Safari booking in Africa as easy as booking an all-inclusive

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Hey! I’m Josh, co-founder at CloudSafari. We’re making safari bookings to Sub-Saharan Africa as easy as booking an all-inclusive (starting with Tanzania). My family has been running tours in Sub-Saharan Africa for over 50 years now (we’ve done the tours/ground-transportation for ex-President Jimmy Carter, National Geographic, Melinda Gates, etc.). This past year I got to spend 5 months on ground in Tanzania and Kenya with my team integrating directly with various accommodation systems to automate the manual tasks that caused safari bookings to take anywhere from 1 week - 6 months to book down to 5 minutes. Our systems check against all availability components and dynamically prices a trip based on high season and low season fees as specified by the Government. Once you’ve selected a feasible date for your trip, our team reviews each booking request and answers any questions needed. We’ve currently launched in Tanzania, and have some packages available on site. We also launched on Product Hunt today! I would really appreciate any feedback on what we could do to make booking a safari more transparent and clear. Also we’ll be doing some future launches for the local guides in the countries we service, stay tuned! Feel free to send me a message directly at josh@mycloudsafari.com for any questions or to give feedback Thanks! :) Our tech: - Next JS Frontend - Express JS backend - Client Models (ZOD objects for messages passed from our backend to frontend and vice versa) - Database Models (ZOD objects for interacting with our database) - Slonik (For queries we can run against ZOD objects specified) - Migrations (Running our SQL queries) - CloudFlare (DNS + R2) - Preview Environments & CI/CD → webapp.io - Deployed on render.com

Visit product View on Hacker NewsMay 30, 2023

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

95%95% 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: model, models, tasks · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

59%59% 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: month, answers · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% 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

19%19% 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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