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Vi

Vim-like undo history for OpenAI API prompts

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Vim-like undo history for OpenAI API prompts

Having been saved by Vim's infinite, persistent, and branched undo for years, I was disappointed that OpenAI's playground has no concept of history whatsoever. Was wasting too much time getting API calls just right, so I built this on the side. The core feature is the branched undo history + diffing between any two historical nodes. I hope it may be useful to someone else, but I would greatly appreciate any feedback - including harsh, but actionable feedback. Unfortunately I do have to require a login to prevent abuse since this is tied to my personal API key.

Visit productView on Hacker NewsJun 27, 2023
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: openai, open · 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 · Strong signals: including · Missing: supports, reddit linkedin, podcasting
63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
49%49% 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, including · Missing: https docs, excited, just released
46%46% 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
19%19% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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