Co

Cohesion – Evidence-Based Idea Prioritization for Teams and Individuals

Hacker News

Cohesion – Evidence-Based Idea Prioritization for Teams and Individuals

Hi HN, I'm excited to share a project I've been working on with a few mates: Cohesion, an idea prioritization tool designed to help teams and individuals make better strategic decisions. The idea came from our own experiences working in teams where creativity needs alignment with long-term thinking. We wanted a way to bridge the gap between high-level strategies and everyday decisions, keeping everyone aligned while staying adaptable. It's based on ideas from evidence-based product management but baked into a tangible tool. With Cohesion, you can: - Continuously evaluate ideas against strategy. - Add evidence (our team version can do this from integrations with some vector-db help) - Re-evaluate in light of new information We’d love for you to try it out, share feedback, and let us know if it’s helpful (or not). You can check it out here: https://cohesiontech.io As an example, I've personally used it during my PhD to help identify promising research avenues—the whole process has helped me avoid time-sinks more than once. Thanks for taking the time to read this and try it out. Your input would mean the world to us!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
93%93% 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: new · Missing: mac, agents, macos
73%73% 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: excited, lua, ide · Missing: https docs, just released, exist
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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
23%23% 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
12%12% 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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