KraspAI Kompass – keep up with new LLMs

Hacker News

Hello HN! I want share KraspAI Kompass, an LLM comparison tool I have been building. Things around LLMs are moving fast, and new models are constantly coming out. It's easy to get disoriented and I personally have had a hard time keeping up. Because I wanted to quickly understand the capabilities of any new model, I built Kompass: a tool that lets you create your own test suite (or suites!) of prompts to evaluate new models with. It allows you to examine and compare performance across models and to figure out which of them are any good for your desired task. For instance, Kompass makes it easy to compare - open-source vs. closed-source (e.g. https://app.krasp.ai/c/b/helpme-software-deve?models=openai-... ) - or US vs. rest of the world (e.g. https://app.krasp.ai/c/b/helpme-office-worker?models=openai-... ) Keen for any feedback! Are there features missing? Models you would like to be added? What use-cases you would interested in? I'd love to hear from you. Thanks a lot for taking the time, I appreciate your help and support!

Visit product View on Hacker NewsSep 11, 2023

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

94%94% 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, new, models · Missing: mac, agents, macos

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: lua · 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.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs

41%41% predicted probability of success on TrustMRR, 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 · 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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