an Amazon CloudFront request

When a major Amazon cloud outage brings everything from smart mattresses to Snapchat grinding to a halt, what does it reveal about our digital fragility—and are we trusting the cloud a little too much? A Single Point of Failure Triggered the Amazon Outage Affecting Million Pluralistic: The mad king's digital killswitch (20 Oct 2025) Trump and Xi will 'consummate' TikTok deal on Thursday, treasury secretary says 3,000 YouTube Videos Exposed as Malware Traps in Massive Ghost Network Operation Can YouTube Replace 'Traditional' TV? All the implications of F1's game-changing TV move Foreign hackers breached a US nuclear weapons plant via SharePoint flaws Browser Promising Privacy Protection Contains Malware-Like Features, Routes Traffic Through China iCloud data helps crack NBA and mob poker scheme Rubbish IT systems cost the US at least $40bn during Covid: study Counter-Strike cosmetics economy loses nearly $2 billion in value overnight GM to introduce eyes-off, hands-off driving system in 2028 WordPress co-founder files countersuit against WP Engine over trademark violations a16z-Backed Startup Sells Thousands of 'Synthetic Influencers' to Manipulate Social Media as a Service Bill Gates-Backed 345 MWe Advanced Nuclear Reactor Secures Crucial US Approval Programmer Gets Doom Running On a Space Satellite Host: Leo Laporte Guests: Richard Campbell and Doc Rock Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: deel.com/twit zapier.com/twit helixsleep.com/twit expressvpn.com/twit zscaler.com/security

News and Updates: Amazon outage knocks out half the internet: A faulty DNS update in Amazon's DynamoDB caused a massive AWS outage, crippling services like Zoom, Alexa, Slack, and major financial platforms. Over 8 million users were affected globally, delaying flights, halting trades, and disrupting daily life. Analysts estimate losses could reach billions, reigniting calls for multi-cloud resilience—and even breaking up Big Tech. Microsoft issues urgent Windows 11 fix: An October update broke Windows Recovery Environment tools, disabling USB keyboards and mice during recovery. Microsoft rushed out patch KB5070773 to restore functionality. Users are urged to install immediately to regain recovery access and avoid potential boot or repair issues. Meta shuts down Messenger desktop apps: Messenger for Windows and macOS will shut down Dec. 15. Users must switch to web access or lose functionality. Messages will remain on Facebook accounts if secure storage is enabled. Meta gave no reason for the change, though declining desktop usage likely drove the decision. GM phases out CarPlay and Android Auto across all models: CEO Mary Barra confirmed GM will remove phone projection from all future vehicles—gas and electric—by 2028. The company is transitioning to a new unified computing platform with Google Gemini AI and in-house apps, part of its push toward a fully integrated infotainment system. GM unveils AI assistant and eyes-off driving system: At its “GM Forward” event, the automaker announced a 2028 launch for its next-gen platform featuring Google Gemini AI, hands-free “eyes-off” driving, and energy systems with home battery leasing. GM calls it a “new era of mobility,” aiming to transform vehicles into intelligent assistants. Apple slashes iPhone Air production amid weak demand: Apple is “drastically” cutting iPhone Air output to near shutdown levels after poor sales and “virtually no demand,” per Nikkei. Customers favor the iPhone 17 Pro lineup for better cameras and battery life. The ultra-thin $999 iPhone Air failed to generate excitement despite its sleek 5.6mm design.

When a major Amazon cloud outage brings everything from smart mattresses to Snapchat grinding to a halt, what does it reveal about our digital fragility—and are we trusting the cloud a little too much? A Single Point of Failure Triggered the Amazon Outage Affecting Million Pluralistic: The mad king's digital killswitch (20 Oct 2025) Trump and Xi will 'consummate' TikTok deal on Thursday, treasury secretary says 3,000 YouTube Videos Exposed as Malware Traps in Massive Ghost Network Operation Can YouTube Replace 'Traditional' TV? All the implications of F1's game-changing TV move Foreign hackers breached a US nuclear weapons plant via SharePoint flaws Browser Promising Privacy Protection Contains Malware-Like Features, Routes Traffic Through China iCloud data helps crack NBA and mob poker scheme Rubbish IT systems cost the US at least $40bn during Covid: study Counter-Strike cosmetics economy loses nearly $2 billion in value overnight GM to introduce eyes-off, hands-off driving system in 2028 WordPress co-founder files countersuit against WP Engine over trademark violations a16z-Backed Startup Sells Thousands of 'Synthetic Influencers' to Manipulate Social Media as a Service Bill Gates-Backed 345 MWe Advanced Nuclear Reactor Secures Crucial US Approval Programmer Gets Doom Running On a Space Satellite Host: Leo Laporte Guests: Richard Campbell and Doc Rock Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: deel.com/twit zapier.com/twit helixsleep.com/twit expressvpn.com/twit zscaler.com/security

以下のようなトピックについて話をしました。 01. H3ロケット7号機、新型補給機HTV-X1打ち上げ成功 JAXA（宇宙航空研究開発機構）は2025年10月26日午前9時00分15秒、種子島宇宙センターから「H3」ロケット7号機の打ち上げを成功させました。このロケットには、新型宇宙ステーション補給機1号機「HTV-X1」が搭載されており、発射から14分4秒後に軌道投入が確認されました。 HTV-X1は、2020年まで運用されていた「HTV（こうのとり）」の後継機として開発された無人補給機です。従来機と比較して大幅に性能が向上しており、貨物搭載能力は質量4トンから5.82トンへ、容積は49立方メートルから78立方メートルへと約1.5倍に拡大されています。打ち上げ時の総質量は約16トンです。 最大の特徴は、ISS（国際宇宙ステーション）での物資補給任務完了後も、最長1.5年間の単独飛行が可能な点です。これにより、軌道上実証プラットフォームとして技術実証や実験ミッションを継続できます。 HTV-X1には、生命維持に必要な窒素・酸素・水の補給タンクや宇宙食、実験機器などの与圧カーゴに加え、船外曝露カーゴとして中型曝露実験アダプタ「i-SEEP」や超小型衛星放出システム「H-SSOD」などの先端技術機器が搭載されています。 分離後のHTV-X1は太陽電池パドルの展開に成功し、10月30日午前0時50分頃にJAXAの油井亀美也宇宙飛行士によってISSにキャッチされる予定です。 02. AWS大規模障害の原因と影響分析 2025年10月20日に発生したAWS大規模障害について、その原因と影響を詳しく解説した記事の要約です。 障害の概要 US-EAST-1リージョンで約4時間にわたる大規模障害が発生。DynamoDBのDNSエンドポイント解決が機能しなくなり、DynamoDB及び依存サービスが連鎖的にダウンしました。 根本原因 AWSのDNS自動更新システムにおける「隠れパターン」の競合が原因でした。このシステムは、DNS PlannerとDNS Enactorの2つのコンポーネントで構成され、高可用性のため3つのAZで独立したインスタンスが同じ機能を実行していました。しかし、異常に長いリトライ処理中に新しいDNSプランが複数生成され、古いプランが適用された直後にクリーンアッププロセスが作動し、現行DNSプランを削除してしまいました。結果、Route 53にDynamoDBエンドポイントのレコードが存在しない「真空」状態が発生しました。 影響範囲 EC2、NLB、Lambdaなどのコアサービスに波及。特に新規インスタンスの起動やLambda関数の作成・更新が不可能になりました。 教訓と今後 筆者は、完全なマルチクラウド化は現実的でないとし、巨大ベンダーへの依存は避けられない一方通行の道だと指摘。しかし責任分散により社会全体でリスクを分担する現在のシステムは合理的だと評価しています。開発者としては、選択した道を信じて進むしかないと結論づけています。 03. GitHub Copilotエージェントの自律開発支援機能 GitHub Copilot コーディング エージェントは、GitHub Copilot Pro、Business、Enterpriseプランで利用可能な自律的なAI開発支援機能です。従来のIDEでのAIアシスタントとは異なり、バックグラウンドで独立して動作し、issueの割り当てやpull request作成依頼を通じてタスクを完了できます。 主な機能として、バグ修正、新機能実装、テストカバレッジ向上、ドキュメント更新、技術的負債への対処が可能です。GitHub Actions環境で動作し、ブランチ作成からコミット、PR作成まで自動化できるため、開発者は複雑な作業により集中できます。 セキュリティ面では、制限された開発環境での動作、copilot/で始まるブランチのみへのアクセス、書き込み権限を持つユーザーのみへの応答など、多層的な保護機能を備えています。また、外部コラボレーターとして扱われ、Actions実行前には承認が必要です。 ただし制限もあり、単一リポジトリでの作業、一度に一つのPR作成のみ、署名済みコミット非対応、セルフホステッドランナー非対応などがあります。現在はClaude Sonnet 4モデルを使用しており、GitHub Actions分とPremiumリクエストを消費します。この機能により、開発チームの生産性向上と作業の効率化が期待できます。 本ラジオはあくまで個人の見解であり現実のいかなる団体を代表するものではありません ご理解頂ますようよろしくおねがいします

In Italia e in Europa, l'energia nucleare rappresenta uno degli argomenti più complessi e dibattuti, eppure spesso la percezione pubblica diverge notevolmente dalla realtà dei fatti. Per comprendere come la tecnologia nucleare si sia evoluta negli ultimi decenni e quali sistemi di sicurezza la caratterizzano oggi, vogliamo ripercorrere gli incidenti storici che hanno plasmato questa industria, da Three Mile Island a Chernobyl, fino a Fukushima, analizzando cosa abbiamo imparato e come le moderne centrali di terza e quarta generazione rispondono a queste lezioni. Proviamo anche a capire cos'è il principio della "difesa in profondità" e come i livelli di protezione ridondanti e indipendenti rendono i reattori straordinariamente sicuri.Nella sezione delle notizie parliamo dei disservizi di Amazon AWS, del debutto italiano di Prime Vision, la modalità di visione per il calcio che integra realtà aumentata e intelligenza artificiale e infine di un impianto di vetrificazione dei rifiuti radioattivi.--Indice--00:00 - Introduzione00:59 - I disservizi di Amazon AWS (IlSole24Ore.com, Luca Martinelli)02:20 - Prime Vision debutta in Italia (DDay.it, Matteo Gallo)03:32 - Una nuova gestione dei rifiuti radioattivi (HDBlog.it, Matteo Gallo)04:59 - La sicurezza nelle centrali nucleari (Matteo Gallo)17:57 - Conclusione--Testo--Leggi la trascrizione: https://www.dentrolatecnologia.it/S7E43#testo--Contatti--• www.dentrolatecnologia.it• Instagram (@dentrolatecnologia)• Telegram (@dentrolatecnologia)• YouTube (@dentrolatecnologia)• redazione@dentrolatecnologia.it--Immagini--• Foto copertina: Wirestock su Freepik--Brani--• Ecstasy by Rabbit Theft• Falling For You by SouMix & Bromar

ChatGPT has launched a browser It's only for Mac though. I tried it and was a little underwhelmed. The first two things I tried to get it to do it failed at. I asked it to get headlines from CNN and the NYTimes but those sites are restricted -- which may be a common issue as you start using it for your every day. Publishers and Apps are thinking about their AI access strategies after being burned from giving so much of their content to Google. I then asked it to draft an email and get it ready in Gmail - but it wasn't any faster than copy and pasting from ChatGPT directly. Not sure there is enough benefit just yet! The big outage earlier the week shows just how reliant we are on AWS Oh boy.. that was a day. 14 hours of downtime with spotty services as different Amazon web services were online and offline. It turned out to be a DNS issue. The ip addresses of the DynamoDB servers were wiped - effectively making them invisible to the internet. It would be like removing all the phone numbers from the phone book - it doesn't make the phone book very useful. Only trouble is that the cloud servers and other AWS services people use rely on that phone book to operate and connect to the internet. It meant they were all taken offline and exposed a bad failure point. It really showed that some companies don't have adequate failovers, or proper multi-cloud implementations. But, it didn't hurt them - their stock actually popped on the day of the outage and they have ended the week up ~5%. LISTEN ABOVESee omnystudio.com/listener for privacy information.

An airhacks.fm conversation with Philipp Page (@PagePhilipp) about: early computing experiences with Windows XP and Intel Pentium systems, playing rally car games like Dirt with split-screen multiplayer, transitioning from gaming to server administration through Minecraft, running Minecraft servers at age 13 with memory limitations and out-of-memory exceptions, implementing caching mechanisms with cron jobs and MySQL databases, learning about SQL injection attacks and prepared statements, discovering connection pooling advantages over PHP approaches, appreciating type safety and Object-oriented programming principles in Java, the tendency to over-abstract and create unnecessary abstractions as junior developers, obsession with avoiding dependencies and implementing frameworks from scratch, building custom Model-View-Controller patterns and dependency injection systems, developing e-learning platform for aerospace industry using PHP Symfony framework, implementing time series forecasting in pure Java without external dependencies, internship and employment at AWS Dublin in Frontier Networking team, working on AWS Outposts and Ground Station hybrid cloud offerings, using python and rust for networking control plane development, learning to appreciate Python despite initial resistance to dynamically typed languages, joining AWS Lambda Powertools team as Java tech lead, maintaining open-source serverless development toolkit, providing utilities for observability including structured JSON logging with Lambda-specific information, implementing metrics and tracing for distributed event-driven architectures, mapping utilities to AWS Well-Architected Framework serverless lens recommendations, caching parameters and secrets to improve scalability and reduce costs, debate about AspectJ dependency and alternatives like Micronaut and quarkus approaches, providing both annotation-based and programmatic interfaces for utilities, newer utilities like Kafka consumer avoiding AspectJ dependency, comparing Micronaut's compiler-based approach and Quarkus extensions for bytecode generation, AspectJ losing popularity in enterprise Java projects, preferring Java standards over external dependencies for long-term maintainability, agents in electricity trading simulations for renewable energy scenarios, comparing on-premise Java capabilities versus cloud-native AWS features, default architecture pattern of Lambda with S3 for persistent storage, using AWS Calculator for cost analysis before architecture decisions, event-driven architectures being native to AWS versus artificially created in traditional Java projects, everything in AWS emitting events naturally through services like EventBridge, filtering events rather than creating them artificially, avoiding unnecessary microservices complexity when simple method calls suffice, directly wiring API Gateway to DynamoDB without Lambda for no-code solutions, using Java for CDK infrastructure as code while minimizing runtime dependencies, maximizing cloud-native features when in cloud versus on-premise optimization strategies, starting with simplest possible architecture and justifying complexity, blue-green deployments and load balancing handled automatically by Lambda, internal AWS teams using Lambda for orchestration and event interception, Lambda as foundational zero-level service across AWS infrastructure, preferring highest abstraction level services like Lambda and ECS Fargate, only dropping to EC2 when specific requirements demand lower-level control, contributing to Powertools for AWS Lambda Python repository before joining team, compile-time weaving avoiding Lambda cold start performance impacts, GraalVM compilation considerations for Quarkus and Micronaut approaches, customer references available on Powertools website, contrast between low-level networking and serverless development, LinkedIn as primary social media platform for professional connections, Powertools for AWS Lambda (Java) Philipp Page on twitter: @PagePhilipp

An airhacks.fm conversation with Ronald Dehuysser (@rdehuyss) about: JobRunner evolution from open source to processing 1 billion jobs daily, carbon-aware job processing using European energy grid data ( ENTSO-E ) for scheduling jobs during renewable energy peaks, correlation between CO2 emissions and energy prices for cost optimization, JobRunner Pro vs Open Source features including workflows and multi-tenancy support, bytecode analysis using ASM for lambda serialization, JSON serialization for job state persistence, support for relational databases and MongoDB with potential S3 and DynamoDB integration, distributed processing with master node coordination using heartbeat mechanism, scale-to-zero architecture possibilities using AWS EventBridge Scheduler, Java performance advantages showing 35x faster than python in benchmarks, cloud migration patterns from on-premise to serverless architectures, criticism of kubernetes complexity and lift-and-shift cloud migrations, cost-driven architecture approach using AWS Lambda and S3, quarkus as fastest Java runtime for cloud deployments, infrastructure as code using AWS CDK with Java, potential WebAssembly compilation for Edge Computing, automatic retry mechanisms with exponential backoff, dashboard and monitoring capabilities, medical industry use case with critical cancer result processing, professional liability insurance for software errors, comparison with executor service for non-critical tasks, scheduled and recurring job support, carbon footprint reduction through intelligent scheduling, spot instance integration for cost optimization, simplified developer experience with single JAR deployment, automatic table creation and data source detection in Quarkus, backwards compatibility requirements for distributed nodes, future serverless edition possibilities Ronald Dehuysser on twitter: @rdehuyss

Sam Johnson, Chief Customer Officer of Jamf, discusses the implementation of AI built on Amazon Bedrock that is a gamechanger in helping Jamf's 76,000+ customers scale their device management operations.Topics Include:Sam Johnson introduces himself as Chief Customer Officer from Jamf companyJamf's 23-year mission: help organizations succeed with Apple device managementCompany manages 33+ million devices for 76,000+ customers worldwide from MinneapolisJamf has used AI since 2018 for security threat detectionReleased first customer-facing generative AI Assistant just last year in 2024Presentation covers why, how they built it, use cases, and future plansJamf serves horizontal market from small business to Fortune 500 companiesChallenge: balance powerful platform capabilities with ease of use and adoptionAI could help get best of both worlds - power and simplicityAI also increases security posture and scales user capabilities significantlyCustomers already using ChatGPT/Claude but wanted AI embedded in productBuilt into product to reduce "doorway effect" of switching digital environmentsCreated small cross-functional team to survey land and build initial trailRest of engineering organization came behind to build the production highwayTeam needed governance layer with input from security, legal, other departmentsEvaluated multiple providers but ultimately chose Amazon Bedrock for three reasonsAWS team support, large community, and integration with existing infrastructureUses Lambda, DynamoDB, CloudWatch to support the Bedrock AI implementationAI development required longer training/validation phase than typical product featuresReleased "AI Assistant" with three skills: Reference, Explain, and Search capabilitiesParticipants:Sam Johnson – Chief Customer Officer, JamfFurther Links:Jamf.comJamf on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

There are over 60 new updates that your hosts Simon, Jillian and Shruthi take you through this week!

Neste episódio do Sem Servidor, conversamos com Denisson Felipe, CTO da Vixting, sobre a jornada real de adoção do serverless em uma empresa de tecnologia focada em saúde ocupacional.Denisson compartilha como começou a programar aos 12 anos, seu primeiro contato com a AWS Lambda em 2015, e os desafios enfrentados ao migrar de uma arquitetura monolítica para uma estrutura moderna baseada em microsserviços e funções serverless. Ele detalha o processo de transformação técnica e cultural da equipe, a estratégia de migração progressiva e os critérios para escolha de bancos como MongoDB e DynamoDB.Alguns destaques do episódio:Como o VixTalks, uma iniciativa interna, ajudou a capacitar o time para a nova arquitetura.Por que eles começaram com NestJS e decidiram simplificar.O uso intensivo de filas com SQS, gateways com roteamento por versão e mais de 300 Lambdas em produção.O racional por trás do serverless first e quando não vale a pena migrar tudo.A importância de formar uma cultura técnica sólida para escalar sem travar o negócio.Esse episódio é um prato cheio para CTOs, arquitetos de software e times técnicos que querem entender como dar os primeiros passos reais com serverless, mesmo com um monolito pesado no colo.

I speak with Daniela Miao of Momento to discuss her journey from DynamoDB at AWS to creating an alternative caching platform for large scale real time applications.Try the best git GUI for macOS and WindowsGrapple git without the grief and try Tower, the best graphical interface for git on macOS and Windows.go.chrischinchilla.com/tower For show notes and an interactive transcript, visit chrischinchilla.com/podcast/To reach out and say hello, visit chrischinchilla.com/contact/To support the show for ad-free listening and extra content, visit chrischinchilla.com/support/

AWS Morning Brief for the week of April 14th, with Corey Quinn.Links:Amazon Route 53 adds public authoritative DNS service to AWS GovCloud (US) RegionsCost Optimization Hub supports DynamoDB and MemoryDB reservation recommendationsLoad Balancer Capacity Unit Reservation for Gateway Load BalancersAnnouncing new AWS Elemental MediaTailor pricing model with lower VOD ad insertion costsHow AWS and Intel make LLMs more accessible and cost-effective with DeepSeekAnnouncing up to 85% price reductions for Amazon S3 Express One ZoneOptimize Amazon VPC Flow Logs analysis with Cribl Stream samplingExploring Data Transfer Costs for AWS Network Load Balancers

An airhacks.fm conversation with Alvaro Hernandez (@ahachete) about: discussion about stackgres as a complete database cloud solution for PostgreSQL, kubernetes as an abstraction layer over infrastructure providing a programmable API, Stackgres offering high availability with primary and replica nodes using patroni, integrated connection pooling with PgBouncer, kubernetes operators and Custom Resource Definitions (CRDs) as a powerful way to extend Kubernetes, day two operations automated through CRDs including benchmarks and version upgrades, Stackgres supporting sharding with Citus for horizontal scaling similar to DynamoDB, Change Data Capture capabilities using embedded debezium, failover mechanisms taking typically 30 seconds with DNS updates, synchronous vs asynchronous replication options affecting data loss during failover, Stackgres being implemented in Java using quarkus, ContainerD as a programmable container runtime that can be used without Kubernetes, Stackgres offering multiple interfaces including CRDs, REST API, and a web console, considerations for running databases on Kubernetes vs cloud-managed services, the advantages of containerization for infrastructure, the challenges of multi-leader setups in PostgreSQL requiring conflict resolution, the value of Kubernetes for on-premises deployments vs cloud environments Alvaro Hernandez on twitter: @ahachete

