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Shared Repository of Links for Teams

Hacker News

Shared Repository of Links for Teams

Hi all at HN, over the past few months, I have been working building a self-managed shared repository of links for teams - Stacks. It is an automated bookmarking service and a noise-less collaboration platform for teams. It provides an easy and pleasant search of your saved content and favorites. Let me know how it works for you. If you currently use another productivity tool or system - let me know in the comments. Use case of stacks Stacks can be used for a variety of purposes, some of the most common use cases include: 1. Bookmark management: Stacks allows users to save and organize all their favorite websites and links in one place, making it easy to find what they need later. 2. Team collaboration: Stacks allows teams to share links, documents, and other resources in one shared space, making it easy for everyone to access and collaborate on important information. 3. Research and content curation: Stacks can be used to save and organize research materials, articles, and other resources for a specific topic or project. 4. Customer Knowledge base: Professionals such as Financial Advisors can curate and create shared knowledge-base for their customers such as best mutual funds, stocks, insurance. Personal trainers can create repository of best workouts. Influencers can curate best products. As a shared repository with google extension, when installed, can provide customers more accurate and trusted information. 5. Personal organization: Stacks can be used to save and organize personal information such as shopping lists, to-do lists, and personal documents. 6. Online Learning: Stacks can be used to save and organize all your online learning materials, such as course materials, video tutorials, and certification links. 7. Information archiving: Stacks can be used to archive and save old information, making it easy to find and access later. 8. Social Media management: Stacks can be used to save and organize all your social media accounts, influencers and information in one place. 9. Digital Scrapbooking: Stacks can be used to save and organize all the digital memories

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user · Missing: mac, agents, macos
74%74% 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.
AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
61%61% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, video, month · Missing: mobile apps, ios, entrepreneurs
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
55%55% 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 · Missing: arr, mrr, revenue
11%11% 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.

Incorrect prediction on native model

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