Gl
Glimpse the multiverse of outcomes before decisions using LLM
Hacker NewsGlimpse the multiverse of outcomes before decisions using LLM
Share cardActual performance
1points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos63%63% 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 · Missing: web3, chat, crypto55%55% predicted probability of success on BetaList, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released37%37% 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, podcasting18%18% 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, revenue16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Correct prediction on native model
Similar products
je
jevals – replacing LLM judges with typed Jev decisions59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
jevals – replacing LLM judges with typed Jev decisions
Hacker News39Gu
Guiding LLM outputs using Zod42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Guiding LLM outputs using Zod
Hacker News3We
Web/API retrieval for LLM's using Crul66%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Web/API retrieval for LLM's using Crul
Hacker News2KV
KV-psi, using Linux PSI to to trim an LLM KV cache55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
KV-psi, using Linux PSI to to trim an LLM KV cache
Hacker News8Re
Redis-LLM – Redis module integrates LLM with Redis45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Redis-LLM – Redis module integrates LLM with Redis
Hacker News2Li
LitLLM the Spiciest LLM Wrapper34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
LitLLM the Spiciest LLM Wrapper
Hacker News1LL
LLM Reasonsers46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
LLM Reasonsers
Hacker News2Re
Resilient LLM23%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Resilient LLM
Hacker News1He
Hegelion – Force your LLM to argue with itself before answering58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Hegelion – Force your LLM to argue with itself before answering
Hacker News1
LLM Hotkey39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
TrustMRROther