Podcasts about json schema

Today, we're sitting down for an in-depth technical discussion with Juan Cruz Viotti, the Founder of Sourcemeta, a top provider of JSON Schema tooling and services. Juan has been working in the space for a long time, so we're delighted to be getting his insight into this field. A lot of people have a love/hate relationship with JSON Schema, and much of that originates from the fact that, despite what people may think, it is a constraint language, not a modelling one. Juan explains to us that one of the reasons people can find it so painful is that the language is extremely powerful and expressive. Through his work, he is determined to help people learn and get to grips with it, so it doesn't seem so intimidating. Finally, he tells us about what Sourcemeta is currently working on, and how they are continuing to evangelise and promote JSON Schema. They are diving head first into the conference space, and will soon have a desktop app to further aid people in their understanding of the language and how it works. Reach out to Juan here: https://www.linkedin.com/in/jviotti/ Check out Sourcemeta: https://www.sourcemeta.com/ Find out more and listen to previous podcasts here: https://www.voxgig.com/podcast Subscribe to our newsletter for weekly updates and information about upcoming meetups: https://voxgig.substack.com/ Join the Dublin DevRel Meetup group here: www.devrelmeetup.com

Karina Nguyen leads research at OpenAI, where she's been pivotal in developing groundbreaking products like Canvas, Tasks, and the o1 language model. Before OpenAI, Karina was at Anthropic, where she led post-training and evaluation work for Claude 3 models, created a document upload feature with 100,000 context windows, and contributed to numerous other innovations. With experience as an engineer at the New York Times and as a designer at Dropbox and Square, Karina has a rare firsthand perspective on the cutting edge of AI and large language models. In our conversation, we discuss:• How OpenAI builds product• What people misunderstand about AI model training• Differences between how OpenAI and Anthropic operate• The role of synthetic data in model development• How to build trust between users and AI models• Why she moved from engineering to research• Much more—Brought to you by:• Enterpret—Transform customer feedback into product growth• Vanta—Automate compliance. Simplify security• Loom—The easiest screen recorder you'll ever use—Find the transcript at: https://www.lennysnewsletter.com/p/why-soft-skills-are-the-future-of-work-karina-nguyen—Where to find Karina Nguyen:• X: https://x.com/karinanguyen_• LinkedIn: https://www.linkedin.com/in/karinanguyen28• Website: https://karinanguyen.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Karina Nguyen(04:42) Challenges in model training(08:21) Synthetic data and its importance(12:38) Creating Canvas(18:33) Day-to-day operations at OpenAI(20:28) Writing evaluations(23:22) Prototyping and product development(26:57) Building Canvas and Tasks(33:34) Understanding the job of a researcher(35:36) The future of AI and its impact on work and education(42:15) Soft skills in the age of AI(47:50) AI's role in creativity and strategy development(53:34) Comparing Anthropic and OpenAI(57:11) Innovations and future visions(01:07:13) The potential of AI agents(01:11:36) Final thoughts and career advice—Referenced:• What's in your stack: The state of tech tools in 2025: https://www.lennysnewsletter.com/p/whats-in-your-stack-the-state-of• Anthropic: https://www.anthropic.com/• OpenAI: https://openai.com/• What is synthetic data—and how can it help you competitively?: https://mitsloan.mit.edu/ideas-made-to-matter/what-synthetic-data-and-how-can-it-help-you-competitively• GPQA: https://datatunnel.io/glossary/gpqa/• Canvas: https://openai.com/index/introducing-canvas/• Barret Zoph on LinkedIn: https://www.linkedin.com/in/barret-zoph-65990543/• Mira Murati on LinkedIn: https://www.linkedin.com/in/mira-murati-4b39a066/• JSON Schema: https://json-schema.org/• Anthropic—100K Context Windows: https://www.anthropic.com/news/100k-context-windows• Claude 3 Haiku: https://www.anthropic.com/news/claude-3-haiku• A.I. Chatbots Defeated Doctors at Diagnosing Illness: https://www.nytimes.com/2024/11/17/health/chatgpt-ai-doctors-diagnosis.html• Cursor: https://www.cursor.com/• How AI will impact product management: https://www.lennysnewsletter.com/p/how-ai-will-impact-product-management• Lee Byron on LinkedIn: https://www.linkedin.com/in/lee-byron/• GraphQL: https://graphql.org/• Claude in Slack: https://www.anthropic.com/claude-in-slack• Sam Altman on X: https://x.com/sama• Jakub Pachocki on LinkedIn: https://www.linkedin.com/in/jakub-pachocki/• Lennybot: https://www.lennybot.com/• ElevenLabs: https://elevenlabs.io/• Westworld on Prime Video: https://www.amazon.com/Westworld-Season-1/dp/B01N05UD06• A conversation with OpenAI's CPO Kevin Weil, Anthropic's CPO Mike Krieger, and Sarah Guo: https://www.youtube.com/watch?v=IxkvVZua28k• Tuple: https://tuple.app/• How Shopify builds a high-intensity culture | Farhan Thawar (VP and Head of Eng): https://www.lennysnewsletter.com/p/how-shopify-builds-a-high-intensity-culture-farhan-thawar—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Congrats to Damien on successfully running AI Engineer London! See our community page and the Latent Space Discord for all upcoming events.This podcast came together in a far more convoluted way than usual, but happens to result in a tight 2 hours covering the ENTIRE OpenAI product suite across ChatGPT-latest, GPT-4o and the new o1 models, and how they are delivered to AI Engineers in the API via the new Structured Output mode, Assistants API, client SDKs, upcoming Voice Mode API, Finetuning/Vision/Whisper/Batch/Admin/Audit APIs, and everything else you need to know to be up to speed in September 2024.This podcast has two parts: the first hour is a regular, well edited, podcast on 4o, Structured Outputs, and the rest of the OpenAI API platform. The second was a rushed, noisy, hastily cobbled together recap of the top takeaways from the o1 model release from yesterday and today.Building AGI with Structured Outputs — Michelle Pokrass of OpenAI API teamMichelle Pokrass built massively scalable platforms at Google, Stripe, Coinbase and Clubhouse, and now leads the API Platform at Open AI. She joins us today to talk about why structured output is such an important modality for AI Engineers that Open AI has now trained and engineered a Structured Output mode with 100% reliable JSON schema adherence. To understand why this is important, a bit of history is important:* June 2023 when OpenAI first added a "function calling" capability to GPT-4-0613 and GPT 3.5 Turbo 0613 (our podcast/writeup here)* November 2023's OpenAI Dev Day (our podcast/writeup here) where the team shipped JSON Mode, a simpler schema-less JSON output mode that nevertheless became more popular because function calling often failed to match the JSON schema given by developers. * Meanwhile, in open source, many solutions arose, including * Instructor (our pod with Jason here) * LangChain (our pod with Harrison here, and he is returning next as a guest co-host)* Outlines (Remi Louf's talk at AI Engineer here)* Llama.cpp's constrained grammar sampling using GGML-BNF* April 2024: OpenAI started implementing constrained sampling with a new `tool_choice: required` parameter in the API* August 2024: the new Structured Output mode, co-led by Michelle* Sept 2024: Gemini shipped Structured Outputs as wellWe sat down with Michelle to talk through every part of the process, as well as quizzing her for updates on everything else the API team has shipped in the past year, from the Assistants API, to Prompt Caching, GPT4 Vision, Whisper, the upcoming Advanced Voice Mode API, OpenAI Enterprise features, and why every Waterloo grad seems to be a cracked engineer.Part 1 Timestamps and TranscriptTranscript here.* [00:00:42] Episode Intro from Suno* [00:03:34] Michelle's Path to OpenAI* [00:12:20] Scaling ChatGPT* [00:13:20] Releasing Structured Output* [00:16:17] Structured Outputs vs Function Calling* [00:19:42] JSON Schema and Constrained Grammar* [00:20:45] OpenAI API team* [00:21:32] Structured Output Refusal Field* [00:24:23] ChatML issues* [00:26:20] Function Calling Evals* [00:28:34] Parallel Function Calling* [00:29:30] Increased Latency* [00:30:28] Prompt/Schema Caching* [00:30:50] Building Agents with Structured Outputs: from API to AGI* [00:31:52] Assistants API* [00:34:00] Use cases for Structured Output* [00:37:45] Prompting Structured Output* [00:39:44] Benchmarking Prompting for Structured Outputs* [00:41:50] Structured Outputs Roadmap* [00:43:37] Model Selection vs GPT4 Finetuning* [00:46:56] Is Prompt Engineering Dead?* [00:47:29] 2 models: ChatGPT Latest vs GPT 4o August* [00:50:24] Why API => AGI* [00:52:40] Dev Day* [00:54:20] Assistants API Roadmap* [00:56:14] Model Reproducibility/Determinism issues* [00:57:53] Tiering and Rate Limiting* [00:59:26] OpenAI vs Ops Startups* [01:01:06] Batch API* [01:02:54] Vision* [01:04:42] Whisper* [01:07:21] Voice Mode API* [01:08:10] Enterprise: Admin/Audit Log APIs* [01:09:02] Waterloo grads* [01:10:49] Books* [01:11:57] Cognitive Biases* [01:13:25] Are LLMs Econs?* [01:13:49] Hiring at OpenAIEmergency O1 Meetup — OpenAI DevRel + Strawberry teamthe following is our writeup from AINews, which so far stands the test of time.o1, aka Strawberry, aka Q*, is finally out! There are two models we can use today: o1-preview (the bigger one priced at $15 in / $60 out) and o1-mini (the STEM-reasoning focused distillation priced at $3 in/$12 out) - and the main o1 model is still in training. This caused a little bit of confusion.There are a raft of relevant links, so don't miss:* the o1 Hub* the o1-preview blogpost* the o1-mini blogpost* the technical research blogpost* the o1 system card* the platform docs* the o1 team video and contributors list (twitter)Inline with the many, many leaks leading up to today, the core story is longer “test-time inference” aka longer step by step responses - in the ChatGPT app this shows up as a new “thinking” step that you can click to expand for reasoning traces, even though, controversially, they are hidden from you (interesting conflict of interest…):Under the hood, o1 is trained for adding new reasoning tokens - which you pay for, and OpenAI has accordingly extended the output token limit to >30k tokens (incidentally this is also why a number of API parameters from the other models like temperature and role and tool calling and streaming, but especially max_tokens is no longer supported).The evals are exceptional. OpenAI o1:* ranks in the 89th percentile on competitive programming questions (Codeforces),* places among the top 500 students in the US in a qualifier for the USA Math Olympiad (AIME),* and exceeds human PhD-level accuracy on a benchmark of physics, biology, and chemistry problems (GPQA).You are used to new models showing flattering charts, but there is one of note that you don't see in many model announcements, that is probably the most important chart of all. Dr Jim Fan gets it right: we now have scaling laws for test time compute, and it looks like they scale loglinearly.We unfortunately may never know the drivers of the reasoning improvements, but Jason Wei shared some hints:Usually the big model gets all the accolades, but notably many are calling out the performance of o1-mini for its size (smaller than gpt 4o), so do not miss that.Part 2 Timestamps* [01:15:01] O1 transition* [01:16:07] O1 Meetup Recording* [01:38:38] OpenAI Friday AMA recap* [01:44:47] Q&A Part 2* [01:50:28] O1 DemosDemo Videos to be posted shortly Get full access to Latent Space at www.latent.space/subscribe