Applications close Monday for the NYC AI Engineer Summit focusing on AI Leadership and Agent Engineering! If you applied, invites should be rolling out shortly.The search landscape is experiencing a fundamental shift. Google built a >$2T company with the “10 blue links” experience, driven by PageRank as the core innovation for ranking. This was a big improvement from the previous directory-based experiences of AltaVista and Yahoo. Almost 4 decades later, Google is now stuck in this links-based experience, especially from a business model perspective. This legacy architecture creates fundamental constraints:* Must return results in ~400 milliseconds* Required to maintain comprehensive web coverage* Tied to keyword-based matching algorithms* Cost structures optimized for traditional indexingAs we move from the era of links to the era of answers, the way search works is changing. You're not showing a user links, but the goal is to provide context to an LLM. This means moving from keyword based search to more semantic understanding of the content:The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share... but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways.All of this is now powered by a $5M cluster with 144 H200s:This architectural choice enables entirely new search capabilities:* Comprehensive result sets instead of approximations* Deep semantic understanding of queries* Ability to process complex, natural language requestsAs search becomes more complex, time to results becomes a variable:People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned... But what if searches can take like a minute or 10 minutes or a whole day, what can you then do?Unlike traditional search engines' fixed-cost indexing, Exa employs a hybrid approach:* Front-loaded compute for indexing and embeddings* Variable inference costs based on query complexity* Mix of owned infrastructure ($5M H200 cluster) and cloud resourcesExa sees a lot of competition from products like Perplexity and ChatGPT Search which layer AI on top of traditional search backends, but Exa is betting that true innovation requires rethinking search from the ground up. For example, the recently launched Websets, a way to turn searches into structured output in grid format, allowing you to create lists and databases out of web pages. The company raised a $17M Series A to build towards this mission, so keep an eye out for them in 2025. Chapters* 00:00:00 Introductions* 00:01:12 ExaAI's initial pitch and concept* 00:02:33 Will's background at SpaceX and Zoox* 00:03:45 Evolution of ExaAI (formerly Metaphor Systems)* 00:05:38 Exa's link prediction technology* 00:09:20 Meaning of the name "Exa"* 00:10:36 ExaAI's new product launch and capabilities* 00:13:33 Compute budgets and variable compute products* 00:14:43 Websets as a B2B offering* 00:19:28 How do you build a search engine?* 00:22:43 What is Neural PageRank?* 00:27:58 Exa use cases * 00:35:00 Auto-prompting* 00:38:42 Building agentic search* 00:44:19 Is o1 on the path to AGI?* 00:49:59 Company culture and nap pods* 00:54:52 Economics of AI search and the future of search technologyFull YouTube TranscriptPlease like and subscribe!Show Notes* ExaAI* Web Search Product* Websets* Series A Announcement* Exa Nap Pods* Perplexity AI* Character.AITranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:10]: Hey, and today we're in the studio with my good friend and former landlord, Will Bryk. Roommate. How you doing? Will, you're now CEO co-founder of ExaAI, used to be Metaphor Systems. What's your background, your story?Will [00:00:30]: Yeah, sure. So, yeah, I'm CEO of Exa. I've been doing it for three years. I guess I've always been interested in search, whether I knew it or not. Like, since I was a kid, I've always been interested in, like, high-quality information. And, like, you know, even in high school, wanted to improve the way we get information from news. And then in college, built a mini search engine. And then with Exa, like, you know, it's kind of like fulfilling the dream of actually being able to solve all the information needs I wanted as a kid. Yeah, I guess. I would say my entire life has kind of been rotating around this problem, which is pretty cool. Yeah.Swyx [00:00:50]: What'd you enter YC with?Will [00:00:53]: We entered YC with, uh, we are better than Google. Like, Google 2.0.Swyx [00:01:12]: What makes you say that? Like, that's so audacious to come out of the box with.Will [00:01:16]: Yeah, okay, so you have to remember the time. This was summer 2021. And, uh, GPT-3 had come out. Like, here was this magical thing that you could talk to, you could enter a whole paragraph, and it understands what you mean, understands the subtlety of your language. And then there was Google. Uh, which felt like it hadn't changed in a decade, uh, because it really hadn't. And it, like, you would give it a simple query, like, I don't know, uh, shirts without stripes, and it would give you a bunch of results for the shirts with stripes. And so, like, Google could barely understand you, and GBD3 could. And the theory was, what if you could make a search engine that actually understood you? What if you could apply the insights from LLMs to a search engine? And it's really been the same idea ever since. And we're actually a lot closer now, uh, to doing that. Yeah.Alessio [00:01:55]: Did you have any trouble making people believe? Obviously, there's the same element. I mean, YC overlap, was YC pretty AI forward, even 2021, or?Will [00:02:03]: It's nothing like it is today. But, um, uh, there were a few AI companies, but, uh, we were definitely, like, bold. And I think people, VCs generally like boldness, and we definitely had some AI background, and we had a working demo. So there was evidence that we could build something that was going to work. But yeah, I think, like, the fundamentals were there. I think people at the time were talking about how, you know, Google was failing in a lot of ways. And so there was a bit of conversation about it, but AI was not a big, big thing at the time. Yeah. Yeah.Alessio [00:02:33]: Before we jump into Exa, any fun background stories? I know you interned at SpaceX, any Elon, uh, stories? I know you were at Zoox as well, you know, kind of like robotics at Harvard. Any stuff that you saw early that you thought was going to get solved that maybe it's not solved today?Will [00:02:48]: Oh yeah. I mean, lots of things like that. Like, uh, I never really learned how to drive because I believed Elon that self-driving cars would happen. It did happen. And I take them every night to get home. But it took like 10 more years than I thought. Do you still not know how to drive? I know how to drive now. I learned it like two years ago. That would have been great to like, just, you know, Yeah, yeah, yeah. You know? Um, I was obsessed with Elon. Yeah. I mean, I worked at SpaceX because I really just wanted to work at one of his companies. And I remember they had a rule, like interns cannot touch Elon. And, um, that rule actually influenced my actions.Swyx [00:03:18]: Is it, can Elon touch interns? Ooh, like physically?Will [00:03:22]: Or like talk? Physically, physically, yeah, yeah, yeah, yeah. Okay, interesting. He's changed a lot, but, um, I mean, his companies are amazing. Um,Swyx [00:03:28]: What if you beat him at Diablo 2, Diablo 4, you know, like, Ah, maybe.Alessio [00:03:34]: I want to jump into, I know there's a lot of backstory used to be called metaphor system. So, um, and it, you've always been kind of like a prominent company, maybe at least RAI circles in the NSF.Swyx [00:03:45]: I'm actually curious how Metaphor got its initial aura. You launched with like, very little. We launched very little. Like there was, there was this like big splash image of like, this is Aurora or something. Yeah. Right. And then I was like, okay, what this thing, like the vibes are good, but I don't know what it is. And I think, I think it was much more sort of maybe consumer facing than what you are today. Would you say that's true?Will [00:04:06]: No, it's always been about building a better search algorithm, like search, like, just like the vision has always been perfect search. And if you do that, uh, we will figure out the downstream use cases later. It started on this fundamental belief that you could have perfect search over the web and we could talk about what that means. And like the initial thing we released was really just like our first search engine, like trying to get it out there. Kind of like, you know, an open source. So when OpenAI released, uh, ChachBt, like they didn't, I don't know how, how much of a game plan they had. They kind of just wanted to get something out there.Swyx [00:04:33]: Spooky research preview.Will [00:04:34]: Yeah, exactly. And it kind of morphed from a research company to a product company at that point. And I think similarly for us, like we were research, we started as a research endeavor with a, you know, clear eyes that like, if we succeed, it will be a massive business to make out of it. And that's kind of basically what happened. I think there are actually a lot of parallels to, of w between Exa and OpenAI. I often say we're the OpenAI of search. Um, because. Because we're a research company, we're a research startup that does like fundamental research into, uh, making like AGI for search in a, in a way. Uh, and then we have all these like, uh, business products that come out of that.Swyx [00:05:08]: Interesting. I want to ask a little bit more about Metaforesight and then we can go full Exa. When I first met you, which was really funny, cause like literally I stayed in your house in a very historic, uh, Hayes, Hayes Valley place. You said you were building sort of like link prediction foundation model, and I think there's still a lot of foundation model work. I mean, within Exa today, but what does that even mean? I cannot be the only person confused by that because like there's a limited vocabulary or tokens you're telling me, like the tokens are the links or, you know, like it's not, it's not clear. Yeah.Will [00:05:38]: Uh, what we meant by link prediction is that you are literally predicting, like given some texts, you're predicting the links that follow. Yes. That refers to like, it's how we describe the training procedure, which is that we find links on the web. Uh, we take the text surrounding the link. And then we predict. Which link follows you, like, uh, you know, similar to transformers where, uh, you're trying to predict the next token here, you're trying to predict the next link. And so you kind of like hide the link from the transformer. So if someone writes, you know, imagine some article where someone says, Hey, check out this really cool aerospace startup. And they, they say spacex.com afterwards, uh, we hide the spacex.com and ask the model, like what link came next. And by doing that many, many times, you know, billions of times, you could actually build a search engine out of that because then, uh, at query time at search time. Uh, you type in, uh, a query that's like really cool aerospace startup and the model will then try to predict what are the most likely links. So there's a lot of analogs to transformers, but like to actually make this work, it does require like a different architecture than, but it's transformer inspired. Yeah.Alessio [00:06:41]: What's the design decision between doing that versus extracting the link and the description and then embedding the description and then using, um, yeah. What do you need to predict the URL versus like just describing, because you're kind of do a similar thing in a way. Right. It's kind of like based on this description, it was like the closest link for it. So one thing is like predicting the link. The other approach is like I extract the link and the description, and then based on the query, I searched the closest description to it more. Yeah.Will [00:07:09]: That, that, by the way, that is, that is the link refers here to a document. It's not, I think one confusing thing is it's not, you're not actually predicting the URL, the URL itself that would require like the, the system to have memorized URLs. You're actually like getting the actual document, a more accurate name could be document prediction. I see. This was the initial like base model that Exo was trained on, but we've moved beyond that similar to like how, you know, uh, to train a really good like language model, you might start with this like self-supervised objective of predicting the next token and then, uh, just from random stuff on the web. But then you, you want to, uh, add a bunch of like synthetic data and like supervised fine tuning, um, stuff like that to make it really like controllable and robust. Yeah.Alessio [00:07:48]: Yeah. We just have flow from Lindy and, uh, their Lindy started to like hallucinate recrolling YouTube links instead of like, uh, something. Yeah. Support guide. So. Oh, interesting. Yeah.Swyx [00:07:57]: So round about January, you announced your series A and renamed to Exo. I didn't like the name at the, at the initial, but it's grown on me. I liked metaphor, but apparently people can spell metaphor. What would you say are the major components of Exo today? Right? Like, I feel like it used to be very model heavy. Then at the AI engineer conference, Shreyas gave a really good talk on the vector database that you guys have. What are the other major moving parts of Exo? Okay.Will [00:08:23]: So Exo overall is a search engine. Yeah. We're trying to make it like a perfect search engine. And to do that, you have to build lots of, and we're doing it from scratch, right? So to do that, you have to build lots of different. The crawler. Yeah. You have to crawl a bunch of the web. First of all, you have to find the URLs to crawl. Uh, it's connected to the crawler, but yeah, you find URLs, you crawl those URLs. Then you have to process them with some, you know, it could be an embedding model. It could be something more complex, but you need to take, you know, or like, you know, in the past it was like a keyword inverted index. Like you would process all these documents you gather into some processed index, and then you have to serve that. Uh, you had high throughput at low latency. And so that, and that's like the vector database. And so it's like the crawling system, the AI processing system, and then the serving system. Those are all like, you know, teams of like hundreds, maybe thousands of people at Google. Um, but for us, it's like one or two people each typically, but yeah.Alessio [00:09:13]: Can you explain the meaning of, uh, Exo, just the story 10 to the 16th, uh, 18, 18.Will [00:09:20]: Yeah, yeah, yeah, sure. So. Exo means 10 to the 18th, which is in stark contrast to. To Google, which is 10 to the hundredth. Uh, we actually have these like awesome shirts that are like 10th to 18th is greater than 10th to the hundredth. Yeah, it's great. And it's great because it's provocative. It's like every engineer in Silicon Valley is like, what? No, it's not true. Um, like, yeah. And, uh, and then you, you ask them, okay, what does it actually mean? And like the creative ones will, will recognize it. But yeah, I mean, 10 to the 18th is better than 10 to the hundredth when it comes to search, because with search, you want like the actual list of, of things that match what you're asking for. You don't want like the whole web. You want to basically with search filter, the, like everything that humanity has ever created to exactly what you want. And so the idea is like smaller is better there. You want like the best 10th to the 18th and not the 10th to the hundredth. I'm like, one way to say this is like, you know how Google often says at the top, uh, like, you know, 30 million results found. And it's like crazy. Cause you're looking for like the first startups in San Francisco that work on hardware or something. And like, they're not 30 million results like that. What you want is like 325 results found. And those are all the results. That's what you really want with search. And that's, that's our vision. It's like, it just gives you. Perfectly what you asked for.Swyx [00:10:24]: We're recording this ahead of your launch. Uh, we haven't released, we haven't figured out the, the, the name of the launch yet, but what is the product that you're launching? I guess now that we're coinciding this podcast with. Yeah.Will [00:10:36]: So we've basically developed the next version of Exa, which is the ability to get a near perfect list of results of whatever you want. And what that means is you can make a complex query now to Exa, for example, startups working on hardware in SF, and then just get a huge list of all the things that match. And, you know, our goal is if there are 325 startups that match that we find you all of them. And this is just like, there's just like a new experience that's never existed before. It's really like, I don't know how you would go about that right now with current tools and you can apply this same type of like technology to anything. Like, let's say you want, uh, you want to find all the blog posts that talk about Alessio's podcast, um, that have come out in the past year. That is 30 million results. Yeah. Right.Will [00:11:24]: But that, I mean, that would, I'm sure that would be extremely useful to you guys. And like, I don't really know how you would get that full comprehensive list.Swyx [00:11:29]: I just like, how do you, well, there's so many questions with regards to how do you know it's complete, right? Cause you're saying there's only 30 million, 325, whatever. And then how do you do the semantic understanding that it might take, right? So working in hardware, like I might not use the words hardware. I might use the words robotics. I might use the words wearables. I might use like whatever. Yes. So yeah, just tell us more. Yeah. Yeah. Sure. Sure.Will [00:11:53]: So one aspect of this, it's a little subjective. So like certainly providing, you know, at some point we'll provide parameters to the user to like, you know, some sort of threshold to like, uh, gauge like, okay, like this is a cutoff. Like, this is actually not what I mean, because sometimes it's subjective and there needs to be a feedback loop. Like, oh, like it might give you like a few examples and you say, yeah, exactly. And so like, you're, you're kind of like creating a classifier on the fly, but like, that's ultimately how you solve the problem. So the subject, there's a subjectivity problem and then there's a comprehensiveness problem. Those are two different problems. So. Yeah. So you have the comprehensiveness problem. What you basically have to do is you have to put more compute into the query, into the search until you get the full comprehensiveness. Yeah. And I think there's an interesting point here, which is that not all queries are made equal. Some queries just like this blog post one might require scanning, like scavenging, like throughout the whole web in a way that just, just simply requires more compute. You know, at some point there's some amount of compute where you will just be comprehensive. You could imagine, for example, running GPT-4 over the internet. You could imagine running GPT-4 over the entire web and saying like, is this a blog post about Alessio's podcast, like, is this a blog post about Alessio's podcast? And then that would work, right? It would take, you know, a year, maybe cost like a million dollars, but, or many more, but, um, it would work. Uh, the point is that like, given sufficient compute, you can solve the query. And so it's really a question of like, how comprehensive do you want it given your compute budget? I think it's very similar to O1, by the way. And one way of thinking about what we built is like O1 for search, uh, because O1 is all about like, you know, some, some, some questions require more compute than others, and we'll put as much compute into the question as we need to solve it. So similarly with our search, we will put as much compute into the query in order to get comprehensiveness. Yeah.Swyx [00:13:33]: Does that mean you have like some kind of compute budget that I can specify? Yes. Yes. Okay. And like, what are the upper and lower bounds?Will [00:13:42]: Yeah, there's something we're still figuring out. I think like, like everyone is a new paradigm of like variable compute products. Yeah. How do you specify the amount of compute? Like what happens when you. Run out? Do you just like, ah, do you, can you like keep going with it? Like, do you just put in more credits to get more, um, for some, like this can get complex at like the really large compute queries. And like, one thing we do is we give you a preview of what you're going to get, and then you could then spin up like a much larger job, uh, to get like way more results. But yes, there is some compute limit, um, at, at least right now. Yeah. People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned, uh, to have search that takes 500 milliseconds. But like search engines like Google, right. No matter how complex your query to Google, it will take like, you know, roughly 400 milliseconds. But what if searches can take like a minute or 10 minutes or a whole day, what can you then do? And you can do very powerful things. Um, you know, you can imagine, you know, writing a search, going and get a cup of coffee, coming back and you have a perfect list. Like that's okay for a lot of use cases. Yeah.Alessio [00:14:43]: Yeah. I mean, the use case closest to me is venture capital, right? So, uh, no, I mean, eight years ago, I built one of the first like data driven sourcing platforms. So we were. You look at GitHub, Twitter, Product Hunt, all these things, look at interesting things, evaluate them. If you think about some jobs that people have, it's like literally just make a list. If you're like an analyst at a venture firm, your job is to make a list of interesting companies. And then you reach out to them. How do you think about being infrastructure versus like a product you could say, Hey, this is like a product to find companies. This is a product to find things versus like offering more as a blank canvas that people can build on top of. Oh, right. Right.Will [00:15:20]: Uh, we are. We are a search infrastructure company. So we want people to build, uh, on top of us, uh, build amazing products on top of us. But with this one, we try to build something that makes it really easy for users to just log in, put a few, you know, put some credits in and just get like amazing results right away and not have to wait to build some API integration. So we're kind of doing both. Uh, we, we want, we want people to integrate this into all their applications at the same time. We want to just make it really easy to use very similar again to open AI. Like they'll have, they have an API, but they also have. Like a ChatGPT interface so that you could, it's really easy to use, but you could also build it in your applications. Yeah.Alessio [00:15:56]: I'm still trying to wrap my head around a lot of the implications. So, so many businesses run on like information arbitrage, you know, like I know this thing that you don't, especially in investment and financial services. So yeah, now all of a sudden you have these tools for like, oh, actually everybody can get the same information at the same time, the same quality level as an API call. You know, it just kind of changes a lot of things. Yeah.Will [00:16:19]: I think, I think what we're grappling with here. What, what you're just thinking about is like, what is the world like if knowledge is kind of solved, if like any knowledge request you want is just like right there on your computer, it's kind of different from when intelligence is solved. There's like a good, I've written before about like a different super intelligence, super knowledge. Yeah. Like I think that the, the distinction between intelligence and knowledge is actually a pretty good one. They're definitely connected and related in all sorts of ways, but there is a distinction. You could have a world and we are going to have this world where you have like GP five level systems and beyond that could like answer any complex request. Um, unless it requires some. Like, if you say like, uh, you know, give me a list of all the PhDs in New York city who, I don't know, have thought about search before. And even though this, this super intelligence is going to be like, I can't find it on Google, right. Which is kind of crazy. Like we're literally going to have like super intelligences that are using Google. And so if Google can't find them information, there's nothing they could do. They can't find it. So, but if you also have a super knowledge system where it's like, you know, I'm calling this term super knowledge where you just get whatever knowledge you want, then you can pair with a super intelligence system. And then the super intelligence can, we'll never. Be blocked by lack of knowledge.Alessio [00:17:23]: Yeah. You told me this, uh, when we had lunch, I forget how it came out, but we were talking about AGI and whatnot. And you were like, even AGI is going to need search. Yeah.Swyx [00:17:32]: Yeah. Right. Yeah. Um, so we're actually referencing a blog post that you wrote super intelligence and super knowledge. Uh, so I would refer people to that. And this is actually a discussion we've had on the podcast a couple of times. Um, there's so much of model weights that are just memorizing facts. Some of the, some of those might be outdated. Some of them are incomplete or not. Yeah. So like you just need search. So I do wonder, like, is there a maximum language model size that will be the intelligence layer and then the rest is just search, right? Like maybe we should just always use search. And then that sort of workhorse model is just like, and it like, like, like one B or three B parameter model that just drives everything. Yes.Will [00:18:13]: I believe this is a much more optimal system to have a smaller LM. That's really just like an intelligence module. And it makes a call to a search. Tool that's way more efficient because if, okay, I mean the, the opposite of that would be like the LM is so big that can memorize the whole web. That would be like way, but you know, it's not practical at all. I don't, it's not possible to train that at least right now. And Carpathy has actually written about this, how like he could, he could see models moving more and more towards like intelligence modules using various tools. Yeah.Swyx [00:18:39]: So for listeners, that's the, that was him on the no priors podcast. And for us, we talked about this and the, on the Shin Yu and Harrison chase podcasts. I'm doing search in my head. I told you 30 million results. I forgot about our neural link integration. Self-hosted exit.Will [00:18:54]: Yeah. Yeah. No, I do see that that is a much more, much more efficient world. Yeah. I mean, you could also have GB four level systems calling search, but it's just because of the cost of inference. It's just better to have a very efficient search tool and a very efficient LM and they're built for different things. Yeah.Swyx [00:19:09]: I'm just kind of curious. Like it is still something so audacious that I don't want to elide, which is you're, you're, you're building a search engine. Where do you start? How do you, like, are there any reference papers or implementation? That would really influence your thinking, anything like that? Because I don't even know where to start apart from just crawl a bunch of s**t, but there's gotta be more insight than that.Will [00:19:28]: I mean, yeah, there's more insight, but I'm always surprised by like, if you have a group of people who are really focused on solving a problem, um, with the tools today, like there's some in, in software, like there are all sorts of creative solutions that just haven't been thought of before, particularly in the information retrieval field. Yeah. I think a lot of the techniques are just very old, frankly. Like I know how Google and Bing work and. They're just not using new methods. There are all sorts of reasons for that. Like one, like Google has to be comprehensive over the web. So they're, and they have to return in 400 milliseconds. And those two things combined means they are kind of limit and it can't cost too much. They're kind of limited in, uh, what kinds of algorithms they could even deploy at scale. So they end up using like a limited keyword based algorithm. Also like Google was built in a time where like in, you know, in 1998, where we didn't have LMS, we didn't have embeddings. And so they never thought to build those things. And so now they have this like gigantic system that is built on old technology. Yeah. And so a lot of the information retrieval field we found just like thinks in terms of that framework. Yeah. Whereas we came in as like newcomers just thinking like, okay, there here's GB three. It's magical. Obviously we're going to build search that is using that technology. And we never even thought about using keywords really ever. Uh, like we were neural all the way we're building an end to end neural search engine. And just that whole framing just makes us ask different questions, like pursue different lines of work. And there's just a lot of low hanging fruit because no one else is thinking about it. We're just on the frontier of neural search. We just are, um, for, for at web scale, um, because there's just not a lot of people thinking that way about it.Swyx [00:20:57]: Yeah. Maybe let's spell this out since, uh, we're already on this topic, elephants in the room are Perplexity and SearchGPT. That's the, I think that it's all, it's no longer called SearchGPT. I think they call it ChatGPT Search. How would you contrast your approaches to them based on what we know of how they work and yeah, just any, anything in that, in that area? Yeah.Will [00:21:15]: So these systems, there are a few of them now, uh, they basically rely on like traditional search engines like Google or Bing, and then they combine them with like LLMs at the end to, you know, output some power graphics, uh, answering your question. So they like search GPT perplexity. I think they have their own crawlers. No. So there's this important distinction between like having your own search system and like having your own cache of the web. Like for example, so you could create, you could crawl a bunch of the web. Imagine you crawl a hundred billion URLs, and then you create a key value store of like mapping from URL to the document that is technically called an index, but it's not a search algorithm. So then to actually like, when you make a query to search GPT, for example, what is it actually doing it? Let's say it's, it's, it could, it's using the Bing API, uh, getting a list of results and then it could go, it has this cache of like all the contents of those results and then could like bring in the cache, like the index cache, but it's not actually like, it's not like they've built a search engine from scratch over, you know, hundreds of billions of pages. It's like, does that distinction clear? It's like, yeah, you could have like a mapping from URL to documents, but then rely on traditional search engines to actually get the list of results because it's a very hard problem to take. It's not hard. It's not hard to use DynamoDB and, and, and map URLs to documents. It's a very hard problem to take a hundred billion or more documents and given a query, like instantly get the list of results that match. That's a much harder problem that very few entities on, in, on the planet have done. Like there's Google, there's Bing, uh, you know, there's Yandex, but you know, there are not that many companies that are, that are crazy enough to actually build their search engine from scratch when you could just use traditional search APIs.Alessio [00:22:43]: So Google had PageRank as like the big thing. Is there a LLM equivalent or like any. Stuff that you're working on that you want to highlight?Will [00:22:51]: The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share. And so if everyone is sharing some Paul Graham essay about fundraising, then like our model is more likely to predict it. So like inherent in our training objective is this, uh, a sense of like high canonicity and like high quality, but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways. That someone refers that Paul Graham, I say, while also learning how important that Paul Graham essay is. Um, so it's like, it's like PageRank on steroids kind of thing. Yeah.Alessio [00:23:26]: I think to me, that's the most interesting thing about search today, like with Google and whatnot, it's like, it's mostly like domain authority. So like if you get back playing, like if you search any AI term, you get this like SEO slop websites with like a bunch of things in them. So this is interesting, but then how do you think about more timeless maybe content? So if you think about, yeah. You know, maybe the founder mode essay, right. It gets shared by like a lot of people, but then you might have a lot of other essays that are also good, but they just don't really get a lot of traction. Even though maybe the people that share them are high quality. How do you kind of solve that thing when you don't have the people authority, so to speak of who's sharing, whether or not they're worth kind of like bumping up? Yeah.Will [00:24:10]: I mean, you do have a lot of control over the training data, so you could like make sure that the training data contains like high quality sources so that, okay. Like if you, if you're. Training data, I mean, it's very similar to like language, language model training. Like if you train on like a bunch of crap, your prediction will be crap. Our model will match the training distribution is trained on. And so we could like, there are lots of ways to tweak the training data to refer to high quality content that we want. Yeah. I would say also this, like this slop that is returned by, by traditional search engines, like Google and Bing, you have the slop is then, uh, transferred into the, these LLMs in like a search GBT or, you know, our other systems like that. Like if slop comes in, slop will go out. And so, yeah, that's another answer to how we're different is like, we're not like traditional search engines. We want to give like the highest quality results and like have full control over whatever you want. If you don't want slop, you get that. And then if you put an LM on top of that, which our customers do, then you just get higher quality results or high quality output.Alessio [00:25:06]: And I use Excel search very often and it's very good. Especially.Swyx [00:25:09]: Wave uses it too.Alessio [00:25:10]: Yeah. Yeah. Yeah. Yeah. Yeah. Like the slop is everywhere, especially when it comes to AI, when it comes to investment. When it comes to all of these things for like, it's valuable to be at the top. And this problem is only going to get worse because. Yeah, no, it's totally. What else is in the toolkit? So you have search API, you have ExaSearch, kind of like the web version. Now you have the list builder. I think you also have web scraping. Maybe just touch on that. Like, I guess maybe people, they want to search and then they want to scrape. Right. So is that kind of the use case that people have? Yeah.Will [00:25:41]: A lot of our customers, they don't just want, because they're building AI applications on top of Exa, they don't just want a list of URLs. They actually want. Like the full content, like cleans, parsed. Markdown. Markdown, maybe chunked, whatever they want, we'll give it to them. And so that's been like huge for customers. Just like getting the URLs and instantly getting the content for each URL is like, and you can do this for 10 or 100 or 1,000 URLs, wherever you want. That's very powerful.Swyx [00:26:05]: Yeah. I think this is the first thing I asked you for when I tried using Exa.Will [00:26:09]: Funny story is like when I built the first version of Exa, it's like, we just happened to store the content. Yes. Like the first 1,024 tokens. Because I just kind of like kept it because I thought of, you know, I don't know why. Really for debugging purposes. And so then when people started asking for content, it was actually pretty easy to serve it. But then, and then we did that, like Exa took off. So the computer's content was so useful. So that was kind of cool.Swyx [00:26:30]: It is. I would say there are other players like Gina, I think is in this space. Firecrawl is in this space. There's a bunch of scraper companies. And obviously scraper is just one part of your stack, but you might as well offer it since you already do it.Will [00:26:43]: Yeah, it makes sense. It's just easy to have an all-in-one solution. And like. We are, you know, building the best scraper in the world. So scraping is a hard problem and it's easy to get like, you know, a good scraper. It's very hard to get a great scraper and it's super hard to get a perfect scraper. So like, and, and scraping really matters to people. Do you have a perfect scraper? Not yet. Okay.Swyx [00:27:05]: The web is increasingly closing to the bots and the scrapers, Twitter, Reddit, Quora, Stack Overflow. I don't know what else. How are you dealing with that? How are you navigating those things? Like, you know. You know, opening your eyes, like just paying them money.Will [00:27:19]: Yeah, no, I mean, I think it definitely makes it harder for search engines. One response is just that there's so much value in the long tail of sites that are open. Okay. Um, and just like, even just searching over those well gets you most of the value. But I mean, there, there is definitely a lot of content that is increasingly not unavailable. And so you could get through that through data partnerships. The bigger we get as a company, the more, the easier it is to just like, uh, make partnerships. But I, I mean, I do see the world as like the future where the. The data, the, the data producers, the content creators will make partnerships with the entities that find that data.Alessio [00:27:53]: Any other fun use case that maybe people are not thinking about? Yeah.Will [00:27:58]: Oh, I mean, uh, there are so many customers. Yeah. What are people doing on AXA? Well, I think dating is a really interesting, uh, application of search that is completely underserved because there's a lot of profiles on the web and a lot of people who want to find love and that I'll use it. They give me. Like, you know, age boundaries, you know, education level location. Yeah. I mean, you want to, what, what do you want to do with data? You want to find like a partner who matches this education level, who like, you know, maybe has written about these types of topics before. Like if you could get a list of all the people like that, like, I think you will unblock a lot of people. I mean, there, I mean, I think this is a very Silicon Valley view of dating for sure. And I'm, I'm well aware of that, but it's just an interesting application of like, you know, I would love to meet like an intellectual partner, um, who like shares a lot of ideas. Yeah. Like if you could do that through better search and yeah.Swyx [00:28:48]: But what is it with Jeff? Jeff has already set me up with a few people. So like Jeff, I think it's my personal exit.Will [00:28:55]: my mom's actually a matchmaker and has got a lot of married. Yeah. No kidding. Yeah. Yeah. Search is built into the book. It's in your jeans. Yeah. Yeah.Swyx [00:29:02]: Yeah. Other than dating, like I know you're having quite some success in colleges. I would just love to map out some more use cases so that our listeners can just use those examples to think about use cases for XR, right? Because it's such a general technology that it's hard to. Uh, really pin down, like, what should I use it for and what kind of products can I build with it?Will [00:29:20]: Yeah, sure. So, I mean, there are so many applications of XR and we have, you know, many, many companies using us for very diverse range of use cases, but I'll just highlight some interesting ones. Like one customer, a big customer is using us to, um, basically build like a, a writing assistant for students who want to write, uh, research papers. And basically like XR will search for, uh, like a list of research papers related to what the student is writing. And then this product has. Has like an LLM that like summarizes the papers to basically it's like a next word prediction, but in, uh, you know, prompted by like, you know, 20 research papers that X has returned. It's like literally just doing their homework for them. Yeah. Yeah. the key point is like, it's, it's, uh, you know, it's, it's, you know, research is, is a really hard thing to do and you need like high quality content as input.Swyx [00:30:08]: Oh, so we've had illicit on the podcast. I think it's pretty similar. Uh, they, they do focus pretty much on just, just research papers and, and that research. Basically, I think dating, uh, research, like I just wanted to like spell out more things, like just the big verticals.Will [00:30:23]: Yeah, yeah, no, I mean, there, there are so many use cases. So finance we talked about, yeah. I mean, one big vertical is just finding a list of companies, uh, so it's useful for VCs, like you said, who want to find like a list of competitors to a specific company they're investigating or just a list of companies in some field. Like, uh, there was one VC that told me that him and his team, like we're using XR for like eight hours straight. Like, like that. For many days on end, just like, like, uh, doing like lots of different queries of different types, like, oh, like all the companies in AI for law or, uh, all the companies for AI for, uh, construction and just like getting lists of things because you just can't find this information with, with traditional search engines. And then, you know, finding companies is also useful for, for selling. If you want to find, you know, like if we want to find a list of, uh, writing assistants to sell to, then we can just, we just use XR ourselves to find that is actually how we found a lot of our customers. Ooh, you can find your own customers using XR. Oh my God. I, in the spirit of. Uh, using XR to bolster XR, like recruiting is really helpful. It is really great use case of XR, um, because we can just get like a list of, you know, people who thought about search and just get like a long list and then, you know, reach out to those people.Swyx [00:31:29]: When you say thought about, are you, are you thinking LinkedIn, Twitter, or are you thinking just blogs?Will [00:31:33]: Or they've written, I mean, it's pretty general. So in that case, like ideally XR would return like the, the really blogs written by people who have just. So if I don't blog, I don't show up to XR, right? Like I have to blog. well, I mean, you could show up. That's like an incentive for people to blog.Swyx [00:31:47]: Well, if you've written about, uh, search in on Twitter and we, we do, we do index a bunch of tweets and then we, we should be able to service that. Yeah. Um, I mean, this is something I tell people, like you have to make yourself discoverable to the web, uh, you know, it's called learning in public, but like, it's even more imperative now because otherwise you don't exist at all.Will [00:32:07]: Yeah, no, no, this is a huge, uh, thing, which is like search engines completely influence. They have downstream effects. They influence the internet itself. They influence what people. Choose to create. And so Google, because they're a keyword based search engine, people like kind of like keyword stuff. Yeah. They're, they're, they're incentivized to create things that just match a lot of keywords, which is not very high quality. Uh, whereas XR is a search algorithm that, uh, optimizes for like high quality and actually like matching what you mean. And so people are incentivized to create content that is high quality, that like the create content that they know will be found by the right person. So like, you know, if I am a search researcher and I want to be found. By XR, I should blog about search and all the things I'm building because, because now we have a search engine like XR that's powerful enough to find them. And so the search engine will influence like the downstream internet in all sorts of amazing ways. Yeah. Uh, whatever the search engine optimizes for is what the internet looks like. Yeah.Swyx [00:33:01]: Are you familiar with the term? McLuhanism? No, it's not. Uh, it's this concept that, uh, like first we shape tools and then the tools shape us. Okay. Yeah. Uh, so there's like this reflexive connection between the things we search for and the things that get searched. Yes. So like once you change the tool. The tool that searches the, the, the things that get searched also change. Yes.Will [00:33:18]: I mean, there was a clear example of that with 30 years of Google. Yeah, exactly. Google has basically trained us to think of search and Google has Google is search like in people's heads. Right. It's one, uh, hard part about XR is like, uh, ripping people away from that notion of search and expanding their sense of what search could be. Because like when people think search, they think like a few keywords, or at least they used to, they think of a few keywords and that's it. They don't think to make these like really complex paragraph long requests for information and get a perfect list. ChatGPT was an interesting like thing that expanded people's understanding of search because you start using ChatGPT for a few hours and you go back to Google and you like paste in your code and Google just doesn't work and you're like, oh, wait, it, Google doesn't do work that way. So like ChatGPT expanded our understanding of what search can be. And I think XR is, uh, is part of that. We want to expand people's notion, like, Hey, you could actually get whatever you want. Yeah.Alessio [00:34:06]: I search on XR right now, people writing about learning in public. I was like, is it gonna come out with Alessio? Am I, am I there? You're not because. Bro. It's. So, no, it's, it's so about, because it thinks about learning, like in public, like public schools and like focuses more on that. You know, it's like how, when there are like these highly overlapping things, like this is like a good result based on the query, you know, but like, how do I get to Alessio? Right. So if you're like in these subcultures, I don't think this would work in Google well either, you know, but I, I don't know if you have any learnings.Swyx [00:34:40]: No, I'm the first result on Google.Alessio [00:34:42]: People writing about learning. In public, you're not first result anymore, I guess.Swyx [00:34:48]: Just type learning public in Google.Alessio [00:34:49]: Well, yeah, yeah, yeah, yeah. But this is also like, this is in Google, it doesn't work either. That's what I'm saying. It's like how, when you have like a movement.Will [00:34:56]: There's confusion about the, like what you mean, like your intention is a little, uh. Yeah.Alessio [00:35:00]: It's like, yeah, I'm using, I'm using a term that like I didn't invent, but I'm kind of taking over, but like, they're just so much about that term already that it's hard to overcome. If that makes sense, because public schools is like, well, it's, it's hard to overcome.Will [00:35:14]: Public schools, you know, so there's the right solution to this, which is to specify more clearly what you mean. And I'm not expecting you to do that, but so the, the right interface to search is actually an LLM.Swyx [00:35:25]: Like you should be talking to an LLM about what you want and the LLM translates its knowledge of you or knowledge of what people usually mean into a query that excellent uses, which you have called auto prompts, right?Will [00:35:35]: Or, yeah, but it's like a very light version of that. And really it's just basically the right answer is it's the wrong interface and like very soon interface to search and really to everything will be LLM. And the LLM just has a full knowledge of you, right? So we're kind of building for that world. We're skating to where the puck is going to be. And so since we're moving to a world where like LLMs are interfaced to everything, you should build a search engine that can handle complex LLM queries, queries that come from LLMs. Because you're probably too lazy, I'm too lazy too, to write like a whole paragraph explaining, okay, this is what I mean by this word. But an LLM is not lazy. And so like the LLM will spit out like a paragraph or more explaining exactly what it wants. You need a search engine that can handle that. Traditional search engines like Google or Bing, they're actually... Designed for humans typing keywords. If you give a paragraph to Google or Bing, they just completely fail. And so Exa can handle paragraphs and we want to be able to handle it more and more until it's like perfect.Alessio [00:36:24]: What about opinions? Do you have lists? When you think about the list product, do you think about just finding entries? Do you think about ranking entries? I'll give you a dumb example. So on Lindy, I've been building the spot that every week gives me like the top fantasy football waiver pickups. But every website is like different opinions. I'm like, you should pick up. These five players, these five players. When you're making lists, do you want to be kind of like also ranking and like telling people what's best? Or like, are you mostly focused on just surfacing information?Will [00:36:56]: There's a really good distinction between filtering to like things that match your query and then ranking based on like what is like your preferences. And ranking is like filtering is objective. It's like, does this document match what you asked for? Whereas ranking is more subjective. It's like, what is the best? Well, it depends what you mean by best, right? So first, first table stakes is let's get the filtering into a perfect place where you actually like every document matches what you asked for. No surgeon can do that today. And then ranking, you know, there are all sorts of interesting ways to do that where like you've maybe for, you know, have the user like specify more clearly what they mean by best. You could do it. And if the user doesn't specify, you do your best, you do your best based on what people typically mean by best. But ideally, like the user can specify, oh, when I mean best, I actually mean ranked by the, you know, the number of people who visited that site. Let's say is, is one example ranking or, oh, what I mean by best, let's say you're listing companies. What I mean by best is like the ones that have, uh, you know, have the most employees or something like that. Like there are all sorts of ways to rank a list of results that are not captured by something as subjective as best. Yeah. Yeah.Alessio [00:38:00]: I mean, it's like, who are the best NBA players in the history? It's like everybody has their own. Right.Will [00:38:06]: Right. But I mean, the, the, the search engine should definitely like, even if you don't specify it, it should do as good of a job as possible. Yeah. Yeah. No, no, totally. Yeah. Yeah. Yeah. Yeah. It's a new topic to people because we're not used to a search engine that can handle like a very complex ranking system. Like you think to type in best basketball players and not something more specific because you know, that's the only thing Google could handle. But if Google could handle like, oh, basketball players ranked by like number of shots scored on average per game, then you would do that. But you know, they can't do that. So.Swyx [00:38:32]: Yeah. That's fascinating. So you haven't used the word agents, but you're kind of building a search agent. Do you believe that that is agentic in feature? Do you think that term is distracting?Will [00:38:42]: I think it's a good term. I do think everything will eventually become agentic. And so then the term will lose power, but yes, like what we're building is agentic it in a sense that it takes actions. It decides when to go deeper into something, it has a loop, right? It feels different from traditional search, which is like an algorithm, not an agent. Ours is a combination of an algorithm and an agent.Swyx [00:39:05]: I think my reflection from seeing this in the coding space where there's basically sort of classic. Framework for thinking about this stuff is the self-driving levels of autonomy, right? Level one to five, typically the level five ones all failed because there's full autonomy and we're not, we're not there yet. And people like control. People like to be in the loop. So the, the, the level ones was co-pilot first and now it's like cursor and whatever. So I feel like if it's too agentic, it's too magical, like, like a, like a one shot, I stick a, stick a paragraph into the text box and then it spits it back to me. It might feel like I'm too disconnected from the process and I don't trust it. As opposed to something where I'm more intimately involved with the research product. I see. So like, uh, wait, so the earlier versions are, so if trying to stick to the example of the basketball thing, like best basketball player, but instead of best, you, you actually get to customize it with like, whatever the metric is that you, you guys care about. Yeah. I'm still not a basketballer, but, uh, but, but, you know, like, like B people like to be in my, my thesis is that agents level five agents failed because people like to. To kind of have drive assist rather than full self-driving.Will [00:40:15]: I mean, a lot of this has to do with how good agents are. Like at some point, if agents for coding are better than humans at all tests and then humans block, yeah, we're not there yet.Swyx [00:40:25]: So like in a world where we're not there yet, what you're pitching us is like, you're, you're kind of saying you're going all the way there. Like I kind of, I think all one is also very full, full self-driving. You don't get to see the plan. You don't get to affect the plan yet. You just fire off a query and then it goes away for a couple of minutes and comes back. Right. Which is effectively what you're saying you're going to do too. And you think there's.Will [00:40:42]: There's a, there's an in-between. I saw. Okay. So in building this product, we're exploring new interfaces because what does it mean to kick off a search that goes and takes 10 minutes? Like, is that a good interface? Because what if the search is actually wrong or it's not exactly, exactly specified to what you mean, which is why you get previews. Yeah. You get previews. So it is iterative, but ultimately once you've specified exactly what you mean, then you kind of do just want to kick off a batch job. Right. So perhaps what you're getting at is like, uh, there's this barrier with agents where you have to like explain the full context of what you mean, and a lot of failure modes happen when you have, when you don't. Yeah. There's failure modes from the agent, just not being smart enough. And then there's failure modes from the agent, not understanding exactly what you mean. And there's a lot of context that is shared between humans that is like lost between like humans and, and this like new creature.Alessio [00:41:32]: Yeah. Yeah. Because people don't know what's going on. I mean, to me, the best example of like system prompts is like, why are you writing? You're a helpful assistant. Like. Of course you should be an awful, but people don't yet know, like, can I assume that, you know, that, you know, it's like, why did the, and now people write, oh, you're a very smart software engineer, but like, you never made, you never make mistakes. Like, were you going to try and make mistakes before? So I think people don't yet have an understanding, like with, with driving people know what good driving is. It's like, don't crash, stay within kind of like a certain speed range. It's like, follow the directions. It's like, I don't really have to explain all of those things. I hope. But with. AI and like models and like search, people are like, okay, what do you actually know? What are like your assumptions about how search, how you're going to do search? And like, can I trust it? You know, can I influence it? So I think that's kind of the, the middle ground, like before you go ahead and like do all the search, it's like, can I see how you're doing it? And then maybe help show your work kind of like, yeah, steer you. Yeah. Yeah.Will [00:42:32]: No, I mean, yeah. Sure. Saying, even if you've crafted a great system prompt, you want to be part of the process itself. Uh, because the system prompt doesn't, it doesn't capture everything. Right. So yeah. A system prompt is like, you get to choose the person you work with. It's like, oh, like I want, I want a software engineer who thinks this way about code. But then even once you've chosen that person, you can't just give them a high level command and they go do it perfectly. You have to be part of that process. So yeah, I agree.Swyx [00:42:58]: Just a side note for my system, my favorite system, prompt programming anecdote now is the Apple intelligence system prompt that someone, someone's a prompt injected it and seen it. And like the Apple. Intelligence has the words, like, please don't, don't hallucinate. And it's like, of course we don't want you to hallucinate. Right. Like, so it's exactly that, that what you're talking about, like we should train this behavior into the model, but somehow we still feel the need to inject into the prompt. And I still don't even think that we are very scientific about it. Like it, I think it's almost like cargo culting. Like we have this like magical, like turn around three times, throw salt over your shoulder before you do something. And like, it worked the last time. So let's just do it the same time now. And like, we do, there's no science to this.Will [00:43:35]: I do think a lot of these problems might be ironed out in future versions. Right. So, and like, they might, they might hide the details from you. So it's like, they actually, all of them have a system prompt. That's like, you are a helpful assistant. You don't actually have to include it, even though it might actually be the way they've implemented in the backend. It should be done in RLE AF.Swyx [00:43:52]: Okay. Uh, one question I was just kind of curious about this episode is I'm going to try to frame this in terms of this, the general AI search wars, you know, you're, you're one player in that, um, there's perplexity, chat, GPT, search, and Google, but there's also like the B2B side, uh, we had. Drew Houston from Dropbox on, and he's competing with Glean, who've, uh, we've also had DD from, from Glean on, is there an appetite for Exa for my company's documents?Will [00:44:19]: There is appetite, but I think we have to be disciplined, focused, disciplined. I mean, we're already taking on like perfect web search, which is a lot. Um, but I mean, ultimately we want to build a perfect search engine, which definitely for a lot of queries involves your, your personal information, your company's information. And so, yeah, I mean, the grandest vision of Exa is perfect search really over everything, every domain, you know, we're going to have an Exa satellite, uh, because, because satellites can gather information that, uh, is not available publicly. Uh, gotcha. Yeah.Alessio [00:44:51]: Can we talk about AGI? We never, we never talk about AGI, but you had, uh, this whole tweet about, oh, one being the biggest kind of like AI step function towards it. Why does it feel so important to you? I know there's kind of like always criticism and saying, Hey, it's not the smartest son is better. It's like, blah, blah, blah. What? You choose C. So you say, this is what Ilias see or Sam see what they will see.Will [00:45:13]: I've just, I've just, you know, been connecting the dots. I mean, this was the key thing that a bunch of labs were working on, which is like, can you create a reward signal? Can you teach yourself based on a reward signal? Whether you're, if you're trying to learn coding or math, if you could have one model say, uh, be a grading system that says like you have successfully solved this programming assessment and then one model, like be the generative system. That's like, here are a bunch of programming assessments. You could train on that. It's basically whenever you could create a reward signal for some task, you could just generate a bunch of tasks for yourself. See that like, oh, on two of these thousand, you did well. And then you just train on that data. It's basically like, I mean, creating your own data for yourself and like, you know, all the labs working on that opening, I built the most impressive product doing that. And it's just very, it's very easy now to see how that could like scale to just solving, like, like solving programming or solving mathematics, which sounds crazy, but everything about our world right now is crazy.Alessio [00:46:07]: Um, and so I think if you remove that whole, like, oh, that's impossible, and you just think really clearly about like, what's now possible with like what, what they've done with O1, it's easy to see how that scales. How do you think about older GPT models then? Should people still work on them? You know, if like, obviously they just had the new Haiku, like, is it even worth spending time, like making these models better versus just, you know, Sam talked about O2 at that day. So obviously they're, they're spending a lot of time in it, but then you have maybe. The GPU poor, which are still working on making Lama good. Uh, and then you have the follower labs that do not have an O1 like model out yet. Yeah.Will [00:46:47]: This kind of gets into like, uh, what will the ecosystem of, of models be like in the future? And is there room is, is everything just gonna be O1 like models? I think, well, I mean, there's definitely a question of like inference speed and if certain things like O1 takes a long time, because that's the thing. Well, I mean, O1 is, is two things. It's like one it's it's use it's bootstrapping itself. It's teaching itself. And so the base model is smarter. But then it also has this like inference time compute where it could like spend like many minutes or many hours thinking. And so even the base model, which is also fast, it doesn't have to take minutes. It could take is, is better, smarter. I believe all models will be trained with this paradigm. Like you'll want to train on the best data, but there will be many different size models from different, very many different like companies, I believe. Yeah. Because like, I don't, yeah, I mean, it's hard, hard to predict, but I don't think opening eye is going to dominate like every possible LLM for every possible. Use case. I think for a lot of things, like you just want the fastest model and that might not involve O1 methods at all.Swyx [00:47:42]: I would say if you were to take the exit being O1 for search, literally, you really need to prioritize search trajectories, like almost maybe paying a bunch of grad students to go research things. And then you kind of track what they search and what the sequence of searching is, because it seems like that is the gold mine here, like the chain of thought or the thinking trajectory. Yeah.Will [00:48:05]: When it comes to search, I've always been skeptical. I've always been skeptical of human labeled data. Okay. Yeah, please. We tried something at our company at Exa recently where me and a bunch of engineers on the team like labeled a bunch of queries and it was really hard. Like, you know, you have all these niche queries and you're looking at a bunch of results and you're trying to identify which is matched to query. It's talking about, you know, the intricacies of like some biological experiment or something. I have no idea. Like, I don't know what matches and what, what labelers like me tend to do is just match by keyword. I'm like, oh, I don't know. Oh, like this document matches a bunch of keywords, so it must be good. But then you're actually completely missing the meaning of the document. Whereas an LLM like GB4 is really good at labeling. And so I actually think like you just we get by, which we are right now doing using like LLM

