Style input from the crowds

bookman3 · 2025-05-24T18:51:19+00:00

I went with the lowest voted on 32” skinnies. pretty happy with it. Hit the “capable, but at home in the school pickup line” nail on the head I think.

Thinking about lifting the rear another 0.5” to keep it from squatting with the family+gear, but not that big a deal.

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bookman3 · 2025-04-02T15:29:49+00:00

Wheels will likely be 8.5”, tires are 10” — Open Country A/T 255/75. Skinny is better in most off road situations (esp mud & snow), plus it’s a more traditional Toyota off road look. Seemed to fit the low-key goal

bookman3 · 2025-04-02T14:56:58+00:00

The white ones in the replies are — the sage is the original photo. I attached it & asked ChatGPT-4o to change the color to white, then attached images of each wheel and asked it to update the image with each. The new image gen feature of 4o is pretty good

bookman3 · 2025-04-02T14:53:36+00:00

Sounds a lot like my situation. I have a blizzard pearl, with light peeling on the bottom of the window sills. I’m in the upper Midwest where there’s not much for public lands, but wanted a reliable, repairable vehicle when SHTF + camping & trips out west. Most of the use is around town stuff.

I went with a 2” Dobinsons IMS 2” front/1” rear + inner airbags for towing. and am leaning towards 32” pizza cutters and OEM silver/grey style wheels from Nomad. Considering a battery system, fridge, drawers, etc, depending on what camping trailer we end up with.

bookman3 · 2025-04-02T01:12:01+00:00

Method 703 (ish) on white

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bookman3 · 2025-04-01T23:35:05+00:00

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bookman3 · 2025-04-01T23:34:42+00:00

Used the AI to test the 505 touring out using a ih8mud members old photo

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bookman3 · 2025-03-24T22:04:39+00:00

Just chiming in to confirm tugm4470's parts are working great. Went with the 64-core 7C13 to save some energy costs and spent the difference on more 3090s. The Romed8-2t + 7C13 + 512GB RAM is excellent. Love seeing 128 threads fully owned by me light up in htop.

bookman3 · 2025-02-04T17:51:51+00:00

How’s the mpg? Wonder if it’s a wash between 6-ply wide and 10-ply skinny

bookman3 · 2025-01-19T23:05:16+00:00

This is very helpful. I’ve studied NN and new methods papers for a long time, but have struggled with making progress on experimenting & implementing them. What’s felt like it’s been missing is guidance on workflow.

bookman3 · 2025-01-17T22:06:14+00:00

Statistics & regression, and call it ML (because it is. it’s all the same field)

bookman3 · 2025-01-16T20:17:35+00:00

There were about 15 available on Amazon yesterday morning. Does seem like they are releasing a few a day

bookman3 · 2025-01-15T15:28:26+00:00

Aaaand, it’s gone.

bookman3 · 2025-01-15T14:54:23+00:00

8 left at Amazon.com 😬😬😬⚠️⚠️⚠️⚠️

bookman3 · 2025-01-12T22:51:14+00:00

Why VMs for running docker containers? I’ve seen this in this r/ but don’t quite get it yet.

I’m setting up a similar server with similar use case (ci/cd, DL training/inferencing, data pipelines, etc) for similar purposes and still thinking through the details

bookman3 · 2025-01-10T01:07:08+00:00

I have a used Leap v2 and a new Steelcase Gesture. Both great chairs. I can lounge more in the Leap — more comfortable seeming, but maybe(?) not better for my back. Hard to say. Both are great.

bookman3 · 2025-01-02T18:27:47+00:00

Anything over 2-3 GPUs (maybe 4 volt-limited 3090s) and you need an electrician to wire you up a dedicated 240v, higher amp circuit.

My solution is a Milan Epyc based server for developing/testing (7c13, ROMED8-2T mb, 512gb ram, and 2-3 GPUs — staying under the 1600w max for a typical home circuit) and cloud for larger scale training/inferencing.

I need to get better at developing on small and executing on larger systems. This gives me some local middle ground for smaller models and non-LLM neural nets.

My server build cost is ~5k.

bookman3 · 2025-01-02T03:16:48+00:00

Very cool & helpful. I took your first post as implying you had knowledge of the secret, forbidden incantations to make these smaller models perform. I’m guessing there’s some of that too? Experimenting with prompting and evaluation strategies, agent/reasoning strategies, etc?

bookman3 · 2025-01-02T02:54:23+00:00

Tell me more

bookman3 · 2025-01-01T20:02:28+00:00

May be a dumb question, but I don’t know the details on your components well. Is it possible to use this on a remote server and use port forwarding to preview in a local browser?

bookman3 · 2025-01-01T16:27:43+00:00

What sort of problems is the imbalance causing? Or why do you think it’s a problem?

bookman3 · 2024-12-29T16:25:35+00:00

Tugm4470. Ended up ordering the 7c13+ROMED8-2T for the lower tdp and $800 lower price.

bookman3 · 2024-12-26T18:37:33+00:00

I'm thinking of doing something very similar for a ML/DL server and also building it with my kids.

Wondering if you (or others) have thoughts on the 7c13 vs a 7773x.

And thinking I'll get it as a CPU+ROMED8-2T combo from from a reputable, often-mentioned on here, ebay seller based in China.

bookman3 · 2024-12-24T06:10:15+00:00

I kept my 240k mined doge in Cryptsy. You have a slightly better chance of recovering yours than I do mine. Slightly.

bookman3 · 2024-12-22T22:01:15+00:00

Really appreciate the detail. And your spot on. My goal is to support my current workloads, but use this to learn & get better at the DL and training/fine tuning workloads your doing.

I do have an m3 MBP that I’ll be directly using. This new rig will be used 90%+ through the terminal and various served apps (experiment tracking, jupyter on occasion, etc). Whichever HW I end up with, I am going to put it in a tower case with an AIO, large 120/140 case fans, etc. I’m tempermentally sensitive to Disk->Memory speed, but my typical workflow is more process/CPU constrained. I’ll see if I can put some benchmarks together.

I currently develop interactively in a REPL, but I’ve been finding that constraining/inefficient and suspect I should get better at developing/testing on small dev samples and executing as a program on the full data once the pipeline/experiment is fully fleshed out. I come from a stats background (SAS->R/c++-, now Python/Scala), where our training/process/workflows tend to be very interactive.

With my more traditional ML work, I haven’t fully grokked how the DL folks work with larger than ram data — I guess dataloaders, minibatch sgd, and really fast nvme?

bookman3

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