An Open Source Alternative to Gemini Deep Research

Hacker News

All - Open Deep Research is an open-source alternative to Gemini Deep Research, designed to generate comprehensive AI-powered reports from web search results. How it Works Search Results: We use the Bing Search API to fetch results for your search term. You can then select the ones you are interested in. Content Extraction: JinaAI processes and extracts detailed content from the retrieved web pages. Report Generation: The app then generates a synthesized, customizable report based on your prompt and selected search results using Gemini. Why Open Deep Research? Open Source: Fully transparent and modifiable for your specific needs. Flexible Workflow: Customize prompts and control search result selection for precise outputs. You can also update the model based on your preferences. Perfect for researchers, content creators, and anyone who wants quick, high-quality insights from online information. Would love your feedback!

Visit product View on Hacker NewsDec 24, 2024

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

96%96% 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: gemini · Missing: supports, reddit linkedin, podcasting

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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 · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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