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rss-bridge 2025-11-04T16:00:00+00:00

185: Workflow Orchestrators

Intro topic: Asymmetric ReturnsNews/Links:NanoChat by Andrej Karpathyhttps://github.com/karpathy/nanochatPydantic AIhttps://www.marktechpost.com/2025/03/25/pydanticai-advancing-generative-ai-agent-development-through-intelligent-framework-design/1000th Starlink this yearhttps://spaceflightnow.com/2025/05/16/live-coverage-spacex-plans-morning-launch-of-starlink-satellites-from-california/ChatGPT Apps SDKhttps://openai.com/index/introducing-apps-in-chatgpt/Book of the ShowPatrickThe Will of the Many by James Islingtonhttps://amzn.to/43IfU8QJasonInterview with DHH (Founder of Ruby on Rails)https://www.youtube.com/watch?v=vagyIcmIGOQPatreon Plug https://www.patreon.com/programmingthrowdown?ty=hTool of the ShowPatrickFactoriohttps://www.factorio.com/ Jasonnip.io Topic: Workflow OrchestratorsWhyBatch jobs (embarrassingly parallel)Long-running tasks (e.g. transcoding video)Checkpointing/resumingHowMessage QueuesContainerizationWorker Pools & AutoscalingHistory & BackfillSteps to run workflows:Containerize the workflow definition and send to the cloudContainerize all the individual tasksSubmit job(s)ExamplesAirflowLegacy but dominantDagsterGreat UX for python developersTemporal: https://temporal.io/The new hotnessRayLow-level but very powerfulKubeflowDesigned for ML workflows, integrated dashboard

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Programming Throwdown

Patrick Wheeler and Jason Gauci

184: Asynchronous Programming

186: Becoming a Manager

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**Intro topic: Asymmetric Returns

**News/Links:

  • NanoChat by Andrej Karpathy
  • Pydantic AI
  • 1000th Starlink this year
  • ChatGPT Apps SDK

Book of the Show

  • Patrick
  • The Will of the Many by James Islington
  • Jason
  • Interview with DHH (Founder of Ruby on Rails)

Patreon Plug https://www.patreon.com/programmingthrowdown?ty=h

Tool of the Show

  • Patrick
  • Factorio
  • Jason

**Topic: Workflow Orchestrators

  • Why
  • Batch jobs (embarrassingly parallel)
  • Long-running tasks (e.g. transcoding video)
  • Checkpointing/resuming
  • How
  • Message Queues
  • Containerization
  • Worker Pools & Autoscaling
  • History & Backfill
  • Steps to run workflows:
  • Containerize the workflow definition and send to the cloud
  • Containerize all the individual tasks
  • Submit job(s)
  • Examples
  • Airflow
  • Legacy but dominant
  • Dagster
  • Great UX for python developers
  • The new hotness
  • Ray
  • Low-level but very powerful
  • Kubeflow
  • Designed for ML workflows, integrated dashboard

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