Vibebox: Your Inbox and Vibes

Hacker News

Hi HN! I'm here to share Vibebox (vibebox.app), a little toy app I built to sprinkle joy across your inbox with friendly animal avatars and messages that transform mundane emails into warm, joyful reads. About Vibebox - Transforms your emails with playful visuals and warm messages. - Aims to reduce stress associated with email management. - Promotes positivity within your daily routine. I'd love your feedback! Is this something you'd actually use if it were built out? I'm convinced it's a fun toy app, but a few early beta testers have said it's turning into their favorite way to read email because it actually reduces the emotional impact of opening their inbox. Inspiration: As you can imagine, Vibebox uses LLMs behind the scenes to vibe-ify your emails. If you're curious about how it works, I gave a talk on an early iteration of Vibebox called "AI for mere mortal web developers" at Austin API Summit last month. - Video: https://www.youtube.com/watch?v=pTQI0emPLDM - Repo: https://github.com/nylas-samples/austin-api-summit-2024

Visit product View on Hacker NewsApr 16, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: inbox, email, visual · Missing: mac, agents, macos

85%85% 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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video, month, way · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% 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

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

nativeThis product was originally launched on this platform.

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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