Ad
Adaptive RAG – How we cut LLM costs without sacrificing accuracy
Hacker NewsAdaptive RAG – How we cut LLM costs without sacrificing accuracy
Share cardActual performance
8points
1comments
Made the leaderboard
Launch Intel predictions
Analyze your own launch →
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto82%82% predicted probability of success on BetaList, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal47%47% 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 released38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting13%13% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Incorrect prediction on native model
Similar products
Mo
Mockingbird is an LLM that outperforms GPT4 on RAG57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Mockingbird is an LLM that outperforms GPT4 on RAG
Hacker News9Wa
WatchLLM – Semantic caching to cut LLM API costs by 70%44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
WatchLLM – Semantic caching to cut LLM API costs by 70%
Hacker News1KeksCode12%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Angular bits to cut your product development costs
Indie HackersAd
Adaptive Geocodes – Truncatable Geocodes27%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Adaptive Geocodes – Truncatable Geocodes
Hacker News3Ad
Adaptive Placeholders30%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Adaptive Placeholders
Hacker News9Ve
Vectorless RAG48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Vectorless RAG
Hacker News11Pa
PageIndex – Vectorless RAG66%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
PageIndex – Vectorless RAG
Hacker News192
Preprocess65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Preprocess maximises RAG performances
Product Hunt+106APIIn
InferShrink – Cut LLM API costs 10x with automatic model routing43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
InferShrink – Cut LLM API costs 10x with automatic model routing
Hacker News2Li
Librarian – Cut token costs by up to 85% for LangGraph and OpenClaw50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Librarian – Cut token costs by up to 85% for LangGraph and OpenClaw
Hacker News8