正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

SITUATION EXPLAINED: Coatue just published a chart showing how fast AI agents are replacing internal white-collar functions at OpenAI itself. • Chart tracks what percent of OpenAI's internal output tokens are generated by Codex vs ChatGPT • Engineering crossed 50% Codex usage at end of 2025, now basically 100%... show more

MTS

444,187 subscribers

18,841 次观看 • 1 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Newsom: "Silicon Valley and Detroit? Not as far apart as you'd think. Blue collar, white collar -- there are a lot of young folks with engineering degrees that have put out 25 resumes that are not getting a callback in this economy. Not just the blue collar worker that lost his or her job. That's a new working coalition that's coming together ... we have to focus on policies that produce visible results."

Newsom: "Silicon Valley and Detroit? Not as far apart as you'd think. Blue collar, white collar -- there are a lot of young folks with engineering degrees that have put out 25 resumes that are not getting a callback in this economy. Not just the blue collar worker that lost his or her job. That's a new working coalition that's coming together ... we have to focus on policies that produce visible results."

Aaron Rupar

36,775 次观看 • 23 天前

OpenAI engineer: "At OpenAI, 85% of our engineers are running hundreds of agents with Codex. The way you do it is graph engineering." In 30-minutes lecture, he explains how a single engineer can now do the work of an entire team, how far this has already gone inside OpenAI, and where it goes next. This is something you can't skip if you don't want to be left behind. Watch it, then read the full guide on graph engineering below.

OpenAI engineer: "At OpenAI, 85% of our engineers are running hundreds of agents with Codex. The way you do it is graph engineering." In 30-minutes lecture, he explains how a single engineer can now do the work of an entire team, how far this has already gone inside OpenAI, and where it goes next. This is something you can't skip if you don't want to be left behind. Watch it, then read the full guide on graph engineering below.

Movez

204,127 次观看 • 25 天前

Google engineer: "Delete your IDE, you don't need it anymore, in 2026 if you're still not building AI agents, it's crazy how behind you are. At Google, 85% of engineers are running agentic loops and graphs, that's how the engineering setup looks now." 25 minutes from a Google engineer with 30 years of experience, explaining where engineering is heading in 2026. This is something you can't skip if you don't want to be left behind. Watch it, then go further with the step-by-step guide below on how to build a system that improves itself.

Google engineer: "Delete your IDE, you don't need it anymore, in 2026 if you're still not building AI agents, it's crazy how behind you are. At Google, 85% of engineers are running agentic loops and graphs, that's how the engineering setup looks now." 25 minutes from a Google engineer with 30 years of experience, explaining where engineering is heading in 2026. This is something you can't skip if you don't want to be left behind. Watch it, then go further with the step-by-step guide below on how to build a system that improves itself.

Anatoli Kopadze

69,544 次观看 • 9 天前

"We are clearly entering a world where a product-minded engineer is now empowered to produce software without writing a line of code for it." In this conversation, Temporal CEO Samar Abbas joins a16z GPs Sarah Wang and Raghu Raghuram to cover: - Why agents are going from short-lived and interactive to long-running and async - Why the engineer of the future manages 15 parallel AI tasks at once - How OpenAI Codex runs millions of concurrent agent executions on Temporal - How real-time context engineering is exploding on Temporal - Why SaaS isn't dead, and value is migrating to APIs 00:00 Introduction 04:03 Temporal's origin story 11:14 Why agents raise the stakes 16:00 Specialized agents need durable RPC 25:20 Deep research agents 30:58 Execution histories as a superpower 39:04 Minimal viable long-running agent architecture 45:07 Context engineering at scale 52:40 Where value accrues: The "five-layer cake" and breakout AI applications Samar Abbas Sarah Wang Raghu Raghuram

"We are clearly entering a world where a product-minded engineer is now empowered to produce software without writing a line of code for it." In this conversation, Temporal CEO Samar Abbas joins a16z GPs Sarah Wang and Raghu Raghuram to cover: - Why agents are going from short-lived and interactive to long-running and async - Why the engineer of the future manages 15 parallel AI tasks at once - How OpenAI Codex runs millions of concurrent agent executions on Temporal - How real-time context engineering is exploding on Temporal - Why SaaS isn't dead, and value is migrating to APIs 00:00 Introduction 04:03 Temporal's origin story 11:14 Why agents raise the stakes 16:00 Specialized agents need durable RPC 25:20 Deep research agents 30:58 Execution histories as a superpower 39:04 Minimal viable long-running agent architecture 45:07 Context engineering at scale 52:40 Where value accrues: The "five-layer cake" and breakout AI applications Samar Abbas Sarah Wang Raghu Raghuram

