Discourse – Wikipedia for Sensitive Topics

Hacker News

We just launched our open beta of Discourse and would love feedback. We’re a group of professionals from across technology & journalism who all have a shared experience working with sensitive topics and misinformation online. We built Discourse as a way to fill a gap we continued to see in our day jobs: a trustworthy and simple way to understand challenging topics. Discourse is unique in that it (a) combines multiple viewpoints into one place and (b) allows for Wiki-style crowd suggestions. While other sources try to be 'fair and balanced' off the bat, our belief is that it takes time, feedback, and iteration to get there - and the team has built the site around this. We’d love your feedback about how we’re presenting and summarizing the full picture to present a more balanced view of complex topics like the Future of AI, the Israeli-Palestinian Conflict, Climate Change, Defunding the Police, and more. Is the formatting easy to follow? Is the wiki component clear? Are opposing viewpoints being presented fairly? We want to hear it all. For more on Discourse check out indiscourse.com/about – thanks for your feedback!

Visit product View on Hacker NewsOct 30, 2023

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

76%76% 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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, 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

36%36% 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: way · Missing: mobile apps, ios, personal

35%35% 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

14%14% 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.

Incorrect prediction on native model

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