Katia, Guillaume, Emmanuel et Antonio discutent Kotlin, Micronaut, Spring Boot, Quarkus, Langchain4j, LLMs en Java, builds reproductible et la question AMA du jour, comment fait-on carrière de dev à 40 ans ? Enregistré le 14 juin 2024 Téléchargement de l'épisode LesCastCodeurs-Episode-313.mp3 News Langages Android avec Kotlin Multiplatform our Flutter avec Dart ? https://developers.googleblog.com/en/making-development-across-platforms-easier-for-developers/ Des licenciements ont continué chez Google et l'équipe Flutter/Dart comme plein d'autres ont été touchées, mais sur les réseaux sociaux les gens ont pensé que Google désinvestissait dans Flutter et Dart. Par ailleurs, côté Android, ils poussent plutôt du côté de Kotlin et KMP, mais naturellement aussi les gens se sont demandé si Google avait pris parti pour pousser plus Kotlin/KMP plutôt que Flutter/Dart. Pour essayer de mieux faire comprendre aux développeurs l'intérêt des deux plateformes, et leurs avantages et inconvénients, les directeurs des deux plateformes ont rédigé un article commun. Si l'on souhaite une expérience plus proche du hardware et des dernières nouveautés d'Android, et d'avoir aussi une UI/UX vraiment native Android, mieux vaut aller du côté de Kotlin/KMP. Si l'on souhaite par contre une expérience multiplateforme Web, mobile, desktop avec une UX commune cross-plateforme, avec également le partage de business logic à partir d'une même base de code, Flutter et Dart sont plus adaptés. Recap de KotlinConf https://x.com/gz_k/status/1793887581433971083?s=46&t=C18cckWlfukmsB_Fx0FfxQ RPC multiplatform la pres Grow with the flow montrant la reecriture en kotlin plus simple que des solutions complexes ailleurs power-assert pour ecrire des tests Kotlin 2.0 et les evolutions majeures Kotlin multiplatforme mainteant stable Kotlin Compose Multiplatform continue a amturer Retour d'experience de la migration d'android jetpack vers Kotlin Multiplatform use cases de coroutines et scope Librairies Quarkus veut aller dans une fondation https://quarkus.io/blog/quarkus-in-a-foundation/ ameliorer l'adoption (encore plus), ameliorer la transparence, et la collaboration, encourager la participatiopn multi vendeur Premiere etape : une gouvernance plus overte Deuxieme etape: bouger dans uen foundation Echange avec la communaute sur la proposition et les fondations cibles Des criteres pour al foudnation (notamment la rapidite de delivery Quarkus 3.11 https://quarkus.io/blog/quarkus-3-11-0-released/ Websocket.next en cours Dev services pour observabilite (grafana, jaegel, open telemetry extension infinispan cache #38448 - Observability extensions - Dev Services, Dev Resources, LGTM #39836 - Infinispan Cache Extension #40309 - WebSockets Next: client endpoints #40534 - WebSockets Next: initial version of security integration #40273 - Allow quarkus:run to launch Dev Services #40539 - Support for OIDC session expired page #40600 - Introduce OidcRedirectFilter LangChain4j 0.31 est sorti https://github.com/langchain4j/langchain4j/releases/tag/0.31.0 Recherche Web pour le RAG avec Google et Tavily RAG avec les bases de données SQL (expérimental) Récupération des resources remontées par le RAG lorsque AiServices retourne un Result Observabilité LLM pour OpenAI pour être notifié des requêtes, réponses et erreurs Intégration de Cohere (embedding), Jina (embedding et re-ranking scoring), Azuere CosmosDB comme embedding store Mise à jour de Gemini avec le parallel function calling et les instructions système Spring Boot 3.3.0 est sorti https://spring.io/blog/2024/05/23/spring-boot-3-3-0-available-now support Class Data Sharing Micrometer sipport de spantag etc Amelioration Spring Security comme JwtAuthenticationCovnerter support docker compose pour les images container bitnami Virtual thread pour les websockets Support sBOM via an actuator SNI for embedded web servers une nouvelle doc via antora Micronaut 4.5 est sortie https://github.com/micronaut-projects/micronaut-platform/releases/tag/v4.5.0 Le serveur basé sur Netty inclus la détection d'opération bloquante et les modules l'utilisant indiqueront à l'utilisateur quand certaines opérations peuvent être redirigée plutôt sur un virtual thread ou dans le thread pool IO Micronaut Data inclus le support de la multitenance avec partitionnement par discriminateur pour JDBC et R2DBC Micronaut Data rajoute le pagination par curseur pour JDBC et R2DBC (important aussi pour Jakarta Data) Support des annotations Jakarta Servlet pour configurer par exemple les servelet filters Support virtual thread et HTTP/2 Un nouveau module JSON Schema pour générer des JSON Schemas pour les records Java Un nouveau module Source Gen pour faire de la génération de source pour Java et Kotlin cross-language Un nouveau module Guice pour importer des modules Guice existants Web Angular 18 est sorti https://blog.angular.dev/angular-v18-is-now-available-e79d5ac0affe Support expérimental pour la détection de changement sans zone Angular.dev est désormais le nouveau site pour les développeurs Angular Material 3, les “deferrable views”, le “built-in control flow” sont maintenant stables et intègrent une série d'améliorations Améliorations du rendu côté serveur telles que le support de l'hydratation i18n, un meilleur débogage, le support de l'hydratation dans Angular Material, et la event replay qui utilise la même bibliothèque que Google Search. Data et Intelligence Artificielle Une version pure Java du LLM Llama3 de Meta https://github.com/mukel/llama3.java/tree/main utilise la future API Vector de Java JLama, un moteur d‘exécution de LLM en Java avec l'api vector https://www.infoq.com/news/2024/05/jlama-llm-inference-java/ basé sur llama.c qui est un moteur d'inference de LLM (l'execution des requetes) jlama implementé avec vector APIs et PamanaTensorOperations plusisures alternatives (native binding, iml0ementation pure en java, scala, kotlin) Target Speech Hearing https://www.infoq.com/news/2024/05/target-speech-hearing/ Nouveau algo Deep Learning de l'Université de Washington permet d'écouter une seule personne de ton choix et effacer tout le bruit autour le système nécessite que la personne portant les écouteurs appuie sur un bouton tout en regardant quelqu'un parler ou simplement en le fixant pendant trois à cinq secondes Permet à un modèle d'apprendre les schémas vocaux du locuteur et de s'y attacher pour pouvoir les restituer à l'auditeur, même s'il se déplace et cesse de regarder cette personne. Selon les chercheurs, cela constitue une avancée significative par rapport aux écouteurs à réduction de bruit existants, qui peuvent annuler efficacement tous les sons, mais ne peuvent pas sélectionner les locuteurs en fonction de leurs caractéristiques vocales. Actuellement, le système ne peut enregistrer qu'un seul locuteur à la fois. Une autre limitation est que l'enregistrement ne réussira que si aucune autre voix forte ne provient de la même direction. L'équipe a mis en open source leur code et leur jeu de données afin de faciliter les travaux de recherche futurs pour améliorer l'audition de la parole cible. Outillage Utiliser LLM pour migrer du framework de testing https://www.infoq.com/news/2024/06/slack-automatic-test-conversion/ Slack a migré 15.000 tests de Enzyme à React Testing Library avec un succès de 80% Migration nécessaire pour le manque de support de Enzyme pour React 18 L'équipe a essayé d'automatiser la conversion avec des transformations AST, mais n'a atteint que 45 % de succès à cause de la complexité des méthodes d'Enzyme et du manque d'accès aux informations contextuelles du DOM. L'équipe a utilisé Claude 2.1 pour la conversion, avec des taux de réussite variant de 40 % à 60 %, les résultats dépendant largement de la complexité des tâches. Suite aux résultats insatisfaisants, l'équipe a décidé d'observer comment les développeurs humains abordaient la conversion des tests unitaires. Les développeurs humains utilisaient leurs connaissances sur React, Enzyme et RTL, ainsi que le contexte du rendu et les conversions AST de l'outil initial pour mieux convertir les tests unitaires. Finalement les ingénieurs de Slack ont combiné transformations AST et LLM en intégrant des composants React rendus et des conversions AST dans les invites, atteignant un taux de réussite de 80 % démontrant ainsi la complémentarité de ces technologies. Claude 2.1 est un modèle de langage de grande taille (LLM) annoncé en novembre 2023 par Anthropic. Il inclut une fenêtre contextuelle de 200 000 tokens, des réductions significatives des taux d'hallucination du modèle, des invites système et permet l'utilisation d'outils. Depuis, Anthropic a introduit la famille de modèles Claude 3, composée de trois modèles distincts, avec des capacités multimodales et une compréhension contextuelle améliorée. Un arbre de syntaxe abstraite (AST) est une représentation arborescente de la structure syntaxique abstraite du code source écrit dans un langage de programmation. Chaque nœud de l'arbre représente une construction du code source. Un arbre de syntaxe se concentre sur la structure et le contenu nécessaires pour comprendre la fonctionnalité du code. Les AST sont couramment utilisés dans les compilateurs et les interpreters pour analyser et examiner le code, permettant diverses transformations, optimisations et traductions lors de la compilation. IDE de test de JetBrains https://blog.jetbrains.com/qa/2024/05/aqua-general-availability/ Aqua, le premier IDE conçu pour l'automatisation des tests, supporte plusieurs langages (Java, Python, JavaScript, TypeScript, Kotlin, SQL) et frameworks de tests (Selenium, Playwright, Cypress). Pourquoi ? Les tests d'applications nécessitent des compétences spécifiques. Aqua, un IDE adapté, est recommandé par les ingénieurs en automatisation des tests. Aqua propose deux plans de licence : un gratuit pour les usages non commerciaux et un payant pour les usages commerciaux. cam me parait un peu contre intuitif a l'heure du devops et du TDD de faire des outils dédiés et donc des equipes ou personnes dédiées Méthodologies Les 10 principes à suivre, selon le créateur de cURL, pour être un bon BDFL (Benevolent Dictator For Life) https://daniel.haxx.se/blog/2024/05/27/my-bdfl-guiding-principles/ Être ouvert et amical Livrer des produits solides comme le roc Être un leader de l'Open Source Privilégier la sécurité Fournir une documentation de premier ordre Rester indépendant Répondre rapidement Suivre l'actualité Rester à la pointe de la technologie Respecter les retours d'information Dans un vieil article de Artima, Guido Van Rossum, le créateur de Python et premier BDFL d'un projet, se remémore un échange de 1995 qui est à l'origine de ce concept https://www.artima.com/weblogs/viewpost.jsp?thread=235725 Guido Van Rossum a été le premier à endosser ce “rôle” Un site compréhensif sur les build reproductibles https://reproducible-builds.org longue doc de la definition aux méthodes pour resoudre des problèmes spécifiques Masterclass de Fabien Olicard: Le Palais Mental https://www.youtube.com/watch?v=u6wu_iY4xd8 Technique pour retenir de l'information plus longtemps que dans sa mémoire courte Les APIs web ne devraient pas rediriger HTTP vers HTTPS https://jviide.iki.fi/http-redirects grosso modo le risque majeur est d'envoyer des données confidentielles en clair sur le réseau le mieux serait de ne pas rediriger vers HTTPS, mais par contre de retourner une vraie erreur explicite notamment les clés d'API et c'est facile de ne pas le,voir vu les redirects. Sécurité Blog de GitHub sur la provenance et l'attestation https://github.blog/2024-04-30-where-does-your-software-really-come-from/ Discute les concepts de securisation de chainne d'approvisionnement de sogiciel et comment elles s'articulent entre elle. A haut niveau discute les hash pour garantir le meme fichier La signature asymetrique pour prouver que j'ai signé (e.g. le hash) et donc que je garantis. L'attenstation qui declare des faits sur un artifact attestation de provenance: source code et instructions de build (SLSA provenance) mais il faut garantir les signature avec une autorite de certification et avec des certificats a courte vide idealement, c'est sigstore MEtionne aussi The Update Framework pour s'appuyer sur cela et garantir des undates non compromis Keycloak 25 est sorti https://www.keycloak.org/2024/06/keycloak-2500-released.html Argon2 pour le hashing de mots de passe Depreciation des adaptateurs (Tomcat, servlet etc) Java 21 et depreciation de Java 17 session utilisatur persistente meme pour les instances online (pour survivre a une rotation de keycloak ameliorations autour des passkeys management et health endpoint sur un port different Et plus Demande aux cast codeurs A 40 ans, tu peux encore être codeur reconnu ? Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 12-14 juin 2024 : Rencontres R - Vannes (France) 13-14 juin 2024 : Agile Tour Toulouse - Toulouse (France) 14 juin 2024 : DevQuest - Niort (France) 18 juin 2024 : Mobilis In Mobile 2024 - Nantes (France) 18 juin 2024 : BSides Strasbourg 2024 - Strasbourg (France) 18 juin 2024 : Tech & Wine 2024 - Lyon (France) 19-20 juin 2024 : AI_dev: Open Source GenAI & ML Summit Europe - Paris (France) 19-21 juin 2024 : Devoxx Poland - Krakow (Poland) 26-28 juin 2024 : Breizhcamp 2024 - Rennes (France) 27 juin 2024 : DotJS - Paris (France) 27-28 juin 2024 : Agi Lille - Lille (France) 4-5 juillet 2024 : Sunny Tech - Montpellier (France) 8-10 juillet 2024 : Riviera DEV - Sophia Antipolis (France) 6 septembre 2024 : JUG Summer Camp - La Rochelle (France) 6-7 septembre 2024 : Agile Pays Basque - Bidart (France) 17 septembre 2024 : We Love Speed - Nantes (France) 17-18 septembre 2024 : Agile en Seine 2024 - Issy-les-Moulineaux (France) 19-20 septembre 2024 : API Platform Conference - Lille (France) & Online 25-26 septembre 2024 : PyData Paris - Paris (France) 26 septembre 2024 : Agile Tour Sophia-Antipolis 2024 - Biot (France) 2-4 octobre 2024 : Devoxx Morocco - Marrakech (Morocco) 7-11 octobre 2024 : Devoxx Belgium - Antwerp (Belgium) 8 octobre 2024 : Red Hat Summit: Connect 2024 - Paris (France) 10 octobre 2024 : Cloud Nord - Lille (France) 10-11 octobre 2024 : Volcamp - Clermont-Ferrand (France) 10-11 octobre 2024 : Forum PHP - Marne-la-Vallée (France) 11-12 octobre 2024 : SecSea2k24 - La Ciotat (France) 16 octobre 2024 : DotPy - Paris (France) 17-18 octobre 2024 : DevFest Nantes - Nantes (France) 17-18 octobre 2024 : DotAI - Paris (France) 30-31 octobre 2024 : Agile Tour Nantais 2024 - Nantes (France) 30-31 octobre 2024 : Agile Tour Bordeaux 2024 - Bordeaux (France) 31 octobre 2024-3 novembre 2024 : PyCon.FR - Strasbourg (France) 6 novembre 2024 : Master Dev De France - Paris (France) 7 novembre 2024 : DevFest Toulouse - Toulouse (France) 8 novembre 2024 : BDX I/O - Bordeaux (France) 13-14 novembre 2024 : Agile Tour Rennes 2024 - Rennes (France) 20-22 novembre 2024 : Agile Grenoble 2024 - Grenoble (France) 21 novembre 2024 : DevFest Strasbourg - Strasbourg (France) 27-28 novembre 2024 : Cloud Expo Europe - Paris (France) 28 novembre 2024 : Who Run The Tech ? - Rennes (France) 3-5 décembre 2024 : APIdays Paris - Paris (France) 4-5 décembre 2024 : DevOpsDays Paris - Paris (France) 4-5 décembre 2024 : Open Source Experience - Paris (France) 6 décembre 2024 : DevFest Dijon - Dijon (France) 22-25 janvier 2025 : SnowCamp 2025 - Grenoble (France) 16-18 avril 2025 : Devoxx France - Paris (France) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via twitter https://twitter.com/lescastcodeurs Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

