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Vibe coding is coming to data analytics. Just showed how to build a custom sales dashboard from Snowflake data using Replit ⠕ Agent - nothing but natural language prompts. No SQL. No Python. No DevOps. Part 2 coming soon! Stay tuned. Timestamps: 0:00 Intro 3:18 Vibe coding coming to...

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Manny Bernabe

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85,357 görüntüleme • 6 ay önce •via X (Twitter)

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Vibe Coding with Codex - Complete Guide Build a Web App, Desktop App & iOS App with Codex + GPT‑5.5 (No Coding Needed, Beginner Friendly) In this video you will learn: > Vibe Coding Basics + Vocab > How to build a web app using Codex > Add db, auth + storage with Firebase > Github Basics > Add AI Features (API's) > Deploy to internet (Vercel) > Convert Web App into Desktop app & iOS App Chapters 00:00 Intro 01:12 Setting up Codex 01:57 The basics of Vibe Coding and Codex 02:20 Projects, Files, App 03:45 Example App - Microsoft Paint 04:25 Running app locally 06:39 Save My Code - Use Github 10:37 Quick Review before building app 12:24 Building a web app - The Prompt 15:32 Creating Web App Project 16:34 Explaining Firebase (Database, Storage, Auth) 18:42 Setting up Firebase Project 22:54 Prompting Codex to build our app 24:49 Inspect Element - Console 26:24 Verify Data being Stored in Database 27:51 Making Changes to App 31:29 Fixing Storage Permissions with Codex 32:20 GPT API - Adding AI to our app 36:20 Making more changes using screenshots 39:02 Queuing vs Steering 40:23 Deploying our app to Vercel (App on Internet) 43:09 Convert web app to desktop app and iOS app 47:15 Web App and Desktop App work Now 48:36 Now let's run the iOS app 50:27 All three apps work! 51:11 Making Changes to iOS app 52:51 Testing agent skill feature of our app 53:55 Summary of what we did
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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

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Data Models - Better AI Code in 5 Minutes Your AI isn't consistent because you're missing one file. I had the same problem until I started using a data model file. In this video, I'm showing you the exact two prompts I use: one generates the data model, the second uses it to generate features without breaking my app. Testing this with AnimeLeak (my actual production app) adding an admin approval feature. Using Droid with Sonnet 4.5 in spec mode, but this works in Cursor, Claude, or whatever you're using. KEY TIMESTAMPS 00:00 - The One File Your AI is Missing 00:44 - Prompt 1: Generate a Data Model 01:12 - The Result: Data Model Documentation 02:12 - Prompt 2: Implement a New Feature 03:00 - Reviewing the AI-Generated Spec 04:17 - Why This Workflow is So Powerful 04:32 - Your 3-Step Action Plan 05:12 - Next Video: Live Code Generation

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This guy literally broke down everything to build a 21 agent dev team in Claude Code: 0:00 - Why one prompt = AI slop 1:55 - Claude Code as your full startup 2:43 - The 21-agent team 4:57 - Inside the system analyst agent 5:52 - Live demo: 0 → TestFlight 8:42 - Define a "good" system analyst first 11:53 - The system analyst workflow 12:17 - Why Confluence + Jira + MCPs matter 15:30 - Classic PM skills > vibe coding 19:15 - The scaffolding that kills spaghetti code 22:10 - Setting up agents inside Claude Code 26:19 - The longest dictation prompt this podcast has seen 35:29 - Why dictation beats typing for AI specs 47:30 - Brand guidelines in Figma Make 55:59 - Idea → prompt → design → app 1:06:27 - Claude Code builds the Figma screens 1:23:59 - Frontend tickets appear in Jira (with Figma links) 1:48:49 - The hockey rules AI app goes live 1:53:56 - Full recap: Claude, Confluence, Figma, Jira, Simulator, TestFlight 2:03:17 - Should PMs get AI PM certificates? 2:08:15 - How to build a PM portfolio that lands FAANG offers 2:13:25 - How to get started this week

