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ssousa666

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PyInfra 3.8.0 3 months ago

I have such mixed feelings about Starlark and Bazel macros. When I write Bazel macros, they're great, the perfect tool for the job. When I encounter macros written by someone else, they are awful, a mistake and the bane of my existence.

My team runs several HarvesterHCI/RKE2 clusters, edge deployments of our validation, simulation and fleet management tools for autonomous vehicles. The Rancher ecosystem has really been a godsend for us.

Excited to experiment with k3k, but worried that I won't have the language to accurately describe the third layer of kubernetes in the stack. Host cluster -> Guest Host Cluster -> Guest Cluster? Host Cluster -> Guest Cluster -> Guest Guest Cluster?

I recently joined a company that writes Python after working for a Kotlin shop for the past four years. I miss Java/Kotlin every single day. I have no gripes with Python itself and I enjoy writing it, but I've still never seen a large python codebase that remains as coherent, structured and understandable as the average large JVM language codebase. It's as if the structure of the language itself materially informs the structure of the codebase as it grows.

Zod 4 1 year ago

I saw your tweet about cracking type inference for recursive schemas. It seems like the solution to this problem is pretty straightforward and simple. Was the solution really as simple as using a getter, or is there some additional complexity that I am failing to grok?

There is a whole class of hybrid incubator/vc firms in the US ("VC Studios") that have used this model and achieved success. Sometimes the model is inverted, in that the VC studio will build the MVP with their small, in-house engineering team before staffing the "real" company and eventually handing the codebase off to founding engineering team.

what did it mean historically? in contemporary usage, it seems to be a synonym for "managed" (or more accurately, a meaningless sobriquet for PAAS that can be easily sold to the huddled masses of "full stack" boot camp grads)

my favorite thing about camelCase is that class instances are (typically) named and cased as the inverse of their pascal-cased class.

val somethingRepository = SomethingRepository()

Is much more visually satisfying and balanced to me than:

something_repository = SomethingRepository()

I've found this very useful as well. My typical workflow (server-side kotlin + spring) has been:

- create migration files locally, run statements against containerized local postgres instance - use a custom data extractor script in IntelliJ's data tool to generate r2dbc DAO files with a commented out CSV table containing column_name, data_type, kotlin_type, is_nullable as headers - let AI assistant handle the rest