In dieser Folge geht es um die Frage, wie man Software in wissenschaftlichen Artikeln zitieren kann. Die Antwort: das Citation File Format (CFF) - an dem mein Gast Stephan Druskat maßgeblich mitgewirkt hat. Stephan und ich hatten uns auf der letzten UK RSE Konferenz in Swansea in einer der Pausen zu einem Schwätzchen getroffen.Inzwischen wird das CFF von etlichen Organisationen wie z.B. GitHub, Zenodo und Zotero  unterstützt und das eScience Center in den Niederlanden unterstützt das Projekt tatkräftig.Hier ein paar Linkshttps://citation-file-format.github.io Homepage vom Citation File Formathttps://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-citation-files GitHub CFF Support und Hilfe Webseitehttps://www.esciencecenter.nl/news/code-citation-was-made-possible-by-research-software-engineers-in-germany-and-the-netherlands/ eScience Center in den Niederlanden und CFFhttps://codemeta.github.io Code Metahttps://json-schema.org JSON Schema https://project.software-metadata.pub Hermes Projekt (Helmholtz)Support the Show.Thank you for listening and your ongoing support. It means the world to us! Support the show on Patreon https://www.patreon.com/codeforthought Get in touch: Email mailto:code4thought@proton.me UK RSE Slack (ukrse.slack.com): @code4thought or @piddie US RSE Slack (usrse.slack.com): @Peter Schmidt Mastadon: https://fosstodon.org/@code4thought or @code4thought@fosstodon.org LinkedIn: https://www.linkedin.com/in/pweschmidt/ (personal Profile)LinkedIn: https://www.linkedin.com/company/codeforthought/ (Code for Thought Profile) This podcast is licensed under the Creative Commons Licence: https://creativecommons.org/licenses/by-sa/4.0/

