ReLLM a Permission sensitive long term context API for LLMs

Hacker News

Hey, we're excited to share ReLLM! ReLLM provides developers with an API to quickly add permission sensitive context for LLM's such as chatGPT. ReLLM goes a step further and also encrypts all of your plain text data at rest. The Motivation: We built ReLLM, because while developing a different application we realized there was not a great way to provide our users context for their GPT questions that was limited to only the data they are allowed to see. ReLLM is fully functional with 2 API endpoints. One to embed data, and one to invoke chat with GPT.

Visit product View on Hacker NewsApr 18, 2023

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

96%96% 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.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist

55%55% 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, way · Missing: mobile apps, ios, personal

49%49% 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

40%40% 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

14%14% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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