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Benchmarking LLM Agents on Consequential Real World Tasks
Hacker NewsBenchmarking LLM Agents on Consequential Real World Tasks
A benchmark that you could run locally to test out LLM & AI agents' abilities to do real-world tasks
Share cardActual performance
3points
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Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, tasks · Missing: mac, macos, cursor92%92% 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.
BetaListSuited for BetaList early-adopters · Strong signals: real world · Missing: web3, chat, crypto64%64% predicted probability of success on BetaList, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released40%40% 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, intuitive34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting23%23% 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, revenue18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Correct prediction on native model
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