"Le dernier auditeur pensait que tout avait été codé par la même personne" Le D.E.V. de la semaine est Simon Parisot, CEO et cofondateur de Blank. Simon a fait un pari, un peu fou, au début de l'aventure Blank : avoir un environnement 100% serverless ! Lambda, DynamoDB, S3, &hellip il connait tous les services AWS, mais n'utilise pas une seule EC2 !! Il vient nous raconter comment il a construit cette plateforme, et surtout pourquoi ! Il nous explique aussi les changements que cela a sur le travail des dev (le dev en local est compllqué), les impératifs de qualité du code que cela implique et aussi comment le recrutement doit s'adapter à ce choix technique.Liens évoqués pendant l'émissionIFTTD avec Olivier Dupuis - Faites entrer le hackeurFramework serverless 🎙️ Soutenez le podcast If This Then Dev ! 🎙️ Chaque contribution aide à maintenir et améliorer nos épisodes. Cliquez ici pour nous soutenir sur Tipeee 🙏Archives | Site | Boutique | TikTok | Discord | Twitter | LinkedIn | Instagram | Youtube | Twitch | Job Board |

AWS Morning Brief for the week of December 16th, 2024, with Corey Quinn. Links:Amazon Bedrock Guardrails reduces pricing by up to 85%Amazon CloudWatch now provides centralized visibility into telemetry configurationsAmazon EC2 F2 instances, featuring up to 8 FPGAs, are generally availableAmazon SES now offers Global Endpoints for multi-region sending resilienceAWS Toolkit for Visual Studio Code now includes Amazon CloudWatch Logs Live TailAccelerate your AWS Graviton adoption with the AWS Graviton Savings DashboardCapture data changes while restoring an Amazon DynamoDB tableUnderstand the benefits of physical replication in Amazon RDS for PostgreSQL Blue/Green DeploymentsHow AWS sales uses Amazon Q Business for customer engagementAWS Network Firewall Geographic IP Filtering launchIssue with DynamoDB local - CVE-2022-1471

Hoje é dia de sobre carreira! No episódio de estreia da série especial do podcast, conversamos com Erika Nagamine, Golden Jacket da AWS, sobre a sua trajetória, sobre as suas decisões, e sobre o poder que a curiosidade teve para lhe impulsionar ao longo de toda a sua carreira. Vem ver quem participou desse papo: Paulo Silveira, o host que gosta de certificação André David, o cohost que está rolando até agora Erika Nagamine, Arquiteta de Soluções Especialista em Dados & AI - Analytics na AWS

In this pre-re:Invent 2024 episode, Luciano and Eoin discuss some of their favorite recent AWS announcements, including improvements to AWS Step Functions, Lambda runtime updates, DynamoDB price reductions, ALB header injection, Cognito enhancements, VPC public access blocking, and more. They share their thoughts on the implications of these new capabilities and look forward to seeing what else is announced at the conference. Overall, it's an exciting time for AWS developers with many new features to explore. Very important: no focus on GenAI in this episode :) AWS Bites is brought to you, as always, by fourTheorem! Sometimes, AWS is overwhelming and you might need someone to provide clear guidance in the fog of cloud offerings. That someone is fourTheorem. Check them out at ⁠fourtheorem.com⁠ In this episode, we mentioned the following resources: The repo containing the code of the AWS Bites website: https://github.com/awsbites/aws-bites-site Orama Search: https://orama.com/ JSONata in AWS Step Functions: https://aws.amazon.com/blogs/compute/simplifying-developer-experience-with-variables-and-jsonata-in-aws-step-functions/ EC2 Auto Scaling improvements: https://aws.amazon.com/about-aws/whats-new/2024/11/amazon-ec2-auto-scaling-highly-responsive-scaling-policies/ Node.js 22 is available for Lambda: https://aws.amazon.com/blogs/compute/node-js-22-runtime-now-available-in-aws-lambda/ Python 3.13 runtime: https://aws.amazon.com/blogs/compute/python-3-13-runtime-now-available-in-aws-lambda/ Aurora Serverless V2 now scales to 0: https://aws.amazon.com/about-aws/whats-new/2024/11/amazon-aurora-serverless-v2-scaling-zero-capacity/ Episode 95 covering Mountpoint for S3: https://awsbites.com/95-mounting-s3-as-a-filesystem/ One Zone caching for Mountpoint for S3: https://aws.amazon.com/about-aws/whats-new/2024/11/mountpoint-amazon-s3-high-performance-shared-cache/ Appending to S3 objects: https://docs.aws.amazon.com/AmazonS3/latest/userguide/directory-buckets-objects-append.html 1 million S3 Buckets per account: https://aws.amazon.com/about-aws/whats-new/2024/11/amazon-s3-up-1-million-buckets-per-aws-account/ DynamoDB cost reduction: https://aws.amazon.com/blogs/database/new-amazon-dynamodb-lowers-pricing-for-on-demand-throughput-and-global-tables/ ALB Headers: https://aws.amazon.com/about-aws/whats-new/2024/11/aws-application-load-balancer-header-modification-enhanced-traffic-control-security/ Cognito Managed Login: https://aws.amazon.com/blogs/aws/improve-your-app-authentication-workflow-with-new-amazon-cognito-features/ Cognito Passwordless Authentication: https://aws.amazon.com/blogs/aws/improve-your-app-authentication-workflow-with-new-amazon-cognito-features/ VPC Block Public Access: https://aws.amazon.com/blogs/networking-and-content-delivery/vpc-block-public-access/ Episode 88 where we talk about VPC Lattice: https://awsbites.com/88-what-is-vpc-lattice/ Direct integration between Lattice and ECS: https://aws.amazon.com/blogs/aws/streamline-container-application-networking-with-native-amazon-ecs-support-in-amazon-vpc-lattice/ Resource Control Policies: https://aws.amazon.com/blogs/aws/introducing-resource-control-policies-rcps-a-new-authorization-policy/ Episode 23 about EventBridge: https://awsbites.com/23-what-s-the-big-deal-with-eventbridge/ EventBridge latency improvements: https://aws.amazon.com/about-aws/whats-new/2024/11/amazon-eventbridge-improvement-latency-event-buses/ AppSync web sockets: https://aws.amazon.com/blogs/mobile/announcing-aws-appsync-events-serverless-websocket-apis/ Do you have any AWS questions you would like us to address? Leave a comment here or connect with us on X/Twitter: - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/eoins⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/loige⁠⁠⁠⁠

Simon is joined by Stephen Liedig to discuss the evolution of serverless technology and its impact on application development, exploring benefits like scalability, cost optimization, and faster dev cycles. They delve into key services and concepts in serverless design, including state machines, event-driven architectures, and observability, highlighting the flexibility and optimization opportunities offered by serverless architecture. - Get started with AWS Serverless (https://aws.amazon.com/serverless) and Application Integration (https://aws.amazon.com/products/application-integration) on the AWS website. - Visit Serverless Land (https://serverlessland.com/) to get the latest information, blogs, videos, code, and learning resources for AWS Serverless. Learn to use and build apps that scale automatically on low-cost, fully-managed serverless architecture. - Implement Serverless best practices and increase your developer velocity with Powertools for AWS (https://powertools.aws.dev/) - Learn by doing! Check out the Serverless Patterns Workshop (https://catalog.workshops.aws/serverless-patterns) to build your first serverless microservice to retrieve data from DynamoDB with Lambda and API Gateway. - Dive even deeper with the Serverless Developer Experience workshop (https://catalog.workshops.aws/serverless-developer-experience) to get hands on experience leveraging serverless application integration patterns, event-driven architectures and orchestration!

In this episode of Startup Project, we chat with Khawaja Shams, Co-founder and CEO of Momento, a serverless caching and messaging service built for interactive applications at scale. Host: Nataraj (Investor at Incisive VC, angel investor, and Senior Product Manager) Guest: Khawaja Shams (Co-founder and CEO of Momento) Website: ⁠Momento Website⁠ LinkedIn: ⁠Nataraj's LinkedIn⁠ | ⁠Khawaja's LinkedIn⁠ [0:00 - 2:00] Khawaja shares his incredible journey—from working on image processing for Mars rovers and communications for interplanetary missions at NASA to building crucial infrastructure at Amazon Web Services (AWS) and ultimately starting Momento. [2:00 - 6:00] Khawaja provides an in-depth look at his early days at NASA, where he was inspired by the company's mission and the potential of cloud computing. He discusses how he prototyped using public datasets on his personal credit card and the challenges of onboarding Amazon as a vendor in the early days of AWS. [6:00 - 10:00] We discuss Khawaja's experience at Amazon, where he witnessed the company's rapid growth and customer obsession firsthand. He details his roles in AWS product engineering and leading key teams, including DynamoDB and Elemental Technologies. [10:00 - 16:00] Khawaja talks about the inspiration behind Momento and how the need for a better caching solution for interactive applications became clear. He explains how Momento addresses the pain points of traditional caching solutions and simplifies development for users. [16:00 - 20:00] We dive deeper into Momento's target customer base and the importance of focusing on verticals like media, gaming, and fintech. Khawaja shares valuable insights on identifying the right customers and building strong design partnerships. [20:00 - 25:00] Khawaja discusses product-market fit and how Momento validated its solution through numerous successful customers. He emphasizes the need for coherence in customer asks and how that provides confidence in the product's direction. [25:00 - 30:00] We talk about B2B growth and marketing strategies, specifically how Momento leverages its existing customer base and focuses on finding similar companies. Khawaja stresses the importance of operational excellence and customer obsession in building trust and advocacy. [30:00 - 35:00] Khawaja shares his thoughts on Amazon's leadership principles and how Momento has cultivated its own unique culture focused on customer centricity and psychological safety. [35:00 - 40:00] We explore the challenges of attracting top talent in a startup environment. Khawaja emphasizes the importance of finding a team you enjoy working with and tackling a problem you believe in. [40:00 - 45:00] Khawaja shares his current consumption habits, including his favorite books and podcasts. He also highlights the importance of mentorship and staying connected with people you admire. [45:00 - 50:00] Khawaja discusses the importance of focus in a startup environment and how prioritizing a few key goals can lead to greater success. [50:00 - 55:00] We finish with a discussion about AI and how Momento plays a crucial role in enabling interactive applications powered by real-time data. #Startup #TechPodcast #Serverless #CloudComputing #AWS #InteractiveApps #B2BMarketing #Entrepreneurship #Leadership #AI #Fintech #MediaTech #GamingTech #ProductMarketFit #Caching #CustomerObsession #FoundersJourney

In this episode of the Modern Web Podcast, Rob Ocel, Danny Thompson, and Adam Rackis talk with Michael Liendo, Senior Developer Advocate at AWS, about building practical AI applications and tackling challenges like scalability, multimodal functionality, and cloud infrastructure choices. Michael shares insights on tools like AWS Amplify and DynamoDB, discusses strategies for managing cloud costs, and explores the evolving role of prompt engineering. Michael previews his upcoming talks at AWS re:Invent on AI and scalable B2B SaaS applications. Chapters 00:00 - Introduction and Guest Welcome 01:30 - Talking Weather and Life in the Midwest 03:00 - Exploring Generative AI and Practical Applications 06:45 - Navigating Cloud Costs and Scalability Considerations 08:30 - Maintaining Creativity and Customization with AI 11:00 - Managed Services vs. On-Prem Infrastructure Debate 15:30 - Choosing a Tech Stack for Side Projects and Startups 18:45 - Learning Cloud: Paths for Full-Stack Cloud Development 22:30 - The Role of Cloud Certifications in Today's Market 26:00 - Preview of Michael's Upcoming Talks at AWS re:Invent 32:00 - Where to Find Michael Online Follow Michael Liendo on Social Media Twitter: https://x.com/focusotter Linkedin: https://www.linkedin.com/in/focusotter/ Sponsored by Wix Studio: wix.com/studio

This week, co-host Conor Bronsdon sits down with Daniela Miao, co-founder and CTO of Momento, to discuss her journey from DynamoDB at AWS to founding the real-time data infrastructure platform Momento. Daniela covers the importance of observability, the decision to rebuild Momento's stack with Rust, and how observability can speed up development cycles. They also explore strategies for aligning technical projects with business objectives, building team trust, and the critical role of communication in achieving success. Tune in for valuable insights on leadership, technical decision-making, and startup growth.Topics:02:01 Why is observability often treated as an auxiliary service?06:14 Making a push for observability13:32 Picking the right metrics to observe and pay attention to15:49 Has the technical shift to Rust paid off?19:23 How did you create trust and buy in from your team to make a switch?26:31 What could other teams learn from Momento's move to Rust?38:15 Advice would you give for other technical founders?Links:Daniela MiaoThe Momento BlogMomento: An enterprise-ready serverless platform for caching and pub/subUnpacking the 2023 DORA Report w/ Nathen Harvey of Google CloudGoogle SRERust Programming LanguageSupport the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever

Hoje é dia de falar de nuvem! Neste episódio, exploramos a surpreendente relação entre a AWS e a Amazon Brasil, e as importantes questões ligadas a dimensionamento, escalabilidade e, é claro, segurança quando o assunto é nuvem. Vem ver quem participou desse papo: André David, o host que fica ligado em palavrinhas-chave Vinny Neves, co-host e Tech Lead na UsTwo Bruno Toffolo, Principal Software Development Engineer na Amazon Gaston Perez, Principal Solutions Architect na AWS

Simon is joined by Jason Hunter, AWS Principal Specialist Solutions Architect, do dive super-deep into how to make the most of DynamoDB. Whether you are new to DynamoDB, or have been using it for years - there is something in this episode for everyone! Shownotes: Jason's Blog Posts: https://aws.amazon.com/blogs/database/author/jzhunter/ The Apache Iceberg blog: https://aws.amazon.com/blogs/database/use-amazon-dynamodb-incremental-export-to-update-apache-iceberg-tables/ Traffic spikes (on-demand vs provisioned): https://aws.amazon.com/blogs/database/handle-traffic-spikes-with-amazon-dynamodb-provisioned-capacity/ Cost-effective bulk actions like delete: https://aws.amazon.com/blogs/database/cost-effective-bulk-processing-with-amazon-dynamodb/ A deep dive on partitions: https://aws.amazon.com/blogs/database/part-1-scaling-dynamodb-how-partitions-hot-keys-and-split-for-heat-impact-performance/ Global tables prescriptive guidance (the 25 page deep dive): https://docs.aws.amazon.com/prescriptive-guidance/latest/dynamodb-global-tables/introduction.html

In this episode, we discuss Luciano's new book project on using Rust to write AWS Lambda functions. We start with a recap on why Rust is a good fit for Lambda, including performance, efficiency, safety, and low cold start times. Luciano provides details on the book's progress so far, the intended audience, and the current published chapters covering Lambda internals, getting started with Rust Lambda, and building a URL shortener app with DynamoDB. We also explore the differences between traditional publishing and self-publishing, and why Luciano chose the self-publishing route for this book. Luciano shares insights into the writing process with AsciiDoc, code samples, SVG image generation, and using Gumroad for distribution. He invites feedback from listeners who have experience with Rust and Lambda.

SummaryIn this episode of What's New in Cloud FinOps, Frank and Stephen discuss a wide range of cloud-related news and updates. They cover topics such as Azure VM hibernation, Azure Compute Fleet, Google Cloud TPU, Amazon EC2 C7i Flex, DynamoDB, AWS Marketplace, Cloud Run, and more. The conversation also delves into the complexities of cloud pricing, energy progress, and the impact of cloud technology on businesses.

This is a recap of the top 10 posts on Hacker News on May 20th, 2024.This podcast was generated by wondercraft.ai(00:36): Statement from Scarlett Johansson on the OpenAI "Sky" voiceOriginal post: https://news.ycombinator.com/item?id=40421225&utm_source=wondercraft_ai(01:48): ICC prosecutor seeks arrest warrants against Sinwar and Netanyahu for war crimesOriginal post: https://news.ycombinator.com/item?id=40414329&utm_source=wondercraft_ai(03:32): 3M executives convinced a scientist forever chemicals in human blood were safeOriginal post: https://news.ycombinator.com/item?id=40414316&utm_source=wondercraft_ai(05:04): Migrating Uber's ledger data from DynamoDB to LedgerStoreOriginal post: https://news.ycombinator.com/item?id=40413891&utm_source=wondercraft_ai(06:46): EnlightenmentwareOriginal post: https://news.ycombinator.com/item?id=40419856&utm_source=wondercraft_ai(08:31): pg_timeseries: Open-source time-series extension for PostgreSQLOriginal post: https://news.ycombinator.com/item?id=40417347&utm_source=wondercraft_ai(10:07): How a 64k intro is made (2017)Original post: https://news.ycombinator.com/item?id=40414565&utm_source=wondercraft_ai(11:42): CVE-2024-4367 – Arbitrary JavaScript execution in PDF.jsOriginal post: https://news.ycombinator.com/item?id=40414718&utm_source=wondercraft_ai(13:21): Rethinking Text Resizing on WebOriginal post: https://news.ycombinator.com/item?id=40418591&utm_source=wondercraft_ai(15:13): Grothendieck's use of equalityOriginal post: https://news.ycombinator.com/item?id=40414404&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

In this episode, we provide a comprehensive overview of DynamoDB, including how it compares to relational databases, when to use it, how to get started, writing and querying data, secondary indexes, and single table design. We share our experiences using DynamoDB and discuss the pros and cons compared to traditional SQL databases.

April has been a month of both highs and lows. At the start of the month, AWS was ordered to pay $525 million in damages, after it was found to have infringed US patent law through some of its core cloud offerings.The month has also seen a high-profile cyber incident – a ransomware attack against Change Healthcare in which personal information was stolen.All of this has unfolded against the backdrop of more innovation in the AI space, with the launch of Llama 3 and news from Google Cloud Next having buoyed market interest.In this episode, Jane and Rory speak once again to Ross Kelly, ITPro's news and analysis editor, to explore some of the month's most notable news items.Read more:AWS fined $525 million after US court rules Amazon S3 storage, DynamoDB services infringed patentsChange Healthcare hit with second ransomware attack of 2024UnitedHealth Group admits to paying ransom after Change Healthcare cyber attackCitrix vulnerability behind Change Healthcare cyber attack, CEO claimsMeta's Llama 3 will force OpenAI and other AI giants to up their gameJust how open are the leading open source AI models?

