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Cohesion – Identifies Repetitive Workflows and Helps You Automate Them

Hacker News

Cohesion – Identifies Repetitive Workflows and Helps You Automate Them

Hi HN, We are building Cohesion, a chrome extension that helps you find repetitive workflows and automate them. We learned that customers often don’t know what can be automated because they are not aware of potential solutions. Additionally, they have a hard time identifying the best automation opportunities across teams because they lack data. Our mvp is a workflow capture system that highlights the most repetitive actions. We plan to release features that help users reduce friction with Copilot, Zapier, and SaaS tools. We are careful about using AI agents because they are unreliable at the moment. Copilot: Predicts what the user will do and asks the user if they want to automate the next steps. Zapier: Automatically generates Zapier templates for the user. SaaS: Recommends a SaaS tool to help the user modify their workflow. Our goal is to build reliable and easy to maintain automations for non-technical users. Please check out the app and let us know what you think. Please mind, this iteration only captures and highlights the most repeated workflows: https://tinyurl.com/3ey8ud94 If you have a few minutes, we would really appreciate your thoughts and feedback on this form: https://forms.gle/UMuRaBiFndNg4mB58 If there are any particular actions that you would like to automate or want to reach out about anything, feel free to send me an email or book a meeting. Email: leon@buildcohesion.com Calendly: https://calendly.com/leon_cohesion/30min Note: The app sends event data to azure to generate titles. You can turn it off on the analytics page.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, user · Missing: mac, macos, cursor
89%89% 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
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
48%48% 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
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
16%16% 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.

Correct prediction on native model

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