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Timeback Profiles – Meet the GitHub-Inspired Workday Dashboard

Hacker News

Timeback Profiles – Meet the GitHub-Inspired Workday Dashboard

Hello HN community, I've been working on a project called Timeback, designed to provide insights into how we spend our time at work, with a specific emphasis on meeting analytics. The genesis of this project was a simple question: "Where does my time go during the workday?" And I realized there was a lack of tools providing clear, actionable answers backed by data. The first step, Timeback Profiles, is inspired by the transparency of GitHub profiles. It's a personal dashboard that represents your workday as a wealth of data, transforming meetings, focus sessions, and completed tasks into a map of your work patterns. But, this isn't just a trophy cabinet. It’s a living, breathing data set, packed with insights for you to take action and drive outcomes. Key metrics like focus time, time spent in meetings, and meeting cost expenses come together in a monthly snapshot, while a heatmap visualizes your activity, making patterns tangible and insights actionable. Your profile includes badges, like "Meeting Budgeter" or "Deep Work Dynamo," which highlight your unique work style and help you better understand your time management. Perhaps most interestingly, Timeback Profiles are shareable. This allows for comparison and learning from others' work patterns, fostering a collective pursuit of effective time management. It's not about boasting or rivalry, but about understanding bandwidth, learning from peers, and making informed decisions about time. Here's an example of a Timeback Profile: https://timeback.so/recmend I'd love for you to try it out, compare it with peers, and share your feedback. Excited to hear your thoughts!

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual, activity, tasks · Missing: mac, agents, macos
90%90% 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
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month, monthly · Missing: mobile apps, ios, entrepreneurs
44%44% 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
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist
34%34% 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
18%18% 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
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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