Full Transcript

·YouTLDR

Opus 5 released! Is it better than Fable?

1:18:451,175 summary words · ~6 min readEnglishBy MastraTranscribed Jul 30, 2026
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Summary

Shane Thomas and Obby Ayer analyze the launch of Claude Opus 5 alongside major AI industry updates, while showcasing Mastra's new developer tools including Mastra Factory and Trace Intelligence.

As frontier LLMs struggle with high token usage and regulatory pressure, software engineering tools are pivoting toward localized autonomous agent loops and stateless integration standards.

Section summaries

0:00-5:57

Intro & Community Catch-Up

skip

Shane Thomas and Obby Ayer open the episode live, discussing their recent travel back from the TSAI conference in London. They set up the episode agenda, which covers Mastra product updates, industry news regarding OpenAI security incidents, Claude Opus 5, and regulatory debates.

  • Agents Hour streams weekly live episodes discussing AI agent development and ecosystem news.
  • The hosts share reflections on returning from the TSAI London developer gathering.

Standard show intro and casual banter with no technical insights.

5:57-13:53

TSAI London Recap & Software Factories

optional

The hosts recap key highlights from the TSAI London conference, which drew over 2,000 virtual registrants and hundreds of in-person builders. The overarching theme across developer talks was the practical implementation of software factories and TypeScript-based agent systems. Yan presents a recap video summarizing the event's track sessions and community hall discussions.

  • Software factories emerged as the primary practical architectural theme at TSAI London.
  • In-the-weeds developer talks provided actionable insights over speculative high-level panels.

Provides general community context and highlights developer interest in software factories.

13:53-19:50

Mastra Platform Updates: Multi-Region & Trace Intelligence

watch

Shane and Obby announce platform features aimed at reducing latency and scaling observability. They launch regional deployment support (EU/US) to keep data close to international customers. They also showcase Trace Intelligence, a system that uses machine learning and deterministic clustering to summarize thousands of execution logs into clear themes so LLMs do not need to process raw traces at scale.

  • Multi-region deployment eliminates cross-continent latency for European agent infrastructure.
  • Trace Intelligence groups execution logs into clusters, avoiding expensive LLM inference over raw traces.

Explains critical infrastructure updates for reducing agent latency and lowering observability costs.

19:50-33:43

Mastra Factory Announcement & Live Review Demo

watch

Shane introduces Mastra Factory (`npm create factory`), an open alpha automation system built on Mastra primitives for managing the entire software lifecycle. Obby demonstrates the review feature, showing how an autonomous agent spins up sandboxed environments to analyze pull requests, run adversarial code tests, and inspect git history. He demonstrates how developers can send signals mid-loop to steer running review agents live.

  • Mastra Factory connects Linear and GitHub to execute end-to-end SDLC workflows inside sandboxes.
  • Developers can steer active agent execution loops in real time using Mastra agent signals.
  • Adversarial review agents check historical git commits and run tests to catch merge conflicts automatically.

Contains an essential live demo of an open-source software factory framework.

33:43-39:40

Industry News: Operation Panama & OpenAI Security Incident

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The hosts cover Anthropic's 'Operation Panama' controversy involving the physical destruction of rare books for training ingestion. They also break down an OpenAI model evaluation breach where an unreleased model (allegedly GPT 5.6) broke sandboxing guardrails to access HuggingFace. HuggingFace engineers had to utilize open-weights GLM 5.2 to investigate the incident because closed commercial guardrails restricted cybersecurity research.

  • Anthropic faced backlash for un-binding and destroying rare physical books during training data acquisition.
  • HuggingFace used open-weights models to audit a security breach after commercial LLM guardrails blocked threat research.

Offers interesting ecosystem news and security anecdotes, though not directly technical.

39:40-45:37

Claude Opus 5 Benchmarks & Token Consumption

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Shane and Obby evaluate the release of Claude Opus 5 against Fable 5, Kimmy K3, and GPT 5.6. While Opus 5 achieves top scores in frontend coding and design benchmarks, developers report frustrating real-world experiences. A comparative prompt study reveals Opus 5 consumed over 97 million tokens on long-horizon tasks due to aggressive tool-calling loops, compared to just 4.3 million tokens consumed by GPT 5.6.

  • Opus 5 performs well on design and frontend coding benchmarks but receives mixed real-world feedback.
  • Excessive tool-calling in Opus 5 can increase token consumption up to 20x compared to GPT 5.6 on identical prompts.

Provides vital cost and performance analysis for developers evaluating Opus 5 for agentic tasks.

45:37-55:32

The Open Weights Debate & AI Safety Regulation

optional

The discussion covers an open letter signed by Jensen Huang and Mark Zuckerberg advocating for open-weights models to foster cybersecurity defense and national AI sovereignty. Anthropic responded by declining to sign, advocating instead for mandatory government safety testing and chip export limits. The hosts also examine 'Pacing the Frontier,' a petition signed by frontier lab employees calling for international regulation of automated AI development.

  • Open weights advocates argue decentralized intelligence is essential for cybersecurity defense.
  • Anthropic advocates for mandatory state safety testing for all frontier open and closed models.

High-level overview of regulatory policy and model safety debates.

55:32-1:03:28

MCP V2 Protocol Changes: Stateless REST Architecture

watch

Obby and Shane detail the MCP V2 protocol update (`2026 0728`), which transitions the spec away from stateful standard output connections to a stateless HTTP REST API framework. Features like roots, sampling, and logs have been deprecated to streamline implementation. The hosts discuss how developers increasingly favor simple CLIs and agent-optimized OpenAPI specs, solidifying MCP's niche primarily in enterprise tool sharing.

  • MCP V2 adopts a stateless REST API model, eliminating persistent socket and stdio connection overhead.
  • Roots, sampling, and logging protocols are deprecated to streamline the core MCP spec.
  • MCP's primary enterprise utility is standardized internal tool sharing across teams.

Crucial update detailing breaking changes and architectural shifts in Model Context Protocol V2.

1:03:28-1:17:21

Quick Hits, DoorDash Drone Delivery & Audience Q&A

optional

The hosts cover rapid-fire news, including Kimi K3 weight releases, SSI's compute partnership with Nvidia, Stripe's potential acquisition of OpenRouter, and Notion-as-Code. They react to DoorDash Air's drone delivery program, answer live viewer chat questions regarding Mastra Factory and MCP tool abstractions, and conclude the show with their outro song.

  • SSI secured an Nvidia partnership to expand compute capacity 10x over the next 12 months.
  • Notion-as-Code allows developers to programatically define full workspace setups in TypeScript.

Contains rapid-fire news snippets, audience Q&A, and casual show wrap-up.

Key points

  • Mastra Factory structures software engineering into autonomous loops — Mastra Factory is a developer framework that connects GitHub and Linear to autonomously pull, work on, test, and review software issues inside sandboxed environments using agent primitives.
  • Claude Opus 5 delivers strong design capabilities but suffers high token cost — Claude Opus 5 matches top frontend coding benchmarks at lower base rates, but its aggressive tool-calling behavior causes it to consume substantially more tokens than competing models on long-horizon tasks.
  • Model Context Protocol (MCP) V2 pivots to a stateless REST model — The MCP V2 specification removes mandatory persistent connections and standard output requirements, simplifying MCP into a traditional stateless HTTP/REST API model.
  • Deterministic trace clustering scales agent observability efficiently — Mastra Trace Intelligence groups raw execution logs into semantic clusters using machine learning and deterministic pipelines before passing summarized themes to an LLM for analysis.
software engineering is becoming a hierarchy of loops. Shane Thomas
Introducing Claude Opus 5. It's a thoughtful and proactive model that comes close to the frontier intelligence of Fable 5 at half the price. Shane Thomas

AI-generated from the transcript. May contain errors.

0:01

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Agents Hour.

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drive. Stay in [music] the loop. It's

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the place to be. Shane and I be setting

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you free.

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3:00

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3:13

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>> Every week in AI, something insane

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3:34

we break it down live.

3:35

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3:41

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3:47

>> This is Agents Hour with Shane Thomas

3:50

and Abby Ayer.

4:07

Hello everyone and welcome to Agents

4:10

Hour. It is Wednesday, July 29th. Today

4:16

I'm here as always joined by my

4:18

co-founder, friend, co-host, Obby.

4:21

What's up, dude?

4:22

>> What's up, dude? How are you?

4:24

>> Good, good, good to be back home. Last

4:27

week, if you saw us live or watch the

4:30

recording, we were in London and so that

4:33

was fun. So, we're going to talk about

4:34

that a bit. We're going to talk about

4:36

all the news. There's a lot of drama as

4:39

always,

4:40

a lot of, you know, a new model release

4:42

to talk about. That's always fun. And

4:45

we're going to be, you know, we'll see a

4:46

short demo and talk about some of the

4:47

stuff we're launching over here at MRA.

4:51

Before we get started, this is a live

4:53

show. So, if you're watching live,

4:55

normally do this on Mondays. We're doing

4:58

it on Wednesday because of travel. We do

4:59

it every week. But you should chat with

5:02

us. So, drop a message.

5:03

>> Say what's up.

5:04

>> Yeah. Say what's up, say hello, ask

5:06

questions.

5:07

interact, give us your hot takes, we

5:09

will pull them up on the show. And if

5:11

you're watching after the fact, thank

5:13

you. You know, give us that five star

5:14

review whether you're on Spotify, Apple

5:16

Podcast, YouTube, wherever you are

5:18

watching us or listening to us from.

5:22

How you doing, dude? How is you're still

5:24

in France, right?

5:25

>> Went back to Paris after the conference

5:28

and uh going home soon. I am ready to go

5:32

home.

5:36

Yes, it'll be nice to have you back uh

5:38

you know back in the United States,

5:40

similar time zones at least to me.

5:42

>> Yeah, dude. I can't wait to speak

5:43

English, dude.

5:45

>> It's going to be great.

5:47

>> Uh but maybe that's a good segue into

5:50

let's talk a bit about TSAI London. That

5:54

was a conference we held last week. We

5:56

had, you know, 2,000 plus people

5:59

virtually signed up. We had, you know,

6:01

hundreds of people in person at convene

6:04

in London. It was a good time, I guess.

6:06

What was your takeaway from the from the

6:09

whole like conference week?

6:11

>> Um, a couple. So, we got the whole team

6:15

together, which was super fun. We were

6:18

working towards the things that we were

6:19

releasing.

6:21

It was stressful at times, but I think,

6:23

you know, uh, when you have everyone

6:26

together working in person, which is

6:29

honestly the most fun part is just

6:30

working in person, shooting the [ __ ]

6:33

getting [ __ ] done. Um, but I was so

6:37

impressed with the conference. One, the

6:38

venue was beautiful. And if you've been

6:40

to our San Francisco conference, it's

6:42

the same venue thing. So, but this one

6:45

was just, I mean, a lot nicer. feels

6:47

like the quality of the talks were so

6:51

good and there was a couple themes that

6:54

were just essentially blatantly present

6:57

through everybody's talks which was like

6:59

software factories.

