Prompt Compression for LLMs App

Hacker News

Hi HN! I've noticed that as the applications of LLMs continue to expand, the demand for tokens is increasing, which also brings significant cost burdens to users. Currently, there aren't many effective prompt compression products on the market, so I've created a simple demo site: https://hello-llm.com . This application includes two features: lossy compression and lossless compression. 1. Lossy compression allows users to specify the compression ratio and is faster; 2. Lossless compression lets the model determine the compression ratio, aiming to retain as much useful information as possible, albeit slower. This is just a demo, and I plan to assess its feasibility and gather enough feedback before deciding whether to launch a formal product. Everyone is welcome to try it out!

Visit product View on Hacker NewsApr 18, 2025

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

88%88% 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.

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

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal

54%54% 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

45%45% 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: users · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

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BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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