Reddit Programming

The Cost of Certainty: Why Perfect is the Enemy of Scale in Distributed Systems
https://www.reddit.com/r/programming/comments/1qokaan/the_cost_of_certainty_why_perfect_is_the_enemy_of/

Even in 2026, no AI can negotiate with the speed of light. ⚛️ As an architect, I’ve realized our biggest expense isn't compute—it’s the Certainty Tax. We pay a massive premium to pretend the world isn't chaotic, but production is pure entropy. I just wrote a deep dive on why we need to stop chasing 100% consistency at scale. Using Pokémon GO as a sandbox, I audited: The Math: Why adding a sidecar can cost you 22 hours of sleep a year. The Sandbox: Why catch history can lie, but player trading must be painfully slow. The Law: How Little’s Law proves that patience in a concurrent system is a liability. If you’ve ever wrestled with PACELC or consensus algorithms, I’d love to hear your thoughts on where you choose to relax your constraints.  submitted by /u/Level-Sink3315 (https://www.reddit.com/user/Level-Sink3315)
[link] (https://open.substack.com/pub/qianarthurwang/p/the-cost-of-certainty-why-perfect?r=6wytu0) [comments] (https://www.reddit.com/r/programming/comments/1qokaan/the_cost_of_certainty_why_perfect_is_the_enemy_of/)

2 views02:55

4 Pyrefly Type Narrowing Patterns that make Python Type Checking more Intuitive
https://www.reddit.com/r/programming/comments/1qolknv/4_pyrefly_type_narrowing_patterns_that_make/

Since Python is a duck-typed language, programs often narrow types by checking a structural property of something rather than just its class name. For a type checker, understanding a wide variety of narrowing patterns is essential for making it as easy as possible for users to type check their code and reduce the amount of changes made purely to “satisfy the type checker”. In this blog post, we’ll go over some cool forms of narrowing that Pyrefly supports, which allows it to understand common code patterns in Python. To the best of our knowledge, Pyrefly is the only type checker for Python that supports all of these patterns. Contents: 1. hasattr/getattr 2. tagged unions 3. tuple length checks 4. saving conditions in variables Blog post: https://pyrefly.org/blog/type-narrowing/ Github: https://github.com/facebook/pyrefly  submitted by /u/BeamMeUpBiscotti (https://www.reddit.com/user/BeamMeUpBiscotti)
[link] (https://pyrefly.org/blog/type-narrowing/) [comments] (https://www.reddit.com/r/programming/comments/1qolknv/4_pyrefly_type_narrowing_patterns_that_make/)

1 view02:56

1 view02:57

1 view02:58

1 view02:59

Panoptic Segmentation using Detectron2
https://www.reddit.com/r/programming/comments/1qopi7p/panoptic_segmentation_using_detectron2/

For anyone studying Panoptic Segmentation using Detectron2, this tutorial walks through how panoptic segmentation combines instance segmentation (separating individual objects) and semantic segmentation (labeling background regions), so you get a complete pixel-level understanding of a scene. It uses Detectron2’s pretrained COCO panoptic model from the Model Zoo, then shows the full inference workflow in Python: reading an image with OpenCV, resizing it for faster processing, loading the panoptic configuration and weights, running prediction, and visualizing the merged “things and stuff” output. Video explanation: https://youtu.be/MuzNooUNZSY Medium version for readers who prefer Medium : https://medium.com/image-segmentation-tutorials/detectron2-panoptic-segmentation-made-easy-for-beginners-9f56319bb6cc Written explanation with code: https://eranfeit.net/detectron2-panoptic-segmentation-made-easy-for-beginners/ This content is shared for educational purposes only, and constructive feedback or discussion is welcome. Eran Feit  submitted by /u/Feitgemel (https://www.reddit.com/user/Feitgemel)
[link] (https://eranfeit.net/detectron2-panoptic-segmentation-made-easy-for-beginners/) [comments] (https://www.reddit.com/r/programming/comments/1qopi7p/panoptic_segmentation_using_detectron2/)

1 view03:00

2 views03:01

JSON vs XML in Embedded Linux - system design trade-offs
https://www.reddit.com/r/programming/comments/1qp07z1/json_vs_xml_in_embedded_linux_system_design/

Data formats define whether systems are lean or bloated. I explored how JSON and XML flow through embedded Linux: - Hardware → Driver → Kernel → Middleware → Application - Real code examples (I²C, sysfs, cJSON, libxml2) - Debugging strategies at every layer - Performance insights: JSON vs XML. Curious how others here approach data structuring in embedded or system-level projects — JSON, XML, or custom formats?  submitted by /u/ImaginaryPin1768 (https://www.reddit.com/user/ImaginaryPin1768)
[link] (https://codewafer.com/blogs/json-xml-in-embedded-linux-full-stack-guide-with-drivers-middleware/) [comments] (https://www.reddit.com/r/programming/comments/1qp07z1/json_vs_xml_in_embedded_linux_system_design/)

2 views05:53