Spatial Contact Management Layout

Hacker News

Hi! I would like to share the connection layout I implemented in Knei. It is used to manage interpersonal relations. This layout spatially communicates information about many relationships. It is formed of columns representing contacts' groups. The column contains people's cards. Every card moves within the column from left to right as time goes by. Hence, people you haven’t talked to for a while will move to the right border of the column. That configuration visually represents 3 properties: a) Different groups — in columns b) Days since the last talk — x coordinate within the column c) Sorting by custom characteristic — y coordinate within the column That way, you can assess a lot of relationships in a glimpse.

Visit product View on Hacker NewsJul 30, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual · Missing: mac, agents, macos

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

32%32% 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

14%14% 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

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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