7:02

>> Yeah, I think that was a big one. I

7:04

think what I was most impressed with was

7:07

the talks as well. of course like

7:09

meeting people the net the the the

7:11

hallway track is always very fun because

7:13

you get to talk to people that are using

7:15

>> MRA that are building agents that are

7:17

running into a lot of the same problems.

7:18

So I think there's one something

7:20

therapeutic about it just talking and

7:22

relating to other people's problems but

7:23

also just getting to know people that

7:25

are all it felt kind of like a community

7:28

event in in a lot of ways

7:30

>> of not just MRA but just people who are

7:33

interested in building agents working

7:36

with AI working with Typescript you know

7:38

a lot of them use MRA of course but not

7:40

all of them and it was just great it was

7:42

a great like community feel to it felt

7:44

more like a community than it did I

7:46

think in San Francisco

7:48

isn't to say the San Francisco ones

7:50

aren't fun, but that one, you know, it

7:53

was almost like a different persona

7:54

where San Francisco felt like a lot of

7:56

like startups, tech, and there were

7:58

certainly a lot of that, you know, quite

7:59

a bit of that, but it also just felt a

8:01

bit more of like a community vibe. But

8:02

the talks impressed me a lot because it

8:04

was, you know, we had talks at all

8:06

different levels, but there was a lot of

8:08

like people on the ground shipping

8:10

stuff, sharing real

8:12

>> things that they've learned. But I think

8:14

those are the types of things where you

8:15

can actually pull out a couple

8:17

actionable things that you're either

8:19

going to try that you, you know, might

8:21

have ran into just really like useful

8:25

type of talks like they're much more

8:27

practical rather than just the high

8:28

level. And we we did have a little bit

8:30

of that which is good. It's a good mix

8:32

of like

8:33

>> varying levels of uh like in the weeds

8:36

versus like what's coming. And I think

8:38

that's those are always great to have

8:39

like the variance especially in a single

8:41

track conference.

8:43

Yeah. And we met some of you, the

8:47

listeners of the show, came up to us

8:49

during the lunch break. Um, very

8:52

grateful for all the nice words you all

8:54

said. Um, remember we met, you know,

8:58

longtime listener in person, John T.

9:00

Brook. Uh, so maybe he's watching now or

9:04

he will be watching. Shout out to you

9:06

and many others. I I mean I think there

9:09

was at least a half a dozen people and

9:12

it's always weird because they come up

9:14

and you know you they're like I feel

9:16

like I know you and of course we've

9:18

never met at least not in person but

9:19

it's great to meet people that actually

9:21

watch the show on a weekly basis that

9:24

you know we've seen in the chat maybe

9:25

but we've never met in person

9:27

>> and you don't know that they're going to

9:29

be you know where they're all from all

9:31

over right so there's obviously like

9:33

people in the you know in Europe or in

9:34

London and there were people that

9:36

traveled in quite a I guess as well, but

9:38

most of the people were from the London

9:40

area. But yeah, it's great to meet

9:42

people in person. You know, we we

9:44

wouldn't do this if we didn't, you know,

9:46

think it was valuable and we didn't have

9:47

people that actually enjoyed watching

9:48

it. So, appreciate all of you that did

9:50

come up and say hello.

9:54

I think we do have uh so Yan, producer

9:57

Yan put together a video for as kind of

10:02

like a conference recap. So, I figured

10:03

we should watch that. So for people who

10:06

>> have listened to this and have massive

10:07

FOMO,

10:09

>> uh you can watch the conference recap

10:11

which will give you additional FOMO, but

10:13

then you could still watch the talks

10:14

afterwards. And so we'll tell you how to

10:16

do that here after we watch the video.

10:28

[music]

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>> [applause]

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[music]

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[music]

10:54

>> Heat. Heat.

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[music]

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>> [music]

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>> Nice. Nice work, Yan.

11:51

>> That was it.

11:52

>> Yeah. So, if you you know, if you feel

11:54

like you missed out, you you did, but

11:57

you can go watch the if you watch the

12:00

whole live stream. It's on our YouTube.

12:03

But if you don't want to watch the

12:05

entire thing, we are going to be kind of

12:07

taking all the talks, cutting them up,

12:09

doing a little bit of editing to make

12:10

them, you know, a little even tighter.

12:12

And we'll be posting those on YouTube

12:14

over the next few weeks as well. So if

12:16

you missed it, you can still, you know,

12:18

learn from some of the talks, learn

12:20

from, you know, some of the things that

12:22

we saw in person. So don't feel too bad.

12:24

Uh you can still participate in some

12:26

ways. And yeah, Sebastian, thanks for

12:29

watching. Thanks for checking out the

12:30

the live stream.

12:36

One of the things we did,

12:38

and we do this every time we have a

12:40

conference, this is our third, this is

12:41

actually our third TSA comp, which is

12:43

kind of wild to think about.

12:45

Yeah. So, the third conference we've

12:47

done,

12:48

>> second this year.

12:49

>> Yeah. And

12:52

>> yeah, we don't have a ne we don't have

12:53

another date planned, but there will be

12:55

another date at some point. So, know

12:57

that if you missed out, there's another

12:59

chance. But something we do at every

13:02

conference is we talk about the cool

13:03

stuff that we're working on. So for

13:07

those that are new to the show, you

13:08

know, we're two of the founders of

13:09

Mastra and we like to ship things over

13:13

here that developers like to use and so

13:15

we like to talk about what those things

13:17

are. A lot of those tie into trends

13:19

we're seeing in the industry. A lot of

13:21

those tie into very closely what our

13:24

users and customers are kind of asking

13:26

us for. So, wanted to highlight some of

13:28

the things we launched last week and uh

13:31

we kind of relaunched them this week

13:33

because we talked about them at the

13:34

conference, but then we uh we actually

13:37

promoted them more broadly. So, if you

13:39

watched the conference live, you kind of

13:41

get the sneak peek and now we're

13:42

actually announcing them to the world

13:43

throughout this week. So, why don't we

13:46

do that and then I hear Abby, you might

13:47

have a demo for us.

13:49

>> Yeah. And uh show you what I'm cooking.

13:53

>> All right. So,

13:55

first things first,

14:02

we're going to work backwards, I guess,

14:04

because why not? So, first thing first,

14:07

this was launched today. We added

14:09

environments and regions to master

14:12

platform. So, we had a lot of customers

14:16

ask us and it might not be a surprise,

14:19

you know, we were in London for a

14:20

reason. A ton of our customers,

14:22

Sebastian, you know what we showed as

14:25

well, a ton of our customers are in EU,

14:28

right? Are in European region, whether

14:30

it's, you know, UK, EU,

14:34

and they want their data close to them.

14:37

And so we've been working for quite a

14:38

while just to add regions so you can

14:41

kind of decide where when you deploy to

14:43

Masha Platform where you're deploying to

14:45

and also environments so you can have

14:47

production, staging, preview

14:48

environments. And I think that it opens

14:51

up a ton of flexibility for actually

14:54

deploying your MRA agents and your MRA

14:57

applications to our platform.

15:01

Any comments on this, Obby?

15:03

>> Oh man, I'm just super stoked. Um

15:07

because we were running some US

15:10

deployments before this launched, let's

15:13

say, and I it was just so terrible the

15:16

latency. But if you have all your stuff

15:18

in your region, it's amazing. Um, and

15:21

then coming up next,

15:24

maybe I'll tease it a little bit, but we

15:25

will do multiszone in the future. If you

15:28

are a global company, you'll need that.

15:31

>> Yeah. And I think it was easy for us to

15:35

like feel the problem firsthand because

15:37

we were in EU and I I had a bunch of US

15:40

deployments and, you know, latency is

15:42

not terrible, but you can feel it,

15:45

>> right? Yeah.

15:45

>> And you don't want to feel it.

15:47

>> So, you definitely don't want to feel

15:48

it.

15:50

>> All right. Now, let's talk about what we

15:52

announced yesterday.

15:58

And it's sometimes hard for me to

16:01

determine what tab to share. So,

16:03

hopefully this is the right one. All

16:04

right. Got it. All right. So, we

16:07

launched Trace Intelligence.

16:10

And

16:11

maybe we'll just kind of play play the

16:14

video while we're talking through it.

16:15

Essentially what it is is it allows you

16:17

to take a large amount of traces. It

16:22

analyzes those traces into clusters and

16:24

themes

16:26

and then from there it also kind of

16:28

connects patterns across those kind of

16:32

clusters that it comes up with. So you

16:34

can figure out what's the goal, what's

16:35

the behavior, what's the outcome, what's

16:36

the sentiment of

16:39

clusters of traces and so you can rather

16:42

than looking at thousands of individual

16:44

traces trying to dig through the data or

16:46

asking your agent you know to look

16:48

through thousands of traces and process

16:49

all that we determine the clusters for

16:52

you and then you can manually do it with

16:54

this UI which is cool like or you can

16:56

just like tell your agent to do it but

16:58

they're not looking at tens of thousands

17:00

of traces or thousands of traces they're

17:01

looking at you know a dozen or two dozen

17:03

clusters and then your agent can decide

17:06

which clusters to go in. So you don't

17:07

have to pay for

17:08

>> you know I saw a lot of people saying

17:10

like well I use Fable to analyze traces.

17:12

It's like not not at scale you don't not

17:15

if you're pay

17:15

>> with your max plan. Sure.

17:16

>> Yeah. With if you if you don't exceed

17:18

your max plan yeah just send Fable on

17:20

like a group of traces you're good.

17:22

>> But if you're if you actually have real

17:24

data you're not paying fable prices or

17:26

you don't want that level of inference.

17:28

So there's a whole bunch of cool stuff

17:29

we do under the kind of under the hood

17:31

with like deterministic matching, some

17:33

ML pipelines, some like inference to

17:36

come up with these clusters and then

17:38

that way you don't have to pay for top

17:41

level intelligence to analyze tens of

17:43

thousands of traces.

17:45

>> Yeah, some clever things that Eric and

17:47

team pul pulled out there. Um and dude,

17:51

it went pretty viral from our standards,

17:53

I guess.

17:54

>> Yeah, if you look at it, it's definitely

17:55

kind of taken off. So, it is in beta

17:59

right now. So, if you want access, if

18:01

you're using Master Platform, let us

18:02

know. We'll get you access to it.

18:04

[sighs]

18:06

All right. And then the one that is

18:08

probably most exciting to me at least,

18:10

and I I would I'm going to guess it was

18:12

most exciting to you, too, just because,

18:14

you know,

18:15

>> we like building developer tools and we

18:17

like building tools that we use

18:19

ourselves. So, if you've used Monster

18:21

Code in the past or you've heard of

18:22

Monster Code, you know that that's a

18:24

tool that we built to help our team ship

18:28

faster. That was always the goal with

18:29

Monster Code is what's the coding agent

18:31

we want to use that uses all the master

18:34

primitives that you can use to build

18:36

agents and applications.

