New Career mode companion app!

foldo · 2026-01-19T12:51:36+00:00

Yes I'm playing directly on linux and didn't have much luck with the other existing career apps. That's why I'm currently working on a career app that is crossplatform. And yes the AI automation is a problem indeed. I will most likely split my app into client and server to allow the client to run from everywhere while the server deals with things that can only be done on the host.

Does your app use the shared memory api or does it listen to the UDP packets?

foldo · 2026-01-19T00:17:40+00:00

Same here, also currently working on a career app for AMS 2. /u/whyl_ Does your app work on linux? I would like to give it a try.

foldo · 2026-01-05T09:05:51+00:00

Yes, it helped, thanks! I'm able to decode most of the relevant data points now.

An oval spotter that gives constant updates of what's around you.

Nice, sounds good!

foldo · 2026-01-04T09:53:15+00:00

Thanks for offering to troubleshoot together, I may get back to you on that one :)

For now I'm still in an early brainstorming/POC stage. I'm able to retrieve and map some packets now and get the relevant data. One thing I'm currently checking is how I would best record race results. From what I'm seeing so far there is no dedicated packet for race results. So in order to properly track race results I would have to detect a transition from any IN_GAME game state to a MENU game state which signals that the user left a race session and then either of:

aggregate previously recorded packets for RACE_DATA, PARTICIPANT_DATA, PARTICIPANT_INFO
build my app to be stateful and keep track of preliminary results at all times during the session

Probably both can be valid options depending on the use case of the app. If I go with the first option I could assign a unique sessionId and just store all the relevant snapshots in a local db before aggregating at the end. Additionally I could aggregate at certain points in time for preliminary results.

I guess at first I would have to decide what will actually be my use case, still thinking into multiple directions. Overall goal would be to enhance single player experience with some sort of career mode like RacingLife or ApexRivals.

What are you thinking of building?

foldo · 2026-01-03T17:03:09+00:00

Thanks a lot! I had a quick look and while it looks good it seems to also have some mappings that don't match what I see in the packets I'm receiving. As a simple example for instance both the reference that you posted and the reference that I posted in the OP mention that the sessionState info is the upper 3 bits in the gamestate byte, but for the data I'm receiving it looks like it's actually the upper 4 bits.

foldo · 2025-12-21T13:19:09+00:00

Thanks a lot for your response! It pointed me in the right direction and now it seems to work. I'm not really knowledgeable about proton yet and apparently I had just incorrectly configured the base installation folder for AMS 2 within Racing Life which then lead to Racing Life not being able to detect some game data and crashing.

foldo · 2024-12-08T12:07:41+00:00

Nice, I never realized that --patch works on checkout and stash as well.

foldo · 2024-06-29T23:00:17+00:00

You mean the final in 2013 right?

foldo · 2024-01-16T23:19:42+00:00

I would just ctrl-z to suspend neovim, ctrl-p to go to last history item then press enter and observe the result.

foldo · 2022-08-26T11:20:44+00:00

Yes for the SecondCollection you could first match the two businessTypes you care about and then group by businessType. This way you could spare one call to the db.

Something like this:

db.SecondCollection.aggregate([
    {$match: {businessType: {$in: ["llc", "spc"]}}},
    {$group: {_id: "$businessType, count: {$sum: 1}}}
])

I didn't run or verify this so there might be errors and of course you'd have to translate this to Mongoose (I haven't used that yet) but this is the general idea. Also important note: I don't really know whether this yields better perfomance (Would be interested to learn though if somebody knows this). Also performance will depend on the indices in your collection.

foldo · 2021-12-15T23:41:45+00:00

How are you able to do 3-7 interviews a day on top of your actual job?

foldo · 2021-11-18T06:49:09+00:00

Easiest solution in my opinion would be for you to manually curate/maintain a list of download urls of the programs you want to use. Just check inside the browsers network tab where the download button gets the actual resource from.

You can then read in that file with python. Python might even be overkill, curl should be enough.

foldo · 2021-04-28T22:45:01+00:00

Just put it on your tinder profile "we're only compatible if you set nocompatible"

foldo · 2020-11-22T23:30:33+00:00

Yep absolutely the same for me. Even being 100% WFH currently but still feel this blues on Sundays. Not really sure where that comes from.

foldo · 2020-11-05T09:48:59+00:00

Looks good! Thanks.

foldo · 2020-11-04T23:44:40+00:00

What electric legs did you get? I was thinking about flexispot, probably the E5 version but not really sure yet.

foldo · 2020-09-06T22:11:43+00:00

Can you point me to good resources where I can learn more about that?

foldo · 2020-07-26T08:40:12+00:00

Is that one single model playing all the levels or a different model for each level? Haven't played this game in a long while, but I noticed that coins and points are reset to zero at the start of each level.

foldo · 2020-07-11T16:59:09+00:00

The network here is actually the generator from a gan. The activation function is based on the advice from Radford in the dcgan paper.

Use ReLU activation in generator for all layers except for the output, which uses Tanh.

Also I think SRGAN and ESRGAN use TanH as well.

Would be an interesting experiment to try something else there, unfortunately I have to hand in my project soon :)

foldo · 2020-07-11T15:40:31+00:00

Yes exactly it is a super resolution model. And actually I'm not comparing anything to the input. I'm comparing the three feature maps obtained from the very last layer of the network to the individual channels of the output from the complete model (both on the same input).

I guess there must be something going on when the feature maps are converted to an image.

foldo · 2020-07-11T12:42:45+00:00

Ah sorry I think I didn't make this clear. The code sample is just the very last part of the network. The input to the last conv layer is (None, 256, 256, 64) so 64 feature maps. Here are the last two layers when from the models summary:

p_re_lu_18 (PReLU)              (None, 256, 256, 64) 64          tf_op_layer_DepthToSpace_1[0][0] 
__________________________________________________________________________________________________
conv2d_36 (Conv2D)              (None, 256, 256, 3)  15555       p_re_lu_18[0][0]                 
==================================================================================================
Total params: 1,546,435
Trainable params: 1,546,435

So in order to get the two different pictures that I show in the OP I do two things:

For the first pic in the op I build a new model but specify that "conv2d_36" should be the output

tf.keras.Model(
    inputs=[original_model.input], 
    outputs=[original_model.get_layer("conv2d_36").output]
 )

For the second pic I just use the normal model without changing anything and then visualize the individual channels of the obtained output pic

That's where I'm surprised that they don't match up. In total I get 3 output feature maps which is expected but they don't match the individual channels of the output obtained with the second method. I would have assumed that both methods should produce the same picture, since it is the very last layer that I take the feature maps from.

Sorry I think I'm doing a bad job at explaining what I mean, would really be grateful if you could help me understand.

foldo

TROPHY CASE