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Building a very useful app in 20 minutes that has - Deep Research (Perplexity) - Claude 3.7 Sonnet - YT Transcript API (SupaData) Without writing a single line of code With the latest Cursor update with web search and Claude 3.7 Sonnet, Vibe Coding got even more vibey. Timestamps: 00:00 Getting Started 00:57 Ideating with excalidraw 02:29 Opening Cursor Starting From Template 05:25 Perplexity Deep Research API and Claude API set up 10:18 OHHH NO AN ERROR! WHATEVER WILL I DO? 11:17 PERPLEXITY AND CLAUDE WORKED! 12:02 IMPROVING DESIGN 13:04 ADDING YOUTUBE TRANSCRIPT API 15:21 Redesign Concept with v0 17:07 This is what we created 19:42 Want to dive deeper into vibe coding?
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Building a very useful app in 20 minutes that has - Deep Research (Perplexity) - Claude 3.7 Sonnet - YT Transcript API (SupaData) Without writing a single line of code With the latest Cursor update with web search and Claude 3.7 Sonnet, Vibe Coding got even more vibey. Timestamps: 00:00 Getting Started 00:57 Ideating with excalidraw 02:29 Opening Cursor Starting From Template 05:25 Perplexity Deep Research API and Claude API set up 10:18 OHHH NO AN ERROR! WHATEVER WILL I DO? 11:17 PERPLEXITY AND CLAUDE WORKED! 12:02 IMPROVING DESIGN 13:04 ADDING YOUTUBE TRANSCRIPT API 15:21 Redesign Concept with v0 17:07 This is what we created 19:42 Want to dive deeper into vibe coding?

Riley Brown

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Vibe Coding: The act of using only your voice in Cursor Composer to build apps, forgetting the code even exists Complete Video Tutorial Some people are skeptical about this new method of programming so why don't we build a ChatGPT clone with real time voice ( Andrej Karpathy 's voice) without typing, using Whispr Flow. For me "vibe coding" is my only option. Because i've never written a line of code. Time Stamps: 00:00 Intro to Vibe Coding (pretty corny) 03:02 Opening Cursor 04:07 Starting Your First Project from Template Link (Pinned to bio of @senior_swc ) 05:12 Building a ChatGPT Clone, Getting Started 06:38 Prompting, API's and Documentation Explanation 08:49 Using Perplexity (Input output custom spaces for coding) 12:07 Vibe Code Prompt 1 13:58 Result of Vibe Coding Prompt 1 15:22 Seeing Prompt 2 15:43 Managing Cursor Composer Context Length 16:25 Designing 17:21 Debugging with Inspect on Web View 18:20 Fixing Formatting 19:04 More Vibing, Lol 20:51 Saving and Uploading Projects to GitHub 21:59 Enhancing the User Experience 22:33 Honoring Andrej Karpathy 26:26 Implementing Real Time Karpathy Voice 28:30 Getting Karpathys Voice (Don't Do this It's Illegal) don't ban me i love you
33:47

Vibe Coding: The act of using only your voice in Cursor Composer to build apps, forgetting the code even exists Complete Video Tutorial Some people are skeptical about this new method of programming so why don't we build a ChatGPT clone with real time voice ( Andrej Karpathy 's voice) without typing, using Whispr Flow. For me "vibe coding" is my only option. Because i've never written a line of code. Time Stamps: 00:00 Intro to Vibe Coding (pretty corny) 03:02 Opening Cursor 04:07 Starting Your First Project from Template Link (Pinned to bio of @senior_swc ) 05:12 Building a ChatGPT Clone, Getting Started 06:38 Prompting, API's and Documentation Explanation 08:49 Using Perplexity (Input output custom spaces for coding) 12:07 Vibe Code Prompt 1 13:58 Result of Vibe Coding Prompt 1 15:22 Seeing Prompt 2 15:43 Managing Cursor Composer Context Length 16:25 Designing 17:21 Debugging with Inspect on Web View 18:20 Fixing Formatting 19:04 More Vibing, Lol 20:51 Saving and Uploading Projects to GitHub 21:59 Enhancing the User Experience 22:33 Honoring Andrej Karpathy 26:26 Implementing Real Time Karpathy Voice 28:30 Getting Karpathys Voice (Don't Do this It's Illegal) don't ban me i love you

Riley Brown

73,901 görüntüleme • 1 yıl önce

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!
9:28

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 görüntüleme • 9 ay önce

i tested a fully local ai coding setup: gemma4 from Google DeepMind running through LM Studio, paired with pi which is a minimal open source coding harness this is part 1 of a two part series. here we cover the why and the complete hands-on setup 0:00 - Running a coding agent fully local 0:47 - Why Pi as the coding agent harness 1:39 - Why Gemma 4 and which variant to pick 2:44 - Architecture: Pi, LM Studio and Gemma 4 3:45 - LM Studio setup and model download 5:01 - Starting the local server 5:19 - API sanity check with curl 6:08 - Installing the Pi coding agent 6:20 - Configuring models.json for local models 8:20 - First run: selecting the model in Pi 8:58 - Fixing the context length error 10:16 - Testing the working setup 10:50 - What is coming in Part 2
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i tested a fully local ai coding setup: gemma4 from Google DeepMind running through LM Studio, paired with pi which is a minimal open source coding harness this is part 1 of a two part series. here we cover the why and the complete hands-on setup 0:00 - Running a coding agent fully local 0:47 - Why Pi as the coding agent harness 1:39 - Why Gemma 4 and which variant to pick 2:44 - Architecture: Pi, LM Studio and Gemma 4 3:45 - LM Studio setup and model download 5:01 - Starting the local server 5:19 - API sanity check with curl 6:08 - Installing the Pi coding agent 6:20 - Configuring models.json for local models 8:20 - First run: selecting the model in Pi 8:58 - Fixing the context length error 10:16 - Testing the working setup 10:50 - What is coming in Part 2

