Claude spinning up a dozens of GPUs when I say "thanks" after he explains how turtles work

Real-Time Evaluation Models for RAG: Who Detects Hallucinations Best?. arxiv.org/abs/2503.21157

Evaluation models for RAG aim to detect incorrect responses in real-time, but can they actually without any ground-truth answers/labels? Just published: A benchmark across six RAG applications comparing popular Evaluation models like: LLM-as-a-Judge, Prometheus, Lynx, HHEM, TLM

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Partnership with MLflow - check it out if you’re trying to improve your LLM Evals

Tomorrow I'm spilling the secrets as to how several Fortune 500 @cleanlabai customers are solving the hardest problem in AI -- producing accurate, compliant, safe fully automated AI Agent responses -- at the AI User Group Conference in SF. Stop by and get your hands dirty and

The best AI agent workflow I’ve used so far is from ManusAI

Remember when they were going to move Silicon Valley to Miami? How'd that work out?

Love when the Pistons blow out the Knicks by -94 Google

If you use NVIDIA AI NeMo Guardrails for LLM app reliability, go ahead and try to integrate our Cleanlab Trustworthy Language Model. Developers can add additional safeguards to address hallucination and untrustworthy responses when building LLM-based applications.

We Cleanlab reproduced, contained, and fixed Cursor’s rogue AI support bot with automated AI Safety software—an incident that sits atop a growing list of customer support AI Agent meltdowns that cause serious damage to trust with customers. Reach out if you care about

Cleanlab is now integrated into langfuse.com's observability platform! We're adding real-time trust scores to LLM outputs to quickly surface the most problematic responses for Langfuse users.

🔍 Unlock generative AI success with quality data! Join #AWS & Cleanlab for an exclusive workshop at SFO Gen AI Loft on May 9, 2025. Learn to build & scale production-ready AI solutions from experts. For developers & decision-makers. Register now! 👉 go.aws/3SeOxwU

Curious about how to systematically evaluate and improve the trustworthiness of your LLM applications? 🤔 Check out how Cleanlab's Trustworthy Language Models (TLM) integrates with #MLflow! TLM analyzes both prompts and responses to flag potentially untrustworthy outputs-no

You can now use Cleanlab with LlamaIndex 🦙 to make your production AI agents trustworthy and actually root cause why certain responses are untrustworthy (knowledge gap/poor retrieval, bad data, hallucination, etc.)

Based on last preseason odds it's not even close too lmao Pacers +6600 15 Warriors +2800 '11 Mavs +2000 '19 Raptors +1850 '23 Nuggets +1800 '04 Pistons +1500

ever since I was a little boy I dreamed of configuring IAM polices for an enterprise AWS environment

🛑Prevent Hallucinated Responses Our integration with Cleanlab allows developers to catch agent failures in realtime To make this more concrete - they put together a blog and a tutorial showing how to do this for a Customer Support agent Blog: cleanlab.ai/blog/prevent-h…

As of midnight tonight, 2050 will be closer than 2000