ObjectiveSee - Interviews with Apple developers

Hacker News

I love reading The Setup (www.usesthis.com) as I like taking a peek in to the tools that help others get their work done. I'm not a fan of pictures of minimalist desks, but I think there's a lot of value in a place where others can share best practices and tips with others in their community. Long story short, I wanted to build a site like this for Apple Mac and iOS developers. I've been blown away at how willing some really great developers have been to participate. There are only 3 interviews up so far, but I'm posting one per week and have commitments to interviews for the next 3 months at this rate. I just wanted to share this with the HN community as I feel that there are many here that may gain some insight or enjoyment out of the site. I'd love any feedback too as this is the first website I have designed on my own (with some help from Twitter Bootstrap.) Justin

Visit product View on Hacker NewsJan 16, 2012

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Launch Intel predictions

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

92%92% 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: mac, apple · Missing: agents, macos, agent

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios, month, way · Missing: mobile apps, personal, entrepreneurs

28%28% 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

13%13% 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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