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I Made Cursor for Prompts Extension

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I Made Cursor for Prompts Extension

Hey HN, Since late 2021, I’ve been building apps using only AI models. Over time, I realized the real problem wasn’t how good the coding models were, but how you write and structure your prompts. When I vibe code, I make a looooot of typos or write unclear prompts. I often find myself reverting to old versions because the AI misunderstood my prompt and made incorrect changes. But in reality, it’s usually not the AI’s fault. It’s my prompt. That’s why I built PromptDC. It enhances your prompts with one click, without leaving your web-based platform or local editor. Just write your prompt in the chat and press your keyboard shortcut or the Enhance button. I hope this extension will be as helpful to you as it is for me. I’d love to hear your feedback. (especially if you try the VSCode extension) Panos

Visit productView on Hacker NewsJun 29, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, model, apps · Missing: mac, agents, macos
98%98% 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
68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
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: apps · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, 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 · 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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