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She literally broke down how to run evals in Claude Code (built the whole thing live): 01:34 - What people get wrong with evals 04:35 - Why product taste is the alpha now 09:28 - Building a PM agent from one prompt 19:00 - Instrumentation without writing code 22:00... show more

Aakash Gupta

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150,362 views • 3 months ago •via X (Twitter)

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New session with Shreya Shankar on How to Automate Evals with AI (correctly) The most important part of the eval workflow is finding issues. Shreya does a live demo of how to steer the AI iteratively to find unknown unknowns Chapter summaries: 00:00:00 Introduction 00:00:55 Why vendors want to automate evals for you 00:01:45 Why no tool can fully automate evals 00:06:00 Mistake #1: Asking AI to just "find the issues" in your data 00:08:11 What good AI-assisted error analysis actually looks like 00:11:12 Live demo: building a review interface from scratch 00:15:10 Annotating traces and building a failure mode taxonomy 00:19:31 Mistake #2: Only reviewing your data once 00:22:53 Mistake #3: Treating all apps with the same accuracy bar 00:25:10 General-purpose agents vs. dedicated eval tools 00:25:48 Summary: three rules for automating evals correctly YT links and more in reply

New session with Shreya Shankar on How to Automate Evals with AI (correctly) The most important part of the eval workflow is finding issues. Shreya does a live demo of how to steer the AI iteratively to find unknown unknowns Chapter summaries: 00:00:00 Introduction 00:00:55 Why vendors want to automate evals for you 00:01:45 Why no tool can fully automate evals 00:06:00 Mistake #1: Asking AI to just "find the issues" in your data 00:08:11 What good AI-assisted error analysis actually looks like 00:11:12 Live demo: building a review interface from scratch 00:15:10 Annotating traces and building a failure mode taxonomy 00:19:31 Mistake #2: Only reviewing your data once 00:22:53 Mistake #3: Treating all apps with the same accuracy bar 00:25:10 General-purpose agents vs. dedicated eval tools 00:25:48 Summary: three rules for automating evals correctly YT links and more in reply

Hamel Husain

34,196 views • 1 month ago

I'm often asked for the best public example of AI evals done right for a real, production product. I finally have an answer. Teresa Torres shares how she shipped an AI interview coach, and used evals to rapidly squash bugs and improve the product. Teresa shows how she: 1. did error analysis FIRST to find real issues (instead of using generic metrics) 😍 2. used Jupyter notebooks to analyze errors 3. built custom annotation tools + custom widgets in notebooks 4. built a LLM-judge and assertions to test for specific errors 5. iterated through this feedback loop until it worked. 6. kept things simple the whole time It's also probably the best commercial for Jupyter notebooks you can imagine. 🥰 Chapter summary below. Link to YT in next thread 00:00:00 - Intro 00:01:45 - The Product: Building an AI Interview Coach 00:06:34 - The Problem: How Do I Know if My AI Coach is Any Good? 00:10:15 - Using Airtable for Traces and Annotation 00:12:15 - Discovering Jupyter Notebooks and Designing the First Evals 00:15:15 - Example Evals: LLM-as-Judge vs. Code-Based Assertions 00:21:00 - Learning Python with ChatGPT to Analyze Eval Results 00:31:00 - VS Code, Custom Tools, and an Eval Investigation Notebook 00:39:45 - Building a Custom Annotation Tool with Claude 00:41:00 - From Personal Project to Production App 00:46:02 - How Should PMs and Engineers Collaborate on AI Products? 00:55:45 - Q&A: Capturing Feedback and Annotations from End Users 00:58:11 - Q&A: Is a Technical Background Necessary to Build AI? 01:02:28 - Q&A: What's Next for Teresa? 01:03:13 - Q&A: Unpacking the Micro-Decisions of Building an AI App

I'm often asked for the best public example of AI evals done right for a real, production product. I finally have an answer. Teresa Torres shares how she shipped an AI interview coach, and used evals to rapidly squash bugs and improve the product. Teresa shows how she: 1. did error analysis FIRST to find real issues (instead of using generic metrics) 😍 2. used Jupyter notebooks to analyze errors 3. built custom annotation tools + custom widgets in notebooks 4. built a LLM-judge and assertions to test for specific errors 5. iterated through this feedback loop until it worked. 6. kept things simple the whole time It's also probably the best commercial for Jupyter notebooks you can imagine. 🥰 Chapter summary below. Link to YT in next thread 00:00:00 - Intro 00:01:45 - The Product: Building an AI Interview Coach 00:06:34 - The Problem: How Do I Know if My AI Coach is Any Good? 00:10:15 - Using Airtable for Traces and Annotation 00:12:15 - Discovering Jupyter Notebooks and Designing the First Evals 00:15:15 - Example Evals: LLM-as-Judge vs. Code-Based Assertions 00:21:00 - Learning Python with ChatGPT to Analyze Eval Results 00:31:00 - VS Code, Custom Tools, and an Eval Investigation Notebook 00:39:45 - Building a Custom Annotation Tool with Claude 00:41:00 - From Personal Project to Production App 00:46:02 - How Should PMs and Engineers Collaborate on AI Products? 00:55:45 - Q&A: Capturing Feedback and Annotations from End Users 00:58:11 - Q&A: Is a Technical Background Necessary to Build AI? 01:02:28 - Q&A: What's Next for Teresa? 01:03:13 - Q&A: Unpacking the Micro-Decisions of Building an AI App

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How do we run evals with LLMs? Interview with Tanishq Singh. This is a real interview question from a big tech company, asked to a candidate in their technical interview round. The video explains the answer in roughly 11 minutes. 00:00 Question - Reliability with LLMs 01:00 Observability & Evals 02:50 What are evals? 06:41 Who does evals? 08:11 Answer relevancy, groundedness, tool sequence 10:39 Bloopers :p AI engineering program for software engineers:

