Schedugram, a web upload tool for Instagram

Hacker News

We got frustrated with Instagram not having a decent API so we built a wrapper on top of their app using physical tablets (you're not allowed to arbitrarily sniff their API calls and just reproduce them, although that's obviously easier). It's now 5 months old after a 4 week MVP build...come a long way! It's my first SaaS startup (previously services startup). More details on how it works here: http://support.schedugr.am/support/solutions/articles/1000044134-how-schedugram Happy to answer any questions :) would love a shoutout/recommendation if you know anyone in the marketing/social media management/etc space!

Visit product View on Hacker NewsMay 31, 2014

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

89%89% 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: physical, using · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · 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: 000, io · Missing: https docs, excited, just released

27%27% 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 · Strong signals: saas · Missing: arr, mrr, revenue

17%17% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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