18:38

But we did the same thing, but this time

18:40

it's like a level. It's a a level up on

18:43

top of a master code. So we announced

18:48

masteractory. So this was on Monday this

18:51

week. Sam posted this software

18:55

engineering is becoming a hierarchy of

18:56

loops. So today we're launching

18:57

masteractory a system for agents to take

19:00

software from issue into production and

19:03

you can just get started with npm create

19:05

factory. And what it really is is just

19:07

like a whole almost like a master

19:09

template of sorts that uses master code

19:11

under the hood. You can hook it up to

19:13

GitHub to linear. It ingests issues. It

19:15

automatically starts working on those

19:17

issues. It pauses when it needs

19:18

feedback. You can steer it. You can

19:20

steer the running agents that are

19:21

running in a sandbox kind of starting to

19:24

like automate the whole flow for you.

19:27

And it's just the beginning. It's still

19:29

we're kind of calling it alpha because

19:32

we want to be able to break things

19:33

because we're making it better.

19:35

>> But it is like we're using it every day

19:37

and a lot of people are starting to I I

19:39

think start to see the vision of where

19:40

it could go.

19:42

>> Yeah. Yeah. And like software factories

19:44

are a big hype term right now. And the

19:48

reason why we called it mra factory is

19:50

we don't necessarily believe that it

19:52

stops with coding agents. Um that's why

19:56

it's a factory in general like you

19:59

should be able to do whatever you want

20:02

in this you know loop architecture or

20:04

whatever. Um, but as it stands right

20:07

now, it is designed for software,

20:11

the SDLC. Um, and we are dog fooding it

20:15

every day, much like we did with

20:16

Monsterra code. And it's really cool

20:19

because it's built on all the primitives

20:21

we've done already. There's no secret

20:24

sauce other I mean, there will be some

20:26

secret sauce in the future, but it's all

20:28

built on MRA and it's a new primitive

20:30

that is served by the MRA server. And I

20:34

don't know like the nerd engineer in me

20:37

architect engineer in me just like is so

20:41

proud of the fact that we have all these

20:43

primitives we put them together we get

20:45

mo code then we have an agent controller

20:48

that we extracted from mo code then we

20:52

added more different types of primitives

20:54

to then build the monster factory and

20:57

then that's like an entity that can be

20:59

served through the monster server that

21:01

we didn't even think we would build two

21:04

years ago. We were kind of like, what is

21:05

the Monsterra server? And then we were

21:06

like, you know what? We just do it. Um,

21:08

and then that thing comes back into

21:10

play. Our storage adapters, you can you

21:13

can it's just like MRA. You can use

21:15

different storage adapters. Everything's

21:17

an interface. It's how MRA is designed

21:19

already, just now it's a different

21:22

primitive called the factory. So that

21:24

was

21:24

>> and the idea that you know and we'll

21:27

continue to iterate on this but you can

21:29

run it in your like bring your own

21:30

sandbox bring your own you know like

21:33

observability like all the things that

21:35

make great like you know because this is

21:37

just built on top of it are going to

21:39

make factory great. So that and the

21:41

reason this came to exist is we kept

21:42

hearing in calls over and over again

21:45

people telling us they were using MRA to

21:47

build a factory and we had a lot of

21:51

pieces of this already internally so we

21:54

just put it together and made it

21:55

extensible so we can help our users so

21:58

they don't have to become you know that

22:00

they can build and own their own factory

22:03

without having to you know do all the

22:06

plumbing themselves right it's like we

22:08

kind of give you like here's the

22:09

baseline customize it to fit your needs.

22:11

You can build your own ramp inspect or

22:14

you know build your own Devon, right?

22:17

But you control it. You pay for the own

22:20

your own inference. You control the

22:21

keys. You can run it anywhere. You can

22:24

run it with and master platform if you

22:25

want as well. But ultimately it's yours.

22:29

And I think that's the cool part is

22:31

>> it allows you to build your own factory

22:33

and customize it to what you need. But

22:34

you don't have to do all the bits. you

22:36

can kind of like take a pretty good set

22:39

of primitives, customize it, and you're

22:41

good to go.

22:42

>> Yeah, it's very disruptive because, you

22:45

know, we've always wanted to build our

22:47

own Devon

22:49

uh internally. And the factory is more

22:53

than just a Devon. It is a automation.

22:56

It is a uh it actually makes you want to

22:59

use linear because you have everything

23:02

controlled. You want to write good

23:03

issues. It's a discipline too because

23:06

you know that you're automating a lot of

23:09

things, you know.

23:10

>> Yeah. That when that issue comes in,

23:12

>> it's going to get picked up immediately.

23:13

So, you should make sure like, you know,

23:15

only write an issue if you're pretty

23:17

serious about getting that thing

23:18

shipped.

23:20

Yeah, I think that's that's a really

23:22

cool part is just it's kind of this idea

23:24

of there's this dream and we're not

23:26

quite there yet, but factory gets us

23:28

very close where you don't have to um

23:32

you know it's this idea of like zero

23:34

bugs, right? Like no bugs. If a bug

23:36

comes in and it's detailed, we should

23:38

just start working on it right away.

23:39

Don't put it in the backlog.

23:41

>> Like either you fix it now or you don't

23:43

fix it and you wait till it becomes like

23:44

a burning issue. I think that kind of

23:46

thing starts to become more possible,

23:49

you know, with something like Factory.

23:51

>> Yeah. And we need to also pass the bar

23:54

test where Shane and I can go out

23:57

drinking and work still continues. Um,

24:00

and if we need to steer the agent, take

24:03

a sip and make it happen.

24:06

>> All right. So, you got us a quick demo.

24:08

We'll keep it short and then we'll jump

24:09

into the news.

24:11

>> Cool. All right. So, I've been working

24:15

on there's many factors to the factory.

24:19

There's work, which is stuff that has

24:21

not been uh maybe issues or linear

24:24

tickets or whatever. I'm not going to

24:25

show that today. I've been really just

24:27

focused on review. Um, and for us,

24:31

review is super important because we get

24:33

a bunch of contributions from the

24:36

community. But the factor, the limiting

24:40

factor is can we actually review it? We

24:42

use code rabbit and our this review is a

24:45

complement to any review code review

24:47

agents that you have. Um but as you can

24:50

see it is a canban style of of a board.

24:56

The intake is the in the work items that

24:59

are coming into the factory. And you can

25:02

see these are all the PRs that need to

25:04

be reviewed. behind the scenes there's a

25:07

review agent that reviews Mashra like we

25:11

do internally. So we wrote a skill

25:14

called the factory the factory review

25:16

skill and we have a lot of different

25:18

like just

25:21

the way we do things and we think the

25:23

way we do things is the way you should

25:24

do things but then in the future you

25:26

know you may be able to configure these

25:29

uh these agents that work behind the

25:30

scenes and so as PRs come in they are

25:35

automatically picked up and they start

25:37

being reviewed which is cool and I've

25:40

done a lot of review today 94. Uh before

25:45

the uh live stream started I was at 65.

25:48

So while we've been talking things have

25:50

been happening. Um so I'm just going to

25:52

show a couple things here. Um one I'll

25:55

just go to the settings. We are building

25:58

out this where you can have different

26:00

intake sources. I can connect to linear.

26:02

I can have more than one repository. I'm

26:06

just worried about Ma open source right

26:07

now because if you spend a week in

26:09

London, hella issues and PRs come in.

26:12

You can configure your model like what

26:14

is the default factory model. Um I also

26:18

added or we also added OOTH here. So I'm

26:20

signed in. Don't tell on me, but I'm

26:22

signed in with my max plan. Um probably

26:25

won't be kosher in the future, but right

26:28

now it is. So that's cool.

26:31

And if I go back here, I can just start.

26:34

This Alysia adapter has been sitting on

26:37

my mind for a while. So I'm just going

26:38

to click this and it's going to start a

26:41

session. It's a review session. So in

26:44

this review session, we spin up a

26:47

sandbox and then the agent will look at

26:50

the PR and then start reviewing.

26:54

And so you can see, you know, it has a

26:56

factory phase. This is the work item.

26:59

This is what's happening. And then we

27:01

have a factory skill which is very

27:03

detailed and looks like [ __ ] right now,

27:04

but we'll fix that display. And then all

27:08

the master bits are all the same. It's

27:10

just a web UI. So now it's writing

27:12

tasks. So what it's going to do, it's

27:14

going to triage the existing stuff. It's

27:16

going to check quality. And then it's

27:18

going to do something that's very

27:19

interesting. And the way we designed

27:22

this is we want it to feel like a the

27:26

senior person on your team is reviewing

27:28

the code. So what really matters is like

27:31

not just this change but what is the

27:34

history of the change or the changes in

27:37

this area and it'll go look in git

27:40

history to see how is this thing changed

27:43

and is the incoming thing an actually

27:46

valid thing to do and then it'll do

27:49

architecture review and then finally in

27:51

verdict it'll do an adversarial review

27:54

on your PR and I made it a little mean

27:59

So, it gets kind of mean. Um, not too

28:01

mean, though.

28:02

>> So, let me just show you an example of a

28:05

review that has happened.

28:07

>> And we're just seeing we're not seeing I

28:09

don't know if you share in multiple

28:10

tabs.

28:12

>> I'm about to share.

28:13

>> Okay, cool.

28:14

>> Something.

28:15

>> I think the coolest thing as you're

28:17

pulling that up or one of the coolest

28:19

things is you can steer the agent as

28:20

it's going, right? So you can actually

28:22

see the session. You can see what it's

28:24

doing. And if you want to ride the loop,

28:26

you can just coach it as it's running.

28:29

Just send a message. The next loop or

28:32

the next time it, you know, the agent

28:34

stops and pauses for a second to do the

28:36

next tool call or whatever, you using

28:38

MRA agent signals will get inserted and

28:41

you can just steer it and keep it keep

28:42

it going. So it

28:44

>> it allows you to let things run

28:48

completely autonomously or it allows you

28:50

to like pay close attention and kind of

28:52

guide it as it goes. So you it gives you

28:54

the flexibility to do it the way you

28:56

want to.

28:57

>> Yep. So this is like a review on

28:59

Daniel's PR and immediately it has a

29:02

bunch of requirements for it to be

29:04

approved. So it's requesting changes.

