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PromptLab–Prompt Chain Iteration for Nontechnical Users

Hacker News

PromptLab–Prompt Chain Iteration for Nontechnical Users

Hey HN! We built a user-friendly tool that allows non-technical domain experts to explore and evaluate the effects of LLM-generated prompt chains on large datasets (via CSV). Our solution wraps around the ChatCompletions API (gpt-3.5-turbo), offering an accessible interface for users who lack the skills to work with Jupyter notebooks or other tooling. The current feature set is minimal, tailored to our friend's specific needs. We're eager to improve and expand the tool, so please share your feedback and suggestions--brutal honesty is okay! Note--we have the space for the OpenAI API key because we don't know how many people will use this and don't want to run up our GPT bill . Sorry for the inconvenience for those who don't have a key :(( Demo video: https://drive.google.com/file/d/15VAjHWt7Btgutp5pc6QPnL8YfLO... Initial CSV from demo: https://drive.google.com/file/d/19LUrXBZ08mIo7p0xJKTwOMdEle8... Result CSV from demo: https://drive.google.com/file/d/1f-kzSvhgcg6Bc0wCODomPl1FwCM...

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, openai · Missing: mac, agents, macos
75%75% 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
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: friendly, interface, users · Missing: plus, platform, intuitive
60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video, google, users · Missing: mobile apps, ios, personal
57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, io · Missing: https docs, excited, just released
57%57% 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
10%10% 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.

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