Un

Uni Beta – a decision making platform for teams

Hacker News

Uni Beta – a decision making platform for teams

Hey HN! - As you go into the weekend check out Uni (https://uni.fyi), we recently came out of closed beta and are excited to share what we’ve built to help remote teams involve the right people from their teams to make smart decisions! We’re an early-stage spinout from Cornell Tech and we see decision making as a natural wedge to actualizing inclusion within companies. Since there are no go-to products out there which facilitate this process fully, decision makers often-times become overwhelmed and frustrated with including people in their decision process resulting in an exclusive culture. Uni helps decision makers bring in their teams into private spaces where everyone can share, discuss, and build up the ideas needed to make smart decisions. Uni’s Momentum Engine also keeps track of when decisions need to be made and reaches out to people via Slack, email, or push notification to help engage the people needed to get to a smart decision. With Uni you can ask questions like: 1. What are our goals for the next week / month / quarter? 2. What trends were hot this week? 3. What are questions, thoughts, or concerns we should talk about at the all hands? Feedback is always welcome, we are particularly interested in how we can extend our integration support to make Uni fit into your existing work-stacks! We also put together some starter templates to make things easy! https://uni.fyi/templates Stay safe and have an awesome weekend! Adrian

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
66%66% 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, email · Missing: mac, agents, macos
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, existing · Missing: https docs, just released, lua
50%50% 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 · Strong signals: platform, exclusive · Missing: plus, intuitive, reviews
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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