It needs some way of clearing all data. I chose "don't know" on calibration because I couldn't be bothered to try to work it out (but my mouse will be on the low end because it's just some random cheap mouse). Then, the training was ridiculous having to move my mouse miles between targets, but there's no way to change the initial calibration step. Not even in settings / data / delete.

I'm not really the target user though. I guess those who are will already know exactly how many DPI their mice are.

writeup on the rationale behind it: https://www.pramit.gg/post/i-made-an-aim-trainer

The aiming is very choppy because it feels like my cursor is "on rails", or stuck in some kind of grid. Not representative of any modern shooting game I've played. I think this would teach the wrong habits if someone were to use it as an aiming coach.

So cool! but somehow the simulation does not work on firefox. the crosshair is just stuck in the middle

Interesting project! I am relatively skilled at FPS with a mouse, but would like to improve with my trackball (so that I don't have to change my setup when playing an FPS). This could cone in handy, though I imagine that there aren't multiple "mouse profiles" built-in? It could be useful for players to understand how much of a difference different mice make.

very cool. project, will evaluate this in detail.

one note, on the "Feel your sensitivity." page, it says " raw input off — OS mouse acceleration is active. Your browser couldn’t give OpenAim raw mouse input, so your OS acceleration curve is reaching the game: the same hand motion turns further when you move faster, which no aim training survives. On Linux: set your mouse’s libinput acceleration profile to “flat” (unaccelerated). Chrome and Edge deliver raw input directly."

but my mouse is definitely unaccelerated (confirmed via `xinput list-props pointer:"Logitech PRO X 2"`). i am unsure if this leads to erroneous results when aim training.

i'm on: Chromium 150, linux, x11 kde plasma.

CS 1.6 scouts and knives was the ultimate aim trainer

does it separate flick errors from tracking errors, or is it just angular delta over time? valorant aim usually feels dominated by confidence-to-fire more than path smoothness.

This looks pretty cool, but I'm pretty sure my imported sens from Deadlock has gone wrong. I have 0.68 in game and it feels significantly slower on the app.

I might use it to compare different mice I have, have you considered adding a comparison option?

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the calibrated sensitivity is correct but the in-game sensitivity is waaaaay higher, it's a bug

You might want to look into this page - it slows my PC to a crawl :-(

Cool app! I also had some ideas a while ago that I think the current breed of aim trainers are still missing.

In-game practice like the Range, DM, and Team DM still feels like the gold standard to me. I generally only use aim trainers when they offer a noticeably higher return per minute, rather than just teaching me how to get good at aim trainer specific mechanics.

Here are a couple of things I think an aim trainer should have:

- Dynamic difficulty: I feel it works best when trying to keep accuracy relatively high, but fluctuating the target accuracy threshold over time and adjusting difficulty dynamically to match it. That keeps you challenged without breaking form.

- Dynamic scenarios: Instead of picking scenarios manually, I think a trainer should ideally detect what you suck at and launch those directly.

- Habit coaching: It would be awesome if a coach flagged bad habits in real time—like clicking too fast (low accuracy), doing a slow tracking movement instead of a sharp flick (mircoflicks), or using tracking when click timing is needed.

- Biomechanics: Finding the exact "perfect" sensitivity feels less important to me than ensuring your arm and wrist are working together properly, altough this might depend on your aiming style.

- Simplicity: A lot of trainers overwhelm you with numbers and menus. I image a simple "click here to start and follow feedback" workflow with nothing else for the user to learn. Seeing a bunch of numbers is less important to me then just getting better (maybe with a final score).

Valorant-specific focus: Trainers often feel too generic for tactical shooters like Valorant, where angle holding, target reading, and micro-corrections dominate.

I built a small aim trainer to experiment with these ideas myself: https://mousecontrol-thomaswelter-fceff2dbe456668c6bedef32db... (the aim trainer is very basic, only made for myself, fixed sens etc).

- In this tool, difficulty scales dynamically while playing (the parameter in parentheses changes on the fly):

- Angle Hold (enemy speed increases): Practicing holding angles against multiple targets. Holding angles is crucial in Valorant (just look at how Primmie plays).

- Follow (standstill window decreases): Forces active mouse correction and reading the target before shooting. I tend to click too fast, so this forces me to slow down and micro-correct.

- Jiggle (jiggle speed increases): Targets perform tight jiggles. I find players who spam small jiggles really hard to hit, so this directly targets that weakness.

- Micro Flicks (snap distance increases): Micro flicks are easy enough to train in the Range, so I mostly use this mode when I am away from my gaming PC.

- Static (target size decreases): Standard static clicking, but with dynamic scaling as targets shrink.

- Strafe (movement speed increases): Moving targets with limited hit windows. When watching pro DM VODs, most kills are on stationary targets, but hitting moving players is still vital and tricky to isolate in-game.

I am still not very good at Valorant but the aim train ideas are interesting. Let me know what you think!

Interesting project. One suggestion I'd make is to reduce the configuration complexity up front on new users. It is kind of overwhelming before you have a feel for how your settings translate to the actual training experience.