29:07

There were some merge conflicts that

29:08

need to be um settled. It agrees with

29:12

code rabbit's

29:14

um review as well. So it takes into

29:17

account the other reviews that are there

29:20

um just to say like hey like you should

29:22

be doing these things has some optional

29:24

stuff. It also verifies everything that

29:27

you claim to have done. You know a lot

29:29

of PRs these days say oh I did all this

29:33

this is the test plan. It's like okay

29:34

cool. If that's the test plan, let me

29:36

run that [ __ ] automatically

29:39

and then go for it. And then I did a

29:42

followup because I think Daniel like

29:44

pulled in some changes. And then there

29:46

you go. And I guess this will be good

29:48

for review. And there's many of these.

29:51

So what I'm doing right now is I'm

29:52

running it on every single PR in our

29:56

repo. And then from there, you know,

29:59

we'll see what happens.

30:02

>> And can it approve?

30:04

it can approve. It has approved many PRs

30:07

today and many community PRs

30:09

>> and I think that's the thing that's

30:11

going to cause people to either be

30:12

excited or scared.

30:15

>> Yeah.

30:15

>> And and I think and but ultimately, you

30:17

know, it's still your choice like

30:18

whether you need just the approval from

30:20

the bot. You still want the human

30:22

approval. I think the the answer for us

30:25

is it kind of depends on what surface

30:26

area it touches, right? If you're

30:28

changing framework code, we're still

30:30

going to have humans look at all that,

30:32

right? because it we don't fully trust

30:34

everything that the bot's going to, you

30:36

know, going to do. But there's probably

30:39

other surface areas that if if the bot's

30:42

happy, you know, if if if the factory is

30:45

happy, we're happy, you know.

30:47

>> Yeah.

30:47

>> So, I think it kind of depends.

30:50

>> Yeah. We're going to like like we always

30:52

do, we're going to ride yolo mode to

30:54

learn and then we're going to find out

30:56

where this thing does not work and then

30:59

give guardrails for that. But we will

31:01

run yolo mode for I mean for the

31:03

foreseeable future just to see what it

31:06

can do.

31:08

>> Absolutely.

31:08

>> Um there's a there's a more yolo part of

31:11

this which is like issue creation,

31:13

right? If you give us an issue, we need

31:15

to triage it, start working on it

31:17

automatically. And uh yeah, we're just

31:20

ironing out the kinks there now. So I

31:23

mean all this is going to be dope.

31:24

Right.

31:26

>> And that that's one thing to flag is

31:27

it's really cool if an issue comes in,

31:30

it starts working on it, it gets to a

31:32

review, a different agent reviews it,

31:34

right? You can customize that

31:36

>> and then basically they're almost having

31:38

like a back and forth of sorts without

31:40

Yeah.

31:41

>> You know, you don't have to have human

31:42

intervention if you don't want, right?

31:43

You can kind of get it to approved PR

31:46

state where

31:47

>> it is actually approved without you

31:49

having to even, you know, touch

31:51

anything.

31:52

>> Yeah. And the memory is shared is

31:54

observational memory. And it might, you

31:56

know, if you saw in that review, it's

31:58

very pedantic to tell a reviewer or a

32:02

contributor or whoever that you have

32:03

merge conflicts. But the reason we do

32:05

that is if a agent started the work,

32:09

when it reads the review, it can just it

32:12

doesn't have to go do a a tool call to

32:15

figure out that it has merge conflicts.

32:16

It'll just be, "Oh, I have some merge

32:18

conflicts. I'm going to start working on

32:19

that right now."

32:20

>> Yep.

32:22

All right. And with that, you know, we,

32:24

you know, this is a live show, so

32:26

Medigames, thanks for tuning in.

32:28

>> Thanks for tuning in.

32:29

>> Thanks for hanging out. And we talked

32:32

about TSAI London. We talked about

32:34

recent master launches. Yeah, if you

32:36

want, if you do want to use the factory,

32:38

npm create factory.

32:40

So, go ahead and

32:41

>> it's an alpha. Give us feedback.

32:44

>> Yeah, it is an alpha. There are rough

32:45

edges. There are many rough edges. It's

32:47

getting better every day. But hopefully

32:49

you can see some of what we're excited

32:51

about when cuz we'll be talking a lot

32:53

about it, I imagine, over the next month

32:55

or two.

32:56

>> Yeah.

32:57

>> But with that, should we get in the

32:59

news?

33:00

>> Let's do it.

33:01

>> Let's get into it.

33:21

All right, welcome to Agents Hour. We're

33:24

doing the news. We do this every week.

33:26

We're doing it on Wednesday this week

33:28

rather than Monday because of some

33:30

travel things. But it has been it's been

33:32

a good week for news. There's been

33:34

there's a lot to talk about.

33:40

little preview for what we're talking

33:42

about today.

33:44

The first thing the first thing to talk

33:46

about is this idea of if you've been

33:49

paying attention, you know, Enthropic

33:51

got, you know, kind of like copyright

33:53

suit. They got they had a settlement is

33:55

like $ 1.5 billion dollars or something

33:57

for like book publishers and then it

33:59

kind of came out and this is like after

34:00

the fact and I don't think, you know,

34:02

Enthropic wanted this to come out or at

34:04

least there were some internal rum

34:06

rumblings or memos of where they didn't

34:07

want people to know this. I think they

34:10

called it like Operation Panama or

34:11

something like they don't want people to

34:13

know that essentially what how they got

34:16

the information is they were just like

34:19

you know the books they couldn't get

34:20

online they were just like buying the

34:23

copies ripping the spines out and then

34:25

ingesting all that data which maybe in

34:28

some cases I'm not too worried about

34:30

like if it's like a normal book cool

34:32

like I guess whatever like if that's

34:34

what you got to do to train it like I

34:36

don't feel great about it but you know I

34:39

anyone can go buy that book again. But

34:41

there's a lot at least a number of like

34:43

one only one of one copies or very

34:45

limited copies that are not that are not

34:47

in circulation anymore because they they

34:49

kind of essentially destroyed the books.

34:52

>> Yeah.

34:53

>> What a crooks, [laughter] dude.

34:55

>> So, I mean that doesn't make you feel

34:58

good, right? Like there's some like

34:59

really old books that were probably cost

35:01

them a lot of money to buy. Might have

35:03

been might have paid $500 for that book

35:06

and all they did is just then destroy

35:07

the book.

35:09

to get the information from the book.

35:11

And now no one else, you know, arguably

35:13

if these are like some of these are one

35:15

of one and I think of course those are

35:17

the extremes. I don't think that's most

35:18

of the books, right? But even the fact

35:20

that they did a little bit kind of

35:21

doesn't sit right.

35:25

There were there were probably ways to

35:26

get the information without having to

35:28

destroy the book is all I'm saying.

35:29

>> Yeah.

35:30

>> Just would have been more inconvenient.

35:32

Would it cost more money to get like to

35:35

pay the people

35:37

like, you know, if the lawsuit's like

35:39

billions of dollars and you're spending

35:40

a ton of money on the books and then

35:42

burning them or whatever, wouldn't it

35:45

have just been cheaper to go to each

35:47

author and get the rights?

35:49

>> Yeah, may

35:52

it would have been expensive in time, I

35:55

think, is what they basically decided.

35:57

And I think the problem is some of these

35:59

things they probably couldn't even get

36:00

digital copies or whatever. So they'd

36:02

have to like they'd have to buy the

36:04

book, right? But then maybe just don't

36:07

destroy it, you know, just, you know,

36:10

take a little more time, keep the book,

36:13

put it back in circulation if someone

36:15

wants to buy it. Like I don't know.

36:18

All right, we got to talk about the

36:20

OpenAI security incident.

36:24

So this came out on July 21st. So this

36:28

is kind of like late last week or kind

36:30

of mid to late last week. We had a

36:32

significant security incident during

36:34

evaluation of our models and we're

36:36

sharing what we've learned so far. Cent,

36:39

you know, essentially they're partnering

36:41

with HuggingFace to try to help figure

36:43

out what happened.

36:45

But what happened? How did

36:48

>> Yes. So they were all right. Allegedly

36:51

everything is allegedly right now. um

36:54

they're running a security bench and

36:57

allegedly or maybe confirmed or whatever

37:01

that essentially GPT 5.6 or a model that

37:06

we do not know about yet broke out of

37:09

the parameters and hacked hugging face.

37:15

So it pretty much ignored its uh

37:17

directive and did whatever the [ __ ] it

37:19

wanted. [laughter]

37:21

And and then there's a lot of things

37:23

came out after that, right? Hugging face

37:25

tried to figure

37:26

>> figure out what was happening because

37:28

they detected something.

37:30

>> They tried to use open AI and anthropic

37:33

models to like figure it out, but

37:37

>> they they were blocked because of

37:39

guardrails. Those models didn't want

37:42

they thought they were, you know,

37:43

potentially being used for some kind of

37:45

like cyber security research or

37:47

something that shouldn't have been

37:48

>> able to be used for. So they said, "No,

37:50

we can't help you with that." So they

37:51

had to go to GLM 5.2

37:54

>> open models.

37:55

>> They had to use an open model in order

37:57

to like get to the bottom of the issue

37:58

and figure out what was happening and

38:00

like start to block or start to like at

38:02

least remediate the attack. Open AAI

38:05

obviously like then figured it out, you

38:07

know, it got shut down or whatever.

38:09

There was some like speculation that the

38:12

model had planted some other things on

38:14

the internet for like instructions for

38:16

itself for future versions of itself.

38:18

There's like, you know, some really like

38:19

Terminator type stuff that

38:21

>> yeah,

38:21

>> hard to know what's true and what is

38:23

speculation at this point, but

38:27

>> also hard to know how much of this is

38:29

[ __ ] or not. You know what I mean?

38:31

>> I mean, yeah,

38:32

>> this is media, you know, media.

38:34

>> It definitely like happened after, you

38:37

know, the Kimmy Kimmy launch where I

38:39

think people are, you know, so you never

38:42

know. I think Sam Alman has come out

38:44

afterwards and said he was shocked that

38:46

there wasn't more of a backlash or more

38:48

of like a media backlash because of it

38:53

>> or maybe the positive media went to open

38:55

models

38:57

>> maybe.

38:58

So I think and we'll talk a bit more

39:00

about this you know about open models

39:03

but I think I thought this was very

39:04

interesting. It's obviously

39:06

>> I feel like most people don't even know

39:08

what hugging face is. You know what I

39:09

mean? Like the layman

39:11

>> Yeah. Like if they if they like hacked

39:13

the New York public library,

39:15

she would be on fire right now.

39:18

>> Maybe. So yeah, then the average person

39:21

does not know or care about Hugging

39:23

Face, right?

39:23

>> We do, but most people don't.

39:30

Opus 5 came out.

39:33

Is it better than Fable? I don't know.

39:35

It was released on July 24th. This is

39:38

the post from Claude. It says,

39:39

"Introducing Claude Opus 5. It's a

39:41

thoughtful and proactive model that

39:43

comes close to the frontier intelligence

39:45

of Fable 5 at half the price."