Today's episode is with Khawaja Shams. Khawaja is the CEO and co-founder of Momento, which is a Serverless Cache.  He used to lead the DynamoDB team at AWS and now he's doing Memento. We talk about a lot of different things, including multi-tenancy and cellular architecture and what it's like to build on AWS and sell infrastructure products to end customers and just a lot of other really good stuff. We hope you enjoy this episode. 01:12 Introduction 03:38 multi-tenancy 08:13 S3 and Tigris 15:09 Aurora 19:11 Momento 31:21 Cellular Architecture 41:16 Most people are doing cross-AZ wrong 52:23 Elasticsearch 01:03:08 Rapid Fire

This Day in Legal History: Civil Rights Act of 1968On April 11, 1968, a significant moment in the history of American civil rights unfolded when President Lyndon B. Johnson signed the Civil Rights Act of 1968, widely known as the Fair Housing Act, into law. This legislation was a watershed in the struggle for equality, aimed at eradicating discrimination in the housing sector. It came as an amendment to the landmark Civil Rights Act of 1964, extending its reach to combat racial, religious, and national origin discrimination in the sale, rental, and financing of housing, as well as in housing-related advertising.The enactment of the Fair Housing Act was the culmination of years of civil rights activism and was influenced by the broader civil rights movement that sought to challenge and dismantle systemic racism across various facets of American life. Its passage was not easy, faced with considerable opposition, and was one of the final acts of civil rights legislation signed by President Johnson. The Act represented not just a legal milestone but a profound statement about the values of equality and justice in American society.Moreover, the Fair Housing Act also laid the groundwork for further legislative efforts to protect individuals from discrimination, including expansions to cover gender, disability, and familial status. This evolving framework reflected a growing recognition of the diverse forms of discrimination that Americans faced and the ongoing need to address these injustices within the legal system.Today, the Fair Housing Act stands as a testament to the enduring struggle for civil rights in the United States. It reminds us of the pivotal role of law in shaping a more equitable society and the continuous effort required to protect and extend these gains. As we reflect on its significance, the Fair Housing Act encourages us to persist in the pursuit of justice and equality for all Americans, acknowledging the progress made and the challenges that remain.Water utilities are bracing for the financial burden of meeting the EPA's new stringent standards for PFAS (per- and polyfluoroalkyl substances) levels in drinking water. The EPA's recent regulation, marking the first-ever limits on PFAS, demands the reduction of "forever chemicals" to nearly zero, specifically setting enforceable limits for certain PFAS compounds at 4 parts per trillion and others at 10 parts per trillion. Legal and industry experts predict this will lead to a slew of legal challenges due to the vast number of water systems—potentially affecting 6,700 systems serving about 100 million people—that will need to implement costly testing and removal technologies. The estimated compliance costs could reach up to $40 billion in initial investments plus $3.8 billion annually, far surpassing the EPA's own estimate of $1.5 billion, with ratepayers likely facing significant increases in water bills. Despite available federal funding for infrastructure and PFAS removal, critics argue it's insufficient to cover the extensive needs. However, proponents of the rule argue the public health benefits, including reduced cancer risks from lower PFAS exposure, justify the high costs. This new regulation is seen as a crucial step in addressing the pervasive issue of PFAS pollution, despite the anticipated financial and legal hurdles ahead.Utilities Brace for Costs of Compliance With New PFAS Water RuleAn Illinois federal jury has ruled that Amazon.com Inc. must pay $525 million to Kove IO Inc. for infringing on three patents associated with distributed cloud storage technology. This decision, emerging from a lawsuit filed by Kove in 2018, indicates that Amazon's infringement was not deemed willful, dismissing Amazon's defenses of non-infringement, invalidity, and unpatentability. The patents in question enable the efficient identification of the multiple servers storing specific data files in the cloud, a technological advancement Kove claims is fundamental to the operation of scalable cloud systems. Kove's lawsuit argued that Amazon Web Services (AWS), specifically its Amazon Simple Storage Service and DynamoDB products, were built upon and benefited significantly from Kove's patented technology. This infringement, according to Kove, was critical to AWS's growth into Amazon's most profitable segment. The case, represented by several law firms on both sides, underscores significant legal and financial implications for Amazon and highlights the value and competitive edge provided by proprietary cloud storage technologies.Amazon Dealt $525 Million Jury Verdict Over Cloud Tech PatentsAhead of the estate tax changes set for 2025, Democrats are targeting the tax avoidance strategies of the wealthy, particularly focusing on the use of trusts. This initiative previews a broader debate around tax reform and the expiration of certain tax cuts from the 2017 tax law. Senators Ron Wyden and Elizabeth Warren, along with the Biden administration, have proposed measures to tighten restrictions on trusts, aiming to curb tax dodges. These strategies include using grantor retained annuity trusts (GRATs) by the ultra-wealthy to transfer assets tax-free to heirs, a method utilized by prominent figures like Nike founder Phillip Knight.Wyden's proposed legislation seeks to impose a minimum remaining trust value and a 15-year term for GRATs, aiming to eliminate the tax benefits of underperforming trusts. Warren's approach includes stricter trust regulations and increased IRS funding to enhance tax avoidance audits. The Biden administration's Greenbook outlines policies estimated to raise $97 billion over ten years through tightened trust restrictions and improved tax administration.Republicans, on the other hand, are advocating for a full repeal of the estate tax, emphasizing the 2017 tax law's increase in exemption amounts. However, the potential for bipartisan agreement exists, particularly on loophole-closing measures that don't involve tax rate increases. Despite efforts to reform, experts caution that as long as the tax code remains complex, individuals will find ways to minimize tax liabilities, underscoring the challenge of achieving comprehensive tax fairness.Warren, Democrats Target Estate Tax Dodges Ahead of 2025 FightThe U.S. Justice Department has finalized a rule that mandates gun dealers to obtain federal licenses and conduct background checks on purchasers, regardless of the sales venue, aiming to close the "gun show loophole." This new regulation broadens the definition of being "engaged in the business" of selling firearms to include those selling at gun shows, online, and other venues, aligning them with the requirements faced by traditional gun stores. An estimated 23,000 individuals in the U.S. who deal guns without a license are expected to be affected, impacting tens of thousands of gun sales annually. U.S. Attorney General Merrick Garland emphasized that the rule applies uniformly across sales platforms, requiring licensure and background checks for anyone selling guns predominantly for profit.The rule, proposed in August and after a public commenting period, will be published in the Federal Register and take effect 30 days post-publication. However, it stops short of establishing universal background checks, allowing certain transfers, like those among family members, without checks. This development follows federal gun reform legislation passed in June 2022 after multiple mass shootings and a Supreme Court decision broadening gun owners' rights. In March 2023, President Joe Biden issued an executive order to expand background checks and called for further Congressional action to mitigate gun violence. The rule is anticipated to face legal challenges from gun rights groups.US to close 'gun show loophole' and require more background checks | Reuters Get full access to Minimum Competence - Daily Legal News Podcast at www.minimumcomp.com/subscribe

In this episode, I spoke with Waldemar Hummer, founder and CTO of LocalStack. We discussed what's new in the latest version of LocalStack and highlighted some of the most interesting additions.One particular highlight for me is the ability to identify IAM permission errors between direct service integrations. For example, when an EventBridge pipe cannot deliver a message to a SQS target. And the ability to use test runs to generate the necessary IAM permissions so they can be added to your Lambda functions.LocalStack v3 also allows running chaos experiments locally by adding random latency spikes, making an entire AWS region unavailable, or simulating DynamoDB throughput-exceeded errors.Lots of exciting new features in LocalStack v3! Waldemar gave us a live demo of some of these features. You can watch the episode on YouTube and watch the demos here.Links from the episode:My webinar with Waldemar after LocalStack v2LocalStack v3 announcementMy blog post on when to use Step Functions vs running everything in LambdaLocalStack is hiring!Opening theme song:Cheery Monday by Kevin MacLeodLink: https://incompetech.filmmusic.io/song/3495-cheery-mondayLicense: http://creativecommons.org/licenses/by/4.0

In this episode, Lane talks to Alex DeBrie, author of the DynamoDB book. Today's talk covers various aspects such as DynamoDB's comparison with Amazon S3, its benefits, use cases, constraints, and cost considerations, while also covering other AWS and Google Cloud services. Alex also shares his insights into his journey of writing the book on DynamoDB and touches on topics like access patterns, secondary indexes, and billing modes. Alex also shares his professional experiences, including consulting vs freelancing, thoughts of entrepreneurial aspirations, and gives helpful advice for those that are considering pursuing a similar career.Learn back-end development - https://boot.devListen on your favorite podcast player: https://www.backendbanter.fmAlex's Twitter: https://twitter.com/alexbdebrieAlex's Website: https://www.alexdebrie.com(00:00) - Introduction (01:27) - Who is Alex DeBrie? (02:39) - What is DynamoDB? (04:15) - EC2 instance (05:50) - Amazon S3 (06:25) - DynamoDB is more like S3 (07:40) - Difference between DynamoDB and S3 (08:20) - What do we mean when we say NoSQL (10:08) - BigQuery and BigTable (12:31) - Some of DynamoDB's benefits (13:15) - When to use DynamoDB (15:58) - Constraint of number of connections (18:06) - DynamoDB is a multi-tenant service (19:21) - How does DynamoDB shake up against something like MongoDB (22:22) - DynamoDB is opinionated, but it provides good results consistently (25:54) - You can only do certain things in DynamoDB, but they are guaranteed to be fast (26:42) - Relational Databases - Theory vs Practicality (31:08) - How Alex came to write a book about DynamoDB (32:15) - What happens when SQL runs, depends heavily on the system underneath (33:57) - DynamoDB doesn't have a query planner (36:08) - Access patterns (38:04) - Use case for Secondary Indexes (39:43) - Costs of DynamoDB (40:45) - Billing modes for DynamoDB (45:26) - Provisioning and planning for expenses (48:40) - Super Mario 64 Hack (49:34) - What Was Alex's Last Full Time Job (51:02) - Consulting vs Freelancing (52:23) - Does Alex see himself going back to a Full Time Job? (53:07) - Does Alex have any entrepreneurial urges? (54:01) - What you should think about before jumping into freelance/consulting (56:01) - Authority in the consulting world (57:11) - Where to find Alex

Evelyn Osman, Principal Platform Engineer at AutoScout24, joins Corey on Screaming in the Cloud to discuss the dire need for developers to agree on a standardized tool set in order to scale their projects and innovate quickly. Corey and Evelyn pick apart the new products being launched in cloud computing and discover a large disconnect between what the industry needs and what is actually being created. Evelyn shares her thoughts on why viewing platforms as products themselves forces developers to get into the minds of their users and produces a better end result.About EvelynEvelyn is a recovering improviser currently role playing as a Lead Platform Engineer at Autoscout24 in Munich, Germany. While she says she specializes in AWS architecture and integration after spending 11 years with it, in truth she spends her days convincing engineers that a product mindset will make them hate their product managers less.Links Referenced:LinkedIn: https://www.linkedin.com/in/evelyn-osman/TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn. My guest today is Evelyn Osman, engineering manager at AutoScout24. Evelyn, thank you for joining me.Evelyn: Thank you very much, Corey. It's actually really fun to be on here.Corey: I have to say one of the big reasons that I was enthused to talk to you is that you have been using AWS—to be direct—longer than I have, and that puts you in a somewhat rarefied position where AWS's customer base has absolutely exploded over the past 15 years that it's been around, but at the beginning, it was a very different type of thing. Nowadays, it seems like we've lost some of that magic from the beginning. Where do you land on that whole topic?Evelyn: That's actually a really good point because I always like to say, you know, when I come into a room, you know, I really started doing introductions like, “Oh, you know, hey,” I'm like, you know, “I'm this director, I've done this XYZ,” and I always say, like, “I'm Evelyn, engineering manager, or architect, or however,” and then I say, you know, “I've been working with AWS, you know, 11, 12 years,” or now I can't quite remember.Corey: Time becomes a flat circle. The pandemic didn't help.Evelyn: [laugh] Yeah, I just, like, a look at that the year, and I'm like, “Jesus. It's been that long.” Yeah. And usually, like you know, you get some odd looks like, “Oh, my God, you must be a sage.” And for me, I'm… you see how different services kind of, like, have just been reinventions of another one, or they just take a managed service and make another managed service around it. So, I feel that there's a lot of where it's just, you know, wrapping up a pretty bow, and calling it something different, it feels like.Corey: That's what I've been low-key asking people for a while now over the past year, namely, “What is the most foundational, interesting thing that AWS has done lately, that winds up solving for this problem of whatever it is you do as a company? What is it that has foundationally made things better that AWS has put out in the last service? What was it?” And the answers I get are all depressingly far in the past, I have to say. What's yours?Evelyn: Honestly, I think the biggest game-changer I remember experiencing was at an analyst summit in Stockholm when they announced Lambda.Corey: That was announced before I even got into this space, as an example of how far back things were. And you're right. That was transformative. That was awesome.Evelyn: Yeah, precisely. Because before, you know, we were always, like, trying to figure, okay, how do we, like, launch an instance, run some short code, and then clean it up. AWS is going to charge for an hour, so we need to figure out, you know, how to pack everything into one instance, run for one hour. And then they announced Lambda, and suddenly, like, holy shit, this is actually a game changer. We can actually write small functions that do specific things.And, you know, you go from, like, microservices, like, to like, tiny, serverless functions. So, that was huge. And then DynamoDB along with that, really kind of like, transformed the entire space for us in many ways. So, back when I was at TIBCO, there was a few innovations around that, even, like, one startup inside TIBCO that quite literally, their entire product was just Lambda functions. And one of their problems was, they wanted to sell in the Marketplace, and they couldn't figure out how to sell Lambda on the marketplace.Corey: It's kind of wild when we see just how far it's come, but also how much they've announced that doesn't change that much, to be direct. For me, one of the big changes that I remember that really made things better for customers—thought it took a couple of years—was EFS. And even that's a little bit embarrassing because all that is, “All right, we finally found a way to stuff a NetApp into us-east-1,” so now NFS, just like you used to use it in the 90s and the naughts, can be done responsibly in the cloud. And that, on some level, wasn't a feature launch so much as it was a concession to the ways that companies had built things and weren't likely to change.Evelyn: Honestly, I found the EFS launch to be a bit embarrassing because, like, you know, when you look closer at it, you realize, like, the performance isn't actually that great.Corey: Oh, it was horrible when it launched. It would just slam to a halt because you got the IOPS scaled with how much data you stored on it. The documentation explicitly said to use dd to start loading a bunch of data onto it to increase the performance. It's like, “Look, just sandbag the thing so it does what you'd want.” And all that stuff got fixed, but at the time it looked like it was clown shoes.Evelyn: Yeah, and that reminds me of, like, EBS's, like, gp2 when we're, like you know, we're talking, like, okay, provision IOPS with gp2. We just kept saying, like, just give yourself really big volume for performance. And it feel like they just kind of kept that with EFS. And it took years for them to really iterate off of that. Yeah, so, like, EFS was a huge thing, and I see us, we're still using it now today, and like, we're trying to integrate, especially for, like, data center migrations, but yeah, you always see that a lot of these were first more for, like, you know, data centers to the cloud, you know. So, first I had, like, EC2 classic. That's where I started. And I always like to tell a story that in my team, we're talking about using AWS, I was the only person fiercely against it because we did basically large data processing—sorry, I forget the right words—data analytics. There we go [laugh].Corey: I remember that, too. When it first came out, it was, “This sounds dangerous and scary, and it's going to be a flash in the pan because who would ever trust their core compute infrastructure to some random third-party company, especially a bookstore?” And yeah, I think I got that one very wrong.Evelyn: Yeah, exactly. I was just like, no way. You know, I see all these articles talking about, like, terrible disk performance, and here I am, where it's like, it's my bread and butter. I'm specialized in it, you know? I write code in my sleep and such.[Yeah, the interesting thing is, I was like, first, it was like, I can 00:06:03] launch services, you know, to kind of replicate when you get in a data center to make it feature comparable, and then it was taking all this complex services and wrapping it up in a pretty bow for—as a managed service. Like, EKS, I think, was the biggest one, if we're looking at managed services. Technically Elasticsearch, but I feel like that was the redheaded stepchild for quite some time.Corey: Yeah, there was—Elasticsearch was a weird one, and still is. It's not a pleasant service to run in any meaningful sense. Like, what people actually want as the next enhancement that would excite everyone is, I want a serverless version of this thing where I can just point it at a bunch of data, I hit an API that I don't have to manage, and get Elasticsearch results back from. They finally launched a serverless offering that's anything but. You have to still provision compute units for it, so apparently, the word serverless just means managed service over at AWS-land now. And it just, it ties into the increasing sense of disappointment I've had with almost all of their recent launches versus what I felt they could have been.Evelyn: Yeah, the interesting thing about Elasticsearch is, a couple of years ago, they came out with OpenSearch, a competing Elasticsearch after [unintelligible 00:07:08] kind of gave us the finger and change the licensing. I mean, OpenSearch actually become a really great offering if you run it yourself, but if you use their managed service, it can kind—you lose all the benefits, in a way.Corey: I'm curious, as well, to get your take on what I've been seeing that I think could only be described as an internal shift, where it's almost as if there's been a decree passed down that every service has to run its own P&L or whatnot, and as a result, everything that gets put out seems to be monetized in weird ways, even when I'd argue it shouldn't be. The classic example I like to use for this is AWS Config, where it charges you per evaluation, and that happens whenever a cloud resource changes. What that means is that by using the cloud dynamically—the way that they supposedly want us to do—we wind up paying a fee for that as a result. And it's not like anyone is using that service in isolation; it is definitionally being used as people are using other cloud resources, so why does it cost money? And the answer is because literally everything they put out costs money.Evelyn: Yep, pretty simple. Oftentimes, there's, like, R&D that goes into it, but the charges seem a bit… odd. Like from an S3 lens, was, I mean, that's, like, you know, if you're talking about services, that was actually a really nice one, very nice holistic overview, you know, like, I could drill into a data lake and, like, look into things. But if you actually want to get anything useful, you have to pay for it.Corey: Yeah. Everything seems to, for one reason or another, be stuck in this place where, “Well, if you want to use it, it's going to cost.” And what that means is that it gets harder and harder to do anything that even remotely resembles being able to wind up figuring out where's the spend going, or what's it going to cost me as time goes on? Because it's not just what are the resources I'm spinning up going to cost, what are the second, third, and fourth-order effects of that? And the honest answer is, well, nobody knows. You're going to have to basically run an experiment and find out.Evelyn: Yeah. No, true. So, what I… at AutoScout, we actually ended up doing is—because we're trying to figure out how to tackle these costs—is they—we built an in-house cost allocation solution so we could track all of that. Now, AWS has actually improved Cost Explorer quite a bit, and even, I think, Billing Conductor was one that came out [unintelligible 00:09:21], kind of like, do a custom tiered and account pricing model where you can kind of do the same thing. But even that also, there is a cost with it.I think that was trying to compete with other, you know, vendors doing similar solutions. But it still isn't something where we see that either there's, like, arbitrarily low pricing there, or the costs itself doesn't really quite make sense. Like, AWS [unintelligible 00:09:45], as you mentioned, it's a terrific service. You know, we try to use it for compliance enforcement and other things, catching bad behavior, but then as soon as people see the price tag, we just run away from it. So, a lot of the security services themselves, actually, the costs, kind of like, goes—skyrockets tremendously when you start trying to use it across a large organization. And oftentimes, the organization isn't actually that large.Corey: Yeah, it gets to this point where, especially in small environments, you have to spend more energy and money chasing down what the cost is than you're actually spending on the thing. There were blog posts early on that, “Oh, here's how you analyze your bill with Redshift,” and that was a minimum 750 bucks a month. It's, well, I'm guessing that that's not really for my $50 a month account.Evelyn: Yeah. No, precisely. I remember seeing that, like, entire ETL process is just, you know, analyze your invoice. Cost [unintelligible 00:10:33], you know, is fantastic, but at the end of the day, like, what you're actually looking at [laugh], is infinitesimally small compared to all the data in that report. Like, I think oftentimes, it's simply, you know, like, I just want to look at my resources and allocate them in a multidimensional way. Which actually isn't really that multidimensional, when you think about it [laugh].Corey: Increasingly, Cost Explorer has gotten better. It's not a new service, but every iteration seems to improve it to a point now where I'm talking to folks, and they're having a hard time justifying most of the tools in the cost optimization space, just because, okay, they want a percentage of my spend on AWS to basically be a slightly better version of a thing that's already improving and works for free. That doesn't necessarily make sense. And I feel like that's what you get trapped into when you start going down the VC path in the cost optimization space. You've got to wind up having a revenue model and an offering that scales through software… and I thought, originally, I was going to be doing something like that. At this point, I'm unconvinced that anything like that is really tenable.Evelyn: Yeah. When you're a small organization you're trying to optimize, you might not have the expertise and the knowledge to do so, so when one of these small consultancies comes along, saying, “Hey, we're going to charge you a really small percentage of your invoice,” like, okay, great. That's, like, you know, like, a few $100 a month to make sure I'm fully optimized, and I'm saving, you know, far more than that. But as soon as your invoice turns into, you know, it's like $100,000, or $300,000 or more, that percentage becomes rather significant. And I've had vendors come to me and, like, talk to me and is like, “Hey, we can, you know, for a small percentage, you know, we're going to do this machine learning, you know, AI optimization for you. You know, you don't have to do anything. We guaranteed buybacks your RIs.” And as soon as you look at the price tag with it, we just have to walk away. Or oftentimes we look at it, and there are truly very simple ways to do it on your own, if you just kind of put some thought into it.Corey: While we want to talking a bit before this show, you taught me something new about GameLift, which I think is a different problem that AWS has been dealing with lately. I've never paid much attention to it because it is the—as I assume from what it says on the tin, oh, it's a service for just running a whole bunch of games at scale, and I'm not generally doing that. My favorite computer game remains to be Twitter at this point, but that's okay. What is GameLift, though, because you want to shining a different light on it, which makes me annoyed that Amazon Marketing has not pointed this out.Evelyn: Yeah, so I'll preface this by saying, like, I'm not an expert on GameLift. I haven't even spun it up myself because there's quite a bit of price. I learned this fall while chatting with an SA who works in the gaming space, and it kind of like, I went, like, “Back up a second.” If you think about, like, I'm, you know, like, World of Warcraft, all you have are thousands of game clients all over the world, playing the same game, you know, on the same server, in the same instance, and you need to make sure, you know, that when I'm running, and you're running, that we know that we're going to reach the same point the same time, or if there's one object in that room, that only one of us can get it. So, all these servers are doing is tracking state across thousands of clients.And GameLift, when you think about your dedicated game service, it really is just multi-region distributed state management. Like, at the basic, that's really what it is. Now, there's, you know, quite a bit more happening within GameLift, but that's what I was going to explain is, like, it's just state management. And there are far more use cases for it than just for video games.Corey: That's maddening to me because having a global session state store, for lack of a better term, is something that so many customers have built themselves repeatedly. They can build it on top of primitives like DynamoDB global tables, or alternately, you have a dedicated region where that thing has to live and everything far away takes forever to round-trip. If they've solved some of those things, why on earth would they bury it under a gaming-branded service? Like, offer that primitive to the rest of us because that's useful.Evelyn: No, absolutely. And honestly, I wouldn't be surprised if you peeled back the curtain with GameLift, you'll find a lot of—like, several other you know, AWS services that it's just built on top of. I kind of mentioned earlier is, like, what I see now with innovation, it's like we just see other services packaged together and releases a new product.Corey: Yeah, IoT had the same problem going on for years where there was a lot of really good stuff buried in there, like IOT events. People were talking about using that for things like browser extensions and whatnot, but you need to be explicitly told that that's a thing that exists and is handy, but otherwise you'd never know it was there because, “Well, I'm not building anything that's IoT-related. Why would I bother?” It feels like that was one direction that they tended to go in.And now they take existing services that are, mmm, kind of milquetoast, if I'm being honest, and then saying, “Oh, like, we have Comprehend that does, effectively detection of themes, keywords, and whatnot, from text. We're going to wind up re-releasing that as Comprehend Medical.” Same type of thing, but now focused on a particular vertical. Seems to me that instead of being a specific service for that vertical, just improve the baseline the service and offer HIPAA compliance if it didn't exist already, and you're mostly there. But what do I know? I'm not a product manager trying to get promoted.Evelyn: Yeah, that's true. Well, I was going to mention that maybe it's the HIPAA compliance, but actually, a lot of their services already have HIPAA compliance. And I've stared far too long at that compliance section on AWS's site to know this, but you know, a lot of them actually are HIPAA-compliant, they're PCI-compliant, and ISO-compliant, and you know, and everything. So, I'm actually pretty intrigued to know why they [wouldn't 00:16:04] take that advantage.Corey: I just checked. Amazon Comprehend is itself HIPAA-compliant and is qualified and certified to hold Personal Health Information—PHI—Private Health Information, whatever the acronym stands for. Now, what's the difference, then, between that and Medical? In fact, the HIPAA section says for Comprehend Medical, “For guidance, see the previous section on Amazon Comprehend.” So, there's no difference from a regulatory point of view.Evelyn: That's fascinating. I am intrigued because I do know that, like, within AWS, you know, they have different segments, you know? There's, like, Digital Native Business, there's Enterprise, there's Startup. So, I am curious how things look over the engineering side. I'm going to talk to somebody about this now [laugh].Corey: Yeah, it's the—like, I almost wonder, on some level, it feels like, “Well, we wound to building this thing in the hopes that someone would use it for something. And well, if we just use different words, it checks a box in some analyst's chart somewhere.” I don't know. I mean, I hate to sound that negative about it, but it's… increasingly when I talk to customers who are active in these spaces around the industry vertical targeted stuff aimed at their industry, they're like, “Yeah, we took a look at it. It was adorable, but we're not using it that way. We're going to use either the baseline version or we're going to work with someone who actively gets our industry.” And I've heard that repeated about three or four different releases that they've put out across the board of what they've been doing. It feels like it is a misunderstanding between what the world needs and what they're able to or willing to build for us.Evelyn: Not sure. I wouldn't be surprised, if we go far enough, it could probably be that it's just a product manager saying, like, “We have to advertise directly to the industry.” And if you look at it, you know, in the backend, you know, it's an engineer, you know, kicking off a build and just changing the name from Comprehend to Comprehend Medical.Corey: And, on some level, too, they're moving a lot more slowly than they used to. There was a time where they were, in many cases, if not the first mover, the first one to do it well. Take Code Whisperer, their AI powered coding assistant. That would have been a transformative thing if GitHub Copilot hadn't beaten them every punch, come out with new features, and frankly, in head-to-head experiments that I've run, came out way better as a product than what Code Whisperer is. And while I'd like to say that this is great, but it's too little too late. And when I talk to engineers, they're very excited about what Copilot can do, and the only people I see who are even talking about Code Whisperer work at AWS.Evelyn: No, that's true. And so, I think what's happening—and this is my opinion—is that first you had AWS, like, launching a really innovative new services, you know, that kind of like, it's like, “Ah, it's a whole new way of running your workloads in the cloud.” Instead of you know, basically, hiring a whole team, I just click a button, you have your instance, you use it, sell software, blah, blah, blah, blah. And then they went towards serverless, and then IoT, and then it started targeting large data lakes, and then eventually that kind of run backwards towards security, after the umpteenth S3 data leak.Corey: Oh, yeah. And especially now, like, so they had a hit in some corners with SageMaker, so now there are 40 services all starting with the word SageMaker. That's always pleasant.Evelyn: Yeah, precisely. And what I kind of notice is… now they're actually having to run it even further back because they caught all the corporations that could pivot to the cloud, they caught all the startups who started in the cloud, and now they're going for the larger behemoths who have massive data centers, and they don't want to innovate. They just want to reduce this massive sysadmin team. And I always like to use the example of a Bare Metal. When that came out in 2019, everybody—we've all kind of scratched your head. I'm like, really [laugh]?Corey: Yeah, I could see where it makes some sense just for very specific workloads that involve things like specific capabilities of processors that don't work under emulation in some weird way, but it's also such a weird niche that I'm sure it's there for someone. My default assumption, just given the breadth of AWS's customer base, is that whenever I see something that they just announced, well, okay, it's clearly not for me; that doesn't mean it's not meeting the needs of someone who looks nothing like me. But increasingly as I start exploring the industry in these services have time to percolate in the popular imagination and I still don't see anything interesting coming out with it, it really makes you start to wonder.Evelyn: Yeah. But then, like, I think, like, roughly a year or something, right after Bare Metal came out, they announced Outposts. So, then it was like, another way to just stay within your data center and be in the cloud.Corey: Yeah. There's a bunch of different ways they have that, okay, here's ways you can run AWS services on-prem, but still pay us by the hour for the privilege of running things that you have living in your facility. And that doesn't seem like it's quite fair.Evelyn: That's exactly it. So, I feel like now it's sort of in diminishing returns and sort of doing more cloud-native work compared to, you know, these huge opportunities, which is everybody who still has a data center for various reasons, or they're cloud-native, and they grow so big, that they actually start running their own data centers.Corey: I want to call out as well before we wind up being accused of being oblivious, that we're recording this before re:Invent. So, it's entirely possible—I hope this happens—that they announce something or several some things that make this look ridiculous, and we're embarrassed to have had this conversation. And yeah, they're totally getting it now, and they have completely surprised us with stuff that's going to be transformative for almost every customer. I've been expecting and hoping for that for the last three or four re:Invents now, and I haven't gotten it.Evelyn: Yeah, that's right. And I think there's even a new service launches that actually are missing fairly obvious things in a way. Like, mine is the Managed Workflow for Amazon—it's Managed Airflow, sorry. So, we were using Data Pipeline for, you know, big ETL processing, so it was an in-house tool we kind of built at Autoscout, we do platform engineering.And it was deprecated, so we looked at a new—what to replace it with. And so, we looked at Airflow, and we decided this is the way to go, we want to use managed because we don't want to maintain our own infrastructure. And the problem we ran into is that it doesn't have support for shared VPCs. And we actually talked to our account team, and they were confused. Because they said, like, “Well, every new service should support it natively.” But it just didn't have it. And that's, kind of, what, I kind of found is, like, there's—it feels—sometimes it's—there's a—it's getting rushed out the door, and it'll actually have a new managed service or new service launched out, but they're also sort of cutting some corners just to actually make sure it's packaged up and ready to go.Corey: When I'm looking at this, and seeing how this stuff gets packaged, and how it's built out, I start to understand a pattern that I've been relatively down on across the board. I'm curious to get your take because you work at a fairly sizable company as an engineering manager, running teams of people who do this sort of thing. Where do you land on the idea of companies building internal platforms to wrap around the offerings that the cloud service providers that they use make available to them?Evelyn: So, my opinion is that you need to build out some form of standardized tool set in order to actually be able to innovate quickly. Now, this sounds counterintuitive because everyone is like, “Oh, you know, if I want to innovate, I should be able to do this experiment, and try out everything, and use what works, and just release it.” And that greatness [unintelligible 00:23:14] mentality, you know, it's like five talented engineers working to build something. But when you have, instead of five engineers, you have five teams of five engineers each, and every single team does something totally different. You know, one uses Scala, and other on TypeScript, another one, you know .NET, and then there could have been a [last 00:23:30] one, you know, comes in, you know, saying they're still using Ruby.And then next thing you know, you know, you have, like, incredibly diverse platforms for services. And if you want to do any sort of like hiring or cross-training, it becomes incredibly difficult. And actually, as the organization grows, you want to hire talent, and so you're going to have to hire, you know, a developer for this team, you going to have to hire, you know, Ruby developer for this one, a Scala guy here, a Node.js guy over there.And so, this is where we say, “Okay, let's agree. We're going to be a Scala shop. Great. All right, are we running serverless? Are we running containerized?” And you agree on those things. So, that's already, like, the formation of it. And oftentimes, you start with DevOps. You'll say, like, “I'm a DevOps team,” you know, or doing a DevOps culture, if you do it properly, but you always hit this scaling issue where you start growing, and then how do you maintain that common tool set? And that's where we start looking at, you know, having a platform… approach, but I'm going to say it's Platform-as-a-Product. That's the key.Corey: Yeah, that's a good way of framing it because originally, the entire world needed that. That's what RightScale was when EC2 first came out. It was a reimagining of the EC2 console that was actually usable. And in time, AWS improved that to the point where RightScale didn't really have a place anymore in a way that it had previously, and that became a business challenge for them. But you have, what is it now, 2, 300 services that AWS has put out, and out, and okay, great. Most companies are really only actively working with a handful of those. How do you make those available in a reasonable way to your teams, in ways that aren't distracting, dangerous, et cetera? I don't know the answer on that one.Evelyn: Yeah. No, that's true. So, full disclosure. At AutoScout, we do platform engineering. So, I'm part of, like, the platform engineering group, and we built a platform for our product teams. It's kind of like, you need to decide to [follow 00:25:24] those answers, you know? Like, are we going to be fully containerized? Okay, then, great, we're going to use Fargate. All right, how do we do it so that developers don't actually—don't need to think that they're running Fargate workloads?And that's, like, you know, where it's really important to have those standardized abstractions that developers actually enjoy using. And I'd even say that, before you start saying, “Ah, we're going to do platform,” you say, “We should probably think about developer experience.” Because you can do a developer experience without a platform. You can do that, you know, in a DevOps approach, you know? It's basically build tools that makes it easy for developers to write code. That's the first step for anything. It's just, like, you have people writing the code; make sure that they can do the things easily, and then look at how to operate it.Corey: That sure would be nice. There's a lack of focus on usability, especially when it comes to a number of developer tools that we see out there in the wild, in that, they're clearly built by people who understand the problem space super well, but they're designing these things to be used by people who just want to make the website work. They don't have the insight, the knowledge, the approach, any of it, nor should they necessarily be expected to.Evelyn: No, that's true. And what I see is, a lot of the times, it's a couple really talented engineers who are just getting shit done, and they get shit done however they can. So, it's basically like, if they're just trying to run the website, they're just going to write the code to get things out there and call it a day. And then somebody else comes along, has a heart attack when see what's been done, and they're kind of stuck with it because there is no guardrails or paved path or however you want to call it.Corey: I really hope—truly—that this is going to be something that we look back and laugh when this episode airs, that, “Oh, yeah, we just got it so wrong. Look at all the amazing stuff that came out of re:Invent.” Are you going to be there this year?Evelyn: I am going to be there this year.Corey: My condolences. I keep hoping people get to escape.Evelyn: This is actually my first one in, I think, five years. So, I mean, the last time I was there was when everybody's going crazy over pins. And I still have a bag of them [laugh].Corey: Yeah, that did seem like a hot-second collectable moment, didn't it?Evelyn: Yeah. And then at the—I think, what, the very last day, as everybody's heading to re:Play, you could just go into the registration area, and they just had, like, bags of them lying around to take. So, all the competing, you know, to get the requirements for a pin was kind of moot [laugh].Corey: Don't you hate it at some point where it's like, you feel like I'm going to finally get this crowning achievement, it's like or just show up at the buffet at the end and grab one of everything, and wow, that would have saved me a lot of pain and trouble.Evelyn: Yeah.Corey: Ugh, scavenger hunts are hard, as I'm about to learn to my own detriment.Evelyn: Yeah. No, true. Yeah. But I am really hoping that re:Invent proves me wrong. Embarrassingly wrong, and then all my colleagues can proceed to mock me for this ridiculous podcast that I made with you. But I am a fierce skeptic. Optimistic nihilist, but still a nihilist, so we'll see how re:Invent turns out.Corey: So, I am curious, given your experience at more large companies than I tend to be embedded with for any period of time, how have you found that these large organizations tend to pick up new technologies? What does the adoption process look like? And honestly, if you feel like throwing some shade, how do they tend to get it wrong?Evelyn: In most cases, I've seen it go… terrible. Like, it just blows up in their face. And I say that is because a lot of the time, an organization will say, “Hey, we're going to adopt this new way of organizing teams or developing products,” and they look at all the practices. They say, “Okay, great. Product management is going to bring it in, they're going to structure things, how we do the planning, here's some great charts and diagrams,” but they don't really look at the culture aspect.And that's always where I've seen things fall apart. I've been in a room where, you know, our VP was really excited about team topologies and say, “Hey, we're going to adopt it.” And then an engineering manager proceeded to say, “Okay, you're responsible for this team, you're responsible for that team, you're responsible for this team talking to, like, a team of, like, five engineers,” which doesn't really work at all. Or, like, I think the best example is DevOps, you know, where you say, “Ah, we're going to adopt DevOps, we're going to have a DevOps team, or have a DevOps engineer.”Corey: Step one: we're going to rebadge everyone with existing job titles to have the new fancy job titles that reflect it. It turns out that's not necessarily sufficient in and of itself.Evelyn: Not really. The Spotify model. People say, like, “Oh, we're going to do the Spotify model. We're going to do skills, tribes, you know, and everything. It's going to be awesome, it's going to be great, you know, and nice, cross-functional.”The reason I say it bails on us every single time is because somebody wants to be in control of the process, and if the process is meant to encourage collaboration and innovation, that person actually becomes a chokehold for it. And it could be somebody that says, like, “Ah, I need to be involved in every single team, and listen to know what's happening, just so I'm aware of it.” What ends up happening is that everybody differs to them. So, there is no collaboration, there is no innovation. DevOps, you say, like, “Hey, we're going to have a team to do everything, so your developers don't need to worry about it.” What ends up happening is you're still an ops team, you still have your silos.And that's always a challenge is you actually have to say, “Okay, what are the cultural values around this process?” You know, what is SRE? What is DevOps, you know? Is it seen as processes, is it a series of principles, platform, maybe, you know? We have to say, like—that's why I say, Platform-as-a-Product because you need to have that product mindset, that culture of product thinking, to really build a platform that works because it's all about the user journey.It's not about building a common set of tools. It's the user journey of how a person interacts with their code to get it into a production environment. And so, you need to understand how that person sits down at their desk, starts the laptop up, logs in, opens the IDE, what they're actually trying to get done. And once you understand that, then you know your requirements, and you build something to fill those things so that they are happy to use it, as opposed to saying, “This is our platform, and you're going to use it.” And they're probably going to say, “No.” And the next thing, you know, they're just doing their own thing on the side.Corey: Yeah, the rise of Shadow IT has never gone away. It's just, on some level, it's the natural expression, I think it's an immune reaction that companies tend to have when process gets in the way. Great, we have an outcome that we need to drive towards; we don't have a choice. Cloud empowered a lot of that and also has given tools to help rein it in, and as with everything, the arms race continues.Evelyn: Yeah. And so, what I'm going to continue now, kind of like, toot the platform horn. So, Gregor Hohpe, he's a [solutions architect 00:31:56]—I always f- up his name. I'm so sorry, Gregor. He has a great book, and even a talk, called The Magic of Platforms, that if somebody is actually curious about understanding of why platforms are nice, they should really watch that talk.If you see him at re:Invent, or a summit or somewhere giving a talk, go listen to that, and just pick his brain. Because that's—for me, I really kind of strongly agree with his approach because that's really how, like, you know, as he says, like, boost innovation is, you know, where you're actually building a platform that really works.Corey: Yeah, it's a hard problem, but it's also one of those things where you're trying to focus on—at least ideally—an outcome or a better situation than you currently find yourselves in. It's hard to turn down things that might very well get you there sooner, faster, but it's like trying to effectively cargo-cult the leadership principles from your last employer into your new one. It just doesn't work. I mean, you see more startups from Amazonians who try that, and it just goes horribly because without the cultural understanding and the supporting structures, it doesn't work.Evelyn: Exactly. So, I've worked with, like, organizations, like, 4000-plus people, I've worked for, like, small startups, consulted, and this is why I say, almost every single transformation, it fails the first time because somebody needs to be in control and track things and basically be really, really certain that people are doing it right. And as soon as it blows up in their face, that's when they realize they should actually take a step back. And so, even for building out a platform, you know, doing Platform-as-a-Product, I always reiterate that you have to really be willing to just invest upfront, and not get very much back. Because you have to figure out the whole user journey, and what you're actually building, before you actually build it.Corey: I really want to thank you for taking the time to speak with me today. If people want to learn more, where's the best place for them to find you?Evelyn: So, I used to be on Twitter, but I've actually got off there after it kind of turned a bit toxic and crazy.Corey: Feels like that was years ago, but that's beside the point.Evelyn: Yeah, precisely. So, I would even just say because this feels like a corporate show, but find me on LinkedIn of all places because I will be sharing whatever I find on there, you know? So, just look me up on my name, Evelyn Osman, and give me a follow, and I'll probably be screaming into the cloud like you are.Corey: And we will, of course, put links to that in the show notes. Thank you so much for taking the time to speak with me. I appreciate it.Evelyn: Thank you, Corey.Corey: Evelyn Osman, engineering manager at AutoScout24. I'm Cloud Economist Corey Quinn, and this is Screaming in the Cloud. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice, and I will read it once I finish building an internal platform to normalize all of those platforms together into one.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business, and we get to the point. Visit duckbillgroup.com to get started.

