Chord, autonomous research to curate the Internet’s favorite stuff

Hacker News

Finding “the best” camera lens, hiking trail, or Python database adapter requires digging through many search results and online discussions. You’re not going to trust the first result you see in a “top-10” marketing blog post. Often, you need to read dozens of articles and Reddit threads to establish consensus on whatever you’re looking for. This is hard work, so we have an AI do this research while you can watch in real time. The final result is a permanent article summarizing consensus views. Unlike closed chatbots, our articles are public by default so improvements submitted by our users compound with time. Our research is composed of several question decomposition + retrieval models executing in parallel, plus iterative reflection points where the model verifies that all parts of a query have been handled. We used Phoenix/LiveView to stream our research steps & the article generation. Would love to hear what you think! Some example articles: 1. Buy-it-for-life thermos: https://chord.pub/article/38472/buy-it-for-life-thermos 2. Best history podcast: https://chord.pub/article/36724/best-history-podcast Give it a try, free + no login required: https://chord.pub Feedback: hello@chord.pub Discord: https://discord.gg/dj3SuTdFEa Become an editor: https://hqpmb45s0nc.typeform.com/to/FxRKHsMA

Visit product View on Hacker NewsMay 12, 2023

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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

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

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

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% 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

19%19% 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, real time · 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

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