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I made an interactive prompt compression demo

Hacker News

I made an interactive prompt compression demo

Hello HN, I am Zachary, co-founder of Promptropy - the world’s first prompt compression API. Linked is a demo for our first public endpoint for context-agnostic text compression. Prompt compression is based on the idea that LLMs don’t need many of the quirks of natural language, allowing them to extract equivalent meaning from a prompt 2-20x smaller than the original one. This allows us to intelligently remove tokens from a prompt—making it nearly unreadable to a human, yet still perfectly clear to the LLM (try this by asking a model like GPT-4 to summarize a long passage vs a compressed one). Please feel free to try out the demo we linked! We would love the HN community’s feedback on what we have built and what can be better. Thank you!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, context · Missing: mac, agents, macos
87%87% 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
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
51%51% 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 · 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 · Strong signals: active · 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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