An Open Source Implementation of Deep Research Using Gemini Flash 2.0

Hacker News

Sup HN, been seeing a lot of open source implementation of deep research but i haven't seen anything that uses Python (or maybe my x feed is just a bunch of TS users) so here's my own implementation using Python, and Gemini Flash! It has three modes: - Fast (1-3min): Quick surface research with 3 parallel queries - Balanced (3-6min): Moderate depth with 7 parallel queries - Comprehensive (5-12min): Deep recursive research that builds query trees and explores counter-arguments This also prints out the research structure as it works, so you can see exactly how it's approaching your topic. All open source.

Visit product View on Hacker NewsFeb 24, 2025

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

95%95% 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: para, gemini · Missing: supports, reddit linkedin, podcasting

51%51% predicted probability of success on Indie Hackers, 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

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

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

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

18%18% 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

11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

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