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Hey there HN! We're Esteban and Esteban and we are looking to get feedback for the new version of our GPT-powered, open-source code contextualizer. We're starting with a VS Code extension that indexes information from git (GitHub, GitLab, or Bitbucket integrations available), Slack and Jira to explain the context around a file or block of code. Finally, we summarize such aggregated context using the power of GPT. As devs we know that it's very annoying to look at a new codebase and start understanding all the nuances, particularly when the person who wrote the code already left the company. With this problem in mind, we decided to build this solution. You'll be able to get into "the ghost" of the person who left the company. Soon, we will also be building a GitHub Action that does the same thing as the VS Code extension but at the time of creating a PR: Index the most relevant information related to this new PR, and add it as a comment. This way we will pro

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Hello, I was running around Germany, hectically navigating public transportation, and getting lost all the time. I noticed that every station had i platforms, each used lists of n buses (trains, whatever) arriving, each has their list of m destinations. That means I would be scanning i x n x m items just to see if I was at the correct stop. As I was nervous, for every bus that arrived, I would rescan the list of stops to double check. I began thinking how I could make a better system. Linked is a very shoddy mockup of how bloom filters could be used to allow passengers O(1) lookup time for which platform+bus is the correct one. I believe it's likely for public transportation to grow increasingly more complex in the future, as population grows, and under the current list-based system, this will make the signage ever more complex. I think some bloom filter mechanism could reduce that complexity. So, here is my fantasy, my day dream. What do you think? Comments URL: https://news.y

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