I created a simple LLM (ChatGPT, Bard) prompt builder

Hacker News

Based on the comments in the previous HN post ( https://news.ycombinator.com/item?id=39201182 ) I made some updates: - Introduced the ability to create/update/delete templates for your custom prompts - Define your own menu options for variables in the template builder - Introduced the ability to create/update/delete examples (predefined values) for the prompts - Saving your current state to browser's LocalStorage so your templates are persisted between browser sessions - Ability to export/load the whole workspace to/from a text file - Navigate with the tab key in the template builder's editable fields - Edit template builder's fields in place

Visit product View on Hacker NewsFeb 8, 2024

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1points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

80%80% 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.

AppSumoStrong fit for a featured deal · Strong signals: builder · Missing: plus, platform, intuitive

64%64% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, chatgpt · Missing: mac, agents, macos

55%55% predicted probability of success on Product Hunt, 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

38%38% 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 · Strong signals: chat, introduce · Missing: web3, crypto, cryptocurrency

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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