For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai

In this webinar, you will gain an introduction to the concept of agentic language models (LMs) and their usage. You will learn about common limitations of…

Contribute to mlvanguards/fraud-audio-detection development by creating an account on GitHub.

Part 1/2: Using Spectogram AI and making the most out of the dataset

Frameworks for LLM Agents have been surprisingly disappointing. I want to offer some principles for building agents based on our own trial & error, and…

Powered by Ray, Anyscale empowers AI builders to run and scale all ML and AI workloads on any cloud and on-prem.

Frameworks for LLM Agents have been surprisingly disappointing. I want to offer some principles for building agents based on our own trial & error, and…

100+ AI Agents, Agent Skills and RAG Apps - Free and Open Source. - Shubhamsaboo/awesome-llm-apps

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Contribute to cfregly/ai-performance-engineering development by creating an account on GitHub.

Is bigger always better? How our optimization space is fundamentally changing. The slow death of scaling and what comes next.

The recording of my talk on the slow death of scaling given at Khipu is now available.

This captures many of the ideas I'm currently…

Why traditional PMF playbooks are failing in 2026, and the new framework that's working (AI PRD Template included)

To deploy agentic AI responsibly and effectively in the enterprise, organizations must progress through a three-tier architecture, Foundation tier, Workflow tier, and Autonomous tier where trust, governance, and transparency precede autonomy.

From a ML Manager's perspective

A practical handbook for engineers building, optimizing, scaling and operating LLM inference systems in production.

What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers? - humanlayer/12-factor-agents

Uncertainty Estimation is an essential ML method for improving robustness in ML models, and building trust/confidence in AI users. In the era of LLMs, it is even more critical because LLMs can be very confident on answers that are very wrong!

My team from…

Best AI teams obsess over measurement and iteration.

If you streamline AI evals, everything else becomes easy.

But we can't take CI/CD from traditional software or ML.

Why?

LLMs introduce unique challenges:

- Non-determinism, no single "right answer"…