You can represent hierarchical data in many ways with one of the most popular formats being JSON. If you are a UI developer, you are likely consuming JSON Data, and if you are a server side engineer, you are providing JSON Data (via REST or Graph APIs, for instance). It is imperative that your JSON Schema looks accurate and is a true structural representation of the problem you are setting out to solve. If it isn't, it's surely going to cause a bit of pain as your product's adoptability grows (think backward compatibility, refactoring, extensibility, and more such challenges). In this course, we will take a recent feature we implemented on our Web App, and design the actual JSON Data Model alongside exploring alternative structures. Purchase course in one of 2 ways: 1. Go to https://getsnowpal.com, and purchase it on the Web 2. On your phone:     (i) If you are an iPhone user, go to http://ios.snowpal.com, and watch the course on the go.     (ii). If you are an Android user, go to http://android.snowpal.com.

Jake and Michael are joined by TJ Miller to try and untangle their confusion about JSON API, Open API, Swagger, and JSON Schema from last episode.This episode is brought to you by our friends at Workvivo - The leading employee communication app.Show links Generate API Documentation for Laravel with Scramble OpenAPI JSON Schema JSON:API Swagger Joe Tennanbaum going full Norton Commander with Laravel Prompts Remote Procedure Call (RPC) spatie/laravel-data Pact Stoplight Redoc SwaggerHub MuleSoft Apiary

Guest Ben Hutton Panelist Richard Littauer Show Notes Hello and welcome to Sustain! The podcast where we talk about sustaining open source for the long haul. In this episode, Richard introduces us to Ben Hutton, a Specification Lead for JSON Schema at Postman. They discuss the evolution and diverse applications of JSON Schema, its funding, and the importance of open standards for interoperability and innovation. The episode delves into real-world use cases, community feedback, and the 10-year vision for JSON Schema. Join Richard and Ben as they explore how JSON Schema is shaping the development stack and its potential impact on various industries. Hit download now to hear more! [00:01:00] Ben describes his work on JSON Schema at Postman. His work entails leading the project, finding ways to move forward, creating visions and roadmaps. [00:01:50] Richard brings up the question of understanding the market value for a Schema like JSON and asks Ben to elaborate on the improvements and ways they are moving forward. Ben explains how JSON Schema has evolved over time to cater to more use cases. [00:03:22] Ben explains that Postman funds his work because JSON Schema is used by The OpenAPI Specification, a standard for defining the interface and data structure of APIs, which is a part of Postman products. [00:04:20] Richard asks about the number of maintainers and community members for JSON Schema, and Ben tells us there are about five or six core maintainers and a community size around 15,000. [00:05:16] What's the importance of open standards and why do they need continuous improvements? Ben explains that the team helps developers understand and use JSON Schemas and supports the implementers of the Schema across different programming languages. [00:07:24] Ben discusses a use case with Six River Systems, illustrating how JSON Schema helped different teams within a company to define their data structures and communicate more effectively, preventing bugs and misunderstandings. [00:09:29] We hear why open standards are important, as Ben states that standards are vital for creating value that people can use and ensure interoperability and they can also spur innovation. [00:11:51] Ben explains that JSON Schema was initially a personal draft within the IETF, but due to lack of alignment and communication issues, they've decided to publish their own standards while still maintaining some principles from the IETF. [00:14:51] What's the difference between JSON Schema and JSON? Ben explains JSON is used for API calls, and JSON Schema defines the stricture of expected JSON data. He also speaks about managing radical change requests, maintaining standards, and the value of community feedback, and he reveals an official JSON Schema test suite and a new tool called Bowtie. [00:18:23] Richard asks about the funding and progression of JSON Schema, given its foundational role and slower pace of development. Ben describes Postman's evolution from a basic API client to a comprehensive API platform. [00:20:01] Ben mentions other companies, such as Retool and Airbnb, that support JSON Schema due to its utility in their own offerings, and he talks about Microsoft's usage and a pending case study on how GitHub uses JSON Schema internally. [00:24:09] Richard asks Ben about his 10-year vision for JSON Schema. Ben envisions JSON Schema being used throughout the entire development stack, from inception to defining data structures and models. He tells us about a case study they used from Open Metadata. [00:27:18] The topic of financial needs for JSON Schema is brought up and Ben is content with current funding but admits they need to assess financial stability. He also tells us about future plans improving compliance and supporting the ecosystem to ensure interoperability across different languages and backgrounds. [00:31:12] Richard asks about potential threats to JSON Schema, and Ben mentions that biggest threat would be if something were to replace JSON, and simplicity and ease of learning are key strengths of JSON and JSON Schema. [00:33:47] Find out where you can learn more about Ben and JSON Schema online. Spotlight [00:35:31] Richard's spotlight is the book, Sir Gawain and the Green Knight by J. R. R. Tolkien and The Green Knight (film). [00:36:08] Ben's spotlight is MeetingBar for Meet, Zoom & Co. Links SustainOSS (https://sustainoss.org/) SustainOSS Twitter (https://twitter.com/SustainOSS?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor) SustainOSS Discourse (https://discourse.sustainoss.org/) podcast@sustainoss.org (mailto:podcast@sustainoss.org) SustainOSS Mastodon (https://mastodon.social/tags/sustainoss) Open Collective-SustainOSS (Contribute) (https://opencollective.com/sustainoss) Richard Littauer Twitter (https://twitter.com/richlitt?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor) Ben Hutton Website (https://benhutton.me/) Ben Hutton Twitter (https://twitter.com/relequestual?lang=en) Ben Hutton Mastodon (https://fosstodon.org/@relequestual) JSON Schema (https://json-schema.org/) JSON Schema Slack (https://json-schema.org/slack) JSON Schema GitHub (https://github.com/json-schema-org) JSON Schema Open Collective (Donate) (https://opencollective.com/json-schema) JSON Schema Twitter (https://twitter.com/jsonschema) Postman (https://www.postman.com/) Open banking (Wikipedia) (https://en.wikipedia.org/wiki/Open_banking) Open Banking (https://www.openbanking.org.uk/) Bowtie (https://json-schema.org/blog/posts/bowtie-intro) Retool (https://retool.com/) Open Metadata (https://open-metadata.org/) Sir Gawain and the Green Knight by J. R. R. Tolkien and E. V. Gordon (https://global.oup.com/academic/product/sir-gawain-and-the-green-knight-9780198114864?cc=us&lang=en&) The Green Knight (film) (https://en.wikipedia.org/wiki/The_Green_Knight_(film)) MeetingBar for Meet, Zoom & Co (https://apps.apple.com/us/app/meetingbar-for-meet-zoom-co/id1532419400?mt=12) Credits Produced by Richard Littauer (https://www.burntfen.com/) Edited by Paul M. Bahr at Peachtree Sound (https://www.peachtreesound.com/) Show notes by DeAnn Bahr Peachtree Sound (https://www.peachtreesound.com/) Special Guest: Ben Hutton.

In this episode, Jake and Michael discuss building your own monitor stand, the mysterious world of React micro-frontends, and get confused about JSON API, Open API, Swagger, and JSON Schema.This episode is brought to you by our friends at Workvivo - The leading employee communication app.Show links DIY monitor stand Micro-frontends Module federation JSON:API OpenAPI vs JSON:API JSON:API, OpenAPI, and JSON Schema working in harmony sixlive/json-schema-assertions

On today's show we are talking about Single Directory Components in Drupal, How they differ from Web Components, and what are their benefits with guest Mateu Bosch & Mike Herchel. We'll also cover Component Libraries: Theme Server as our module of the week. For show notes visit: www.talkingDrupal.com/416 Topics What are Single Directory Components? Where did the idea of adding Single Directory Components to Drupal come from? Where does support for this stand in Drupal Core? Fully supported? Still need a contrib module? How do they differ from Web Components? (Mike will take this one) How does Single Directory Components make Drupal Theme development easier? What is the point of creating a schema for an SDC? Can modules or themes override SDCs? How? Can SDC be integrated into component library systems like Storybook? How? Any other helpful contrib modules that enhance SDCs? Does this at all help a headless? How can someone get involved or help contribute to Single Directory Components? Resources Single Directory Components https://www.drupal.org/project/sdc JSON Schema https://json-schema.org/ SDC Display https://www.drupal.org/project/sdc_display SDC Styleguide https://www.drupal.org/project/sdc_styleguide Cl Devel https://www.drupal.org/project/cl_devel CL Server https://www.drupal.org/project/cl_server CL Generator https://www.drupal.org/project/cl_generator SDC Documentation https://www.drupal.org/project/drupal/issues/3345922 Mike's blog https://herchel.com/ SDC Slack Channel (Components channel in Drupal Slack) #components https://drupal.slack.com/archives/C4EDNHFGS Drupal Board Elections https://www.drupal.org/association/board/elections Guests Mike Herchel - herchel.com @mikeherchel Mateu Bosch - mateuaguilo.com Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Andy Blum - andy-blum.com - andy_blum Module of the Week with Martin Anderson-Clutz - @mandclu Component Libraries: Theme Server This module lets you use component libraries, like Storybook, in your Drupal project, without Twig.js!

Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org Brian: @brianokken@fosstodon.org Show: @pythonbytes@fosstodon.org Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesdays at 11am PT. Older video versions available there too. Michael #1: Pydantic v2 released Pydantic V2 is compatible with Python 3.7 and above. There is a migration guide. Check out the bump-pydantic tool to auto upgrade your classes Brian #2: Two Ways to Turbo-Charge tox Hynek Not just tox run-parallel or tox -p or tox --``parallel , but you should know about that also. The 2 ways Build one wheel instead of N sdists Run pytest in parallel tox builds source distributions, sdists, for each environment before running tests. that's not really what we want, especially if we have a test matrix. It'd be better to build a wheel once, and use that for all the environments. Add this to your tox.ini and now we get one wheel build [testenv] package = wheel wheel_build_env = .pkg It will save time. And a lot if you have a lengthy build. Run pytest in parallel, instead of tox in parallel, with pytest -n auto Requires the pytest-xdist plugin. Can slow down tests if your tests are pretty fast anyway. If you're using hypothesis, you probably want to try this. There are some gotchas and workarounds (like getting coverage to work) in the article. Michael #3: Awesome Pydantic A curated list of awesome things related to Pydantic!

Summary Batch vs. streaming is a long running debate in the world of data integration and transformation. Proponents of the streaming paradigm argue that stream processing engines can easily handle batched workloads, but the reverse isn't true. The batch world has been the default for years because of the complexities of running a reliable streaming system at scale. In order to remove that barrier, the team at Estuary have built the Gazette and Flow systems from the ground up to resolve the pain points of other streaming engines, while providing an intuitive interface for data and application engineers to build their streaming workflows. In this episode David Yaffe and Johnny Graettinger share the story behind the business and technology and how you can start using it today to build a real-time data lake without all of the headache. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) Your host is Tobias Macey and today I'm interviewing David Yaffe and Johnny Graettinger about using streaming data to build a real-time data lake and how Estuary gives you a single path to integrating and transforming your various sources Interview Introduction How did you get involved in the area of data management? Can you describe what Estuary is and the story behind it? Stream processing technologies have been around for around a decade. How would you characterize the current state of the ecosystem? What was missing in the ecosystem of streaming engines that motivated you to create a new one from scratch? With the growth in tools that are focused on batch-oriented data integration and transformation, what are the reasons that an organization should still invest in streaming? What is the comparative level of difficulty and support for these disparate paradigms? What is the impact of continuous data flows on dags/orchestration of transforms? What role do modern table formats have on the viability of real-time data lakes? Can you describe the architecture of your Flow platform? What are the core capabilities that you are optimizing for in its design? What is involved in getting Flow/Estuary deployed and integrated with an organization's data systems? What does the workflow look like for a team using Estuary? How does it impact the overall system architecture for a data platform as compared to other prevalent paradigms? How do you manage the translation of poll vs. push availability and best practices for API and other non-CDC sources? What are the most interesting, innovative, or unexpected ways that you have seen Estuary used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Estuary? When is Estuary the wrong choice? What do you have planned for the future of Estuary? Contact Info Dave Y (mailto:dave@estuary.dev) Johnny G (mailto:johnny@estuary.dev) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Estuary (https://estuary.dev) Try Flow Free (https://dashboard.estuary.dev/register) Gazette (https://gazette.dev) Samza (https://samza.apache.org/) Flink (https://flink.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/apache-flink-with-fabian-hueske-episode-57/) Storm (https://storm.apache.org/) Kafka Topic Partitioning (https://www.openlogic.com/blog/kafka-partitions) Trino (https://trino.io/) Avro (https://avro.apache.org/) Parquet (https://parquet.apache.org/) Fivetran (https://www.fivetran.com/) Podcast Episode (https://www.dataengineeringpodcast.com/fivetran-data-replication-episode-93/) Airbyte (https://www.dataengineeringpodcast.com/airbyte-open-source-data-integration-episode-173/) Snowflake (https://www.snowflake.com/en/) BigQuery (https://cloud.google.com/bigquery) Vector Database (https://learn.microsoft.com/en-us/semantic-kernel/concepts-ai/vectordb) CDC == Change Data Capture (https://en.wikipedia.org/wiki/Change_data_capture) Debezium (https://debezium.io/) Podcast Episode (https://www.dataengineeringpodcast.com/debezium-change-data-capture-episode-114/) MapReduce (https://en.wikipedia.org/wiki/MapReduce) Netflix DBLog (https://netflixtechblog.com/dblog-a-generic-change-data-capture-framework-69351fb9099b) JSON-Schema (http://json-schema.org/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Watch on YouTube About the show Sponsored by Microsoft for Startups Founders Hub. Connect with the hosts Michael: @mkennedy@fosstodon.org Brian: @brianokken@fosstodon.org Michael #1: Jupyter Server 2.0 is released! Jupyter Server provides the core web server that powers JupyterLab and Jupyter Notebook. New Identity API: As Jupyter continues to innovate its real-time collaboration experience, identity is an important component. New Authorization API: Enabling collaboration on a notebook shouldn't mean “allow everyone with access to my Jupyter Server to edit my notebooks”. What if I want to share my notebook with e.g. a subset of my teammates? New Event System API: jupyter_events—a package that provides a JSON-schema-based event-driven system to Jupyter Server and server extensions. Terminals Service is now a Server Extension: Jupyter Server now ships the “Terminals Service” as an extension (installed and enabled by default) rather than a core Jupyter Service. pytest-jupyter: A pytest plugin for Jupyter Brian #2: Converting to pyproject.toml Last week, episode 314, we talked about “Tools for rewriting Python code” and I mentioned a desire to convert setup.py/setup.cfg to pyproject.toml Several of you, including Christian Clauss and Brian Skinn, let me know about a few tools to help in that area. Thank you. ini2toml - Automatically translates .ini/.cfg files into TOML “… can also be used to convert any compatible .ini/.cfg file to TOML.” “ini2toml comes in two flavours: “lite” and “full”. The “lite” flavour will create a TOML document that does not contain any of the comments from the original .ini/.cfg file. On the other hand, the “full” flavour will make an extra effort to translate these comments into a TOML-equivalent (please notice sometimes this translation is not perfect, so it is always good to check the TOML document afterwards).” pyproject-fmt - Apply a consistent format to pyproject.toml files Having a consistent ordering and such is actually quite nice. I agreed with most changes when I tried it, except one change. The faulty change: it modified the name of my project. Not cool. pytest plugins are traditionally named pytest-something. the tool replaced the - with _. Wrong. So, be careful with adding this to your tool chain if you have intentional dashes in the name. Otherwise, and still, cool project. validate-pyproject - Automated checks on pyproject.toml powered by JSON Schema definitions It's a bit terse with errors, but still useful. $ validate-pyproject pyproject.toml Invalid file: pyproject.toml [ERROR] `project.authors[{data__authors_x}]` must be object $ validate-pyproject pyproject.toml Invalid file: pyproject.toml [ERROR] Invalid value (at line 3, column 12) I'd probably add tox Don't forget to build and test your project after making changes to pyproject.toml You'll catch things like missing dependencies that the other tools will miss. Michael #3: aws-lambda-powertools-python Via Mark Pender A suite of utilities for AWS Lambda Functions that makes distributed tracing, structured logging, custom metrics, idempotency, and many leading practices easier Looks kinda cool if you prefer to work almost entirely in python and avoid using any 3rd party tools like Terraform etc to manage the support functions of deploying, monitoring, debugging lambda functions - Tracing: Decorators and utilities to trace Lambda function handlers, and both synchronous and asynchronous functions Logging - Structured logging made easier, and decorator to enrich structured logging with key Lambda context details Metrics - Custom Metrics created asynchronously via CloudWatch Embedded Metric Format (EMF) Event handler: AppSync - AWS AppSync event handler for Lambda Direct Resolver and Amplify GraphQL Transformer function Event handler: API Gateway and ALB - Amazon API Gateway REST/HTTP API and ALB event handler for Lambda functions invoked using Proxy integration Bring your own middleware - Decorator factory to create your own middleware to run logic before, and after each Lambda invocation Parameters utility - Retrieve and cache parameter values from Parameter Store, Secrets Manager, or DynamoDB Batch processing - Handle partial failures for AWS SQS batch processing Typing - Static typing classes to speedup development in your IDE Validation - JSON Schema validator for inbound events and responses Event source data classes - Data classes describing the schema of common Lambda event triggers Parser - Data parsing and deep validation using Pydantic Idempotency - Convert your Lambda functions into idempotent operations which are safe to retry Feature Flags - A simple rule engine to evaluate when one or multiple features should be enabled depending on the input Streaming - Streams datasets larger than the available memory as streaming data. Brian #4: How to create a self updating GitHub Readme Bob Belderbos Bob's GitHub profile is nice Includes latest Pybites articles, latest Python tips, and even latest Fosstodon toots And he includes a link to an article on how he did this. A Python script that pulls together all of the content, build-readme.py and fills in a TEMPLATE.md markdown file. It gets called through a GitHub action workflow, update.yml and automatically commits changes, currently daily at 8:45 This happens every day, and it looks like there are only commits if Note: We covered Simon Willison's notes on self updating README on episode 192 in 2020 Extras Brian: GitHub can check your repos for leaked secrets. Julia Evans has released a new zine, The Pocket Guide to Debugging Python Easter Eggs Includes this fun one from 2009 from Barry Warsaw and Brett Cannon >>> from __future__ import barry_as_FLUFL >>> 1 2 True >>> 1 != 2 File "[HTML_REMOVED]", line 1 1 != 2 ^ SyntaxError: invalid syntax Crontab Guru Michael: Canary Email AI 3.11 delivers First chance to try “iPad as the sole travel device.” Here's a report. Follow up from 306 and 309 discussions. Maps be free New laptop design Joke: What are clouds made of?

