A web-app to explore topics using LLM

Hacker News

Lately, I've been tinkering with llama.cpp and the ollama server. The speed of these tools caught my attention, even on my modest 4060 setup. I was quite impressed with the generation quality of models like Mistral. But I was a bit unhappy at the same time because whenever I explore a topic, there is a lot of typing involved when using the chat interface. So I needed a tool to not only give a response but also generate a set of "suggestions" which can be explored further just by clicking. My experience in front-end development is limited. Nonetheless, I tinkered together a small web app to achieve the same goal. It is built with vuejs3+vuetify. Code: https://github.com/charstorm/llmbinge/

Visit product View on Hacker NewsDec 26, 2023

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

82%82% 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.

AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

54%54% 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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

13%13% 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.

Correct prediction on native model

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