The Sundown WebGPU game and SIM engine

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I just pushed up the initial version of a new WebGPU + JS engine I've been working on. It is free and open source under the MIT license. Hopefully it's of use to someone other than me! Here are some of the features: * WebGPU renderable abstractions * Flexible render graph for crafting render and compute pipelines * Simple, expressive material system for crafting custom shaders and materials * Gameplay simulation layer system for adding layered, modular functionality * ECS system for more efficient processing, using TypedArrays where possible * Simple, context-based input system, allowing you to set up different input schemes and contexts * Built-in PBR shaders * Auto instancing and draw batching of meshes using a specialized mesh task queue * Helpers for loading GTLF models, tracking performance scopes, named IDs and more. As an aside, I am forking this to pursue the more ambitious goal of building a visual-first AI building platform. Not for generative AI, but for all types of AI algorithms and techniques. An intuitive way to build MLPs, Bayesian systems, RL pipelines and more. The idea is that making AI crafting more intuitive will open the door up for more people to bootstrap us towards a better future. Anybody should be able to creatively come up with new types of intelligence, given sufficiently powerful building blocks. Researchers have been crafting the building blocks of AI for decades, but it's high time we start giving those building blocks to everyone so anyone can craft entirely new kinds of tools and intelligences.

Visit product View on Hacker NewsJul 21, 2024

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, pipe · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: platform, intuitive, efficient · Missing: plus, reviews, host

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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