What are some recommendations to consider when running Apache Kafka® in production? Jun Rao, one of the original Kafka creators, as well as an ongoing committer and PMC member, shares the essential wisdom he's gained from developing Kafka and dealing with a large number of Kafka use cases.Here are 6 recommendations for maximizing Kafka in production:1. Nail Down the Operational PartWhen setting up your cluster, in addition to dealing with the usual architectural issues, make sure to also invest time into alerting, monitoring, logging, and other operational concerns. Managing a distributed system can be tricky and you have to make sure that all of its parts are healthy together.  This will give you a chance at catching cluster problems early, rather than after they have become full-blown crises. 2. Reason Properly About Serialization and Schemas Up FrontAt the Kafka API level, events are just bytes, which gives your application the flexibility to use various serialization mechanisms. Avro has the benefit of decoupling schemas from data serialization, whereas Protobuf is often preferable to those practiced with remote procedure calls; JSON Schema is user friendly but verbose. When you are choosing your serialization, it's a good time to reason about schemas, which should be well-thought-out contracts between your publishers and subscribers. You should know who owns a schema as well as the path for evolving that schema over time.3. Use Kafka As a Central Nervous System Rather Than As a Single ClusterTeams typically start out with a single, independent Kafka cluster, but they could benefit, even from the outset, by thinking of Kafka more as a central nervous system that they can use to connect disparate data sources. This enables data to be shared among more applications. 4. Utilize Dead Letter Queues (DLQs)DLQs can keep service delays from blocking the processing of your messages. For example, instead of using a unique topic for each customer to which you need to send data (potentially millions of topics),  you may prefer to use a shared topic, or a series of shared topics that contain all of your customers. But if you are sending to multiple customers from a shared topic and one customer's REST API is down—instead of delaying the process entirely—you can have that customer's events divert into a dead letter queue. You can then process them later from that queue.5. Understand Compacted TopicsBy default in Kafka topics, data is kept by time. But there is also another type of topic, a compacted topic, which stores data by key and replaces old data with new data as it comes in. This is particularly useful for working with data that is updateable, for example, data that may be coming in through a change-data-capture log. A practical example of this would be a retailer that needs to update prices and product descriptions to send out to all of its locations. 6. Imagine New Use Cases Enabled by Kafka's Recent Evolution The biggest recent change in Kafka's history is its migration to the cloud. By using Kafka there, you can reserve your engineering talent for business logic. The unlimited storage enabled by the cloud also means that you can truly keep data forever at reasonable cost, and thus you don't have to build a separate system for your historical data needs.EPISODE LINKSKafka Internals 101 Watch in videoKris Jenkins' TwitterUse PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)   

Mike is starting work at Stripe! What the world looks like for Open API and JSON Schema going forward https://json-schema.org/blog/posts/json-schema-joins-the-openjsf Mozilla's 2020 Layoffs Writing for APIs You Won't Hate - https://github.com/orgs/apisyouwonthate/projects/4 

An airhacks.fm conversation with Mark Sailes (@MarkSailes3) about: Checkout episode "#168 Serverless Java on AWS" with Mark, AWS Lambda Powertools for Java was was initiated in 2020, AWS Lambda Powertools for Java started with logging tracing and custom metrics, the major use cases for AWS Lambda Powertools, lambda best practices are implemented as modules, Lambda Powertools Java Logging and structured logging in JSON format with additional context provided with annotation, including the correct amount of data, logging writes to standard out, Lambda, metrics and the AWS CloudWatch Embedded Metrics Format (EMF), AWS Lambda and metrics scraping, Lambda Powertools Java Metrics, providing Lambdas to AWS CloudWatch via EMF, synchronous AWS CloudWatch calls are expensive, secrets and configuration management with parameters, AWS Systems Manager Parameter Store support, parameter caching, Lambda Java-like tracing with AWS X-Ray, Lambda Powertools annotation for X-Ray, adding exceptions to AWS X-Ray, adding correlation id support for cross Lamba logging, AWS Lambda Powertools for Java is an incubator, support for CloudFormation custom resources, the SQS and SNS message offloading to S3, validation support of business objects with JSON-Schema and JMESPath, the killer Use Case for AOP, writing ugly code for performance Mark Sailes on twitter: @MarkSailes3, Mark's blog: mark-sailes.medium.com

On this episode of the podcast, Thomas Betts talks with Kin Lane about managing your API lifecycle using standards and specifications, including OpenAPI, AsyncAPI, and JSON Schema. These specifications and the tooling based on them can help reduce communication problems, by creating documentation, generating code, and automating testing. Read a transcript of this interview: https://bit.ly/3s6uTYo Subscribe to our newsletters: - The InfoQ weekly newsletter: www.infoq.com/news/InfoQ-Newsletter/ - The Software Architects' Newsletter [monthly]: www.infoq.com/software-architects-newsletter/ Upcoming Virtual Events - events.infoq.com/ QCon London: https://qconlondon.com/ - April 4-6, 2022 / London, UK QCon Plus: https://plus.qconferences.com/ - May 10-20, 2022 InfoQ Live: https://live.infoq.com/ - Feb 22, 2022 - June 21, 2022 - July 19, 2022 - August 23, 2022 Follow InfoQ: - Twitter: twitter.com/infoq - LinkedIn: www.linkedin.com/company/infoq/ - Facebook: www.facebook.com/InfoQdotcom/ - Instagram: @infoqdotcom - Youtube: www.youtube.com/infoq

This episode is brought to you by Mindsize. If you’re looking for monthly WooCommerce support, look no further than Mindsize.com You know how it goes, everything I mention here will be linked up in the newsletter and the blog post. Check out thewpminute.com for the links. In the News There was a lot of excitement this week around LTDs (LifeTime Licensing Deals). There were several posted reactions to the email sent from Delicious Brains, the new owners of Advanced Custom Fields. The email was not well-received (to say the least) by some users that have had Lifetime Licensing because it was asking for a part-time donation for the product. Twitter exploded with reactions and many in the WordPress community responded as well. We covered this on the WPMinute and Sarah Gooding also wrote about both perspectives — positive and negative — in her article over on the WPTavern. The bottom line is that the lifetime licenses are tough, and very few still remain in the WordPress space. ACF (read: Brad) will continue to honor the pricing for legacy customers. With the recent delay of WordPress 5.9 the team is looking for testers for Beta 1. Angela Jin posted the link for the helpful testing guide. Feel free to participate and let them know how you “broke” it. Testing is very important for a successful release. A JSON Schema for theme.json and one for block.json are now available to help with building block-based themes. The schema can be used by code editors to provide things like tooltips, autocomplete, and validation while editing theme.json or block.json. The WP Live Streams Directory pick of the week “Building Modern WordPress Plugins With Plugin Machine (Part 2)*” presented by Josh Pollock, formerly of WPCaldera, on December 7th at 11pm UTC / 6pm EST / 3pm PST. In Part 1 of his talk, Josh laid the foundation of the mess that modern tooling has become for plugin development. In Part 2, Josh will show us a demo of Plugin Machine, a new app he’s building that helps developers create plugins and add features to them easily. You can catch this by registering for the Pittsburgh WordPress Developers and Designers meetup. Other News From Our Contributors Shopify Engineering announced that they had their biggest Black Friday Cyber Monday ever in 2021. They were proud of the uptime and traffic across the infrastructure along with their partner Google Cloud. Liam Dempsey shared this post by Andy Stitt thanking WordPress for helping him find Digital Accessibility. This is a great article of how WordPress helped Andy advance and allow him to concentrate on accessibility now. Giving Tuesday And now, I'd like to introduce you to Mary Job, who's leading the

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. The key features are: Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). One of the fastest Python frameworks available. Fast to code: Increase the speed to develop features by about 200% to 300%. * Fewer bugs: Reduce about 40% of human (developer) induced errors. * Intuitive: Great editor support. Completion everywhere. Less time debugging. Easy: Designed to be easy to use and learn. Less time reading docs. Short: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs. Robust: Get production-ready code. With automatic interactive documentation. Standards-based: Based on (and fully compatible with) the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema.