Jeff Morris, VP of Product & Solutions Marketing at Couchbase, joins Corey on Screaming in the Cloud to discuss Couchbase's new columnar data store functionality, specific use cases for columnar data stores, and why AI gets better when it communicates with a cleaner pool of data. Jeff shares how more responsive databases could allow businesses like Dominos and United Airlines to create hyper-personalized experiences for their customers by utilizing more responsive databases. Jeff dives into the linked future of AI and data, and Corey learns about Couchbase's plans for the re:Invent conference. If you're attending re:Invent, you can visit Couchbase at booth 1095.About JeffJeff Morris is VP Product & Solutions Marketing at Couchbase (NASDAQ: BASE), a cloud database platform company that 30% of the Fortune 100 depend on.Links Referenced:Couchbase: https://www.couchbase.com/TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn. This promoted guest episode of Screaming in the Cloud is brought to us by our friends at Couchbase. Also brought to us by Couchbase is today's victim, for lack of a better term. Jeff Morris is their VP of Product and Solutions Marketing. Jeff, thank you for joining me.Jeff: Thanks for having me, Corey, even though I guess I paid for it.Corey: Exactly. It's always great to say thank you when people give you things. I learned this from a very early age, and the only people who didn't were rude children and turned into worse adults.Jeff: Exactly.Corey: So, you are effectively announcing something new today, and I always get worried when a database company says that because sometimes it's a license that is going to upset people, sometimes it's dyed so deep in the wool of generative AI that, “Oh, we're now supporting vectors or whatnot.” Well, most of us don't know what that means.Jeff: Right.Corey: Fortunately, I don't believe that's what you're doing today. What have you got for us?Jeff: So, you're right. It's—well, what I'm doing is, we're announcing new stuff inside of Couchbase and helping Couchbase expand its market footprint, but we're not really moving away from our sweet spot, either, right? We like building—or being the database platform underneath applications. So, push us on the operational side of the operational versus analytic, kind of, database divide. But we are announcing a columnar data store inside of the Couchbase platform so that we can build bigger, better, stronger analytic functionality to feed the applications that we're supporting with our customers.Corey: Now, I feel like I should ask a question around what a columnar data store is because my first encounter with the term was when I had a very early client for AWS bill optimization when I was doing this independently, and I was asking them the… polite question of, “Why do you have 283 billion objects in a single S3 bucket? That is atypical and kind of terrifying.” And their answer was, “Oh, we built our own columnar data store on top of S3. This might not have been the best approach.” It's like, “I'm going to stop you there. With no further information, I can almost guarantee you that it was not.” But what is a columnar data store?Jeff: Well, let's start with the, everybody loves more data and everybody loves to count more things, right, but a columnar data store allows you to expedite the kind of question that you ask of the data itself by not having to look at every single row of the data while you go through it. You can say, if you know you're only looking for data that's inside of California, you just look at the column value of find me everything in California and then I'll pick all of those records to analyze. So, it gives you a faster way to go through the data while you're trying to gather it up and perform aggregations against it.Corey: It seems like it's one of those, “Well, that doesn't sound hard,” type of things, when you're thinking about it the way that I do, in terms of a database being more or less a medium to large size Excel spreadsheet. But I have it on good faith from all the customer environments. I've worked with that no, no, there are data stores that span even larger than that, which is, you know, one of those sad realities of the world. And everything at scale begins to be a heck of a lot harder. I've seen some of the value that this stuff offers and I can definitely understand a few different workloads in which case that's going to be super handy. What are you targeting specifically? Or is this one of those areas where you're going to learn from your customers?Jeff: Well, we've had analytic functionality inside the platform. It just, at the size and scale customers actually wanted to roam through the data, we weren't supporting that that much. So, we'll expand that particular footprint, it'll give us better integration capabilities with external systems, or better access to things in your bucket. But the use case problem is, I think, going to be driven by what new modern application requirements are going to be. You're going to need, we call it hyper-personalization because we tend to cater to B2C-style applications, things with a lot of account profiles built into them.So, you look at account profile, and you're like, “Oh, well Jeff likes blue, so sell him blue stuff.” And that's a great current level personalization, but with a new analytic engine against this, you can maybe start aggregating all the inventory information that you might have of all the blue stuff that you want to sell me and do that in real-time, so I'm getting better recommendations, better offers as I'm shopping on your site or looking at my phone and, you know, looking for the next thing I want to buy.Corey: I'm sure there's massive amounts of work that goes into these hyper-personalization stories. The problem is that the only time they really rise to our notice is when they fail hilariously. Like, you just bought a TV, would you like to buy another? Now statistically, you are likelier to buy a second TV right after you buy one, but for someone who just, “Well, I'm replacing my living room TV after ten years,” it feels ridiculous. Or when you buy a whole bunch of nails and they don't suggest, “Would you like to also perhaps buy a hammer?”It's one of those areas where it just seems like a human putting thought into this could make some sense. But I've seen some of the stuff that can come out of systems like this and it can be incredible. I also personally tend to bias towards use cases that are less, here's how to convince you to buy more things and start aiming in a bunch of other different directions where it starts meeting emerging use cases or changing situations rapidly, more rapidly than a human can in some cases. The world has, for better or worse, gotten an awful lot faster over the last few decades.Jeff: Yeah. And think of it in terms of how responsive can I be at any given moment. And so, let's pick on one of the more recent interesting failures that has popped up. I'm a Giants fan, San Francisco Giants fan, so I'll pick on the Dodgers. The Dodgers during the baseball playoffs, Clayton Kershaw—three-time MVP, Cy Young Award winner, great, great pitcher—had a first-inning meltdown of colossal magnitude: gave up 11 runs in the first inning to the Diamondbacks.Well, my customer Domino's Pizza could end up—well, let's shift the focus of our marketing. We—you know, the Dodgers are the best team in baseball this year in the National League—let's focus our attention there, but with that meltdown, let's pivot to Arizona and focus on our market in Phoenix. And they could do that within minutes or seconds, even, with the kinds of capabilities that we're coming up with here so that they can make better offers to that new environment and also do the decision intelligence behind it. Like, do I have enough dough to make a bigger offer in that big market? Do I have enough drivers or do I have to go and spin out and get one of the other food delivery folks—UberEats, or something like that—to jump on board with me and partner up on this kind of system?It's that responsiveness in real, real-time, right, that's always been kind of the conundrum between applications and analytics. You get an analytic insight, but it takes you an hour or a day to incorporate that into what the application is doing. This is intended to make all of that stuff go faster. And of course, when we start to talk about things in AI, right, AI is going to expect real-time responsiveness as best you can make it.Corey: I figure we have to talk about AI. That is a technology that has absolutely sprung to the absolute peak of the hype curve over the past year. OpenAI released Chat-Gippity, either late last year or early this year and suddenly every company seems to be falling all over itself to rebrand itself as an AI company, where, “We've been working on this for decades,” they say, right before they announce something that very clearly was crash-developed in six months. And every company is trying to drape themselves in the mantle of AI. And I don't want to sound like I'm a doubter here. I'm like most fans; I see an awful lot of value here. But I am curious to get your take on what do you think is real and what do you think is not in the current hype environment.Jeff: So yeah, I love that. I think there's a number of things that are, you know, are real is, it's not going away. It is going to continue to evolve and get better and better and better. One of my analyst friends came up with the notion that the exercise of generative AI, it's imprecise, so it gives you similarity things, and that's actually an improvement, in many cases, over the precision of a database. Databases, a transaction either works or it doesn't. It has failover or it doesn't, when—Corey: It's ideally deterministic when you ask it a question—Jeff: Yes.Corey: —the same question a second time, assuming it's not time-bound—Jeff: Gives you the right answer.Corey: Yeah, the sa—or at least the same answer.Jeff: The same answer. And your gen AI may not. So, that's a part of the oddity of the hype. But then it also helps me kind of feed our storyline of if you're going to try and make Gen AI closer and more accurate, you need a clean pool of data that you're dealing with, even though you've got probably—your previous design was such that you would use a relational database for transactions, a document database for your user profiles, you'd probably attach your website to a caching database because you needed speed and a lot of concurrency. Well, now you got three different databases there that you're operating.And if you're feeding data from each of those databases back to AI, one of them might be wrong or one of them might confuse the AI, yet how are you going to know? The complexity level is going to become, like, exponential. So, our premise is, because we're a multi-modal database that incorporates in-memory speed and documents and search and transactions and the like, if you start with a cleaner pool of data, you'll have less complexity that you're offering to your AI system and therefore you can steer it into becoming more accurate in its response. And then, of course, all the data that we're dealing with is on mobile, right? Data is created there for, let's say, your account profile, and then it's also consumed there because that's what people are using as their application interface of choice.So, you also want to have mobile interactivity and synchronization and local storage, kind of, capabilities built in there. So, those are kind of, you know, a couple of the principles that we're looking at of, you know, JSON is going to be a great format for it regardless of what happens; complexity is kind of the enemy of AI, so you don't want to go there; and mobility is going to be an absolute requirement. And then related to this particular announcement, large-scale aggregation is going to be a requirement to help feed the application. There's always going to be some other bigger calculation that you're going to want to do relatively in real time and feed it back to your users or the AI system that's helping them out.Corey: I think that that is a much more nuanced use case than a lot of the stuff that's grabbing customer attentions where you effectively have the Chat-Gippity story of it being an incredible parrot. Where I have run into trouble with the generative story has been people putting the thing that the robot that's magic and from the future has come up with off the cuff and just hurling that out into the universe under their own name without any human review, and that's fine sometimes sure, but it does get it hilariously wrong at some points. And the idea of sending something out under my name that has not been at least reviewed by me if not actually authored by me, is abhorrent. I mean, I review even the transactional, “Yes, you have successfully subscribed,” or, “Sorry to see you go,” email confirmations on stuff because there's an implicit, “Hugs and puppies, love Corey,” at the end of everything that goes out under my name.Jeff: Right.Corey: But I've gotten a barrage of terrible sales emails and companies that are trying to put the cart before the horse where either the, “Support rep,” quote-unquote, that I'm speaking to in the chat is an AI system or else needs immediate medical attention because there's something going on that needs assistance.Jeff: Yeah, they just don't understand.Corey: Right. And most big enterprise stories that I've heard so far that have come to light have been around the form of, “We get to fire most of our customer service staff,” an outcome that basically no one sensible wants. That is less compelling than a lot of the individualized consumer use cases. I love asking it, “Here's a blog post I wrote. Give me ten title options.” And I'll usually take one of them—one of them is usually not half bad and then I can modify it slightly.Jeff: And you'll change four words in it. Yeah.Corey: Yeah, exactly. That's a bit of a different use case.Jeff: It's been an interesting—even as we've all become familiar—or at least junior prompt engineers, right—is, your information is only going to be as good as you feed the AI system—the return is only going to be as good—so you're going to want to refine that kind of conversation. Now, we're not trying to end up replacing the content that gets produced or the writing of all kinds of pros, other than we do have a code generator that works inside of our environment called Capella iQ that talks to ChatGPT, but we try and put guardrails on that too, right, as always make sure that it's talking in terms of the context of Couchbase rather than, “Where's Taylor Swift this week,” which I don't want it to answer because I don't want to spend GPT money to answer that question for you.Corey: And it might not know the right answer, but it might very well spit out something that sounds plausible.Jeff: Exactly. But I think the kinds of applications that we're steering ourselves toward can be helped along by the Gen AI systems, but I don't expect all my customers are going to be writing automatic blog post generation kinds of applications. I think what we're ultimately trying to do is facilitate interactions in a way that we haven't dreamt of yet, right? One of them might be if I've opted into to loyalty programs, like my United account and my American Express account—Corey: That feels very targeted at my lifestyle as well, so please, continue.Jeff: Exactly, right? And so, what I really want the system to do is for Amex to reward me when I hit 1k status on United while I'm on the flight and you know, have the flight attendant come up and be like, “Hey, you did it. Either, here's a free upgrade from American Express”—that would be hyper-personalization because you booked your plane ticket with it, but they also happen to know or they cross-consumed information that I've opted into.Corey: I've seen them congratulate people for hitting a million miles flown mid-flight, but that's clearly something that they've been tracking and happens a heck of a lot less frequently. This is how you start scaling that experience.Jeff: Yes. But that happened because American Airlines was always watching because that was an American Airlines ad ages ago, right, but the same principle holds true. But I think there's going to be a lot more of these: how much information am I actually allowing to be shared amongst the, call it loyalty programs, but the data sources that I've opted into. And my God, there's hundreds of them that I've personally opted into, whether I like it or not because everybody needs my email address, kind of like what you were describing earlier.Corey: A point that I have that I think agrees largely with your point is that few things to me are more frustrating than what I'm signing up, for example, oh, I don't know, an AWS even—gee, I can't imagine there's anything like that going on this week—and I have to fill out an entire form that always asked me the same questions: how big my company is, whether we have multiple workloads on, what industry we're in. And no matter what I put into that, first, it never remembers me for the next time, which is frustrating in its own right, but two, no matter what I put in to fill that thing out, the email I get does not change as a result. At one point, I said, all right—I'm picking randomly—“I am a venture capitalist based in Sweden,” and I got nothing that is differentiated from the other normal stuff I get tied to my account because I use a special email address for those things, sometimes just to see what happens. And no, if you're going to make me jump through the hoops to give you the data, at least use it to make my experience better. It feels like I'm asking for the moon here, but I shouldn't be.Jeff: Yes. [we need 00:16:19] to make your experience better and say, you know, “Here's four companies in Malmo that you ought to be talking to. And they happen to be here at the AWS event and you can go find them because their booth is here, here, and here.” That kind of immediate responsiveness could be facilitated, and to our point, ought to be facilitated. It's exactly like that kind of thing is, use the data in real-time.I was talking to somebody else today that was discussing that most data, right, becomes stale and unvaluable, like, 50% of the data, its value goes to zero after about a day. And some of it is stale after about an hour. So, if you can end up closing that responsiveness gap that we were describing—and this is kind of what this columnar service inside of Capella is going to be like—is react in real-time with real-time calculation and real-time look-up and real-time—find out how you might apply that new piece of information right now and then give it back to the consumer or the user right now.Corey: So, Couchbase takes a few different forms. I should probably, at least for those who are not steeped in the world of exotic forms of database, I always like making these conversations more accessible to folks who are not necessarily up to speed. Personally, I tend to misuse anything as a database, if I can hold it just the wrong way.Jeff: The wrong way. I've caught that about you.Corey: Yeah, it's—everything is a database if you hold it wrong. But you folks have a few different options: you have a self-managed commercial offering; you're an open-source project, so I can go ahead and run it on my own infrastructure however I want; and you have Capella, which is Couchbase as a service. And all of those are useful and have their points, and I'm sure I'm missing at least one or two along the way. But do you find that the columnar use case is going to disproportionately benefit folks using Capella in ways that the self-hosted version would not be as useful for, or is this functionality already available in other expressions of Couchbase?Jeff: It's not already available in other expressions, although there is analytic functionality in the self-managed version of Couchbase. But it's, as I've mentioned I think earlier, it's just not as scalable or as really real-time as far as we're thinking. So, it's going to—yes, it's going to benefit the database as a service deployments of Couchbase available on your favorite three clouds, and still interoperable with environments that you might self-manage and self-host. So, there could be even use cases where our development team or your development team builds in AWS using the cloud-oriented features, but is still ultimately deploying and hosting and managing a self-managed environment. You could still do all of that. So, there's still a great interplay and interoperability amongst our different deployment options.But the fun part, I think, about this is not only is it going to help the Capella user, there's a lot of other things inside Couchbase that help address the developers' penchant for trading zero-cost for degrees of complexity that you're willing to accept because you want everything to be free and open-source. And Couchbase is my fifth open-source company in my background, so I'm well, well versed in the nuances of what open-source developers are seeking. But what makes Couchbase—you know, its origin story really cool too, though, is it's the peanut butter and chocolate marriage of memcached and the people behind that and membase and CouchDB from [Couch One 00:19:54]. So, I can't think of that many—maybe Red Hat—project and companies that formed up by merging two complementary open-source projects. So, we took the scale and—Corey: You have OpenTelemetry, I think, that did that once, but that—you see occasional mergers, but it's very far from common.Jeff: But it's very, very infrequent. But what that made the Couchbase people end up doing is make a platform that will scale, make a data design that you can auto partition anywhere, anytime, and then build independently scalable services on top of that, one for SQL++, the query language. Anyone who knows SQL will be able to write something in Couchbase immediately. And I've got this AI Automator, iQ, that makes it even easier; you just say, “Write me a SQL++ query that does this,” and it'll do that. But then we added full-text search, we added eventing so you can stream data, we added the analytics capability originally and now we're enhancing it, and use JSON as our kind of universal data format so that we can trade data with applications really easily.So, it's a cool design to start with, and then in the cloud, we're steering towards things like making your entry point and using our database as a service—Capella—really, really, really inexpensive so that you get that same robustness of functionality, as well as the easy cost of entry that today's developers want. And it's my analyst friends that keep telling me the cloud is where the markets going to go, so we're steering ourselves towards that hockey puck location.Corey: I frequently remark that the role of the DBA might not be vanishing, but it's definitely changing, especially since the last time I counted, if you hold them and use as directed, AWS has something on the order of 14 distinct managed database offerings. Some are general purpose, some are purpose-built, and if this trend keeps up, in a decade, the DBA role is going to be determining which of its 40 databases is going to be the right fit for a given workload. That seems to be the counter-approach to a general-purpose database that works across the board. Clearly you folks have opinions on this. Where do you land?Jeff: Oh, so absolutely. There's the product that is a suite of capabilities—or that are individual capabilities—and then there's ones that are, in my case, kind of multi-model and do lots of things at once. I think historically, you'll recognize—because this is—let's pick on your phone—the same holds true for, you know, your phone used to be a watch, used to be a Palm Pilot, used to be a StarTAC telephone, and your calendar application, your day planner all at the same time. Well, it's not anymore. Technology converges upon itself; it's kind of a historical truism.And the database technologies are going to end up doing that—or continue to do that, even right now. So, that notion that—it's a ten-year-old notion of use a purpose-built database for that particular workload. Maybe sometimes in extreme cases that is the appropriate thing, but in more cases than not right now, if you need transactions when you need them, that's fine, I can do that. You don't necessarily need Aurora or RDS or Postgres to do that. But when you need search and geolocation, I support that too, so you don't need Elastic. And then when you need caching and everything, you don't need ElastiCache; it's all built-in.So, that multi-model notion of operate on the same pool of data, it's a lot less complex for your developers, they can code faster and better and more cleanly, debugging is significantly easier. As I mentioned, SQL++ is our language. It's basically SQL syntax for JSON. We're a reference implementation of this language, along with—[AsteriskDB 00:23:42] is one of them, and actually, the original author of that language also wrote DynamoDB's PartiQL.So, it's a common language that you wouldn't necessarily imagine, but the ease of entry in all of this, I think, is still going to be a driving goal for people. The old people like me and you are running around worrying about, am I going to get a particular, really specific feature out of the full-text search environment, or the other one that I pick on now is, “Am I going to need a vector database, too?” And the answer to me is no, right? There's going—you know, the database vendors like ourselves—and like Mongo has announced and a whole bunch of other NoSQL vendors—we're going to support that. It's going to be just another mode, and you get better bang for your buck when you've got more modes than a single one at a time.Corey: The consensus opinion that's emerging is very much across the board that vector is a feature, not a database type.Jeff: Not a category, yeah. Me too. And yeah, we're well on board with that notion, as well. And then like I said earlier, the JSON as a vehicle to give you all of that versatility is great, right? You can have vector information inside a JSON document, you can have time series information in the document, you could have graph node locations and ID numbers in a JSON array, so you don't need index-free adjacency or some of the other cleverness that some of my former employers have done. It really is all converging upon itself and hopefully everybody starts to realize that you can clean up and simplify your architectures as you look ahead, so that you do—if you're going to build AI-powered applications—feed it clean data, right? You're going to be better off.Corey: So, this episode is being recorded in advance, thankfully, but it's going to release the first day of re:Invent. What are you folks doing at the show, for those who are either there and for some reason, listening to a podcast rather than going to getting marketed to by a variety of different pitches that all mention AI or might even be watching from home and trying to figure out what to make of it?Jeff: Right. So, of course we have a booth, and my notes don't have in front of me what our booth number is, but you'll see it on the signs in the airport. So, we'll have a presence there, we'll have an executive briefing room available, so we can schedule time with anyone who wants to come talk to us. We'll be showing not only the capabilities that we're offering here, we'll show off Capella iQ, our coding assistant, okay—so yeah, we're on the AI hype band—but we'll also be showing things like our mobile sync capability where my phone and your phone can synchronize data amongst themselves without having to actually have a live connection to the internet. So, long as we're on the same network locally within the Venetian's network, we have an app that we have people download from the Apple Store and then it's a color synchronization app or picture synchronization app.So, you tap it, and it changes on my screen and I tap it and it changes on your screen, and we'll have, I don't know, as many people who are around standing there, synchronizing, what, maybe 50 phones at a time. It's actually a pretty slick demonstration of why you might want a database that's not only in the cloud but operates around the cloud, operates mobile-ly, operates—you know, can connect and disconnect to your networks. It's a pretty neat scenario. So, we'll be showing a bunch of cool technical stuff as well as talking about the things that we're discussing right now.Corey: I will say you're putting an awful lot of faith in conductivity working at re:Invent, be it WiFi or the cellular network. I know that both of those have bitten me in various ways over the years. But I wish you the best on it. I think it's going to be an interesting show based upon everything I've heard in the run-up to it. I'm just glad it's here.Jeff: Now, this is the cool part about what I'm talking about, though. The cool part about what I'm talking about is we can set up our own wireless network in our booth, and we still—you'd have to go to the app store to get this application, but once there, I can have you switch over to my local network and play around on it and I can sync the stuff right there and have confidence that in my local network that's in my booth, the system's working. I think that's going to be ultimately our design there because oh my gosh, yes, I have a hundred stories about connectivity and someone blowing a demo because they're yanking on a cable behind the pulpit, right?Corey: I always build in a—and assuming there's no connectivity, how can I fake my demos, just because it's—I've only had to do it once, but you wind up planning in advance when you start doing a talk to a large enough or influential enough audience where you want things to go right.Jeff: There's a delightful acceptance right now of recorded videos and demonstrations that people sort of accept that way because of exactly all this. And I'm sure we'll be showing that in our booth there too.Corey: Given the non-deterministic nature of generative AI, I'm sort of surprised whenever someone hasn't mocked the demo in advance, just because yeah, gives the right answer in the rehearsal, but every once in a while, it gets completely unglued.Jeff: Yes, and we see it pretty regularly. So, the emergence of clever and good prompt engineering is going to be a big skill for people. And hopefully, you know, everybody's going to figure out how to pass it along to their peers.Corey: Excellent. We'll put links to all this in the show notes, and I look forward to seeing how well this works out for you. Best of luck at the show and thanks for speaking with me. I appreciate it.Jeff: Yeah, Corey. We appreciate the support, and I think the show is going to be very strong for us as well. And thanks for having me here.Corey: Always a pleasure. Jeff Morris, VP of Product and Solutions Marketing at Couchbase. This episode has been brought to us by our friends at Couchbase. And I'm Cloud Economist Corey Quinn. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice along with an angry comment, but if you want to remain happy, I wouldn't ask that podcast platform what database they're using. No one likes the answer to those things.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business and we get to the point. Visit duckbillgroup.com to get started.

