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ActionPrompt – A Rails Plugin for Managing Your LLM Prompts

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ActionPrompt – A Rails Plugin for Managing Your LLM Prompts

Hi Everyone! I've just extracted this from our code base. As LLMs have become ubiquitous in web applications, I've noticed that prompts intended for Claude or GPT have become scattered throughout our codebase or buried within objects. Often, these prompts were built inline through string manipulation. My thinking was two-fold, 1) Let's come up with a simple pattern for organizing and rendering these prompts, and 2) Let's make them easy to review. This draws heavy inspiration from ActionMailer::Preview.

Visit productView on Hacker NewsSep 20, 2024
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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, code · Missing: mac, agents, macos
88%88% 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
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
47%47% 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
34%34% 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
16%16% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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