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Built a Chrome Extension to Organize X (Twitter) DMs

Hacker News

Built a Chrome Extension to Organize X (Twitter) DMs

Hi HN, I recently built a simple Chrome extension called Twinbox to help organize DMs on X (formerly Twitter - yes, I reminded you again haha). The idea came from my own frustration with managing multiple conversations – it’s easy for things to get lost, especially when juggling personal, work, and project-related chats. The extension lets you: - Create folders to categorize your DMs (e.g., “Work,” “Friends,” “Projects”). - Keep things tidy and easier to follow up on. It’s free to use, lightweight, and privacy-focused – all the organization happens locally in your browser. I’d love to hear your thoughts: Do you also find X DMs hard to manage? Are there any features that would make this more helpful? Thanks for reading! I built this to scratch my own itch, but I’m curious if it’s something others might find useful too.

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Product HuntOn track for Day 1 leaderboard · Strong signals: inbox · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
38%38% 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
34%34% 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
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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