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New top story on Hacker News: Show HN: Khoj – Chat Offline with Your Second Brain Using Llama 2

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Show HN: Khoj – Chat Offline with Your Second Brain Using Llama 2
57 by 110 | 7 comments on Hacker News.
Hi folks, we're Debanjum and Saba. We created Khoj as a hobby project 2+ years ago because: 1. Search on the desktop sucked; we just had keyword search on the desktop vs google for the internet 2. Natural language search models had become good and easy to run on consumer hardware by this point Once we made Khoj search incremental, I completely stopped using the default incremental search (C-s) in Emacs. Since then Khoj has grown to support more content types, deeper integrations and chat (using ChatGPT). With Llama 2 released last week, Chat models are finally good and easy enough to use on consumer hardware for the chat with docs scenario. # Overview Khoj is a desktop application to search and chat with your personal notes, documents and images It is accessible from within Emacs, Obsidian or your Web browser It works with org-mode, markdown, pdf, jpeg files and notion, github repositories It is open-source and can work without internet access (e.g on a plane) # Chat Extract answers and create content from your existing knowledge base Online or Offline: Chat without internet using Llama 2 or with internet using GPT3.5+ depending on your requirements Example: "What was that book Trillian mentioned at Zaphod's birthday last week" We personally use the chat feature regularly to find links, names and addresses (especially on mobile) and collate content across multiple, messy notes # Search Quickly find relevant notes, documents or images using natural language Does not use internet Example: Search for "bought flowers at grocery store" will find notes about "roses at wholefoods" # Quickstart pip install khoj-assistant && khoj See https://ift.tt/kiq03Mx for detailed instructions We also have desktop apps (in beta) at https://ift.tt/GkSnjIJ if you want to try them out --- Please do try out Khoj and let us know if it works for your use-cases? Looking forward to the feedback!

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New top story on Hacker News: Ask HN: What boosted your confidence as a new programmer?

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Ask HN: What boosted your confidence as a new programmer?
28 by optbuild | 32 comments on Hacker News.
When anyone starts out in a new craft, even after grasping the fundamentals of the tools they are quite shaky and low on confidence until they have a significant experience in doing something that ultimately ramps up their confidence in their craftsmanship. Similar things happen with novice programmers when they start out. Then they read a beautiful codebase which they can fully understand and replicate, or build a project from scratch, or read a book or take a class on a subject. And their confidence is tremendously boosted thereafter. What was it for you? How did you gain this confidence to take the first step from being a tinkerer to being a skilled craftman?

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Tuesday, July 4, 2023

Monday, July 3, 2023

New top story on Hacker News: Show HN: JobLens AI-powered job search for 'Who Is Hiring'

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Show HN: JobLens – AI-powered job search for 'Who Is Hiring'
6 by hubraumhugo | 0 comments on Hacker News.
There are existing HN job aggregators, but I thought we could take it a step further. Inspired by an insightful comment on a previous thread ( https://ift.tt/aE5zdt3 ), I built a tool that aggregates job postings and intelligently categorizes them based on user-specific preferences: * Country and remote work preferences * Employer type (e.g., startup, corporation, government) * Industry * Technologies used * Role type (developer, architect, product owner, etc.) * Salary range (where available) One of the superpowers of LLMs is reformatting information from any format X to any other format Y. We leverage this to map all the unstructured job postings into the same unified structure. The new GPT functions feature and the extended context windows are really helpful for this. Instead of having to build a custom NER pipeline, it works very well with GPT out-of-the box. One challenge is keeping the filters consistent and merging of duplicates. Embeddings help with that. What's next: * Integrate additional sources. We can generate web scrapers and data processing steps on the fly that extract and transform data into the same structure. * Add location distance filters. * Expand beyond jobs to monitor personalized data like events or real estate. Imagine using AI to rate local events from multiple sources based on your preferences, considering factors like your interests and distance from home. * Smaller improvements based on your feedback :)

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New top story on Hacker News: Ask HN: Who wants to be hired? (July 2023)

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Ask HN: Who wants to be hired? (July 2023)
32 by whoishiring | 122 comments on Hacker News.
Share your information if you are looking for work. Please use this format: Location: Remote: Willing to relocate: Technologies: Résumé/CV: Email: Readers: please only email these addresses to discuss work opportunities.

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