Amir Szekely, Owner at CloudSnorkel, joins Corey on Screaming in the Cloud to discuss how he got his start in the early days of cloud and his solo project, CloudSnorkel. Throughout this conversation, Corey and Amir discuss the importance of being pragmatic when moving to the cloud, and the different approaches they see in developers from the early days of cloud to now. Amir shares what motivates him to develop open-source projects, and why he finds fulfillment in fixing bugs and operating CloudSnorkel as a one-man show. About AmirAmir Szekely is a cloud consultant specializing in deployment automation, AWS CDK, CloudFormation, and CI/CD. His background includes security, virtualization, and Windows development. Amir enjoys creating open-source projects like cdk-github-runners, cdk-turbo-layers, and NSIS.Links Referenced: CloudSnorkel: https://cloudsnorkel.com/ lasttootinaws.com: https://lasttootinaws.com camelcamelcamel.com: https://camelcamelcamel.com github.com/cloudsnorkel: https://github.com/cloudsnorkel Personal website: https://kichik.com TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn, and this is an episode that I have been angling for for longer than you might imagine. My guest today is Amir Szekely, who's the owner at CloudSnorkel. Amir, thank you for joining me.Amir: Thanks for having me, Corey. I love being here.Corey: So, I've been using one of your open-source projects for an embarrassingly long amount of time, and for the longest time, I make the critical mistake of referring to the project itself as CloudSnorkel because that's the word that shows up in the GitHub project that I can actually see that jumps out at me. The actual name of the project within your org is cdk-github-runners if I'm not mistaken.Amir: That's real original, right?Corey: Exactly. It's like, “Oh, good, I'll just mention that, and suddenly everyone will know what I'm talking about.” But ignoring the problems of naming things well, which is a pain that everyone at AWS or who uses it knows far too well, the product is basically magic. Before I wind up basically embarrassing myself by doing a poor job of explaining what it is, how do you think about it?Amir: Well, I mean, it's a pretty simple project, which I think what makes it great as well. It creates GitHub runners with CDK. That's about it. It's in the name, and it just does that. And I really tried to make it as simple as possible and kind of learn from other projects that I've seen that are similar, and basically learn from my pain points in them.I think the reason I started is because I actually deployed CDK runners—sorry, GitHub runners—for one company, and I ended up using the Kubernetes one, right? So, GitHub in themselves, they have two projects they recommend—and not to nudge GitHub, please recommend my project one day as well—they have the Kubernetes controller and they have the Terraform deployer. And the specific client that I worked for, they wanted to use Kubernetes. And I tried to deploy it, and, Corey, I swear, I worked three days; three days to deploy the thing, which was crazy to me. And every single step of the way, I had to go and read some documentation, figure out what I did wrong, and apparently the order the documentation was was incorrect.And I had to—I even opened tickets, and they—you know, they were rightfully like, “It's open-source project. Please contribute and fix the documentation for us.” At that point, I said, “Nah.” [laugh]. Let me create something better with CDK and I decided just to have the simplest setup possible.So usually, right, what you end up doing in these projects, you have to set up either secrets or SSM parameters, and you have to prepare the ground and you have to get your GitHub token and all those things. And that's just annoying. So, I decided to create a—Corey: So much busy work.Amir: Yes, yeah, so much busy work and so much boilerplate and so much figuring out the right way and the right order, and just annoying. So, I decided to create a setup page. I thought, “What if you can actually install it just like you install any app on GitHub,” which is the way it's supposed to be right? So, when you install cdk-github-runners—CloudSnorkel—you get an HTML page and you just click a few buttons and you tell it where to install it and it just installs it for you. And it sets the secrets and everything. And if you want to change the secret, you don't have to redeploy. You can just change the secret, right? You have to roll the token over or whatever. So, it's much, much easier to install.Corey: And I feel like I discovered this project through one of the more surreal approaches—and I had cause to revisit it a few weeks ago when I was redoing my talk for the CDK Community Day, which has since happened and people liked the talk—and I mentioned what CloudSnorkel had been doing and how I was using the runners accordingly. So, that was what I accidentally caused me to pop back up with, “Hey, I've got some issues here.” But we'll get to that. Because once upon a time, I built a Twitter client for creating threads because shitposting is my love language, I would sit and create Twitter threads in the middle of live keynote talks. Threading in the native client was always terrible, and I wanted to build something that would help me do that. So, I did.And it was up for a while. It's not anymore because I'm not paying $42,000 a month in API costs to some jackass, but it still exists in the form of lasttootinaws.com if you want to create threads on Mastodon. But after I put this out, some people complained that it was slow.To which my response was, “What do you mean? It's super fast for me in San Francisco talking to it hosted in Oregon.” But on every round trip from halfway around the world, it became a problem. So, I got it into my head that since this thing was fully stateless, other than a Lambda function being fronted via an API Gateway, that I should deploy it to every region. It didn't quite fit into a Cloudflare Worker or into one of the Edge Lambda functions that AWS has given up on, but okay, how do I deploy something to every region?And the answer is, with great difficulty because it's clear that no one was ever imagining with all those regions that anyone would use all of them. It's imagined that most customers use two or three, but customers are different, so which two or three is going to be widely varied. So, anything halfway sensible about doing deployments like this didn't work out. Again, because this thing was also a Lambda function and an API Gateway, it was dirt cheap, so I didn't really want to start spending stupid amounts of money doing deployment infrastructure and the rest.So okay, how do I do this? Well, GitHub Actions is awesome. It is basically what all of AWS's code offerings wish that they were. CodeBuild is sad and this was kind of great. The problem is, once you're out of the free tier, and if you're a bad developer where you do a deploy on every iteration, suddenly it starts costing for what I was doing in every region, something like a quarter of per deploy, which adds up when you're really, really bad at programming.Amir: [laugh].Corey: So, their matrix jobs are awesome, but I wanted to do some self-hosted runners. How do I do that? And I want to keep it cheap, so how do I do a self-hosted runner inside of a Lambda function? Which led me directly to you. And it was nothing short of astonishing. This was a few years ago. I seem to recall that it used to be a bit less well-architected in terms of its elegance. Did it always use step functions, for example, to wind up orchestrating these things?Amir: Yeah, so I do remember that day. We met pretty much… basically as a joke because the Lambda Runner was a joke that I did, and I posted on Twitter, and I was half-proud of my joke that starts in ten seconds, right? But yeah, no, the—I think it always used functions. I've been kind of in love with the functions for the past two years. They just—they're nice.Corey: Oh, they're magic, and AWS is so bad at telling their story. Both of those things are true.Amir: Yeah. And the API is not amazing. But like, when you get it working—and you know, you have to spend some time to get it working—it's really nice because then you have nothing to manage, ever. And they can call APIs directly now, so you don't have to even create Lambdas. It's pretty cool.Corey: And what I loved is you wind up deploying this thing to whatever account you want it to live within. What is it, the OIDC? I always get those letters in the wrong direction. OIDC, I think, is correct.Amir: I think it's OIDC, yeah.Corey: Yeah, and it winds up doing this through a secure method as opposed to just okay, now anyone with access to the project can deploy into your account, which is not ideal. And it just works. It spins up a whole bunch of these Lambda functions that are using a Docker image as the deployment environment. And yeah, all right, if effectively my CDK deploy—which is what it's doing inside of this thing—doesn't complete within 15 minutes, then it's not going to and the thing is going to break out. We've solved the halting problem. After 15 minutes, the loop will terminate. The end.But that's never been a problem, even with getting ACM certificates spun up. It completes well within that time limit. And its cost to me is effectively nothing. With one key exception: that you made the choice to use Secrets Manager to wind up storing a lot of the things it cares about instead of Parameter Store, so I think you wind up costing me—I think there's two of those different secrets, so that's 80 cents a month. Which I will be demanding in blood one of these days if I ever catch you at re:Invent.Amir: I'll buy you beer [laugh].Corey: There we go. That'll count. That'll buy, like, several months of that. That works—at re:Invent, no. The beers there are, like, $18, so that'll cover me for years. We're set.Amir: We'll split it [laugh].Corey: Exactly. Problem solved. But I like the elegance of it, I like how clever it is, and I want to be very clear, though, it's not just for shitposting. Because it's very configurable where, yes, you can use Lambda functions, you can use Spot Instances, you can use CodeBuild containers, you can use Fargate containers, you can use EC2 instances, and it just automatically orchestrates and adds these self-hosted runners to your account, and every build gets a pristine environment as a result. That is no small thing.Amir: Oh, and I love making things configurable. People really appreciate it I feel, you know, and gives people kind of a sense of power. But as long as you make that configuration simple enough, right, or at least the defaults good defaults, right, then, even with that power, people still don't shoot themselves in the foot and it still works really well. By the way, we just added ECS recently, which people really were asking for because it gives you the, kind of, easy option to have the runner—well, not the runner but at least the runner infrastructure staying up, right? So, you can have auto-scaling group backing ECS and then the runner can start up a lot faster. It was actually very important to other people because Lambda, as fast that it is, it's limited, and Fargate, for whatever reason, still to this day, takes a minute to start up.Corey: Yeah. What's wild to me about this is, start to finish, I hit a deploy to the main branch and it sparks the thing up, runs the deploy. Deploy itself takes a little over two minutes. And every time I do this, within three minutes of me pushing to commit, the deploy is done globally. It is lightning fast.And I know it's easy to lose yourself in the idea of this being a giant shitpost, where, oh, who's going to do deployment jobs in Lambda functions? Well, kind of a lot of us for a variety of reasons, some of which might be better than others. In my case, it was just because I was cheap, but the massive parallelization ability to do 20 simultaneous deploys in a matrix configuration that doesn't wind up smacking into rate limits everywhere, that was kind of great.Amir: Yeah, we have seen people use Lambda a lot. It's mostly for, yeah, like you said, small jobs. And the environment that they give you, it's kind of limited, so you can't actually install packages, right? There is no sudo, and you can't actually install anything unless it's in your temp directory. But still, like, just being able to run a lot of little jobs, it's really great. Yeah.Corey: And you can also make sure that there's a Docker image ready to go with the stuff that you need, just by configuring how the build works in the CDK. I will admit, I did have a couple of bug reports for you. One was kind of useful, where it was not at all clear how to do this on top of a Graviton-based Lambda function—because yeah, that was back when not everything really supported ARM architectures super well—and a couple of other times when the documentation was fairly ambiguous from my perspective, where it wasn't at all clear, what was I doing? I spent four hours trying to beat my way through it, I give up, filed an issue, went to get a cup of coffee, came back, and the answer was sitting there waiting for me because I'm not convinced you sleep.Amir: Well, I am a vampire. My last name is from the Transylvania area [laugh]. So—Corey: Excellent. Excellent.Amir: By the way, not the first time people tell me that. But anyway [laugh].Corey: There's something to be said for getting immediate responsiveness because one of the reasons I'm always so loath to go and do a support ticket anywhere is this is going to take weeks. And then someone's going to come back with a, “I don't get it.” And try and, like, read the support portfolio to you. No, you went right into yeah, it's this. Fix it and your problem goes away. And sure enough, it did.Amir: The escalation process that some companies put you through is very frustrating. I mean, lucky for you, CloudSnorkel is a one-man show and this man loves solving bugs. So [laugh].Corey: Yeah. Do you know of anyone using it for anything that isn't ridiculous and trivial like what I'm using it for?Amir: Yeah, I have to think whether or not I can… I mean, so—okay. We have a bunch of dedicated users, right, the GitHub repo, that keep posting bugs and keep posting even patches, right, so you can tell that they're using it. I even have one sponsor, one recurring sponsor on GitHub that uses it.Corey: It's always nice when people thank you via money.Amir: Yeah. Yeah, it is very validating. I think [BLEEP] is using it, but I also don't think I can actually say it because I got it from the GitHub.Corey: It's always fun. That's the beautiful part about open-source. You don't know who's using this. You see what other things people are working on, and you never know, is one of their—is this someone's side project, is it a skunkworks thing, or God forbid, is this inside of every car going forward and no one bothered to tell me about that. That is the magic and mystery of open-source. And you've been doing open-source for longer than I have and I thought I was old. You were originally named in some of the WinAMP credits, for God's sake, that media player that really whipped the llama's ass.Amir: Oh, yeah, I started real early. I started about when I was 15, I think. I started off with Pascal or something or even Perl, and then I decided I have to learn C and I have to learn Windows API. I don't know what possessed me to do that. Win32 API is… unique [laugh].But once I created those applications for myself, right, I think there was—oh my God, do you know the—what is it called, Sherlock in macOS, right? And these days, for PowerToys, there is the equivalent of it called, I don't know, whatever that—PowerBar? That's exactly—that was that. That's a project I created as a kid. I wanted something where I can go to the Run menu of Windows when you hit Winkey R, and you can just type something and it will start it up, right?I didn't want to go to the Start menu and browse and click things. I wanted to do everything with the keyboard. So, I created something called Blazerun [laugh], which [laugh] helped you really easily create shortcuts that went into your path, right, the Windows path, so you can really easily start them from Winkey R. I don't think that anyone besides me used it, but anyway, that thing needed an installer, right? Because Windows, you got to install things. So, I ended up—Corey: Yeah, these days on Mac OS, I use Alfred for that which is kind of long in the tooth, but there's a launch bar and a bunch of other stuff for it. What I love is that if I—I can double-tap the command key and that just pops up whatever I need it to and tell the computer what to do. It feels like there's an AI play in there somewhere if people can figure out how to spend ten minutes on building AI that does something other than lets them fire their customer service staff.Amir: Oh, my God. Please don't fire customer service staff. AI is so bad.Corey: Yeah, when I reach out to talk to a human, I really needed a human.Amir: Yes. Like, I'm not calling you because I want to talk to a robot. I know there's a website. Leave me alone, just give me a person.Corey: Yeah. Like, you already failed to solve my problem on your website. It's person time.Amir: Exactly. Oh, my God. Anyway [laugh]. So, I had to create an installer, right, and I found it was called NSIS. So, it was a Nullsoft “SuperPiMP” installation system. Or in the future, when Justin, the guy who created Winamp and NSIS, tried to tone down a little bit, Nullsoft Scriptable Installation System. And SuperPiMP is—this is such useless history for you, right, but SuperPiMP is the next generation of PiMP which is Plug-in Mini Packager [laugh].Corey: I remember so many of the—like, these days, no one would ever name any project like that, just because it's so off-putting to people with sensibilities, but back then that was half the stuff that came out. “Oh, you don't like how this thing I built for free in the wee hours when I wasn't working at my fast food job wound up—you know, like, how I chose to name it, well, that's okay. Don't use it. Go build your own. Oh, what you're using it anyway. That's what I thought.”Amir: Yeah. The source code was filled with profanity, too. And like, I didn't care, I really did not care, but some people would complain and open bug reports and patches. And my policy was kind of like, okay if you're complaining, I'm just going to ignore you. If you're opening a patch, fine, I'm going to accept that you're—you guys want to create something that's sensible for everybody, sure.I mean, it's just source code, you know? Whatever. So yeah, I started working on that NSIS. I used it for myself and I joined the forums—and this kind of answers to your question of why I respond to things so fast, just because of the fun—I did the same when I was 15, right? I started going on the forums, you remember forums? You remember that [laugh]?Corey: Oh, yeah, back before they all became terrible and monetized.Amir: Oh, yeah. So, you know, people were using NSIS, too, and they had requests, right? They wanted. Back in the day—what was it—there was only support for 16-bit colors for the icon, so they want 32-bit colors and big colors—32—big icon, sorry, 32 pixels by 32 pixels. Remember, 32 pixels?Corey: Oh, yes. Not well, and not happily, but I remember it.Amir: Yeah. So, I started just, you know, giving people—working on that open-source and creating up a fork. It wasn't even called ‘fork' back then, but yeah, I created, like, a little fork of myself and I started adding all these features. And people were really happy, and kind of created, like, this happy cycle for myself: when people were happy, I was happy coding. And then people were happy by what I was coding. And then they were asking for more and they were getting happier, the more I responded.So, it was kind of like a serotonin cycle that made me happy and made everybody happy. So, it's like a win, win, win, win, win. And that's how I started with open-source. And eventually… NSIS—again, that installation system—got so big, like, my fork got so big, and Justin, the guy who works on WinAMP and NSIS, he had other things to deal with. You know, there's a whole history there with AOL. I'm sure you've heard all the funny stories.Corey: Oh, yes. In fact, one thing that—you want to talk about weird collisions of things crossing, one of the things I picked up from your bio when you finally got tired of telling me no and agreed to be on the show was that you're also one of the team who works on camelcamelcamel.com. And I keep forgetting that's one of those things that most people have no idea exists. But it's very simple: all it does is it tracks Amazon products that you tell it to and alerts you when there's a price drop on the thing that you're looking at.It's something that is useful. I try and use it for things of substance or hobbies because I feel really pathetic when I'm like, get excited emails about a price drop in toilet paper. But you know, it's very handy just to keep an idea for price history, where okay, am I actually being ripped off? Oh, they claim it's their big Amazon Deals day and this is 40% off. Let's see what camelcamelcamel has to say.Oh, surprise. They just jacked the price right beforehand and now knocked 40% off. Genius. I love that. It always felt like something that was going to be blown off the radar by Amazon being displeased, but I discovered you folks in 2010 and here you are now, 13 years later, still here. I will say the website looks a lot better now.Amir: [laugh]. That's a recent change. I actually joined camel, maybe two or three years ago. I wasn't there from the beginning. But I knew the guy who created it—again, as you were saying—from the Winamp days, right? So, we were both working in the free—well, it wasn't freenode. It was not freenode. It was a separate IRC server that, again, Justin created for himself. It was called landoleet.Corey: Mmm. I never encountered that one.Amir: Yeah, no, it was pretty private. The only people that cared about WinAMP and NSIS ended up joining there. But it was a lot of fun. I met a lot of friends there. And yeah, I met Daniel Green there as well, and he's the guy that created, along with some other people in there that I think want to remain anonymous so I'm not going to mention, but they also were on the camel project.And yeah, I was kind of doing my poor version of shitposting on Twitter about AWS, kind of starting to get some traction and maybe some clients and talk about AWS so people can approach me, and Daniel approached me out of the blue and he was like, “Do you just post about AWS on Twitter or do you also do some AWS work?” I was like, “I do some AWS work.”Corey: Yes, as do all of us. It's one of those, well crap, we're getting called out now. “Do you actually know how any of this stuff works?” Like, “Much to my everlasting shame, yes. Why are you asking?”Amir: Oh, my God, no, I cannot fix your printer. Leave me alone.Corey: Mm-hm.Amir: I don't want to fix your Lambdas. No, but I do actually want to fix your Lambdas. And so, [laugh] he approached me and he asked if I can help them move camelcamelcamel from their data center to AWS. So, that was a nice big project. So, we moved, actually, all of camelcamelcamel into AWS. And this is how I found myself not only in the Winamp credits, but also in the camelcamelcamel credits page, which has a great picture of me riding a camel.Corey: Excellent. But one of the things I've always found has been that when you take an application that has been pre-existing for a while in a data center and then move it into the cloud, you suddenly have to care about things that no one sensible pays any attention to in the land of the data center. Because it's like, “What do I care about how much data passes between my application server and the database? Wait, what do you mean that in this configuration, that's a chargeable data transfer? Oh, dear Lord.” And things that you've never had to think about optimizing are suddenly things are very much optimizing.Because let's face it, when it comes to putting things in racks and then running servers, you aren't auto-scaling those things, so everything tends to be running over-provisioned, for very good reasons. It's an interesting education. Anything you picked out from that process that you think it'd be useful for folks to bear in mind if they're staring down the barrel of the same thing?Amir: Yeah, for sure. I think… in general, right, not just here. But in general, you always want to be pragmatic, right? You don't want to take steps are huge, right? So, the thing we did was not necessarily rewrite everything and change everything to AWS and move everything to Lambda and move everything to Docker.Basically, we did a mini lift-and-shift, but not exactly lift-and-shift, right? We didn't take it as is. We moved to RDS, we moved to ElastiCache, right, we obviously made use of security groups and session connect and we dropped SSH Sage and we improved the security a lot and we locked everything down, all the permissions and all that kind of stuff, right? But like you said, there's stuff that you start having to pay attention to. In our case, it was less the data transfer because we have a pretty good CDN. There was more of IOPS. So—and IOPS, specifically for a database.We had a huge database with about one terabyte of data and a lot of it is that price history that you see, right? So, all those nice little graphs that we create in—what do you call them, charts—that we create in camelcamelcamel off the price history. There's a lot of data behind that. And what we always want to do is actually remove that from MySQL, which has been kind of struggling with it even before the move to AWS, but after the move to AWS, where everything was no longer over-provisioned and we couldn't just buy a few more NVMes on Amazon for 100 bucks when they were on sale—back when we had to pay Amazon—Corey: And you know, when they're on sale. That's the best part.Amir: And we know [laugh]. We get good prices on NVMe. But yeah, on Amazon—on AWS, sorry—you have to pay for io1 or something, and that adds up real quick, as you were saying. So, part of that move was also to move to something that was a little better for that data structure. And we actually removed just that data, the price history, the price points from MySQL to DynamoDB, which was a pretty nice little project.Actually, I wrote about it in my blog. There is, kind of, lessons learned from moving one terabyte from MySQL to DynamoDB, and I think the biggest lesson was about hidden price of storage in DynamoDB. But before that, I want to talk about what you asked, which was the way that other people should make that move, right? So again, be pragmatic, right? If you Google, “How do I move stuff from DynamoDB to MySQL,” everybody's always talking about their cool project using Lambda and how you throttle Lambda and how you get throttled from DynamoDB and how you set it up with an SQS, and this and that. You don't need all that.Just fire up an EC2 instance, write some quick code to do it. I used, I think it was Go with some limiter code from Uber, and that was it. And you don't need all those Lambdas and SQS and the complication. That thing was a one-time thing anyway, so it doesn't need to be super… super-duper serverless, you know?Corey: That is almost always the way that it tends to play out. You encounter these weird little things along the way. And you see so many things that are tied to this is how architecture absolutely must be done. And oh you're not a real serverless person if you don't have everything running in Lambda and the rest. There are times where yeah, spin up an EC2 box, write some relatively inefficient code in ten minutes and just do the thing, and then turn it off when you're done. Problem solved. But there's such an aversion to that. It's nice to encounter people who are pragmatists more than they are zealots.Amir: I mostly learned that lesson. And both Daniel Green and me learned that lesson from the Winamp days. Because we both have written plugins for Winamp and we've been around that area and you can… if you took one of those non-pragmatist people, right, and you had them review the Winamp code right now—or even before—they would have a million things to say. That code was—and NSIS, too, by the way—and it was so optimized. It was so not necessarily readable, right? But it worked and it worked amazing. And Justin would—if you think I respond quickly, right, Justin Frankel, the guy who wrote Winamp, he would release versions of NSIS and of Winamp, like, four versions a day, right? That was before [laugh] you had CI/CD systems and GitHub and stuff. That was just CVS. You remember CVS [laugh]?Corey: Oh, I've done multiple CVS migrations. One to Git and a couple to Subversion.Amir: Oh yeah, Subversion. Yep. Done ‘em all. CVS to Subversion to Git. Yep. Yep. That was fun.Corey: And these days, everyone's using Git because it—we're beginning to have a monoculture.Amir: Yeah, yeah. I mean, but Git is nicer than Subversion, for me, at least. I've had more fun with it.Corey: Talk about damning with faint praise.Amir: Faint?Corey: Yeah, anything's better than Subversion, let's be honest here.Amir: Oh [laugh].Corey: I mean, realistically, copying a bunch of files and directories to a.bak folder is better than Subversion.Amir: Well—Corey: At least these days. But back then it was great.Amir: Yeah, I mean, the only thing you had, right [laugh]?Corey: [laugh].Amir: Anyway, achieving great things with not necessarily the right tools, but just sheer power of will, that's what I took from the Winamp days. Just the entire world used Winamp. And by the way, the NSIS project that I was working on, right, I always used to joke that every computer in the world ran my code, every Windows computer in the world when my code, just because—Corey: Yes.Amir: So, many different companies use NSIS. And none of them cared that the code was not very readable, to put it mildly.Corey: So, many companies founder on those shores where they lose sight of the fact that I can point to basically no companies that died because their code was terrible, yeah, had an awful lot that died with great-looking code, but they didn't nail the business problem.Amir: Yeah. I would be lying if I said that I nailed exactly the business problem at NSIS because the most of the time I would spend there and actually shrinking the stub, right, there was appended to your installer data, right? So, there's a little stub that came—the executable, basically, that came before your data that was extracted. I spent, I want to say, years of my life [laugh] just shrinking it down by bytes—by literal bytes—just so it stays under 34, 35 kilobytes. It was kind of a—it was a challenge and something that people appreciated, but not necessarily the thing that people appreciate the most. I think the features—Corey: Well, no I have to do the same thing to make sure something fits into a Lambda deployment package. The scale changes, the problem changes, but somehow everything sort of rhymes with history.Amir: Oh, yeah. I hope you don't have to disassemble code to do that, though because that's uh… I mean, it was fun. It was just a lot.Corey: I have to ask, how much work went into building your cdk-github-runners as far as getting it to a point of just working out the door? Because I look at that and it feels like there's—like, the early versions, yeah, there wasn't a whole bunch of code tied to it, but geez, the iterative, “How exactly does this ridiculous step functions API work or whatnot,” feels like I'm looking at weeks of frustration. At least it would have been for me.Amir: Yeah, yeah. I mean, it wasn't, like, a day or two. It was definitely not—but it was not years, either. I've been working on it I think about a year now. Don't quote me on that. But I've put a lot of time into it. So, you know, like you said, the skeleton code is pretty simple: it's a step function, which as we said, takes a long time to get right. The functions, they are really nice, but their definition language is not very straightforward. But beyond that, right, once that part worked, it worked. Then came all the bug reports and all the little corner cases, right? We—Corey: Hell is other people's use cases. Always is. But that's honestly better than a lot of folks wind up experiencing where they'll put an open-source project up and no one ever knows. So, getting users is often one of the biggest barriers to a lot of this stuff. I've found countless hidden gems lurking around on GitHub with a very particular search for something that no one had ever looked at before, as best I can tell.Amir: Yeah.Corey: Open-source is a tricky thing. There needs to be marketing brought into it, there needs to be storytelling around it, and has to actually—dare I say—solve a problem someone has.Amir: I mean, I have many open-source projects like that, that I find super useful, I created for myself, but no one knows. I think cdk-github-runners, I'm pretty sure people know about it only because you talked about it on Screaming in the Cloud or your newsletter. And by the way, thank you for telling me that you talked about it last week in the conference because now we know why there was a spike [laugh] all of a sudden. People Googled it.Corey: Yeah. I put links to it as well, but it's the, yeah, I use this a lot and it's great. I gave a crappy explanation on how it works, but that's the trick I've found between conference talks and, dare I say, podcast episodes, you gives people a glimpse and a hook and tell them where to go to learn more. Otherwise, you're trying to explain every nuance and every intricacy in 45 minutes. And you can't do that effectively in almost every case. All you're going to do is drive people away. Make it sound exciting, get them to see the value in it, and then let them go.Amir: You have to explain the market for it, right? That's it.Corey: Precisely.Amir: And I got to say, I somewhat disagree with your—or I have a different view when you say that, you know, open-source projects needs marketing and all those things. It depends on what open-source is for you, right? I don't create open-source projects so they are successful, right? It's obviously always nicer when they're successful, but—and I do get that cycle of happiness that, like I was saying, people create bugs and I have to fix them and stuff, right? But not every open-source project needs to be a success. Sometimes it's just fun.Corey: No. When I talk about marketing, I'm talking about exactly what we're doing here. I'm not talking take out an AdWords campaign or something horrifying like that. It's you build something that solved the problem for someone. The big problem that worries me about these things is how do you not lose sleep at night about the fact that solve someone's problem and they don't know that it exists?Because that drives me nuts. I've lost count of the number of times I've been beating my head against a wall and asked someone like, “How would you handle this?” Like, “Oh, well, what's wrong with this project?” “What do you mean?” “Well, this project seems to do exactly what you want it to do.” And no one has it all stuffed in their head. But yeah, then it seems like open-source becomes a little more corporatized and it becomes a lead gen tool for people to wind up selling their SaaS services or managed offerings or the rest.Amir: Yeah.Corey: And that feels like the increasing corporatization of open-source that I'm not a huge fan of.Amir: Yeah. I mean, I'm not going to lie, right? Like, part of why I created this—or I don't know if it was part of it, but like, I had a dream that, you know, I'm going to get, oh, tons of GitHub sponsors, and everybody's going to use it and I can retire on an island and just make money out of this, right? Like, that's always a dream, right? But it's a dream, you know?And I think bottom line open-source is… just a tool, and some people use it for, like you were saying, driving sales into their SaaS, some people, like, may use it just for fun, and some people use it for other things. Or some people use it for politics, even, right? There's a lot of politics around open-source.I got to tell you a story. Back in the NSIS days, right—talking about politics—so this is not even about politics of open-source. People made NSIS a battleground for their politics. We would have translations, right? People could upload their translations. And I, you know, or other people that worked on NSIS, right, we don't speak every language of the world, so there's only so much we can do about figuring out if it's a real translation, if it's good or not.Back in the day, Google Translate didn't exist. Like, these days, we check Google Translate, we kind of ask a few questions to make sure they make sense. But back in the day, we did the best that we could. At some point, we got a patch for Catalan language, I'm probably mispronouncing it—but the separatist people in Spain, I think, and I didn't know anything about that. I was a young kid and… I just didn't know.And I just included it, you know? Someone submitted a patch, they worked hard, they wanted to be part of the open-source project. Why not? Sure I included it. And then a few weeks later, someone from Spain wanted to change Catalan into Spanish to make sure that doesn't exist for whatever reason.And then they just started fighting with each other and started making demands of me. Like, you have to do this, you have to do that, you have to delete that, you have to change the name. And I was just so baffled by why would someone fight so much over a translation of an open-source project. Like, these days, I kind of get what they were getting at, right?Corey: But they were so bad at telling that story that it was just like, so basically, screw, “You for helping,” is how it comes across.Amir: Yeah, screw you for helping. You're a pawn now. Just—you're a pawn unwittingly. Just do what I say and help me in my political cause. I ended up just telling both of them if you guys can agree on anything, I'm just going to remove both translations. And that's what I ended up doing. I just removed both translations. And then a few months later—because we had a release every month basically, I just added both of them back and I've never heard from them again. So sort of problem solved. Peace the Middle East? I don't know.Corey: It's kind of wild just to see how often that sort of thing tends to happen. It's a, I don't necessarily understand why folks are so opposed to other people trying to help. I think they feel like there's this loss of control as things are slipping through their fingers, but it's a really unwelcoming approach. One of the things that got me deep into the open-source ecosystem surprisingly late in my development was when I started pitching in on the SaltStack project right after it was founded, where suddenly everything I threw their way was merged, and then Tom Hatch, the guy who founded the project, would immediately fix all the bugs and stuff I put in and then push something else immediately thereafter. But it was such a welcoming thing.Instead of nitpicking me to death in the pull request, it just got merged in and then silently fixed. And I thought that was a classy way to do it. Of course, it doesn't scale and of course, it causes other problems, but I envy the simplicity of those days and just the ethos behind that.Amir: That's something I've learned the last few years, I would say. Back in the NSIS day, I was not like that. I nitpicked. I nitpicked a lot. And I can guess why, but it just—you create a patch—in my mind, right, like you create a patch, you fix it, right?But these days I get, I've been on the other side as well, right? Like I created patches for open-source projects and I've seen them just wither away and die, and then five years later, someone's like, “Oh, can you fix this line to have one instead of two, and then I'll merge it.” I'm like, “I don't care anymore. It was five years ago. I don't work there anymore. I don't need it. If you want it, do it.”So, I get it these days. And these days, if someone creates a patch—just yesterday, someone created a patch to format cdk-github-runners in VS Code. And they did it just, like, a little bit wrong. So, I just fixed it for them and I approved it and pushed it. You know, it's much better. You don't need to bug people for most of it.Corey: You didn't yell at them for having the temerity to contribute?Amir: My voice is so raw because I've been yelling for five days at them, yeah.Corey: Exactly, exactly. I really want to thank you for taking the time to chat with me about how all this stuff came to be and your own path. If people want to learn more, where's the best place for them to find you?Amir: So, I really appreciate you having me and driving all this traffic to my projects. If people want to learn more, they can always go to cloudsnorkel.com; it has all the projects. github.com/cloudsnorkel has a few more. And then my private blog is kichik.com. So, K-I-C-H-I-K dot com. I don't post there as much as I should, but it has some interesting AWS projects from the past few years that I've done.Corey: And we will, of course, put links to all of that in the show notes. Thank you so much for taking the time. I really appreciate it.Amir: Thank you, Corey. It was really nice meeting you.Corey: Amir Szekely, owner of CloudSnorkel. I'm Cloud Economist Corey Quinn, and this is Screaming in the Cloud. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice, along with an insulting comment. Heck, put it on all of the podcast platforms with a step function state machine that you somehow can't quite figure out how the API works.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business and we get to the point. Visit duckbillgroup.com to get started.