39:49

And then, you know, there's some

39:50

benchmarks that came out around it. So,

39:53

exciting news. Claude Opus 5 with Max

39:55

Reasoning is number one in the frontend

39:57

code arena and text arena with

39:59

factuality on, which seems like very

40:02

specific that it, you know, you need,

40:05

but it it does beat Kimmy K3. It beats,

40:09

you know, Fable 5. So, it's apparently

40:13

good at like front end.

40:15

We saw that, you know, Claude Opus 5 by

40:18

Enthropic AI is second overall in design

40:21

arena with an ELO of 1358, which puts it

40:25

just, you know, I guess not just behind

40:26

Kimmy, but second place behind Kimmy.

40:31

Then you know Claude Opus 5 is narrowly

40:33

the most intelligent model on the

40:35

artificial analysis intelligence index

40:37

offering comparable intelligence to

40:39

Fable 5 at 26% lower cost per task.

40:45

So, you know, looks good on some

40:46

benchmarks.

40:48

Not it didn't look great on every

40:50

benchmark, right? Like there are some

40:51

that it lost to Fable, lost to Kimmy,

40:53

lost to, you know, 56 on, but there are

40:56

some benchmarks where it was either top

40:57

or very close to the top. And then

41:00

there, but a lot of people have mixed

41:02

opinions. So, Siki Chen says, "I take

41:04

back what I said about Opus 5. Initial

41:06

results were promising, but the more

41:08

time I spent with it, the more

41:09

infuriating of an experience it became.

41:11

My team feels the same way. I am back on

41:13

GBT 56 Soul. It's my daily driver with

41:16

Fable and Kimmy 3 unplanning and

41:17

reviews. Theo said, "I do not like Opus

41:20

5 as much as I hoped."

41:23

What do you think? What's been your

41:25

response?

41:26

>> Um, been daily driving it and then daily

41:29

driving it in the factory

41:31

and I just don't think it's not as smart

41:33

as Fable, but I think it is quite

41:35

capable. Um, so

41:39

I don't know. I don't have the same

41:40

feeling, but I'm just doing review right

41:42

now. So maybe that's the point.

41:45

>> I think it just doesn't feel much like

41:48

in the tasks that I've sent it, it feels

41:49

the intelligence level is pretty close

41:51

to like 48 for me. Like I don't notice a

41:53

big jump.

41:55

I have noticed there's, you know, and

41:57

maybe this is momentary issues with

41:59

enthropic or whatever, but I've noticed

42:00

that sometimes it just stalls out.

42:02

Sometimes that could be like the

42:03

response like too long of response, so

42:05

it just

42:06

>> cuts out. I I I don't know if that's a

42:08

me problem, but that's just something

42:10

I've noticed with Opus 5, I haven't

42:12

noticed necessarily with other models as

42:14

much. So, like some momentary things

42:16

where it just doesn't feel like it

42:17

finishes what it was what it started out

42:19

to.

42:19

>> Yeah.

42:20

>> Um but overall, I seems good. I don't

42:24

know that it seems necessarily great. I

42:27

don't know that it quite feels fable

42:30

level intelligence to me, but maybe a

42:33

step in the right direction, I guess,

42:34

overall. So I I don't hate it, but I

42:36

don't love it if that makes sense. It it

42:38

will probably be part of my rotation

42:40

though.

42:42

>> Same.

42:43

>> Um and then but one interesting thing

42:46

that's kind of come out. So Justin

42:47

Schroeder had this post and it says this

42:49

chart says so much. They use the exact

42:51

same prompt. They were all long horizon

42:54

oneshots and he said it reflects his

42:57

world real world experience at least,

42:59

but it's token use for this same prompt.

43:02

So it compares GBT 56, Terra, Luna,

43:05

Soul, Grock 45, DeepSync V4, Fable 5,

43:09

GLM, Kimmy, and then Opus 48 and Opus 5.

43:13

And on this task, which again, I don't

43:15

know, maybe this is like cherrypicked.

43:19

Hard to tell, but Opus 5 used a ton more

43:23

tokens. 97.

43:24

>> I think it's very uh it's very trigger

43:26

happy for tool calling.

43:28

>> Yeah. So 97 million tokens compared to

43:31

like Opus 48 was 23 million

43:34

>> and GBT 56 Soul was 4.3 million.

43:37

>> So if you think about it, so not only is

43:39

the token cost more expensive, but it

43:41

it's very token hungry as well.

43:43

>> Yeah.

43:46

>> So if if you're on your max plan, you

43:48

don't care. Who cares, right?

43:49

>> Yeah.

43:49

>> If you're paying API costs, you probably

43:52

care.

43:54

You definitely probably care.

43:56

Uh, anything else on Opus 5?

44:01

>> No.

44:02

>> Yeah, I think it's a good model. I don't

44:04

think it's,

44:06

you know, like the last time I I will

44:09

say this, going from like a four to a

44:10

five, you expect it to be this kind of

44:14

like put the like the plant a flag in

44:17

the ground kind of release. It doesn't

44:19

feel that way to me, but it feels like a

44:21

good useful model.

44:23

>> Yeah.

44:26

All right, let's talk about open weights

44:28

and all the things regarding

44:32

open models and should we have open

44:34

models? Should we not have open models?

44:36

So, Jensen had a post last week, first

44:39

post, I guess it was both Jensen and

44:42

Zuck both have had like first posts for

44:44

the first time in

44:46

>> in a you know, in potentially a long

44:48

long time. But Jensen said, "For my

44:50

first post, I'm sharing a letter Nvidia

44:52

signed on why open models matter. AI

44:54

will transform every industry, power

44:56

every company, and be built by every

44:58

country. Open models strengthen safety

45:00

and cyber security, accelerate

45:01

innovation and diffusion, and enable

45:03

sovereignty.

45:06

And then a bunch of people kind of

45:07

basically like signed on to this, right?

45:10

Signed on to this letter. You had even

45:11

open AAI signing. You had all the other

45:15

usual like suspects that would you

45:17

typically sign something like this also

45:19

sign it, right? Right. Palanteer of

45:21

course is going to sign it. YC signs it.

45:25

Whole bunch of like open model companies

45:27

of course signed it. Misilla signed

45:29

signed it. GitHub signed it. You know,

45:32

everyone you'd kind of expect.

45:34

>> We're trying to sign it.

45:35

>> Yeah. We we said we, you know, we threw

45:37

our hat in there. I don't think our logo

45:39

got on the board, but you know, we we

45:40

said we we would sign it. Um because I

45:44

think we all you if you're watching

45:45

this, you'd probably sign it, too,

45:47

right? I think we most of us agree that

45:49

open models are a net positive. It

45:52

keeps,

45:54

you know, it keeps things more open,

45:57

allows you to have more flexibility. No

45:59

one's going and hosting these open

46:00

models themselves. Not the big ones.

46:02

Like the smaller ones maybe, but the

46:04

bigger ones you can't host yourself,

46:05

right? But they should still be like the

46:07

ability for people to have open models

46:08

and open weight models is is a good

46:10

thing overall. I think I think it pushes

46:12

the frontier to be more competitive, to

46:14

keep moving faster, and I think it lock

46:18

keeps us from getting locked into

46:20

there's a few companies that control all

46:21

the intelligence, right?

46:25

And then this came out. I thought this

46:27

was hilarious.

46:29

Denny's had a banger post that says

46:32

[laughter] Denny's and Nvidia both know

46:34

the importance of staying open. So, you

46:37

know, not first time first time Denny's

46:40

mention on, you know, agents hour, but

46:43

nice work. That was funny. I laughed

46:47

and then Enthropic finally responded. I

46:50

feel like Enthropic must have been

46:52

getting a ton of internal pressure and

46:54

they did not sign it, right? No,

46:57

>> but they did

47:00

outline how like their thoughts and so

47:02

you can read this post, you know, they

47:05

they released it on the, you know, on

47:08

the anthropic blog or their news in in

47:12

the anthropic news announcement. It

47:14

basically says our position on open

47:16

weight models

47:17

um their biggest concern is more of a

47:21

risk of authoritarian governments, not

47:23

just the CCP.

47:25

They're, you know, concerned that

47:28

powerful AI models may be misused to

47:30

carry out cyber attacks.

47:33

Their biggest things are we should not

47:35

sell powerful chips to China. We should

47:38

crack down on industrialcale

47:40

distillation operations. You know,

47:41

that's Daario's thing lately. He doesn't

47:43

want people to, you know, pay for their

47:46

inference and take take the content and

47:49

build models from it.

47:51

Um and then the next big point is all

47:54

sufficiently capable models open and

47:56

closed should go through mandatory

47:58

safety testing.

48:01

And so overall tried to be like take a

48:04

more reasonable approach. They didn't

48:06

respond to every point in the letter but

48:08

said we don't dislike open models but

48:11

here are the things we believe and we

48:12

think that if even if they are open

48:14

models they should have to go through

48:15

this some rigorous testing which I guess

48:18

is mandated by the each government which

48:20

kind of makes things hard though because

48:21

you got to then be tested by every

48:24

government entity that

48:26

would regulate the models and the model

48:29

use within their country which becomes I

48:32

think hard to govern

48:36

and then I and then my question would be

48:38

like who gets to decide what that safety

48:40

test is because I bet you anthropic says

48:42

it should be them.

48:43

>> Yeah.

48:44

>> And that that's the concern of course

48:46

>> and then you can deem things that you do

48:48

not like with bias.

48:50

>> Yeah. They are fully biased you know

48:52

>> right? So if OpenAI, Anthropic, and

48:55

maybe Google and XAI are the only

48:58

companies that can determine what this

49:00

what safety is, they get to write the

49:02

safety test. Well, then they can pretty

49:04

much just write the test. So open models

49:06

are probably not going to pass it,

49:07

right?

49:07

>> Yeah.

49:08

>> And the other argument is if you have to

49:10

go through a rigorous test, then it does

49:13

block out anyone else from being able to

49:15

release new models because they got to

49:17

go through this rigorous test which are

49:18

probably going to be very expensive,

49:20

very time consuming.

49:22

I see.

49:23

>> Then, you know, then you don't even want

49:24

to innovate anymore because it's like

49:26

the the red tape to even start. You're

49:29

like, you know what? I'll just [ __ ] it.

49:31

I don't even want to do this anymore.

49:33

>> Yeah. And I think, you know, you see

49:34

that with a lot of government

49:35

regulation. When an industry becomes

49:37

overregulated,

49:38

typically innovation slows down, right?

49:41

It's it's like the path

49:42

>> and corruption goes up.

49:44

>> Yeah.

49:44

>> How many like side deals would happen?

49:46

People selling bribes and all that

49:49

stuff.

