Thriving Thriving C++ C+
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Environment-Smulation sheeepsoup/Environment-Smulation

Vulkan terrain renderer with GPU erosion. Currently features procedural terrain, 1M-droplet hydraulic erosion, and smart texture blending. More coming: infinite worlds, vegetation, water, clouds. Built from scratch — follow the journey!

HEALTH 90 / Healthy
// solo builder compute-shadercpperosion-simulationgamedevgpugraphicslearningpersonal-projectprocedural-generationreal-time-rendering

// readme

🌄 Vulkan 环境渲染模拟器


🇬🇧 English

📖 Introduction

A graphics rendering project learning Vulkan from scratch, aiming to build a real-time rendering simulator for natural environments including terrain, vegetation, water bodies, clouds, and oceans.

This project is planned for long-term development. Welcome to star and follow my progress! ⭐

Currently completed: terrain generation and hydraulic erosion simulation. More environmental elements will be added progressively.


✨ Current Features

  • 🏔️ Procedural Terrain Generation — Multi-layer noise blending based on FastNoiseLite for highly detailed terrain
  • 💧 GPU Hydraulic Erosion Simulation — Compute shader parallel simulation with 1 million water droplets, creating natural gullies and sediment deposits
  • 🎨 Intelligent Texture Blending — Automatically blends grass/dirt/rock/snow based on slope, height, normal direction, and water flow
  • 🌫️ Atmospheric Fog — Distance-based fog rendering for enhanced scene depth
  • 🖱️ Free Camera Control — WASD movement + mouse rotation for full 3D observation

✨ Features

Erosion-simulated…

The Undervalued Score +

How much a project earns versus how much attention it actually gets. Above 50 means the work is outrunning its audience. Recomputed nightly from commit velocity, contributor effort, issue resolution, fork utility, release cadence, and project maturity — divided by a logarithmic reach factor.

score  = signal / reach

signal = 0.25·commit_velocity   // commits in last 90 days (cap 30)
       + 0.20·contributor_work  // unique authors × velocity (cap 100)
       + 0.20·issue_resolution  // closed ÷ total issues
       + 0.20·fork_ratio        // forks ÷ stars (proxy for real usage)
       + 0.10·release_cadence   // releases in 90 days (cap 3)
       + age_bonus              // +0 to +0.30 after 6 months
       + homepage_bonus         // +0.05 if homepage is set

reach  = log₁₀(stars + watchers + 10)
The Health Score +

Is the project alive and maintained right now? A 0–100 pulse recomputed nightly from commit recency, rhythm, how fast issues close, and how quickly PRs get merged.

health = 0.35·recency       // days since last commit (90d decay)
       + 0.25·cadence       // commit rhythm consistency
       + 0.20·issue_health  // closed ÷ total issues
       + 0.20·pr_health     // merged ÷ total PRs
Health bands +

The colour and label on every card come straight from the health score.

Healthy   80 – 100   active, responsive, regular releases
Stable    60 – 79    maintained, steady, no alarms
Quiet     40 – 59    slowing down — watch this one
At Risk    0 – 39    going dark · candidate for rescue
// Tags — what each label means +

Tags are independent behavioral signals computed nightly. A project can hold multiple at once. They drive the home page sections.

solo_builder      one person holds > 80% of commits (last 180d)
needs_contributors has open "help wanted" or "good first issue" labels
hidden_gem        < 100 stars · active in last 3 months · documented
legacy_hero       repo > 5 years old · committed this year
fork_magnet       forks/stars > 0.5 · used as template or dependency
release_machine   5+ releases in the last 90 days
under_pressure    > 10 open issues · ≤ 2 contributors · health ≥ 60
community_watch   watchers > stars · devs tracking before the public
community_hub     GitHub Discussions enabled · > 20 discussions
funded            maintainer has active funding channel
Why rank against stars at all? +

Stars are an outcome, not effort. A project with 8 stars and daily commits is doing more interesting work than one coasting on 8k. We measure the building, then divide by the attention already received — so the genuinely undervalued rise to the top.

// stars   = lagging indicator
// commits = leading indicator
// we rank by the leading one