troysk

Other folks were correct and I was wrong! It has come out as GLM 5.3 Flash.

It is probably from Google and is probably hosted on Vertex AI. Opencode announced that responses from Ox Alpha should be better and soon posted about Vertex eu and us multi region update in their changelog. I could be a Gemini model or a new one based on GLM based on tokenizer. Also the amount of inference it is providing for free is something only google can support with its TPUs. So maybe a GLM based model running on TPUs.

don't get the $5 ones, they are probably just presence detector or at best distance detector and probably work on 24Ghz. Get the 60Ghz for breathing, heart rate, posture etc.

Unfortunately no. But fortunately, it isn't something new I have built. I use a Seed Studio sensor MR60BHA2 which has an ESP32 which sends the data to Home Assistant through ESPHome. Once it is in Home Assistant you can do automations and notifications. I mostly use it for elderly care. I have another DFRobot C1001 sensor but waiting for ESPHome to add support for it. It has fall detection also which is why I am planning to replace it. They are fairly accurate; ~90%; has been my experience. They work better when mounted on ceiling and have a cone like coverage.

I do it using MMWave sensor, 60Ghz one. Want to have more of them but installation is a pain as these need to be mounted on ceiling so WiFi based sensor would be awesome!

+1! Most LLMs can already output Mathpix markdown. I prompt it to do so and it gives the code and this use a rendering library to show the scalable and selectable equations. No wonder facebook nougat also uses it. Good stuff!

both openai and claude vision models are able do that for me. It is more expensive than tesseract which can run on cpu but I assume it will become similarly cheap in the near future with open models and as AI becomes ubiquitous.

Segmenting at lower resolution and then using them at higher resolution using resolution multipliers don't work as other items bleed in. FastSAM paper has some interesting ideas on doing this with CNNs which I guess SAM2 have superseded. However, the complication in the pipeline is not worth the result as I find vision LLMs are able to do almost the same task within the same OCR prompt.

How to segment the document without LLM?

I prefer to do all of this in 1 step with an LLM with a good prompt and few shots.

With so many passes with images, the costs/time will be high with ViT being slower.