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I built UnderKey to stop re-explaining chat contexts to ChatGPT

Hacker News

I built UnderKey to stop re-explaining chat contexts to ChatGPT

Hi everyone, I’m a freelance developer working with clients and teams across different countries. One of my biggest daily pains was switching between contexts: formal to informal, English to Spanish, casual to professional — often in the same hour. I used ChatGPT to help, but it was tedious. Every time, I had to re-explain the relationship, the tone I needed, and the background of the conversation. It slowed me down badly. After 3 months of coding in my spare time, I built UnderKey. It’s a minimal app that creates “chat profiles” — lightweight containers where I can set notes, translation preferences, tone, and context once. Then I can generate replies, correct drafts, or translate messages on the fly without re-briefing the AI every time. Right now it covers just the essentials I needed: profiles, smart notes, translations, and quick AI-generated messages. I would love feedback on the product, especially from others juggling multiple conversations across languages and contexts. Thanks for reading.

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Actual performance

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
92%92% 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: context, chatgpt, notes · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, 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
24%24% 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
20%20% 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, smart · 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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