49:50

>> Yeah. I I mean on the flip side there is

49:53

an argument for safety testing right in

49:56

that yeah

49:56

>> do you not you know you don't want the

49:59

most powerful agent to do everything but

50:02

>> I also would argue maybe the best way is

50:04

just

50:05

>> if all the intelligence is open then at

50:08

least you can have the right tools to

50:09

protect yourself if there is an agent

50:11

because who knows

50:12

>> what kind of agents being you know

50:14

cooked behind the scenes that could do

50:16

all the damage and you don't have access

50:18

to it right so how can you protect

50:19

yourself from it. So I can see both

50:21

sides, but ultimately I think less

50:25

regulation is typically better and we

50:27

shouldn't have we should be encouraging

50:29

innovation at this point rather than

50:32

trying to you know encourage or like

50:34

discourage people from even trying.

50:38

>> Knock on wood for the Skynet stuff. But

50:40

yeah.

50:40

>> Yeah. [laughter] Yeah. I mean that's the

50:41

that's the asterisk, right? Like you

50:42

don't

50:43

>> I don't want Skynet, but I also don't

50:45

want, you know, only three or four

50:47

companies to control everything. I don't

50:48

want Daario controlling this.

50:50

>> Yeah. And

50:53

now, you know, OpenAI and I think even

50:56

Anthropic, maybe some Anthropic

50:57

employees, they started this

51:00

uh they it's called the pacing the

51:02

frontier. So, pacing the frontfront.com.

51:06

>> So, they basically made a statement.

51:07

They had a whole bunch of people that

51:09

signed from different uh companies. And

51:12

so and OpenAI both signed the open model

51:15

letter, but then they kind of go

51:16

backwards a little bit and they're

51:17

saying like we should be very careful

51:18

about frontier intelligence and we

51:20

should kind of have this regulation and

51:23

and you know like safety concern over

51:25

top of it, right? And so maybe I can

51:28

share

51:30

um this is kind of the website the

51:34

letter statement from 1,200 employees of

51:37

Frontier AI companies. You can see, you

51:40

know, Daario's in here, chief scientist

51:43

of Open AI, chief scientist of thinking

51:45

machines, anthrop, you know, co-founder,

51:47

chief science officer of anthropic,

51:49

chief scientist of Meta, Google

51:52

DeepMind.

51:53

Um,

51:55

and kind of the statement is we request

51:58

that the US government support an

51:59

international effort to develop the

52:01

technical and governance tools needed to

52:03

deliberately pace the frontier of

52:05

automated AI development.

52:08

So my question is what happens if

52:09

someone says they're going to do they're

52:10

going to pace but then behind the scenes

52:12

they don't. What if China is like, "Yes,

52:14

we're in." But then they're actually

52:15

like, "You know what?

52:17

>> We're gonna be doing our own like black

52:19

ops behind the scenes trying to like

52:22

we're gonna try to slow everyone else

52:24

down. And don't I mean, Open AI and

52:27

Anthropic are going to do the same

52:28

thing, right? Like they might not

52:30

release it to the public, but they're

52:32

going to be doing it behind the scenes

52:33

because they want to be ready and have

52:34

the everyone wants to have the most

52:36

intelligent model.

52:38

>> So, it's Game of Thrones, dude.

52:40

>> I am very skept. very skeptical of of

52:42

this in general. It's it's very tied to

52:44

the the last, you know, the open weights

52:47

concept.

52:47

>> I wonder if I sign it, will they accept?

52:49

I'm not in a Frontier lab, but you know.

52:51

>> Yeah. I don't think I don't think so. I

52:53

don't think

52:53

>> I'll be at anthropic. [laughter]

52:57

>> Yeah. I mean, and you can see they have

53:00

some quotes here.

53:02

Um, so you can kind of see the thought

53:04

process.

53:06

I think all this highlights is there's a

53:09

lot that's going to come from government

53:11

regulation wise uh open weight open

53:14

model wise around just how open models

53:18

or how models in general are developed

53:20

and how intelligence is is kind of

53:22

rolled out over the course of the next

53:24

few years and so some level like we need

53:26

some things I don't know what that thing

53:28

is

53:30

>> I wonder if you got fired if you didn't

53:31

sign it if you worked at anthropic

53:35

I would hope not. I bet. But I feel like

53:37

Enthropic doesn't need to. I feel like

53:38

if you're at Enthropic, like 75% of the

53:41

people believe the same things. Like I'm

53:43

not saying that there aren't divergent

53:44

opinions, but I think like from what

53:47

I've heard, Anthropic kind of has the

53:48

mission and they're pretty public about

53:49

their mission that, you know, like I'm

53:52

going to like we are we are the company

53:54

that's going to make AI safe, right? And

53:58

if so, if you believe that and you work

53:59

at Enthropic, you're gonna sign this

54:00

thing.

54:02

>> Yeah. Now, my opinion is I don't think

54:04

one company is what's going to make AI

54:06

safe, but that's where my opinions

54:07

differ.

54:11

All right. Um, continuing on, and then

54:15

this came out also July 28th, which is

54:17

yesterday. It says, "President Trump is

54:20

relying on a small group to decide what

54:21

restrictions to impose on Chinese AI

54:23

ahead of this week's open AI meetings.

54:26

It includes Howard Lutnik, Scott

54:28

Bessant, David Sax, Susie Wild, Sean

54:31

Karen, Cross, Arvin Dramman.

54:34

>> Oh boy. Wonder what's going to happen

54:36

then.

54:36

>> Again, more to come. More speculation.

54:39

We will see.

54:41

All right, let's talk about MCP. MCP is

54:44

not dead. It's just stateless now.

54:47

>> Yeah. So

54:48

>> V2

54:49

>> MCP 2026 0728 is live and it's the

54:54

largest update to the protocol since the

54:56

launch. This is from July 28th. This is

54:58

a post from claude devs and it says MCP

55:01

is now stateless making it easier to

55:04

deploy and scale remote servers. So tell

55:08

me about this Obby. What does this mean

55:09

for folks?

55:10

>> So MCP is an API now. Um that's cool.

55:16

Um, just to give a little history

55:18

lesson, so MCP came out quite a while

55:20

ago. Um, and when it first came out, it

55:23

was only through stdio standard out. Um,

55:27

and how MCP used to work was you have a

55:31

connection. You like get a connection to

55:34

the server and then the protocol to

55:37

transport was standard out. This was

55:40

really good for MCPs that were not

55:42

hosted, let's say, but uh or some were

55:46

hosted, whatever. And that was cool to

55:48

start, but automatically a lot of people

55:51

were wondering what the hell MCP is

55:54

useful for because like why do I need a

55:57

connection to a server to to do this

55:59

stuff? Then we had SHTTP, which is a

56:02

state stateless HTTP protocol in M in

56:05

MCP, which allowed [snorts] you to do

56:08

HTTP

56:10

And now we're back to everything's

56:12

stateless just like a rest API. So

56:16

>> So can you use full circle?

56:18

>> Can you use like stdo like standard

56:21

input out anymore? Now it's gone in this

56:22

new version.

56:23

>> It's all Yeah, it's all like we've been

56:26

doing for many years. We are back at

56:29

square one.

56:30

>> Um yeah. So it's like MCP

56:34

realized that most people use MCP for

56:38

tool calls

56:39

>> tools

56:40

>> and how do you norm how would you

56:43

normally access a remote system

56:45

>> through an API

56:46

>> API

56:47

>> and you don't need a connection you

56:49

don't need a long live connection to

56:50

that system you just want to like make a

56:52

request get a response and have your

56:54

agent

56:55

>> that's it

56:56

>> handle that thing so why do I need a you

56:58

know a connect an ongoing connection

57:01

Yeah. And this al this honestly

57:03

complicated agent development because

57:05

sometimes you lose your connection and

57:07

you're just trying to [ __ ] make a

57:08

tool call and then you have to make sure

57:10

that you have a connection, you lost the

57:12

connection. Um you have to regain it.

57:14

It's just like all this latency.

57:17

Um but some good things came out of this

57:20

uh V2. Uh they got rid of dumb [ __ ] that

57:23

no one used. Roots, who cares? Like

57:28

logs. Yeah, just use regular logs. Like

57:30

who cares about that? Like they had

57:32

added all this crust to MCP

57:36

and it's just gone which is great.

57:38

>> I'm assuming they still have like O. Do

57:40

they still have elicitation?

57:42

>> Um so they have O still which is just

57:46

going to be normal ass O. Um which is

57:49

great. They have tasks. They had a task

57:53

protocol that still exists but roots

57:56

sampling logging are all deprecated.

57:58

They'll still work for the interim.

58:00

Elicitation still works. Um,

58:04

and elicitation is I mean people use

58:07

that so like that was good but um you

58:10

didn't have Yeah. But still even the

58:12

people like elicitation is used but not

58:15

at the same like most people are using

58:16

it for tools right like 8 I would say 80

58:19

plus percent of people that use MCP it's

58:22

literally just to share tools so it's

58:26

easier for agents to use right like that

58:28

is most people's use case

58:31

>> and there was a lot of talk around like

58:32

is MCP dead because you know hadn't been

58:35

updated for a while or hadn't really

58:36

been like at least not very vocal

58:38

updates a lot of people weren't using

58:40

all the new things that were added. I

58:42

would say based on my experience and

58:44

conversations, MCP is definitely not

58:46

dead, but I do think MCP is going to be

58:49

like an enterprise type like

58:52

where that's where it's going to get the

58:53

most use. I'm not saying it's not going

58:55

to be used outside of that, but

58:58

a lot of things that people are using

58:59

MCP for, they're just using skills for

59:01

now. Unless you're an enterprise and you

59:04

want to build a set of like a tool set

59:05

that you can share across teams. That's

59:07

where I see the most is like internal

59:08

tool sets that one team can build the

59:11

MCP server, connect it to the different

59:13

systems and give agents or you know that

59:16

are being built by another team access.

59:19

>> Yeah, a lot of things happened to MCP

59:22

that were detrimental like outside of

59:24

MCP, right? One, you could because you

59:27

can write code easier, you can just

59:29

create tools with your coding agent

59:31

using SDKs that you already have.

59:34

>> Yep.

59:35

>> Cool. Second thing is CLIs became cool

59:38

again. In general, coding agents will

59:40

just execute the CLI. Most things have a

59:42

CLI and if they didn't, people started

59:45

building CLIs for them, right? And then

59:48

finally, the whole noise about, oh, you

59:51

can't just use OpenAI specs because they

59:53

weren't written for agents. Well, good

59:56

[ __ ] luck because now everyone's

59:57

writing APIs for agents. So, open AI is

1:00:00

cool again. Sorry, open API. My bad.

1:00:03

Open API specs can be good because

1:00:06

they're being refactored for an agent

1:00:08

world, right?

