Web Search Powered by GPT and Bing

Hacker News

We built a search engine interface on top of OpenAI GPT 3.5 and Microsoft Bing that summarizes and cites top search results in response to natural language questions. By using search results, the AI is able to reference recent news and provide citations for specific facts. Our interface offers concise answers, without having to click through links, scroll past irrelevant content, or read ads. No login is required; no personal data is collected. We believe in the power of combining the intuitive UI of web search with the intelligence of large language models. The search engine does indexing work and interpretation is made by the LLM (a la OpenAI WebGPT https://openai.com/blog/webgpt/ ). We are also working on using LLMs to reword queries before searching and to make searches more conversational. Additionally, we are building new features that allow you to curate the result of your query, e.g., by omitting references that are not relevant and expanding the list of references that are relevant. New references could be added either “breadth-first” (include more results from the original query) or “depth-first” (summarize more content from one of the pages in the search results and find related links). Curious what the community thinks, and what features come to mind when you use this interface. Twitter: https://twitter.com/perplexity_ai/status/1600551871554338816 Discord: https://t.co/R4G21AmwQ7

Visit product View on Hacker NewsDec 7, 2022

Share card

Actual performance

37points

11comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

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

91%91% 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 · Missing: supports, reddit linkedin, podcasting

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

71%71% 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: intuitive, interface · Missing: plus, platform, reviews

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Correct prediction on native model

Similar products

GPT3 Powered Web Search with Streamlit39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

GPT3 Powered Web Search with Streamlit

Hacker News2

ELI5 Powered by GPT-355%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ELI5 Powered by GPT-3

Hacker News134

I made a GPT-powered webscraper51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I made a GPT-powered webscraper

Hacker News2

GPT-4-powered web searches for developers84%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

GPT-4-powered web searches for developers

Hacker News1,401

Worldbuilding Experiments with GPT-336%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Worldbuilding Experiments with GPT-3

Hacker News2

Autosummarized HN (With GPT-3)51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Autosummarized HN (With GPT-3)

Hacker News6

I composed a sonata with GPT-3 DaVinci-003 and you can too51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I composed a sonata with GPT-3 DaVinci-003 and you can too

Hacker News3

GPT Classifies HN Titles57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

GPT Classifies HN Titles

Hacker News6

Visualized GPT57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Visualized GPT

Hacker News3

Cerebras-GPT-2.7B finetuned on Stanford Alpaca dataset65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Cerebras-GPT-2.7B finetuned on Stanford Alpaca dataset

Hacker News4

Browse all products Analyze your launch