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In this episode of Infrastructure Matters – Insider Edition, Steven Dickens is joined by AWS's Ajay Nair, General Manager, AWS Lambda, for a conversation focusing on the topic of serverless computing and its place within AWS's broader portfolio. Ajay explains that serverless has evolved from its initial definition as a way to run code without managing servers to a broader operational model focused on delivering value to customers without getting bogged down in managing infrastructure. He emphasizes that serverless allows customers to delegate outcomes like security, scale, performance, and availability to AWS experts, enabling them to focus on their unique business needs. Their discussion covers: Definition of Serverless: Serverless is an operational model that enables businesses to run or build applications without the need to manage low-level infrastructure. It allows customers to delegate infrastructure responsibilities to AWS, freeing them to concentrate on delivering value to their customers. AWS's Evolving Role: AWS has evolved to meet the diverse needs of its customers. Some customers require differentiated infrastructure and hardware, while others seek a more hands-off approach. AWS provides a spectrum of choices, from fully managed serverless services like Lambda to more hands-on options like EC2 instances, allowing customers to select what works best for their workloads. Benefits of Serverless: Customers adopting serverless benefit from lower total cost of ownership, elasticity, reliability, and speed. Serverless enables them to focus on innovation and faster delivery of applications, as AWS takes care of infrastructure management, performance optimization, and security. Serverless Across AWS's Portfolio: AWS is extending the serverless operational model across its entire portfolio, not just infrastructure. This includes databases (e.g., Redshift Serverless and DynamoDB), IoT services, machine learning platforms (e.g., SageMaker), and industry-specific solutions (e.g., healthcare). AWS aims to provide a range of serverless options to meet the needs of different application classes. Ajay Nair encourages customers to think "serverless first" for new development projects, emphasizing that serverless computing brings agility and cost efficiency to AWS users, allowing them to innovate faster and do less manual infrastructure management.

В этом выпуске: обсуждаем кресла, сломанность e-mail и языка C, а также распределенные транзакции в DynamoDB. Шоуноты: [00:01:39] Чему мы научились за неделю [00:05:18] Кресло AndaSeat Kaiser 3, XL Гусиный выпуск — Episode 0260 « DevZen Podcast AndaSeat Kaiser 3 игровое кресло купить от производителя с гарантией Double Dragon Gaiden: Rise Of The Dragons /… Читать далее →

Welcome episode 219 of The Cloud Pod podcast - where the forecast is always cloudy! Today your hosts are Justin and Jonathan, and they discuss all things cloud, including clickstream analytics, databricks, Microsoft Entra, virtual machines, Outlook threats, and some major changes over at the Google Cloud team.  Titles we almost went with this week: TCP is not Entranced with Entra ID The Cave you Fear to Entra, Holds the Treasure you Seek Microsoft should rethink Entra rules for their Email A big thanks to this week's sponsor: Foghorn Consulting, provides top-notch cloud and DevOps engineers to the world's most innovative companies. Initiatives stalled because you have trouble hiring?  Foghorn can be burning down your DevOps and Cloud backlogs as soon as next week.

Andreas Wittig, Co-Author of Amazon Web Services in Action and Co-Founder of marbot, joins Corey on Screaming in the Cloud to discuss ways to keep a book up to date in an ever-changing world, the advantages of working with a publisher, and how he began the journey of writing a book in the first place. Andreas also recalls how much he learned working on the third edition of Amazon Web Services in Action and how teaching can be an excellent tool for learning. Since writing the first edition, Adreas's business has shifted from a consulting business to a B2B product business, so he and Corey also discuss how that change came about and the pros and cons of each business model. About AndreasAndreas is the Co-Author of Amazon Web Services in Action and Co-Founder of marbot - AWS Monitoring made simple! He is also known on the internet as cloudonaut through the popular blog, podcast, and youtube channel he created with his brother Michael. Links Referenced: Amazon Web Services in Action: https://www.amazon.com/Amazon-Services-Action-Andreas-Wittig/dp/1617295116 Rapid Docker on AWS: https://cloudonaut.io/rapid-docker-on-aws/ bucket/av: https://bucketav.com/ marbot: https://marbot.io/ cloudonaut.io: https://cloudonaut.io TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn. It's been a few years since I caught up with Andreas Wittig, who is also known in the internet as cloudonaut, and much has happened since then. Andreas, how are you?Andreas: Hey, absolutely. Thank you very much. I'm happy to be here in the show. I'm doing fine.Corey: So, one thing that I have always held you in some high regard for is that you have done what I have never had the attention span to do: you wrote a book. And you published it a while back through Manning, it was called Amazon Web Services in Action. That is ‘in action' two words, not Amazon Web Services Inaction of doing absolutely nothing about it, which is what a lot of companies in the space seem to do instead.Andreas: [laugh]. Yeah, absolutely. So. And it was not only me. I've written the book together with my brother because back in 2015, Manning, for some reason, wrote in and asked us if we would be interested in writing the book.And we had just founded our own consulting company back then and we had—we didn't have too many clients at the very beginning, so we had a little extra time free. And then we decided, okay, let's do the book. And let's write a book about Amazon Web Services, basically, a deep introduction into all things AWS. So, this was 2015, and it was indeed a lot of work, much more [laugh] than we expected. So, first of all, the hard part is, what do you want to have in the book? So, what's the TOC? What is important and must be in?And then you start writing and have examples and everything. So, it was really an interesting journey. And doing it together with a publisher like Manning was also really interesting because we learned a lot about writing. You have kind of a coach, an editor that helps you through that process. So, this was really a hard and fun experience.Corey: There's a lot of people that have said very good things about writing the book through a traditional publisher. And they also say that one of the challenges is it's a blessing and a curse, where you basically have someone standing over your shoulder saying, “Is it done yet? Is it done yet? Is it done yet?” The consensus that seems to have emerged from people who have written books is, “That was great, please don't ever ask me to do it again.”And my operating theory is that no one wants to write a book. They want to have written a book. Which feels like two very different things most of the time. But the reason you're back on now is that you have gone the way of the terrible college professor, where you're going to update the book, and therefore you get to do a whole new run of textbooks and make everyone buy it and kill the used market, et cetera. And you've done that twice now because you have just recently released the third edition. So, I have to ask, how different is version one from version two and from version three? Although my apologies; we call them ‘editions' in the publishing world.Andreas: [laugh]. Yeah, yeah. So, of course, as you can imagine, things change a lot in AWS world. So, of course, you have to constantly update things. So, I remember from first to second edition, we switched from CloudFormation in JSON to YAML. And now to the third edition, we added two new chapters. This was also important to us, so to keep also the scope of the book in shape.So, we have in the third edition, two new chapters. One is about automating deployments, recovering code deploy, [unintelligible 00:03:59], CloudFormation rolling updates in there. And then there was one important topic missing at all in the book, which was containers. And we finally decided to add that in, and we have now container chapter, starting with App Runner, which I find quite an interesting service to observe right now, and then our bread and butter service: ECS and Fargate. So, that's basically the two new chapters. And of course, then reworking all the other chapters is also a lot of work. And so, many things change over time. Cannot imagine [laugh].Corey: When was the first edition released? Because I believe the second one was released in 2018, which means you've been at this for a while.Andreas: Yeah. So, the first was 2015, the second 2018, three years later, and then we had five years, so now this third edition was released at the beginning of this year, 2023.Corey: Eh, I think you're right on schedule. Just March of 2020 lasted three years. That's fine.Andreas: Yeah [laugh].Corey: So, I have to ask, one thing that I've always appreciated about AWS is, it feels like with remarkably few exceptions, I can take a blog post written on how to do something with AWS from 2008 and now in 2023, I can go through every step along with that blog post. And yeah, I might have trouble getting some of the versions and services and APIs up and running, but the same steps will absolutely work. There are very few times where a previously working API gets deprecated and stops working. Is this the best way to proceed? Absolutely not.But you can still spin up the m1.medium instance sizes, or whatever it was, or [unintelligible 00:05:39] on small or whatever the original only size that you could get was. It's just there are orders of magnitude and efficiency gains you can do by—you can go through by using more modern approaches. So, I have to ask, was there anything in the book as you revised it—two times now—that needed to come out because it was now no longer working?Andreas: So, related to the APIs that's—they are really very stable, you're right about that. So, the problem is, our first few chapters where we have screenshots of how you go through the management—Corey: Oh no.Andreas: —console [laugh]. And you can probably, you can redo them every three months, probably, because the button moves or a step is included or something like that. So, the later chapters in the book, where we focus a lot on the CLI or CloudFormation and stuff like—or SDKs, they are pretty stable. But the first few [ones 00:06:29] are a nightmare to update all those screenshots. And then sometimes, so I was going through the book, and then I noticed, oh, there's a part of this chapter that I can completely remove nowadays.So, I can give you an example. So, I was going through the chapter about Simple Storage Service S3, and I—there was a whole section in the chapter about read-after-write consistency. Because back then, it was important that you knew that after updating an object or reading an object before it was created the first time, you could get outdated versions for a little while, so this was eventually consistent. But nowadays, AWS has changed that and basically now, S3 has this strong read-after-write consistency. So, I basically could remove that whole part in the chapter which was quite complicated to explain to the reader, right, so I [laugh] put a lot of effort into that.Corey: You think that was confusing? I look at the sea of systems I had to oversee at one company, specifically to get around that problem. It's like, well, we can now take this entire application and yeet it into the ocean because it was effectively a borderline service to that just want to ens—making consistency guarantees. It's not a common use case, but it is one that occurs often enough to be a problem. And of course, when you need it, you really need it. That was a nice under-the-hood change that was just one day, surprise, it works that way. But I'm sure it was years of people are working behind the scenes, solving for impossible problems to get there, and cetera, et cetera.Andreas: Yeah, yeah. But that's really cool is to remove parts of the book that are now less complicated. This is really cool. So, a few other examples. So, things change a lot. So, for example, EFS, so we have EFS, Elastic File System, in the book as well. So, now we have new throughput modes, different limits. So, there's really a lot going on and you have to carefully go through all the—Corey: Oh, when EFS launched, it was terrible. Now, it's great just because it's gotten so much more effective and efficient as a service. It's… AWS releases things before they're kind of ready, it feels like sometimes, and then they improve with time. I know there have been feature deprecations. For example, for some reason, they are no longer allowing us to share out a bucket via BitTorrent, which, you know, in 2006 when it came out, seemed like a decent idea to save on bandwidth. But here in 2023, no one cares about it.But I'm also keeping a running list of full-on AWS services that have been deprecated or have the deprecations announced. Are any of those in the book in any of its editions? And if and when there's a fourth edition, will some of those services have to come out?Andreas: [laugh]. Let's see. So, right after the book was published—because the problem with books is they get printed, right; that's the issue—but the target of the book, AWS, changes. So, a few weeks after the printed book was out, we found out that we have an issue in our one of our examples because now S3 buckets, when you create them, they have locked public access enabled by default. And this was not the case before. And one of our example relies on that it can create object access control lists, and this is not working now anymore. [laugh].So yeah, there are things changing. And we have, the cool thing about Manning is they have that what they call a live book, so you can read it online and you can have notes from other readers and us as the authors along the text, and there we can basically point you in the right direction and explain what happened here. So, this is how we try to keep the book updated. Of course, the printed one stays the same, but the ebook can change over time a little bit.Corey: Yes, ebooks are… at least keeping them updated is a lot easier, I would imagine. It feels like that—speaking of continuous builds and automatic CI/CD approaches—yeah, well, we could build a book just by updating some text in a Git repo or its equivalent, and pressing go, but it turns out that doing a whole new print run takes a little bit more work.Andreas: Yeah. Because you mentioned the experience of writing a book with a publisher and doing it on your own with self-publishing, so we did both in the past. We have Amazon Web Services in Action with Manning and we did another book, Rapid Docker on AWS in self-publishing. And what we found out is, there's really a lot of effort that goes into typesetting and layouting a book, making sure it looks consistent.And of course, you can just transform some markdown into a epub and PDF versions, but if a publisher is doing that, the results are definitely different. So, that was, besides the other help that we got from the publisher, very helpful. So, we enjoyed that as well.Corey: What is the current state of the art—since I don't know the answer to this one—around updating ebook versions? If I wind up buying an ebook on Kindle, for example, will they automatically push errata down automatically through their system, or do they reserve that for just, you know, unpublishing books that they realized shouldn't be on the Marketplace after people have purchased them?Andreas: [laugh]. So—Corey: To be fair, that only happened once, but I'm still giving them grief for it a decade and change later. But it was 1984. Of all the books to do that, too. I digress.Andreas: So, I'm not a hundred percent sure how it works with the Kindle. I know that Manning pushes out new versions by just emailing all the customers who bought the book and sending them a new version. Yeah.Corey: Yeah. It does feel, on some level, like there needs to be at least a certain degree of substantive change before they're going to start doing that. It's like well, good news. There was a typo on page 47 that we're going to go ahead and fix now. Two letters were transposed in a word. Now, that might theoretically be incredibly important if it's part of a code example, which yes, send that out, but generally, A, their editing is on point, so I didn't imagine that would sneak through, and 2, no one cares about a typo release and wants to get spammed over it?Andreas: Definitely, yeah. Every time there's a reprint of the book, you have the chance to make small modifications, to add something or remove something. That's also a way to keep it in shape a little bit.Corey: I have to ask, since most people talk about AWS services to a certain point of view, what is your take on databases? Are you sticking to the actual database services or are you engaged in my personal hobby of misusing everything as a database by holding it wrong?Andreas: [laugh]. So, my favorite database for starting out is DynamoDB. So, I really like working with DynamoDB and I like the limitations and the thing that you have to put some thoughts into how to structure your data set in before. But we also use a lot of Aurora, which really find an interesting technology. Unfortunately, Aurora Serverless, it's not becoming a product that I want to use. So, version one is now outdated, version two is much too expensive and restricted. So—Corey: I don't even know that it's outdated because I'm seeing version one still get feature updates to it. It feels like a divergent service. That is not what I would expect a version one versus version two to be. I'm with you on Dynamo, by the way. I started off using that and it is cheap is free for most workloads I throw at it. It's just a great service start to finish. The only downside is that if I need to move it somewhere else, then I have a problem.Andreas: That's true. Yeah, absolutely.Corey: I am curious, as far as you look across the sea of change—because you've been doing this for a while and when you write a book, there's nothing that I can imagine that would be better at teaching you the intricacies of something like AWS than writing a book on it. I got a small taste of this years ago when I shot my mouth off and committed to give a talk about Git. Well, time to learn Git. And teaching it to other people really solidifies a lot of the concepts yourself. Do you think that going through the process of writing this book has shaped how you perform as an engineer?Andreas: Absolutely. So, it's really interesting. So,I added the third edition and I worked on it mostly last year. And I didn't expect to learn a lot during that process actually, because I just—okay, I have to update all the examples, make sure everything work, go through the text, make sure everything is up to date. But I learned things, not only new things, but I relearned a lot of things that I wasn't aware of anymore. Or maybe I've never been; I don't know exactly [laugh].But it's always, if you go into the details and try to explain something to others, you learn a lot about that. So, teaching is a very good way to, first of all gather structure and a deep understanding of a topic and also dive into the details. Because when you write a book, every time you write a sentence, ask the question, is that really correct? Do I really know that or do I just assume that? So, I check the documentation, try to find out, is that really the case or is that something that came up myself?So, you'll learn a lot by doing that. And always come to the limits of the AWS documentation because sometimes stuff is just not documented and you need to figure out, what is really happening here? What's the real deal? And then this is basically the research part. So, I always find that interesting. And I learned a lot in during the third edition, while was only adding two new chapters and rewriting a lot of them. So, I didn't expect that.Corey: Do you find that there has been an interesting downstream effect from having written the book, that for better or worse, I've always no—I always notice myself responding to people who have written a book with more deference, more acknowledgment for the time and effort that it takes. And some books, let's be clear, are terrible, but I still find myself having that instinctive reaction because they saw something through to be published. Have you noticed it changing other aspects of your career over the past, oh, dear Lord, it would have been almost ten years now.Andreas: So, I think it helped us a lot with our consulting business, definitely. Because at the very beginning, so back in 2015, at least here in Europe and Germany, AWS was really new in the game. And being the one that has written a book about AWS was really helping… stuff. So, it really helped us a lot for our consulting work. I think now we are into that game of having to update the book [laugh] every few years, to make sure it stays up to date, but I think it really helped us for starting our consulting business.Corey: And you've had a consulting business for a while. And now you have effectively progressed to the next stage of consulting business lifecycle development, which is, it feels like you're becoming much more of a product company than you were in years past. Is that an accurate perception from the outside or am I misunderstanding something fundamental?Andreas: You know, absolutely, that's the case. So, from the very beginning, basically, when we founded our company, so eight years ago now, so we always had to go to do consulting work, but also do product work. And we had a rule of thumb that 20% of our time goes into product development. And we tried a lot of different things. So, we had just a few examples that failed completely.So, we had a Time [Series 00:17:49] as a Service offering at the very beginning of our journey, which failed completely. And now we have Amazon Timestream, which makes that totally—so now the market is maybe there for that. We tried a lot of things, tried content products, but also as we are coming from the software development world, we always try to build products. And over the years, we took what we learned from consulting, so we learned a lot about, of course, AWS, but also about the market, about the ecosystem. And we always try to bring that into the market and build products out of that.So nowadays, we really transitioned completely from consulting to a product company, as you said. So, we do not do any consulting anymore with one few exception with one of our [laugh] best or most important clients. But we are now a product company. And we only a two-person company. So, the idea was always how to scale a company without growing the team or hiring a lot of people, and a consulting business is definitely not a good way to do that, so yeah, this was why always invested into products.And now we have two products in the AWS Marketplace which works very well for us because it allows us to sell worldwide and really easily get a relationship up and running with our customers, and that pay through their AWS bill. So, that's really helping us a lot. Yeah.Corey: A few questions on that. At first it always seems to me that writing software or building a product is a lot like real estate in that you're doing a real estate development—to my understanding since I live in San Francisco and this is a [two exit 00:19:28] town; I still rent here—I found though, that you have to spend a lot of money and effort upfront and you don't get to start seeing revenue on that for years, which is why the VC model is so popular where you'll take $20 million, but then in return they want to see massive, outsized returns on that, which—it feels—push an awful lot of perfectly sustainable products into things that are just monstrous.Andreas: Hmm, yeah. Definitely.Corey: And to my understanding, you bootstrapped. You didn't take a bunch of outside money to do this, right?Andreas: No, no, we have completely bootstrapping and basically paying the bills with our consulting work. So yeah, I can give you one example. So, bucketAV is our solution to scan S3 buckets for malware, and basically, this started as an open-source project. So, this was just a side project we are working on. And we saw that there is some demand for that.So, people need ways to scan their objects—for example, user uploads—for malware, and we just tried to publish that in the AWS Marketplace to sell it through the Marketplace. And we don't really expect that this is a huge deal, and so we just did, I don't know, Michael spent a few days to make sure it's possible to publish that and get in shape. And over time, this really grew into an important, really substantial part of our business. And this doesn't happen overnight. So, this adds up, month by month. And you get feedback from customers, you improve the product based on that. And now this is one of the two main products that we sell in the Marketplace.Corey: I wanted to ask you about the Marketplace as well. Are you finding that that has been useful for you—obviously, as a procurement vehicle, it means no matter what country a customer is in, they can purchase it, it shows up on the AWS bill, and life goes on—but are you finding that it has been an effective way to find new customers?Andreas: Yes. So, I definitely would think so. It's always funny. So, we have completely inbound sales funnel. So, all customers find us through was searching the Marketplace or Google, probably. And so, what I didn't expect that it's possible to sell a B2B product that way. So, we don't know most of our customers. So, we know their name, we know the company name, but we don't know anyone there. We don't know the person who buys the product.This is, on the one side, a very interesting thing as a two-person company. You cannot build a huge sales process and I cannot invest too much time into the sales process or procurement process, so this really helps us a lot. The downside of it is a little bit that we don't have a close relationship with our customers and sometimes it's a little tricky for us to find important person to talk to, to get feedback and stuff. But on the other hand, yeah, it really helps us to sell to businesses all over the world. And we sell to very small business of course, but also to large enterprise customers. And they are fine with that process as well. And I think, even the large enterprises, they enjoy that it's so easy [laugh] to get a solution up and running and don't have to talk to any salespersons. So, enjoy it and I think our customers do as well.Corey: This is honestly the first time I've ever heard a verifiable account a vendor saying, “Yeah, we put this thing on the Marketplace, and people we've never talked to find us on the Marketplace and go ahead and buy.” That is not the common experience, let's put it that way. Now true, an awful lot of folks are selling enterprise software on this and someone—I forget who—many years ago had a great blog post on why no enterprise software costs $5,000. It either is going to cost $500 or it's going to cost 100 grand and up because the difference is, is at some point, you'd have a full-court press enterprise sales motion to go and sell the thing. And below a certain point, great, people are just going to be able to put it on their credit card and that's fine. But that's why you have this giant valley of there is very little stuff priced in that sweet spot.Andreas: Yeah. So, I think maybe it's important to mention that our products are relatively simple. So, they are just for a very small niche, a solution for a small problem. So, I think that helps a lot. So, we're not selling a full-blown cloud security solution; we only focus on that very small part: scanning S3 objects for malware.For example, on marbot,f the other product that we sell, which is monitoring of AWS accounts. Again, we focus on a very simple way to monitor AWS workloads. And so, I think that is probably why this is a successful way for us to find new customers because it's not a very complicated product where you have to explain a lot. So, that's probably the differentiator here.Corey: Having spent a fair bit of time doing battle with compliance goblins—which is, to be clear, I'm not describing people; I'm describing processes—in many cases, we had to do bucket scanning for antivirus, just to check a compliance box. From our position, there was remarkably little risk of a user-generated picture of a receipt that is input sanitized to make sure it is in fact a picture, landing in an S3 bucket and then somehow infecting one of the Linux servers through which it passed. So, we needed something that just checked the compliance box or we would not be getting the gold seal on our website today. And it was, more or less, a box-check as opposed to something that solved a legitimate problem. This was also a decade and change ago. Has that changed to a point now where there are legitimate threats and concerns around this, or is it still primarily just around make the auditor stop yelling at me, please?Andreas: Mmm. I think it's definitely to tick the checkbox, to be compliant with this, some regulation. On the other side, I think there are definitely use cases where it makes a lot of sense, especially when it comes to user-generated content of all kinds, especially if you're not only consuming it internally, but maybe also others can immediately start downloading that. So, that is where we see many of our customers are coming with that scenario that they want to make sure that the files that people upload and others can download are not infected. So, that is probably the most important use case.Corey: There's also, on some level, an increasing threat of ransomware. And for a long time, I was very down on the ideas of all these products that hit the market to defend S3 buckets against ransomware. Until one day, there was an AWS security blog post talking about how they found it. And yeah, we've we have seen this in the wild; it is causing problems for companies; here's what to do about it. Because it's one of those areas where I can't trust a vendor who's trying to sell me something to tell me that this problem exists.I mean, not to cast aspersions, but they're very interested, they're very incentivized to tell that story, whereas AWS is not necessarily incentivized to tell a story like that. So, that really brought it home for me that no, this is a real thing. So, I just want to be clear that my opinion on these things does in fact, evolve. It's not, “Well, I thought it was dumb back in 2012, so clearly it's still dumb now.” That is not my position, I want to be very clear on that.I do want to revisit for a moment, the idea of going from a consultancy that is a services business over to a product business because we've toyed with aspects of that here at The Duckbill Group a fair bit. We've not really found long-term retainer services engagements that add value that we are comfortable selling. And that means as a result that when you sell fixed duration engagements, it's always a sell, sell, sell, where's the next project coming from? Whereas with product businesses, it's oh, the grass is always greener on the other side. It's recurring revenue. Someone clicks, the revenue sticks around and never really goes away. That's the dream from where I sit on the services side of the fence, wistfully looking across and wondering what if. Now that you've made that transition, what sucks about product businesses that you might not have seen going into it?Andreas: [laugh]. Yeah, that a good question. So, on the one side, it was really also our dream to have a product business because it really changes the way we work. We can block large parts of our calendar to do deep-focus work, focus on things, find new solutions, and really try to make a solution that really fits to problem and uses all the AWS capabilities to do so. And on the other side, a product business involves, of course, selling the product, which is hard.And we are two software engineers, [laugh] and really making sure that we optimize our sales and there's search engine optimization, all that stuff, this is really hard for us because we don't know anything about that and we always have to find an expert, or we need to build a knowledge ourself, try things out, and so on. So, that whole part of selling the product, this is really a challenge for us. And then of course, product business evolves a lot of support work. So, we get support emails multiple times per hour, and we have to answer them and be as fast as possible with that. So, that is, of course, something that you do not have to do with consulting work.And not always that, the questions are many times really simple questions that pointed people in the right direction, find part of the documentation that answers the question. So, that is a constant stream of questions coming in that you have to answer. So, the inbox is always full [laugh]. So, that is maybe a small downside of a product business. But other than that, yeah, compared to a consulting business, it really gives us many flexibilities with planning our work day around the rest of our lives. That's really what we enjoy about a product company.Corey: I was very careful to pick an expensive problem that was only a business-hours problem. So, I don't wind up with a surprise, middle-of-the-night panic phone call. It's yeah, it turns out that AWS billing operate during business hours in the US Pacific Time. The end. And there are no emergencies here; there are simply curiosities that will, in the fullness of time take weeks to get resolved.Andreas: Mmm. Yeah.Corey: I spent too many years on call, in that sense. Everyone who's built a product company the first time always says the second time, the engineering? Meh, there are ways to solve that. Solving the distribution problem. That's the thing I want to focus on next.And I feel like I sort of went into this backwards in that I don't really have a product to sell people but I somehow built an audience. And to be honest, it's partly why. It's because I didn't know what I was going to be doing after 18 months and I knew that whatever it was going to be, I needed an audience to tell about it, so may as well start the work of building the audience now. So, I have to imagine if nothing else, your book has been a tremendous source of building a community. When I mentioned the word cloudonaut to people who have been learning AWS, more often than not, they know who you are.Andreas: Yeah.Corey: Although I admit they sometimes get you confused with your brother.Andreas: [laugh]. Yes, that's not too hard. Yeah, yeah, cloudonaut is definitely—this was always our, also a side project of we was just writing about things that we learned about AWS. Whenever we, I don't know, for example, looked into a new series, we wrote a blog post about that. Later, we did start a podcast and YouTube videos during the pandemic, of course, as everyone did. And so, I think this was always fun stuff to do. And we like sharing what we learn and getting into discussion with the community, so this is what we still do and enjoy as well, of course. Yeah.Corey: I really want to thank you for taking the time to catch up and see what you've been up to these last few years with a labor of love and the pivot to a product company. If people want to learn more, where's the best place for them to find you?Andreas: So definitely, the best place to find me is cloudonaut.io. So, this basically points you to all [laugh] what I do. Yeah, that's basically the one domain and URL that you need to know.Corey: Excellent. And we will put that in the show notes, of course. Thank you so much for taking the time to speak with me today. I really appreciate it.Andreas: Yeah, it was a pleasure to be back here. I'm big fan of podcasts and also of Screaming in the Cloud, of course, so it was a pleasure to be here again.Corey: [laugh]. You are always welcome. Andreas Wittig, co-author of Amazon Web Services in Action, now up to its third edition. And of course, the voice behind cloudonaut. I'm Cloud Economist Corey Quinn, and this is Screaming in the Cloud. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice along with an angry, insulting comment that I will at one point be able to get to just as soon as I find something to deal with your sarcasm on the AWS Marketplace.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business and we get to the point. Visit duckbillgroup.com to get started.

