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Emdash – Slack/Zoom alternative for distributed team collaboration

Hacker News

Emdash – Slack/Zoom alternative for distributed team collaboration

Hi HN, I’m Phil, one of the co-founders building emdash. Previously, I was an early engineer at Facebook and led Customer Products at Square. We’ve focused on making chat and video work together so distributed teams can stay aligned without drowning in information. You can try it here: https://emdash.io . It frustrated us how easily important conversations would happen and then disappear. Slack never quite matched how we worked. Channels were too coarse which led to noisy notifications and broken search. Zoom meetings weren’t much better–unless someone took perfect notes (which rarely happened), video calls became black holes of lost knowledge. We spent too much time trying to find the information we needed to do our jobs. To address this, we’re testing a few concepts and would appreciate your feedback on the value of the following: (1) Automatically record, summarize, and transcribe your team’s video chats. We store meeting content directly inside discussions to facilitate search and discovery. (2) Make it easy to manage & organize conversations of varying scope. A chat between team members can be forked into a dedicated Discussion with its own audience permissions and subscription. Individual messages or entire Discussions can be moved after the fact. Conversations can evolve unpredictably, so having the right tools to keep them organized post-hoc was important to us. (3) Improve search with AI and hierarchical information retrieval. We use LLMs to uncover insights, summarize content, and connect the dots across related discussions, meetings, and documents. You can ask questions like “What are the team’s priorities this week?” or “What did we decide to do with feature X?” and get back a generative response AND deep links into the original chats and meetings. Try it out: https://emdash.io and tell us what you think!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, notes · Missing: mac, agents, macos
96%96% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: calls · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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