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Cost&Time AI Estimator for MVP and Full Product. Seeking Your Feedback

Hacker News

Cost&Time AI Estimator for MVP and Full Product. Seeking Your Feedback

Hi HN community, We're developing a free tool that aims to help founders and product teams estimate the costs of developing an MVP versus a full-scale product. Here's the gist: 1. You input a description of your idea into our AI Estimator. 2. It provides you with two estimates: one for an MVP and another for a complete product. We're sharing it here because we value the critical and insightful feedback that the HN community is known for. The tool is in its early stages, and we're looking to refine its accuracy and user experience. If you have a moment, please try it out: https://www.allcancode.com/time-to-market We'd be grateful for any feedback on the following: 1. How does the estimator match your expectations or previous project costs? 2. Is the user interface intuitive enough? 3. What additional features would you want in such a tool? Feel free to DM me or comment below with your thoughts. Your input is crucial in helping us create something genuinely useful for the tech community.

Visit productView on Hacker NewsNov 9, 2023
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, code · Missing: mac, agents, macos
59%59% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive, interface · Missing: plus, platform, reviews
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% 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.

Incorrect prediction on native model

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