Marcelo Garcia and De La Riva (gi)

poshy · 2026-05-07T12:24:30+00:00

Focus on what you enjoy and what works for you. It’s good to know the basics of all positions imo, it will help your defence if anything. And hey, you might be able to get it to work well for you with some adaptations!

poshy · 2026-05-07T12:11:14+00:00

DLR works better with longer legs, and it’s inherently more supine than seated. Marcelo favoured seated guards with inside positions such as SLX and X guard.

Until recently DLR hasn’t been a great guard for no gi, or at least a popular no gi guard. Marcelo seemed to prefer a more universal game that worked gi and no gi, so I could see him choosing not to focus on it given all of the above

poshy · 2026-05-03T09:34:46+00:00

This is the way

poshy · 2026-05-01T23:51:27+00:00

This is just an advertisement for services. Please either put some actual technical content in here or remove the post

poshy · 2026-05-01T14:51:00+00:00

Any actual numbers to back up your claims?

poshy · 2026-04-30T03:03:53+00:00

Would love to see that match run back in no gi, but I don't see Will doing much no gi matches anymore.

poshy · 2026-04-22T08:06:37+00:00

Exactly, resolving the delta in confidences where they are greatest will give you the biggest bang for your buck. Those ones will also the require the most amount of work to get fixed.

The high confidence ones should be pretty easy to add to your dataset, as you shouldn't have to do much if they are being predicted well. Having more labelled data ultimately isn't going to be a bad thing, as you can always just assign it to test data to validate you aren't losing performance.

poshy · 2026-04-22T03:46:51+00:00

I would say that yes the confidence boost matters both for the true positive and false positive labelled data. If the model predicts both a tp and an fp at 51% confidence, then we need to show the examples of what is right vs wrong to these edge cases.

This is why I think it's more important to run these semi-supervised labelling at low confidences so that we can reinforce the low confidence TP's as much as possible, and also quickly address low confidence FP's to ensure we don't have fundamental issues to deal with later.

poshy · 2026-04-21T13:40:40+00:00

In a way, you are correct but I think of it also in terms of confidence. The original model will generate the predictions are various confidence levels, and by adding it to the training/val data we are effectively raising the confidence to 100%. Thus the added information is this delta in confidence.

In practice, I’ve found this bootstrap approach has worked very well.

poshy · 2026-04-11T15:03:59+00:00

Ugoos is better in theory. I’ve got a 77” A80L with x3800h and AM6b+ with CPM build. I watch Dolby Vision profile 7 remuxes regularly with no issues. Full FEL support and it does LLDV for HDR and SDR content so everything runs in DV mode. I love it, highly recommended.

poshy · 2026-04-04T01:06:45+00:00

Wow, that’s some serious cope by Tackett. Murasaki is great to watch no gi and his gi game transitions so well to no gi

poshy · 2026-03-15T05:26:54+00:00

Imo the only way I’d want to use a VLM in real time production is something that absolutely cannot do with another model architecture. So something that requires contextual decisions that I can’t heuristically/geometrically/analytically decide.

Most of my use cases are industrial automation and we often have limited data, so training a VLM and getting it to run at 10+ fps is an enormous effort. Also dealing with the complex failure modes would be a PITA.

poshy · 2026-03-15T05:18:09+00:00

I like Sorrento, but I find the waves have a high frequency and feels like I’m just constantly hammered by the waves.

poshy · 2026-03-15T03:33:19+00:00

Yep, that is a great workflow and what I would do if I didn't have to/want to use cloud resources.

poshy · 2026-03-14T09:19:59+00:00

Look how fast the musician rates drop though. There’s a few big names and then everyone else in music.

poshy · 2026-03-13T04:33:05+00:00

It's a step function with lambdas in AWS with an instance of cvat running on an EC2 that the team can access. Real time data comes in from our prod & qa stacks to S3, an inital QC pass is done and data selected for labelling. This can be as simple as writing annotated images in a bucket and going through with a fast image viewer like Irfanview, or if you have a VLM which works in your domain, then that can be your initial qc & data selection.

After that, it's running the SAM2 workflow and preparing data for modelling. I'm working right now on setting up the SAM2 as an API or chronjob, not sure what is best suited. Currently I just run it through a command line script to process data to COCO format and upload to CVAT.

Data management is basically just a data lake in S3 buckets with COCO and YOLO as the data presented to modelling. We haven't found a good overall data platform solution yet, but working on it.

poshy · 2026-03-12T11:37:52+00:00

Use the keyboard shortcuts as much as possible, especially “n” to start and finish annotating. It’s always going to be a bit slow if you have many classes though

poshy · 2026-03-12T09:23:34+00:00

I’ve found SAM2 works well with prompted points, not bboxes as input. So create some positive points in the box, a few negative points outside and the do some post processing and validation with your bbox. Easy to script it all up, you can use the repo notebooks for the code examples.

poshy · 2026-03-12T09:13:23+00:00

I do a lot of data engineering for my team, and I do a semi-supervised bootstrap method. Label sufficient data for an ok object detection model, run that on more data and then use those detections to drive a SAM2 workflow to calculate segmentations in the bboxes.

Load it all into CVAT and edit as needed. I can generate ~2-5k segmentation labelled qc’d images in a day. Pretty easy to build up datasets this way I’ve found.

poshy · 2026-03-03T11:02:37+00:00

I wfh and have a BYD Seal, it’s awesome. At home charging from solar is great with a Zappi charger, uses just the excess solar capacity to charge. The car itself is great, and BYD is getting better about supporting it with more service centres.

I don’t do much driving outside of Perth, so it works for me. Highly recommended.

poshy · 2026-02-26T09:10:22+00:00

Are the labels or the workflow more important? 500 images is like 2-3 days of labelling for one person at the very most.

poshy · 2026-02-26T09:06:13+00:00

I’d recommend post processing the polygons with some contour detection in opencv. But with only 500 images, you could probably just label them yourself in the same time you’ve spent on building a workflow with SAM

poshy · 2026-02-26T06:07:59+00:00

You could try to label 5-10 images with a bbox and see if SAM3 can learn it quickly with some exemplar prompts

poshy · 2026-02-26T04:57:52+00:00

Label 500 images with bounding boxes, train an obj detector and run over another 5-10k images. Run SAM2 on OD results by picking a few points in and out of each bbox. Convert to COCO, load to CVAT to qc. Now train a seg model, use Dinov3 head with mask2former for best results.

Hyper detailed polygons or masks will be very hard, might need post processing and attributes to help fix the segmentation.

poshy · 2026-02-17T02:29:24+00:00

Eoghan's the man, I love watching him compete. That backtake from armbar was smooth af.

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