Tea time #devlog #indiedev

That's looking a bit less placeholder-y. Probably too large. Also definitely too penisy. #indielife #indiegames #DevLog

Step. Sway. Pray the next board holds. #Graytail #indiegame #pixelart #devlog

[自作ゲームエンジン] 回転の編集時に、オイラー角とQuaternionの変換時に、ジンバルロックで値が変わってしまう問題を解決していました！ 前々から気になっていたので、解決できてうれしいです！ #CurryEngine #devlog

Ultima Ratio Regum 0.11 devlog #59 is up! Making huge and rapid strides in generating other kinds of clues, including those which hint at what *will* be generated in places not even generated yet: markrjohnsongames.com/2026/0… #gamedev #indiedev #roguelike #screenshotsaturday

🛠️ DevLog – Early Heads-Up on L3 Mainnet Infra Assessment A quick heads-up on another area we may start looking at a bit earlier. 🔹 Current direction We may do some earlier assessment on the L3 mainnet (@Arbitrum Orbit) infra side, mainly around: - infra shape - RPC path - baseline setup - supporting services and readiness 🔹 What this means This is not an execution/update post yet. It is more about starting to assess what the L3 mainnet baseline would require so we have a clearer picture earlier. 🔹 Why this matters The goal is to surface: - infra assumptions - RPC requirements - setup gaps - operational needs before those become heavier work later. 🔹 Current takeaway So for now, this is just an early signal that some L3 mainnet infra assessment may start sooner, even though there is nothing actionable from it yet. #Cortensor #DevLog #L3 #Mainnet #Infra #RPC

Hello Undergooners! We've decided to post our first devlog sharing some info about development. Banner by: @rabbit5gamer #Undertale #Undergoon #NSFW (Link in the replies)

#robloxart #roblox #backrooms #devlog Lobby for my upcoming roblox game hehe

New screenshots and our first devlog: "What is GRAFT?" - check it out on Steam!

devlog; I have onnx models running in my ML framework, again. But now with graph capture not just eager execution. I'm trying to roughly model it after PyTorch. I'm doing something similar to TorchFX, doing symbolic tracing with fake tensors (using names instead of ids, no memory allocations, ...) to record ops. Tensor ops execute eagerly, and are traced if run within `Graph::capture()` (I used to hide this by doing some wiring behind the scenes and use thread locals to store state). During capture, the ops record nodes into a graph. Tensors are aliased so recorded nodes refer to named tensors. When graphs run, they create "guards" which store metadata that is later used for checking inputs and outputs (names, data types, shapes, etc. kinda like in TorchDynamo). Optimization currently just involves pruning dead nodes, some validations and topological sort. I have a custom CUDA allocator that can reuse blocks, which is useful if you know what and when to allocate ahead-of-time. After that, it gets messy. There's a "compile" step that chooses "providers" (cuDNN, cuTENSOR, or custom kernels compiled with nvrtc) that graphs are executed with. These libraries require some annoying maintenance and aren't that straightforward to use. For the "TorchInductor" part I only have ideas not something that can run end-to-end yet.

TOONSOULS - Devlog ----------------------------- - I lost the whole project - Rebuilding System - Slope and Platforms