Scrive – Simplify Your LinkedIn Messaging

Hacker News

I recently built Scrive, a browser extension aimed at making LinkedIn messaging more straightforward. During my job search, I found it tedious to craft personalized messages, often resorting to copying drafts into AI tools for refinement. This experience led me to develop Scrive. What It Does: 1-One-Click Message Generation: Automatically creates a professional message for you. 2-Draft Enhancement: Improves your existing drafts to ensure they are polished and effective. Technical Details: Scrive is built with JavaScript and leverages OpenAI's GPT-4 API for language processing. I'm eager to hear your thoughts and feedback to help improve Scrive. Thanks for checking it out!

Visit product View on Hacker NewsFeb 27, 2025

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

86%86% 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: openai, open · Missing: mac, agents, macos

74%74% 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

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

47%47% 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: personal · Missing: mobile apps, ios, entrepreneurs

29%29% 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

14%14% 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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