Nidhi Singh

16,289 görüntüleme • 1 ay önce

How to build an iOS app that uses the best AI image generator... IN UNDER 37 minutes. And get it on the Apple App Store... With Zero Coding... STACK: 1. New web app for vibecode 2. Ideogram New Character Generation Feature 3. FAL API 4. Claude 4 Sonnet and GPT-5 for coding TIMESTAMPS: 00:00 Intro 00:51 Setting Up the AI Prompt 01:12 Understanding and Using APIs 03:47 First Prompt on the vibecode website 08:04 Ideogram Works Now! 08:24 Changes we want to make to the app 12:25 Aspect Ratio of Thumbnails, FIXED! 15:23 Generating and Reviewing Thumbnails 16:36 Implementing New Features 19:52 Final Adjustments before App Store Submission 21:18 The Apps Final Form 22:42 Shipping our app to the app store Video with Muhammad Zulali
26:56

How to build an iOS app that uses the best AI image generator... IN UNDER 37 minutes. And get it on the Apple App Store... With Zero Coding... STACK: 1. New web app for vibecode 2. Ideogram New Character Generation Feature 3. FAL API 4. Claude 4 Sonnet and GPT-5 for coding TIMESTAMPS: 00:00 Intro 00:51 Setting Up the AI Prompt 01:12 Understanding and Using APIs 03:47 First Prompt on the vibecode website 08:04 Ideogram Works Now! 08:24 Changes we want to make to the app 12:25 Aspect Ratio of Thumbnails, FIXED! 15:23 Generating and Reviewing Thumbnails 16:36 Implementing New Features 19:52 Final Adjustments before App Store Submission 21:18 The Apps Final Form 22:42 Shipping our app to the app store Video with Muhammad Zulali

Riley Brown

52,563 görüntüleme • 1 yıl önce

Solidus Ai Tech Presents Agent Forge: Transforming AI Technology! Agent Forge is more than a platform—it's a revolution in how users interact with AI agents. Accessible to everyone, from seasoned professionals to newcomers, it empowers users to: 🔹Create Autonomous AI Agents: Automate processes, execute strategies, and stay ahead 24/7. 🔹 Gain Real-Time Insights: Stay informed with market updates via leading data platforms like CoinGecko and CoinMarketCap. 🔹 Simplify Complex Systems: Build automated workflows and manage digital strategies effortlessly, with no coding required. 🔹 Optimize Financial Management: Track data, optimize resources, and manage workflows with ease. 🔹 Breaking Barriers in AI Automation: Agent Forge makes advanced AI accessible to all, eliminating traditional technical challenges. 💡 Powered by AITECH HPC Data Center, Agent Forge ensures speed, security, and eco-friendly operations. 🗓 Coming Soon Agent Forge isn’t just a launch—it’s a leap forward.
1:13

Solidus Ai Tech Presents Agent Forge: Transforming AI Technology! Agent Forge is more than a platform—it's a revolution in how users interact with AI agents. Accessible to everyone, from seasoned professionals to newcomers, it empowers users to: 🔹Create Autonomous AI Agents: Automate processes, execute strategies, and stay ahead 24/7. 🔹 Gain Real-Time Insights: Stay informed with market updates via leading data platforms like CoinGecko and CoinMarketCap. 🔹 Simplify Complex Systems: Build automated workflows and manage digital strategies effortlessly, with no coding required. 🔹 Optimize Financial Management: Track data, optimize resources, and manage workflows with ease. 🔹 Breaking Barriers in AI Automation: Agent Forge makes advanced AI accessible to all, eliminating traditional technical challenges. 💡 Powered by AITECH HPC Data Center, Agent Forge ensures speed, security, and eco-friendly operations. 🗓 Coming Soon Agent Forge isn’t just a launch—it’s a leap forward.

