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PrismaGPT – Ask how to query against your schema using natural language

Hacker News

PrismaGPT – Ask how to query against your schema using natural language

Hi HN! I often ask ChatGPT how to construct Prisma queries, especially when things get a bit more complicated. Since I do that on the regular, I decided to put together a tool that is specific to that purpose: PrismaGPT ( https://gpt.howtoprisma.com/ ) With PrismaGPT, you can drop in your Prisma schema and ask questions like "how do I get a list of users and all their related posts". You'll get results for both a Prisma Client query as well as raw SQL. Hopefully it's useful, especially if you're newer to Prisma and want to get going quickly!

Visit productView on Hacker NewsApr 21, 2023
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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, chatgpt · Missing: mac, agents, macos
68%68% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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