highly sophisticated technology that exchanges information with everyone around the world.

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I am Imsuparmin, this time I will discuss about a very sophisticated technology that allows you all in searching for the latest news and information that certainly useful for you, it is only in
you are curious.? Let’s look at each other. is a highly sophisticated project that serves to unite users and other professionals to give them all the knowledge they know in their respective areas of expertise, and will get tokens as rewards, which can be taken in real life as well. And all of that is supported in a transparency that can be seen by everyone and the sophistication of blockchain technology. This serves as a basis for integrating and integrating educators and entrepreneurs who can exchange values ​​and be given incentives for the tasks they undertake. It not only stops by giving incentives, but also tracks learning and records knowledge in its core system. uses Blockchain technology for two main activities — to increase the value of the knowledge token, and to store valuable information to advertisers, educators, etc. with the highest level of transparency.This Knowledge is the Score of knowledge form the core of the platform. This knowledge score helps determine the user’s knowledge by tracking and measuring user knowledge about a large set of topics. The platform also helps measure the level of user interest based on the number of correct answers and the wrong answers provided by them. The Knowledge Score consists of the Knowledge Line, Interest Line, Review Line. This line is designed to basically determine the depth and breadth of one’s knowledge, or the level of one’s interest in a particular topic or just how one’s actions are perceived by others.
They also build something called a funnel of knowledge that ideally helps to classify and segregate users based on the scores of knowledge they gain. Knowledge score technology will help users track their own level of knowledge.


When our team consisted of just a handful of people, it was easy to share and discover research findings and techniques. But as our team has grown, issues that were once minor have become more significant. Take the case of Jennifer, a new data scientist looking to expand on work produced by a colleague on the topic of host rejections. Here’s what we’d see happening:

  1. Jennifer asks other people on the team for any previous work, and is sent a mixed bag of presentations, emails, and Google Docs.
  2. The previous work doesn’t have the up-to-date code. Jennifer tracks down the local copy on the original author’s machine or an outdated GitHub link.
  3. After fiddling with the code, Jennifer realizes it’s slightly different from what made the previous plots. Jennifer decides to either adapt the deviated code or start from scratch.
  4. After spending time reproducing the results, or giving up and starting from scratch, she does her work.
  5. Jennifer distributes the results through a presentation, email, or Google Doc, perpetuating the cycle.

Based on conversations with other companies, this experience is all too common. As an organization grows, the cost of transmitting knowledge across teams and across time increases. An inefficient and anarchic research environment raises this cost, slowing down analysis and the speed of decision making. Thus, a more streamlined solution can expedite the rate at which decisions are made and keep the company nimble atop a growing base of knowledge.


  • Reproducibility — There should be no opportunity for code forks. The entire set of queries, transforms, visualizations, and write-up should be contained in each contribution and be up to date with the results.
  • Quality — No piece of research should be shared without being reviewed for correctness and precision.
  • Consumability — The results should be understandable to readers besides the author. Aesthetics should be consistent and on brand across research.
  • Discoverability — Anyone should be able to find, navigate, and stay up to date on the existing set of work on a topic.
  • Learning — In line with reproducibility, other researchers should be able to expand their abilities with tools and techniques from others’ work.

With these tenets in mind, we surveyed the existing set of tools that had solved these problems in isolation. We noticed that R Markdowns and iPython notebooks solved the issue of reproducibility by marrying code and results. Github provided a framework for a review process, but wasn’t well adapted to content outside of code and writing, such as images. Discoverability was usually based on folder organization, but other sites such as Quora were structuring many-to-one topic inheritance with tags. Learning was based on whatever code had been committed online, or via personal relationships.

Together, we combined these ideas into one system. Our solution combines a process around contributing and reviewing work, with a tool to present and distribute it. Internally, we call it the Knowledge Repo.

At the core there is a Git repository, to which we commit our work. Posts are written in Jupyter notebooks, Rmarkdown files, or in plain Markdown, but all files (including query files and other scripts) are committed. Every file starts with a small amount of structured meta-data, including author(s), tags, and a TLDR. A Python script validates the content and transforms the post into plain text with Markdown syntax. We use GitHub’s pull request system for the review process. Finally, there is a Flask web-app that renders the Repo’s contents as an internal blog, organized by time, topic, or contents.

On top of these tools, we have a process focused on making sure all research is high quality and consumable. Unlike engineering code, low quality research doesn’t create metric drops or crash reports. Instead, low quality research manifests as an environment of knowledge cacophony, where teams only read and trust research that they themselves created.

To prevent this from happening, our process combines the code review of engineering with the peer review of academia, wrapped in tools to make it all go at startup speed. As in code reviews, we check for code correctness and best practices and tools. As in peer reviews, we check for methodological improvements, connections with preexisting work, and precision in expository claims. We typically don’t aim for a research post to cover every corner of investigation, but instead prefer quick iterations that are correct and transparent about their limitations. Our tooling includes internal R and Python libraries to maintain on-brand, aesthetic consistency, functions to integrate with our data warehouse, and file processing to fit R and Python notebook files to GitHub pull requests.

Figure 1 — A screenshot of the knowledge feed showing the summary cards for two posts.
Figure 2 — An example of a post examining gap days in host acceptance decision making.

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