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Colab.ing – A Tool to Capture and Organize Team Ideas for Your Company

Hacker News

Colab.ing – A Tool to Capture and Organize Team Ideas for Your Company

Hello HN community, Over the past 11 months, I've been working on a side project called colab.ing. It's a tool designed to help organizations capture, organize, and act on ideas and feedback from their team members. Problem Statement: In many companies, valuable ideas from employees often get lost in the daily workflow. Whether it's process improvements, customer insights, or innovative concepts, these ideas sometimes don't reach the right people or are forgotten amidst other priorities. Solution: https://colab.ing colab.ing aims to provide a simple and effective platform where team members can submit their ideas and feedback. These submissions are then organized and made accessible for decision-makers to review and act upon. Key Features Easy Submission: A straightforward way for employees to share their thoughts. Organized Dashboard: Ideas are categorized for easy navigation and prioritization. Feedback Ownership: Assign responsibility for implementing specific ideas within the team. Slack integration: Connect a slack workspace, and your users, and submit feedback directly from inside your Slack workspace! Dashboard: Get a analytical breakdown of where your feedback is coming from, and who's engaging with it. Upcoming Enhancements: Customizable tags and categories to fit your organization's needs. ROI tracking to measure the impact of implemented ideas. Advanced customization options in a premium tier. Why I Built This I believe that the people working within an organization have invaluable insights that can drive growth and improvement. By providing a platform to harness this collective knowledge, companies can foster innovation and make more informed decisions. Research also backs this up! Looking for Feedback and Early Users I'm eager to get feedback from the HN community to help improve colab.ing. If you're interested: Try It Out: Visit colab.ing to explore the platform. Early Adopters Program: For organizations willing to collaborate closely, you can join our Early Adopters program: Early Adopters Signup: https://www.colab.ing/#early-adopters What I'd Love Feedback On Usability: Is the platform intuitive? Value Proposition: Does this address a need you've observed in organizations? Feature Suggestions: What functionalities would make this tool more useful for you? Contact Feel free to share your thoughts in the comments or reach out directly at sam@colab.ing. Thank you for taking the time to read this. I'm looking forward to your feedback!

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Indie HackersFits the IH revenue-focused audience · Strong signals: organizations · Missing: supports, reddit linkedin, podcasting
89%89% 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: slack, user · Missing: mac, agents, macos
79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, intuitive, users · Missing: plus, reviews, host
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, users, way · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
26%26% 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: growth · 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 · Strong signals: collaborate · 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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