Show HN：一种新型 FPS 瞄准训练器

Show HN：一种新型 FPS 瞄准训练器
Show HN: A new kind of FPS aim trainer

该训练软件提供高度自定义功能，以优化性能、人机工程学及数据追踪。 **视觉与界面自定义：** 用户可通过多种主题、目标样式及色盲友好预设来定制环境。“动态场景”与“缓解眼部疲劳”等设置可减轻视觉压力，丰富的准星调整功能则支持精细化的个性化设置。 **训练反馈：** 该平台提供全面的视听反馈，包括击中标记、追踪音效、节奏节拍器及屏幕外刷新提示。用户还可使用“禅模式”和HUD切换功能，以进行专注的练习。 **技术与数据管理：** 软件支持微调灵敏度、视场角（FOV）调整及原始输入设置。集成的教练功能可实现灵敏度自动调节、训练项目管理及进度基准对比。 **隐私与分析：** 数据被用于构建基于科学的玩家模型。默认情况下，匿名的练习总结将汇入该群体模型以提高训练准确性，而原始鼠标轨迹及排行榜参与度则完全由用户自选。所有遥测设置均会记录在回放文件中，以确保分析与用户的特定配置保持一致。系统优先考虑用户控制权，允许完整的数据导出、导入及彻底删除会话历史记录。

Hacker News | 最新 | 过往 | 评论 | 提问 | 展示 | 招聘 | 提交 | 登录 **Show HN: 一种新型 FPS 瞄准训练器 (pramit.gg)** 12 点 | pmazumder | 1 小时前 | 隐藏 | 过往 | 收藏 | 3 条评论我玩了很久《无畏契约》（Valorant）并且感到很抓狂，所以我做了一款瞄准训练器。它不只是通过场景打分，而是分析你原始的准星移动，以发掘你在运动控制和感知上的原始弱点。它还会将灵敏度和难度作为任务的一部分进行选择，并为你定制难度最优的练习列表，助你更快地学习和进步！帮助 **pmazumder** 12 分钟前 | 下一条 [-] 关于其设计原理的文章：https://www.pramit.gg/post/i-made-an-aim-trainer 回复 **King-Aaron** 19 分钟前 | 上一条 [-] CS 1.6 的狙击和刀战才是终极瞄准训练器。回复 **psalaun** 17 分钟前 | 父评论 [-] 哇，现在涌现出好多回忆，谢谢！回复考虑申请 YC 2026 年秋季班吧！申请通道开放至 7 月 27 日。指南 | 常见问题 | 列表 | API | 安全 | 法律 | 申请 YC | 联系搜索：

原文

Appearance

Theme Target colorPresets are colorblind-friendly against every theme. Target style Playfield background

Arena moodEarned backdrop palettes — free play only. Locked ones show what unlocks them.

Target finishEarned target skins — free play only. Rated drills always use the standard target.

Living sceneBackdrop shifts between drills & drifts slowly to fight visual fatigue. Never touches targets or contrast. Reduced eye strainDims & warms the scene and UI for long sessions — lowers luminance load. Targets keep full contrast. MotionReduce disables UI transitions; Auto follows your OS.

Crosshair

Style Size Thickness Gap Color theme OutlineKeeps the crosshair visible over targets. On-target reactionOff by default for clean telemetry.

Input & camera

Sensitivity° per mouse count. Mouse DPIOptional. Enables cm/360 and hand-travel modeling. Import sensConverts your in-game sensitivity. Field of viewHorizontal; stamped into every replay. Invert Y

Raw inputWhether the browser delivers unaccelerated mouse motion. OpenAim never accelerates; this is the browser/OS layer.

Coach

Challenge pointTarget success rate drills are solved to. ExplanationsFull detail always stays behind the ? chip. Session lengthDrills per coached session. FocusLet the coach choose, or lock the session to one axis. AutoGainSens auto-tuning from measured under/overshoot.

Data

Capture levelSummary skips raw mouse/camera streams; deep analysis needs Full. Auto-download replaySave the .oar after every completed run.

Your data

Aim Commons

Anonymous drill summaries are contributed by default to train the shared, science-based player model everyone starts from and that renormalizes your ratings to real peers. No raw mouse traces, no identity, no device info — and you can opt out or revoke below anytime. Raw replays and the public leaderboard stay off unless you turn them on.

Contribute runsSends drill summaries + capability features (no raw mouse, no device info) to sharpen the population model everyone starts from. LeaderboardRank on the task-Elo boards under a public handle. HandleYour public leaderboard name. Share replaysAdvanced: uploads raw mouse-movement traces (mildly biometric) for deeper analysis and spectating. Requires the two above.

Statuslocal only

Settings that change the meaning of telemetry (FOV, sens, target style, crosshair, invert, capture level) are recorded in each replay's .oar header, so analysis reflects the settings the run used.