# JiepengTan/Lockstep-Tutorial

帧同步 教程

Repository: https://github.com/JiepengTan/Lockstep-Tutorial
Canonical: https://ross.abutalabs.com/products/lockstep-tutorial
Language: C#
License Family: other
Topics: lockstep, tutorial, lockstepengine
Last push: 2020-03-23T05:33:57+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2570, "days_push": 2354, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1197, forks 280 (observed 2026-08-28T04:03:57.490398+00:00)

## What it is
A Chinese-language tutorial series teaching lockstep synchronization for multiplayer games, covering basic lockstep and prediction/rollback lockstep in C#. It pairs with the LockstepEngine library and includes video lessons on Bilibili.

## Use cases
- learn lockstep synchronization for multiplayer games
- implement prediction and rollback netcode
- understand deterministic game logic and desync debugging
- build a lockstep-based RTS or ARPG
- study client-server replay architecture in games

## When to choose
- you want a structured, video-backed introduction to lockstep and rollback techniques
- you are building deterministic multiplayer game logic in C#

## When to avoid
- you need a production-ready, maintained netcode library rather than a tutorial
- you need English-language documentation
- you need ECS-based lockstep, which is explicitly out of scope

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: simulation, networking, developer-tools
- domain: tutorials
- platform: cpp, cross-platform
- tags: lockstep, deterministic-sync, rollback-netcode, multiplayer-games, csharp, game-networking, game-development, real-time

## Member repositories
- JiepengTan/Lockstep-Tutorial (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:57.490398+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:21:17.118937+00:00, confidence not recorded.
  - readme: https://github.com/JiepengTan/Lockstep-Tutorial (fetched 2026-08-28T04:03:57.490398+00:00, sha 9bbdf96ebbee)
- Data as of 2026-08-30T08:39:29.467469+00:00.