Chipper AI/RAG Interface for Tinkerers (Ollama, Haystack RAG, Python)

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I started this project as a way to help my girlfriend with her new book and to learn a bit about LLMs and RAGs. The idea was to use local embeddings to ask questions about characters and explore creative possibilities, all while keeping everything local. What began as a bunch of a wild colelction of scripts is now growing into a labour of love pet-project. You maybe know how it sometimes goes :) It's not finished and polished yet, but I've made some good progress and brought it to a much better overall state over Christmas day. I'd love to show it to a few people and get some feedback and contributors. Your thoughts could really help me improve it and make it even better.

Visit product View on Hacker NewsDec 30, 2024

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3points

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

86%86% 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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive

46%46% 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

15%15% 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

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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