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Products that do what I built an AI-Powered semantic search for Mac does
After getting frustrated with macOS's Spotlight search, e.g., typing "driver license" doesn't give me anything unless the file name matches exactly, I thought, why not index my entire Documents folder? This way, I can find that one PDF or image buried deep in subfolders using natural language queries. So I built SmartSearch; it uses SentenceTransformers for embeddings and FAISS for fast similarity search. Best of all, it runs locally on your computer. Github: https://github.com/neberej/smart-search/ Demo:…
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As a grad student (and an ADHDer), I had trouble doing literature review systematically. To combat this, I made a website that finds similar papers using the meaning of the thing I am looking for. I used MixedBread's [^1] embedding model to generate vectors from the abstracts. I store and search similar vectors using Milvus [^2] and finally use Gradio [^3] to serve the frontend. I update the vector database weekly by pulling the metadata dataset from Kaggle [^4]. To speed up the search process on my free oracle instance, I binarise the embeddings and use Hamming distance as a metric. I would…
2024 · papermatch.mitanshu.tech
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Hi HN, I built an open-source AI agent that has already indexed and can search the entire Epstein files, roughly 100M words of publicly released documents. The goal was simple: make a large, messy corpus of PDFs and text files immediately searchable in a precise way, without relying on keyword search or bloated prompts. What it does: - The full dataset is already indexed - You can ask natural language questions - Answers are grounded and include direct references to source documents - Supports both exact text lookup and semantic search Discussion around these files is often fragmented. This…
Jan 2026 · epstein.trynia.ai
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Sep 2025 · github.com
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Hi HN! I'm Jonathan and I built Hacker Search (https://hackersearch.net), a semantic search engine for Hacker News. Type a keyword or a description of what you're interested in, and you'll get top links from HN surfaced to you along with brief summaries. Unlike HN's otherwise very valuable search feature, Hacker Search doesn't require you to get your keywords exactly right. That's achieved by leveraging OpenAI's latest embedding models alongside more traditional indexes extracted from the scraped and cleaned up contents of the links. I think there are many more interesting things…
2024 · hackersearch.net
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2020 · lacona.app
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I wrote this tool to get familiar with CLIP model, I know many people have written similar tools with CLIP before, but I'm new to machine learning and writing a classic tool helps my study. The unusual thing with my version is, it is in pure Node.js, with the power of node-mlx, a Node.js machine learning framework. The repo in the link is mostly about implementing indexing and CLI, the code of the model implementation lives as a Node.js module: https://github.com/frost-beta/clip . Hope this helps other learners!
2024 · github.com
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Dear HN Community, I am a long time fan and first-time contributor. I just launched a developer focused semantic search platform and wanted to share it with the community. The idea is simple: upload structured or unstructured documents, select the fields you want to index and tag as metadata, and instantly get a clean search API you can use in your own app. Here is what it currently supports: - Manage your own tenants and projects - Upload .json and .txt files (support for .pdf, .docx, .xlsx, .yml, etc. coming soon) - Expose 3 APIs: search, upload document (embeddings), and delete document -…
2025 · aisearch.vpuna.com
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Would appreciate a star (and happy for ideas on improving indexing speed/embedding quality)!
May 2026 · github.com
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I was experimenting with building a local dataset generator with deep research workflow a while back and that got me thinking. what if the same workflow could run on my own files instead of the internet. being able to query pdfs, docs or notes and get back a structured report sounded useful. so I made a small terminal tool that does exactly that. I point it to local files like pdf, docx, txt or jpg. it extracts the text, splits it into chunks, runs semantic search, builds a structure from my query, and then writes out a markdown report section by section. it feels like having a lightweight…
2025 · github.com
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ImageSage finds the image you remember.

It is a private, local AI gallery. Point it at the folders you choose and it reads them in place, building an index that stays on your PC. That gives you two things Windows file search cannot: search that understands what is inside your images, and AI
20d ago · apps.microsoft.com
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Introducing Nia Vault, a CLI that lets you query your local markdown/text files using natural language. What it does: Semantic search over local folders and notes Works across multiple synced directories RAG-style answers with citations from your own files How it works: Calls `POST /search/query` with `local_folders` Uses `search_mode: sources` to return answers + file references Example: vault ask "What are my notes about project planning?" OSS: https://github.com/chenxin-yan/nia-vault
Feb 2026 · github.com
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Hey HN! I've build a simple tool to index and search your documents. This uses two great open source libraries: apache tika (for extracting content from docs) and apache lucene (for searching). It's been built with kotlin ktor as a web framework. You can index all kind of files (i.e doc, docx, xls, ppt, pdf, txt, html even ORC pdfs) and then search them using very advanced queries like "always contain X", "never contain X", "X near Y", wildcard search, proper stemming support etc. We're using it on my work where we have hundreds of thousands of doc/docx/pdf files and it works…
2024 · github.com
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Turn web search results into AI-organized folders
Jul 2026 · onesearch-download.adultdongsoo0516.chatgpt.site
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