Alternatives
Products that do what n8n Node Explorer does
Discover the right n8n community node faster
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2024 · blog.wilsonl.in
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Hey HN! I just published https://meet.hn, a map to find hackers in your city. How it works? Demo of the signup process: https://x.com/meet_hn/status/1834918518904746329 1. Fill the form: username, city+country 2. Copy the text generated in the box below the form, and paste it in your HN description. 3. Click "Add me on the map" Optionnaly (it's recommended!) you can add links to your socials as well as some tags to showcase your interests. Why does it exist? I created this because, despite its harsh reputation on the internet, I love the HN community. I…
2024
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Hi HN! Thanks for your attention to my post. It was a big challenge to run most of Node.js packages in browser, fast moreover. Virtual File system, resolve import/export. I got cold many times, depressions, burned out, yet still alive and finished it. Many guys helped me with an advice. Many users give a lot of positive feedback. There are 200,000 monthly unique users. I work full time now because of the freemium business model. To be honest - I am happy after many years of hard work.
2022 · playcode.io
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We (Nick, Dens, Denzell, Fede, Drew, Aaryan, and Daniel) have been building HN Discovery, a discovery-focused search engine for Hacker News, in our spare time for the past 6 months and are excited to show it! It adds the following features relative to the existing keyword search interface and preserves the existing ones: - no-JS version (hnnojs.trieve.ai) - site:{required_site} and site:{negated-site} filters - public analytics - LLM generated query suggestions based on random stories - recommendations - dense vector semantic search - SPLADE fulltext search - RAG AI chat - order by…
2024 · hn.trieve.ai
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Hi HN, I spent a few weeks looking at the top HN posts of all time. This included exploration, clustering, creating visualizations, and zooming in on what (to me personally) seems like some of the best discussions on here. Three things in this post: 1- The interesting groups of HN posts 2- The interactive visualizations that you can explore in your browser 3- The data from this exploration -- this includes CSV of the titles as well as the text embeddings of 3,000 Ask HN articles. Blog post about this whole process here: [1] ============ 1- The interesting groups of HN posts From the…
2022
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Hey HN! Over the past few months, I've been working on building Omni - a workplace search and chat platform that connects to apps like Google Drive/Gmail, Slack, Confluence, etc. Essentially an open-source alternative to Glean, fully self-hosted. I noticed that some orgs find Glean to be expensive and not very extensible. I wanted to build something that small to mid-size teams could run themselves, so I decided to build it all on Postgres (ParadeDB to be precise) and pgvector. No Elasticsearch, or dedicated vector databases. I figured Postgres is more than capable of handling the level…
Mar 2026 · github.com
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I built this tool to help me find interesting discussions on Hacker News. I love reading HN discussions almost more than the articles themselves. However, I found that full text search, although highly performant, is not always good at surfacing interesting discussions on a certain topic -- especially if you don't know what to search for exactly. I built this by scraping the most recent ~6 million posts (that's about 2 years of history) and putting the resulting posts and their vector embeddings into Postgres. Let me know what could be improved, and if you'd like a more detailed writeup of…
2024 · searchhacker.news
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2024 · app.airtrain.ai
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2024 · blog.wilsonl.in
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I made a HN client that allows you to explore comments in a breadth-first manner. Compared to the classic depth-first approach, it works much better for the larger threads - you don't lose the context and can easily skip discussions you're not interested in. Video preview: https://imgur.com/a/tzBdpXw
2025 · apps.apple.com
- 22AS
We built a search engine that shows you the most engaging stories/topics being shared across Twitter, Facebook, Linkedin, and Google+. We crawled over 15 million articles the past 3 months, retrieved the total number of Facebook likes, tweets, Google+’s etc and built a search index around it. Here's what our infrastructure looks like: Rails/Redis: We use the Sidekiq gem as a message queue. We have hundreds of workers that do the crawling, data mining, and number crunching. ElasticSearch: We built the search index using ElasticSearch, with the data imported from our Postgres…
2013 · buzzsumo.com
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2024 · github.com
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