Convert Text – Offline Text Transformation for Developers and Writers

Hacker News

Hi HN! I’m excited to share Convert Text, a lightweight tool I built to simplify the way we work with text. It’s designed to transform text quickly and efficiently—offline and privately. The idea came from my own frustrations as a developer and occasional writer. I often needed to switch between multiple online tools for tasks like converting text to camelCase, cleaning up formatting, or counting characters. But those tools were slow, filled with ads, or required an internet connection. I wanted something faster, distraction-free, and more privacy-focused. So I built Convert Text! It runs entirely offline, works instantly, and supports a range of use cases for developers, writers, and anyone who handles text daily. Key Features: 100% Offline: Keeps your data private—no internet required. Comprehensive Format Support: Title Case, PascalCase, snake_case, CamelCase, and more. Text Analysis: Count words, characters, sentences, and paragraphs at a glance. Web Tools: HTML encoding/decoding, URL formatting, Base64 conversion. Developer-Specific Tools: Clean formats (strip spaces, trim whitespace), and encoding options. Building this tool has been incredibly exciting, and I’d love for the HN community to try it and share feedback. What do you think? Are there any features you’d like to see added? Thanks for checking it out!

Visit product View on Hacker NewsFeb 20, 2025

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

88%88% 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.

AppSumoMay struggle as an AppSumo deal · Strong signals: occasional, efficient · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: tasks, coding · Missing: mac, agents, macos

41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% 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: way, para · Missing: mobile apps, ios, personal

26%26% 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

15%15% 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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