Databomz: Organize and Share AI Prompts via Chrome Extension and WebApp

Hacker News

I built Databomz to fix a recurring problem in my AI workflow — scattered prompts, lost versions, and no simple way to reuse or share them with teammates. Databomz includes: - A Chrome Extension to capture prompts instantly from any LLM (ChatGPT, Claude, Gemini, etc.) - Paste prompts directly from the extension into your chats or save them in one click - A WebApp with workspaces with tags, folders, and version history - Search to instantly find prompts by keyword or tag - Sharing controls for individuals and teams - A Public Prompt Library for discovery and inspiration There’s a Forever Free version that already covers a lot of features — it’s meant to be genuinely useful for solo users, not just a demo. Website: https://www.databomz.com Would love feedback from anyone managing multiple AI prompts or collaborating across tools. What’s the hardest part of keeping your prompts organized?

Visit product View on Hacker NewsOct 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, user, chatgpt · Missing: mac, agents, macos

77%77% 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 · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: recurring · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, 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

17%17% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

Correct prediction on native model

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