> As of my last update in January 2022, there is no public information or evidence to suggest that Sam Bankman-Fried is a felon or fraudster. He is primarily known for his roles in the cryptocurrency industry, particularly as the CEO of Alameda Research and the co-founder and CEO of FTX Exchange.
Is FTX bankrupt?
> As of my last update in January 2022, FTX, the cryptocurrency exchange co-founded by Sam Bankman-Fried, was not known to be bankrupt. In fact, FTX had been experiencing significant growth and had secured notable partnerships and sponsorships in various sectors.
dolphin-2.1-mistral-7b.Q6_K running with llama.cpp
Is FTX bankrupt?
""Yes, FTX filed for bankruptcy on November 11th, 2022 after experiencing massive financial difficulties.""
Is Sam Bankman-Fried a felon and fraudster?
""Sam Bankman-Fried was charged with multiple counts and pled guilty to most of them. He's currently awaiting sentencing, so he's technically not yet considered a convicted felon or fraudster.""
This is a port of Meta's Segment Anything computer vision model which allows easy segmentation of shapes in images. Originally written in Python, Yavor Ivanov has ported it to C++ using the GGML library created by Georgi Gerganov which is optimized for CPU instead of GPU, specifically Apple Silicon M1/M2. The repo is still in it's early stage
Do you know how the time to do the image embedding takes? In SAM, most of the time is spent generating a very expensive embedding (prohibitive for real-time object detection). From the timing on your page it looks like yours is also similarly slow, but I'm curious how it compares to the pytorch Meta implementation.
Depends on the machine, number of threads selected and the model checkpoint used (Vit-B or Vit-L or Vit-B). The video demo attached is running on Apple M2 Ultra and using the Vit-B model. The generation of the image embedding takes ~1.9s there and all the subsequent mask segmentations take ~45ms.
However, I am now focusing on improving the inference speed by making better use of ggml and trying out quantization. Once I make some progress in this direction I will compare to other SAM alternatives and benchmark more thoroughly.
it's fascinates me how social platforms are never able to control their own narrative, most YouTuber's hate YouTube, most Redditors complain about Reddit. the volume of negative sentiment must be enormous for them not to have configured their recommendation algorithms to suppress content that is anti-them
There's just no need. Negative sentiment doesn't hurt the balance sheet, and users will be back to get their fix tomorrow, every time. Youtube keeps making ads more intrusive and people keep watching. What people say they care about, and what they are actually willing to take action to change, are extremely disconnected.