a16z

45,989 次观看 • 6 个月前

As the Musk-vs-Altman fight is becoming pay-per-view TV, xAI is rebuilding, Anthropic is surging, and the economics of intelligence are starting to break old models, all while OpenAI sits on a $852B war chest. -- xAI admits it “was not built right the first time,” and Colossus 2 is now reportedly training 7 models at once. -- Software engineering roles are up 67,000, while roughly 80,000 layoffs hit other white-collar functions in Q1. -- Anthropic is making the biggest product pivot in AI from systems that answer questions to managed agents that actually do the work. - South Korea is mandating 40% solar rooftops, while the DOE is backing $800M in microreactors.

As the Musk-vs-Altman fight is becoming pay-per-view TV, xAI is rebuilding, Anthropic is surging, and the economics of intelligence are starting to break old models, all while OpenAI sits on a $852B war chest. -- xAI admits it “was not built right the first time,” and Colossus 2 is now reportedly training 7 models at once. -- Software engineering roles are up 67,000, while roughly 80,000 layoffs hit other white-collar functions in Q1. -- Anthropic is making the biggest product pivot in AI from systems that answer questions to managed agents that actually do the work. - South Korea is mandating 40% solar rooftops, while the DOE is backing $800M in microreactors.

Peter H. Diamandis, MD

37,719 次观看 • 4 个月前

🚨Elon Musk:“In 6-12 months GrokBot will be the #1 tool for building agentic systems. In a year, 100% of code will be written by LLMs.” At SpaceXAI, more than 70% of engineers are already using GrokBot to build self-learning agentic systems. In a 40-minute talk, Elon sits down with SpaceXAI engineers and discusses what the future of AI engineering is really going to look like.This conversation is honestly worth more than 2 hours of any Stanford lecture on AI engineering. Watch it today, then go read the article below on how to build a full Grok bot agents team from scratch. Who’s ready to see the future of AI engineering from the people actually building it?

🚨Elon Musk:“In 6-12 months GrokBot will be the #1 tool for building agentic systems. In a year, 100% of code will be written by LLMs.” At SpaceXAI, more than 70% of engineers are already using GrokBot to build self-learning agentic systems. In a 40-minute talk, Elon sits down with SpaceXAI engineers and discusses what the future of AI engineering is really going to look like.This conversation is honestly worth more than 2 hours of any Stanford lecture on AI engineering. Watch it today, then go read the article below on how to build a full Grok bot agents team from scratch. Who’s ready to see the future of AI engineering from the people actually building it?

Hosna ⚖️ בניטה

16,111 次观看 • 10 天前

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 01:45 - Build an LLM from scratch 17:22 - Learn how to actually use AI models 30:03 - Master prompt engineering 52:35 - Coordinate 100 agents as one human Most people are still learning how to prompt one model. Jeff Dean is showing the full stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating agent systems is the new one. This is 27 years of Google AI compressed into one hour. Bookmark and watch it today Then read the full Graph Engineering guide below ↓

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 01:45 - Build an LLM from scratch 17:22 - Learn how to actually use AI models 30:03 - Master prompt engineering 52:35 - Coordinate 100 agents as one human Most people are still learning how to prompt one model. Jeff Dean is showing the full stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating agent systems is the new one. This is 27 years of Google AI compressed into one hour. Bookmark and watch it today Then read the full Graph Engineering guide below ↓

Lunar

102,011 次观看 • 27 天前

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 0% → 01:45 - build an LLM from scratch 30% → 17:22 - learn how to actually use AI models 60% → 30:03 - master prompt engineering 100% → 52:35 - coordinate 100 agents as one human Most people are still learning how to prompt a single model. Jeff Dean is showing the entire stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating entire agent systems is the next one. This is 27 years of Google AI compressed into a single hour. Bookmark it and watch today. Then read the full Graph Engineering guide below ↓

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 0% → 01:45 - build an LLM from scratch 30% → 17:22 - learn how to actually use AI models 60% → 30:03 - master prompt engineering 100% → 52:35 - coordinate 100 agents as one human Most people are still learning how to prompt a single model. Jeff Dean is showing the entire stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating entire agent systems is the next one. This is 27 years of Google AI compressed into a single hour. Bookmark it and watch today. Then read the full Graph Engineering guide below ↓