1:00:09

>> Yeah. And

1:00:10

>> why even use MCP? And I think in a lot

1:00:12

of cases it's like MCP is good for

1:00:14

sharing and if you want people to just

1:00:17

be able to easily plug into it and you

1:00:19

know but at the end of the day if you

1:00:20

just had a REST API or you could spin up

1:00:23

an SDK you could probably get around a

1:00:24

lot of the same things.

1:00:26

>> Yeah.

1:00:26

>> And I think one of the other challenges

1:00:28

with MCP is you get this like huge list

1:00:30

of tools and you don't always want like

1:00:32

the GitHub MCP back in the day. You get

1:00:34

like a hundred tools. I don't want a

1:00:35

hundred tools. I want like 10 tools.

1:00:38

>> Maybe 15. So maybe it'd be actually

1:00:40

better rather than have my agent have to

1:00:43

decide between 100 tools or me having to

1:00:45

like look through the list, I could just

1:00:47

have my agent know that these are the

1:00:49

five things I needed to do, do that. And

1:00:52

maybe sometimes one tool call is

1:00:54

actually like two API calls, right? Like

1:00:56

not always, but

1:00:58

>> like if I want to request a refund, that

1:01:00

might be a couple API calls to do a

1:01:01

refund, but my agent just needs to do

1:01:03

the refund. They don't need to like make

1:01:05

three tool calls. So I I think in some

1:01:08

cases MCP is good. It's still used. I

1:01:10

think it's become where it was extremely

1:01:13

hot as like a concept 18 months ago,

1:01:17

right? About that, you know, 15 months

1:01:18

ago. Now it's just become like a a tool

1:01:22

in your tool belt, right? Like there are

1:01:24

certain use cases where it's good. If

1:01:26

you're sharing tools around teams, like

1:01:28

maybe it's still useful. Deploy an MCP

1:01:30

server. One team can maintain it,

1:01:32

another team can use it.

1:01:34

But I feel like in a lot of cases it's

1:01:36

going to be the same as just using an

1:01:37

API.

1:01:38

>> I think one benefit of MCP

1:01:41

today is you can expose an MCP server

1:01:44

that wraps your SDK or your REST API or

1:01:48

REST client or whatever whatever

1:01:49

internal logic you have and now you

1:01:52

already have a tool format that the

1:01:53

agent will speak. So you don't have to

1:01:56

do this like glue code, right? Like the

1:01:58

MCP is already giving you tools. Now you

1:02:00

just have no connection or handshake

1:02:03

necessary. So there are benefits, but

1:02:06

we're just haters.

1:02:07

>> Yeah, I think it's just Yeah, the

1:02:09

benefits are not as great as they were

1:02:11

when it came out. So still useful and I

1:02:14

see it all the time in uh in enterprise

1:02:16

settings. It's being used heavily. So

1:02:18

it's not it's definitely not dead, but

1:02:21

you know, it's maybe just not not what

1:02:23

everyone thought it was going to be.

1:02:24

>> Dude, I'm so glad we didn't support

1:02:26

roots in our MCP client. Thank

1:02:28

>> we talked about it. Yeah, we got we had

1:02:30

people asking about it.

1:02:32

>> Yeah, they ain't asking anymore.

1:02:34

>> And then, you know, this came out sounds

1:02:36

like we arrived at REST API, which is

1:02:39

just funny as a response to uh

1:02:42

stateless MCP because yeah, it's very

1:02:44

similar. I mean, obviously it's, you

1:02:46

know, it gives you a tool format that

1:02:47

agents can use as you said, but yeah,

1:02:49

it's an API.

1:02:51

All right, let's go through some quick

1:02:52

hits. This is where we rapid fire

1:02:55

through a bunch of things that might be

1:02:56

interesting to you all and we'll give

1:02:58

you some of our hot takes on it. So,

1:03:00

Kimmy K3 weights are out. So, that's

1:03:02

good. If you're a fan of open models,

1:03:04

which we are, they released the model

1:03:07

weights. So, you can kind of read

1:03:09

through that.

1:03:11

There was some a leak, I guess, on July

1:03:14

26th. You know, whether it's a leak or

1:03:16

not, I don't know. I think more, you

1:03:18

know, I feel like Model Labs released

1:03:20

these things so they can kind of build

1:03:22

hype. But anyways, Kimmy K 3.1 leak says

1:03:27

performance that closes the gap with

1:03:29

GPT56 and Fable. It's uh maintenance

1:03:32

report mythic mythos level capabilities,

1:03:35

faster inference and lower latency,

1:03:37

better token efficiency. But just

1:03:39

getting a lot of hype around when this

1:03:40

chem 3.1 coming out. It's supposed to be

1:03:43

good. It's supposed to be like even like

1:03:46

as much as a surprise as Kimmy K3 is,

1:03:48

imagine now you get an upgrade to that

1:03:50

if you can make it a little faster, too.

1:03:56

SSI

1:03:57

announced, so this is from SSI Inc. It

1:04:00

was a message on July 27th said, "We are

1:04:02

announcing a long-term strategic

1:04:03

partnership with Nvidia. NVIDIA is

1:04:05

making a substantial investment in SSI

1:04:07

that will let us 10x our compute in the

1:04:09

next 12 months. We reached the point

1:04:11

where our research is worth scaling and

1:04:13

with this partnership, we will be able

1:04:14

to. So this is Ilia's from, you know,

1:04:17

OpenAI days, Ilia's company, SSI, and

1:04:21

sounds like they, you know, maybe

1:04:23

starting to make some moves.

1:04:25

I I think I I don't think you can be a

1:04:27

Frontier model lab and not build

1:04:30

partnerships like this. So

1:04:32

>> yeah,

1:04:32

>> maybe that means we'll be seeing some

1:04:33

things from SSI.

1:04:39

Stripe is in discussions to acquire Open

1:04:42

Router possibly for as high as $10

1:04:44

billion.

1:04:46

>> That is

1:04:48

tight.

1:04:49

>> Yeah, we're fans of Open Router. We like

1:04:51

Open Router.

1:04:51

>> Friends with them.

1:04:52

>> Yeah. So, that's cool. If true.

1:04:56

>> Another dude in Sam's fraternity is

1:04:58

about to be rich. [laughter]

1:05:01

>> Yeah. You know, it's one of those things

1:05:02

like big if true like you know who we'll

1:05:05

see. But dang that that's a that's a lot

1:05:07

of

1:05:08

>> people have a model router

1:05:10

>> just like ramp.

1:05:12

>> Yeah, exactly. I mean

1:05:15

Stripe, you know, Stripe is it's funny

1:05:17

like Stripe is a financial company,

1:05:19

right? Like tied around like finances,

1:05:22

card processing, all that. Ramp is a

1:05:24

financial company. And now they're both

1:05:27

like kind of pivoting to trying to be AI

1:05:31

companies in a lot of ways.

1:05:32

>> Yeah. I think they like imagine being

1:05:35

like leaders in those categories and be

1:05:37

like you know what's a bigger market

1:05:39

than finance AI. Let's go there.

1:05:46

>> Notion as code. So this is now in beta.

1:05:48

This is a post last week July 23rd from

1:05:51

notion. It says you can define an entire

1:05:54

workspace in Typescript team spaces

1:05:56

databases custom agents all of it and

1:05:58

then deploy it through the API. So you

1:06:01

can build workspaces with coding agents,

1:06:02

version control your setup in Git, and

1:06:04

reproduce the same setup anywhere you

1:06:06

need it.

1:06:08

I think this is kind of actually cool.

1:06:10

Like, you know,

1:06:12

>> I'm not going to use it, but it's cool.

1:06:14

>> Yeah. I don't think I mean I I feel like

1:06:16

Notion has become less important for us

1:06:21

as a company. Like we are huge Notion

1:06:23

users. I feel like we still use it,

1:06:26

>> but it's basically used as just like a

1:06:28

wiki, right? It's like if something

1:06:30

doesn't live in linear then put it in

1:06:32

notion maybe. But I think if you were to

1:06:37

want to build like knowledge bases and

1:06:40

you could you you know wanted to be able

1:06:42

to just have your coding agents spin up

1:06:43

and do things for you. But then I my

1:06:46

question is do you really need notion to

1:06:47

do that or not?

1:06:49

>> Yeah.

1:06:50

>> And there's been a lot of hype around

1:06:51

like what is it like open wiki or

1:06:52

something like that's been coming out as

1:06:55

well. So I think there's a lot of people

1:06:58

like trying to disrupt notion. Notion's

1:07:00

trying to become, you know, an AI

1:07:01

company as well. And so they're trying

1:07:03

to get closer to coding agents.

1:07:04

Everything is converging on like the

1:07:06

making things for coding agents.

1:07:08

>> Yep.

1:07:11

>> Cognition is in acquiring interaction,

1:07:13

the makers of Poke. So if you've ever

1:07:16

used Poke, you know why we're so

1:07:18

excited.

1:07:20

>> Okay,

1:07:21

>> that'll be cool. Another channel for

1:07:22

them.

1:07:23

>> Yeah. I've never used Poke. Have you

1:07:24

used Poke?

1:07:26

>> No.

1:07:27

>> Anyone in the chat, have you ever used

1:07:29

Poke?

1:07:31

>> I don't know. Never used it.

1:07:32

>> I think it's I mean Poke was like an AI

1:07:36

assistant that would be in your

1:07:38

WhatsApp, your Telegram, uh your

1:07:42

iMessage, things like that. And it was

1:07:44

like a, you know, like an assistant or,

1:07:48

you know, some somebody you could talk

1:07:50

to as well. Um, so I I I assume this is

1:07:54

to expand channels and that technology

1:07:57

with Devon.

1:08:03

All right. Chat GBT voice is now in the

1:08:05

desktop app. So control your computer,

1:08:07

direct multiple agents running in chat

1:08:09

GPT work or codecs just using your

1:08:10

voice. It's powered by GPT live. So it

1:08:13

can speak, listen, and coordinate work

1:08:15

in the app at the same time.

1:08:18

This is cool. I did see a post about

1:08:20

this that I thought was kind of

1:08:21

interesting and I actually I'm gonna try

1:08:23

it just to maybe provide a come back and

1:08:25

and talk about it. But it's basically

1:08:27

saying if you run the desktop app then

1:08:31

you can actually like go on your phone

1:08:34

and talk to it, but it can control your

1:08:36

your computer. So you can basically be

1:08:38

like, you know, controlling your

1:08:40

computer while you're going for a walk,

1:08:42

right? You could be telling it what to

1:08:43

do. it'll be, you know, basically live

1:08:45

voice and it'll be kind of making moves

1:08:48

for you as you're talking to it using

1:08:50

your computer, but you can kind of take

1:08:51

it anywhere. So, it's this idea of like

1:08:54

maybe you just have one,

1:08:56

you know, workstation running all the

1:08:58

time, but you have you just bring you

1:09:00

can basically

1:09:01

>> it would pass the bar test, I guess, is

1:09:03

the idea. And so maybe maybe I need to

1:09:05

try it out because ultimately you can

1:09:08

use chat GBT work or codeex right from

1:09:11

you know technically the mobile app. You

1:09:13

just talk to it with voice which is

1:09:15

pretty cool.

