MiniSearch, a minimalist search engine with integrated browser-based AI

Hacker News

Hey everyone! I’m excited to announce the release of my last project, MiniSearch. I admire Perplexity.ai, Phind.com, You.com, Bing, Bard and all these search engines integrated with AI chatbots. And as a curious developer, I took the chance and created my own version. Using Web-LLM and Transformers.js to provide browser-based text-generation models on desktop and mobile, I built a minimalist self-hosted search app on which an AI analyses the results, comments on them and responds to your query summarising the info. In the backend, it still queries a real search engine, but besides that, there's no other remote connection happening. For running in the browser and on mobiles, lightweight models are required, so we can't expect them to give stellar answers, but there are a few advantages of using this over the services as mentioned earlier: - Availability: The AI will always be available and respond with the maximum available speed from the device. - Privacy: Besides the queries that go anonymously to the actual search engine, nothing else leaves your device. - No ads/trackers: Get the relevant links clean and fast without being tracked. - Customization: As it's open-source, you can fork it and re-style it any way you want. You can get started with MiniSearch by cloning the repository from GitHub ( https://github.com/felladrin/MiniSearch ) and running it locally or by using it online on this HugginFace Space: https://felladrin-minisearch.hf.space (Alternative Space address: https://huggingface.co/spaces/Felladrin/MiniSearch ) You can even set it as your browser's address-bar search engine using the query pattern ` https://felladrin-minisearch.hf.space/?q=%s ` (where your query replaces %s). At the moment of this writing, the app is using TinyLlama and LaMini-Flan-T5 models, but there's an option to try to use larger models like Mistral 7B (not recommended, though, as it could be slow and break the fast-search experience). That's what I had to share. Thanks for reading! Your feedback means the world to me! Please don't hesitate to reach out if you have any questions or suggestions or want to learn more.

Visit product View on Hacker NewsOct 15, 2023

Share card

Actual performance

17points

6comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, perplexity · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, llama, ide · Missing: https docs, just released, exist

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: answers, way · Missing: mobile apps, ios, personal

28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Wiby – A Minimalist’s Search Engine64%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Wiby – A Minimalist’s Search Engine

Hacker News59

SQL integrated with a distributed transactional search engine.64%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

SQL integrated with a distributed transactional search engine.

Hacker News2

AskHadith – A Hadith search engine based on Mongo text search67%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AskHadith – A Hadith search engine based on Mongo text search

Hacker News8

Minimalist, text only search engine with prefixes to play with63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Minimalist, text only search engine with prefixes to play with

Hacker News5

Whimso – preference based place search engine60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Whimso – preference based place search engine

Hacker News5

Femme Advisors39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Browser-based AI voice sessions with fictional AI advisors

Indie Hackers1ai

A reactive search engine45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A reactive search engine

Hacker News10

Vineyard - A search engine for Vine60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Vineyard - A search engine for Vine

Hacker News3

Sentiment Search Engine65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Sentiment Search Engine

Hacker News12

We made a search engine60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

We made a search engine

Hacker News3

Browse all products Analyze your launch