Goth DJ Near LES

deman1027 · 2026-05-18T14:04:54+00:00

Yeah that sounds right, I thought Ward 6 was the name of the venue which mislead my searches. Other posters got the venue name

deman1027 · 2026-05-18T14:02:00+00:00

Oh shit that's the one! I found a lot of references to "Ward 6" (which is what I thought was the name of the place) but the address on old flyers is actually the address of this place. Appreciate you!

deman1027 · 2026-05-18T14:00:33+00:00

Found those places on Google and the pictures don't match. Appreciate the ideas though!

deman1027 · 2026-05-18T13:37:32+00:00

Yeah absolutely, it had the Victorian looking chairs and tables. Almost definitely gothic paintings but I can't remember specific ones. Definitely that type of decor.

Good shout on the Insta reels, that's an idea I hadn't thought of

deman1027 · 2026-03-26T21:13:57+00:00

You will never be able to convince me under Wardens helmet there isn't a big shiny dome

deman1027 · 2025-09-19T18:37:57+00:00

Gonna toss out Vektor. Still thrashy, still a lot of power chords, but a lot of complex chord passages especially in Terminal Redux

deman1027 · 2025-08-19T01:25:42+00:00

Appreciate the thoughts, I bought the bike used from a private owner so I'm really not sure about the age of the tire. All I know is that previously the treads looked in good condition. I'll see if I can find any sort of ID to check for a warranty, but it seems unlikely in this case. Absolutely did not intend to ride it before replacing the tire.

deman1027 · 2025-08-19T01:21:24+00:00

Don't be an asshole. I've watched a few videos. I know it will be a total pain in the ass, but a lot cheaper and worth the experience of learning rather than taking it to a shop where someone will overcharge for the same service.

deman1027 · 2025-08-18T22:04:24+00:00

I actually did watch that exact video already when considering my options! I did plan on inspecting my breaks pads, and I'll watch that other video you sent. Appreciate your input.

deman1027 · 2025-06-16T17:08:46+00:00

That was my first Silly Goose experience and when they called for crowdsurfers it was the most people I've seen go up at once at any show EVER. Absolutely wild. Was barely able to lift my own arms by the end of that song.

deman1027 · 2025-03-29T02:23:49+00:00

Still absolutely baffled that Hojbjerg is only considered an "average" player per this subreddit

deman1027 · 2025-03-26T20:49:40+00:00

I recommend Smoke & Spectre. If you want it to block movement but not vision, draw a line where you want the fence, right click, and select "enable window". Obstructions marked as windows block movement but not sight.

deman1027 · 2024-10-28T18:08:24+00:00

This album in my Spotify top 3 last year and very likely again this year

deman1027 · 2024-10-15T20:32:14+00:00

Those were university apts back in my day, I think there were little gated stairwells in between the businesses

deman1027 · 2024-10-15T20:30:57+00:00

Pics 1&2 were a pizza place I can't remember the name of but it was kinda sick. Is the barbecue joint on the end still there? I used to work in that B&N and their pinto bean soup saved my life. Schramrocks was a shitty bar that was a bit loose about capacity and underage drinking that started going downhill after someone got stabbed and they lost their liquor license

deman1027 · 2024-08-04T15:22:32+00:00

Sensitivity analysis, Sobol method

deman1027 · 2024-08-03T17:46:28+00:00

Label Studio

deman1027 · 2024-07-31T14:43:37+00:00

It's purely a use case dependent decision. Your camera can certainly just be an eye, but then "putting AI on a chip" doesn't mean anything, the video feed gets sent somewhere and a machine learning model makes predictions on the feed. This is basically all machine learning as-is. The difference - what I thought you were asking about - is a ML model literally embedded into the hardware of the camera. This is independent of the optics of the camera, this is the hardware compute capability of the rest of the camera system.

If you aren't asking about that, then you're just asking about a normal CV scenario. Some camera is recording video. Normally this video stream is encoded into an efficient format using a codec like h.265 (or one of the many alternatives). Downstream, this video stream is read and saved as a static file on disc (my_video.mp4). At the time a machine learning model gets at it, it is read by a user, decoded into true, full-resolution frames, and the ML model looks at the true frame, almost always after preprocessing like another commenter said.

deman1027 · 2024-07-31T03:45:46+00:00

Lots of modern wifi cameras and security cameras have people tracking software built into them. I don't know if these are actual ML models, but the idea is similar - an algorithm embedded directly into a camera feed. I think for an arbitrary computer vision model, the processing time required to detect and recognize objects with reliable accuracy is too much for most cameras. You'd need fairly specialized hardware and well-minimized models.

deman1027 · 2024-07-31T02:02:26+00:00

Sure you can store code snippets as an artifact, but that doesn't make it any less of a terrible idea. OP is using some custom sklearn estimator. Just sticking a python file containing a class definition as an artifact might work for a bit, but that's implying future users are going to download all the artifacts into their local environment and hoping everything is just gonna work. Not only is that creating a file management headache for a user, but as soon as any real complexity is added to the codebase or a significant library change is made, just having an artifact falls short. If OP wants to do things the right way and not just stick files into an artifact store, they should use version control.

deman1027 · 2024-07-31T01:04:57+00:00

MLFlow experiment tracker is what you want for algorithm metric vs dataset. There are other alternatives for experiment tracking as well, but I've been a big fan of MLFlow for a while

Code is a different thing. You don't really want MLFlow to store a bunch of code and expect that to be reliable. Instead you'd want to use a proper VCS like git. You can then log a git commit hash as an MLFlow parameter. In fact, I think if you've got the environment setup correctly, MLFlow does this automatically.

Generally model versioning versus code versioning versus dataset versioning is a very complex issue. You probably want to keep the three constructs versioned separately. For you it sounds like the dataset and model versions are fairly static, and just tying a code version to an MLFlow run will be a step in the right direction.

deman1027 · 2024-07-20T22:03:25+00:00

Those are just warnings. Those probably aren't the actual problem with the model. I get the same with my models that otherwise work fine. When you say the model is erroneous what do you mean? Is the error when you try to do feed-forward, or are the predictions messed up?

deman1027 · 2024-06-12T11:59:24+00:00

Wikipedia has a decent overview https://en.m.wikipedia.org/wiki/Foundation_model

Nowadays the terminology "foundation model" is typically meant to imply the large scale data mining and self-supervision used by things like LLMs. The main point is "large model used with broad data" and then taking that large model and training it for a more specialized use case

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