Personalized Task Time estimation for Devs (getmodelli.com)

Hacker News

Hi HN, I am founder of Modelli. A tool that automates sprint planning and task time estimation for Saas teams. Check out our platform here: [ https://www.getmodelli.com/ ] Currently the burden of accurately estimating task times and managing sprints is on devs/PMs. This process is manual, error-prone, and time-consuming. Modelli uses historical data & Dev Skills to provide personalized tasks time estimates. Modelli helps teams focus on what they do best: building great products. I am hoping to get feedback from founders, devs and product managers who deal with sprint planning on a regular basis. Try it out and let us know what you think! Looking forward to any thoughts and feedback!

Visit product View on Hacker NewsMar 16, 2024

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

75%75% 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

51%51% 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: ide, io · Missing: https docs, excited, just released

43%43% 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 · Missing: mobile apps, ios, entrepreneurs

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · 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

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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