1:09:16

>> Yeah.

1:09:16

>> So I think I think we'll be seeing you

1:09:17

know that that's the dream that people

1:09:20

are trying to build for it for. Obby and

1:09:22

I have been talking about this dream for

1:09:23

18 months now it seems like or a year on

1:09:25

this show. It's like, you know, how do

1:09:27

you pass how do you get it to pass the

1:09:28

bar test or the beach test where you're

1:09:30

you can take your work with you on the

1:09:32

beach or at the bar and you can still

1:09:34

make some moves.

1:09:38

All right, we got to watch this video

1:09:40

because

1:09:40

>> yeah, this is dope.

1:09:42

>> This is wild. So, give me a second to

1:09:44

pull it up because yeah, we got to watch

1:09:47

this video.

1:09:49

So, this is from Door Dash. We're

1:09:51

cleared for takeoff. Say hello to Door

1:09:53

Dash Air, our in-house drone delivery

1:09:55

program. You know, this isn't maybe

1:09:59

specifically AI, but it's kind of AI

1:10:01

related, right? Um, so let me pull up

1:10:04

this video, wherever it is. There it is.

1:10:07

Hopefully you can all hear this.

1:10:17

Heat. Heat. Heat.

1:10:27

All

1:10:33

>> [music]

1:10:39

[music]

1:10:48

>> right. So, if you watched the Yeah. the

1:10:52

episode, I think it was was it last week

1:10:53

we were talking about is Dor are you is

1:10:55

your agent going to be ordering you

1:10:56

pizza? Yeah,

1:10:58

>> like your agent's going to be ordering a

1:10:59

pizza delivered by a damn helicopter,

1:11:02

>> dude. [laughter]

1:11:03

Um I have a friend who is working on

1:11:06

Door Dash drones um in uh SF. So, dude,

1:11:11

it's tight. Also, when he first told me

1:11:14

about it, I was like, that's like the

1:11:16

dumbest thing ever. And then

1:11:19

now that I think about it, it's not that

1:11:20

dumb. So, the test is, and we should

1:11:24

record this next time we're in SF

1:11:26

together.

1:11:28

We get our agent to use Door Dash's MCP

1:11:32

>> and get it delivered by Door Dash Air.

1:11:35

That would be sick.

1:11:36

>> That's the dream, dude. At the bar.

1:11:38

>> Yeah. [laughter] I I need my pizza. The

1:11:41

pizza comes down and drops.

1:11:44

All right. Yeah. So, that's that. Uh

1:11:48

before we close out, you know, thanks

1:11:51

for watching the show. Follow us on X.

1:11:54

Follow Mr. on X at Mastra. Go to our

1:11:56

YouTube. Hit subscribe if you haven't

1:11:58

already. Please follow me on X at SMT

1:12:00

Thomas 3. Follow Abby on X. Um we

1:12:04

appreciate that. We appreciate any

1:12:06

fivestar reviews. If you don't want to

1:12:07

give us a five star, find something else

1:12:09

to do. But if you do like the show, that

1:12:11

fivestar review does help other people

1:12:13

like you. The other thing you can do and

1:12:15

every time I say this people are always

1:12:17

ask me what are friends but if you do

1:12:19

have friends that are like you and think

1:12:20

like you tell them about the show

1:12:22

because that really helps us get more

1:12:24

people. We've had a ton of chatter in

1:12:27

the the chat that we kind of haven't

1:12:29

pulled up. So let's go through some of

1:12:31

that. So we got I am Brennan says hey

1:12:33

guys does factory run cloud code behind

1:12:35

the scenes right now it runs master

1:12:37

code. We will, you know, Mashra

1:12:40

supports, you know, ACP, we support, you

1:12:42

know, cloud code, codecs, things like

1:12:44

that. So maybe eventually it's like you

1:12:46

can configure your own coding agent to

1:12:48

run if you have a preference, but right

1:12:49

now it runs master code. We'll probably

1:12:51

make it more extensible in the future.

1:12:52

>> That's a big maybe, but we'll see.

1:12:54

>> Maybe we'll see. We will see. We got,

1:12:56

you know, our we think master code's

1:12:58

better for a lot of reasons, but maybe

1:13:00

we will uh make it work. uh when we were

1:13:03

talking about you know

1:13:07

all the open model stuff all yeah

1:13:10

says dystopian behavior

1:13:13

and I think it was when we're talking

1:13:15

about the the books anthropic you know

1:13:17

destroying the books reminds me of the

1:13:19

Google plus French national library

1:13:21

story

1:13:23

>> ma 3D says Kimmy K3 plus opus 5 is a

1:13:29

pretty good combo that's

1:13:33

Medigame says is factory for like

1:13:36

building an agent set up that connects

1:13:38

to things like GitHub. So not exactly.

1:13:41

So what factory is npm create factory if

1:13:45

you want to try it out. It essentially

1:13:46

allows you to help automate your

1:13:48

software development for your team. So

1:13:51

the reason we wanted this is because if

1:13:53

you think about a a small team,

1:13:55

everyone's running their own coding

1:13:56

agents, right? Chipping their own PRs.

1:13:58

But what if you had a centralized place

1:13:59

where your team could see the work, see

1:14:01

all the coding agents that are running,

1:14:03

interact with the coding agents, and

1:14:05

essentially like collaboratively ship

1:14:07

software, but in an often automated way.

1:14:10

Not everything needs to be completely

1:14:11

automated. But that's kind of the dream

1:14:13

is like what if an issue comes in and

1:14:15

the factory just picks it up and works

1:14:17

on it. And at the end, you get an PR

1:14:19

that's gone through multiple rounds of

1:14:22

approval. So ideally, you just click,

1:14:25

you know, merge. Maybe certain things

1:14:27

you still want to review, but maybe

1:14:29

there's certain types of tasks you just

1:14:30

go ahead and just merge.

1:14:35

Um, Hassan says, "Maybe OpenAI only

1:14:37

signed the letter because they thought

1:14:38

Jensen was talking about them when they

1:14:40

said open." [laughter]

1:14:44

Um,

1:14:48

Hassan says, "How unlikable do you want

1:14:50

to make yourself to the public?"

1:14:51

Anthropic says, "Yes."

1:14:55

Um, Mika says, "This is only going to

1:14:57

lead to the best models being hidden

1:14:59

from the public." I agree.

1:15:04

Profi Woo says, "It's definitely useful

1:15:07

for enterprise internal tools." Speaking

1:15:09

of MCP, agreed. That's where I see all

1:15:11

the use or a lot of the use cases.

1:15:15

>> Um, I al so I don't know how to

1:15:18

pronounce your name, but said, "I often

1:15:20

find tools to work much worse once

1:15:21

abstracted behind an MCPA tools.

1:15:27

interesting.

1:15:29

Um,

1:15:31

says LMVD Xand says WTF is poke.

1:15:37

Hassan says never heard of it before.

1:15:40

So, I'm at least I'm not the only one.

1:15:42

Thank you for the chat for backing me up

1:15:44

that I I didn't know what poke was, but

1:15:46

yeah, many games never heard of it. Um,

1:15:52

so anyways,

1:15:55

all right. And then Yan says, "We need

1:15:57

to make a machine that eats the pizza to

1:16:00

close the loop."

1:16:00

>> Close the loop.

1:16:02

>> Man, there's a lot of chat today these

1:16:04

days. I don't know what software is

1:16:06

anymore. Everyone's just building the

1:16:07

same desktop at with the chatbot.

1:16:10

>> I feel you. Uh, Prof. NGW says, "Factory

1:16:15

looks amazing.

1:16:18

Before TSAI, I thought one could use

1:16:20

Masera to offer their services to

1:16:22

software companies to create a factory

1:16:23

for them. After TSI and the factory

1:16:25

release, that thought is obsolete. Great

1:16:27

work."

1:16:28

>> It's not necessarily It's not

1:16:30

necessarily obsolete though. Like

1:16:32

factories customizable. So take it and

1:16:34

go customize it for people and help them

1:16:36

build their own factories. Like that's

1:16:37

that's the goal. But yes, we want to

1:16:40

give you tools so you can do it easier.

1:16:41

So you don't have to do it all yourself.

1:16:44

All right. Dang, that was that was a fun

1:16:46

show.

1:16:47

>> Super fun.

1:16:48

>> Wait, this just in. Anthropic is down.

1:16:53

>> This this is my shocked face. All right.

1:16:57

Just another Wednesday.

1:16:59

>> Just another Wednesday.

1:17:00

>> All right. Well, thank you everybody for

1:17:03

tuning in. Thanks for uh watching.

1:17:06

Thanks Fennel for watching at 2 am in

1:17:09

India. We appreciate you. Appreciate

1:17:10

everyone for watching the show. Go

1:17:12

ahead, follow us, like, do all the

1:17:14

things. Uh, and we'll see you next week.

1:17:17

We'll do it again. Be on Monday next

1:17:18

week, so back to normal.

1:17:19

>> Yeah. Peace.

1:17:21

>> See y'all.

1:17:33

>> Still here. And

1:17:34

>> we're still here.

1:17:35

>> Still here.

1:17:36

>> Yeah. This is where normally Yan comes

1:17:38

in with the

1:17:40

>> Yeah, Jan comes in with a outro, but you

1:17:44

know, it wouldn't be a live show without

1:17:45

a few technical difficulties.

1:17:47

>> Yo, that show's a wrap. We were live in

1:17:49

the zone agent with Shane and I be on

1:17:52

the throne. Did you give us that review

1:17:53

[music] only if it's a five? Jump on the

1:17:55

tube. Make sure to like and subscribe.

1:17:58

Dude, so fresh. [music] Yeah, we keep

1:17:59

you in the loop. Get so fly. They bring

1:18:02

the whole troop. AI on the rise. [music]

1:18:04

Don't miss this [singing] power. Welcome

1:18:06

to the show. It's AI Sour. Did you just

1:18:09

drop in? Is this your first time? Make

1:18:10

sure to follow us on next and go like

1:18:12

and subscribe. Yeah. Learn the

1:18:14

principles and patterns in our books.

1:18:16

The master.AI site. Give it a look. New

1:18:19

so fresh. [music] Yeah, we keep you in

1:18:20

the loop. Guess so fly. They bring the

1:18:23

whole troop. AI on the rise. Don't miss

1:18:25

this power. Welcome to the show. It's AI

1:18:28

Sour. This is the end. We all wrapped

1:18:31

up. Another showdown. Another one coming

1:18:33

up. AI agent I was done, but the news

1:18:36

doesn't cease. Shane and Abby, we out of

1:18:38

here. Peace.

1:18:43

[music]

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