Many users assume shared conversations are only seen by friends or colleagues — but when you use OpenAI’s share feature, those chats get now indexed by search engines like Google.
Meaning: your “private” AI prompts could end up very public. This is called Google dorking — and it’s shockingly effective.
Over 70,000 chats are now publicly viewable. Some are harmless. Others? They might expose sensitive strategies, internal docs, product plans, even company secrets.
OpenAI currently does not block indexing. So if you’ve ever shared something thinking it’s “just a link” — it might now be searchable by anyone. You can even build a bot to crawl and analyze these.
Welcome to the new visibility layer of AI. I can’t say I am surprised…
It lets you load any .cube LUT right in your browser, see the RGB curves, and use a split view on the Granger Test Image to compare the original vs. LUT-applied version in real time — perfect for spotting hue shifts, saturation changes, and contrast tweaks.
In color technology, color depth also known as bit depth, is either the number of bits used to indicate the color of a single pixel, OR the number of bits used for each color component of a single pixel.
When referring to a pixel, the concept can be defined as bits per pixel (bpp).
When referring to a color component, the concept can be defined as bits per component, bits per channel, bits per color (all three abbreviated bpc), and also bits per pixel component, bits per color channel or bits per sample (bps). Modern standards tend to use bits per component, but historical lower-depth systems used bits per pixel more often.
Color depth is only one aspect of color representation, expressing the precision with which the amount of each primary can be expressed; the other aspect is how broad a range of colors can be expressed (the gamut). The definition of both color precision and gamut is accomplished with a color encoding specification which assigns a digital code value to a location in a color space.