AITECH CLOUD NETWORK

171,755 görüntüleme • 1 yıl önce

I'm a dev now (thanks to caffeine) In this video, I show how I went from content creator to building real apps on-chain using just natural language prompts ☕ #ICP 0:00 Intro 0:51 First look at the app 3:18 First prompt 4:01 Picking a better name for the app & designing the logo 5:22 Building an image hosting website 6:41 Second Chess website draft 7:40 Second prompt 8:08 Uploading the logo to the blockchain 8:35 Second Draft 8:47 Trying to fix the AI opponent 8:57 Third draft 9:23 Changing the app's name & logo 10:04 Closing thoughts
10:19

I'm a dev now (thanks to caffeine) In this video, I show how I went from content creator to building real apps on-chain using just natural language prompts ☕ #ICP 0:00 Intro 0:51 First look at the app 3:18 First prompt 4:01 Picking a better name for the app & designing the logo 5:22 Building an image hosting website 6:41 Second Chess website draft 7:40 Second prompt 8:08 Uploading the logo to the blockchain 8:35 Second Draft 8:47 Trying to fix the AI opponent 8:57 Third draft 9:23 Changing the app's name & logo 10:04 Closing thoughts

Alex (BCP)

12,552 görüntüleme • 10 ay önce

Vibe coding is like politics, there is a lot of fluff and very few answers to the hard questions. 1. Of every $$ a vibe coding platform makes, what % goes to Anthropic? 2. How does the margin change when switching between models? 3. Do vibe coding platforms really have any defensibility… really? 4. Does Cursor and Cognition come down to eat consumer or do consumer platforms like Lovable, Replit and Base44 move up and eat more of the dev workflow? 5. Salesforce, Atlassian, Cloudflare, of all incumbents adding vibe coding, which is best and worst? Spotify 👉 Youtube 👉 Apple Podcasts 👉 My 6 takeaways with 👇 Timestamps: 00:00 Intro 01:13 The Decision Behind Selling Base44 11:56 How Base44 Thinks About Defensibility 16:37 Mapping the Future of the Market in 3–5 Years 23:18 I’m Worried About Google 30:47 Margins Do Not Matter 37:15 What’s the Metric for User Success? 41:52 Do Early Revenue Numbers Actually Matter in AI? 49:33 Where Should Smart Money Go Today? 53:01 How does Base44 beat Cursor? 57:47 Will the AI Boom Hit a Revenue Speed Bump? 01:01:13 Are AI Builders Overvalued or Is Wix Undervalued? 01:03:45 Does Not Being in Silicon Valley Help or Hurt You? 01:05:07 Quick-Fire Round
1:18:29

Vibe coding is like politics, there is a lot of fluff and very few answers to the hard questions. 1. Of every $$ a vibe coding platform makes, what % goes to Anthropic? 2. How does the margin change when switching between models? 3. Do vibe coding platforms really have any defensibility… really? 4. Does Cursor and Cognition come down to eat consumer or do consumer platforms like Lovable, Replit and Base44 move up and eat more of the dev workflow? 5. Salesforce, Atlassian, Cloudflare, of all incumbents adding vibe coding, which is best and worst? Spotify 👉 Youtube 👉 Apple Podcasts 👉 My 6 takeaways with 👇 Timestamps: 00:00 Intro 01:13 The Decision Behind Selling Base44 11:56 How Base44 Thinks About Defensibility 16:37 Mapping the Future of the Market in 3–5 Years 23:18 I’m Worried About Google 30:47 Margins Do Not Matter 37:15 What’s the Metric for User Success? 41:52 Do Early Revenue Numbers Actually Matter in AI? 49:33 Where Should Smart Money Go Today? 53:01 How does Base44 beat Cursor? 57:47 Will the AI Boom Hit a Revenue Speed Bump? 01:01:13 Are AI Builders Overvalued or Is Wix Undervalued? 01:03:45 Does Not Being in Silicon Valley Help or Hurt You? 01:05:07 Quick-Fire Round

Harry Stebbings

17,514 görüntüleme • 8 ay önce

A Google Cloud engineer just showed how to build a full app with Claude from scratch he spent 26 minutes live on stage doing what most teams take weeks to do worth more than any $500 vibe-coding course here's what he covers: > zero to deployed app in a single session > handling five engineering roles alone with Claude > the exact workflow Google uses internally > no team, no setup, just Claude and a goal the people who figure out what Claude can actually do are building things everyone else thinks requires a team that's exactly why I wrote a step by step guide on how to build your first AI agent the guide is in the article below
26:23

