Jig – a tool to define, compute and monitor metrics

Hacker News

Hi HN, 8 months ago, I posted “Ask HN: I built it nobody came, what now?” and got a ton of (not very optimistic) feedback [1]. I took away 3 things: 1. The message wasn’t properly targeted 2. The onboarding experience was terrible. 3. Someone posted some advice to find leads which actually worked, yeah! So here we are. 8 months later. I unfortunately didn’t have a lot of time to put in the tool itself. But I improved the website, improved the onboarding, and got a paying customer who seems to really like the software. So here it is in its current form. Let me know what you think would make the tool/website better! [1] https://news.ycombinator.com/item?id=26734079

Visit product View on Hacker NewsDec 22, 2021

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

77%77% 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: new · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

72%72% 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, way · 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 · Missing: plus, platform, intuitive

42%42% 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

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.

Correct prediction on native model

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