Welcome to the newest episode of The Cloud Pod podcast - where the forecast is always cloudy! Ryan, Jonathan, and Matt are your hosts this week as we discuss all things cloud, including updates to Terraform, pricing updates in GCP SCC, AWS Blueprint, DMS Serverless, and Snowball - as well as all the discussion on Microsoft quantum safe computing and ethical AI you could possibly want!  A big thanks to this week's sponsor: Foghorn Consulting, provides top-notch cloud and DevOps engineers to the world's most innovative companies. Initiatives stalled because you have trouble hiring?  Foghorn can be burning down your DevOps and Cloud backlogs as soon as next week.

Aaron Torres and Ala Shiban are from Klotho, which powers Infrastructure Copilot, the most advanced infrastructure design tool that understands how to define, connect, and scale your infrastructure-as-code. Victoria talks to Aaron and Ala about the Klotho engine, Klotho the CLI tool, and InfraCopilot and how they work together to help enable developer teams to iterate on applications and features quickly. Klotho (https://klo.dev/) Infrastructure Copilot (https://infracopilot.io/) Follow Klotho on Github (https://github.com/klothoplatform/klotho), Discord (https://discord.com/invite/4wwBRqqysY), Twitter (https://twitter.com/GetKlotho), or LinkedIn (https://www.linkedin.com/company/klothoplatform/). Follow Aaron Torres on LinkedIn (https://www.linkedin.com/in/torresaaron/), or Twitter (https://twitter.com/aarontorres). Follow Ala Shiban on LinkedIn (https://www.linkedin.com/in/alashiban/) or Twitter (https://twitter.com/AlaShiban). Follow thoughtbot on Twitter (https://twitter.com/thoughtbot) or LinkedIn (https://www.linkedin.com/company/150727/). Become a Sponsor (https://thoughtbot.com/sponsorship) of Giant Robots! Transcript: VICTORIA: This is the Giant Robots Smashing Into Other Giant Robots Podcast, where we explore the design, development, and business of great products. I'm your host, Victoria Guido. And with me today is Aaron Torres and Ala Shiban from Klotho, which powers Infrastructure Copilot, the most advanced infrastructure design tool that understands how to define, connect, and scale your infrastructure-as-code. Aaron and Ala, thank you for joining me. ALA: Thank you for having us. AARON: Yeah, thank you very much. VICTORIA: Well, great. I wanted to just start with a little bit of a icebreaker; maybe tell me a little bit more about what the weather is like where you're currently at. AARON: So I'm in St. Louis, Missouri. Right now, it is definitely...it feels like summer finally. So we're getting some nice, warm days and clear skies. ALA: And I'm in LA. And it's gloomier than I would like compared to what it's been in the last few years. But I'll take it if this means we're getting closer to summer. VICTORIA: Right. And I'm not too far from you, Ala, in San Diego, and it's a little chillier than I would prefer as well. But that's what we get for living close to the beach. So there's always trade-offs. Well, wonderful. I'm so excited to talk to you about your product here today. Let me start with a question about, let's say, I'm a non-technical founder, and I've just heard about your product. What's your pitch to someone in that position on the value of your tool? ALA: For somebody who isn't technical, I would say you can enable your team, your developer team, to quickly iterate on their applications or features and let InfraCopilot and Klotho take care of taking that application or features and deploy them and getting them running on the cloud. VICTORIA: Okay. So maybe I've been thinking about having to hire an AWS engineer or someone who's an infrastructure engineer. I could consider getting a tool like Klotho and Infrastructure Copilot to allow my developers to take on more of that responsibility themselves. ALA: Absolutely. VICTORIA: Gotcha. Okay, well, great. So let me ask about how did it all get started? What was the impetus that set you on this journey ALA: Both Aaron and I used to work at Riot Games, and I used to lead the cloud services org at Riot. I had about 50 people, 40 engineers, as part of a larger 120-person org, infrastructure platform org, which was tasked with building the platform that runs League of Legends, VALORANT, for 200 million people all around the world, in China. Full DevOps mode for Riot developers and full ops mode for running in China. It took us three years, a lot of effort. And by the time we were done, it was already legacy, and that seemed broken to me. We were already getting started to do another round of upgrades and iterations. At that point, I decided to leave. But I couldn't let go of this feeling that we shouldn't have had to spend so many years solving a problem only for it not to be solved. And based on research and conversations, it was clear that this was an industry-wide phenomena. And so I went about trying to figure out why that happens and then how we can solve it, and that's how Klotho came about. VICTORIA: That's so interesting. And I've certainly been a part of similar situations where you spend so much time solving a big problem and infrastructure only to get to the end of it and realize now you have a whole nother set of problems. [laughs] And you get upgrade. And they've also invented new ways of doing things in the cloud that you want to be able to take advantage of. So you had that time with Riot Games and League of Legends and building this globally responsive infrastructure. What lessons learned did you take from that into building Klotho and building your product, Infrastructure Copilot? AARON: We learned a bunch of things. One of the more difficult problems to solve isn't technical at all; it's organizational and understanding how the organization flows and how the different teams interact with each other. So we really endeavor to solve that problem. I mean, our product is a technical product, but it is meant to help bridge that gulf and make that problem a little bit easier as well. Otherwise, yeah, exactly to your point, part of the problem with these migrations is that new technology comes along. And there's definitely a feeling of when you hire new developers, they are excited about the new thing, and there's other reasons as well. But you get this kind of natural, eternal migration going to the newer technology. VICTORIA: That makes sense. And you bring up a great point on some of the issues, not being technical but organizational. And when I look at a lot of infrastructure-as-code tools, when we get to security, I wonder how it fits in with the organizational requirements for security, right? Like, you have to have defined groups who have defined access to different levels and have the tools in place to be able to manage identities in your organization. So I'm curious how that fits into what you built with Klotho and the Infrastructure Copilot. ALA: The way we think about infrastructure is as a set of intents or things that developers, and operators, cloud engineers, infrastructure engineers are trying to satisfy or to do. So you have tasks. You're trying to build a solution. You're trying to build an architecture or add something to it. And organizations have constraints, whether it's their own Terraform, or their own ruleset, or security expectations, or compliance expectations. And the way we look at this dynamic is those rules are encoded in a way that Klotho, which is a cloud compiler, it has the ability to reason about both the application and the infrastructure-as-code and enforce or at least warn about mismatches between the constraints that the organization sets, and what the developer or operator are trying to do, or the intent that is being described high level or low level within the tools. And then that is reflected both visually and in code and in the infrastructure-as-code, one or more. And so it's very much rooted in how the entire set of technologies and product and tools are designed. VICTORIA: Got it. So do you see the tool will be more fit for the market of larger development shops who maybe have existing infrastructure but want to experiment with a different way of managing it for their developers? ALA: It depends. So because we went about solving the problem rather than just building a specific vertical or a specific stack piece, we try to only play in this space of intelligent editing and intelligent understanding of the alignment between infrastructure and code. And so you could, as a developer, effectively with Klotho, write a plain application and have it be running in the cloud without knowing anything in the underlying cloud systems. It will set up storage, and persistence, and security, and secrets. All those elements are easily accessible within the code itself. It can also work in the context of a company where the infrastructure or platform team have set those rules and guidance within the tools. And then, developers can continue working the way they expect to work, either in code or in the infrastructure-as-code layer. And it would still allow them to do the same intents that they want only within that sandbox. Or if they can't be satisfied because they're trying to do something that isn't allowed, they have a mechanism of, one, knowing that but also asking, in our case, InfraCopilot to help it reshape what it's doing, what they're doing into the sandbox and the trade-offs that that brings in. VICTORIA: Got it. So you can both start from scratch and start a brand new application using it, or you can integrate it with your existing rules and systems and everything that already exists. ALA: Exactly. VICTORIA: Gotcha. Yeah, I think one interesting thing we've found with very new founders who are building their application for the first time is that there are some essential things, like, they don't even have an identity store like a Google [laughs] or Microsoft Azure Directory. So starting to work in the cloud, there are some basic elements you have to set up first that's a little bit of a barrier. So it sounds like what you're saying with Klotho is that you wouldn't necessarily have those same issues. Or how would you get that initial, like, cloud accounts set up? AARON: Yeah. So, for the situation where you're bootstrapping everything from scratch, you've done nothing; we haven't invested much in setting up the initial accounts. But assuming you get to the point where you have AWS credentials, and you're able to hit the AWS API using the CLI, that's sort of where we can take over. So, yeah, like, I would say right now, as a business, it's definitely where the value is coming is going to be these mid-sized companies. But for that scenario specifically, bootstrapping and starting something from scratch, if you have that initial setup in place, it's one of the fastest ways to go from a concept to something running in the cloud. ALA: And if you think about the two tools that we're building, there's Klotho, which InfraCopilot...or the Klotho engine, which Klotho the CLI tool uses and InfraCopilot uses. The Klotho engine is responsible for the intelligence. It knows how to translate things like I want a web API that talks to DynamoDB. And it will literally create everything or modify everything that is needed to give you that and plug in your code. You can also say things in a much higher level degree, which things like I want a lambda which handles 10,000 users. And I want it to be lowest latency talking to an RDS Instance or to a Postgres database. And what that would do is, in our side, in the Klotho engine, we understand that there needs to be a VPC and subnets, and spin up RDS, and connect an RDS proxy. Because for connection pooling with lambda specifically, you need one to scale to that degree of scale. And so that is the intelligence that is built into the Klotho engine if you want to start from the infrastructure. If you want to start from code, all you have to do is bring in the Redis instance, the Redis SDK, and, let's say, your favorite web framework, and just add the annotations or the metadata that says, I want this web framework to be exposed to the internet, and I want this Redis to be persisted in the cloud. And you run Klotho. And what comes out the other end is the cloud version that does that for you. And it's one command away from getting it to run. VICTORIA: So that's interesting how the two tools work together and how a developer might be able to get things spun up quickly on the cloud without having to know the details of each particular AWS service. And reading through your docs, it sounds like once you have something working in the cloud, then you'll also get automated recommendations on how to improve it for cost and reliability. Is that right? ALA: That's where we're headed. VICTORIA: Gotcha. I'm curious; for Aaron, it sounds like there is more in that organizational challenges that you alluded to earlier. So you want to be able to deliver this capability to developers. But what barriers have you found organizationally to getting this done? AARON: So I'm going to speak specifically on infrastructure here because I think this is one of the biggest ones we've seen. But typically, when you get to a larger-sized company, we'll call it a mid-sized company with, you know, a couple hundred engineers or more, you get to the point where it doesn't make sense for every team to own their entire vertical. And so you want to really put the cloud knowledge into a central team. And so you tend to build either a platform team, or an infrastructure team, or a cloud team who sort of owns how cloud resources are provisioned, which ones they support, et cetera. And so, really, some of the friction I'm talking about is the friction between that team and developer teams who really just want to write their application and get going quickly. But you don't have to fall within the boundaries set by that central team. To give, like, a real concrete example of that, if you wanted to prototype a new technology, like, let's say that some new database technology came out and you wanted to use it, it's a very coordinated effort between both teams in terms of the roadmap. Like, the infrastructure team needs to get on that roadmap, that they need to make a sandbox and how that's going to work. The code team needs to make an application to test it. And the whole thing requires a lot more communication than just tech. VICTORIA: Yeah, no, I've been part of kind of one of those classic DevOps problems. It's where now you've built the ops team and the dev team, [laughs] and now you're back to those coordination issues that you had before. So, if I were a dev using Klotho or the infrastructure-as-code copilot, I would theoretically have access to any AWS sandbox account. And I could just spin up whatever I wanted [laughs] within the limits that could be defined by your security team or by your, you know, I'm sure there's someone who's setting a limit on the size of databases you could spin up for fun. Does that sound right? AARON: Yeah, that's totally right. And in addition to just limits, it's also policies. So a good example is maybe in production for databases, you have a data retention policy. And you have something like we need to keep three months of backups for this amount of time. We want to make sure that if someone spins up a production database from any of those app teams, that they will follow their company policy there and not accidentally, like, lose data where it has to be maintained for some reason. ALA: That's an important distinction where we have our own set of, you know, best practice or rules that are followed roughly in the industry. But also, the key here is that the infrastructure central teams in every company can describe the different rulesets and guidelines, guardrails within the company on what developers can do, not only in low-level descriptions like instance sizes or how much something is, whether it's Spot Instances always or not in production versus dev. But also be able to teach the system when a developer says, "I want a database," spin up a Postgres database with this configuration that is wired to the larger application that they have. Or, if I want to run a service, then it spins up the correct elements and configures them to work, let's say, Kubernetes pods, or lambdas, or a combination based on what the company has described as the right way for that company to do things. And so it gives flexibility to not know the specific details but still get the company's specific way of doing them. And the key here is that we're trying to codify the communication patterns that do happen, and they need to happen if there's no tools to facilitate it between the infrastructure platform team and the feature teams. Only in this case, we try and capture that in a way that the central teams can define it. And the developers on feature teams can consume it without having as much friction. VICTORIA: So that will be different than, like, an infrastructure team that's putting out everything in Terraform and doing pull requests based off GitHub repository to that. It makes it a little more easier to read, and understand, and share the updates and changes. AARON: Right. And also, I mean, so, like, the thing you're describing of, like, the central team, having Terraform tends to be, like, these golden templates. Like they say, "If you want to make a database, here's your database template." And then you get a lot of interesting issues like drift, where maybe some teams are using the old versions of the templates, and they're not picking up the new changes. And how do you kind of reconcile all that? So it is meant to help with all of those things. VICTORIA: That makes a lot of sense. And I'm curious, what questions came up in the customer discovery process for this product that surprised you? ALA: I think there's one...I don't know that it was a question, but I think there was...So, when we started with Klotho, Klotho has the ability to enable a code-first approach, which means that you give the tool to developers as the infrastructure or platform team, or if you're a smaller shop, then you can just use Klotho directly. You set the rules on what's allowed or what's not allowed, and then developers can work very freely. They can describe very succinctly how to turn a plain object, SDK, et cetera, how to build larger architectures very quickly with a few annotations that we describe and that give cloud powers. We had always thought that some teams will feel that this encroaches on their jobs. We've heard from people on infra, you know, platform teams, "This is amazing. But this is my job." And so, one of our hypotheses was that we are encroaching into what they see as their responsibility. And we built more and more mechanisms that would clean up that interface and give them the ability to control more so they can free themselves up, just like most automations that happen in the world, to do more things. What happened later surprised us. And by having a few or several more discoveries, we found out that the feeling isn't a fear of the tool replacing their job. The fear or worry is that the tool will make their jobs boring, what is left of the job be boring, and nobody wants to go to work and not have cool and fun things to do. And because I think we all, on a certain degree, believe that, you know, if we take away some of the work that we're doing, we'll find something higher level and harder to solve, but until that exists in people's minds, there's nothing there. And therefore, they're left with whatever they don't want to do or didn't want to do. And so that's where we tried to take a step back from all the intelligence the Klotho engine provides through that code-first Klotho. And we built out focusing on one of the pillars in the tech to create InfraCopilot, which helps with keeping or making the things that we already do much simpler but also in a way that maintains and does it in a fun way. VICTORIA: That makes sense because my understanding of where to use AI and where to use machine learning for best purposes is to automate those, like, repetitive, boring tasks and allow people to focus on the creative and more interesting work, right? ALA: Yes and no. The interesting bit about our approach to ML is that we don't actually use machine learning or ChatGPT for any of the intelligence layers, meaning we don't ask ChatGPT to generate Terraform or any kind of GPT model to analyze a certain aspect of the infrastructure. That is all deterministic and happens in the Klotho engine. That is the uniqueness of why this always works rather than if GPT happened to get it right. What we use ML for is the ability to parse the intent. So we actually use it as a language model to parse the intent from what the user is trying to convey, meaning I want a lambda with an API gateway. What we get back from our use of ML is the user has asked for a lambda, an AWS lambda, and API gateway and that they be connected. That is the only thing we get back. And that is fed into the Klotho engine. And then, we do the intelligence to translate that to an actual architecture. VICTORIA: That's a really cool way to use natural language processing to build cloud infrastructure. MID-ROLL AD: Are you an entrepreneur or start-up founder looking to gain confidence in the way forward for your idea? At thoughtbot, we know you're tight on time and investment, which is why we've created targeted 1-hour remote workshops to help you develop a concrete plan for your product's next steps. Over four interactive sessions, we work with you on research, product design sprint, critical path, and presentation prep so that you and your team are better equipped with the skills and knowledge for success. Find out how we can help you move the needle at: tbot.io/entrepreneurs. VICTORIA: I'm curious; you said you're already working on some issues about being able to suggest improvements for cost reduction and efficiencies. What else is on your roadmap for what's coming up next? AARON: So there's a bunch of things in the long-term roadmap. And I'll say that, like, in the short term, it's much more about just expanding the breadth of what we support. If you think about just generating all the different permutations and types of infrastructure, it's, like, a huge matrix problem. Like, there's many, many dimensions that you could go in. And if you add an extra cloud or you add an extra capability, it expands everything. So you can imagine, like, testing it to make sure things work, and everything becomes very complicated. So, really, a lot of what we're doing is still foundational and trying to just increase the breadth, make the intent processing more intelligent, make the other bits work. And then one of the areas right now is for our initial release of the product; we chose to use Discord as our interface for the chatbot. And the reason for that is because it gives us a lot of benefits of having sort of the community built in and the engagement built in where we can actually talk with users and try and understand what they're doing. However, we really have a lot of UI changes and expansions that we'd like to do. And even from some of our early demo material, we have things like being able to right-click and being able to configure your lambda directly from the UI. So there's a lot of areas there that we can expand into an intent, too, once we get sort of the foundational stuff done, as an example. The intelligence bit is a much bigger process, like, there's a lot of things to unpack there. So I won't talk about it too much. But if we were to just talk about the most simple things, it'd be setting up alerts somehow and then feeding into our system that, like, we're hitting those alerts, and we have to make modifications. A good example of that would be, like, configuring auto scaling on an instance for [inaudible 22:17]. So we can get some of those benefits now. The bigger vision of what we want to do with optimization requires a lot more exploration and also the ability to look at what's happening to your application while it's running in the cloud. ALA: Let me maybe shed a bit more light on the problems we're trying to solve and where we're headed. When it comes to optimization, to truly optimize a cloud application, you have to reason about it on the application level rather than on the one service level. To do that, we have to be able to look at the application as an application. And today, there's a multi-repo approach to building cloud applications. So one of the future work that we're going to do is be able to reason about existing infrastructure-as-code from different portions of the teams or organization or even multiple services that the same team works and link them together. So, when we look at reasoning about an architecture, it is within the entire context of the application rather than just the smaller bits and pieces. That's one layer. Another layer is being able to ingest the real runtime application metrics and infrastructure metrics, let's say, from AWS or Azure into the optimizer system to be able to not only say, oh well, I want low latency. Then this is hard-coded to use a Fargate instance instead of a lambda. But more realistically, being able to see what that means in lambda world and maybe increase the concurrency count. Because we know that within the confines of cost limitations or constraints that the company wants to have, it is more feasible and cost-effective to raise the minimum concurrency rate of that lambda instead of using Fargate. You can only do that by having real-time data, or aggregated data come from the performance characteristics of the applications. And so that's another layer that we're going to be focusing on. The third one is, just like Aaron said, being able to approach that editing experience and operational experience, not just through one system like InfraCopilot but also through a web UI, or an app, or even as an extension to other systems that want to integrate with Klotho's engine. The last thing that I think is key is that we're still holding on to the vision that infrastructure should be invisible to most developers. Infrastructure definition is similar to how we approach assembly code. It's the bits and pieces. It's the underlying components, the CPUs, the storage. And as long as we're building microservices in that level of fidelity, of like, thinking about the wiring and how things interconnect, then we're not going to get the gains of 10x productivity building cloud applications. We have to enable developers and operators to work on a higher abstraction. And so our end game, where we're headed, is still what we want to build with Klotho, which is the ability to write code and have it be translated into what's allowed in the infrastructure within the constraints of the underlying platforms that infrastructure or platform teams set for the rest of the organization. It can be one set or multiple sets, but it's still that type of developers develop, and the infrastructure teams set them up to be able to develop, and there's separation. VICTORIA: Those are all really interesting problems to be solving. I also saw on your roadmap that you have published on Klotho that you're thinking of open-sourcing Klotho on GitHub. AARON: So, at this point, we already have the core engine of Klotho open-sourced, so the same engine that's powering InfraCopilot and Klotho, the tool itself is open source today. So, if anyone wants to take a look, it is on github.com/klothoplatform/klotho. VICTORIA: Super interesting. And it sounds like you mentioned you have a Discord. So that's where you're also getting feedback from developers on how to do this. And I think that challenge you mentioned about creating abstractions so that developers don't have to worry as much about the infrastructure and platform teams can just enable them to get their work done; I'm curious what you think is the biggest challenge with that. It seems like a problem that a lot of companies are trying to solve. So, what's the biggest challenge? And I think what do you think is unique about Klotho and solving that challenge? AARON: I guess what I would say the biggest challenge today is that every company is different enough that they all saw this in a slightly different way. So it's like, right now, the tools that are available are the building blocks to make the solution but not the solution itself. So, like, every cloud team approaches it on, let's build our own platform. We're building our own platform that every one of our developers is going to use. In some cases, we're building, like, frameworks and SDKs that everyone's going to use. But then the problem is that you're effectively saying my company is entering the platform management business. And there's no way the economies of scale will make sense forever in that world. So I think that's the biggest issue. And I think the reason it hasn't been solved is it's just a very hard problem. There's many approaches, but there's not a clear solution that kind of brings it all together. And I think our product is positioned better than most to solve some of the higher-level abstractions. It still doesn't solve the whole problem. There's still some things that are going to be tricky. But the idea is, if you can get to the point where you're using some of our abstractions, then you've guaranteed yourself portability into the future, like, your architecture will be able to evolve, even in technologies that don't exist yet once they become available. ALA: To tack on to what Aaron said, a key difference, and to our knowledge, this doesn't exist in any other tool or technology, is a fundamentally new architecture we call adaptive architecture. It is not microservices. It is not monoliths. It's a superset that combines all the benefits from monoliths, microservices, and serverless if you consider it a different platform or paradigm. What that means is that you get the benefits without the drawbacks. And the reason we can do it is because of the compiler approach that we're taking, where everything in the architecture that we produce is interchangeable. The team has decided to use Kubernetes, a specific version of Kubernetes with Istio. That works great. And, a year later, it turns out that that choice no longer scales well for the use. And we need to use Linkerd. The problem in today's world and what companies have to do is retrofit everything and not only the technology itself, but it's the ripple effects of changing it into everything else that all the other choices that were made that depended on it. In the Klotho world, because of the compilation step or the compilation approach and its extensibility, you could say, I want to take out Istio and replace it with Linkerd. And it would percolate all the changes that need to happen everywhere for that to maintain its semantic behavior. To our knowledge, that doesn't exist anywhere today. VICTORIA: So it would do, maybe not, like, would do migrations for you as well? ALA: I think migrations are a special case. When it comes to stateless things, yes. When it comes to data, we are much more conservative. Again, bringing what we've learned in different companies in, a lot of solutions try to solve all the things versus we're trying to play in a very specific niche, which is the adaptive architecture of it all. But if you want to move data, there's fantastic tools for it, and we will guide you through getting the access to the actual underlying services and, say, great, write a migration system, or we can generate for you. But you will run it to move the data from, let's say, Postgres to MySQL or from being able to drain a unit on Kubernetes to a lambda. Some of those things are much more automatic. And the transition happened through the underlying technologies like Terraform or Pulumi. Others will require you to take a step, not because we can't do it for you but we want to be conservative with the choices. AARON: I would also add that another aspect of this is that we don't position ourselves as being the center of the universe for these teams. Like a lot of products, you kind of have to adopt the platform, and everything has to plug into it, and if you don't adhere, it doesn't work. We're trying very, very hard with our design to make it so that existing apps will continue to function like they've always functioned. If app developers want to continue using direct SDKs and managing config themselves, they can absolutely do that. And then they'll interact well with Klotho apps that are also in that same company. So we're trying to make it so that you can adopt incrementally without having to go all in. VICTORIA: So that makes a lot of sense. So it's really helpful if you're trying to swap out those stateless parts of your infrastructure and you want to make some changes there. And then, if you were going to do a data migration, it would help you and guide you to where additional tools might be needed to do that. And at your market segment, you're really focusing on having it be an additional tool, as opposed to, like, an all-encompassing platform. Did I get it all right? [crosstalk 31:07] ALA: Exactly. VICTORIA: [laughs] Cool. All right. Well, that's exciting. That's a lot of cool things that you all are working on. I'm curious how overall the workload is for you two. How big of a team do you have so far? How are you balancing out this work of creating something new and exciting that has such a broad potential scope? AARON: Yeah. So, right now, the team is currently six people. So it's Ala and I, plus four additional engineers is the current team. And in terms of, like, where we're focusing, the real answer is that it's somewhat reactive, and it's very fast. So, like, it could be, like...in fact, Copilot went from ideation to us acting on it extremely quickly. And it wasn't even in the pipeline before that. So I'd actually say the biggest challenge has been where do we sort of focus our energy to get the best results? And a lot of where we spend our time is sort of meta-process of, like, making sure we're investing in the right things. ALA: And I think that comes from both Aaron and I have been in the industry for over 15 years. We don't, you know, drop everything and now switch to something new. We're very both tactical and strategic with the pace and when we pivot. But the idea is when we decide to change and focus on something that we think will be higher value, and it's almost always rooted in the signals and hypotheses that we set out to kind of learn from, from every iteration that we go after. We are not the type that would say, "Oh, we saw this. Let's drop everything, and let's go do it." I think we've seen enough in the industry that there's a measure of knowing when to switch, and when to refocus, and what to do when these higher tidbits come, and then being able to execute aggressively when that choice or decision happens. VICTORIA: Are there any trends that you're watching right now that the outcome would influence a change in direction for you? ALA: Not technically. I think what we're seeing in the industry is there's no real approaches to solving the problem. I would say most of the solutions and trends that we're seeing are...I call them streamlined complexity. We choose a set of technologies, and we make that easy. We make the SaaS version, and it can do these workloads, and it makes that easy. But the minute you step out of the comfort zone of those tools, you're back into the nightmare that building distributed systems brings with it, and then you're back to, you know, square one. What we're trying to do is fundamentally solve the problem. And we haven't seen many at least make a lot of headway there. We are seeing a few of the startups that are starting to think in the same vein, which is the zeitgeist. And that's fantastic. We actually work with them closely to try and broaden the category. VICTORIA: Right. Do you feel that other companies who are working in a similar problem space that there is...is it competitive between each other? Or do you think it's actually more collaborative? ALA: It depends on the companies and what they're trying to achieve. Every set of companies have different incentives. So Google, Amazon, and Microsoft have, you know, are incentivized to keep you on their clouds. They may care less about what they have in there as long as you are happy to stay. So you'll see more open source being adopted. You will see Amazon trying to copy or operationalize a lot of open-source tools. Microsoft will give their...because they are working with larger companies to have more vertical solutions. Google is trying to catch up. If you look at startups, you will see some focus more on developers. You'll see others focus on infra team. So it really depends on the intersection of the companies, and then they either collaborate or they compete, depending on how it affects their strategy. In our case, we recognize that our competition is the incumbents and the current way of doing things. And so we are happy to collaborate with all the startups that are doing something in the vicinity of what we're doing, startups like Ampt, and Encore, and Winglang. And there's several others. We have our own Slack channel where we talk about, like, where we're headed or at least what we can do to support one another. VICTORIA: Great. And I wonder if that's part of your business decision to open source your product as well or if there are other factors involved. ALA: I think the biggest factor that we've seen, realistically, is the expectation in the developer community to have a core that is open source, not even the source available model but to have an open-source core that they can rely on always existing and referencing when, you know if the company disappears or Oracle buys them. And so I would say that that was the biggest determining factor in the end to open-sourcing the Klotho engine. It's a very pragmatic view. VICTORIA: That makes sense. Well, I wanted to make sure we had time to ask one of my favorite questions that I ask on the podcast, and you can both answer. But if you could go back in time to when you first started this project, what advice would you give yourself? ALA: I guess the advice that I would give is keep selling and start selling as early as you can, even before the vision is realized. Or let's say you're making kind of headway towards what you'll wind up sharing and giving companies, the lead time to creating the opportunities and the belief and the faith that you can solve problems for companies, and the entire machinery of doing that is a lot more complex than most founders, I think, or at least first-time founders or, honestly, myself have found it to be. AARON: Yeah. If I try and answer that same question, it's very challenging. I guess my perspective now is there's nothing I could tell myself that would make me go any faster because a lot of it really is the journey. Like, the amount of stuff that we've learned in the last year of working on this and exploring and talking with people and everything else has been so vast that there's nothing I can communicate to past me that would prepare me any better. So [laughs] I think I would try just my best to be encouraging to just stick with it. VICTORIA: Well, that's good. And who knows what you're going to learn in the next year that [laughs] probably might not help you in the past either? That's wonderful. Do you have any final takeaways for our listeners today or anything you'd like to promote? ALA: So, from my lens, I've always wanted to do a startup but felt that the life setting wasn't quite ready. And a lot of the startup culture is talking about younger, earlier founders. I think having had the industry experience and understanding both the organizational and technical challenges, knowing more people, and engineers, and founders, potential founders, has been vastly more helpful than what I would have been able to pull off ten years ago. So, if you are thinking maybe it's too late, it is not. It's probably easier in some regards now. And yeah, check out InfraCopilot. It's on infracopilot.io. We would love to have you try it out and go on this journey with us. AARON: Yeah, I would definitely echo that. I mean, sort of the same thing on the journey. Like, it's never too late to start. And yeah, like, I would say being in the industry and actually seeing these problems first-hand makes it so much more fulfilling to actually try and solve them. VICTORIA: That's [inaudible 38:15]. I'm excited to see what you all accomplish. And I appreciate you coming on the show. You can subscribe to the show and find notes along with a complete transcript for this episode at giantrobots.fm. If you have questions or comments, you can email us at hosts@giantrobots.fm. And you could find me on Twitter @victori_ousg or on Mastodon @vguido@thoughtbot.social. This podcast is brought to you by thoughtbot and produced and edited by Mandy Moore. Thanks for listening. See you next time. ANNOUNCER: This podcast is brought to you by thoughtbot, your expert strategy, design, development, and product management partner. We bring digital products from idea to success and teach you how because we care. Learn more at thoughtbot.com. Special Guests: Aaron Torres and Ala Shiban.