How to build your own knowledge engine? Community open application

Feynman method of learning ·  Metacosmic trend · The knowledge base · Knowledge map · Recommendation engine  

shadow ：

from 2018 From the year onwards , I've been thinking about knowledge engines . How to realize a suitable knowledge engine ？ lately , I learned a lot about the consumption scenarios of digital content , In my submission Knowledge engine should be integrated into the consumption scenario of digital content . Only in this way can we have output , somewhat Learn2earn The feeling of .

Metacosmic trend

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Meta Digital clothing store

The little red book R-space 

Virtual fashion

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Super QQ Show

Digital human image

Virtual space

The digital content industry

Super QQ Show

Plug the unreal engine QQ in , Users can pinch their faces 、 change 、 Make the same , At the same time, it can also have its own virtual space , Decorate your virtual space by buying and selling various virtual goods .

QQ channel

A new way of entertainment cooperation , The user can go through QQ Channel found from different places 、 Engage in different occupations , But they have common hobbies , Chat on the common channel 、 live broadcast 、 A literary creation ……

About  Learn2earn , Much like Feynman learning method ：

Feynman method of learning ：

In a scene that needs to be taught to others Teach - Replace learning with teaching . In a way that others can understand and in the simplest language , Explain this knowledge to people who are not familiar with it .

how Put Feynman learning method , Become a software system ？

In the process of building a knowledge engine , I realize that data is the core , At first, it was in a way similar to reptiles , Extensive indexing of all kinds of information , But it's too broad , The knowledge engine built in this way , Not very easy to use . therefore , I also built a knowledge base .

Dynamic update , I organize my knowledge whenever I have time , In the play label , adopt Label arrangement 、 Update knowledge , Accumulated a lot of labels . Content classified by label , covers ： The product application 、 file 、 Research Report 、 trend 、 The paper 、 Case study 、 Open source code 、 Speech 、PPT Documents and other materials .

Meta universe · Interdisciplinary knowledge base ：

Has accumulated 1913 Data for , We need to go to the next stage . The first step in upgrading ： The label system is organized and open source , Display in the form of atlas , Further help users expand their knowledge points .

shadow ：

stay 2021/9/24 I recorded a thought ： Go to traditional experts 、 Leading enterprises 、 Head strength , Look for opportunities in emerging fields . Subvert the business model of traditional media , Provide a new social media experience , Not because of where the needs are to be solved , But what kind of social media experience it can provide , Users to experience , To create value .

Digitization , Content integration based on knowledge engine . fragmentation , Automatically defragment the content generated by chatting and socializing . Programmable economy ？ Exchange what value ？ Consolidate workflow .

ML686 ：

These have just been manually modified recently APP Recommended weight . I feel like this recommended algorithm , Used for a long time, the content is very homogeneous , Excessive involution , Not at all 「 new blood 」 Get into , I manually ordered all the content I used to watch 「 Content not recommended / Don't like 」, After disrupting these weights again , New content came in About knowledge engine , It's just that 「 I subscribe to 」, You can also add a little 「 Areas I didn't focus on 」,「 Non conventional field 」 And so on. , Sometimes cross domain knowledge , It will be very useful at some time in the future .

shadow ：

Good suggestion ！ This classification is also quite clear , It needs to be communicated to users .

opus ：

Welcome to join # Knowledge engine research group , Explore knowledge base products suitable for you .

remarks ： Knowledge engine

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