Sponsored by Datadog: pythonbytes.fm/datadog Special guest: Sebastián Ramírez Live stream Watch on YouTube Brian #1: Python Developers Survey 2020 Results Using Python for? Lots of reductions in percentages. Increases in Education, Desktop, Games, Mobile, and Other Python 3 vs 2 94% Python3 vs 90% last year Python 3.8 has 44% of Python 3 usage, 3.5 or lower down to 3% environment isolation 54% virtualenv (I assume that includes venv) 32% Docker 22% Conda Web frameworks 46% Flask 43% Django 12% FastAPI … 2% Pyramid :( … Unit testing 49% pytest 28% unittest 13% mock OS 68% Linux, 48% Windows, 29% Mac, 2% BSD, 1% other CI: Gitlab, Jenkins, Travis, CircleCI … (Where’s GH Actions?) Editors: PyCharm, VS Code, Vim, … Lots of other great stuff in there Michael #2: Django Ninja - Fast Django REST Framework via Marcus Sharp and Adam Parkin (Codependent Codr) independently Django Ninja is a web framework for building APIs with Django and Python 3.6+ type hints. This project was heavily inspired by FastAPI (developed by Sebastián Ramírez) Key features: Easy: Designed to be easy to use and intuitive. FAST execution: Very high performance thanks to Pydantic and async support. Fast to code: Type hints and automatic docs lets you focus only on business logic. Standards-based: Based on the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema. Django friendly: (obviously) has good integration with the Django core and ORM. Production ready: Used by multiple companies on live projects. Benchmarks are interesting Example api = NinjaAPI() @api.get("/add") def add(request, a: int, b: int): return {"result": a + b} Sebastian #3: Pydantic 1.8 Hypothesis plugin (for property-based testing). Support for [NamedTuple](https://pydantic-docs.helpmanual.io/usage/types/#namedtuple) and [TypedDict](https://pydantic-docs.helpmanual.io/usage/types/#typeddict) in models. Support for [Annotated](https://pydantic-docs.helpmanual.io/usage/schema/#typingannotated-fields) types, e.g.: def some_func(name: Annotated[str, Field(max_length=256)] = 'Bar'): pass Annotated makes default and required values more “correct” in terms of types. E.g. the editor won't assume that a function's parameter is optional because it has a default value of Field(``'``Bar``'``, max_length=256), this will be especially useful for FastAPI dependency functions that could be called directly in other places in the code. Michael #4: Google, Microsoft back Python and Rust programming languages Partially via Will Shanks Google and Microsoft join and strengthen forces with the foundations behind the Python and Rust programming languages The companies will get to help shape their future. Microsoft has joined Mozilla, AWS, Huawei and Google as founding members of the Rust Foundation. Google donated $350,000 to the Python Software Foundation (PSF), making the company the organization's first visionary sponsor. Google is investing in improved PyPI malware detection, better foundational Python tools and services, and hiring a CPython Developer-in-Residence for 2021. Other PSF sponsors include Salesforce, a sustainability sponsor contributing $90,000. Microsoft, Fastly, Bloomberg and Capital One are maintaining sponsors contributing $60,000 apiece. You’ll find Talk Python Training over at the PSF Sponsors as well. Microsoft has shown an interest in Rust, particularly for writing secure code: “Rust programming changes the game when it comes to writing safe systems software” Microsoft is forming a Rust programming team to contribute engineering efforts to the language's ecosystem, focusing on the compiler, core tooling, documentation and more. Brian #5: Semantic Versioning Will Not Save You Hynek Schlawack Version numbers are usually 3 decimals separated by dots. SemVer is Major.Minor.Micro Implied promise is that if you depend on something and anything other than the Major version changes, your code won’t break. In practice, you have to be proactive Have tests with good coverage Pin your dependencies Regularly try to update your dependencies and retest If they pass, pin new versions If not, notify the maintainer of a bug or fix your code Block the versions that don’t work Consequences: ZeroVer Version conflicts mayhem Consider CalVer Sebastian #6: OpenAPI 3.1.0 It was released on February. Now the OpenAPI schemas are in sync and based on the latest version of JSON Schema. That improves compatibility with other tools. E.g. frontend components auto-generated from JSON Schema. Very small details to adjust in Pydantic and FastAPI, but they are actually more “strictly compatible” with OpenAPI 3.1.0, as they were made with the most recent JSON Schema available at the moment. The differences are mainly in one or two very specific corner cases. Note: OpenAPI 3.1.0 might not be Python-specific enough, so, in that case, I have an alternative topic: IDOM, which is more or less React in Python on the server with live syncing with the browser. Extras Michael Installing Python - training.talkpython.fm/installing-python boto3 types update (via Dean Langsam) - seems like boto type annotations is not maintained anymore, and the rabbit hole of github links sends you to *mypy_boto3_builder *(they have a gif example). Traverse up from the cwd to look for [HTML_REMOVED].venv[HTML_REMOVED] virtual environments #75 [](https://github.com/brettcannon/python-launcher/issues/75)[CLOSED](https://github.com/brettcannon/python-launcher/issues/75)[] Talk Python: AMA 2021 Episode Brian Thanks to Matthew Casari and NOAA for the great shirts. Joke More code comments jokes try { } finally { // should never happen } /* You may think you know what the following code does. * But you dont. Trust me. * Fiddle with it, and you'll spend many a sleepless * night cursing the moment you thought youd be clever * enough to "optimize" the code below. * Now close this file and go play with something else. */ const int TEN=10; // As if the value of 10 will fluctuate... // I am not responsible for this code. // They made me write it, against my will. // If this code works, it was written by Paul DiLascia. // If not, we don't know who wrote it options.BatchSize = 300; //Madness? THIS IS SPARTA!

On this week's episode, Steph and Chris trade some consulting and everyone comes out a winner. Steph talks about a win and a loss on the battlefield of refactoring, and Chris shares a related effort around identifying and removing unused code. Chris shares a pattern his team has been using with a special "demo" flag to provide small enhancements but otherwise keep sales demos within the product. Steph then shares some friction related to using dependabot on her team's project that hints at more foundational ideas at the intersection of workflow, team dynamics, testing, deployment. And finally, Chris asks Steph for her thoughts on how best to add testing around the structure of API responses. This episode is brought to you by Datadog (http://datadog.com/thebikeshed). Click through to get a free 14-day trial and a free Datadog t-shirt! Coverband (https://github.com/danmayer/coverband) for production code coverage Flipper feature flag gem (https://github.com/jnunemaker/flipper) Dependabot (https://dependabot.com/) JSON Schema (https://json-schema.org/) Swagger (https://swagger.io/) rspec-request_snapshot (https://github.com/CareMessagePlatform/rspec-request_snapshot) Say no to more process, say yes to trust (https://thoughtbot.com/blog/say-no-to-more-process-say-yes-to-trust) One electron theory (https://en.wikipedia.org/wiki/One-electron_universe)

Confluent Platform 5.5 introduces long-awaited JSON Schema and Protobuf support in Confluent Schema Registry and across other platform components. Support for Protobuf and JSON Schema in Schema Registry provides the same assurances of data compatibility and consistency we already had with Avro, while opening up Kafka to more businesses, applications, and use cases that are built upon those data serialization formats. Tushar Thole (Engineering Leader, Confluent) and David Araujo (Product Manager, Confluent) share about these new improvements to Confluent Schema Registry, the differences between Apache Avro™, Protobuf, and JSON Schemas, how to treat optional fields, some of the arguments between Avro and Protobuf, and why it took some time for Schema Registry to support JSON Schemas and Protobuf.Later, they talk about custom plugins, adding another layer of safety in Confluent Platform 5.5, and their vision for data governance.EPISODE LINKSIntroducing Confluent Platform 5.5Confluent Platform Now Supports Protobuf, JSON Schema, and Custom FormatsDownload Confluent PlatformGetting Started with Protobuf in Confluent CloudRead articles by Robert Yokota Schema Validation with Confluent Platform 5.4 Playing Chess with Confluent Schema RegistryJSON Schema specsSend feedback to datagovernance@confluent.ioFully managed Apache Kafka as a service! Try free.Join the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent Developer

On this week's episode, Steph and Chris catch up in their first recording of 2020. They discuss git workflows and the surprisingly strong opinions often associated with them, testing at all levels of your application, Steph gives a quick summary of her Ember adventures, and they round out the discussion with some new years systems building and Star Wars reviews. This episode is brought to you by Clubhouse (http://go.thoughtleaders.io/1658120200117). Click through to get 2 free months on any paid plan. Ember Documentation (https://emberjs.com/learn/) JSON Schema (https://json-schema.org/) Pretender (https://github.com/pretenderjs/pretender) Apollo GraphQL (https://www.apollographql.com/) React Testing Library (https://testing-library.com/docs/react-testing-library/intro) Write good commit messages by blaming others (https://thoughtbot.com/blog/write-good-commit-messages-by-blaming-others) (German's blog post) Prettier (https://prettier.io/)

Chris and Desmond are back! In this episode we talk about Desmond’s exploration into Property-Based testing, and then do a deep dive into authorization and access control in Chris’ GraphQL API. Property-based testing is a new world for both of us, but we do our best to tell you all about what libraries to use and how we’re thinking of using it. Chris is deep in some reworking of the authorization code in his GraphQL API and we go into depth talking about how it is structured, some of the challenges of building a flexible authorization system and then how this integrates back into a system. After talking about authorization we then go into validation of parameters in our APIs and how we think about typing and validating them at the boundaries, and in our Ecto Schemas. ## Links * EMPEX LA (https://empex.co/la) * StreamData Library for property testing (https://github.com/whatyouhide/stream_data) * PropEr (https://github.com/proper-testing/proper) * PropSchema (https://github.com/podium/prop_schema) * Property-Based Testing with PropEr (https://pragprog.com/book/fhproper/property-based-testing-with-proper-erlang-and-elixir) * StreamData: Property-based testing and data generation for Elixir (https://elixir-lang.org/blog/2017/10/31/stream-data-property-based-testing-and-data-generation-for-elixir/) * Canada Library (https://github.com/jarednorman/canada) * GraphQL Scalar Types in Absinthe (https://hexdocs.pm/absinthe/custom-scalars.html) * JSON Schema in Elixir (https://github.com/jonasschmidt/ex_json_schema) * API versioning at Stripe (https://stripe.com/blog/api-versioning)

