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A Tool for Prompt Exploration

Hacker News

A Tool for Prompt Exploration

When my co-founder and I started working with Large language models, we struggled to find the right prompts to get the most optimal results for the tasks we had in mind. That’s why we built Prompt Studio, a way for us to fine-tune our LLM prompts. And now we are launching it as an early preview. Prompt Studio is aimed to help you quickly run variations of your prompts, connect to different language models and write/test prompt templates. Our features as of today are: - Create reusable templates. - Keep track of the text you generate - Compare results from different models - Create sets of data to test your prompt with or use as examples - Chat mode. We are continuously working on this and refining it. Some of our next features are: - Prompt the LLMs on files that you upload to the platform - Collaborate with your colleagues, create workspaces - Chain several prompts together for more complex use cases - Connect to a custom language model through a REST api. In the long run, we want to allow you to connect to other types of AI systems as well. Do you work with machine learning prompts and you don’t mind the occasional bug? Or do you know someone who works with prompts? We would love to get your feedback on our tool and learn more about what features you would want to see in Prompt Studio.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, models · Missing: agents, macos, agent
94%94% 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: started · Missing: supports, reddit linkedin, podcasting
93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, occasional · Missing: plus, intuitive, reviews
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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, collaborate · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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