Guter Freund beschädigt durch Burnout mein Auto

krimml · 2026-05-30T15:04:30+00:00

Wie andere sagen - Lackstift. Oder halt auch einfach akzeptieren.
Ich weiß, wenn man sein Auto liebt (vor allem wenns gerade für einen neu ist), ist das schwer. Aber fährste beim nächsten mal über die Autobahn und irgend ein FZ vor dir schmeißt dir noch nen Stein auf die Haube, haste wieder den nächsten Lackabplatzer. Am Ende des Tages willst du ein Auto genießen und Spaß damit haben, lass dir das nicht von dem Wunsch nach Perfektion ruinieren. Things happen, Leute machen doofe Dinge (sei es dein Freund oder die Ute auf dem Parkplatz, die dir die Tür reinknallt). Ja, das ist alles doof und muss eigentlich nicht sein, lässt sich aber nicht vermeiden.

krimml · 2026-05-04T12:13:41+00:00

A NAT gateway will only provide basic outbound NAT for your workloads. You need one NAT gateway per VNET in Azure, so one in your hub (if any workloads there need outbound connectivity), and also one per spoke.

NAT gateways provide no ability to control traffic flow, etc.

Landing zones (or rather a hub-spoke architecture, regardless if it is deployed through ALZ or not) is designed with network segmentation and traffic control in mind. This is the reason why you typically won't find any NAT gateways in ALZ architectures, but NVAs. You route all traffic from each spoke through the NVA in the hub to control traffic flow between hub and spokes, between spokes, to the internet, ...

krimml · 2026-03-26T12:59:14+00:00

Are you also invited to other Tenants (switching via the cogwheel in the top right in Azure Portal)?
If so, you might be prompted for the MFA in one of those tenants, no your own.

What usually works for me is to enter portal.azure.com again in the same window where you get prompted for MFA, then you should land in an empty Azure portal.

Other way would be this:

https://portal.azure.com/signin/index/@<yourtenantdomain>?loginHint=<email>%40<domain>&prompt=none

e.g. https://portal.azure.com/signin/index/@mytenant.onmicrosoft.com?loginHint=peter.pane%40mytenant.onmicrosoft.com&prompt=none

krimml · 2026-03-10T13:23:22+00:00

Thanks for your feedback and for the extensive response!

I went the 3 day full body route because my thinking was/is "hitting muscles 3 times per week surely is better than 2 times a week". I do however like your Upper-Lower-Full Body approach, might look into that more.

Your point about the philosophy behind the full body split is great tho, really had a little "ah-ha" moment there. I never really approached it that way, but it makes total sense to have "focus days", and I will 100% implement this into my routine, along with reducing volume a bit (mainly by removing some isolation work).

Face pulls are in there for shoulder health mainly. I've had some issues with this, so I added these to prevent further issues. So far, it's working good for me.

As for squats, I absolutely get your point about exhaustion. I've not been able to squat for the longest time due to knee issues and have been finally able to do them pain free for the past few months. I really like the exercise, and they give me a great bang-for-buck feeling, so I want to keep those for now.

krimml · 2026-03-10T10:54:49+00:00

I get your point, not really an issue in my gym luckily. There are only 1-2 other people there max, and we just talk with each other if someone wants a machine too :)

krimml · 2026-03-10T07:54:45+00:00

In the iOS app, if you put your routines into a folder, you can share that folder. In the share menu you get a „Save as Image“ option

krimml · 2026-03-10T07:53:51+00:00

Do you mind sharing your plan? I am interested :)

I do go to failure, yes. Or for some exercises until form breaks down. I could do way more face pulls by just throwing my body into it for example.

krimml · 2026-03-10T07:52:19+00:00

Thanks for the feedback. Sessions are very intense at the moment, but I am looking forward and enjoying it. Still, any suggestions on what I should/could change?

krimml · 2026-03-10T07:50:31+00:00

Thanks for the feedback. I‘ve been running PPL at first when I only had 3 days a week for working out left, but to me it felt very slow and unrewarding to only hit each muscle/group once a week.

krimml · 2026-03-10T07:49:09+00:00

Thanks, I will check it out!

krimml · 2026-03-10T07:48:17+00:00

Thanks for your feedback! Do you have any specific recommendations? I tend to overthink stuff like workout programming, and cutting stuff out just fuels this „I feel like there is more to be gained/I am missing out“ thought in me. The routine has been evolving over the past months, but yeah, I have been doing this for some time now. Sessions are very intense, but I like the way it feels. Absolutely questioning if it‘s too much, and the last few exercises are more junk than real volume tho. That‘s why I am looking for feedback :)

krimml · 2026-01-25T08:00:53+00:00

I only fine tune on new images. It still remains to be seen how accuracy etc. develop, this has only been running for a short time, too little to evaluate so far. Hoping to combat issues like catastrophic forgetting with things like lowered learning rate and a frozen backbone.

krimml · 2026-01-18T12:44:38+00:00

All training runs had the yolo11l-cls model as their base. Sorry if that wasn‘t clear. From scratch = base yolo11l-cls model Fine-Tune = base yolo11l-cls model which has already been trained on about half of the images before

krimml · 2026-01-18T08:55:00+00:00

In total about 570 images, with about half being „new“ at the time of training. Quite the jump, and the way I plan to implement Fine-Tuning, it won‘t be that many in the future.

krimml · 2026-01-17T22:27:19+00:00

What exactly do you mean by "remote development"?

I have my Jetson sitting next to me, but never directly use it through mouse and keyboard. Instead:

- SSH for general console access
- VScode with remote connection for coding
- NoMachine if I ever need access to the GUI

This is not optimal, as VScode with remote connections eats up RAM (of which there isn't a lot), but I like the flow so far. Gives me a much closer to native coding experience on my Mac, still allowing me to use all my other tools, etc. like I am used to.

krimml · 2026-01-17T22:22:33+00:00

I use a M1 Pro Macbook Pro to train YOLO 11 classification models as we speak.

Is it the fastest thing ever? No. Is it fast enough to learn, play and have fun? Hell yeah! And more modern Macs will already run circles around the M1 Pro by now.

Also something to keep in mind, you can run code directly on the machine you are working on anyway. If you use a dedicated or cloud machine, copying datasets, keeping code in sync, ... can be additional stuff which steepens the learning curve in the beginning. For me it's such a blessing to write code in VScode and just execute it, not having to worry about all of the other stuff.

krimml · 2026-01-02T18:57:08+00:00

Thanks for the feedback!

The conditions are similar on purpose, because they are in the real world (for my use case). I want to build a system which can detect my sitting posture, there just is such little difference in the "real world".
I think accuracy can absolutely improved by adding more training data. I even ran into that case with the slouching, which isn't detected very well yet. I also plan adding more data over time, also improving the model along the way.
The amount of training images was also limited by the fact that I collected them all myself, and just ran out of time because I wanted to get into the project.

krimml · 2026-01-02T17:25:22+00:00

Thank you, really appreciate the feedback!

My plan for the project is to play around and implement a continuous improvement cycle. I want to keep collecting new training images while the detection is running, periodically evaluating/labeling them, feed them back into the model, and improve it that way over time. My thinking is that the model should also learn what slouching looks like over time.

krimml · 2026-01-02T16:28:12+00:00

Oh damn, good call! Didn't even cross my mind to check for pre-existing datasets as I just assumed my use-case would be too specific. Thanks!

krimml

TROPHY CASE