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35,079 views • 12 days ago

This guy literally broke down how to become a $1.4M "builder PM" with n8n, Claude Code, and OpenClaw: 1:53 - What a "builder PM" actually is 6:04 - Your first agent in n8n (live build) 14:18 - Why every agent needs these 4 things 21:35 - The multi-agent eval loop 29:47 - Where n8n dies 33:39 - When to graduate to Claude Code 35:08 - What broke in December 2025 47:17 - The self-improving PRD reviewer 1:02:28 - Mocks and prototypes without designers 1:05:15 - OpenClaw and the new agent OS 1:22:06 - What AI PM interviews look like now

This guy literally broke down how to become a $1.4M "builder PM" with n8n, Claude Code, and OpenClaw: 1:53 - What a "builder PM" actually is 6:04 - Your first agent in n8n (live build) 14:18 - Why every agent needs these 4 things 21:35 - The multi-agent eval loop 29:47 - Where n8n dies 33:39 - When to graduate to Claude Code 35:08 - What broke in December 2025 47:17 - The self-improving PRD reviewer 1:02:28 - Mocks and prototypes without designers 1:05:15 - OpenClaw and the new agent OS 1:22:06 - What AI PM interviews look like now

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Our new podcast on evals, with Max Niederman, Ege Erdil, and Stephen Yang. 0:00:00 – What's an eval, and how's it different from an RL environment? 0:19:33 – Why are models bad at building an emulator when the task is fully verifiable? 0:42:00 – How does training on bad data teach models to write terrible code? 1:04:00 – Why is continual learning still so bad? 2:25:24 – Why haven't software engineers been replaced when coding is basically solved? Listen to the Mechanize Podcast on YouTube, Spotify, etc. Enjoy!

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62,263 views • 5 months ago

California is America's most populous, prosperous, and perhaps most beautiful state, and it sets trends for the rest of the country. But it's also perhaps the most broken state. Today, California Sheriff Chad Bianco is running for Governor and wants to lead California back to sanity: zero-income tax, oil independence, parental rights, and so much more. Can he do it? 00:00:00 - Intro 00:03:18 - Understanding California Governor elections 00:06:10 - Sheriff Chad’s background 00:14:40 - What does it mean to be sheriff? 00:18:12 - Gavin Newsom Rant 00:21:13 - Making life better in Riverside County 00:35:17 - Discretion in Law Enforcement 00:48:28 - Policy Differences Between Candidates 00:56:22 - Steve Hilton’s criticism of Chad 01:04:11 - First person to end the riots 01:10:59 - Eliminating Income Tax?! 01:20:00 - Wildlife 01:23:29 - Parental Rights, Education, Vaccines 01:27:53 - Pregnancy Resource Centers 01:35:19 - Why are you going to win?

California is America's most populous, prosperous, and perhaps most beautiful state, and it sets trends for the rest of the country. But it's also perhaps the most broken state. Today, California Sheriff Chad Bianco is running for Governor and wants to lead California back to sanity: zero-income tax, oil independence, parental rights, and so much more. Can he do it? 00:00:00 - Intro 00:03:18 - Understanding California Governor elections 00:06:10 - Sheriff Chad’s background 00:14:40 - What does it mean to be sheriff? 00:18:12 - Gavin Newsom Rant 00:21:13 - Making life better in Riverside County 00:35:17 - Discretion in Law Enforcement 00:48:28 - Policy Differences Between Candidates 00:56:22 - Steve Hilton’s criticism of Chad 01:04:11 - First person to end the riots 01:10:59 - Eliminating Income Tax?! 01:20:00 - Wildlife 01:23:29 - Parental Rights, Education, Vaccines 01:27:53 - Pregnancy Resource Centers 01:35:19 - Why are you going to win?

lilaroseshow

76,657 views • 5 months ago

Is Jason Arday just a liar? Peter Boghossian joins me on DarkHorse to discuss. 00:00:00 Jason Arday Exposes the Academy 00:02:34 Sponsor: Sundays for Dogs 00:04:25 Sponsor: Vanman 00:06:18 Evergreen Was the Warning 00:14:15 How DEI Creates a Niche for Fraud 00:17:27 What If Arday Was the Ultimate Hoax? 00:24:18 Sponsor: Branch Basics 00:26:10 When DEI Reaches High-Stakes Fields 00:34:53 Trust, Race and Meritocracy 00:50:46 The Academy Is Broken 00:58:08 What Can Be Done? 01:02:18 Will AI Replace the Trades? 01:07:33 The West Has an Honesty Crisis 01:10:44 The Consent of the Governed 01:15:43 Why We Need Difficult Conversations

Is Jason Arday just a liar? Peter Boghossian joins me on DarkHorse to discuss. 00:00:00 Jason Arday Exposes the Academy 00:02:34 Sponsor: Sundays for Dogs 00:04:25 Sponsor: Vanman 00:06:18 Evergreen Was the Warning 00:14:15 How DEI Creates a Niche for Fraud 00:17:27 What If Arday Was the Ultimate Hoax? 00:24:18 Sponsor: Branch Basics 00:26:10 When DEI Reaches High-Stakes Fields 00:34:53 Trust, Race and Meritocracy 00:50:46 The Academy Is Broken 00:58:08 What Can Be Done? 01:02:18 Will AI Replace the Trades? 01:07:33 The West Has an Honesty Crisis 01:10:44 The Consent of the Governed 01:15:43 Why We Need Difficult Conversations

Bret Weinstein

97,083 views • 15 days ago