I built a tool to fix the hassle of copying email templates

Hacker News

Hi HN, As a support rep, I spent months copying email templates from sticky notes. It was time-consuming and frustrating, especially when formatting got messed up. That’s why I built Tempstr — a tool that lets you instantly access and copy your most-used templates, complete with preserved formatting, all without leaving your browser tab. Tempstr works seamlessly with the Chromium extension I also developed, which uses a restricted API key to securely fetch your templates. If repetitive email tasks have been a headache for you, I’d love for you to check it out and share your feedback! Miguel

Visit product View on Hacker NewsDec 18, 2024

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Actual performance

1points

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Launch Intel predictions

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

75%75% 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: email, tasks, notes · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

45%45% 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 · Missing: mobile apps, ios, personal

40%40% 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

18%18% 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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