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Understanding FSR 4
https://www.reddit.com/r/programming/comments/1otn9lj/understanding_fsr_4/

<!-- SC_OFF -->After AMD accidentally leaked the source code to FSR 4 I decided to figure out how it works <!-- SC_ON --> submitted by /u/mer_mer (https://www.reddit.com/user/mer_mer)
[link] (https://woti.substack.com/p/understanding-fsr-4) [comments] (https://www.reddit.com/r/programming/comments/1otn9lj/understanding_fsr_4/)
Adopting Static Analysis Early Transforms Large Codebases (and Why It’s Not Just About Tools)
https://www.reddit.com/r/programming/comments/1otrwe0/adopting_static_analysis_early_transforms_large/

<!-- SC_OFF -->Hey all,
In many mid to large scale projects I’ve observed (40K+ lines of code and growing), the real gains come not from just “installing a tool” but from adopting the mindset behind static analysis integration early and consistently. Below is a breakdown of the why, how, pit falls, plus top vetted external resources. I hope this adds value to your coding/architecture workflows. <!-- SC_ON --> submitted by /u/Digitalunicon (https://www.reddit.com/user/Digitalunicon)
[link] (https://deepsource.com/blog/engineering-manager-guide-to-static-analysis?utm_source=chatgpt.com) [comments] (https://www.reddit.com/r/programming/comments/1otrwe0/adopting_static_analysis_early_transforms_large/)
Indexing, Partitioning, Sharding - it is all about reducing the search space
https://www.reddit.com/r/programming/comments/1ou7hn7/indexing_partitioning_sharding_it_is_all_about/

<!-- SC_OFF -->When we work with a set of persisted in the database data, we most likely want our queries to be fast. Whenever I think about optimizing certain data query, be it SQL or NoSQL, I find it useful to think about these problems as Search Space problems: How much data must be read and processed in order for my query to be fulfilled? Building on that, if the Search Space is big, large, huge or enormous - working with tables/collections consisting of 10^6, 10^9, 10^12, 10^15... rows/documents - we must find a way to make our Search Space small again. Fundamentally, there is not that many ways of doing so. Mostly, it comes down to: Changing schema - so that each table row or collection document contains less data, thus reducing the search space Indexing - taking advantage of an external data structure that makes searching fast Partitioning - splitting table/collection into buckets, based on the column that we query by often Sharding - same as Partitioning, but across multiple database instances (physical machines) <!-- SC_ON --> submitted by /u/BinaryIgor (https://www.reddit.com/user/BinaryIgor)
[link] (https://binaryigor.com/reducing-the-search-space.html) [comments] (https://www.reddit.com/r/programming/comments/1ou7hn7/indexing_partitioning_sharding_it_is_all_about/)
Day 15: Gradients and Gradient Descent
https://www.reddit.com/r/programming/comments/1ou9cp1/day_15_gradients_and_gradient_descent/

<!-- SC_OFF -->1. What is a Gradient? Your AI’s Navigation System Think of a gradient like a compass that always points toward the steepest uphill direction. If you’re standing on a mountainside, the gradient tells you which way to walk if you want to climb fastest to the peak. In yesterday’s lesson, we learned about partial derivatives - how a function changes when you tweak just one input. A gradient combines all these partial derivatives into a single “direction vector” that points toward the steepest increase in your function. # If you have a function f(x, y) = x² + y² # The gradient is [∂f/∂x, ∂f/∂y] = [2x, 2y] # This vector points toward the steepest uphill direction For AI systems, this gradient tells us which direction to adjust our model’s parameters to increase accuracy most quickly. Resources https://aieworks.substack.com/p/day-15-gradients-and-gradient-descent https://github.com/sysdr/aiml/tree/main/day15/day15_gradients <!-- SC_ON --> submitted by /u/Designer_Bug9592 (https://www.reddit.com/user/Designer_Bug9592)
[link] (https://aieworks.substack.com/p/day-15-gradients-and-gradient-descent) [comments] (https://www.reddit.com/r/programming/comments/1ou9cp1/day_15_gradients_and_gradient_descent/)
Building a cross-platform project scaffolding engine: template detection, safe copying, and Git-aware initialization
https://www.reddit.com/r/programming/comments/1oua8df/building_a_crossplatform_project_scaffolding/

<!-- SC_OFF -->I’ve been working on a small cross-platform project scaffolding tool and kept running into problems that weren’t documented anywhere. Figured the technical notes might be useful to others.
It’s not fully polished yet, but the core ideas work. 1. Template detection
I wanted templates to identify themselves automatically without a predefined list. Ended up using a mix of signature files (package.json, go.mod, pyproject.toml) plus a lightweight ignore system to avoid walking massive folders. 2. Safe copying
Copying templates sounds trivial until you hit symlinks, Windows junctions, and binary assets. I settled on simple rules: never follow symlinks, reject junctions, treat unknown files as binary, and only apply placeholder replacement on verified text files. 3. CLI quirks on Windows and Linux
ANSI coloring, arrow-key navigation, and input modes behave differently everywhere. Raw input mode plus a clear priority between NO_COLOR, --color, and --no-color kept things mostly sane. 4. Optional Git integration
Initialize a repo, pull a matching .gitignore, create the first commit, but avoid crashing if Git isn’t installed or the user disables it. The project isn’t fully done yet, but the current implementation is open source here for anyone curious about the details: maybe for people that are programming already for a long time this sounds easy but for me creating a project for the first time without really copying parts from stackoverflow or other tutorials was a real prestation. <!-- SC_ON --> submitted by /u/kajvans (https://www.reddit.com/user/kajvans)
[link] (https://github.com/kajvans/Foundry/blob/main/README.md) [comments] (https://www.reddit.com/r/programming/comments/1oua8df/building_a_crossplatform_project_scaffolding/)