HelpMoji – Build Co-pilots for your favorite Games with Mixtral-8x7B

Hacker News

In the last few months, I have been building RAG applications for different personal purposes. Creating game guides and wikis have always been a long term interest of mine. Some weeks ago, I decided to finetune a model on Juno to help my teenage cousin with his gameplay. The results were very impressive. Especially for strategic guidance and assistance. We applied the same AI fine-tuning on Genshin and Fortnite and got equally good results. I decided to spend sometime polishing the code and created a website for it. It helps you finetune an AI on game-specific content and create a Copilot for any game. Connecting it with Mixtral has been a gamechanger as I can now offer the product at absolutely no significant costs or loss of performance.

Visit product View on Hacker NewsDec 19, 2023

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

81%81% 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, code · Missing: mac, agents, macos

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

36%36% 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

35%35% 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

18%18% 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.

Correct prediction on native model

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