LL
LLM-Tournament – Have 4 Frontier Models Duke It Out over 5 Rounds
Hacker NewsLLM-Tournament – Have 4 Frontier Models Duke It Out over 5 Rounds
I had this idea earlier today and wrote this article: https://github.com/Dicklesworthstone/llm_multi_round_coding_... In the process, I decided to automate the entire method, which is what the linked project here does.
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, coding · Missing: mac, agents, macos68%68% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released55%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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal33%33% 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, podcasting15%15% 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, revenue13%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, crypto10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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