Lunar

23,624 次观看 • 2 天前

WOW! I'm so excited about this. OpenAI Developers said Codex was good at Computer Use, but I wasn't prepared for this. For the last two weeks I've been working on a Computer Use skill to work with Linux. And while I had some success, it was a pretty frustrating experience. That is...until the breakthrough. Using accessibility tools, Codex can now control my entire computer, not just the browser. There are limits to this, of course, but what a time to be alive. This Computer Use skill will unlock and entirely new set of automations, all powered by Codex. Demonstration below 👇

WOW! I'm so excited about this. OpenAI Developers said Codex was good at Computer Use, but I wasn't prepared for this. For the last two weeks I've been working on a Computer Use skill to work with Linux. And while I had some success, it was a pretty frustrating experience. That is...until the breakthrough. Using accessibility tools, Codex can now control my entire computer, not just the browser. There are limits to this, of course, but what a time to be alive. This Computer Use skill will unlock and entirely new set of automations, all powered by Codex. Demonstration below 👇

am.will

79,698 次观看 • 5 个月前

Introducing: AI agents that ship product videos whenever you ship features. Engineering teams today are releasing product updates faster than ever before. But creating videos for every new release still takes weeks. Now, whenever you ship a feature to your software, Clueso deploys a team of AI agents to create launch videos, product demos, help tutorials, and more. Content can finally ship as quickly as code. Get access at:

Introducing: AI agents that ship product videos whenever you ship features. Engineering teams today are releasing product updates faster than ever before. But creating videos for every new release still takes weeks. Now, whenever you ship a feature to your software, Clueso deploys a team of AI agents to create launch videos, product demos, help tutorials, and more. Content can finally ship as quickly as code. Get access at:

Akash Anand

45,277 次观看 • 1 个月前

The next big thing is Asynchronous Coding Agents. Such Agents change our role as engineers from Conductor (directing one agent at a time) to Orchestrator (defining tasks for a "fleet" of agents working in parallel), for very well-defined tasks with human review. GitHub's Copilot Agent, as covered at #githubuniverse, is one such example I've been trying and liking. There are a number of options also worth considering, including Jules (also great!), Codex web by OpenAI Developers, and Anthropic's Claude Code for web. Editors like Cursor are also exploring this with some very interesting UX ideas. For the more advanced, orchestrators like Claude Squad, Magnet and Conductor by (nice balance on the engineering/product spectrum) are certainly worth considering. Some caveats: Orchestration has been working well for me in small-medium sized projects: for fixing UI bugs, adding small features etc. This can be good for tackling migrations, upgrading dependencies etc. Enterprise teams are very much still figuring out their "Conductor" patterns but eventually, I do see more orchestration becoming a standard part of the engineering workflow.

The next big thing is Asynchronous Coding Agents. Such Agents change our role as engineers from Conductor (directing one agent at a time) to Orchestrator (defining tasks for a "fleet" of agents working in parallel), for very well-defined tasks with human review. GitHub's Copilot Agent, as covered at #githubuniverse, is one such example I've been trying and liking. There are a number of options also worth considering, including Jules (also great!), Codex web by OpenAI Developers, and Anthropic's Claude Code for web. Editors like Cursor are also exploring this with some very interesting UX ideas. For the more advanced, orchestrators like Claude Squad, Magnet and Conductor by (nice balance on the engineering/product spectrum) are certainly worth considering. Some caveats: Orchestration has been working well for me in small-medium sized projects: for fixing UI bugs, adding small features etc. This can be good for tackling migrations, upgrading dependencies etc. Enterprise teams are very much still figuring out their "Conductor" patterns but eventually, I do see more orchestration becoming a standard part of the engineering workflow.

Addy Osmani

17,510 次观看 • 10 个月前

Citadel Founder Ken Griffin at the Stanford Leadership Forum 2026 discussing how AI agents compress elite analytical work from months into hours: "It has been interesting to watch...work that we would usually do with people with master's and PhDs in finance over the course of weeks or months, being done by AI agents over the course of hours or days. So these are not mid-tier white-collar jobs. These are like extraordinarily high-skilled jobs being ...automated by agentic AI. And I got to tell you, I went home one Friday, actually fairly depressed by this because you could just see how this was going to have such a dramatic impact on society."

