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Building the future of LLMs. Cofounder & CEO, Lamini. CS Faculty at Stanford. MIT Technology Review’s 35 Under 35. (Speaker).
CAT Challenge 🐱 Apply to hack with us on your CAT. ~1 hr to get to an extremely high-accuracy LLM agent (eg. 95%, 99%, etc.). https://lnkd.in/gR_Fh3m8 Uses: - LLM-as-judge - Data labeling agent - Tool selection - Intent detection Let's build some CATs together :)
Building the future of LLMs. Cofounder & CEO, Lamini. CS Faculty at Stanford. MIT Technology Review’s 35 Under 35. (Speaker).
I'm so excited to launch Lamini's Classifier Agent Toolkit, aka. CAT! 🚀🐱 CAT hunts & tags the important signals 🐭 in a vast amount of data — so devs can easily create *agentic classifiers*. ❌ Manual data labeling ❌ Large, slow general LLM calls that can only handle 20-30 categories with mid accuracy ✅ CAT has helped our customers tag 2,000 pages across 1,000 categories in just 3.6 seconds with 99.9% accuracy. Dev time? A few hours to a few days. Hallucinations? Approaching zero. *Meow*. Some common agentic classifiers with CAT: ◽️ Customer service agents that extract user intent ◽️ Finding high severity tickets, so your teams can prioritize urgent issues ◽️ Triage legacy application code based on importance, to prioritize development ◽️ Analyze sentiment in earnings calls, reviews, posts, surveys, etc. More on it 👉 https://lnkd.in/gh-w97Gb Demo from one of our amazing architects, Scott Gay https://lnkd.in/gSGYKvqG This was a huge effort by the entire Lamini team 🎀 Happy holidays, hope you like our gift 🎁 Reach out anytime to fill our inbox with cheer at [email protected] (we read, we respond!)
Principal Data Scientist | Gen-AI Engineer | MLOps | LLM | RAG | Chess Expert
4dCan't wait to try this amazing one as well! 😼