CQ

CQ2 – a tool for thoughtful and coherent discussions

Hacker News

CQ2 – a tool for thoughtful and coherent discussions

Hi HN! I'm Anand, and I’m launching CQ2 ( https://cq2.co ) – a tool for thoughtful and coherent discussions. Discussions using existing written communication tools often turn into a tangle of sub-discussions around quotes, making it hard to follow. There's no clear view of all the comments in a specific sub-discussion and how the sub-discussion is related to its parent, forcing constant scrolling and hopping between comments to gather context for a meaningful response. On top of all that, it's tough to tell if any key points were overlooked. Meetings are hit-or-miss. When it comes to complex and lengthy discussions, they often go nowhere. Instead of thoughtful responses, you often get knee-jerk reactions. Many meetings benefit from taking a break to analyse deeper or reflect further, but there is never enough time. CQ2 is a better way to discuss: It allows creating n-level threads around specific quotes (as well as whole comments). No mess of threads and no more copy-pasting quotes! One can see all the comments and parent threads of a specific thread in the same view. It's easier to tell if any key points were overlooked because it let's you clearly see which parts of the discussion became sub-discussions and which didn't. And, it’s open source! We are currently under heavy development, and if our passion for thoughtful and coherent discussions resonates with you, we would love to learn about your team, your frustrations with discussions, and shape the future of CQ2, together. You can try the demo and sign up for early access from our site ( https://cq2.co ).

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Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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Product HuntOn track for Day 1 leaderboard · Strong signals: context, using, open · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
32%32% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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.

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