Citadel Founder Ken Griffin at the Stanford Leadership Forum 2026 discussing how AI agents compress elite analytical work from months into hours: "It has been interesting to watch...work that we would usually do with people with master's and PhDs in finance over the course of weeks or months, being done by AI agents over the course of hours or days. So these are not mid-tier white-collar jobs. These are like extraordinarily high-skilled jobs being ...automated by agentic AI. And I got to tell you, I went home one Friday, actually fairly depressed by this because you could just see how this was going to have such a dramatic impact on society."

The Transcript

186,329 次观看 • 3 个月前

The same signals that marked the bottom are now showing up as signs of a local top "A lot of the signals we saw that were very key signs of a bottom are now showing up as key signs of a local top. I do not think this move is over, I'm just getting excited to hopefully buy some dips as price drops here. Bitcoin has a tendency not to give you the dips you want, but that's what I'm looking for" "Those signals are overbought RSI now. We had almost historically high RSI on the daily chart. We're seeing bearish divergence with overbought RSI on lower timeframes, meaning strength abating even as price continued up" "And we're at key levels of resistance. The 50 moving average on the weekly chart, we basically went right up to it and got rejected. That's the kind of area you'd be looking for price to retrace below, to gather up strength to go back above"

The same signals that marked the bottom are now showing up as signs of a local top "A lot of the signals we saw that were very key signs of a bottom are now showing up as key signs of a local top. I do not think this move is over, I'm just getting excited to hopefully buy some dips as price drops here. Bitcoin has a tendency not to give you the dips you want, but that's what I'm looking for" "Those signals are overbought RSI now. We had almost historically high RSI on the daily chart. We're seeing bearish divergence with overbought RSI on lower timeframes, meaning strength abating even as price continued up" "And we're at key levels of resistance. The 50 moving average on the weekly chart, we basically went right up to it and got rejected. That's the kind of area you'd be looking for price to retrace below, to gather up strength to go back above"

The Wolf Of All Streets

34,989 次观看 • 9 天前

Lead of Claude Code: "100% of our code at Anthropic is now shipped by Claude. Over 30% of it is written using /loops. Loop engineering is the future of coding. Loops are as big a shift as move from source coding to agents" in a 1-hour podcast, a Claude Code lead engineer shares the internal setup for building the #1 coding tool. It’s the most useful breakdown of how the Anthropic team uses Claude internally that I’ve seen on YouTube. Worth more than a $500 vibe-coding course.

Lead of Claude Code: "100% of our code at Anthropic is now shipped by Claude. Over 30% of it is written using /loops. Loop engineering is the future of coding. Loops are as big a shift as move from source coding to agents" in a 1-hour podcast, a Claude Code lead engineer shares the internal setup for building the #1 coding tool. It’s the most useful breakdown of how the Anthropic team uses Claude internally that I’ve seen on YouTube. Worth more than a $500 vibe-coding course.

Codez

37,770 次观看 • 2 个月前

Happy to properly launch Anna, the proactive AI agent for parents! Uncovering a bit of the technology behind the scenes! Building Anna is where I learned: 💾 Memory as plain text sucks. You need structured memory. Like a full-blown PostgreSQL DB that stores your tasks and calendar in a structured manner. Most harnesses are good at coding-related stuff. Let it do the query. Don't let it vibe-search the memory. Let it vibe your SQL query 💭 Dreaming is a useful concept for enhancing memory to feed the LLM context. But DO NOT vibe your dream. Asking your agent to "hey, just dream and keep the relevant memory around" is a recipe for deleting a bunch of important information and keeping trash around. Your dream needs to have some Taxonomy (or better, Ontology). What information is important? For who? With what object? What can they do? And again, these are impossible to describe and act well without a proper schema 🔄 Loop Engineering is important for smoothing out rough edges in the system we build. But even expensive loop engineering with a state-of-the-art model can't out-engineer bad system design. The highest leverage an AI Engineer can do is actually building the right system design, and having an eye on both product delight and engineering scalability There are several more insights that I plan to cover in a dedicated video about Agentic AI Engineering. But it's actually a huge relief that the future of software engineering... is still software engineering

