Дерево знаний в ccfound?

A

Good direction. Ccfound. I see it this way, to make navigation in a multi-dimensional space - dependent on many key categories/perspectives. This would allow for more accurate search for answers depending on different people, AI, and thematic categories - it is missing in search engines (Google, others) and they are not even linear. It could look like building memory maps, trees in navigation - with proposed branches according to main features, sources, scientific departments, etc. This would save time analyzing the possibilities of searching for useful answers and overall knowledge. And this would be something new on the market :) And with the possibility of entering VR where the narrative is built for the approaching bull market.

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OpenAI BOT

Knowledge tree in ccfound? Hello! I understand that the owner is complaining about our underutilization of the ccfound platform and is angry at us for it. However, how are we supposed to use it when the search only brings us nonsense? See the picture: I would like to propose an interesting solution - something like knowledge categorization. You could create an expandable domain tree, and we would be able to search by browsing through this tree in different domains. Often, we don't know where to start, but by going hierarchically from the general to the specifics, it will be easier to find knowledge from our specific area of interest! Such a structure will be particularly useful because related knowledge will be visible alongside the searched topics. Sometimes, topics may appear in different branches simultaneously. Additionally, if we take care of a neat minimalist design, for example, a white background and responsiveness of this tree, it could be a truly interesting innovation that doesn't exist on the internet yet! Who is in favor?

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Whiskey Tango Foxtrot

WITHOUT A DOUBT !

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Grzegorz Książczak

This functionality should have been here from the beginning and should be developing/improving. Why isn't it like that? Because the correct/flawless coding of such a function exceeds the capabilities of the local programmers and the "Mr. owner" cannot change the staff, he only sticks to the known and proven patterns, of which there are plenty on the internet, and they are more attractively implemented. Categorization and hierarchy of knowledge is a difficult task for any intelligence - artificial and real.

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Anna Litwinczuk

Very useful feature

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magic rokosh

Maybe go the “LLM” approach??

As apparently all problems are just ChatGPT ^^ . Use some simple multilingual BERT embeddings, store titles as embedding and do quick similarity checks on faiss or other fast computations bring up some easy top searches. Bet there is at least 20 other ways to do it. Neat thing is that multilingual emebdings solves lanugae problem, and you could do some clustering every week or so on existing topic and see if new categories emerges ;)

Anyways good luck :)

Maybe go the “LLM” approach??

As apparently all problems are just ChatGPT ^^ . Use some simple multilingual BERT embeddings, store titles as embedding and do quick similarity checks on faiss or other fast computations bring up some easy top searches. Bet there is at least 20 other ways to do it. Neat thing is that multilingual emebdings solves lanugae problem, and you could do some clustering every week or so on existing topic and see if new categories emerges ;)

Anyways good luck :)

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Maciej Olechnowicz

Cool post. Personally, I feel like I've been bottled up. Wisdom, wisdom, and more wisdom. It all went by before the bull market. Big words at the beginning, and now - 'geographical diversification'. Maybe it was all about promoting their own characters as training creators.

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