TT

TTShift – build a work schedule from the text requirements

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TTShift – build a work schedule from the text requirements

I want to create a work schedule builder that is (1) easy to start with and (2) offers great flexibility. And now I have a very basic MVP that I want to show. Here are some things it can handle while building your schedule: * Set the number of workers needed per shift and specify constraints. Ex: each shift has a minimum of 8 workers and 2 of them must be certified staff that can be assigned to hazardous tasks. * Respecting mutually exclusive shifts. Ex: If the worker is assigned to a night shift, he can not be assigned for a day shift on the same or following day. * Take into account each worker’s availability and the maximum number of shifts they can work. Ex: John is only available on weekdays, and can do 3 shifts per week. * Enforce mutually and non-mutually exclusive constraints. Ex: if the worker is assigned to handle the cash register during the morning shift, he cannot also be assigned as a floor supervisor during that same shift, since both roles require full attention. * Prioritize certain constraints over others to ensure critical tasks are covered, even if not all shifts are fully staffed. Ex: In the event of understaffing, prioritize essential roles like healthcare or safety positions. * Takes into account workers' preferences regarding who they work with or who they prefer to avoid. Ex: Sophie, Emma, and Noah have a great rapport and boost productivity when scheduled together so consider this when building the shift. If you want to try it, keep in mind that it's just an MVP to play around with. Sometimes you'll have to be extra verbose with your requirements to get the desired result. I really appreciate your feedback.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
74%74% 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: tasks · Missing: mac, agents, macos
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: exclusive, builder · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
39%39% 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
21%21% 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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