Happy to properly launch Anna, the proactive AI agent for parents! Uncovering a bit of the technology behind the scenes! Building Anna is where I learned: 💾 Memory as plain text sucks. You need structured memory. Like a full-blown PostgreSQL DB that stores your tasks and calendar in a structured manner. Most harnesses are good at coding-related stuff. Let it do the query. Don't let it vibe-search the memory. Let it vibe your SQL query 💭 Dreaming is a useful concept for enhancing memory to feed the LLM context. But DO NOT vibe your dream. Asking your agent to "hey, just dream and keep the relevant memory around" is a recipe for deleting a bunch of important information and keeping trash around. Your dream needs to have some Taxonomy (or better, Ontology). What information is important? For who? With what object? What can they do? And again, these are impossible to describe and act well without a proper schema 🔄 Loop Engineering is important for smoothing out rough edges in the system we build. But even expensive loop engineering with a state-of-the-art model can't out-engineer bad system design. The highest leverage an AI Engineer can do is actually building the right system design, and having an eye on both product delight and engineering scalability There are several more insights that I plan to cover in a dedicated video about Agentic AI Engineering. But it's actually a huge relief that the future of software engineering... is still software engineering

Gogo | Dota for Toxicity

30,766 次观看 • 2 个月前

"I ran a startup for 5 years. I thought we moved fast. Then I joined OpenAI—and realized I had no idea what fast actually meant." Head of Product for OpenAI's Codex Alexander Embiricos and I chat about: 🔸 How OpenAI built the Sora Android app (which hit #1) in just 18 days by using Codex 🔸 Codex growing 20x since August 🔸 How they're thinking about the dev bottleneck shifting from coding to reviewing AI-generated work 🔸 Why coding will be a core competency for every AI agent—because writing code is how agents use computers best 🔸 Why the bottleneck to AGI-level productivity isn’t model capability—it’s human typing speed 🔸 The vision of Codex, and how they plan to win Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 Fin — The #1 AI agent for customer service: 🏆 Atlassian Jira Product Discovery — Confidence to build the right thing:

"I ran a startup for 5 years. I thought we moved fast. Then I joined OpenAI—and realized I had no idea what fast actually meant." Head of Product for OpenAI's Codex Alexander Embiricos and I chat about: 🔸 How OpenAI built the Sora Android app (which hit #1) in just 18 days by using Codex 🔸 Codex growing 20x since August 🔸 How they're thinking about the dev bottleneck shifting from coding to reviewing AI-generated work 🔸 Why coding will be a core competency for every AI agent—because writing code is how agents use computers best 🔸 Why the bottleneck to AGI-level productivity isn’t model capability—it’s human typing speed 🔸 The vision of Codex, and how they plan to win Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 Fin — The #1 AI agent for customer service: 🏆 Atlassian Jira Product Discovery — Confidence to build the right thing:

Lenny Rachitsky

334,564 次观看 • 8 个月前

Tonight I sat back from my screen and couldn't speak. Computational engineering logic fused with modern CAD, and what came out wasn't just a design. It was a glimpse. Every generation thinks it's living at the hinge of history. Ours actually is! The machines we imagined in dark theaters as kids are being born right now, not in billion-dollar labs, but at desks like mine. And you can start building them.

Tonight I sat back from my screen and couldn't speak. Computational engineering logic fused with modern CAD, and what came out wasn't just a design. It was a glimpse. Every generation thinks it's living at the hinge of history. Ours actually is! The machines we imagined in dark theaters as kids are being born right now, not in billion-dollar labs, but at desks like mine. And you can start building them.

Dylan Small

48,200 次观看 • 1 个月前

White-collar jobs are getting hit harder by AI than blue-collar ones right now. Tasks like writing, data analysis, coding, research, and admin work are exactly what current AI excels at, unlike physical manufacturing work. One analysis puts white-collar automation potential at around 30%, compared to under 1% for blue-collar roles. Goldman Sachs says AI could automate 25% of all US work hours, with 300 million jobs globally exposed, though actual displacement over 10 years might be more like 6-7% of workers. What’s actually happening: AI was linked to about 4.5% of job losses in 2025, and in April 2026 it accounted for 26% of layoffs in one report. Entry-level white-collar roles, junior analysts, coders, paralegals, customer service, are seeing the biggest squeeze, with hiring slowing sharply in AI-exposed fields. BCG projects 50-55% of US jobs will change significantly in the next couple years, but only 10-15% get fully eliminated in the next five. A lot of roles are getting augmented, people using AI tools to work faster rather than being replaced outright. It’s still early, so the big wave of losses some CEOs are warning about hasn’t fully materialized yet. The real story so far is fewer new office jobs being created, especially at the entry level.

