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Posts tagged #Inferact

Funding Raises: #Anthropic +$10B; #CoreWeave $2B; #Waabi $750M; #RedwoodMaterials $425M; #RicursiveIntelligence $300M; #Decagon $250M; #UpwindSecurity $250M; #TenpointTherapeutics $235M; #Synthesia $200M; #UpscaleAI $200M; #FlappingAirplanes $180M; #Inferact $150M; #PaleBlueDotAI $150M

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“훈련은 돈만 쓴다, 진짜 돈은 추론서 벌어”…2000억 실탄 챙긴 ‘인퍼랙트’ CEO의 일침 vLLM의 주역들이 설립한 인퍼랙트가 1.5억 달러의 실탄을 확보하며 AI 경제학의 패러다임 전환을 선언했습니다. 수조 원을 쏟아붓는 모델 훈련은 결국 ‘비용’일 뿐, AI가 사용자에게 정보를 전달하고 가치를 창출하는 유일한 순간은 ‘추론’이라는 일침입니다. AI포스트 핵심 요약 ✅

📉 "훈련은 밑 빠진 독에 물 붓기?"
시드 투자로만 2,182억 원 챙긴 '인퍼랙트'의 독설

오픈소스 추론 엔진의 끝판왕 'vLLM' 팀이 만든 인퍼랙트가 전장에 뛰어들었습니다. 이제 AI 산업의 승자는 '누가 더 큰 모델을 가졌느냐'가 아니라 '누가 더 효율적으로 추론하느냐'에서 갈릴 것입니다.
www.aipostkorea.com/news/article...

#인퍼랙트 #Inferact #vLLM #사이먼모 #AI인프라 #추론의경제학 #시드투자 #a16z #테크트렌드

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Inferact raises $150M to commercialize vLLM, enhancing AI inference efficiency. Backed by Andreessen Horowitz & Lightspeed. #AI #Inference #TechFunding #vLLM #Inferact Link: thedailytechfeed.com/inferact-rai...

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Andreessen Horowitz just pumped $150M into Inferact’s seed round, pushing its valuation to $800M. The startup’s open‑source vLLM engine could reshape AI model inference. Curious? Dive in. #Inferact #vLLM #SeedFunding

🔗 aidailypost.com/news/andrees...

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A diagram illustrating an example of error detection in a Webshop task. The user query at the top requests “white blackout shades that are 66 inches in width and 66 inches in height, easy to install, and under $90.” The “Actor Agent” box below shows the agent’s reasoning, actions, and thoughts as it searches for and selects a product (B09LS7KQMC) offering custom-cut cellular shades. Four colored boxes labeled (A) Direct Prompt, (B) Multi-Step Evaluation, (C) InferAct: Task Inference Unit, and (D) InferAct: Task Verification Unit compare evaluation methods. The direct and multi-step evaluations incorrectly mark the result as correct, while InferAct correctly detects a mismatch between “custom-sized blackout shades” and the user’s requested fixed size of 66×66 inches. The figure caption explains that InferAct successfully identifies this misalignment, unlike other methods.

A diagram illustrating an example of error detection in a Webshop task. The user query at the top requests “white blackout shades that are 66 inches in width and 66 inches in height, easy to install, and under $90.” The “Actor Agent” box below shows the agent’s reasoning, actions, and thoughts as it searches for and selects a product (B09LS7KQMC) offering custom-cut cellular shades. Four colored boxes labeled (A) Direct Prompt, (B) Multi-Step Evaluation, (C) InferAct: Task Inference Unit, and (D) InferAct: Task Verification Unit compare evaluation methods. The direct and multi-step evaluations incorrectly mark the result as correct, while InferAct correctly detects a mismatch between “custom-sized blackout shades” and the user’s requested fixed size of 66×66 inches. The figure caption explains that InferAct successfully identifies this misalignment, unlike other methods.

🤖 𝗛𝗼𝘄 𝗰𝗮𝗻 𝘄𝗲 𝘀𝘁𝗼𝗽 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗳𝗿𝗼𝗺 𝘁𝗮𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝘄𝗿𝗼𝗻𝗴 𝗮𝗰𝘁𝗶𝗼𝗻—𝙗𝙚𝙛𝙤𝙧𝙚 𝙞𝙩 𝙝𝙖𝙥𝙥𝙚𝙣𝙨?
Imagine your shopping agent accidentally buying something expensive you didn’t want! 💸 #InferAct keeps your AI agents reliable and safe by rectifying misaligned actions 𝗯𝗲𝗳𝗼𝗿𝗲 they occur🧠✅

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