A Google Cloud engineer just showed how to build a full app with Claude from scratch he spent 26 minutes live on stage doing what most teams take weeks to do worth more than any $500 vibe-coding course here's what he covers: > zero to deployed app in a single session > handling five engineering roles alone with Claude > the exact workflow Google uses internally > no team, no setup, just Claude and a goal the people who figure out what Claude can actually do are building things everyone else thinks requires a team that's exactly why I wrote a step by step guide on how to build your first AI agent the guide is in the article below

Anatoli Kopadze

459,568 görüntüleme • 2 ay önce

How to build long-horizon AI agents: behavior specs, ontologies, process supervision - my conversation with Mitchell Troyanovsky, co-founder of Basis 01:09 Why Everyone at Basis Was Whispering to AI when Stephanie Palazzolo walked in 04:12 Accounting as "an Intelligence Over the Economy" 06:11 What Makes an Agent Truly Long-Horizon 08:24 Inside an Autonomous, Multi-Day Tax Return 10:19 Agents That Hand Off Like Senior Engineers 11:17 A Brief History of Agents: From ReAct to Today 12:33 Why LLMs Have No Long-Term Memory 14:13 Why AutoGPT Didn't Live Up to Its Promise 15:51 The Three Breakthroughs: Opus 3, o1, o3 17:07 Why Reasoning Models Unlocked Agents 18:23 "Let's Verify Step by Step": The Road Not Taken 20:32 Pushing Back on the METR Chart 22:09 Why Coding Agents Won First 25:14 Why Real-World Agents Are Harder 26:55 How Accountants Verify Non-Deterministic Work 29:18 You Can't Scale Tax Returns Like Math 33:16 100 Evals Pass - So What? 35:53 Right Answer, Wrong Process 36:37 Behavior Specs, Explained 39:58 How Specific Should Behaviors Be? 42:18 Context Is Runtime Training Data 44:21 Who Judges the Judge? 46:45 The Move 37 Objection 50:02 The Magic Box Mental Model 52:41 "Nothing Has Changed Since o3" 54:56 Open-Sourcing Behavior Specs with Ankur Goyal Braintrust 59:45 Ontologies: A World for Agents to Live In 01:04:20 Documentation as Codebase 01:06:33 Why the Founding Fathers Were Context Engineers 01:09:05 Onboarding 300 Brilliant Alien Employees 01:11:10 Self-Improving Agent Systems 01:12:50 The Context Mistake Agent Builders Make 01:14:29 RL on Behavior Adherence 01:17:01 Will the Bitter Lesson Swallow the Harness 01:18:46 "Technical Moats Are Not Real Moats" 01:21:03 Advice for AI Builders
1:22:51

How to build long-horizon AI agents: behavior specs, ontologies, process supervision - my conversation with Mitchell Troyanovsky, co-founder of Basis 01:09 Why Everyone at Basis Was Whispering to AI when Stephanie Palazzolo walked in 04:12 Accounting as "an Intelligence Over the Economy" 06:11 What Makes an Agent Truly Long-Horizon 08:24 Inside an Autonomous, Multi-Day Tax Return 10:19 Agents That Hand Off Like Senior Engineers 11:17 A Brief History of Agents: From ReAct to Today 12:33 Why LLMs Have No Long-Term Memory 14:13 Why AutoGPT Didn't Live Up to Its Promise 15:51 The Three Breakthroughs: Opus 3, o1, o3 17:07 Why Reasoning Models Unlocked Agents 18:23 "Let's Verify Step by Step": The Road Not Taken 20:32 Pushing Back on the METR Chart 22:09 Why Coding Agents Won First 25:14 Why Real-World Agents Are Harder 26:55 How Accountants Verify Non-Deterministic Work 29:18 You Can't Scale Tax Returns Like Math 33:16 100 Evals Pass - So What? 35:53 Right Answer, Wrong Process 36:37 Behavior Specs, Explained 39:58 How Specific Should Behaviors Be? 42:18 Context Is Runtime Training Data 44:21 Who Judges the Judge? 46:45 The Move 37 Objection 50:02 The Magic Box Mental Model 52:41 "Nothing Has Changed Since o3" 54:56 Open-Sourcing Behavior Specs with Ankur Goyal Braintrust 59:45 Ontologies: A World for Agents to Live In 01:04:20 Documentation as Codebase 01:06:33 Why the Founding Fathers Were Context Engineers 01:09:05 Onboarding 300 Brilliant Alien Employees 01:11:10 Self-Improving Agent Systems 01:12:50 The Context Mistake Agent Builders Make 01:14:29 RL on Behavior Adherence 01:17:01 Will the Bitter Lesson Swallow the Harness 01:18:46 "Technical Moats Are Not Real Moats" 01:21:03 Advice for AI Builders

Matt Turck

14,518 görüntüleme • 3 gün önce