En el episodio 45 del podcast de http://www.entredevyops.es/ TBD. Blog EntreDevYOps - http://www.entredevyops.es Twitter EntreDevYOps - https://twitter.com/EntreDevYOps LinkedIn EntreDevYOps - https://www.linkedin.com/in/entre-dev-y-ops-a7404385/ Patreon EntreDevYOps - https://www.patreon.com/edyo Amazon EntreDevYOps - https://amzn.to/2HrlmRw Enlaces comentados: Web de Fernando Doglio - https://www.fernandodoglio.com/ The importance of standards in development teams - https://blog.logrocket.com/standards-and-why-you-need-them-b48309053e41 GitFlow - https://nvie.com/posts/a-successful-git-branching-model/ No te olvides de poner el WHERE en el DELETE FROM - https://www.youtube.com/watch?v=i_cVJgIz_Cs GitHub Flow - https://guides.github.com/introduction/flow/ Good commit messages - https://chris.beams.io/posts/git-commit/ Semantic commit messages - https://seesparkbox.com/foundry/semantic_commit_messages Conventional commits - https://www.conventionalcommits.org/ GitHub: About issue and pull request templates - https://help.github.com/articles/about-issue-and-pull-request-templates/ GitHub: Setting guidelines for repository contributors - https://help.github.com/articles/setting-guidelines-for-repository-contributors/ GitHub: Creating issue templates for your repository - https://help.github.com/articles/creating-issue-templates-for-your-repository/ GitHub: Creating a pull request template for your repository - https://help.github.com/articles/creating-a-pull-request-template-for-your-repository/ Python PEP8 - https://www.python.org/dev/peps/pep-0008/ black - https://github.com/ambv/black yapf - https://github.com/google/yapf gofmt - https://golang.org/cmd/gofmt/ checkstyle - http://checkstyle.sourceforge.net/ Semantic versioning - https://semver.org/ JSON Schema - https://json-schema.org

In episode 83 of Does Not Compute, Sean and Paul talk about Sean's new ultrawide monitor & recording interface, their new found addiction with the game Factorio, and using JSON Schema and JSONB to create flexible yet validatable data storage with Postgres.

In episode 56 of Does Not Compute, Sean and Paul talk about Visual Studio Code, JSON Schema, and how learning complicated tools like Redux and Vuex take up more time up front, but can empower you to build complex applications efficiently

Derek and Laila discuss Derek's excitement for Elixir and Phoenix. Is Elixir as fun to write as Ruby? Is Phoenix a better Rails? Elixir and Phoenix Routes in Phoenix Using ctags with Elixir Static Assets in Phoenix ja_serializers ecto Is There a JSON Schema describing JSON API? Elixir 1.2 Map and MapSet scale better ExMachina - factories for Elixir Elixir Typespecs and Behaviours

Derek is joined by Gordon Fontenot for a discussion of the JSON API specification, problems consuming it from Swift, and the future of functional programming in Swift. This episode of The Bike Shed is sponsored by: Code School: Entertaining online learning for existing and aspiring developers. Leave a review on our iTunes page to be entered to win a free month of Code School. Links / Show Notes JSON API Argo: Functional JSON parsing in Swift Swift Optionals Spine: A Swift JSON API client Curry: Swift framework for function currying. HAL: Hypertext Application Language SOAP JSON Schema Runes Build Phase- For more of Gordon's insight into baseball and iOS development Gordon on Twitter Cookie Clicker Swarm Sim

Check out RailsClips on Kickstarter!!   02:01 - Richard Kennard Introduction Twitter GitHub Kennard Consulting Metawidget 02:04 - Geraint Luff Introduction Twitter 02:07 - David Luecke Introduction Twitter GitHub 02:57 - Object-relational Mapping (ORM) NoSQL Duplication 10:57 - Online Interface Mapper (OIM) CRUD (Create, Read, Update, Delete) UI (User Interface) 12:53 - How OIMs Work Form Generation Dynamic Generation Static Generation Duplication of Definitions Runtime Generation 16:02 - Editing a UI That’s Automatically Generated Shape Information => Make Obvious Choice 23:01 - Why Do We Need These? 25:24 - Protocol? Metawidget 27:56 - Plugging Into Frameworks backbone-forms JSON Schema 33:48 - Making Judgement Calls WebComponents, React JSON API AngularJS 49:27 - Example OIMs JSON Schema Metawidget Jsonary 52:08 - Testing Picks The Legend of Zelda: Majora's Mask 3D (AJ) 80/20 Sales and Marketing: The Definitive Guide to Working Less and Making More by Perry Marshall (Chuck) A Wizard of Earthsea by Ursula K. Le Guin (Chuck) Conform: Exposing the Truth About Common Core and Public Education by Glenn Beck (Chuck) Miracles and Massacres: True and Untold Stories of the Making of America by Glenn Beck (Chuck) 3D Modeling (Richard) Blender (Richard) Me3D (Richard) Bandcamp (David) Zones of Thought Series by Vernor Vinge (David) Citizenfour (Geraint) Solar Fields (Geraint) OpenPGP.js (Geraint) forge (Geraint)

Check out RailsClips on Kickstarter!!   02:01 - Richard Kennard Introduction Twitter GitHub Kennard Consulting Metawidget 02:04 - Geraint Luff Introduction Twitter 02:07 - David Luecke Introduction Twitter GitHub 02:57 - Object-relational Mapping (ORM) NoSQL Duplication 10:57 - Online Interface Mapper (OIM) CRUD (Create, Read, Update, Delete) UI (User Interface) 12:53 - How OIMs Work Form Generation Dynamic Generation Static Generation Duplication of Definitions Runtime Generation 16:02 - Editing a UI That’s Automatically Generated Shape Information => Make Obvious Choice 23:01 - Why Do We Need These? 25:24 - Protocol? Metawidget 27:56 - Plugging Into Frameworks backbone-forms JSON Schema 33:48 - Making Judgement Calls WebComponents, React JSON API AngularJS 49:27 - Example OIMs JSON Schema Metawidget Jsonary 52:08 - Testing Picks The Legend of Zelda: Majora's Mask 3D (AJ) 80/20 Sales and Marketing: The Definitive Guide to Working Less and Making More by Perry Marshall (Chuck) A Wizard of Earthsea by Ursula K. Le Guin (Chuck) Conform: Exposing the Truth About Common Core and Public Education by Glenn Beck (Chuck) Miracles and Massacres: True and Untold Stories of the Making of America by Glenn Beck (Chuck) 3D Modeling (Richard) Blender (Richard) Me3D (Richard) Bandcamp (David) Zones of Thought Series by Vernor Vinge (David) Citizenfour (Geraint) Solar Fields (Geraint) OpenPGP.js (Geraint) forge (Geraint)

Check out RailsClips on Kickstarter!!   02:01 - Richard Kennard Introduction Twitter GitHub Kennard Consulting Metawidget 02:04 - Geraint Luff Introduction Twitter 02:07 - David Luecke Introduction Twitter GitHub 02:57 - Object-relational Mapping (ORM) NoSQL Duplication 10:57 - Online Interface Mapper (OIM) CRUD (Create, Read, Update, Delete) UI (User Interface) 12:53 - How OIMs Work Form Generation Dynamic Generation Static Generation Duplication of Definitions Runtime Generation 16:02 - Editing a UI That’s Automatically Generated Shape Information => Make Obvious Choice 23:01 - Why Do We Need These? 25:24 - Protocol? Metawidget 27:56 - Plugging Into Frameworks backbone-forms JSON Schema 33:48 - Making Judgement Calls WebComponents, React JSON API AngularJS 49:27 - Example OIMs JSON Schema Metawidget Jsonary 52:08 - Testing Picks The Legend of Zelda: Majora's Mask 3D (AJ) 80/20 Sales and Marketing: The Definitive Guide to Working Less and Making More by Perry Marshall (Chuck) A Wizard of Earthsea by Ursula K. Le Guin (Chuck) Conform: Exposing the Truth About Common Core and Public Education by Glenn Beck (Chuck) Miracles and Massacres: True and Untold Stories of the Making of America by Glenn Beck (Chuck) 3D Modeling (Richard) Blender (Richard) Me3D (Richard) Bandcamp (David) Zones of Thought Series by Vernor Vinge (David) Citizenfour (Geraint) Solar Fields (Geraint) OpenPGP.js (Geraint) forge (Geraint)

Ryo Nakamuraさんをゲストに迎えて、JSON Schema, Rails, Postgres, RSpec, ChatOps, Markdown, 絵文字などについて話しました。 Show Notes Qiita クックパッドとマイクロサービス cookpad/garage JSON Schema and Hyper-Schema r7kamura/jdoc JSON SchemaとAPIテスト - YAPC::Asia 2014 Heroku | JSON Schema for the Heroku Platform API JSON Hyper-Schemaのようなサービスディスクリプションがうまくいかない理由 rails/jbuilder rails-api/active_model_serializers apotonick/roar Upgrading GitHub to Rails 3 with Zero Downtime Continuous Delivery at GitHub // Speaker Deck cookpad/chanko PostgreSQL: Documentation: 9.4: JSON Types miyagawa/mongery Query Documents - MongoDB Manual Query Mongo: MySQL to MongoDB Query Translator Rubyテスティングフレームワークの歴史 Transpec - The RSpec Syntax Converter Autodoc - r7km/s r7kamura/ruboty Ruby製HubotクローンのRubotyをSlackで動かす - Qiita jimmycuadra/lita The Twelve-Factor App Heroku | Introducing Heroku Button Hubot Scripting チャット経由でデプロイする - Qiita Markdownを拡張して独自記法をつくる - Qiita increments/qiita-markdown html-pipeline: Chainable Content Filters Open sourcing Twitter emoji for everyone | Twitter Blogs Six Apart絵文字 特集 : 絵文字が開いてしまった「パンドラの箱」 Unicode proposes a way to let an emoji black man and white woman hold hands Advent Calendar - Qiita