White-collar jobs are getting hit harder by AI than blue-collar ones right now. Tasks like writing, data analysis, coding, research, and admin work are exactly what current AI excels at, unlike physical manufacturing work. One analysis puts white-collar automation potential at around 30%, compared to under 1% for blue-collar roles. Goldman Sachs says AI could automate 25% of all US work hours, with 300 million jobs globally exposed, though actual displacement over 10 years might be more like 6-7% of workers. What’s actually happening: AI was linked to about 4.5% of job losses in 2025, and in April 2026 it accounted for 26% of layoffs in one report. Entry-level white-collar roles, junior analysts, coders, paralegals, customer service, are seeing the biggest squeeze, with hiring slowing sharply in AI-exposed fields. BCG projects 50-55% of US jobs will change significantly in the next couple years, but only 10-15% get fully eliminated in the next five. A lot of roles are getting augmented, people using AI tools to work faster rather than being replaced outright. It’s still early, so the big wave of losses some CEOs are warning about hasn’t fully materialized yet. The real story so far is fewer new office jobs being created, especially at the entry level.

Patricia 🇺🇸

74,890 次观看 • 3 个月前

Three months ago, Codex was trash for knowledge work. Now it's my daily driver. I use it for writing, recruiting, deep engineering work, and everything in between. It even keeps me at inbox 0. I chatted with Every 🧱's head of growth Austin Austin Tedesco on Every 🧱's AI & I about what changed, and why he now spends 80% of his working time in the Codex desktop app too. We get into: - How Codex went from making Austin feel like an idiot to being the place he goes to get stuff done, including complex tasks like writing go-to-market plans using existing material from Slack, Notion, and meeting transcripts. - Why the Codex’s desktop app, which is faster and more reliable than Claude Desktop/Cowork, is the real differentiator. - How I source candidates with Codex by having it identify career arcs, not keywords—my go-to move is identifying organizations likely to teach the skills Every needs for a role, and then find candidates from that pool who have since gone on to work in AI. This is a must-watch for anyone who's wondering whether it’s finally time to give Codex a try. Watch below! Timestamps How Codex went from a tool for senior engineers to a daily driver for knowledge work: 00:00:57 How Claude Code proved that a great coding agent works for any knowledge work: 00:02:42 Austin's switch to Codex: 00:07:24 How Austin set up Codex with folders, keys, and reviewer agents: 00:13:48 Using Codex to brainstorm automations across Gmail, Slack, and Notion: 00:18:24 How Austin manages the human review step when Codex is drafting communications: 00:22:42 Using Codex to build specialized agents inspired by product executive Claire Vo: 00:28:54 Synthesizing meeting transcripts and Slack threads into a go-to-market plan: 00:31:09 Building a live KPI tracker in Notion that agents can read: 00:40:15 Using Codex for recruiting: 00:44:54

Three months ago, Codex was trash for knowledge work. Now it's my daily driver. I use it for writing, recruiting, deep engineering work, and everything in between. It even keeps me at inbox 0. I chatted with Every 🧱's head of growth Austin Austin Tedesco on Every 🧱's AI & I about what changed, and why he now spends 80% of his working time in the Codex desktop app too. We get into: - How Codex went from making Austin feel like an idiot to being the place he goes to get stuff done, including complex tasks like writing go-to-market plans using existing material from Slack, Notion, and meeting transcripts. - Why the Codex’s desktop app, which is faster and more reliable than Claude Desktop/Cowork, is the real differentiator. - How I source candidates with Codex by having it identify career arcs, not keywords—my go-to move is identifying organizations likely to teach the skills Every needs for a role, and then find candidates from that pool who have since gone on to work in AI. This is a must-watch for anyone who's wondering whether it’s finally time to give Codex a try. Watch below! Timestamps How Codex went from a tool for senior engineers to a daily driver for knowledge work: 00:00:57 How Claude Code proved that a great coding agent works for any knowledge work: 00:02:42 Austin's switch to Codex: 00:07:24 How Austin set up Codex with folders, keys, and reviewer agents: 00:13:48 Using Codex to brainstorm automations across Gmail, Slack, and Notion: 00:18:24 How Austin manages the human review step when Codex is drafting communications: 00:22:42 Using Codex to build specialized agents inspired by product executive Claire Vo: 00:28:54 Synthesizing meeting transcripts and Slack threads into a go-to-market plan: 00:31:09 Building a live KPI tracker in Notion that agents can read: 00:40:15 Using Codex for recruiting: 00:44:54

Dan Shipper 📧

55,561 次观看 • 4 个月前